{"dataset": "lmsys/lmsys-chat-1m", "conversation_id": "6863e33486cd49f596cbf2aa42c150b8", "conversation_index": 380099, "turn_index": 0, "tokens_gpt_oss_120b": 1007, "prompt": "Holdings Data: 'Holding, Number of shares, NAME_1 price/ Average price per share ($), Client investment ($), Cost basis ($), Price per share on Feb 28 ($), Value on Feb 28 ($), Unrealized (tax) gain or loss ($), Investment return ($), Holding period\nFEDERATED HERMES, , , , , , , , ,\nSTRATEGIC VALUE DIVIDEND, , , , , , , , ,\nFUND IS, , , , , , , , ,\nSymbol: SVAIX, , , , , , , , ,\nTrade date: Feb 4 21, 1366.279, 5.160, 7050.00, 7050.00, 6.110, 8347.96, 1297.96, , LT\nTotal reinvested, 57.410, 5.678, , 326.02, 6.110, 350.77, 24.75, ,\nEAI: $313 Current yield: 3.60%\nSecurity total, 1423.689, 5.181, 7050.00, 7376.02, , 8698.73, 1322.71, 1648.73,\nO'SHAUGHNESSY MARKET LEADERS VALUE FUND CLASS, , , , , , , , ,\nI, , , , , , , , ,\nSymbol: OFVIX, , , , , , , , ,\nTrade date: Feb 4 21, 470.628, 14.979, 7050.00, 7050.00, 17.710, 8334.81, 1284.81, , LT\nTotal reinvested, 8.717, 17.859, , 155.68, 17.710, 154.38, -1.30, ,\nEAI: $159 Current yield: 1.87%, , , , , , , , ,\nSecurity total, 479.345, 15.032, 7050.00, 7205.68, , 8489.19, 1283.51, 1439.19,\nPACE portfolio total, , , $14100.00, $14581.70, , $17187.92, $2606.22, $3087.92,\nANGEL OAK MULTI-STRATEGY, , , , , , , , ,\nINCOME FUND CLASS INSTL, , , , , , , , ,\nSymbol: ANGIX, , , , , , , , ,\nTrade date: Sep 23 20, 2408.841, 10.179, 24522.00, 24522.00, 10.110, 24353.38, -168.62, , LT\nTotal reinvested, 155.558, 10.351, , 1610.26, 10.110, 1572.69, -37.57, ,\nEAI: $1220 Current yield: 4.71%, , , , , , , , ,\nSecurity total, 2564.399, 10.190, 24522.00, 26132.26, , 25926.07, -206.19, 1404.07,\nNAME_2 & NAME_3 PREFERRED, , , , , , , , ,\nSEC & INC FUND I, , , , , , , , ,\nSymbol: CPXIX, , , , , , , , ,\nTrade date: Sep 23 20, 740.474, 13.910, 10300.00, 10300.00, 13.330, 9870.51, -429.49, , LT\nTotal reinvested, 57.946, 14.199, , 822.81, 13.330, 772.42, -50.39, ,\nEAI: $539 Current yield: 5.06%, , , , , , , , ,\nSecurity total, 798.420, 13.931, 10300.00, 11122.81, , 10642.93, -479.88, 342.93, '\nGet ONLY the following five pieces of information for each holding from the given holdings data: the company name, its symbol or CUSIP, the quantity or number of shares, the price or NAME_1 price of each share, and the market value or value without outputting anything. The SYMBOL column must be short, no more than 15 characters and must COMPLETELY UNIQUELY identify the row. Give ONLY a CSV response with the retrieved five properties for each holding where every output row is a unique security."} {"dataset": "zai-org/LongAlign-10k", "example_id": "fa18b5601651871b7448f88a65f303b2254581a528df42d3", "conversation_index": 1304, "turn_index": 0, "tokens_gpt_oss_120b": 12990, "prompt": "Paracosm: A Test Framework for Autonomous Driving Simulations\n\nRupak Majumdar\n\nAman Mathur\n\nMarcus Pirron\n\nLaura Stegner\n\nDamien Zufferey\n\nA Paracosm program consists of parameterized reactive components such as the test vehicle, the environment, road networks, other actors and their behaviors, and monitors. The test input generation scheme guarantees good coverage over the parameter space. The test scenario depicted here shows a test vehicle stopping for a jaywalking pedestrian.\n\nIntroduction\n\nBuilding autonomous driving systems requires complex and intricate engineering effort. At the same time, ensuring their reliability and safety is an extremely difficult task. There are serious public safety and trust concerns1, aggravated by recent accidents involving autonomous cars2. Software in such vehicles combine well-defined tasks such as trajectory planning, steering, acceleration and braking, with underspecified tasks such as building a semantic model of the environment from raw sensor data and making decisions using this model. Unfortunately, these underspecified tasks are critical to the safe operation of autonomous vehicles. Therefore, testing in large varieties of realistic scenarios is the only way to build confidence in the correctness of the overall system.\n\nRunning real tests is a necessary, but slow and costly process. It is difficult to reproduce corner cases due to infrastructure and safety issues; one can neither run over pedestrians to demonstrate a failing test case, nor wait for specific weather and road conditions. Therefore, the automotive industry tests autonomous systems in virtual simulation environments. Simulation reduces the cost per test, and more importantly, gives precise control over all aspects of the environment, so as to test corner cases.\n\nA major limitation of current tools is the lack of customizability: they either provide a GUI-based interface to design an environment piece-by-piece, or focus on bespoke pre-made environments. This makes the setup of varied scenarios difficult and time consuming. Though exploiting parametricity in simulation is useful and effective, the cost of environment setup, and navigating large parameter spaces, is quite high. Prior works have used bespoke environments with limited parametricity. More recently, programmatic interfaces have been proposed to make such test procedures more systematic. However, the simulated environments are largely still fixed, with no dynamic behavior.\n\nA Paracosm program consists of parameterized reactive components such as the test vehicle, the environment, road networks, other actors and their behaviors, and monitors. The test input generation scheme guarantees good coverage over the parameter space. The test scenario depicted here shows a test vehicle stopping for a jaywalking pedestrian.\n\nIn this work, we present Paracosm, a programmatic interface that enables the design of parameterized environments and test cases. Test parameters control the environment and the behaviors of the actors involved. Paracosm supports various test input generation strategies, and we provide a notion of coverage for these. Rather than computing coverage over intrinsic properties of the system under test (which is not yet understood for neural networks ), our coverage criteria is over the space of test parameters. Figure 2 depicts the various parts of a Paracosm test. A Paracosm program represents a family of tests, where each instantiation of the program’s parameters is a concrete test case.\n\nParacosm is based on a synchronous reactive programming model. Components, such as road segments or cars, receive streams of inputs and produce streams of outputs over time. In addition, components have graphical assets to describe their appearance for an underlying visual rendering engine and physical properties for an underlying physics simulator. For example, a vehicle in Paracosm not only has code that reads in sensor feeds and outputs steering angle or braking, but also has a textured mesh representing its shape, position and orientation in 3D space, and a physics model for its dynamical behavior. A Paracosm configuration consists of a composition of several components. Using a set of system-defined components (road segments, cars, pedestrians, etc.) combined using expressive operations from the underlying reactive programming model, users can set up complex temporally varying driving scenarios. For example, one can build an urban road network with intersections, pedestrians and vehicular traffic, and parameterize both, environment conditions (lighting, fog), and behaviors (when a pedestrian crosses a street).\n\nStreams in the world description can be left “open” and, during testing, Paracosm automatically generates sequences of values for these streams. We use a coverage strategy based on $k$-wise combinatorial coverage for discrete variables and dispersion for continuous variables. Intuitively, $k$-wise coverage ensures that, for a programmer-specified parameter $k$, all possible combinations of values of any $k$ discrete parameters are covered by tests. Low dispersion ensures that there are no “large empty holes” left in the continuous parameter space. Paracosm uses an automatic test generation strategy that offers high coverage based on random sampling over discrete parameters and deterministic quasi-Monte Carlo methods for continuous parameters.\n\nLike many of the projects referenced before, our implementation performs simulations inside a game engine. However, Paracosm configurations can also be output to the OpenDRIVE format for use with other simulators, which is more in-line with the current industry standard. We demonstrate through various case studies how Paracosm can be an effective testing framework for both qualitative properties (crash) and quantitative properties (distance maintained while following a car, or image misclassification).\n\nOur main contributions are the following:\n\nWe present a programmable and expressive framework for programmatically modeling complex and parameterized scenarios to test autonomous driving systems. Using Paracosm one can specify the environment’s layout, behaviors of actors, and expose parameters to a systematic testing infrastructure.\n\nWe define a notion of test coverage based on combinatorial $k$-wise coverage in discrete space and low dispersion in continuous space. We show a test generation strategy based on fuzzing that theoretically guarantees good coverage.\n\nWe demonstrate empirically that our system is able to express complex scenarios and automatically test autonomous driving agents and find incorrect behaviors or degraded performance.\n\nParacosm through Examples\n\nWe now provide a walkthrough of Paracosm through a testing example. Suppose we have an autonomous vehicle to test. Its implementation is wrapped into a parameterized class:\n\n [numbers=none, basicstyle=\\small\\ttfamily]\nAutonomousVehicle(start, model, controller) { \n void run(...) {... } }\n\nwhere the model ranges over possible car models (appearance, physics), and the controller implements an autonomous controller. The goal is to test this class in many different driving scenarios, including different road networks, weather and light conditions, and other car and pedestrian traffic. We show how Paracosm enables writing such tests as well as generate test inputs automatically.\n\nA test configuration consists of a composition of reactive objects. The following is an outline of a test configuration in Paracosm, in which the autonomous vehicle drives on a road with a pedestrian wanting to cross. We have simplified the API syntax for the sake of clarity and omit the enclosing Test class. In the code segments, we use ‘:’ for named arguments.\n\n// Test parameters\nlight = VarInterval(0.2, 1.0) // value in [0.2, 1.0]\nnlanes = VarEnum({2,4,6}) // value is 2, 4 or 6\n// Description of environment\nw = World(light:light, fog:0)\n// Create a road segment\nr = StraightRoadSegment(len:100, nlanes:nlanes)\n// The autonomous vehicle controlled by the SUT\nv = AutonomousVehicle(start:...,model:...,controller:...)\n// Some other actor(s)\np = Pedestrian(start:.., model:...,...)\n// Monitor to check some property\nc = CollisionMonitor(v) \n// Place elements in the world\nrun_test(env: {w, r, v, p}, test_params: {light, nlanes}, monitors: {c}, iterations: 100)\n\nAn instantiation of the reactive objects in the test configuration gives a scene—all the visual elements present in the simulated world. A test case provides concrete inputs to each “open” input stream in a scene. A test case determines how the scene evolves over time: how the cars and pedestrians move and how environment conditions change. We go through each part of the test configuration in detail below.\n\nReactive Objects.\n\nThe core abstraction of Paracosm is a reactive object. Reactive objects capture geometric and graphical features of a physical object, as well as their behavior over time. The behavioral interface for each reactive object has a set of input streams and a set of output streams. The evolution of the world is computed in steps of fixed duration which corresponds to events in a predefined tick stream. For streams that correspond to physical quantities updated by the physics simulator, such as position and speeds of cars, etc., appropriate events are generated by the underlying physics simulator.\n\nInput streams provide input values from the environment over time; output streams represent output values computed by the object. The object’s constructor sets up the internal state of the object. An object is updated by event triggered computations. Paracosm provides a set of assets as base classes. Autonomous driving systems naturally fit reactive programming models. They consume sensor input streams and produce actuator streams for the vehicle model. We differentiate between static environment reactive objects (subclassing Geometric) and dynamic actor reactive objects (subclassing Physical). Environment reactive objects represent “static” components of the world, such as road segments, intersections, buildings or trees, and a special component called the world. Actor reactive objects represent components with “dynamic” behavior: vehicles or pedestrians. The world object is used to model features of the world such as lighting or weather conditions. Reactive objects can be composed to generate complex assemblies from simple objects. The composition process can be used to connect static components structurally–such as two road segments connecting at an intersection. Composition also connects the behavior of an object to another by binding output streams to input streams. At run time, the values on that input stream of the second object are obtained from the output values of the first. Composition must respect geometric properties—the runtime system ensures that a composition maintains invariants such as no intersection of geometric components. We now describe the main features in Paracosm, centered around the test configuration above.\n\nTest Parameters.\n\nUsing test variables, we can have general, but constrained streams of values passed into objects. Our automatic test generator can then pick values for these variables, thereby leading to different test cases (see Figure 3). There are two types of parameters: continuous (VarInterval) and discrete (VarEnum). In the example presented, light (light intensity) is a continuous test parameter and nlanes (number of lanes) is discrete.\n\nWorld.\n\nThe World is a pre-defined reactive object in Paracosm with a visual representation responsible for atmospheric conditions like the light intensity, direction and color, fog density, etc. The code segment\n\nw = World(light:light, fog:0)\n\nparameterizes the world using a test variable for light and sets the fog density to a constant (0).\n\nReactive streams represented by a marble diagram. A change in the value of test parameters nlanes or light changes the environment, and triggers a change in the corresponding sensor (output) stream camera.\n\nRoad Segments.\n\nIn our example, StraightRoadSegment was parameterized with the number of lanes. In general, Paracosm provides the ability to build complex road networks by connecting primitives of individual road segments and intersections. (A detailed example is presented in the Appendices.)\n\nIt may seem surprising that we model static scene components such as roads as reactive objects. This serves two purposes. First, we can treat the number of lanes in a road segment as a constant input stream that is set by the test case, allowing parameterized test cases. Second, certain features of static objects can also change over time. For example, the coefficient of friction on a road segment may depend on the weather condition, which can be a function of time.\n\nAutonomous Vehicles & System Under Test (SUT).\n\nAutonomousVehicle, as well as other actors, extends the Physical class (which in turn subclasses Geometric). This means that these objects have a visual as well as a physical model. The visual model is essentially a textured 3D mesh. The physical model contains properties such as mass, moments of inertia of separate bodies in the vehicle, joints, etc. This is used by the physics simulator to compute the vehicle’s motion in response to external forces and control input. In the following code segment, we instantiate and place our test vehicle on the road:\n\nv = AutonomousVehicle(start:r.onLane(1, 0.1), model:CarAsset(...), controller:MyController(...))\n\nThe start parameter “places” the vehicle in the world (in relative coordinates). The model parameter provides the implementation of the geometric and physical model of the vehicle. The controller parameter implements the autonomous controller under test. The internals of the controller implementation are not important; what is important is its interface (sensor inputs and the actuator outputs). These determine the input and output streams that are passed to the controller during simulation. For example, a typical controller can take sensor streams such as image streams from a camera as input and produce throttle and steering angles as outputs. The Paracosm framework “wires” these streams appropriately. For example, the rendering engine determines the camera images based on the geometry of the scene and the position of the camera and the controller outputs are fed to the physics engine to determine the updated scene. Though simpler systems like openpilot use only a dashboard-mounted camera, autonomous vehicles can, in general, mix cameras at various mount points, LiDARs, radars, and GPS. Paracosm can emulate many common types of sensors which produce streams of data. It is also possible to integrate new sensors, which are not supported out-of-the-box, by implementing them using the game engine’s API.\n\nOther Actors.\n\nA test often involves many actors such as pedestrians, and other (non-test) vehicles. Apart from the standard geometric (optionally physical) properties, these can also have some pre-programmed behavior. Behaviors can either be only dependent on the starting position (say, a car driving straight on the same lane), or be dynamic and reactive, depending on test parameters and behaviors of other actors. In general, the reactive nature of objects enables complex scenarios to be built. For example, here, we specify a simple behavior of a pedestrian crossing a road. The pedestrian starts crossing the road when a car is a certain distance away. In the code segments below, we use ‘_’ as shorthand for a lamdba expression, i.e., “f(_)” is the same as “x => f(x)”.\n\n [numbers=none, basicstyle=\\small\\ttfamily]\nPedestrian(value start, value target, carPos, value dist, value speed) extends Geometric {\n ... // Initialization\n // Generate an event when the car gets close\n trigger = carPos.Filter( abs(_ - start) < dist )\n // target location reached\n done = pos.Filter( _ == target )\n // Walk to the target after trigger fires\n tick.SkipUntil(trigger).TakeUntil(done).foreach(... /* walk with given speed */ )\n}\n\nMonitors and Test Oracles.\n\nParacosm provides an API to provide qualitative and quantitative temporal specifications. For instance, in the following example, we check that there is no collision and ensure that the collision was not trivially avoided because our vehicle did not move at all.\n\n [numbers=none, basicstyle=\\small\\ttfamily]\n// no collision\nCollisionMonitor(AutonomousVehicle v) extends Monitor {\n assert(v.collider.IsEmpty()) }\n// cannot trivially pass the test by staying put\nDistanceMonitor(AutonomousVehicle v, value minD) extends Monitor {\n pOld = v.pos.Take(1).Concat(v.pos)\n D = v.pos.Zip(pOld).Map( abs(_ - _) ).Sum()\n assert(D >= minD)\n}\n\nThe ability to write monitors which read streams of system-generated events provides an expressive framework to write temporal properties, something that has been identified as a major limitation of prior tools. Monitors for metric and signal temporal logic specifications can be encoded in the usual way.\n\nSystematic Testing of Paracosm Worlds\n\nTest Inputs and Coverage\n\nWorlds in Paracosm directly describe a parameterized family of tests. The testing framework allows users to specify various strategies to generate input streams for both, static, and dynamic reactive objects in the world.\n\nTest Cases.\n\nA test of duration $T$ executes a configuration of reactive objects by providing inputs to every open input stream in the configuration for $T$ ticks. The inputs for each stream must satisfy const parameters and respect the range constraints from VarInterval and VarEnum. The runtime system manages the scheduling of inputs and pushing input streams to the reactive objects. Let $\\mathsf{In}$ denote the set of all input streams, and $\\mathsf{In} = \\mathsf{In}_D \\cup \\mathsf{In}_C$ denote the partition of $\\mathsf{In}$ into discrete streams and continuous streams respectively. Discrete streams take their value over a finite, discrete range; for example, the color of a car, the number of lanes on a road segment, or the position of the next pedestrian (left/right) are discrete streams. Continuous streams take their values in a continuous (bounded) interval. For example, the fog density or the speed of a vehicle are examples of continuous streams.\n\nCoverage.\n\nIn the setting of autonomous vehicle testing, one often wants to explore the state space of a parameterized world to check “how well” an autonomous vehicle works under various situations, both qualitatively and quantitatively. Thus, we now introduce a notion of coverage. Instead of structural coverage criteria such as line or branch coverage, our goal is to cover the parameter space. In the following, for simplicity of notation, we assume that all discrete streams take values from ${\\{ 0,1 \\}}$, and all continuous streams take values in the real interval $[0,1]$. Any input stream over bounded intervals—discrete or continuous—can be encoded into such streams. For discrete streams, there are finitely many tests, since each co-ordinate is Boolean and there is a fixed number of co-ordinates. One can define the coverage as the fraction of the number of vectors tested to the total number of vectors. Unfortunately, the total number of vectors is very high: if each stream is constant, then there are already $2^{n}$ tests for $n$ streams. Instead, we consider the notion of $k$-wise testing from combinatorial testing. In $k$-wise testing, we fix a parameter $k$, and ask that every interaction between every $k$ elements is tested. Let us be more precise. Suppose that a test vector has $N$ co-ordinates, where each co-ordinate can get the value $0$ or $1$. A set of tests $A$ is a $k$-wise covering family if for every subset ${\\{ i_1, i_2,\\ldots, i_k \\}} \\subseteq {\\{ 1,\\ldots, N \\}}$ of co-ordinates and every vector $v\\in {\\{ 0,1 \\}}^k$, there is a test $t\\in A$ whose restriction to the $i_1,\\ldots, i_k$ is precisely $v$.\n\nFor continuous streams, the situation is more complex: since any continuous interval has infinitely many points, each corresponding to a different test case, we cannot directly define coverage as a ratio (the denominator will be infinite). Instead, we define coverage using the notion of dispersion. Intuitively, dispersion measures the largest empty space left by a set of tests. We assume a (continuous) test is a vector in $[0,1]^N$: each entry is picked from the interval $[0,1]$ and there are $N$ co-ordinates. Dispersion over $[0,1]^N$ can be defined relative to sets of neighborhoods, such as $N$-dimensional balls or axis-parallel rectangles. Let us define ${\\mathcal{B}}$ to be the family of $N$-dimensional axis-parallel rectangles in $[0,1]^N$, our results also hold for other notions of neighborhoods such as balls or ellipsoids. For a neighborhood $B\\in{\\mathcal{B}}$, let $\\mathit{vol}(B)$ denote the volume of $B$. Given a set $A \\subseteq [0,1]^N$ of tests, we define the dispersion as the largest volume neighborhood in ${\\mathcal{B}}$ without any test: $$\\mathsf{dispersion}(A) = \\sup{\\{ \\mathrm{vol}(B) \\mid B\\in{\\mathcal{B}}\\mbox{ and } A \\cap B = \\emptyset \\}}$$ A lower dispersion means better coverage.\n\nLet us summarize. Suppose that a test vector consists of $N_D$ discrete co-ordinates and $N_C$ continuous co-ordinates; that is, a test is a vector $(t_D, t_C)$ in ${\\{ 0,1 \\}}^{N_D} \\times [0, 1]^{N_C}$. We say a set of tests $A$ is $(k, \\varepsilon)$-covering if\n\n 1. for each set of $k$ co-ordinates ${\\{ i_1,\\ldots, i_k \\}} \\subseteq {\\{ 1,\\ldots, N_D \\}}$ and each vector $v\\in {\\{ 0,1 \\}}^k$, there is a test $(t_D, t_C) \\in {\\{ 0,1 \\}}^{N_D} \\times [0,1]^{N_C}$ such that the restriction of $t_D$ to the co-ordinates $i_1,\\ldots, i_k$ is $v$; and\n\n 2. for each $(t_D, t_C)\\in A$, the set ${\\{ t_C \\mid (t_D, t_C)\\in A \\}}$ has dispersion at most $\\epsilon$.\n\nTest Generation\n\nThe goal of our default test generator is to maximize $(k, \\epsilon)$ for programmer-specified number of test iterations or ticks.\n\n$k$-Wise Covering Family.\n\nOne can use explicit construction results from combinatorial testing to generate $k$-wise covering families. However, a simple way to generate such families with high probability is random testing. The proof is by the probabilistic method (see also ). Let $A$ be a set of $2^k(k \\log N - \\log \\delta)$ uniformly randomly generated ${\\{ 0,1 \\}}^N$ vectors. Then $A$ is a $k$-wise covering family with probability at least $1-\\delta$.\n\nLow Dispersion Sequences.\n\nIt is tempting to think that uniformly generating vectors from $[0,1]^N$ would similarly give low dispersion sequences. Indeed, as the number of tests goes to infinity, the set of randomly generated tests has dispersion $0$ almost surely. However, when we fix the number of tests, it is well known that uniform random sampling can lead to high dispersion ; in fact, one can show that the dispersion of $n$ uniformly randomly generated tests grows asymptotically as $O((\\log \\log n/n)^{\\frac{1}{2}})$ almost surely. Our test generation strategy is based on deterministic quasi-Monte Carlo sequences, which have much better dispersion properties, asymptotically of the order of $O(1/n)$, than the dispersion behavior of uniformly random tests. There are many different algorithms for generating quasi-Monte Carlo sequences deterministically (see, e.g., ). We use Halton sequences. For a given $\\epsilon$, we need to generate $O(\\frac{1}{\\epsilon})$ inputs via Halton sampling. In Section 4.2, we compare uniform random and Halton sampling.\n\nCost Functions and Local Search.\n\nIn many situations, testers want to optimize parameter values for a specific function. A simple example of this is finding higher-speed collisions, which intuitively, can be found in the vicinity of test parameters that already result in high-speed collisions. Another, slightly different case is (greybox) fuzzing, for example, finding new collisions using small mutations on parameter values that result in the vehicle narrowly avoiding a collision. Our test generator supports such quantitative objectives and local search. A quantitative monitor evaluates a cost function on a run of a test case. Our test generation tool generates an initial, randomly chosen, set of test inputs. Then, it considers the scores returned by the Monitor on these samples, and performs a local search on samples with the highest/lowest scores to find local optima of the cost function.\n\nImplementation and Tests\n\nRuntime System and Implementation\n\nParacosm uses the Unity game engine to render visuals, do runtime checks and simulate physics (via PhysX ). Reactive objects are built on top of UniRx, an implementation of the popular Reactive Extensions framework. The game engine manages geometric transformations of 3D objects and offers easy to use abstractions for generating realistic simulations. Encoding behaviors and monitors, management of 3D geometry and dynamic checks are implemented using the game engine interface.\n\nA simulation in Paracosm proceeds as follows. A test configuration is specified as a subclass of the EnvironmentProgramBaseClass. Tests are run by invoking the run_test method, which receives as input the reactive objects that should be instantiated in the world as well as additional parameters relating to the test. The run_test method runs the tests by first initializing and placing the reactive objects in the scene using their 3D mesh (if they have one) and then invoking a reactive engine to start the simulation. The system under test is run in a separate process and connects to the simulation. The simulation then proceeds until the simulation completion criteria is met (a time-out or some monitor event).\n\nOutput to Standardized Testing Formats.\n\nThere have been recent efforts to create standardized descriptions of tests in the automotive industry. The most relevant formats are OpenDRIVE and OpenSCENARIO(only recently finalized). OpenDRIVE describes road structures, and OpenSCENARIO describes actors and their behavior. Paracosm currently supports outputs to OpenDRIVE. Due to the static nature of the specification format, a different file is generated for each test iteration/configuration.\n\nEvaluation\n\nWe evaluate Paracosm with respect to the following research questions (RQs):\nRQ 1: Does Paracosm’s programmatic interface enable the easy design of test environments and worlds?\nRQ 2: Do the test input generation strategies discussed in Section 3 effectively explore the parameter space?\nRQ 3: Can Paracosm help uncover poor performance or bad behavior of the SUT in common autonomous driving tasks?\n\nMethodology.\n\nTo answer RQ 1, we develop three independent environments rich with visual features and other actors, and use the variety generated with just a few lines of code as a proxy for ease of design. To answer RQ 2, we use coverage maximizing strategies for test inputs to all the three environments/case studies. We also use and evaluate cost functions and local search based methods. To answer RQ 3, we test various neural network based systems and demonstrate how Paracosm can help uncover problematic scenarios. A summary of the case studies presented here is available in Table [tab:case_study_summary]. In the Appendices, we present more case studies, specifically experiments on many pre-trained neural networks, busy urban environments and studies exploiting specific testing features of Paracosm.\n\n[tab:case_study_summary]\n\nAn overview of our case studies. Note that even though the Adaptive Cruise Control study has 2 discrete parameters, we calculate k-wise coverage for 3 as the 2 parameters require 3 bits for representation.\n Road segmentation Jaywalking pedestrian Adaptive Cruise Control \nSUT VGGNet CNN NVIDIA CNN NVIDIA CNN \nTraining 191 images 403 image & car control samples 1034 image & car control samples \nTest params 3 discrete 2 continuous 3 continuous, 2 discrete \nTest iters 100 100, 15s timeout 100, 15s timeout \nMonitor Ground truth Scored Collision Collision & Distance \nCoverage $k = 3$ with probability $\\sim 1$ $\\epsilon = 0.041$ $\\epsilon = 0.043$, $k = 3$ with probability $\\sim 1$\n\n\nCase Studies\n\nRoad segmentation\n\n[fig:road_seg_training]\n\n[fig:road_seg_test]\n\n[fig:road_seg]\n\n[tab:kittiseg_summary]\n\nSummary of results of the road segmentation case study. Each combination of parameter values is presented separately, with the parameter values used for training in bold. We report the SUT’s average true positive rate (% of pixels corresponding to the road that are correctly classified) and false positive rate (% of pixels that are not road, but incorrectly classified as road).\n# lanes # cars Lighting # test iters True positive (%) False positive (%)\n2 5 Noon 12 70% 5.1% \n2 5 Evening 14 53.4% 22.4% \n2 0 Evening 12 51.4% 18.9% \n2 0 Noon 12 71.3% 6% \n4 5 Evening 10 60.4% 7.1% \n4 5 Noon 16 68.5% 20.2% \n4 0 Evening 13 51.5% 7.1% \n4 0 Noon 11 83.3% 21% \n\n\nUsing Paracosm’s programmatic interface, we design a long road segment with several vehicles. The vehicular behavior is to drive on their respective lanes with a fixed maximum velocity. The test parameters are the number of lanes ($\\{2,\\: 4\\}$), number of cars in the environment ($\\{0,\\: 5\\}$) and light conditions ($\\{Noon,\\: Evening\\}$). Noon lighting is much brighter than the evening. The direction of lighting is also the opposite. We test a deep CNN called VGGNet, that is known to perform well on several image segmentation benchmarks. The task is road segmentation, i.e., given a camera image, identifying which pixels correspond to the road. The network is trained on 191 dashcam images captured in the test environment with fixed parameters ($2$ lanes, $5$ cars, and $Noon$ lighting), recorded at the rate of one image every $1/10^{th}$ second, while manually driving the vehicle around (using a keyboard). We test on 100 images generated using Paracosm’s default test generation strategy (uniform random sampling for discrete parameters). Table [tab:kittiseg_summary] summarizes the test results. Tests with parameter values far away from the training set are observed to not perform so well. As depicted in Figure [fig:road_seg], this happens because varying test parameters can drastically change the scene.\n\nJaywalking pedestrian.\n\n[tbl:ped_crossing]\n\nResults for the jaywalking pedestrian case study.\nTesting strategy Dispersion ( $\\epsilon$) % fail Max. collision \nRandom 0.092 $7\\%$ 10.5 m/s \nHalton 0.041 $10\\%$ 11.3 m/s \nRandom+opt/collision 0.109 $13\\%$ 11.1 m/s \nHalton+opt/collision 0.043 $20\\%$ 11.9 m/s \nRandom+opt/almost failing 0.126 $13\\%$ 10.5 m/s \nHalton+opt/almost failing 0.043 $13\\%$ 11.4 m/s \n\n\nWe now test over the environment presented in Section 2. The environment consists of a straight road segment and a pedestrian. The pedestrian’s behavior is to cross the road at a specific walking speed when the autonomous vehicle is a specific distance away. The walking speed of the pedestrian and the distance of the autonomous vehicle when the pedestrian starts crossing the road are test parameters. The SUT is a CNN based on NVIDIA’s behavioral cloning framework. It takes camera images as input, and produces the relevant steering angle or throttle control as output. The SUT is trained on 403 samples obtained by driving the vehicle manually and recording the camera and corresponding control data. The training environment has pedestrians crossing the road at various time delays, but always at a fixed walking speed (1 m/s). In order to evaluate RQ 2 completely, we evaluate the default coverage maximizing sampling approach, as well as explore two quantitative objectives: first, maximizing the collision speed, and second, finding new failing cases around samples that almost fail. For the default approach, the CollisionMonitor as presented in Section 2 is used. For the first quantitative objective, this CollisionMonitor’s code is prepended with the following calculation:\n\n// Score is speed of car at time of collision \ncoll_speed = v.speed.CombineLatest(v.collider, (s,c) => s).First()\n\nThe score coll_speed is used by the test generator for optimization. For the second quantitative objective, the CollisionMonitor is modified to give high scores to tests where the distance between the autonomous vehicle and pedestrian is very small:\n\n [numbers=none, basicstyle=\\small\\ttfamily]\nCollisionMonitor(AutonomousVehicle v, Pedestrian p) extends Monitor {\n minDist = v.pos.Zip(p.pos).Map(1/abs(_-_)).Min()\n coll_score = v.collider.Map(0)\n // Score is either 0 (collision) or 1/minDist\n score = coll_score.DefaultIfEmpty(minDist)\n assert(v.collider.IsEmpty())\n}\n\nWe evaluate the following test input generation strategies:\n\nRandom sampling\n\nHalton sampling,\n\nRandom or Halton sampling with local search for the two quantitative objectives.\n\nWe run 100 iterations of each strategy with a 15 second timeout. For random or Halton sampling, we sample 100 times. For the quantitative objectives, we first generate 85 random or Halton samples, then choose the top 5 scores, and finally run 3 simulated annealing iterations on each of these 5 configurations. Table [tbl:ped_crossing] presents results from the various test input generation strategies. Clearly, Halton sampling offers the lowest dispersion (highest coverage) over the parameter space. This can also be visually confirmed from the plot of test parameters (Figure [fig:Pedestrian_halton_100]). There are no big gaps in the parameter space. Moreover, we find that test strategies optimizing for the first objective are successful in finding more collisions with higher speeds. As these techniques perform simulated annealing repetitions on top of already failing tests, they also find more failing tests overall. Finally, test strategies using the second objective are also successful in finding more (newer) failure cases than simple Random or Halton sampling.\n\nRandom sampling (no opt.)\n\n[fig:Pedestrian_rand_100]\n\nRandom + opt. / maximizing collision.\n\n[fig:Pedestrian_rand_sa]\n\nRandom + opt. / almost failing.\n\n[fig:Pedestrian_random_fuzzing]\n\nHalton sampling (no opt.)\n\n[fig:Pedestrian_halton_100]\n\nHalton + opt. / maximizing collision.\n\n[fig:Pedestrian_halton_sa]\n\nHalton + opt. / almost failing.\n\n[fig:Pedestrian_halton_fuzzing]\n\nAdaptive Cruise Control.\n\nInitial offset (X-axis) vs. max. speed (Y-axis).\n\n[fig:ACC_OffsetvSpeed]\n\nInitial offset (X-axis) vs. fog density (Y-axis).\n\n[fig:ACC_OffsetvFog]\n\nMax. speed (X-axis) vs. fog density (Y-axis).\n\n[fig:ACC_SpeedvFog]\n\n[tbl:acc_results]\n\nParameterized test on Adaptive Cruise Control, separated for each value of discrete parameters, and low and high values of continuous parameters. A test passes if there are no collisions and no inactivity (the overall distance moved by the test vehicle is more than 5 m. The average offset (in m) maintained by the test vehicle to the lead car (for passing tests) is also presented.\n \n(r)2-3(r)4-7 (r)8-9(r)10-11(r)12-13 2 4 Black Red Yellow Blue $< 24$ $\\geq 24$ $< 5.5$ $\\geq 5.5$ $< 0.5$ $\\geq 0.5$\nTest iters 54 46 24 22 27 27 51 49 52 48 51 49 \nCollisions 7 7 3 3 6 2 6 8 8 6 12 0 \nInactivity 12 4 4 4 6 2 9 7 9 7 1 15 \nOffset (m) 42.4 43.4 46.5 48.1 39.6 39.1 33.7 52.7 38.4 47.4 36.5 49.8 \n\n\nWe now create and test in an environment with our test vehicle following a car (lead car) on the same lane. The lead car’s behavior is programmed to drive on the same lane as the test vehicle, with a certain maximum speed. This is a very typical driving scenario that engineers test their implementations on. We use $5$ test parameters: the initial lead of the lead car to the test vehicle ($[8 \\: m,\\: 40 \\: m]$), the lead car’s maximum speed ($[3 \\: m/s,\\: 8 \\: m/s]$), density of fog3 in the environment ($[0,1]$), number of lanes on the road ($\\{2,\\: 4\\}$), and color of the lead car ($\\{Black, \\: Red, \\: Yello, \\: Blue\\}$). We use both, CollisionMonitor4 and DistanceMonitor, as presented in Section 2. A test passes if there is no collision and the autonomous vehicle moves atleast 5 m during the simulation duration (15 s).\n\nWe use Paracosm’s default test generation strategy, i.e., Halton sampling for continuous parameters and Random sampling for discrete parameters (no optimization or fuzzing). The SUT is the same CNN as in the previous case study. It is trained on 1034 training samples, which are obtained by manually driving behind a red lead car on the same lane of a 2-lane road with the same maximum velocity (5.5 m/s) and no fog.\n\nThe results of this case study are presented in Table [tbl:acc_results]. Looking at the discrete parameters, the number of lanes does not seem to contribute towards a risk of collision. Surprisingly, though the training only involves a Red lead car, the results appear to be the best for a Blue lead car. Moving on to the continuous parameters, the fog density appears to have the most significant impact on test failures (collision or vehicle inactivity). In the presence of dense fog, the SUT behaves pessimistically and does not accelerate much (thereby causing a failure due to inactivity). These are all interesting and useful metrics about the performance of our SUT. Plots of the results projected on to continuous parameters are presented in Figure [fig:ACC].\n\nResults and Analysis\n\nWe now summarize the results of our evaluation with respect to our RQs:\nRQ 1: All the three case studies involve varied, rich and dynamic environments. They are representative of tests engineers would typically want to do, and we parameterize many different aspects of the world and the dynamic behavior of its components. These designs are at most $70$ lines of code. This provides confidence in Paracosm’s ability of providing an easy interface for the design of realistic test environments.\nRQ 2: Our default test generation strategies are found to be quite effective at exploring the parameter space systematically, eliminating large unexplored gaps, and at the same time, successfully identifying problematic cases in all the three case studies. The jaywalking pedestrian study demonstrates that optimization and local search are possible on top of these strategies, and are quite effective in finding the relevant scenarios. The adaptive cruise control study tests over $5$ parameters, which is more than most related works, and even guarantees good coverage of this parameter space. Therefore, it is amply clear that Paracosm’s test input generation methods are useful and effective.\nRQ 3: The road segmentation case study uses a well-performing neural network for object segmentation, and we are able to detect degraded performance for automatically generated test inputs. Whereas this study focuses on static image classification, the next two, i.e., the jaywalking pedestrian and the adaptive cruise control study uncover poor performance on simulated driving, using a popular neural network architecture for self driving cars. Therefore, we can safely conclude that Paracosm can find bugs in various different kinds of systems related to autonomous driving.\n\nThreats to Validity\n\nThe internal validity of our experiments depends on having implemented our system correctly and, more importantly, trained and used the neural networks considered in the case studies correctly. For training the networks, we followed the available documentation and inspected our examples to ensure that we use an appropriate training procedure. We watched some test runs and replays of tests we did not understand. Furthermore, our implementation logs events and we also capture images, which allow us to check a large number of tests.\n\nIn terms of threats to external validity, the biggest challenge in this project has been finding systems that we can easily train and test in complex driving scenarios. Publicly available systems have limited capabilities and tend to be brittle. Many networks trained on real world data do not work well in simulation. We therefore re-train these networks in simulation. An alternative is to run fewer tests, but use more expensive and visually realistic simulations. Our test generation strategy maximizes coverage, even when only a few test iterations can be performed due to high simulation cost.\n\nRelated Work\n\nTraditionally, test-driven software development paradigms have advocated testing and mocking frameworks to test software early and often. Mocking frameworks and mock objects allow programmers to test a piece of code against an API specification. Typically, mock objects are stubs providing outputs to explicitly provided lists of inputs of simple types, with little functionality of the actual code. Thus, they fall short of providing a rich environment for autonomous driving. Paracosm can be seen as a mocking framework for reactive, physical systems embedded in the 3D world. Our notion of constraining streams is inspired by work on declarative mocking.\n\nTesting Cyber-Physical Systems.\n\nThere is a large body of work on automated test generation tools for cyber-physical systems through heuristic search of a high-dimensional continuous state space. While much of this work has focused on low-level controller interfaces rather than the system level, specification and test generation techniques arising from this work—for example, the use of metric and signal temporal logics or search heuristics—can be adapted to our setting. More recently, test generation tools have started targeting autonomous systems under a simulation-based semantic testing framework similar to ours. In most of these works, visual scenarios are either fixed by hand, or are constrained due to the model or coverage criteria. These analyses are shown to be preferable to the application of random noise on the input vector. Additionally, a simulation-based approach filters benign misclassifications from misclassifications that actually lead to bad or dangerous behavior. Our work extends this line of work and provides an expressive language to design parameterized environments and tests. AsFault uses random search and mutation for procedural generation of road networks for testing. AC3R reconstructs test cases from accident reports.\n\nTo address problems of high time and infrastructure cost of testing autonomous systems, several simulators have been developed. The most popular is Gazebo for the ROS robotics framework. It offers a modular and extensible architecture, however falls behind on visual realism and complexity of environments that can be generated with it. To counter this, game engines are used. Popular examples are TORCS, CARLA, and AirSim Modern game engines and support creation of realistic urban environments. Though they enable visually realistic simulations and enable detection of infractions such as collisions, the environments themselves are difficult to design. Designing a custom environment involves manual placement of road segments, buildings, and actors (as well as their properties). Performing many systematic tests is therefore time-consuming and difficult. While these systems and Paracosm share the same aims and much of the same infrastructure, Paracosm focuses on procedural design and systematic testing, backed by a relevant coverage criteria.\n\nAdversarial Testing.\n\nAdversarial examples for neural networks introduce perturbations to inputs that cause a classifier to classify “perceptually identical” inputs differently. Much work has focused on finding adversarial examples in the context of autonomous driving as well as on training a network to be robust to perturbations. Tools such as DeepXplore, DeepTest, DeepGauge, and SADL define a notion of coverage for neural networks based on the number of neurons activated during tests compared against the total number of neurons in the network and activation during training. However, these techniques focus mostly on individual classification tasks and apply 2D transformations on images. In comparison, we consider the closed-loop behavior of the system and our parameters directly change the world rather than apply transformations post facto. We can observe, over time, that certain vehicles are not detected, which is more useful to testers than a single misclassification. Furthermore, it is already known that structural coverage criteria may not be an effective strategy for finding errors in classification. We use coverage metrics on the test space, rather than the structure of the neural network. Alternately, there are recent techniques to verify controllers implemented as neural networks through constraint solving or abstract interpretation. While these tools do not focus on the problem of autonomous driving, their underlying techniques can be combined in the test generation phase for Paracosm.\n\nFuture Work and Conclusion\n\nDeploying autonomous systems like self-driving cars in urban environments raises several safety challenges. The complex software stack processes sensor data, builds a semantic model of the surrounding world, makes decisions, plans trajectories, and controls the car. The end-to-end testing of such systems requires the creation and simulation of whole worlds, with different tests representing different world and parameter configurations. Paracosm tackles these problems by\n\nenabling procedural construction of diverse scenarios, with precise control over elements like layout roads, physical and visual properties of objects, and behaviors of actors in the system, and\n\nusing quasi-random testing to obtain good coverage over large parameter spaces.\n\nIn our evaluation, we show that Paracosm enables easy design of environmnents and automated testing of autonomous agents implemented using neural networks. While finding errors in sensing can be done with only a few static images, we show that Paracosm also enables the creation of longer test scenarios which exercise the controller’s feedback on the environment. Our case studies focused on qualitative state space exploration. In future work, we shall perform quantitative statistical analysis to understand the sensitivity of autonomous vehicle behavior on individual parameters.\n\nIn the future, we plan to extend Paracosm’s testing infrastructure to also aid in the training of deep neural networks that require large amounts of high quality training data. For instance, we show that small variations in the environment result in widely different results for road segmentation. Generating data is a time consuming and expensive task. Paracosm can easily generate labelled data for static images. For driving scenarios, we can record a user manually driving in a parameterized Paracosm environment and augment this data by varying parameters that should not impact the car’s behavior. For instance, we can vary the color of other cars, positions of pedestrians who are not crossing, or even the light conditions and sensor properties (within reasonable limits).\n\nAcknowledgements\n\nThis research was funded in part by the Deutsche Forschungsgemeinschaft project 389792660-TRR 248 and by the European Research Council under the Grant Agreement 610150 (ERC Synergy Grant ImPACT).\n\nIn these Appendices to the main paper, we present additional case studies performed using Paracosm. We also provide additional description and a code sample for connection and composition of road elements, and a sample output to OpenDRIVE.\n\nTesting networks trained on standard datasets\n\nMany autonomous driving systems have on-board components for computer vision tasks like road segmentation, traffic light and traffic sign classification, vehicle detection, and optical flow. In the following tests, instead of training the SUT (a deep neural network) inside our simulation environment, we test components trained on real world datasets. We present results for road detection and vehicle detection.\n\nTest environment.\n\nFor the tests, we designed a highly parameterized environment using Paracosm’s programmatic interface. The environment consisted of 4 StraightRoadSegments connected by a CrossIntersection.\n\nThe test has three discrete parameters and three continuous parameters:\n\nThe number of lanes is either $2$ or $4$ (discrete).\n\nThe light condition corresponds to a morning, noon, or an evening drive (discrete).\n\nThe number of other cars on the road ranges from $2$ to $9$ (discrete).\n\nThe camera focal length is in the $[18,22]$ mm interval (continuous).\n\nThe height of the mounting point of the camera varies from $1.9$m to $2.2$m (continuous).\n\nFinally, the camera looks slightly down with a pitch angle between $-10$ and $-12$ degrees (continuous).\n\nMany of our parameters correspond to the vehicle’s camera. These were chosen because in preliminary tests, small perturbations to the camera’s properties led to drastically different results (see Figure [fig:focal_width_rd]). We perform 100 test iterations using Paracosm’s default test generation scheme.\n\n[fig:focal10]\n\n[fig:focal34]\n\n[fig:focal_width_rd]\n\nRoad segmentation.\n\nThe SUTs here take RGB images as input and return those pixels that are estimated to be a part of the road. We tested:\n\nthe convolutional neural network from Simonyan and Zisserman (popular as VGGNet),\n\nMultinet from Teichmann, Weber et al., a top performer on the KITTI Road Estimation Benchmark, and\n\nthe fully convolutional network by Long, Shelhamer and Darrell,\n\nAll three are trained on the KITTI road segmentation dataset (289 images). The first and third networks do not have a name, so we use initials of the authors’ names, SZ and LSD, respectively. Figure [fig:focal_graph] shows the results for the 100 test iterations ($x$-axis). We plot results in the order in which the tests were performed. The $y$-axis shows the percentage of the “ground truth” road identified as road by the method. A cursory look did not reveal any correlation between road segmentation performance and parameter choice. We observe that SZ is the best performer overall. What is quite striking is the results of LSD: in these tests, it either performs well, or not at all. Except for the poorly performing examples of LSD, false positives are not an issue, generally. Our hypothesis is that the networks do not generalize sufficiently from the limited training data and images that are too different from training lead to poor results.\n\nRoad segmentation results (% of the ground truth).\n\n[fig:focal_graph]\n\n\n[fig:car_detection]\n\nVehicle detection rates for the two SUTs.\n\nVehicle detection.\n\nThe SUTs here take RGB images as input and return bounding boxes around pixels that correspond to vehicles. Figure [fig:car_detection] shows an example of a vehicle detection system’s output. To detect other vehicles in the vicinity of the autonomous car, we used:\n\nthe single shot multibox detector (SSD), a deep neural network, trained with the Pascal Object Recognition Database Collection,\n\nMultinet like in the previous experiment.\n\nFigure 4 summarizes the results. The results are again in the order of the tests. In this experiment, we did not observe any false positives. Overall, Multinet performs better than SSD but these systems are much closer than in the previous experiment. While the detection rates may look disappointing, factors like occlusion as seen in Figure [fig:car_detection] make it difficult to detect all the cars. The two experiments presented here highlight the fact that even with quite narrow parameter ranges (especially for the camera), the quality of results can vary widely.\n\nAdditional tests on driving behavior\n\nIn the case studies that follow, we test the NVIDIA behavioral cloning framework to perform tests on autonomous vehicle behavior. The SUT takes RGB images as input and returns the corresponding throttle or steering control to be applied. The network is trained inside Paracosm’s simulation environment and the specific training procedure is also described for each case study presented. The primary aim of these case studies is to highlight specific testing features of Paracosm.\n\nRandom (dispersion is $0.105$)\n\n[fig:Pedestrian_rand_100]\n\nHalton (dispersion is $0.041$)\n\n[fig:Pedestrian_halton_100]\n\nRandom vs. Halton sampling for the pedestrian crossing experiment over various test iterations. The test parameters are the walking speed of the pedestrian ([0.5, 10] m/s) and distance from the car when the pedestrian starts crossing ([5, 60] m).\n \n(r)2-3(r)4-5 # tests Random Halton Random Halton \n50 $0.200$ $0.083$ $6\\%$ $8\\%$ \n100 $0.105$ $0.041$ $6\\%$ $8\\%$ \n200 $0.051$ $0.029$ $7\\%$ $8\\%$ \n400 $0.025$ $0.011$ $8.75\\%$ $8.25\\%$\n\n\nDynamic Pedestrian Behavior (low dispersion sequences revisited).\n\nThis case study has already been presented in the main paper. We now present results that underline the importance of low-dispersion sequences and how coverage improves with more test iterations. Note that the parameter ranges here are different (wider) than the study presented in the main paper. However, the SUT is the same. Figure [fig:PedestrianHalton] demonstrates the advantage of low-dispersion sampling over random sampling. Samples are more spread out for the Halton sequence (low-dispersion). In Table [tbl:crossing], we report the difference between random and Halton sampling for various numbers of test iterations. Halton sampling gives much better dispersion and even leads to more failure cases being revealed (especially for fewer test iterations).\n\nDistance covered (Z-axis, [0,120] m) in changing fog (X-axis, $[0,1]$) and light (Y-axis, $[0,1]$) conditions tested with 400 iterations of the Halton sequence. Green dots and red crosses denote the absence or presence of a collision. The car is trained with a fog density of 0 and light intensity of 0.5.\n\nChanging Environmental Settings.\n\nAs mentioned in the main paper, reactive variables can be used to parameterize environment settings so as to describe a large class of configurations. To demonstrate this, we train a model at fixed light intensity and no fog. This case study is similar to the Adaptive Cruise Control case study presented in the main paper. Here, we analyse the autonomous vehicle’s performance when the light intensity and fog density are varied. We report the overall distance covered, and whether a collision happened. Each test lasts 15 seconds. Parameter values are generated using the Halton sequence. Results are aggregated in Figure 5. The car performs best around the parameter values it was trained on. The distance that the car covers drops-off fairly quickly as fog density increases. Perturbations to light intensity often lead to scenarios with collisions.\n\nTest vehicle braking on seeing a red car coming from the opposite direction, even though there is a large distance to the lead car (car on the same lane).\n\n[fig:brake_opp_car]\n\nSpeed (X-axis, $[0,40]$ km/h) over time (Y-axis, $[0,8]$ sec) of car trained to follow a red car in the presence of another car coming from the opposite direction. Depending on the color of the incoming car, the speed of the car changes vis-à-vis the baseline driving with no other car.\n\n[fig:features_geometric]\n\nFeatures of Geometric Components.\n\nFor the case study above, the SUT is trained to follow a red lead car driving in front of it on a two-lane road. Under ideal conditions (conditions under which the SUT is trained), it is observed that the autonomous vehicle indeed follows the red lead car while maintaining a safe distance and not colliding with it. We now test how the SUT reacts to cars coming from the other direction. Though the test vehicle’s throttle should not be affected by cars coming from the other direction, it is likely that the SUT learnt to slow the car down when there are several red pixels in the camera image. Indeed, this seems to be the case. When we test with a red car coming from the other direction, our autonomous vehicle slows down in response to this car being close (see Figure [fig:brake_opp_car]). Speed is picked up again once this car passes. Perhaps more surprisingly, the vehicle also slows down when the car coming from the other direction is yellow or green, but has no affect when the car is blue. Figure 6 has plots of speed vs. time for the various cases, with the baseline being no car coming from the other direction.\n\nConnections of road segments (and OpenDRIVE output)\n\nT-Intersection with 2 lanes, long road segments, and traffic and pedestrian lights.\n\nT-Intersection with 4 lanes, short road segments, and no traffic or pedestrian lights.\n\n[fig:tintersection-opendrive_simple]\n\nIn this section we provide a code example to demonstrate composition of road elements using the connect operation between road elements. Paracosm supports complex road elements such as cross-intersections, T-intersections, and roundabouts. Connections can be established using the connect method, that takes physical connection identifiers and road elements as arguments. The connections are directed in order to compute the positions of the elements. One road element becomes the parent and it’s children are positioned relative to its position and the specified connection points. After an object is connected, a new composite road element which encapsulates all road elements along with requisite transformations (rotations and translations) is returned. The following example shows how road segments can be connected into a road network.\n\nlen = VarInterval(5, 100)\nnlanes = VarInterval({2, 4})\n// Create a parameterized T-intersection and three straight road elements (east, south, west)\nt = TIntersection(nlanes:nlanes)\ne = StraightRoadSegment(len:len, nlanes:nlanes)\ns = StraightRoadSegment(len:len, nlanes:nlanes)\nw = StraightRoadSegment(len:len, nlanes:nlanes)\n// connect and get new composite object\nnet = t.connect((t.ONE, e, e.TWO),\n (t.TWO, s, s.ONE), \n (t.THREE, w, w.ONE))\n\nIn this example, the T-intersection is not given a specific position or orientation. It is therefore instantiated at the origin. Road elements connecting to it are then positioned with respect to it. After connection, the composite road element net can be used for tests in simulation or to a standardized format (OpenDRIVE). Figure [fig:tintersections] shows some samples in the OpenDRIVE viewer.\n\nConnecting elements has two purposes. First, it allows Paracosm to perform sanity checks like proper positioning of road elements. Second, it creates an overlay graph of the road networks which can easily be followed by environment controlled vehicles. When a road network is created, the runtime system of Paracosm checks that compositions of road elements and intersections are topologically and geometrically valid. All road elements must be connected to a matching road correctly (for example, a 2-lane road segment cannot be connected to a 6-lane road segment directly), there can be no spatial overlaps between road segments, and the positions of the connection points must match.\n\nA large grid world with several connected road elements viewed in the default 3D simulator.\n\nIn general, Paracosm inherits all programming features of the underlying imperative programming model as well as reactive programming with streams. Thus, one can build complex urban settings through composition and iteration. For instance, the grid world shown in Figure 8 was created by iterating a simple road network.\n\nThis chapter is licensed under the terms of the Creative CommonsAttribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.\n\n.28em plus.1em minus.1em The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intendeduse is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.\n\n 1. https://www.weforum.org/agenda/2019/08/self-driving-vehicles-public-trust/↩\n\n 2. https://www.ntsb.gov/investigations/AccidentReports/Reports/HWY18MH010-prelim.pdf↩\n\n 3. 0 denotes no fog and 1 denotes very dense fog (exponential squared scale).↩\n\n 4. the monitor additionally calculates the mean distance of the test vehicle to the lead car during the test, which is used for later analysis.↩\n\n\n\nAccording to the results on the adaptive cruise control case study, which test parameter had the most significant impact on test failures involving collisions or vehicle inactivity?"} {"dataset": "lmsys/lmsys-chat-1m", "conversation_id": "12343ed24d8c4010a349dcf19ef03721", "conversation_index": 608811, "turn_index": 20, "tokens_gpt_oss_120b": 1160, "prompt": "Dec 29\nARRAIAL DO CABO SUPEARRAIAL DO CA\n\n$101.50\n\nDec 28\nBOOKING.COM BRASIL SSAO PAULO\n\n$565.86\n\nDec 28\nDROGARIA ARAUJO BELO HORIZONT\n\n$40.48\n\nDec 28\nLAMBEIJOS PET SHOP BELO HORIZONTE\n\n$15.37\n\nDec 28\nUNIVET LTDA BELO HORIZONTE MG\n\n$24.58\n\nDec 24\nHOTEL MONTE FELICE GRAMADO RS\n\n$15.70\n\nDec 23\nHR CAFE GRAMADO LTDAGRAMADO\n\n$83.55\n\nDec 23\nSNOWLAND PARTICIPACOGRAMADO RS\n\n$31.18\n\nDec 22\nAPLPAY ADEGA DO JACAGRAMADO BR\n\n$8.73\n\nDec 21\nGRAMADO TERMAS PARK GRAMADO RS\n\n$24.14\n\nDec 21\nGRAMADO TERMAS PARK GRAMADO RS\n\n$30.83\n\nDec 21\nHOTEL MONTE FELICE GRAMADO RS\n\n$159.95\n\nDec 21\nHOTEL NAME_2 GRAMADGRAMADO\n\n$2.75\n\nDec 21\nRESTAURANTE NOVA ERAPORTO ALEGRE\n\n$84.03\n\nDec 20\nCANTINA PASTASCIUTTAGRAMADO RS\n\n$49.71\n\nDec 19\nFARMACIA SAO JOAO GRAMADO\n\n$16.65\n\nDec 19\nLA DIVINA RESTAURANTGRAMADO\n\n$62.69\n\nDec 18\nHOTEL NAME_2 GRAMADGRAMADO\n\n$182.41\n\nDec 18\nMC2 ESTACIONAMENTOS GRAMADO RS\n\n$17.08\n\nDec 18\nOLIVAS GRAMADO BR\n\n$66.17\n\nDec 18\nTHE WAFFLE KING GRAMADO BR\n\n$7.21\n\nDec 16\nBARBEARIA SEU ELIAS BELO HORIZONTE MG\n\n$57.88\n\nDec 16\nINTEREST CHARGE ON PAY OVER TIME PURCHASES\n\n$17.80\n\nDec 16\nOURO MINAS BELO HORIZONTE BR\n\n$1.71\n\nDec 16\nOURO MINAS BELO HORIZONTE BR\n\n$56.93\n\nDec 14\nMULTIPLAN ADMINISTRABELO HORIZONTE\n\n$4.58\n\nDec 14\nO QUINTAL GRANU CAFEBELO HORIZONTE MG\n\n$56.67\n\n\nDec 13\nTOKAI SAVASSI BELO HORIZONTE\n\n$21.53\n\nDec 11\nLATE FEE\n\n$29.00\n\nDec 10\nEPA PLUS 013 BELO HORIZONT\n\n$79.54\n\nDec 10\nSAMS CONTAGEM\n\n$106.09\n\nDec 9\nDROGARIA ARAUJO BELO HORIZONT\n\n$6.20\n\nDec 8\nPIZZARELLA BELO HORIZONT\n\n$28.89\n\nDec 5\nOLEGARIO BELO HORIZONTE MG\n\n$31.76\n\nDec 3\nESTACIONAMENTO MINASBELO HORIZONT\n\n$3.10\n\nDec 3\nMINAS GRILL BELO HORIZONTE MG\n\n$14.07\n\nDec 3\nPOSTO MINAS SHOPPINGBELO HORIZONTE\n\n$41.27\n\nNov 16\nMEMBERSHIP FEE\n\n$695.00\n\nMay 31\nPending\nSTROBEL AMERICA\n\n$900.00\n\nMay 31\nPending\nTHEODORO E FILHOS DISTRIBUIDOR\n\n$5.14\n\n\nMay 30\nAPLPAY MGPOWER BELO HORIZONT\n\n$149.80\n\nMay 30\nMERCADOLIVRE*MERCADOSANTANA DE PARNAIBA\n\n$25.74\n\nMay 27\nDROGARIA ARAUJO BELO HORIZONT\n\n$1.51\n\nMay 27\nPERSARENTACAR CONTAGEM\n\n$21.57\n\nMay 27\nSUPERMERCADO E PADARBELO HORIZONTE\n\n$11.40\n\nMay 27\nSUPERMERCADO E PADARBELO HORIZONTE\n\n$104.23\n\nMay 26\nDROGARIA ARAUJO BELO HORIZONT\n\n$133.57\n\nMay 24\nWWW.STROBELAMERICA.CDOVER DE\n\n$1,787.00\n\nMay 22\nDROGARIA ARAUJO BELO HORIZONT\n\n$2.82\n\nMay 22\nSUPERMERCADO E PADARBELO HORIZONTE\n\n$42.54\n\nMay 21\nBETA DE PRATA LTDA ETIRADENTES\n\n$17.20\n\nMay 21\nJARDINS DE SANTO TIRADENTES\n\n$22.03\n\nMay 21\nPAG*ICASEIPRESENTES SAO PAULO\n\n$129.51\n\nMay 21\nPOSTO CN LTDA BELO HORIZONTE\n\n$52.35\n\nMay 21\nPOUSADA RECANTO A TIRADENTES\n\n$0.81\n\nMay 19\nPOSTO VIP BELO HORIZONT\n\n$22.73\n\nMay 19\nP"} {"dataset": "lmsys/lmsys-chat-1m", "conversation_id": "d312019f9a4d414cb77837dedc0091cd", "conversation_index": 376376, "turn_index": 8, "tokens_gpt_oss_120b": 998, "prompt": "아래 내용은 시간여행이라는 책을 요약한 내용입니다. 아래의 글을 바탕으로 2000자 내외의 칼럼을 작성해주세요.\n\n사람들은 몇 살 때부터 자기 혼자만의 생각을 즐기지 못하게 됐을까요?\n\n깨어 있으면서도 다른 사람들의 소리를 듣지 않는, 그런 방법이 어디 없을까?\n\n혹시 이런거 생각 안 해 봤니? 네가 하기 싫은 일에 대해 말하면 하고 싶은 일을 할 수 없다는 사실 말야.\n\n반대로 말해 볼게. 네가 하고 싶은 일에 대해 말하거나, 또 하고 싶은 일을 하고 있다고 느낄 때, 어땠니, 즉시 그 일을 할 수 있었지? 하늘을 나는 일 말야.\n\n가장 중요한 것은, 하고 싶은 일을 하고 있다는 느낌이야.\n\n정말로 하고 싶은 일이 있다면, 무엇을 하고 싶으며 왜 그 일을 하고 싶은지에 대해 생각해보란 말이야 . 바로 지금 그 일을 하는 중이라고 느낄 때까지 말야. 이게 바로 네가 배운 가장 중요한 교훈이야.\n\n고통의 사슬을 끊는 방법- 느낌으로 알 수 있다. 하기 싫은 일에 대해 생각하거나 말할 때, 항상 나쁜 감정을 갖게 된다. 고통의 사슬에서 빠져나올 수 있는 효과가 가장 빠른 방법은 감사한 마음을 가지는 것이다.\n\n1. 내가 하기 싫은 일을 알아내는 것\n2. 내가 하고 싶은 일을 결정하는 것\n3. 하고 싶은 일을 알아내자마자 실제로 그 일을 하고 있다고 느껴야 한다\n4. 내 소망을 행동으로 옮기는 단계\n\n우주에 존재하는 모든 사람들과 모든 사물은 끌림의 법칙을 따르고 있다.\n\n매 순간 나는 내가 원하는 상황을, 혹은 내가 원하지 않는 상황을 선택할 수 있다.\n\n내가 행복하거나 감사하는 마음을 가지고 다른 사람이나 사물의 긍정적인 면을 보면, 내가 원하는 상황과 조화를 이루게 된다.\n\n항상. 언제나 내 느낌을 믿어야 한다. 내 느낌이 바로 나의 길잡이다.\n\n내가 무엇에 관심을 가지느냐에 따라 내 기분이 좋아지거나 나빠지는 것이다.\n\n내가 할 일은 행복이 들어올 수 있게 나의 밸브를 열어주는 것\n\n오늘 나의 기분이 좋아지면 내일과 그 다음 날 일어날 상황도 마음에 들 것이다\n\n내 느낌과 생각이 어떻게 연결돼 있는지 이제 알겠어. 상황은 변하지 않아! 내 생각이 변하는거야.\n\n감사하는 마음은 항상 거기에 있다. 원래부터 거기에 있었다.\n\n나 자신이 행복하지 않으면 다른 사람에게도 행복을 나눠줄 수 없다\n\n내 몸은 진정한 내가 아니다. 단지 진정한 내가 재미있게 놀고 성장하고 기뻐할 수 있도록 해주는 도구일 뿐이다\n\n내 생각에 따라 내 기쁨과 고통을 선택할 수 있다는 것이다.\n\n내 주변의 상황이 내 감정을 통제하기 시작하면 난 항상 덫에 걸리게 된다. 내 느낌을 통제할 수 있도록 연습해야 한다. 그래야 내 생각도 통제하게 되고, 결국 진정한 자유를 누릴 수 있다.\n\n내 삶을 아주 소중하게 생각하면, 다른 사람들의 행동이나 말 때문에 괴로워하지 않게 될지도 몰라\n\n내가 원하지 않는 상황에 신경을 쓰면 결국 내가 원하는 상황을 거부하게 되는 것이다. 그렇기 때문에 내가 원하지 않는 상황을 구별해 내고 내가 원하는 상황을 찾아서 ‘좋아’라고 말하는 일이 가장 중요하다.\n\n내가 원하지 않는 상황을 밀어내려고 하기보다 내가 원하는 상황을 느긋하게 생각한다. 물론 말도 방향을 일러주지만, 느낌이야 말로 내가 받아들이는지 거부하는지를 가장 정확하게 알려주는 안내자다. 내가 원하는 상황에 대해 계속 이야기하면 모든 상황이 점점 좋아질 것이다.\n\n행복의 물결은 항상 나를 향해 흐르고 있다. 나는 매 순간 그 물결을 받아들이거나 거부한다. 그 물결을 받아들이거나 거부할 수 있는 사람은 오직 나 뿐이다.\n\n나는 모든 일이 순조롭게 흘러간다는 믿음을 전해야 한다. 나 자신의 선명한 경험을 많은 사람들에게 말하면 사람들은 자신이 밀어내야 할 상황은 세상에 없다는 사실을 이해하게 될것이다. 밀어내는 행위 자체가 행복을 쫓아내는 일이라는 사실도 또한 알게 될것이다."} {"dataset": "lmsys/lmsys-chat-1m", "conversation_id": "3a1bbab03247483d8016d0e7cae6e423", "conversation_index": 831882, "turn_index": 0, "tokens_gpt_oss_120b": 1256, "prompt": "Take the following text and put it into a Salesforce order JSON structure.\n\n2022 2022 | תנש LA.O.22.072522 שכר תנמזה דדוובבככלל :ןימזמ 'סמ מ\"עב הבונת ללאאררששיי תתססננככ ::ההננייממזזממ ההננחחתת הוקת חתפ,300 .ד.ת 21 םייפכ עיגי 4910201 ירשב תסנכ :ןסח מ םש 0508875313 6986028 יגח רומיל :ןופלט ::ממ..עע noyt@tnuva.co.il :ינורטקלא ראוד 33008811 ::םםככררפפססממ 17/01/23:הקפסא .ת 16/01/23 :אתכמסא .ת 03-7367706 :יצרא זכרמ ןופלט הרושל כ\"הס 'חיל ריחמ הדימ 'חי תומכ םיטרפ קפס דוק # 3% ןוטרק ל1ירט הבונת בלח ח\"ש 1,099.20 4.58 רטיל 240.00 4131074 1 ןירדהמ ח\"ש 117.12 7.32 רטיל 16.00 רטיל 1 רשעומ היוס הקשמ 4125578 2 1 ירט יעבט םעטב טייל היוס הקשמ ח\"ש 122.72 7.67 רטיל 16.00 4132552 3 רטיל ח\"ש 352.10 35.21 הדיחי 10.00 לכימ לקשמב 5% תירגלוב סואריפ 4120818 4 מ\"עב ח\"ש 113.90 22.78 םרגוליק 5.00 ידסומה קושל יעבט ופוט 10322786 5 הנכות 5% רג 50 עיבג הניבג הבונת תונבל ח\"ש 18.72 0.78 הדיחי 24.00 47782 6 צדב תוישעת ח\"ש 262.14 43.69 םרגוליק 6.00 גק 3 ידסומ קמע יתיתפ 4121341 7 ח\"ש 141.12 8.82 רטיל 16.00 םידקש+ תלוביש הקשמ 10325619 8 340 תויגוע םעטב GO בלח הקשמ גומלא ח\"ש 112.20 5.61 רטיל 20.00 10329686 9 ל\"מ ח\"ש 80.80 4.04 הדיחי 20.00 למ 250 קובקבב הבונת וקוש 4126872 10 תיבמ ח\"ש 69.44 4.34 רטיל 16.00 %1 ל1 קקפ +טרק ירט הבונת בלח 42435 11 Almog ח\"ש 37.85 37.85 םרגוליק 1.00 הינדעמל 5% תנדועמ תיתפצ 4136291 12 150 עיבג %3 ןבדבוד טרוגוי הלפוי ח\"ש 28.16 3.52 הדיחי 8.00 40066 13 ERP םרג 150 עיבג %3 קסרפא טרוגוי הלפוי תועצמאב ח\"ש 28.16 3.52 הדיחי 8.00 40080 14 םרג ח\"ש 2,583.63 החנה ינפל םוכס קפוה ח\"ש -0.15 החנה ח\"ש 2,583.63 מ\"עמ ינפל כ\"הס ח\"ש 439.22 מ\"עמ 17.00% ח\"ש 3,023.00 םולשתל כ\"הס 01/05/23 :ךיראת דע םולשתל .ישאר תונמזה זכרמ רושיא אלל וז הנמזהב יוניש עצבל ןיא * .םייקלח םיחולשמ ןיא * .הנמזהה ףוגב אלש םיטירפ לש הקפסא ןיא * 16/01/23 15:12 2 ךותמ 1 דומע 2022 2022 | תנש LA.O.22.072522 שכר תנמזה דדוובבככלל :ןימזמ 'סמ מ\"עב הבונת ללאאררששיי תתססננככ ::ההננייממזזממ ההננחחתת הוקת חתפ,300 .ד.ת 21 םייפכ עיגי 4910201 ירשב תסנכ :ןסח מ םש 0508875313 6986028 יגח רומיל :ןופלט ::ממ..עע noyt@tnuva.co.il :ינורטקלא ראוד 33008811 ::םםככררפפססממ 17/01/23:הקפסא .ת 16/01/23 :אתכמסא .ת 03-7367706 :יצרא זכרמ ןופלט מ\"עב הנכות תוישעת גומלא תיבמ Almog ERP תועצמאב קפוה בר דובכב לשא.ל 16/01/23 15:12 2 ךותמ 2 דומע"} {"dataset": "zai-org/LongAlign-10k", "example_id": "c9e3c0ac0c49ddef44fecbd340f4e1b1d979051d73febed2", "conversation_index": 3096, "turn_index": 0, "tokens_gpt_oss_120b": 11922, "prompt": "Former Secretary of State Condi Rice and Aretha Franklin teamed up for charity.\nThe former U.S. secretary of state and Franklin took the stage Tuesday evening at Philadelphia’s Mann Music Center in a rare duet for Rice, the classically trained pianist, and Franklin, the divalicious voice of a generation. Their aim was to raise money for inner-city youth of Philadelphia and Detroit and awareness for music and the arts.\nBut these are the same jackasses who love to tout their patriotism and love to throw 9/11 around when they are cheerleading for more war...all while claiming to support the heroes. In fact, the images and memories of 9/11 have been at the core of the Republican brand since September 11th, 2001. They have turned that disaster into slogans for their campaigns...time and time again. There were several years where you couldn't get them to stop campaigning on 9/11.... you couldn't avoid it if you were at a Republican event. Oh the irony.\nI bet these lying, hypocritical pieces of scum sleep well at night. I don't know how they do it... but I bet they do.\nThe bill was the James Zadroga 9/11 Health and Compensation Act. Read more from Huffpo.\nCongressman Anthony Weiner lashed out at Republicans. (This man should be Speaker of the House, Majority Leader, or Whip - with all due respect to Hoyer & Clyburn).\nLoved his response...where he basically told a Republican to sit _______ down. Most of the Dems in Congress are spineless...can't say I have seen this much passion before out of any of them. They are far too passive.\nShirley Sherrod, the fired and subsequently vindicated Department of Agriculture employee, said today she will sue the conservative blogger who posted edited video on the Internet last week that made her appear racist.\nThe posted video resulted in Sherrod being fired by the Obama administration, which was followed by public apologies from President Obama and Agriculture Secretary Tom Vilsack for dismissing her without learning the whole story. Even Fox News Host Bill O'Reilly apologized when it became apparent that the video he aired on his show was incomplete and that Sherrod was telling a story of personal growth, not bigotry.\nBut Andrew Breitbart, the blogger who posted the spliced video of Sherrod in the first place, has remained unapologetic, despite the fact that the full video features Sherrod telling an NAACP meeting how she became a better person and overcame her biases.\nAs originally posted, Sherrod spoke about not helping a white farmer as much as she could have. But the instance occurred a quarter century ago. The point of Sherrod's story was that she had been wrong. And the farmer in question jumped to her defense.\nI hope she sues the hell out of that jackass. Hopefully she can find a way to garnish his future earnings.\nIn his piece \"Endgame in Afghanistan\", British reporter Sean Smith provides a rare glimpse of the raw reality of the war in Afghanistan for American troops.\nI initially supported the idea of a surge in troops for Afghanistan because it was sold as part of a comprehensive strategy to make sure that the U.S. would get out or reduce its presence in a reasonable amount of time. But as I have read more, and observed the situation...it is becoming clear that this is not the case. So, for me, the pendulum is swinging the other way. I actually had doubts about Afghanistan as far back as 2005, when it became clear that it was becoming a misguided quagmire. Without a comprehensive approach and not enough troops, the situation was allowed to get out of control under the Bush Administration. Without addressing the economic situation, the dire poverty of the people, education, the poppy farming (a huge problem), failing to win the support of the people, and not having enough troops to hold ground... it was bound to turn into an ugly stalemate. But I also understood from the beginning - a belief that is now reaffirmed- that trying to build a nation out of a part of the world where the ingredients for such a task simply don't exist in any practical sense. In Afghanistan there is generally no sense of nationalism, a fractious & heterogeneous population with verious different tribal and ethnic groups, life being driven at the local level, and thus no sense of collective national pride to motivate men to want to stand up an Army and fight for their own Country. In other words, the whole mission is built on a false premise.\nIt is clear that corruption is strangling efforts there. Furthermore, it is clear that the Afghans are not taking responsibility for their own security... at least the enthusiasm just doesn't seem to be there. So if they aren't willing to fight... we certainly shouldn't have our soldiers fighting and dying there. In order to gain control of Afghanistan, it would require at least 500,000 American/NATO troops. I (and others) have said that for years. There is no way that the U.S. or its NATO allies will ever send that many troops into this war. Hell, the U.S., with its military stretched so thin...and with its hunger to maintain some sort of empire with troops all over the world, doesn't have the troops to send. So what are we doing? Furthermore, we have to consider the fact that the nation is broke. We can't afford it. With no clear definition of what success is...what winning is.... and no clear, achievable goals... this war (with an initial attack that I believed was warranted back in 2002, although dubious) has changed over the last 9 years into something that no longer makes sense. So my view of this war has changed quite a bit, especially over the last 18 months.\nBut it is clear that the Obama Administration has no plans to get out of Afghanistan anytime soon. Americans were sold fools gold with the promise of a troop drawdown...as I suspected. President Obama can't leave & is planning to have our troops there for a number of years down the road (I will go into that a little more in a separate commentary). Although I think the crap about terrorists attacking us from Afghanistan...and it will be doomsday if we leave... we must stay there so they don't hit us at home...is all a bunch of BS.\nWe are looking at 3-5 more years of this madness (at a minimum). Hard to understand...and getting harder everyday. I will write more on that in a future post.\nMore examples of how the Right makes things up. I have been trying to tell you all... they're crazy. But they are crazy and organized...which is what bugs the hell out of me.\nAnyone who has read this blog long enough should know how I feel about the NAACP. I think the time for this organization has come and gone. It's a relic of history.\nThis isn't Dubois' NAACP. This is a group that is trying to hang on to its glorious past, while looking for something to do in the present to appear relevant. That might explain why they tried to take on the Tea Party.... a complete 180 for an organization that had been dead for many years on the political front. Their rustiness might explain why they chose their issue, without realizing that they were going up against the most powerful PR/Political machine in modern American history (the Republican Right wing media). In other words, they should have had their **** together. It's as if the NAACP arrived here from another planet...and didn't know how the game was played. That's why NAACP President Ben Jealous (who was one of the first \"leaders\" to throw Shirley Sherrod under the bus without checking the story) ended up being...in his words... \"Snookered\". I mean.. they discarded Mrs. Sherrod like a dirty diaper. Yet another stain on an already mud soaked organization.\nStatement from Ben Jealous (in his effort to stay in front of whatever Faux News and others may have had planned).\nBen Jealous failed to get his facts together before making such an important statement. The result? He came away looking like a clown. The President of an organization with a legacy like that of the NAACP can't afford to be that sloppy. Even Mrs. Sherrod could not believe the way she had been discarded.\nThe NAACP is an embarrassment. This is a group that gives \"image\" awards to child molesters, rappers, & criminals....and holds them up as pillars of the \"Black Community\" who others should emulate. Disgusting. They don't represent me. It seems as if I am saying this to myself (and to others) more and more often these days - \"they don't represent me\". It comes to mind whenever there is an issue of race, where one group or leader is acting on behalf of Black people, or being perceived as speaking for an entire group. This situation makes me say the same thing once again - \"they don't represent me\". I always find myself explaining this to co-workers, as well as the fact that Blacks aren't a monolith.\nThe way that the NAACP President treated Sherrod only reinforces my view of the organization. On top of everything else, they have shown themselves to be sloppy & incompetent, at best. I am glad the group at least made an attempt to take on the Tea Party.... but it is clear that they were ill equipped for the mission. This is an organization that is stuck in the 20th Century. It's not ready for a multimedia war with the PR/Media behemoth of Right wing media. The NAACP's failure was a result of the fact that the organization is run by a bloated board of dozens of people (mostly old folks) who have held the group back for years....preventing modernization.\nEnter Amy Alexander... a former writer for Ben Jealous himself. Alexander recently wrote a revealing piece describing her experiences at the NAACP. She wrote the piece in response to the Sherrod fiasco. Her story provides an inside look at the organization and confirms what I suspected for years. It's a sad reflection of an organization that was once relevant and respected. But did I also mention that the story is hilarious? Alexander doesn't disappoint when it comes to keeping the reader's attention.\nPhoto taken from Gina McCauley's blog, What About Our Daughters.\nRepublicans successfully filibustered the Disclose Act this week. Once again, they have shown themselves to be hypocrites. On one hand they criticize the corrupt insider deal-making nature of Washington D.C. yet they don't want the public to know who is bankrolling Republican political ads. That would screw up their entire program of deceiving the American people.\nBut the bill isn't completely dead yet. It could come back for consideration later in the year. However, passing this legislation will be an uphill climb.\nBettye LaVette- Salt of the Earth (as good as the original? I think so).\nThe Obama Administration is under serious pressure to extend the Bush tax cuts for the rich. Insiders have been suggesting over the past week, that Obama & Co. is flirting with the idea of caving. Extending these tax cuts would dig the nation into an even bigger debt hole. Once again Republicans - who claim to be concerned about annual deficits & the national debt - are saying one thing and doing another, as is normal for them.\nThe Republican media has carefully framed the expiration of the tax cut as a tax increase by Obama. The way that they manipulate the public through the media is just uncanny. They are making the Bush tax policy appear as if it was a permanent law and the evil Democrats are going to radically change tax policy. Of course this is all BS. The tax policy was never permanent...which is why the legislation is sun shining to be reconsidered by Congress. The cuts were temporary. This misconception (that Republicans purposely created) has allowed them to frame the scheduled expiration as an increase. This has been another textbook example of the power & influence of Conservative right wing media.\nCommentary on why the Bush tax cuts should be allowed to expire.\nAlthough I think they waited a good six months too long. Better late than never. Read on.\nProgressive Talk show host and attorney Mike Papantonio discusses Right-wing extremism with civil rights watchdog Mark Potok.\nAhhh... nothing like the sights and sounds of Racist Republican policies falling apart. And so it begins. Round one goes to the sane & rational. But the Right vows to fight on.\nPHOENIX — A federal judge stepped into the fight over Arizona's immigration law at the last minute Wednesday, blocking the heart of the measure and defusing a confrontation between police and thousands of activists that had been building for months.\nProtesters who gathered at the state Capitol and outside the U.S. Embassy in Mexico City cheered when they heard the news. The governor, the law's authors and anti-illegal immigration groups vowed to fight on.\n\"It's a temporary bump in the road,\" Gov. Jan Brewer said.\nThe Clinton appointee said the controversial sections should be put on hold until the courts resolve the issues, including sections that required officers to check a person's immigration status while enforcing other laws.\nBolton delayed provisions that required immigrants to carry their papers and banned illegal immigrants from soliciting employment in public places — a move aimed at day laborers. In addition, she blocked officers from making warrantless arrests of suspected illegal immigrants for crimes that can lead to deportation.\nIt looks like this will drag on for quite some time, and will eventually be heard by the U.S. Supreme Court. The law was full of problems and had measures that were unconstitutional, even after the Arizona legislature tried to clean up their own mess by changing the text.\nSee our previous postings on the Arizona Immigration Law here and here.\nHis ideas on foreign policy and Arms Control warrant a second look.\nA recent piece in the New York Times, by writer Louis Uchitelle, highlights the difficulties faced by the millennial generation, also called Generation Y. But it hasn't been much better for my generation (Generation X).\nThe Great Depression damaged the self-confidence of the young, and that is beginning to happen now, according to pollsters, sociologists and economists. Young men in particular lost a sense of direction, Glen H. Elder Jr., a sociologist at the University of North Carolina, found in his study, “Children of the Great Depression.” In some cases they were forced into work they did not want — the issue for Scott Nicholson.\nMilitary service in World War II, along with the G.I. Bill and a booming economy, restored well-being; by the 1970s, when Mr. Elder did his retrospective study, the hardships of the Depression were more a memory than an open sore. “They came out of the war with purpose in their lives, and by age 40 most of them were doing well,” he said, speaking of his study in a recent interview.\nThe outlook this time is not so clear. Starved for jobs at adequate pay, the millennials tend to seek refuge in college and in the military and to put off marriage and child-bearing. Those who are working often stay with the jobs they have rather than jump to better paying but less secure ones, as young people seeking advancement normally do. And they are increasingly willing to forgo raises, or to settle for small ones.\nSee reports on Black poverty and the struggle with recession from the BBC and MSNBC. The Loop also has a good piece on childhood poverty from last year.\nI gave up on the American Dream some years ago. Now I am just seeking survival. I realized the Dream had become an empty slogan.... that the chances of achieving it were slim to none. I almost feel as if I have been lied to all this time. I started to feel this way at age 27 or 28, when I had begun to run-in-place. I just wasn't achieving, even though I was working everyday and studying hard. The last 5-10 years pretty much confirmed the doubts going through my mind at the time.\nNow I am about $80,000.00 in debt with no prospects for meaningful employment. The chances that I will be able to pay this money back is pretty close to zero. Over the last year I have been engaging in all sorts of maneuvers (including going back to school and making matters worse with more debt) to keep creditors at bay just a little while longer. But at some point in the next year or two, I will be looking at default. Once I run out of options and default, I will be ineligible for many....well.... basically all jobs in the Government sector...where I want to work. American Dream? Where?\nAnd the thought of finding a mate, developing relationships, and starting a family have been out of the question. Not even an option for me.... never has been. Men, of course, are damn near exclusively defined by what they do....how much money they earn, their ability to support a family, yadda yadda yadda. This is how women rate men. This is especially the case in the U.S., where women are shallow & materialistic to the extreme.\nIf a man can't meet certain professional/financial expectations...if you don't have a job with a respectable salary, if you can't support a family.... then you aren't considered a man at all. Chances for actually attracting a decent mate under such circumstances are remote. So in the last decade (really more than that) I have actually never bothered to try with any serious effort. All due to this elusive \"American Dream\".... trying to hold out for a meaningful job opportunity. The lost decade has really worn me down.\n(btw... I think one of the next bubbles to pop will be student loans....as students who leave college end up like me... stuck in BS service jobs earning less than $30,000 a year....or can't find work at all).\nNeedless to say I really hate my life...and the prospects for the future are bleak. With the incompetence of the Obama Administration....not having a clue of what the Hell to do to stimulate the economy...and with all the wars, the misguided foreign policy, and the financial mess (global and personal), I believe things will get worse.\nWhen asked if it is now harder or easier to attain \"the American Dream\" than it was for their parents' generation, 60 percent of Xavier's 1,022 respondents said it's getting harder; 68 percent, meanwhile, said it will be even harder for their children than it is for them.\nThe poll was conducted Feb. 14-21 by Fairbank, Maslin, Maullin, Metz & Associates (FM3) for Xavier's Institute for Politics and the American Dream, reaching respondents over 18 years old via land lines and cell phones. Margin of error was +/-3.1 percent. Xavier plans to release a similar poll every year.\nEven as people think it's getting harder to achieve the dream, Xavier found, they still believe--more or less--that it's possible with hard work: 35 percent said the American dream is \"entirely\" dependent on hard work, while 53 percent said it's roughly an even mix of hard work and good luck/circumstances. And 67 percent think they can achieve it in their lifetimes.\nFifty-eight percent, meanwhile, said America itself is in decline.\nI actually am a little more pessimistic about the \"hard work\" theme in the Xavier survey, because even with hard work, the Dream seems out of reach. And more important than that was the belief by the majority of respondents that the U.S. was in decline. (Something I have been saying for years).\nRead the summary of the Xavier University survey conducted earlier this year. See pdf.\nIs The American Dream Possible for Most Anymore? (Discussions w/ Barbara Ehrenreich).\nImpact of Corporate Based Economy (some links may be dead).\nE.J. Dionne posted a great commentary today on Truthdig.\nCan a nation remain a superpower if its internal politics are incorrigibly stupid?\nHe recently offered a rather brutal budget that includes severe cutbacks. I have doubts about some of them, but at least Cameron cared enough about reducing his country’s deficit that alongside the cuts, he also proposed an increase in the value-added tax from 17.5 percent to 20 percent. Imagine: a fiscal conservative who really is a fiscal conservative.\nThe simple truth is that the wealthy in the United States—the people who have made almost all the income gains in recent years—are undertaxed compared with everyone else.\nRead the Full Commentary from Truthdig.\nThis runs contrary to the Conservative argument that Obama has been doing nothing and wants to open the borders.\nConvicted felon Byron Williams loaded up his mother's Toyota Tundra with guns, strapped on his body armor and headed to San Francisco late Saturday night with one thing in mind: to kill workers at the American Civil Liberties Union and an environmental foundation, prosecutors say.\nWilliams, an anti-government zealot on parole for bank robbery, had hoped to \"start a revolution\" with the bloodshed at the ACLU and the Tides Foundation in San Francisco, authorities said.\nBut before he made it to the city, Williams was stopped at early Sunday by California Highway Patrol officers for speeding and driving erratically on westbound Interstate 580 west of Grand Avenue in Oakland.\nPolice say he then initiated a chaotic, 12-minute gunbattle with officers, firing a 9mm handgun, a.308-caliber rifle and a shotgun. He reloaded his weapons when he ran out of ammunition and stopped only after officers shot him in areas of his body not covered by his bullet-resistant vest, authorities said.\nOn Tuesday, Williams, 45, of Groveland (Tuolumne County) appeared in an Oakland courtroom on charges that he tried to murder four CHP officers. Authorities described him as a heavily armed man determined not to return to prison. Bullets from the suspect's rifle could penetrate ballistic body armor and vehicles, police said.\nAfter he was wounded and taken to Highland Hospital in Oakland, Williams told investigators \"his intention was to start a revolution by traveling to San Francisco and killing people of importance at the Tides Foundation and the ACLU,\" Oakland police Sgt. Michael Weisenberg wrote in a court affidavit.\nThe Tides Foundation is a liberal not for profit that supports Progressive causes. The otherwise obscure group is often the subject of attacks by Glenn Beck.\nRead more on Glenn Beck and the Tides Foundation from TPM. Also see commentary from the Guardian.\nWilliams was upset about \"Left-wing agenda\" of Congress. From TPM: (note: His mother, Janice Williams, seems to be as much of a Tea Party nut as her son is).\nThe man, identified by local news reports as Byron Williams of Groveland, was allegedly pulled over for driving erratically by the California Highway Patrol. As the officers approached his truck, they saw several guns and ammunition, according to police, and they saw the suspect reach for a handgun.\nThe officers ran back to their car and called for backup as the man allegedly opened fire. Officers reported seeing a handgun, a shotgun and a rifle, and that the shooter fired at least two of the weapons. The shootout -- which according to CHP involved 10 police officers and lasted about eight minutes -- left the suspect, who was wearing body armor, seriously wounded. He was taken to the hospital and is listed in stable condition.\nTwo officers were hurt by broken glass, but none were shot.\n\"There is no doubt in our mind, given the body armor and the extensive amount of ammunition he had, that he was on his way to do a very serious crime against either someone or a group of people,\" CHP Sgt. Trent Cross said.\nOfficers discovered a binder labeled \"California\" in the truck. It has so far been described only as a \"list.\"\nWhen local reporters called the truck's registrant, Janice Williams, she realized her truck, and her guns, were gone.\nWilliams told the San Francisco Chronicle that Byron, her 45-year-old son, was upset by \"the way Congress was railroading through all these left-wing agenda items.\"\n\"He hasn't been able to get a job because he's an ex-felon and nobody will hire him,\" she said, adding that he was angry about his unemployment and about \"what's happening to our country.\"\n\"I have no doubt it is him,\" Williams said. \"He's been upset with the direction the country is going.... He feels the people of this country are being raped by our government and politicians.\"\nJanice Williams said she kept the guns, which were locked in safe, because \"eventually, I think we're going to be caught up in a revolution.\" She also told the Chronicle that she had warned Byron that \"he didn't have to be on the front lines.\"\nIt's ironic that he was angry about unemployment, but was too much of an idiot to understand how the economy works and how Republicans sent the nation over a cliff. He didn't understand that the Recession officially started in 2007 and the fiscal crisis and job losses started in the Fall of 2008, caused by the failed policies of his Republican President. Nor did he understand that employment is a lagging economic indicator and will take years to recover. And it appears that he was also clueless about the fact that Republicans (for weeks) were trying to block any unemployment benefits that he may or may not have been entitled to.\nThe irony is amazing. And it's not just with this nut. We see this ignorance flowing all throughout the Tea Party crowd.\nIn another display of stunning hypocrisy, congressional Republicans continue their fight to reward the rich during the recession. During the upcoming campaigns, Republicans will claim to represent every American, but that’s hardly the case when it comes to tax policy. This time the battle is over whether Congress should extend Bush’s 2001 and 2003 Bush tax cuts and the GOP is siding with the rich over the middle-class.\nThis will come as no surprise to observant court watchers. Under John Roberts, but not necessarily because of John Roberts, is the most conservative it has been in decades. The general rightward shift will continue for the foreseeable future until one of the five conservative justices leaves (Kagan is replacing a liberal in John Paul Stevens). This is unlikely to happen anytime soon as one conservative justice, Anthony Kennedy has indicated he'd like to wait out Obama.\nKURTZ: Something really striking happened when I tried to book the segment you're about to see about minorities and the media.\nI talked to several very prominent African-American journalists who said they would love to come on the program but the subject was just too sensitive to discuss publicly, or their bosses did not want them speaking out in public.\nLook at the people who have gotten the latest primetime hosting jobs in cable news: Lawrence O'Donnell at MSNBC; Eliot Spitzer and Kathleen Parker at CNN. They join people like Sean and Bill and Keith and Rachel and Anderson. They join the Sunday show hosts and the evening news anchors and the principal network morning hosts. Not an African-American face among them except for GMA's Robin Roberts.\nIf you were 77-years-old, your spouse recently died and you faced two separate bouts of cancer would you continue working, or would you kick back and relax? This is the decision facing Supreme Court Justice Ruth Bader Ginsburg.\nKeith Olbermann nailed it with this commentary. Definitely one of the best he has ever done.\nAs I mentioned in my earlier comments, this is not just an issue of race. Race, in my view, should not necessarily be the focus of discussion. We don't need another race dabate. To be honest, they just don't do much. We have had that debate half a dozen times (or more) since Obama announced his run for the Presidency. What the nation should really be focused on is the obscene level of power held by Republican right wing media. This is something that I have blogged/written about probably dozens of times and it is the focus of my sidebar commentary, written a couple of years ago. I tried to warn people about just how big of a threat right wing media is.\nThe right wing media is simply the communications department for the Republican political machine. They have a very specific political agenda. Telling the truth and presenting actual news is not part of their program. It is a behemoth that is so powerful that when they tell Democrats - especially the Obama Administration - to jump, the Obama Administration asks how high? In my warning about Conservative media, I explain what their aims are... and that the playbook is an open secret.\nThe national mainstream media seems to ignore this information imbalance. No one wants to look into why Republican right wing media is allowed to control so much of the national debate. No one wants to look deeper into how Faux news (Radio Rwanda) is allowed to set the tone... by creating a certain narrative, and why mainstream media accepts their narrative as a legitimate starting point for their own reporting. Basically they take the Fox News narrative and they run with it...often without verifying information. Keep in mind, Fox News is, for all practical purposes, a propaganda operation. Fox News is so far removed from legitimate journalism that it's like night and day. In other words, legitimate news organizations have little reason to trust Fox News the way that they have been. Fox hasn't done the kind of reporting that deserves such a high level of trust. So the way that other networks parrot Fox (and other right wing outlets) is just incomprehensible to me. Furthermore, it's equally troubling how other networks fall over themselves to provide a platform for Republican right wing propaganda pundits, many of whom have little to no credibility. To make matters worse, these pundits are often unchallenged when they appear on the more legitimate networks. So what we have today is a situation where mainstream media is complicit in the misinforming and dumbing down of Americans. They have been co-opted by Republicans and used for propaganda purposes. This leaves a social/political environment where it is very difficult for a robust countervailing view to grow and challenge Conservative opinion.\nI have been sounding the alarm on this for a few years now. The dangers of Conservative media are real. We just had another domestic terrorist attack thwarted this week, this time in California... although it didn't make it as a top story on most networks (perhaps because this has become so common). But the suspect was another Terrorist & Tea Party nut angry about left-leaning politics. Read more here, here and here. He opened fire on police on a California freeway after realizing he was caught. Luckily this terrorist was stopped after a long shootout before he could carry out any mass killings. But can you guess what fueled him? (Check the links above).\nWhat we are in the midst of right now is an ideological war for the hearts and minds of Americans. It's a fight for the soul of the Country. Within that war is an information/PR battle. While President Obama shut down his campaign and his communications/PR war room... the Republicans never did. Republicans never ended their campaign after 2008, they simply changed its focus. Instead of running on a platform (as if they ever had one) or asking for votes, they shifted the focus to discrediting Obama, delegitimizing his Presidency, framing him as un-American, as an outsider, as dangerous, weak on security, etc. Republican/right wing media is in an all out war against President Obama, on two fronts. One is an information/image war... the other focuses on Policy (political obstruction, so that he is seen as a failure).\nThe kind of misinformation campaigns being waged right now... are, in many respects, similar to the kind of nonsense the CIA was doing 30 years ago in Countries around the world to confuse/mislead local populations, by putting out media stories favorable to the U.S., etc. The corporate PR companies today use some of those same strategies. The only difference now is that the tactics are being used against American citizens.\nProgressives are at a disadvantage, because they have no media infrastructure that can match what is coming out of Conservative media. The right is just too powerful when it comes to media dominance. It's just not a fair fight. And this is occurring at a time when we have a President who has become synonymous with the term 'PR disaster'. If the White House had a strong war room, had strong PR people, a President who was more engaged and in tune with what is happening on Main Street, someone controlling his image...who has his back, a team of brilliant advisors providing the best guidance & information, then the Democrats and this President would be in a better position to combat the right wing media. But none of these things exist in this White House in my opinion. There is no PR/media war room. If there is one.. they are incompetent...and those running it should be fired immediately. There are no strong PR people. If he has them, they should be fired tomorrow. If the President is engaged...and in tune with the Country, then I don't see it. I have been a serious observer of politics for 20 years...this is one of the worst Administrations in recent memory when it comes to basic situational awareness. How in the Hell do you go to Maine for vacation while the folks in the Gulf are still suffering and are struggling to keep their hotels and restaurants open, struggling to make their rent, struggling to save their livelihoods -and AFTER you encouraged Americans to go and spend money? Yeah.... we all need a vacation every now and then, but sometimes the Captain of the ship has to lead... especially on a ship that is taking on water. If you can't vacation in the Gulf, at least put off your vacation until things settle down....just out of respect for those who are suffering. If you must have a vacation... send the First Lady and the kids.... while you stay in Washington. This is about leadership folks. A walking PR disaster indeed.\nIf he has someone in charge of image... they should be fired forthwith. If President Obama has brilliant advisors... they have been giving the him terrible advice, at least on the image/PR front. We have had 747's flying over Manhattan for photo ops scaring the daylights out of New Yorkers (beyond comprehension in a post 9/11 World), the White House not responding to attacks from the right (even ridiculous lies), Van Jones & others being forced out, the President commenting on issues that he should not have commented on..at least without knowing all the facts, the lack of a clear plan...and the huge absence of at least the image of the President attempting to create jobs & alleviate the problem of unemployment....instead he is out playing Golf, the bungling of the PR response to the Gulf oil spill, the bungling of Gitmo, using horrible strategy in the Healthcare debate, allowing the right to dictate his foreign policy, reaching out to Republicans...even working to water down legislation for them just to be fooled in the end with the knowledge that they were never going to sign on to his policies, and generally looking weak as a man and as a President. Now enter Shirley Sherrod. This is perhaps the most breathtaking of all of the PR debacles thus far. This is one for the history books. It will take me quite a while to wrap my brain around this. But it basically encapsulates, in one event, all of these PR weaknesses...and all the examples of incompetence.\nThis whole situation is a perfect example of why Progressives need to establish their own PR/media infrastructure. If they don't, they will continue to struggle with Conservative media for years. The right will continue to poison the airwaves and threaten livelihoods, and they will continue to win elections when they shouldn't.\nBut Progressives have been slow to respond. This week, for the first time, I heard at least two major Progressive commentators acknowledge what I stated years ago... that we are in the midst of an ideological war for the hearts and minds of Americans. There has finally been an acknowledgment that there is an ongoing, well orchestrated PR war against President Obama and Progressive politics. Maybe this incident was the wake-up call that Progressives needed. It took years just to get to a point where this could be acknowledged. Unfortunately I didn't hear many calls for the establishment of a robust Progressive media infrastructure to rival the Republican machine (not by telling lies...but simply by telling the truth...doing real journalism). And yes, I support bringing back the Fairness Doctrine.\nThe most effective antidote for the madness that we are seeing today in the U.S., is a well informed electorate. There are other things that I believe must be done as well as part of reaching that goal.... such as requiring a certain amount of civics education, global education, geography, World history, political science, constitutional studies, etc... in all schools, public and private. It should be Federal Law. But the end goal should be a well informed electorate. This is what new media could help to accomplish.\nThe sooner Americans wake up to what KO, I & others are saying on this... the better.\nSeries of videos from the Shirley Sherrod Debacle.\nMany media organizations are rushing to seek repudiations of Shirley Sherrod from African-American leaders such as the NAACP’s Ben Jealous who quickly renounced Sherrod.\nBefore throwing Ms. Sherrod completely “under a bus,” I hope that they will consider the following remarks from the wife of the farmer whom Sherrod was supposedly “half-heartedly” helping.\nThe video in question originally shown on Fox turns out to be a FRAUD.\nIn case you want to see the videos in the Shirley Sharrod scam/fraud.\nI called, pressed 8, and then left a voicemail.\nGet the entire Sherrod speech, hear comments from the White farmer, and see other videos under the fold.\nWhite House spokesman Robert Gibbs sent Democrats into tailspin when he acknowledged Republicans could take control of the House in November. He's backtracked since, but his comments remind us of all the reasons we don't want to see the GOP back in power.\nI've been Black in America longer than 3 days.\nThe Tea Party’s leaders’ claims of race-neutrality ring hollow given their racially-inflammatory words and strategy. I exposed the Tea Party’s double-talk here at some length yesterday. Here’s new video from Think Progress (courtesy of Eric Wingerter over at the NAACP – thanks) from actual Tea Party rallies among their rank and file members out that highlights the rampant racism motivating the their critique of the Obama Administration.\nBlogger, Professor, & author Christopher Chambers discusses the Tea Party on the RT network.\nDr. Errington Thompson covered this today as well.\nWhat do you call a Black Man Who Is President of the United States?\nDylan Ratigan: He(President Barack Obama) didn’t do it. when the wall street guys got across the table from him you’re going to change our tax code, little boy? i think not because i tell you when you’re 75 or 80-year-old billionaire from new york who is looking at any government in this country that is trying to play with the tax code, you know who wins? the 80-year-old billionaire in new york every time. if it’s teddy roosevelt in the white house who is not intimidated by these types of people, he might say, listen, i don’t care who you are and how rich you are it’s not going happen but with this guy he bends over every time.\nThe First Lady discusses the threat that obesity poses to the Black Community.\nWhen someone volunteers to join the Armed Services of this country...to put their lives on the line for this country..\nthe least, and I do mean the least we can do in return, is make sure that they are taken care of if they return to this country damaged - IN ANY WAY.\nThis has not been the case, especially during the Bush years, where they fought increasing services to the common soldier, and did what they could to rig the system so that the soldiers that needed help, couldn't get help.\nThis sea change from the Obama Administration is wonderful, and should be praised.\nWill Obama Turn On Young Voters?\nAmazing stat for you: In presidential elections, the last time the Democratic candidate won a majority of the white vote was LBJ in 1964. Yes, it’s true. It’s been 46 years since a Democrat pulled off 50% of the white vote. This tells me a lot of things.\nWhites, males in particular, took a chance on Obama because they appreciated his even temper, his educational pedigree, and a belief he would look at all angles before committing to policy.\nMalia Obama,the daughter of US President Barack Obama and First Lady Michelle Obama, makes her way to board Marine One May 27, 2010 on the South Lawn of the White House in Washington, DC. Obama and his family were heading to Chicago to spend the Memorial Day weekend.\nFirst Daughter Malia Ann Obama turns 12 today. She's an All-American Girl.\nHave a great Holiday with family and friends.\nEnjoy a little Ray Charles.\nWith her 13th Grand Slam under her belt, Serena passes Billie Jean King on the all-time list of Grand Slam winners.\nThis is why I love the idea of Progressives taking their video cameras to Right wing events. I'm not sure if this was the case in this situation, but it shows that such a strategy could yield political treasure. Catching these hucksters in their macaca moments, or when they are telling lies should be an important part of Progressive efforts to get off of defense and take back the PR initiative.\nPropaganda and lies are a huge part of the GOP strategy. That goes for the Tea Party as well. They have to lie. So it should be the duty of Progressives to catch them when they do.\nSteele made a huge gaffe at a recent Republican event.... his biggest gaffe of all, according to Chris Good of the Atlantic. And the calls for Steele to resign have started to pour in. Steele stuck his foot....and leg in his mouth by making the claim that the war in Afghanistan was a war of Obama's choosing. Of course it was a war perpetrated by George W. Bush in 2002 and heavily supported by Republicans ever since. Steele's attempt to make the invasion Obama's idea now that it's becoming unpopular is amazing.\nHe went on to slam the war effort. Ironically, in his attempt to lie and mislead, Steele ended up accidentally telling a few truths about how daunting a task Afghanistan is and how the effort to nation build may be unrealistic. OOppps!\nBut once he was caught...he turned around and released a phony statement that basically said he didn't mean it, and he gave assurances that he supports the troops and the war effort. Hilarious.\nHe also describes the McChyrstal fiasco as \"comical\".\nI was perturbed to learn this week that NPR chose rapper/singer Lauryn Hill for their 50 Great Voices Series. This is their list of the 50 greatest singers ever, based in part on suggestions/voting from listeners. Each week, for the rest of this year, NPR will feature a new artist. Are you kidding me?\nI guess this is the point where I should add another disclaimer... I am not a fan of Lauryn Hill. (I'm not going to gain much support from this commentary). I have never cared for her music... and anyone reading this blog long enough should know how I feel about rap and the Hip Hop culture. However, I do respect Hill for being a talented musician. I can recall flipping through the channels several years ago and stumbling upon her unplugged performance. She's talented, there are no ifs ands, or buts about that.\nShe's a pretty good singer in my humble opinion. But one of the 50 Great voices in the entire world, EVER? Let's stop with the nonsense. Who made this decision at NPR? This is the point where NPR's 50 Greats adventure went from a serious project to more of a joke. This seemed to be more of a PR effort on the part of NPR to reach out to a younger, more Hip Hop oriented demographic... a group that doesn't listen to NPR in great numbers on a regular basis.\nOne big irony here is that Mary Christine Brockert & Roberta Flack have yet to make the list and may never be chosen. I doubt if NPR will pick both, and chances are slim that even one will be recognized. Yet Hill has borrowed heavily from these two singers during her career....covering their performances, using their riffs, their phrasing, their style and so on. Hill doesn't come close to Brockert or Flack when it comes to the art of singing. They would both blow Hill off stage. Hill's voice has a limited range...and her singing style is much more forced, her delivery more contrived. Her voice may be natural, but she's not a natural singer.\nAnother issue is the fact that Hill has had a limited career compared to the all-time great singers around the world. There is not that much material to base such a big decision on. Hill has benefited from an era of sampling and technology to enhance her performances and boost her career.\nI was lucky enough to have seen Brockert live in concert in St. Louis back in 1994. I was stunned by the performance... to this day I shudder thinking about what I saw and heard that night. How could such a powerful voice come out of such a small package, I thought to myself. THAT is a singer.... I can recall how she held one note for somewhere in the ballpark of 30 seconds...(not hyperbole), just to play with the crowd...which was screaming & throwing roses on stage at that point. Circular breathing perhaps? I'm not sure. But I had never heard a singer like her before or since.\nThe generations of \"singers\" who came after Luther Vandross and Whitney Houston (in her prime) just never quite measured up for me. Perhaps it's my old age (only 37 this month). But I have always identified with older generations of artists. That's not to say that the current crop of young singers isn't talented... there are definitely good singers still around...but they are hit & miss.\nThe list of 50 Great voices were supposed to be the very best in the world...the best ever...the best that some Countries had to offer...the best that some cultures had to offer. On an exclusive list like that, a Lauryn Hill just doesn't measure up in my book. If this were a list of 500 Great voices... then there might be enough room to fit her in. But this is a list of 50 of the best throughout modern human history.... since the introduction of the vinyl record over 100 years ago.\nHill is a folk hero to generations of young Black Americans (those 35 & under), although I don't really understand why. I have never understood this phenomenon. But that folk hero status may have something to do with the admiration her supporters have for her and may ultimately be the reason for the selection. That probably played a bigger role in her selection than her actual impact, voice, or singing prowess.\nThe cult of Lauryn Hill is one of many things in the \"Black Community\" that never made any sense to me...someone looking at it objectively from another perspective. Perhaps its that identity thing again... The fact that I don't identify with today's Black culture, and certainly don't identify with Hip Hop culture (which has largely taken the place of a real Black culture), probably has something to do with my bewilderment. But that's not a bad thing... because it allows me to make an unbiased assessment. I have been a connoisseur of good music for a long time...and I think I can say objectively & with confidence that Lauryn Hill doesn't make the cut.\nDid Congressman Gutierrez Fail Government 101?\nDisclaimer: I fully support immigration reform...and have supported it since this latest debate started (under George W. Bush).\nHowever, I have been extremely annoyed recently by Rep. Luis V. Gutierrez (D-IL) and his verbal attacks aimed at President Obama. Gutierrez and his supporters have been lobbying the President hard to magically make comprehensive immigration reform a reality. But really....what exactly do they want Obama to do? Am I missing something here?\nThe President can't create and pass legislation. That's the job of the Congress. Obama can't sign into law what doesn't exist. And even if Nancy Pelosi, Steny Hoyer, Harry Reid & others were to craft some sort of legislation...it would barely get through the House, and would be dead on arrival in the Senate. The math simply doesn't work and everyone knows it (at least everyone except for Gutierrez). I wrote several months ago that the part of Obama's agenda that dealt with immigration reform was probably unachievable and would likely have to be dropped from his list of goals. It's a lost cause. I never believed it should have been something that President Obama should even try...especially after seeing what happened to George W. Bush (by his own Party). It would be a huge waste of political capital, after he already wasted vast amounts of political capital in his first year, fighting for what ended up being a bad health care reform bill in my view. Obama could waste another year on immigration reform and be left with nothing to show for it in the end. Meanwhile, he would be so weakened by it that he wouldn't be able to get anything else accomplished. If that's not bad enough, his efforts would simply be used as a basis for Republicans to energize their supporters going into the midterm elections... creating even bigger losses for Democrats than would have been the case otherwise.\nA Republican President wasn't able to do it with a Republican Congress... Republicans blocked the effort. They are going to be even more aggressive in blocking Obama. Obviously nothing can be done before the mid-terms, and Gutierrez has to know this. Most members of Congress are worried about re-election and aren't going to touch the taboo subject (made taboo by Republicans/Tea Partiers). It's radioactive. There are just certain political and mathematical realities that cannot be ignored.\nOn the other side of the midterms, Republicans are expected to win back one, if not both Houses of Congress...making the passage of any legislation on comprehensive immigration reform impossible. Even under assessments friendly to the Democrats, Republicans are expected to gain so many seats...that even if they come up short on regaining the majority in the House or Senate, they will still be able to block legislation. So I just don't understand what Gutierrez, and his supporters, are so upset about. Why are they upset with Obama? Do they really believe he is Superman or some sort of political MacGyver? President Obama cannot make a proclamation and declare something to be law. Congressman Gutierrez and his supporters should be lobbying the other members of Congress.... not just the President (and perhaps they shouldn't be focused on the President at all in this case). Gutierrez should target Congressional Republicans in particular. That's where he should focus his anger. Not at the President.\nI guess Elena Kagan is such a blank slate that Republicans have to find others to attack.... even the dearly departed. They were apparently so desperate this week that they dug up a class paper that Kagan wrote decades ago, before she even entered law school. Of course they failed miserably with that effort.\nBut what annoyed me most was the way that Republican Senators on the Judiciary Committee used Kagan to attack Thurgood Marshall - a giant and American hero. Listen to the highlights of the hearings from last week, where Marshall is repeatedly brought up, attacked and diminished by Republicans. The effort was led by Senators Lindsey Graham, John Kyl, and Jeff Sessions. Their racism was plain to see and it was clear that they were playing to their base - their white Southern audiences back home. By targeting Marshall, they were attacking civil rights, desegregation, and equal justice...all the things he stood for. In their attacks (in front of at least one Marshall family member) they painted Marshall as a radical...as a judicial \"activist\". Marshall's opinions as a judge -upholding the idea of fairness, equal rights, etc- were out of the mainstream (although there is no evidence of that whatsoever). What they were really criticizing was Marshall's career before he became a judge. They were basically saying that Brown v. Topeka Board of Education was not decided correctly and was a result of Marshall's work as an attorney & agitator, and a result of an activist Supreme Court which overturned years of segregation. They suggested that since racism, esp. Jim Crow, was the law of the land, and was well established, settled law.... someone like Thurgood Marshall was a radical and activist because he came along and stirred things up by daring to challenge what had been legal precedent prior to May 1954. In other words, these Senators were sending the not-so-subtle message that Plessy v. Ferguson, the 1896 case that upheld segregation in schools, should have been allowed to stand as it was settled law. Racist to the core.\nWhy has the national corporate media allowed this to go almost unchallenged? I saw the segments on MSNBC...but I have not heard much from any other outlet. Unreal.\nSenator Al Franken provided a pretty good rebuttal - see video.\nBesides Al Franken.... few Senators/House members have spoken out against this blatant racism.\nSee Thurgood Marshall Jr's response. Hear an interview with Thurgood Marshall Jr. from NPR.\nThis comes on top of efforts by racist jackasses like Glenn Beck who want to hijack the anniversary of the 1963 March on Washington as a way to mock Dr. Martin Luther King and the Civil Rights struggle. Beck says he wants to \"restore honor\" and dignity to America..... as opposed to MLK, advancements in Civil Rights, and that nigra being elected President.\nIs the U.S. Headed For a Greek Style Economic Collapse?\nFinancial Historian Niall Ferguson thinks the U.S. could be headed for a big fall- IF it stays on its current course of spending much more than it collects in revenue. Hear discussion from OnPointRadio. (A Must Listen). He points out what I have been saying here since I started... this business as usual nonsense is unsustainable. Business as usual meaning spending billions (now well over a Trillion) on wars that we don't need, being afraid to talk about a sensible tax policy...because Republicans have turned just the mention of taxes into a \"taboo\", not working fast enough to build and feed a Green industry- continuing to assume that it will magically blossom on its own, not working hard enough to build small/medium businesses and to create jobs, and not investing in educating future generations (why do I have to go $80,000.00 in debt before I even have a chance to live...just because I want an education? True story....my story). Other countries educate their people at very low cost or for free in some cases. They put a priority on people, rather than huge military industrial complex's or phony corrupt stock markets.\nI am not as downbeat as some of the voices of gloom and doom. I don't think that the U.S. is headed for a quick collapse - at least not yet. In my mind, I am keeping my fingers crossed that it doesn't happen. The U.S. came close to this in the Fall of 2008. A collapse of the big banks was narrowly avoided. I am skeptical for the future though. The Obama administration can barely get a financial reform bill approved in the House & Senate - in fact, there are currently not enough votes to get the bill through the Senate. Republicans are blocking any efforts to make Progress on reforming an out of date system. With the prospect of clueless American voters returning these same Republicans to power in November of this year and again in 2012, there is no reason to be hopeful. Republicans are talking from both sides of their mouths. On one hand, Republicans say they are concerned about the debt, the deficit, and want to control spending, yet they blocked the Presidents Debt Commission - a commission tasked with steering the Country clear of complete economic collapse. I realize that this is part of the Republican Party's efforts to weaken Obama's ability to govern, so that he fails. But shouldn't they be more concerned about the Country???? Just a little concerned?\nI believe the U.S. may be headed for several months, if not years, of stagnant growth and high unemployment which will only make the debt problems worse. With the lack of revenue from business growth and job creation, the U.S. will have to borrow more to maintain basic services. Cuts...and I mean massive cuts, will be necessary if the U.S. is to avoid a Greek-like crisis. But I just don't see that happening. Politicians from both parties are more concerned with their political careers. David Walker, former comptroller general of the United States, warns that by 2035, the U.S. will only be able to afford to pay the interest on the debt and nothing else. Unreal! Why isn't this issue on the front burner?\n1. For now...the U.S. maintains the advantage of being the biggest economy in the World...and the biggest consumer. This means that other nations (who are now the producers) will be cooperative, for the most part, on trade, monetary policy, and will want to make sure that their chief consumer remains stable....so their economies can stay afloat.\n2. The U.S. currency is still....for now... the main reserve currency for the globe..... for now.\n3. The U.S. isn't as leveraged (debt as % of GDP) as many other nations in Europe.\nSo there is hope... but there has to be some action. Right now... no real action is on the horizon. That's what bothers me. The U.S. is stuck on stupid...stuck in some sort of perpetual malaise, thanks to the Republican Party and a stupid electorate that keeps supporting these jackasses.\n\nWho did the writer say should be the next Speaker of the House, Majority Leader, or Whip in place of the current Democratic leaders?"} {"dataset": "lmsys/lmsys-chat-1m", "conversation_id": "94cf32fa83d3496db05c153cd8d7ecde", "conversation_index": 933994, "turn_index": 8, "tokens_gpt_oss_120b": 1021, "prompt": "T 16-177,79,7 162,98,7 196,97,8 161,117,8 206,122,8 167,137,8 207,141,8 170,141,8 193,141,8 171,169,9 198,168,9 173,192,9 200,192,10;248,62,8 261,73,8 242,73,8 268,86,8 235,84,8 270,98,8 239,99,8 259,98,8 245,98,8 262,119,9 247,120,8 267,140,9 250,140,9;97,104,6 104,110,7 79,113,7 111,118,7 77,128,7 102,129,7 94,131,7 101,139,7 85,139,7 106,155,8 78,137,7 102,170,8 80,159,8;314,103,9 297,117,9 328,119,10 291,136,10 329,129,10 287,158,10 338,159,10 297,165,11 321,165,11 298,173,11 315,170,11 298,206,12 316,207,12;212,58,8 221,67,8 202,66,8 225,83,8 195,71,7 226,95,8 199,73,8 216,94,8 204,94,8 217,111,8 205,111,8 218,120,8 205,121,8;124,98,6 130,103,7 117,105,6 136,111,7 109,109,7 132,100,7 115,100,6 131,122,7 119,123,7 140,117,7 110,129,7 134,121,7 121,121,7;287,60,8 300,85,9 321,85,9 295,92,9 324,92,9 295,92,9 328,93,9 302,111,9 318,111,9 287,60,8 313,115,10 289,120,9 287,60,8\nT 17-192,84,7 164,100,7 196,100,7 160,122,7 206,124,8 166,140,8 206,142,8 170,143,8 193,142,8 171,172,8 197,170,9 172,194,9 198,193,9;248,64,8 262,74,8 242,74,8 268,88,8 236,86,7 270,100,8 237,100,8 258,100,8 244,99,8 261,121,8 246,121,8 265,142,9 248,142,8;97,108,6 103,113,6 78,115,7 110,120,7 75,131,7 102,131,7 93,133,7 100,141,7 84,142,7 106,140,7 77,139,7 111,174,8 79,174,8;209,61,7 218,69,7 199,68,7 224,84,7 191,75,7 224,97,7 198,74,7 214,98,7 201,97,7 212,122,8 204,121,8 209,145,8 206,144,8;122,100,6 129,105,6 115,107,6 135,113,7 108,111,6 131,101,6 114,102,6 130,125,7 117,125,7 141,119,7 109,122,7 132,122,7 107,121,7;292,80,8 298,88,8 315,89,8 292,96,8 315,102,8 293,108,8 315,109,8 299,112,8 311,112,8 297,121,9 310,121,9 295,112,8 309,121,9"} {"dataset": "lmsys/lmsys-chat-1m", "conversation_id": "dea119f1215b4e528e450a94fa4db644", "conversation_index": 64388, "turn_index": 4, "tokens_gpt_oss_120b": 995, "prompt": "d'après les données de la course reunion 1 course 2 qu'elle cheval est susceptible de gagner la course. 1 PREMIER ORDRE DEMURO C. GUARNIERI M.\nH3 59 13 2NAME_1 6NAME_1 1NAME_1 2NAME_1 1NAME_1 (22) 9NAME_1 4NAME_1 2NAME_1 2NAME_1\nWhitecliffsofdover-Law And Order\n2 DURANGO BARZALONA M. GRAFFARD (S) FH.\nH3 57.5 9 10NAME_1 5NAME_1 4NAME_1 (22) 1NAME_1 4NAME_1 10NAME_1\nDark Angel-Cersei\n3 GOGUEN SPAISE MOSSE G. DE MIEULLE (S) J.\nM3 57 6 7NAME_1 4NAME_1 3NAME_1 2NAME_1 (22) 4NAME_1 1NAME_1 2NAME_1 3NAME_1 6NAME_1\nDabirsim-Lisa Road\n4 WOODSTOCK CITY {GB} LEMAITRE NAME_2. HEAD (S) CHR.\nM3 57 11 1NAME_1 5NAME_1 5NAME_1 3NAME_1 3NAME_1 (22) 3NAME_1\nChurchill-White Witch\n5 AALTO LECOEUVRE C. DEVIN (S) HF.\nM3 57 16 3NAME_1 (22) 4NAME_1 1NAME_1 7NAME_1\nZelzal-Au Dessus\n6 MADAME DE SAXE {IRE} PESLIER O. LE DREN DOLEUZE R.\nNAME_25 57 12 5NAME_1 1NAME_1 (22) 5NAME_1 8NAME_1\nSaxon Warrior-Sariette\n7 SAYED PICCONE T. PANTALL HA.\nM3 56.5 10 4NAME_1 1NAME_1 2NAME_1 (22) 4NAME_1\nFrench Fifteen-Al Zarqa\n8 ENDS OF THE EARTH {GB} MURZABAYEV NAME_11. PANTALL HA.\nM3 56.5 8 6NAME_1 5NAME_1 4NAME_1 (22) 1NAME_1\nTerritories-Heavenly Scent\n9 OPIANA {GB} GUYON M. MONFORT (S) ED.\nNAME_25 56 3 1NAME_1 2NAME_1 3NAME_1 (22) 8NAME_1\nAnodin-Cosmique\n10 SEE THE LIGHT BOUTIN Hug. HERNON G.\nH3 55.5 2 1NAME_1 2NAME_1 (22) 5NAME_1 7NAME_1 8NAME_1 3NAME_1 2NAME_1 10NAME_1\nPedro The Great-Miss Raven\n11 I'M NAME_2 BELIEVER (oeil Aus) NAME_26 NAME_13.\nM3 55 5 1NAME_1 1NAME_1 6NAME_1 (22) 5NAME_1 5NAME_1 3NAME_1 9NAME_1\nSeabhac-Winds Up\n12 SOLEIL D'ARIZONA MENDIZABAL I. MONFORT (S) FR.\nNAME_25 55 4 6NAME_1 5NAME_1 1NAME_1\nPrince Gibraltar-Roxanne\n13 SAADIYAT {IRE} MADAMET NAME_2. LAFFON-PARIAS C.\nNAME_25 55 14 6NAME_1 3NAME_1 (22) 2NAME_1\nLope De Vega-Sweepstake\n14 MORE CRASTUS NAME_2. FERLAND (S) C.\nNAME_25 54.5 7 1NAME_1 8NAME_1 1NAME_1 2NAME_1 (22) 6NAME_1 5NAME_1 2NAME_1\nShalaa-Sport Game\n15 LARISSA'S WORLD POUCHIN NAME_2. WATTEL (S) S.\nNAME_25 54.5 1 8NAME_1 5NAME_1 2NAME_1\nSeabhac-Lake Baino"} {"dataset": "lmsys/lmsys-chat-1m", "conversation_id": "6375176063304a298e4c78d61dff7379", "conversation_index": 744550, "turn_index": 0, "tokens_gpt_oss_120b": 968, "prompt": "Holdings Data: 'Holding, Number of shares, NAME_1/ Average price per share ($), Client investment ($), Cost basis ($), Price per share on Feb 28 ($), Value on Feb 28 ($), Unrealized (tax) gain or loss ($), Investment return ($), Holding period\nFEDERATED HERMES, , , , , , , , ,\nSTRATEGIC VALUE DIVIDEND, , , , , , , , ,\nFUND IS, , , , , , , , ,\nSymbol: SVAIX, , , , , , , , ,\nTrade date: Feb 4 21, 1366.279, 5.160, 7050.00, 7050.00, 6.110, 8347.96, 1297.96, , LT\nTotal reinvested, 57.410, 5.678, , 326.02, 6.110, 350.77, 24.75, ,\nEAI: $313 Current yield: 3.60% Security total, 1423.689, 5.181, 7050.00, 7376.02, , 8698.73, 1322.71, 1648.73,\nO'SHAUGHNESSY MARKET LEADERS VALUE FUND CLASS, , , , , , , , ,\nI, , , , , , , , ,\nSymbol: OFVIX, , , , , , , , ,\nTrade date: Feb 4 21, 470.628, 14.979, 7050.00, 7050.00, 17.710, 8334.81, 1284.81, , LT\nTotal reinvested, 8.717, 17.859, , 155.68, 17.710, 154.38, -1.30, ,\nEAI: $159 Current yield: 1.87%, , , , , , , , ,\nSecurity total, 479.345, 15.032, 7050.00, 7205.68, , 8489.19, 1283.51, 1439.19,\nPACE portfolio total, , , $14100.00, $14581.70, , $17187.92, $2606.22, $3087.92,\nNAME_2 MULTI-STRATEGY, , , , , , , , ,\nINCOME FUND CLASS INSTL, , , , , , , , ,\nSymbol: ANGIX, , , , , , , , ,\nTrade date: Sep 23 20, 2408.841, 10.179, 24522.00, 24522.00, 10.110, 24353.38, -168.62, , LT\nTotal reinvested, 155.558, 10.351, , 1610.26, 10.110, 1572.69, -37.57, ,\nEAI: $1220 Current yield: 4.71%, , , , , , , , ,\nSecurity total, 2564.399, 10.190, 24522.00, 26132.26, , 25926.07, -206.19, 1404.07,\nNAME_3 & NAME_4 PREFERRED, , , , , , , , ,\nSEC & INC FUND I, , , , , , , , ,\nSymbol: CPXIX, , , , , , , , ,\nTrade date: Sep 23 20, 740.474, 13.910, 10300.00, 10300.00, 13.330, 9870.51, -429.49, , LT\nTotal reinvested, 57.946, 14.199, , 822.81, 13.330, 772.42, -50.39, ,\nEAI: $539 Current yield: 5.06%, , , , , , , , ,\nSecurity total, 798.420, 13.931, 10300.00, 11122.81, , 10642.93, -479.88, 342.93,'\n\nGet ONLY the following five pieces of information for each holding from the given holdings data: the company name, its symbol or CUSIP, the quantity or number of shares, the price or NAME_1, and the market value or value without outputting anything. Give ONLY an CSV response with the retrieved five properties for each holding:"} {"dataset": "zai-org/LongAlign-10k", "example_id": "9730354cdd3be173f55b58be764ce71bee3b3ae42517ced6", "conversation_index": 7422, "turn_index": 0, "tokens_gpt_oss_120b": 6233, "prompt": "/* language: CSS */\nbody\n{\nfont-size:.75em;\nfont-family: Verdana, Helvetica, Sans-Serif;\ncolor: #696969;\n}\na:link\n{\ncolor: #034af3;\ntext-decoration: underline;\n}\na:visited\n{\ncolor: #505abc;\n}\na:hover\n{\ncolor: #1d60ff;\ntext-decoration: none;\n}\na:active\n{\ncolor: #12eb87;\n}\na.selected\n{\ntext-decoration: overline;\n}\np\n{\nmargin-bottom: 20px;\nline-height: 1.6em;\n}\nul\n{\n list-style-type: none;\n margin: 0; \n padding: 0;\n}\n/* Primary Layout - Start */\n.page\n{\n/* width: 90%; */\nmargin-left: auto;\nmargin-right: auto;\n}\n#header\n{\nposition: relative;\nmargin-bottom: 0px;\ncolor: #000;\npadding: 0;\n}\n#header h1\n{\nfont-weight: bold;\npadding: 5px 0;\nmargin: 0;\ncolor: Gray;\nborder: none;\nfont-family: Arial, Helvetica, sans-serif;\nfont-size: 32px!important;\n}\n#header>.nof-action\n{\nmargin-top:10px;\nfloat: right;\npadding: 6px;\n}\n#header #title\n{\ndisplay:block;\nfloat:left;\ntext-align:left;\nmargin-right: 1em;\n}\n#main\n{\npadding: 30px 30px 15px 30px;\nbackground-color: #fff;\nmargin-bottom: 30px;\n}\n#footer\n{\ncolor: #999;\npadding: 10px 0;\ntext-align: center;\nline-height: normal;\nmargin: 0;\nfont-size:.9em;\n}\n.nof-object\n{\npadding: 3px;\n}\ndiv.nof-standalonetable>.nof-object, div.nof-objectview>.nof-object, div.nof-objectedit>.nof-object, div.nof-actiondialog>.nof-object\n{\nclear: left; /* So that it doesn't follow the wrapper history */\n}\n/* Primary Layout - End */\n\n/* History - Start */\n.nof-history\n{\nClear: left;\n}\n.nof-history.nof-object\n{\nFloat: left;\n}\n.nof-history button\n{\nFloat: left;\nheight:20px;\ncolor: #777777;\nmargin-bottom: 10px;\npadding-top: 1px;\n}\n.nof-history img\n{\nheight:16px;\ndisplay: inline;\nvertical-align:top;\nmargin: -2px 5px -4px 0;\n}\n.nof-history a:link\n{\ncolor: #438aff;\n}\n.nof-history a:visited\n{\ncolor: #707adc;\n}\n/* History - End */\n\n/* Tabbed History - Start */\n.nof-tabbed-history\n{\n height: 36px;\n border-bottom: 1px solid #777777;\n}\n\n/*all tabs*/\n.nof-tab\n{\nFloat: left;\nborder: 1px solid #777777;\nborder-top-left-radius: 5px;\nborder-top-right-radius: 5px;\nborder-bottom: none;\nbackground-color: #d3dce0;\npadding: 0px 3px 0px 3px;\n}\n\n.transient\n{\n Float: left;\n border: 1px solid #777777;\n margin-top: 10px;\n border-top-left-radius: 5px;\n border-top-right-radius: 5px;\n border-bottom: none;\n background-color: #efeeef;\n padding-top: 10px;\n padding-bottom: 10px;\n padding-left: 5px;\n padding-right: 5px;\n}\n\n.nof-tab img,.nof-tab a,.nof-tab form\n{\n float: left;\n padding-top: 10px;\n}\n.nof-tab.active\n{\nborder-bottom: 1px solid #efeeef;\nbackground-color: #efeeef;\n}\n.nof-tab a\n{\n width: 40px;\n text-overflow: ellipsis;\n white-space: nowrap;\n overflow: hidden;\n padding: 10px 0px 10px 3px;\n}\n.nof-tab.active a\n{\n width:auto;\n text-overflow:initial;\n white-space: normal;\n overflow: auto;\n}\n.nof-tab form button.nof-clear-item\n{\nFloat: left;\nheight:16px;\nwidth: 16px;\ncolor: #777777;\nbackground: transparent url(\"../Images/tab-close.png\") no-repeat;\ntext-indent: -1000px;\nmargin-right: 0px;\nborder: none;\npadding: 10px 0px 15px 0px;\n}\n\n.nof-tab form button.nof-clear-item:hover\n{\n background: transparent url(\"../Images/tab-close-hover.png\") no-repeat;\n}\n.nof-tabbed-history,.nof-tab\n{\n position: relative;\n}\n\n.nof-tabbed-history > form\n{\n display: none;\n}\n\n.nof-tab img\n{\nheight:16px;\n}\n.nof-tab a:link\n{\ncolor: #438aff;\ntext-decoration: none;\n}\n\n.nof-tab a:hover\n{\ntext-decoration: underline;\nbackground-color: initial;\n}\n\n.nof-tab.active a:hover\n{\ntext-decoration: none;\n}\n\n.nof-tab a:visited\n{\ncolor: #438aff;\n}\n.nof-tab.ui-menu\n{\n position: absolute;\n top: 30px;\n z-index: 10;\n}\n\n.nof-tab.ui-menu a\n{\n width: auto;\n}\n\n/* Tabbed History - End */\n\n\n/* Menus - Start */\ndiv.nof-servicelist\n{\nCLEAR:left;\nWIDTH: 100%;\nHEIGHT: 34px;\nBORDER: 0;\nMARGIN: 0;\nPADDING: 0;\nLIST-STYLE-TYPE: none;\nLIST-STYLE-IMAGE: none;\nBACKGROUND-COLOR: #cccccc;\n}\n/*Global menu styles*/\ndiv.nof-menu a {\nTEXT-DECORATION: none;\nCOLOR: #083755;\n}\ndiv.nof-servicelist div.nof-menu {\nPOSITION: relative;\nDISPLAY: block;\nFLOAT: left;\nPADDING: 0;\nMARGIN: 0;\nBORDER-RIGHT: white 1px solid;\nFONT: 8pt verdana, arial, helvetica;\n}\ndiv.nof-servicelist div.nof-menu > div.nof-menuname {\nDISPLAY: block;\nBORDER: 0;\nPADDING:10px 8px 0px 8px;\nBACKGROUND-COLOR: #87a8c3;\nCOLOR: #083755;\nTEXT-ALIGN: center;\nTEXT-DECORATION: none;\nZ-INDEX: 500;\nHEIGHT:24px;\nBORDER-BOTTOM: white 1px solid;\n}\ndiv.nof-servicelist div.nof-menu div.nof-menuname:hover {\nBACKGROUND: #75b755;\n}\ndiv.nof-objectview div.nof-menu, div.nof-standalonetable div.nof-menu {\nPOSITION: relative;\nFLOAT: left;\nDISPLAY: block;\nBORDER: 0px;\nPADDING: 10px 8px 0px 8px;\nBACKGROUND-COLOR: #87a8c3;\nCOLOR: #083755;\nFONT: 8pt verdana, arial, helvetica;\nTEXT-ALIGN: center;\nTEXT-DECORATION: none;\nHEIGHT:24px;\nBORDER-BOTTOM: white 1px solid;\nmargin-top: 17px;\n/*margin-left: 10px;*/\n}\ndiv.nof-objectview div.nof-menu:hover, div.nof-standalonetable div.nof-menu:hover {\nBACKGROUND: #75b755;\n}\ndiv.nof-objectview div.nof-menu[title=\"No Actions Available\"]\n{\ncolor: #666666;\nbackground: #cccccc;\n}\ndiv.nof-standalonetable div.nof-menu[title=\"No Actions Available\"]\n{\n display: none;\n}\ndiv.nof-menuitems {\nZ-INDEX: 2000;\nPOSITION: absolute;\nDISPLAY: none;\nTOP: 34px;\nLEFT: 0px;\nWIDTH: 170px;\nMARGIN: 0;\nPADDING: 0px;\nLIST-STYLE-TYPE: none;\nLIST-STYLE-IMAGE: none;\nFONT: 8pt verdana, arial, helvetica;\nBORDER-RIGHT:solid 50px transparent;\nBORDER-BOTTOM:solid 30px transparent;\n}\n\ndiv.nof-menu:hover div.nof-menuitems {\nDISPLAY: block;\n}\n.nof-menuitems form.nof-action {\nBORDER:0;\nMARGIN:0;\nPADDING:0;\n}\n.nof-menu button {\nBORDER:0;\nZ-INDEX: 2000;\nBACKGROUND-COLOR:#a5de8a;\nTEXT-ALIGN:left;\nFONT: 8pt verdana, arial, helvetica;\n}\n.nof-menu button:hover {\nBACKGROUND-COLOR:#75b755;\n}\n.nof-menuitems button,.nof-menuitems button:hover {\nZ-INDEX: 2000;\nDISPLAY:block;\nFLOAT:left;\nPOSITION:relative;\nCLEAR: left;\nWIDTH: 170px;\nMARGIN:0;\nPADDING-TOP: 4px;\nPADDING-RIGHT: 8px;\nPADDING-BOTTOM: 4px;\nPADDING-LEFT: 8px;\nBORDER-TOP: 1px #ffffff solid;\n}\n.nof-menuitems div.nof-action {\nZ-INDEX: 2000;\nDISPLAY:block;\nFLOAT:left;\nPOSITION:relative;\nCLEAR: left;\nWIDTH: 154px;\nMARGIN:0;\nPADDING-TOP: 4px;\nPADDING-RIGHT: 8px;\nPADDING-BOTTOM: 4px;\nPADDING-LEFT: 8px;\nBORDER-TOP: 1px #ffffff solid;\nBACKGROUND-COLOR:#cccccc;\nCOLOR:#666666;\nTEXT-ALIGN:left;\n}\n.nof-menuitems div.nof-action:hover {\nCOLOR:#999999;\n}\n/* Styling of SubMenu elements */\ndiv.nof-submenuitems {\nMARGIN:0 10px 0 10px;\nPADDING:0;\nLIST-STYLE:none;\nDISPLAY:none;\nWIDTH:170px;\nPOSITION:absolute;\nTOP:-1px;\nLEFT:160px;\n/*BORDER:0px solid #ffffff;*/\nborder-bottom: solid 30px transparent;\nborder-right: solid 30px transparent;\n}\ndiv.nof-submenu:hover > div.nof-submenuitems{\nDISPLAY: block;\n}\ndiv.nof-submenu button:hover, div.nof-submenu:hover{\nBACKGROUND: #75b755;\nBORDER-TOP:1px solid white;\n}\n.nof-submenu {\nZ-INDEX: 2000;\nPOSITION: relative;\nCLEAR: left;\nWIDTH: 146px;\nFLOAT:left;\nBORDER-TOP:1px solid white;\nPADDING-TOP: 4px;\nPADDING-RIGHT: 14px;\nPADDING-BOTTOM: 4px;\nPADDING-LEFT: 10px;\nBACKGROUND: #a5de8a;\nBACKGROUND-IMAGE:url(\"../Images/SubMenuPointer.png\");\nBACKGROUND-REPEAT:no-repeat;\nBACKGROUND-POSITION: right 50%;\nCOLOR:#000000;\nTEXT-ALIGN: left;\n}\ndiv.nof-submenuitems button {\nPOSITION:relative;\nZ-INDEX:2000;\nCLEAR:left;\nFLOAT:left;\nWIDTH:170px;\nBORDER-TOP:1px solid #ffffff;\nPADDING:4px 10px;\nBACKGROUND:#a5de8a;\nTEXT-ALIGN:left;\n}\n.nof-submenuitem button:hover{\nBACKGROUND: #75b755;\n}\n/* PROPERTY_based menu styles below... */\n.nof-property div.nof-menu,.nof-parameter div.nof-menu {\nFLOAT:right;\nposition:relative;\n}\n.nof-property div.nof-menu div.nof-menuname {\nDISPLAY: block;\nFLOAT:right;\nHEIGHT:17px;\nBORDER:1px solid white;\npadding:4px 4px 4px 4px;\nBACKGROUND-COLOR: #c2e6b2;\n}\n.nof-parameter div.nof-menu div.nof-menuname {\nDISPLAY: block;\nFLOAT:right;\nHEIGHT:17px;\n/* BORDER:1px solid white; */\npadding:4px 4px 4px 4px;\nBACKGROUND-COLOR: #9dbdd9;\n}\n.nof-property div.nof-submenu div.nof-menuname {\nDISPLAY: block;\nFLOAT:left;\nBORDER:0px solid red;\nMARGIN:0;\nHEIGHT:10px;\nPADDING:3px;\n}\n.nof-parameter div.nof-submenu div.nof-menuname {\nDISPLAY: block;\nFLOAT:left;\nBORDER:0px solid red;\nMARGIN:0;\nHEIGHT:15px;\nPADDING:0;\nMARGIN-LEFT:-2px;\nBACKGROUND-COLOR: #a5de8a;\n}\n.nof-parameter div.nof-submenu div.nof-menuname:hover {\nBACKGROUND-COLOR: #75b755;\n}\n.nof-property.nof-menuitems,.nof-parameter.nof-menuitems {\nZ-INDEX: 2000;\nPOSITION: absolute;\nFLOAT:left;\nDISPLAY: none;\nTOP: 24px;\nLEFT: 00px;\nWIDTH: 170px;\nBORDER:0px solid red;\nMARGIN: 0;\nPADDING: 0px;\nLIST-STYLE-TYPE: none;\nLIST-STYLE-IMAGE: none;\nFONT: 8pt verdana, arial, helvetica;\n}\n\n/*Find menus in Popup-dialogs*/\n\n.popup-dialog.nof-parameterlist\n{\n min-width: 500px;\n}\n\n.popup-dialog.nof-parameterlist > button\n{\n margin-top: 30px;\n}\n\n.popup-dialog.nof-menuitems\n{\n left: -135px;\n}\n\n.popup-dialog.nof-submenuitems\n{\n left: -180px;\n}\n\n/* Menus - End */\n\n/* Finder Menu - Start */\n/* Float the 'Find' menu to the right, within the property/parameter. */\n.nof-menu#Find\n{\nfloat: right;\n}\n/* Results of Find should be pale blue when within a dialog parameter... */\n.nof-parameter.nof-object.nof-collection-list table\n{\nbackground-color: #9dbdd9;\n}\n.nof-parameter.nof-object.nof-collection-list table div.nof-object\n{\nbackground-color: #9dbdd9;\n}\n/*... and pale green within a property in an edit view */\n.nof-property.nof-object.nof-collection-list table\n{\nbackground-color: #c2e6b2;\n}\n.nof-property.nof-object.nof-collection-list table div.nof-object\n{\nbackground-color: #c2e6b2;\n}\n/* Finder Menu - End */\n\n/* Property List - Start */\n.nof-propertylist\n{\nclear: left;\ndisplay: table;\nwidth:700px;\nfont-family:Verdana, Arial, Helvetica, sans-serif;\nfont-size:12px;\ncolor: #083755;\nmargin-top: 10px;\npadding-top: 10px;\n}\n.nof-property {\ndisplay: table-row;\nborder-bottom:1px solid white;\nbackground-color:#9dbdd9;\nheight:30px;\n}\n.nof-property label {\ndisplay: table-cell;\nbackground-color:#87a8c3;\ntext-align:right;\nvertical-align:middle;\nfont-weight:bold;\nborder-right:1px solid white;\nborder-bottom:1px solid white;\nwidth:160px;\npadding:3px;\n}\n.nof-property div.nof-value,.nof-property div.nof-object,.nof-property.nof-collection-summary {\ndisplay: table-cell;\nbackground-color:#9dbdd9;\nvertical-align:middle;\npadding:3px;\nborder-bottom:1px solid white;\n}\n/* Drag and drop */\n.nof-property div.nof-object.nof-validdrop.nof-withindrop {\nbackground-color:#00cc00;\n}\n.nof-property div.nof-object.nof-validdrop {\nbackground-color:#ffffff;\n}\n.nof-property select {\ndisplay:inline;\nvertical-align:middle;\npadding:3px;\n}\n.nof-property.nof-collection-list, .nof-property.nof-collection-table {\ndisplay: table-cell;\nbackground-color:#9dbdd9;\nvertical-align:middle;\npadding:0px;\nmargin-right:0px;\nborder-bottom:1px solid white;\n}\n.nof-collection-summary div.nof-object {\nborder-bottom:0px;\n}\n.nof-property img{\nheight:24px;\ndisplay: inline;\nvertical-align:middle;\nmargin: -2px 5px -4px 0;\n}\n.nof-property a,.nof-parameter a {\nheight:24px;\ndisplay: inline;\nvertical-align:middle;\nmargin: 0px 5px -4px 10px;\n}\n/* Float the 'Find' menu to the right, within the property/parameter. */\n.nof-object.nof-menu#Find {\nfloat: right;\n}\n/* For inputs marked up with Multiline attribute */\nTextArea\n{\nwidth: 520px;\n/* Not sure why but it appears these are not inherited from property list, so repeated here*/\nfont-family:Verdana, Arial, Helvetica, sans-serif;\nfont-size:12px;\ncolor: #083755;\n}\nTextArea[readonly=\"ReadOnly\"]\n{\nbackground-color:#9dbdd9;\n}\n.nof-property div form\n{\nfloat: left;\n}\n/* Property List - End */\n.nof-actiondialog {\nborder:1px solid #ffffff;\nbackground-color:#DCF2D3;\npadding:5px;\nwidth:700px;\nmargin:20px 0 0 0;\n}\n.nof-property.nof-actiondialog{\nwidth:540px;\n}\n.nof-parameter.nof-actiondialog {\nwidth:540px;\nbackground-color:#b4d3ee;\n}\n.nof-parameter.nof-actiondialog label {\nbackground-color:#87a8c3;\ncolor: white;\nborder-bottom:0;\n}\n.nof-parameter.nof-actiondialog.nof-value {\nbackground-color:#9dbdd9;\ncolor: white;\nborder-bottom:0;\n}\n.nof-parameterlist\n{\nclear: left;\ndisplay: table;\nwidth:100%;\nfont-family:Verdana, Arial, Helvetica, sans-serif;\nfont-size:12px;\ncolor: #083755;\nmargin-top: 10px;\npadding-top: 10px;\n}\n.nof-parameter {\ndisplay: table-row;\nborder-bottom:1px solid white;\nheight:30px;\n}\n.nof-parameter label {\ndisplay: table-cell;\nbackground-color:#A5DE8A;\ntext-align:right;\nvertical-align:middle;\nfont-weight:bold;\nborder-right:1px solid #DCF2D3; /* Light Green borders */\nborder-bottom:1px solid #DCF2D3;\nwidth:160px;\npadding:3px;\ncolor:#326F16; /*Darkest Green text */\n}\n.nof-parameter div.nof-value,.nof-parameter div.nof-object,.nof-parameter.nof-collection-summary {\ndisplay: table-cell;\nbackground-color:#C2E6B2;\nvertical-align:middle;\npadding:3px;\nborder-bottom:1px solid #DCF2D3;\n}\n/* Drag and drop */\n.nof-parameter div.nof-object.nof-validdrop.nof-withindrop {\nbackground-color:#00cc00;\n}\n.nof-parameter div.nof-object.nof-validdrop {\nbackground-color:#ffffff;\n}\n.nof-parameter select {\ndisplay: inline;\nvertical-align:middle;\npadding:3px;\n}\n.nof-parameter.nof-collection-list, .nof-parameter.nof-collection-table {\ndisplay: table-cell;\nvertical-align:middle;\npadding:0px;\nmargin-right:0px;\nborder-bottom:1px solid white;\n}\n.nof-parameter.nof-collection-summary div.nof-object {\nborder-bottom:0px;\n}\n.nof-parameter img {\nheight:24px;\nvertical-align:middle;\nmargin: -4px 5px -4px 0;\n}\n.nof-parameter div.nof-value div.nof-menu,.nof-parameter div.nof-object div.nof-menu,.nof-parameter.nof-collection-summary div.nof-menu {\nfloat:right;\nheight:24px;\nborder:1px solid white;\n}\n.nof-parameter div.nof-value div.nof-menu a,.nof-parameter div.nof-object div.nof-menu a,.nof-parameter.nof-collection-summary div.nof-menu a {\npadding: 5px 10px 5px 10px;\n}\n.nof-parameter div.nof-menu.nof-menuitems,.nof-parameter div.nof-object div.nof-menu.nof-menuitems,.nof-parameter.nof-collection-summary div.nof-menu.nof-menuitems {\ntop:24px;\n}\ninput[type=\"text\"]\n{\nwidth: 200px;\nborder: 1px solid #CCC;\n}\ninput[type=\"password\"]\n{\nwidth: 200px;\nborder: 1px solid #CCC;\n}\n.nof-parameter Button[name=\"Details\"]\n{\nborder: none;\nbackground-color: transparent;\ncolor: #034af3;\ntext-decoration: underline;\n}\n/* Tables - Start */\n.nof-collection-table,.nof-collection-list\n{\nclear: left;\npadding-top: 10px;\n}\ntable\n{\nborder: solid 1px #e8eef4;\nborder-collapse: collapse;\nwidth: 100%;\ncolor: #083755;\n}\n.nof-collection-table table,.nof-collection-list table\n{\nborder: solid 1px #e8eef4;\nborder-collapse: collapse;\nwidth: 540px;\npadding: 0px -6px 0 -5px;\n}\ntable td\n{\npadding: 5px;\nborder: solid 1px #e8eef4;\nvertical-align: middle;\n}\n.nof-property table div.nof-object\n{\ndisplay: table-cell;\nvertical-align: middle;\npadding: 0px;\nborder-bottom: 0;\n}\n.nof-property table div.nof-value\n{\ndisplay: table-cell;\nvertical-align: middle;\npadding: 0px;\nborder-bottom: 0;\n}\n.nof-parameter table div.nof-object\n{\ndisplay: table-cell;\nvertical-align: middle;\npadding: 0px;\nborder-bottom: 0;\n}\ntable td.nof-remove\n{\nborder-bottom: solid 1px #9dbdd9;\nborder-right: solid 1px #9dbdd9;\nborder-top: solid 2px #9dbdd9;\nbackground-color: #9dbdd9;\n}\ntable td img\n{\nheight: 24px;\nvertical-align: middle;\nmargin: -4px 5px -4px 0;\n}\n/* Padding either side of links within a table... */\ntable td a\n{\nmargin: 0px 10px 0px 10px;\nposition: relative;\ntop: -2px;\n}\ntable.ui-datepicker-calendar a\n{\nmargin: 0 0px;\n}\ntable th\n{\npadding: 6px 5px;\ntext-align: left;\nbackground-color: #678dab;\nborder: solid 1px #ffffff;\ncolor: #fff;\n}\ntable thempty\n{\nheight: 0;\npadding: 0;\nborder-top: 1px solid #ffffff;\nborder-left: 1px solid #9dbdd9;\nborder-right: 1px solid #9dbdd9; /*border-bottom: solid 1px #9dbdd9;*/\nbackground-color: #9dbdd9;\n}\ntd.nof-object button\n{\nfloat: right;\n}\ntd.nof-object button[title=\"Select\"]\n{\nfloat: none;\ndisplay: inline;\n}\n/* Tables - End */\n\n/* Paging - Start*/\n.nof-paging.nof-page-number\n{\npadding-top: 4px;\nfont-weight:bold;\nfloat:left;\n}\n.nof-paging.nof-total-count\n{\npadding-top: 4px;\nfont-weight:bold;\nfloat:right;\n}\n.nof-paging button\n{\nwidth: 24px;\nheight: 24px;\npadding: 30px 0 0;\nmargin: 0;\nborder: 0;\noverflow: hidden;\ncursor: pointer;\ncursor: hand;\ntext-indent: -1000em;\n}\n.nof-paging button[title=\"First\"]\n{\nbackground: transparent url(\"../Images/First.png\") no-repeat;\n}\n.nof-paging button[title=\"First\"][disabled=\"disabled\"]\n{\nbackground: transparent url(\"../Images/First-disabled.png\") no-repeat;\n}\n.nof-paging button[title=\"Previous\"]\n{\nbackground: transparent url(\"../Images/Previous.png\") no-repeat;\n}\n.nof-paging button[title=\"Previous\"][disabled=\"disabled\"]\n{\nbackground: transparent url(\"../Images/Previous-disabled.png\") no-repeat;\n}\n.nof-paging button[title=\"Next\"]\n{\nbackground: transparent url(\"../Images/Next.png\") no-repeat;\n}\n.nof-paging button[title=\"Next\"][disabled=\"disabled\"]\n{\nbackground: transparent url(\"../Images/Next-disabled.png\") no-repeat;\n}\n.nof-paging button[title=\"Last\"]\n{\nbackground: transparent url(\"../Images/Last.png\") no-repeat;\n}\n.nof-paging button[title=\"Last\"][disabled=\"disabled\"]\n{\nbackground: transparent url(\"../Images/Last-disabled.png\") no-repeat;\n}\n/* Paging - End */\n\n/* User Messages - Start */\n.field-validation-error,.nof-mandatory-field-indicator\n{\ncolor: #ff0000;\n}\n.input-validation-error\n{\nborder: 1px solid #ff0000;\nbackground-color: #ffeeee;\n}\n.validation-summary-errors\n{\nfont-weight: bold;\ncolor: #ff0000;\n}\n/* User Messages - End */\n\n/* Buttons - Start */\nbutton.nof-maximize\n{\n width: 16px;\n height: 16px;\n margin: 0;\n border: 0;\n overflow: hidden;\n cursor: pointer;\n cursor: hand;\n text-indent: -1000em;\n background: transparent url(\"../Images/Max.png\") no-repeat;\n}\nbutton.nof-minimize\n{\n width: 16px;\n height: 16px;\n margin: 0;\n border: 0;\n overflow: hidden;\n cursor: pointer;\n cursor: hand;\n text-indent: -1000em;\n background: transparent url(\"../Images/Min.png\") no-repeat;\n}\nbutton.nof-summary\n{\n width: 16px;\n height: 16px;\n margin: 0;\n border: 0;\n overflow: hidden;\n cursor: pointer;\n cursor: hand;\n text-indent: -1000em;\n background: transparent url(\"../Images/Min.png\") no-repeat;\n}\nbutton.nof-list\n{\n width: 16px;\n height: 16px;\n margin-right: 4px;\n border: 0;\n overflow: hidden;\n cursor: pointer;\n cursor: hand;\n text-indent: -1000em;\n background: transparent url(\"../Images/List.png\") no-repeat;\n}\nbutton.nof-table\n{\n width: 16px;\n height: 16px;\n margin: 0;\n border: 0;\n overflow: hidden;\n cursor: pointer;\n cursor: hand;\n text-indent: -1000em;\n background: transparent url(\"../Images/Table.png\") no-repeat;\n}\ndiv.nof-property div.nof-object form[action*=\"EditObject\"]\n{\n float: right;\n}\n\n/* Buttons - End */\n\n/* Errors - Start */\n.error \n{\n clear: left;\n}\n/* Errors - End */\n.nof-viewmodel > form >.nof-menu > div.nof-menuitems {\n display: block;\n position: relative;\n margin: 20px;\n width: auto;\n}\n\n.nof-viewmodel > form >.nof-menu > div.nof-menuitems > button,.nof-viewmodel > form >.nof-menu > div.nof-menuitems > button:hover {\nwidth: auto;\npadding: 10px;\nmargin: 10px;\nfloat: right;\n}\n\n.nof-viewmodel.nof-propertylist.nof-menu {\ndisplay: none; /*Hide the Find menu always*/\n}\n\n.nof-viewmodel.nof-propertylist\n{\n margin-top: 50px;\n}\n\n.nof-viewmodel.nof-menuname\n{\n display: none;\n}\n/*NOF 6.0 - New styling*/\n.body-content,.content-wrapper {\n padding-top: 30px;\n margin: auto;\n max-width: 1170px;\n}\n\n*,\n*:before,\n*:after {\n -webkit-box-sizing: content-box;\n -moz-box-sizing: content-box;\n box-sizing: content-box;\n}\n\n.nof-tab,.nof-tab:after,\n.nof-tab form,.nof-tab form:after,\n.nof-tab form button,.nof-tab form button:after {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\n\nbutton[title=\"OK\"], button[title=\"Apply\"], button[title=\"Edit\"], button[title=\"Save\"], button[name=\"Cancel\"]{\nbackground-color: #d3dce0;\npadding: 7px;\nmargin-right: 8px;\nmargin-top: 8px;\nborder: none;\nfont-size: 12px;\n}\n\nWhat CSS property is used to specify the box model for sizing the tab and form elements?"} {"dataset": "lmsys/lmsys-chat-1m", "conversation_id": "c0c828a890514c2e8409547b5276561c", "conversation_index": 227503, "turn_index": 2, "tokens_gpt_oss_120b": 985, "prompt": "Главная\nФутбол\nХоккей\nБаскетбол\nАвто\nТеннис\nБокс/MMA/UFC\nФигурное катание\nЛыжи\nБиатлон\nМедиафутбол\n\nЛуис Суарес\n\n\nМатч-центр\nНовости\nВидео\nБлоги\nПодкасты\nСтатусы\nБукмекеры\nИгры\nFantasy РПЛ\nКиберспорт\nРегистрация\nВход\nРеклама 18+Вчерашние матчи Матч-центр\n Футбол. Лига Конференций 2023/2024 таблица календарь статистика\n17:00\n Тобол– : –Хонка \n18:00\n Арарат-Армения– : –Эгнатия \n18:00\n Пюник– : –Транс \n20:00\n Алашкерт– : –Арсенал Тиват \n21:00\n Вадуц– : –Неман \n21:30\n Железничар– : –Динамо Минск\n Футбол. Кубок КОНКАКАФ 2023 таблица календарь статистика\nзавершен\n США1 : 1 пПанама \nзавершен\n Ямайка0 : 3Мексика\n Теннис. Уимблдон 2023. Полуфинал сетка календарь\n15:30\n Свитолина – : –Вондроушова \n17:00\n Жабер – : –Соболенко\nКто и сколько заработал на Хвиче? Расследование великой схемы\nКто и сколько заработал на Хвиче? Расследование великой схемы\nКак в «Друзьях Оушена».\n\n+125\n59\nГЛАВНЫЕ НОВОСТИ\nФутбол\n11:51 Месси опередил Холанда в голосовании за лучшего футболиста года от ESPN 9\nТеннис\n11:30 Спецпроект Хорошо знаете историю «Уимблдона»? Проверьте себя\nТеннис\n11:27 Агент Мирры Андреевой опроверг информацию о смене гражданства: «Она является только гражданкой России и будет далее выступать за свою страну» 28\nФутбол\n11:15 Деле Алли рассказал, что 6 недель провел в реабилитационном центре: «Я впал в зависимость от снотворного» 30\nФутбол\n10:36 Маттеус о Кейне в «Баварии»: «100 млн евро за игрока, которому почти 30 – это спорно. Продлить Левандовского было бы дешевле» 34\nФутбол\n10:30 Fantasy Малком, Промес, Сперцян и Чалов – самые дорогие в Fantasy РПЛ-2023/24 (9 млн). В прошлом сезоне столько не стоил никто 9\nФутбол\n10:23 Месси уступил в голосовании за лучшего спортсмена года от ESPN. Награду получил игрок НФЛ Махоумс 77\nФутбол\n10:05 Гендиректор «Зенита» на вопрос о давлении на Вендела: «Игрок под контрактом обязан находиться в команде» 60\nЛегкая атлетика\n09:57 Кастер Семеня о победе в Европейском суде по правам человека: «Я в восторге. Всегда выступала против любой дискриминации в спорте» 44\nБаскетбол\n09:14 Леброн Джеймс объявил, что продолжит карьеру: «Когда не смогу выкладываться на все 100%, тогда и закончу. Этот день не сегодня» 47\nПОКАЗАТЬ БОЛЬШЕ\nНовости Sports.ru в соцсетях и не только\n\n\n\n\n\nНОВОСТИ МОЕЙ КОМАНДЫ\nВыберите любимую команду\n\n\nВыберите вид спорта\nФУТБОЛ\n11:48 Семак о Венделе: «Пищи для размышлений нет, игрока – тоже. Тонкостей его контракта я не знаю, за это отвечает руководство» 7\n11:30 Кузяев о «Гавре»: «Понимаю, что не будем бороться за титул, но хочу попробовать себя в Европе. Франция – идеальный выбор для "} {"dataset": "zai-org/LongAlign-10k", "example_id": "33926435bb7e2877d05e7f2c275ee93d2b4b7aec9665e3d5", "conversation_index": 5382, "turn_index": 0, "tokens_gpt_oss_120b": 11244, "prompt": "2004-11-22 Assigned to MICROSOFT CORPORATION reassignment MICROSOFT CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: BARGERON, DAVID M., KRAMER, AXEL, RUIZ, RAFAEL R., ZAHER, MAGED N.\nAccording to an aspect of the present invention, a method for storing a digital annotation is provided. The method includes obtaining a notification of a creation of a digital annotation and identifying an anchor associated with the digital annotation. Once the digital annotation has been received and the anchor identified, a resource: locators representative of a location of the anchor is stored. The resource: locators includes a locator that contains at least one locator part. Additionally, a determination is made as to whether there is a content associated with the digital annotation, and if so, a resource: contents representative of the content associated with the digital annotation is stored.\nThis application claims the benefit of U.S. Provisional Application No. 60/514,443, filed Oct. 24, 2003, titled ANNOTATION OBJECT MODEL, which is incorporated by reference herein.\nIn general, the present invention relates to computer software and computer information storage, and in particular, to a system and method for processing digital annotations.\nAdvancements in computing devices, networks, storage devices, and digital inputs for computing devices has increased the variety of ways in which a user can interface with digital information. In particular, users now have the capability to digitally annotate, or mark up, electronic data. An “annotation,” as used herein, is additional data a user may anchor or associate with some original information. To “anchor” an annotation, as used herein, means to fix the annotation so that its association relative to some portion of original content remains the same. Typical examples of an annotation in the physical world are sticky notes attached to a document, the “sign here” flag attached to a document, bookmarks, pen scribbles on a document, etc. Digital annotations are similar to physical annotations except that they are associated with electronic data.\nNot only has the advancement of computing technology provided individuals with the ability to digitally annotate documents, the improvement in computer networking has provided users with the increased ability to transfer digital content and to interact with multiple users connected to a network, such as the Internet. For example, a user in California can digitally annotate a document and e-mail that document with the digital annotation to an individual in New York, who may then view the document and the digital annotation.\nAlthough users can digitally annotate digital content, current digital annotation techniques are limited in their flexibility and storage structure. Typically, digital annotations may only be anchored to one location within a document. Additionally, current digital annotation techniques do not store together an anchor and the content around the anchor. Not storing an anchor and the content around the anchor together requires that the digital annotations remain tied to the original information. This limits a user's ability to share portions of information, and to query digital annotations from multiple sources of information. For example, if a user has digitally annotated multiple different sources of information, such as two digital documents and a spreadsheet, the user may only search for digital annotations within each document individually. Still further, to query existing digital annotations, a user is required to query the original information source that contains the digital annotation. A user who does not have access to the original source of information does not have the ability to search for digital annotations that may be present.\nThus, based on the above-mentioned problems associated with existing digital annotation techniques, there is a need for a system and method that overcomes the deficiencies of existing digital annotation technology and provides an annotation object model that enables digital annotations where anchors and content around the anchors may be stored and represented at the same time and independent of the original information. Also desirable is a system and method that allows for multiple anchors for a single digital annotation. Still further, a system and storage structure is needed that provides users the ability to search for and view digital annotations independent of the original source of information.\nAccording to an aspect of the present invention, a method for storing a digital annotation is provided. The method includes obtaining a notification of a creation of a digital annotation and identifying an anchor associated with the digital annotation. Once the digital annotation has been received and the anchor identified, a resource object representative of a location of the anchor is stored. The resource object includes a locator that contains at least one locator part. Additionally, a determination is made as to whether there is any content associated with the digital annotation, and if so, a resource object representative of the content associated with the digital annotation is stored.\nAccording to another aspect of the present invention, an annotation object model for storing a digital annotation is provided. The annotation object model includes an annotation class, a resources class, and a locators class. The annotation class includes an author identification, identifying an author of the digital annotation, and at least one reference to a collection of resource objects. The resources class is configured to identify the anchor or the cargo of the digital annotation. The locators class includes at least one locator part. Each of the locator parts serve to locate the annotation's association with the content.\nAccording to still another aspect of the present invention, a method for querying for an existing digital annotation is provided. The method includes receiving a query parameter, determining a query type, and querying an annotation store for annotation objects matching the query parameter. The results returned from the query are compiled and at least one digital annotation that matches the query parameters is returned.\nFIG. 9 is a flow diagram illustrative of a digital annotation query routine, in accordance with an embodiment of the present invention.\nGenerally described, the present invention relates to a system and method for processing digital annotations. Processing can include, but is not limited to, creating, modifying, storing, and querying. In one aspect, digital annotation anchors, the annotation content of those anchors, and potentially the content surrounding those anchors (annotated content) may be stored together so that the digital annotation may be accessed, modified, and/or queried independent of the original information. In another aspect, the present invention may also provide for digital annotations with multiple anchors and multiple types of annotation content and annotated content. Additionally, queries may be performed on existing digital annotations resulting in the return of digital annotations, and the annotated content associated with those digital annotations, matching the query parameters.\nAs a result of the different aspects of the present invention, digital annotations may be associated with a specific portion, or portions, of electronic data, regardless of the format of the data and/or the storage location of the data. Additionally, in an example of a networked computing system, a user may create digital annotations within electronic data residing on a computing device at a first location. The digital annotations may be queried and obtained by a computing device at a second location, regardless of whether the computing device has direct access to the original data. In such an example, not only is the digital annotation accessible, but a user without the original data may also obtain the annotated content, thereby providing context to the digital annotation.\nAs will be appreciated, embodiments of the present invention may be utilized to digitally annotate any type of electronic data. For example, digital annotations may be created on a digital document, audio, spreadsheet, image, video, e-mail, slide presentation, or any other form of electronic data. While any form of digital data may be utilized by embodiments of the present invention, for consistency and ease of explanation, the example of a digital document (referred to herein as a “document”) will be utilized. The use of documents to describe examples and embodiments of the present invention is not intended to limit the scope of the present invention in any way and it will be understood that embodiments of the present invention are equally applicable to any type of electronic data.\nEmbodiments of the present invention expand the scope of types of possible digital annotations to include any form of digital metadata that is added to a document which does not change the content integrity of the document. For example, a digital annotation may be, but is not limited to, a digital “sticky note” (en electronic note included inside a document), a text comment, a snippet, inking in the margin of a document, a margin-bar (vertical indication next to content), highlights over existing content of a document, symbols, underlines, bookmarks (a pointer into some content that helps a user navigate and re-find a particular location), a hyperlink to some other document that the user has added to a document and annotated grouping or annotated relationships (a user might select the number of files and add a comment to that group, or the user might underline two sections in a document and connect both with one margin note). Processing (e.g., creation, storage, and querying) of exemplary digital annotations will be described in further detail below.\nFIG. 1A is a block diagram of an annotation object model graph 100 that may be utilized to represent any type of digital annotation, in accordance with an embodiment of the present invention. An annotation object model graph 100 includes three objects: an annotation object 101, two resources objects 105, and a locators object 107. Utilizing those three object types (i.e. classes), any type of digital annotation may be represented and stored regardless of the different combinations of data within each class. For example, the annotation object model graph 100 provides the ability to store annotation objects representative of digital annotations such as text comments, snippets, etc., which require different combinations from the locators class and the resources class. Utilizing the three abstract classes to define all digital annotations provides an object model that is concrete, but flexible.\nAs described in more detail below (FIG. 1B), the annotation class 101 which defines the digital annotation itself, includes information about the annotation, anchor resources for the annotation and cargo resources for the annotation. Anchor resources represent what the annotation is anchored on and cargo resources represent the content of the annotation. The resources are defined by the resource objects 105. Instances of resource objects include resource: locators 105A and a resource: contents 105B. The resource: locators 105A represents the characterization of the annotated content with which the annotation is associated, and possibly the “annotated content”. The resource: contents 105B represents the “annotation content.” Annotation content, as used herein, is content that defines the digital annotation, such as the text of a textual comment, the digital ink strokes of a hand drawn underline, the audio data from a spoken comment, etc. Annotated content, as used herein, is content from a document that has been annotated or is associated with a digital annotation. For example, consider a digital text bubble annotation that is anchored to the last name of a person in a human resources application. The resource: locators 105A contains a reference to the last name for the given employee, and the resource: contents 105B may contain the text of the text bubble annotation (annotation content).\nWhen a resource refers to a location, it is expressed with a locator 107A, which will be described in greater detail below. When a resource contains content directly, it is expressed by the content itself 109. When resources provide both reference and content, the digital annotation is expressed using both a locator 107A and content 109. Utilizing resources that contain both a locator 107A and the content 109 may be beneficial when a user wants to keep a reference to a document as well as a particular portion of the document itself. For example, it may be beneficial for clipping annotations to keep a reference to the clipping source (the original document) as well as a copy of the content of the clipping.\nAdditionally, an annotation object may include multiple references to a document, multiple references to multiple documents, and contain multiple contents as literal representations of the information that is referenced. In such embodiments, the annotation object model graph 100 may include any number and combination of resource: locators, resource: contents, locators, and content. For example, the locator 107A of the resource: locators 105A for a digital sticky note annotation may identify the location of the anchor of the sticky note; the locator 107A for a digital highlight annotation may express where the highlight is, etc. A resource containing a locator can also be used for content. This supports two scenarios. One, using preexisting data as the annotation content, e.g., an image attached to a paragraph, and two, leveraging native storage for well-known media types, e.g., an audio note as annotation content becomes an audio item instead of being literally represented in a digital annotation.\nFIG. 1B illustrates the annotation class 101 of the annotation object model graph 100 illustrated in FIG. 1A, in accordance with an embodiment of the present invention. Structurally the annotation class 101 typically includes a unique digital annotation identification (“unique ID”) 121A, a digital annotation type 121B, the digital annotation authors 121C identifying who created and/or changed the digital annotation, a creation date and time 121D, a modification date and time 121E, a reference to a collection of resources for anchors 121F of a digital annotation, a reference to a collection of resources for content 121G of the digital annotation, and relationships 121H.\nRelationships 121H express how anchors and content relate to one another. A relationship 121H may structurally include directionality (unidirectional or bidirectional), a name of the relationship, a source, and a destination. For example, a relationship 121H may identify a digital hyperlink annotation in a document that refers to some other document. Such a link is identified by directionality. Relationships 121H may also identify directionality between anchors and the annotated content. For example, an ink scribble might indicate arrows from the annotated content to a margin bar and from a margin bar to other annotated content.\nReferring to FIG. 1C, resources of the resource class may be used to represent either anchors or cargo of an annotation. For example, an instance of a resource may identify the location of an annotation's anchor (resource: locator) and another instance of a resource may identify the content type of the annotation (resource: content). Structurally, objects of the resource class, such as resource: locators 105A, may include a locator 107A. Alternatively, objects of the resource class may include content 109. A locator 107A, as defined by the locator class, describes the location or the identification of a particular item of information. Each locator 107A may contain zero or more locator parts, such as locator part 107B. Any number of locator parts may be contained within a locator 107A. Each locator may include one or more representations. Each representation may be considered as referring to the same item. For example, each representation may refer to the annotated content with which the annotation is associated. However, each representation may express an alternative way of referring to this annotated content.\nLocator parts may represent, but are not limited to: an identification of a digital annotation 111A, a marker 111B, a character offset 111C, a fingerprint 111D, a robust anchor 111E, and a range 111F. An identification 111A provides a unique identification for the locator 107A. A marker 111B provides an embedded unique identification for the locator 107A. A character offset 111C identifies the distance of the digital annotation from a particular location, such as the beginning of the document. Fingerprints 111D contain a unique hash-code for information associated with a digital annotation to identify the information. A robust anchor 111E determines anchoring by best statistical fit location based on key words and information saved with an anchor of the annotation. One skilled in the relevant art will appreciate that additional or alternative locator parts may be included in the locator 107A.\nEach locator part 107B is compared against data that is determined by evaluating the locator parts that precede it in the locator 107A. For example, a group of locator parts that make up a locator 107A may identify a location within a document where an annotation belongs. For instance, the locator \\\\ABCD\\public\\misc.doc#3rdParagraph indicates that an annotation is associated with the third paragraph of the document misc.doc located in the \\public directory on the \\\\ABCD server. \\\\ABCD, \\public, misc.doc, and 3rdParagraph are each locator parts that make up the locator \\\\ABCD public\\misc.doc#3rdParagraph.\nIn one embodiment, the data itself for each locator part 107B may be expressed in Extensible Markup Language (“XML”) that conforms to a given type of an XML Schema Definition (“XSD schema”). In such an example, the locator part 107B structurally contains an XML Schema Identifier (“XSI type name”), identifying the XML schema type the data adheres to, a name space identifying the name space for the schema type, and data in XML representing the locator part. The use of XML and associated schemas is only an exemplary description, and any other language or schema type may be utilized with embodiments of the present invention. For example, the data for a locator part 107B may be represented in SGML.\nWith continued reference to FIG. 1C, structurally, the resource: contents 105B may contain content 109 that represents annotation content. In one embodiment, annotation content contained within the content 109 may conform to a given type of an XSD schema. In such an example, the content 109 structurally contains an XSI type name, identifying the XML schema type the data adheres to, and a name space Uniform Resource Identifier (“URI”), and the data itself in XML representing the annotation content. Examples of annotation content include, but are not limited to, text, images, ink, audio, Extensible Hyper-text Markup Language (“XHTML”), Transaction Authority Markup Language (“XAML”), video, spreadsheets, documents, etc.\nFIG. 2A illustrates a computing device-i 200 which may be used for processing digital annotations on a document, such as a margin-bar 205, in accordance with an embodiment of the present invention. The computing device-1 200 may be embodied as any one of a variety of computing devices that may be utilized for processing digital annotations. Examples of the computing device-1 200 include, but are not limited to, personal computing devices, handheld computing devices, server-based computing devices, personal digital assistants, mobile telephones, stand-alone memory devices, electronic devices having some type of memory, whether external or internal, removable or permanent, and the like.\nIn one embodiment of the present invention, the computing device-1 200 includes a storage device 213, a display 201, and one or more input devices 209. Also included in the computing device-1 200 is an operating system 215. Any type of operating system may be used with embodiments of the present invention. The operating system 215 of the computing device-1 200 may be used to control and execute digital annotation software 217 for performing different aspects of the present invention. In an illustrative embodiment of the present invention, the digital annotation software 217 may be a stand-alone software product. Alternatively, the digital annotation software 217 may be a software component integrated into other software products.\nWith continuing reference to FIG. 2A, a user (not shown) may view a document 203 on the display 201. Using an input device 209, such as a mouse pointer, a user can create a digital annotation 205 on the document 203. The digital annotation 205 is anchored to information within the document 203 that is proximate to the digital annotation 205. That information becomes annotated content 207. In an embodiment of a single computing device, such as computing device-1 200, the digital annotation 205 and the annotated content 207 to which it is anchored are represented as an annotation object, according to the annotation object model graph 100, and stored in an annotation store 211 contained on a storage device 213. In an alternative embodiment, the digital annotation 205 and a reference to the annotated content 207 is represented as an annotation object, according to the annotation object model graph 100, and stored in the annotation store 211. Storing an annotation object representative of the digital annotation 205 and the annotated content 207, or a reference to the annotated content 207, provides the user with the ability to perform several different functions with respect to the digital annotation 205 and the annotated content 207.\nFor example, after creation of the digital annotation 205, a user, at a later time, can view the document 203 and the digital annotation 205 would be represented on that document at the location it was originally created. Additionally, a user wanting to find the digital annotation 205 can perform a query on the annotation store 211 and the resulting digital annotation 205 and the associated annotated content 207 will be returned to the user either within the document 203 or independent of the document 203. Still further, a user can modify the digital annotation 205. For example, a user can expand the size of the digital annotation 205, thereby increasing the amount of annotated content 207. A user can delete the digital annotation 205, thereby removing the annotation object representative of the digital annotation 205 and the annotated content 207 from the annotation store 211 which resides on the storage device 213. Still further, a user can associate the digital annotation 205 with yet another portion of the document 203. For example, the user can anchor the digital annotation 205 to a second set of information within the document 203 such as text 219. The second set of information within the document 203 also becomes annotated content. Providing multiple anchors for a digital annotation increases the flexibility with which a document may be annotated.\nFIG. 2B illustrates an alternative embodiment of the present invention. Similar to the embodiment described with reference to FIG. 2A, the system illustrated in FIG. 2B includes a network of several computing devices 220, 230, 240. Computing devices 220 and 240 may be similar to computing device-1 200 (FIG. 2A) and include displays 221, 241, as well as input devices (not shown), an operating system (not shown), and possibly a storage device (also not shown). In the embodiment illustrated in FIG. 2B, the computing device-2 220 is connected to a network 250, such as the Internet or a private network, which may also be connected to any number of additional computing devices, such as computing device-3 240. Additionally, the computing device-2 220 may also be networked with a database 230 which may include an annotation store 231, similar to the annotation store 211 illustrated in FIG. 2A. Locating the annotation store 231 on the database 230 which is networked with multiple computing devices 220, 240 through the network 250 provides the ability for any of the multiple computing devices to search for and access digital annotations created at any of the computing devices, such as computing device-2 220 and/or computing device-3 240.\nIn an alternative embodiment, the computing devices 220, 240 may be integrated to share storage devices residing on those computing devices, thereby providing the ability for one computing device, such as computing device-2 220, to search the storage device of a second computing device, such as computing device-3 240, for existing annotation objects located on one of those computing devices.\nWith continuing reference to FIG. 2B, a user may create a digital annotation 225 which is anchored to information of a document 223. That information becomes annotated content 227. The digital annotation 225 and the associated annotated content 227 are defined by an annotation object, according to the annotation object model graph 100, and may be transferred from computing device-2 220 through the network 250 and stored in the annotation store 231 on the database 230. In such an embodiment, the digital annotation 225 may be generated and the associated annotation object which is stored on the annotation store 231 is created using techniques similar to those described with respect to FIG. 2A, and as described in further detail below.\nIn a networked embodiment, a user at a separate computing device, such as computing device-3 240, may query the annotation store 231, via the network 250, for an annotation object. For example, a user at computing device-3 240 may query the annotation store 231 for all annotation objects representative of digital annotations that are in the form of a margin bar within a document. The results of that query would return a digital annotation 245 with annotated content 247 which is representative of the digital annotation 225 created on computing device-2 220 and the annotated content 227 associated with that digital annotation 225. The digital annotation 245 and the associated annotated content 247 may be provided to a user at computing device-3 240 regardless of whether that user has a copy of the original document 223 on which the original digital annotation 225 was created. Additionally, the user at computing device-2 220 may query the annotation store 231 via the network 250 for any existing annotation objects representative of a digital annotation, and be provided with the option of viewing the digital annotation within the original document or alternatively, view the digital annotation and the associated annotated content independent of the document.\nIn yet another embodiment, a combination of the embodiments described with reference to FIG. 2A and FIG. 2B may be realized. In particular, digital annotations created on one computing device, such as computing device-1 200 (FIG. 2A) and/or computing device-2 220 (FIG. 2B), may have the representative annotation object according to the annotation object model graph 100 stored on a local storage device, such as storage device 213, and also stored in a network based annotation store 231 residing on a database 230. Regardless of where the annotation object representing a digital annotation, such as digital annotation 205, is stored, as described above, the annotation object model graph itself, and resulting instances of annotation objects, remain concrete, but flexible.\nFIG. 3 is a block diagram illustrating an example of an instance of annotation object 301, as part of the annotation object graph 300, for a digital annotation having only one anchor. For example, a digital annotation having only one anchor may be, but is not limited to, a bookmark, which is a pointer to some location within a document. A user typically creates bookmarks to assist in navigation within a larger document. Even though an annotation object 300 for a digital bookmark annotation in its simplest form only contains one anchor, thereby necessitating only one resource: locators 305A, the locator 307A of the annotation object 300 typically includes more than one locator part, since the bookmark is pointing into a document. For example, the locator 307A may include two locator parts, locator part-1 311A and locator part-2 311B. In particular, locator part-1 311A identifies a document to which the digital bookmark annotation is associated, while locator part-2 311B identifies the particular location within the document where the digital bookmark annotation is anchored. For example, if the document is named document1.doc and the annotation is in the fourth paragraph, locator part-1 311A is document1.doc and locator part-2 311B is 4thParagraph.\nIn addition to the annotation object 300 containing one anchor which refers to resource 305A with locator 307A, the cargo of the annotation object 300 refers to a collection of resources that describe the content of the digital annotation itself, in this example, a bookmark. In particular, the cargo of the annotation object 300 references the resource: content 305B which includes the content 309 of the annotation. In an alternative embodiment, for structured annotations such as bookmarks, the annotation object may not include the content of the annotation type but instead refer to the type of annotation. Additionally, the annotation object 301 typically includes the author that created the digital annotation, the date and time it was created, and a date and time if it was modified.\nFIG. 4 illustrates a block diagram of an instance of annotation object 401, as part of the annotation object graph 400, representing a digital annotation that has two anchors, such as a digital hyperlink annotation. In the example of a digital hyperlink annotation, the two anchors are a source and a destination of the hyperlink. In order to adequately define digital annotations having two anchors, each anchor of the annotation object refers to a collection of resources. In particular, the instance of the source anchor refers to the resource: locators 405A and the instance of the destination anchor refers to the resource: locators 405B. The resource: locators 405A, for the hyperlink example, is defined by a locator 407A, that is typically positioned within some subpart of a document. Thus, there are at least two locator parts, locator part-1 411A, and locator part-2 411B. Locator part-1 411A identifies the document in which the source anchor of the digital annotation was created and locator part-2 411B identifies the particular location of the source anchor within that document.\nThe destination anchor, represented by locator 407B, may also have one or more locator parts. The number of locator parts identifying a destination anchor is dependent upon whether the destination is identified as a whole or as a part. For example, the destination anchor of the digital annotation may be to another document itself, identified as a whole, thereby only needing one locator part 411C to identify the destination document. If the destination anchor is to a particular location within the same document as the source anchor, it may be sufficiently identified with one locator part defining the particular location of the destination anchor within the original document. Alternatively, if the destination is a particular location within another document, the locator 407B may include at least two locator parts for the destination anchor, one locator part 411C identifying the destination document and the second locator part 411D identifying the particular location of the destination anchor within the destination document.\nIn addition to the annotation object 401 including references to a collection of resources for the anchors, a copy of the annotated content from those locations may also be included in the annotation object 401. For example, if the annotated content is to be included in the annotation object 401, the source anchor would also be represented by resource: contents 405C that includes the annotated content 409.\nThe annotation object 401 also describes the structure of the digital annotation. The annotation object 401 identifies the author of the digital annotation, the date and time it was created, and the date and time if it was modified.\nFIG. 5 is yet another example of an instance of an annotation object 501, as part of the annotation object model 500, in accordance with an embodiment of the present invention. In this example, the annotation object 501 represents a digital annotation containing one anchor and simple annotation cargo. Digital annotations of this type include, but are not limited to, a margin bar, highlight, underline, bookmark, or symbol. For these types of digital annotations, there are generally some visual properties of the digital annotation that need to be included as annotation cargo in the content 509 of the annotation object 501. For example, included as annotation cargo in the content 509 of an annotation object 501 representative of a margin bar, is the width and color of the margin bar.\nFor digital annotations having one anchor and simple annotation cargo, the annotation object 501 includes resource: locators 505A for the anchor and resource: contents 505B for the cargo of the anchor. The resource: contents 505B may include the actual annotation content 509 of the digital annotation. For example, the annotation content 509 for the margin bar includes, but is not limited to, the color of the margin bar and the width of the margin bar. The resource: locators 505A includes a locator 507A with locator parts defining the digital annotation and its anchor position. Again, referring to the example of a digital margin bar annotation, the locator 507A may contain two locator parts, locator part-1 511A and locator part-2 511B. Locator part-1 511A identifies the document in which the digital annotation is contained and locator part-2 511B identifies the location of the digital annotation within the document. The annotation object 501 may also include a copy of the annotated content at the identified location of the digital annotation.\nFIG. 5 is also illustrative of an annotation object 501, according to the annotation object graph 500, representative of digital annotations containing one anchor and complex annotation cargo. Such digital annotations include, but are not limited to, sticky notes, text comments, and footnotes. A sticky note is similar to the physical sticky notes that can be attached to a particular object. However, unlike physical sticky notes, electronic sticky notes can include different kinds of annotation cargo, such as rich text, ink, audio, images, video, etc.\nIn contrast to simple digital annotations, like margin bars and highlights, the annotation content type for digital annotations such as sticky notes is much more complex and thus, the final user experience with the digital annotation is more complex. From a representation point of view, all that changes within the annotation object 501 is the annotation content 509.\nAlso included in the annotation object 501 are resources 505A, 505B, and a locator 507A. Still referring to the example of a digital sticky note annotation, the resource: locators 505A for the anchor includes a locator 507A having numerous locator parts 511A, 511B. In particular, locator part-1 511A identifies a document in which the digital annotation was created and locator part-2 511B identifies the particular location within that document where the sticky note is to be displayed, i.e., its anchor location. The resource: contents 505B contains the annotation cargo 509 of the sticky note, such as text within the digital sticky note annotation. The annotation object 501 for a sticky note also identifies authors, the date and time it was created, a date and time if the digital annotation was modified, a reference to the content, and a reference to the resource.\nFIG. 6 illustrates block diagram of an instance of an annotation object, as part of the annotation object graph, representative of digital annotations containing two or more anchors and annotation cargo. Examples of such digital annotations include, but are not limited to, annotation relationships and annotated grouping. For such digital annotations, a user might select a number of files as a group and add a comment to that group. Alternatively, a user might underline two sections in one document and connect both sections with one digital margin note annotation. Both cases may be represented by using two or more anchors and annotation cargo. The two anchors might reference resources that identify sections of the same document or identify sections of different documents.\nThe annotation object 601, according to the annotation object graph 600, includes a an instance of the first anchor that references a collection of resources. In particular, the source anchor references the resource: locators-A 605A and the destination anchor references the resource: locators-B 605B. The anchors may be in the same document or in different documents. Each resource 605A, 605B includes locators 607A and 607B, respectively, each having numerous locator parts identifying the document and the location within the document at which the anchor resides. In particular, locator-A 607A may include locator part-1 611A, identifying the document in which the digital annotation was created, and locator part-2 611B, identifying where in the document the first anchor of the digital annotation is positioned. Likewise, locator-B 607B includes two locator parts, locator part-3 611C, identifying the document (either the same document or a different document), and a locator part-4 611D identifying the position of the second anchor of the digital annotation within the document.\nThe cargo of the annotation object references the resource: contents 605C of the resources class, which contains the annotation content 609 of the digital annotation. For example, if the digital annotation was a highlight connecting two sections, the annotation content 609 may include the color and shape of the digital annotation. Additionally, the annotation object 601 defines the digital annotation itself. Such information includes, but is not limited to, an identification of the author of the digital annotation, the date and time the digital annotation was created, and a date and time if the digital annotation was modified. As with the other examples, the annotation object 601 may also contain a copy of the annotated content from the document(s).\nIn an alternative embodiment, if the digital annotation only points to a section of a first document and to a second document as a whole, locator-B 607B may only include one locator part. That locator part would include a reference to the second document.\nFor each annotation object graph, such as the examples described above, typically one locator within a resource will describe a reference to data. However, there are some cases where multiple locators for one item of data may be useful, since the data can be retrieved in multiple ways. For example, the same document may be identified in many different ways (a url, a file path, or an id for the document in a database, etc.). Likewise, you can identify a position within a document multiple different ways... character offset, robust anchor, paragraph hash, etc. Additionally, annotation content may also be represented in multiple formats. For example, a handwritten margin note has an ink representation, but it may also have a translated text representation and an image representation. In such an example, each type of representation may be contained within one or more resource: content.\nFIGS. 7-9 illustrate different routines that may be implemented according to embodiments of the present invention. One skilled in the relevant art will appreciate that the routines may be implemented on a single computing device or distributed to a number of computing devices. FIGS. 7-9 illustrate blocks for performing specific functions. In alternative embodiments, more or fewer blocks may be used. In an embodiment of the present invention, a block may represent a software program, a software object, a software function, a software subroutine, a software method, a software instance, a code fragment, a hardware operation, or a user operation, singly or in combination.\nFIG. 7 is a flow diagram illustrative of annotation object creation routine 700 implemented by a computing device, such as computing device-1 200 (FIG. 2A), or computing devices 220 and/or 240 (FIG. 2B), in accordance with the embodiment of the present invention. The routine begins at block 701 and at block 703, a creation of a digital annotation is received. In an illustrative embodiment of the present invention, the digital annotation creation may be received from any input device that interfaces with a computing device, such as computing device-1 200. For example, an input device may be a mouse, keyboard, digital pen, etc., which is used to interact with a document that is displayed on a display of the computing device. At block 705, a determination is made as to the category type of the digital annotation that is being created. In one example, there are four categories of digital annotations: embellishments, attachments, relationships, and actions. Each of the four categories contains a number of predefined digital annotation types.\nReferring to FIG. 8, a digital annotation category determination subroutine 800, corresponding to block 705 (FIG. 7), implemented according to an aspect of the present invention, is described in further detail. The subroutine begins at block 801 and at block 803 the internal structure of a digital annotation is identified. The internal structure of a digital annotation may include multiple items. For example, the internal structure may identify how many anchors exist, whether annotation content exists, whether annotated content exists, etc. Upon determination of the internal structure of a digital annotation at block 803, at block 805, a category for the digital annotation is selected based on the identified internal structure. At block 807, the digital annotation category routine completes, returning control to the annotation object creation routine 700 (FIG. 7).\nAfter a category has been determined, at block 707, a type within that identified category is determined for the digital annotation. The digital annotation type implies a certain, well-defined, annotation content for the cargo of the annotation object. As described above, annotation content referenced by the cargo of an annotation object may be either referenced by a resource referring to the annotation cargo, and/or actually contained in the content of the annotation object. Additionally, as described above, the anchor of the digital annotation may reference a resource that includes the annotated content from the document that is associated with the digital annotation.\nFor example, for a digital ink underlined annotation, the annotation type is “underlined.” The anchor of the digital annotation object may refer to a resource: locator for the digital annotation identifying which piece of the source document is underlined, and refer to a resource: content that includes the actual information of the document that is underlined (annotated content). The cargo of the annotation object may reference a resource: content for the annotation content, such as an ink content object. The ink content object (annotation content) contains information as to what color the underline is and how thick it is drawn (as part of the ink object description), and the binary ink itself. An annotation object for an underline that was not done in ink may contain only an underline content object (annotation content) in the content. Such an annotation content contains information on color and thickness of the underline.\nFor a margin note that refers to an audio stream, the annotation type is “margin note.” The representative annotation object contains two resources. One resource, a resource: locator, identifies a reference to the audio content, and the other resource, content, defines the annotation content of the digital margin note annotation. The annotation object for the margin note contains information as to the default offset in the margin relative to the anchor position, the background color used for the margin note, a collapsed or expanded state, an identification of a collapsed icon if used, etc.\nThe categories that may be determined in block 705 each contain numerous types, that are determined for the digital annotation in block 707. In particular, for the embellishment category, the different types of digital annotations include, but are not limited to, highlight, bracket, margin bar, underline, grouping, bookmark, and symbol. As described above, for a highlight the annotation content includes, but is not limited to, a color and a thickness of the highlight. For a margin bar, the annotation content includes, but is not limited to, color, width, and the horizontal offset from the margin of the document. With underline, the annotation content includes, but is not limited to, the color and the width of the underline. For grouping, which indicates grouping of content from a document pointed to by an anchor (e.g., a circle around some words) the annotation content includes, but is not limited to, color, width, and kind. The kind may be the kind of grouping, such as a lasso or circle. For a bookmark, the annotation content includes, but is not limited to, the size of the bookmark, the horizontal offset from the margin, and location (e.g., top, bottom, left, right).\nFor the attachment category, the types of digital annotations include, but are not limited to, margin note, sticky note, footnote, and endnote. Additionally, each digital annotation may include an anchor type containing one or more pieces of associated annotation content. Each of the pieces of annotation content is referenced by the annotation object cargo and identified by an annotation content type. Examples of annotation content types include, but are not limited to, text, ink, audio, image, XAML, and documents. Annotation content types such as text, ink, audio, and image are defined by their properties so that they may be used as literal content. Alternatively, if they are referenced using a locator, their type is stated as part of the locator.\nFor a margin note, the associated annotation content may include a background color, collapsed or expanded, offset in margin, width, and height. For a sticky note, the annotation content may include a background color, collapsed or expanded, offset from anchor, width, and height. For a footnote, the annotation content may include background color, and an anchor indicator. For an endnote, the annotation content may include background color, and an anchor indicator.\nIn the relationships category, the types of digital annotations include, but are not limited to, links and connectors. A link points from one digital annotation to others (potentially vice versa) and may let the user navigate to other digital annotations. A connector indicates that two digital annotations are related. For example, a line from a digital annotation to a note may be a connector. If a connector is drawn in ink, the ink is added as annotation content in a representative annotation object.\nOnce the annotation type has been determined, at block 709, the position of the digital annotation is identified and an anchor for the digital annotation is created. As described above, an anchor for a digital annotation is defined by resource: locators that includes a locator with one or more locator parts, and stored in the annotation object. In addition to identifying the position of the anchor, at block 709 the cargo for the anchor is generated and stored. As described above, the cargo for the anchor references resources that include the content of the annotation itself. At decision block 711, a determination is made as to whether there is annotated content from the annotated document that is to be included in the annotation object. Annotated content may be the text surrounding the digital annotation or other types of information, as described above. If there is no annotated content to be included, at decision block 715, a determination is made as to whether there are additional locations for which anchors need to be defined for the digital annotation. If it is determined at decision block 715 that there are additional anchors to be defined, the routine returns to block 709 and repeats.\nReferring back to decision block 711, if it is determined that there is annotated content that is to be included in the annotation object, at block 713, the annotated content is obtained and included as a resource object of the anchor. In particular, the resources of the anchor will include a resource: content that includes the annotated content. After the annotated content is obtained, at decision block 715, the determination discussed above is made as to whether there are additional anchors to be defined. If there are no additional anchors to be defined, at block 717 an instance of an annotation object, according to the annotation object graph, representative of the digital annotation created by the user is generated and stored. After the annotation object is generated and stored, the routine completes, as illustrated by block 719.\nFIG. 9 is a flow diagram illustrative of a digital annotation query routine 900 which may be performed by a computing device on an annotation store containing annotation object(s), in accordance with an embodiment of the present invention.\nAt block 901, the digital annotation query routine 900 begins and at block 903, query parameters from a user are obtained. Query parameters may include any type of typical search parameters, such as a keyword search or other type of content search. Additionally, the query parameters may be based upon a digital annotation type search, such as searching for digital annotations which are underlines, highlights, margin bars, an annotation category search, etc.\nAt block 905, the type of query to be performed is determined. For example, a query type may be a request to only return one matching digital annotation. Alternatively, it may be requested to return all matching digital annotations. After the query parameters are obtained at block 903 and the query type is determined at block 905, the annotation store is queried for annotation objects matching the query parameters.\nAt decision block 909 a determination is made as to whether the query type identified at block 905 was a single digital annotation query. If the query type was not a single digital annotation query, at decision block 917, a subsequent determination is made as to the number of matching annotation objects resulting from the query performed at block 907. If there are no matching query results to the query performed by digital annotation query routine 900, at block 921, the query routine returns an empty list and completes at block 923. Alternatively, if there is at least one match to the query parameters, at block 919, a list of all matches is returned and presented to the user.\nReferring back to decision block 909, if it is determined that the query type requested was to return only one match, at decision block 911 it is determined whether there is at least one match returned from the query of annotation objects performed at block 907. If there is no match returned by the query, at block 915 a “no result” is presented to the user through a display and the routine completes at block 923. However, if there is at least one match, at block 913, the match is returned and displayed to the user. If only one match was requested and multiple matches were returned, the system selects only one of those matches to be returned and presents that match to the user. Selection of one match may be accomplished through any variety of selection techniques. For example, selection may be accomplished by selecting the first annotation object identified by the query and returning the corresponding digital annotation. After providing the appropriate response to a user, the digital annotation query routine 900 completes at block 923.\nIt will be appreciated that the embodiments described above in FIGS. 3-9 may be embodied on either a stand-alone computing device as illustrated in FIG. 1A, or in a networked environment as described in FIG. 2B, or in any other computing device configuration.\nstoring a second resource representative of a content associated with the digital annotation if it is determined that there is content associated with the digital annotation.\ndetermining a category representative of the digital annotation.\n3. The method of claim 2, wherein determining a category is dependent upon an internal structure of the digital annotation.\n4. The method of claim 2, wherein the determined category is selected from a group of categories including an embellishment, attachment, relationship, and action.\ndetermining a type within the determined category for the digital annotation.\n6. The method of claim 1, wherein the digital annotation includes a plurality of anchors.\n7. The method of claim 6, wherein a first anchor is within a first item of electronic data and a second anchor is within a second item of electronic data.\n8. The method of claim 6, wherein the digital annotation is a hyperlink including a source anchor and a destination anchor.\n9. The method of claim 8, wherein the source anchor identifies a first location within a first item of electronic data and the destination anchor identifies a second item of electronic data.\n10. The method of claim 1, wherein the at least one locator part is representative of a portion of the digital annotation.\n11. The method of claim 1, wherein the content is annotation content associated with the digital annotation.\n12. The method of claim 1, wherein the content is annotated content.\n13. The method of claim 1, further including the limitation of storing a relationship defining a style of the digital annotation.\n14. The method of claim 13, wherein the style of the digital annotation identifies a directionality of the digital annotation.\n15. The method of claim 1, wherein the content includes a plurality of annotation content.\na locators class including at least one locator part, wherein each locator part is representative of a portion of the digital annotation.\n17. The annotation object model of claim 16, wherein the resource class includes annotation content associated with the digital annotation.\n18. The annotation object model of claim 16, wherein the resource class includes a reference to annotation content associated with the digital annotation.\na second collection of resources, wherein the second collection of resources includes both annotated content associated with the digital annotation, and a reference to annotated content associated with the digital annotation.\n20. The annotation object model of claim 16, wherein the digital annotation is selected from a group of digital annotations consisting of: a sticky note, a text comment, a snippet, inking, a margin bar, a highlight, a symbol, an underline, a bookmark, and a hyperlink.\n21. The annotation object model of claim 16, wherein an annotation object according to the annotation object model is stored in an annotation store.\n22. The annotation object model of claim 21, wherein the annotation store includes a plurality of annotation objects.\n23. The annotation object model of claim 21, wherein the digital annotation is created on a computing device and wherein the annotation store is located on the same computing device.\n24. The annotation object model of claim 21, wherein the digital annotation is created on a first computing device and the annotation store is located on a second computing device.\n25. The annotation object model of claim 24, wherein an annotation object according to the annotation object model is generated on the first computing device and stored on the second computing device.\n26. The annotation object model of claim 16, wherein the annotation object is stored separate from an item of electronic data that is being annotated.\nreturning a digital annotation associated with an annotation object that matches the query parameters.\n28. The method of claim 27, wherein the query parameter is a keyword.\n29. The method of claim 27, wherein the query parameter is a content search parameter, identifying a particular content to query for.\n30. The method of claim 27, wherein the query parameter is an annotation type.\n31. The method of claim 27, wherein the query type is a request identifying how the query results are to be returned.\n32. The method of claim 27, wherein the query type identifies that only one matching digital annotation is to be returned.\n33. The method of claim 27, wherein the query type identifies that all matching digital annotations are to be returned.\n34. The method of claim 27, wherein querying an annotation store includes querying an annotation store for annotation objects.\n\nIf a query returns multiple matching digital annotations but the query type specified to only return one result, what method could be used to select which annotation is returned?"} {"dataset": "lmsys/lmsys-chat-1m", "conversation_id": "11e16a86ee0149f284eebdeca2b2d2be", "conversation_index": 808723, "turn_index": 4, "tokens_gpt_oss_120b": 981, "prompt": "### 지침: \n아래 정보에서 질문에 간단하게 마크다운 테이블로 답변한다. \n만약 답을 할 수 없으면 모르겠음이라고 답한다.\n\n### 정보:\n달콤한 국내산 꿀수박 그대로 탱거리열 대표브랜드 믿니다] 멈들거리를 고민혈 필요없이 통해 믿고 드시면 됩니다 \n 제배환 수분 가득 달콤한 국내산 국내산꿀수박 동부가애정과 신선하고건강한 점성을다해 │기원 노력에 문리망에서 자란 동산물 입니다. 동산물 입니다. 건강함 동산물입니다. 수분가득 국내산 시원하고 │재배한 몸이오고 과일 팽산지 동가에 플하되는상품염증용량 [정성을 다해비운 멈짐 편별하여 고객담께 동산물 입니다! \n 수분공급 국내산 꿀수박 미텔제(판관하세요) 주박원 수분이 많아 그당 먹어도 시원하지만 더 시원하게 먹으려면 병장 보관 니다 남원 수박은 밀폐용 기에 담아 보관하고 자른 수박원 밑에 접시 등으로 받치고 담아아 아래 수박이 무르지 않습니다. 여름과일의 대명사 시원스러운 색깔에 달고 시원한 맛과 가득해 칼증을불어 주에 더위를 가시게 하는 데는최고의 과일 품새산수분가득결정셀룰로오 \n 입에서 퍼지는 시원한 단맛 매정을 담아 동부가길러번 산지생산 그대로 [철저한 [선별을 통해 플은상품으로 보내드립니다. 다양한음식 이렇게 드셔보세요 다양하게 \n 미월게 보내드려요 날씨 상황에 따라 포장 방식은 변경 딜쉬 있습니다. \n제품명 국내산 달콤한 꿀 수박 무게 4-5kg 내외 원재료명 및 함량 수박 100% 원산지 국내산 보관방법 냉장보관 (수형 후 섭취를 권장합니다) 제조원 부산광역시 반여동 고객센터 02-2138 5193 010- -2470 소비기한은 신선식품으로 보관방법에 따라 상 수형 후 안내 방법에 따라 보관하시며 빠른 섭취를 권장합니다. 안내사항 지금 주문하면 언제 올까요? 모전 p시이전 [결제건에 담일배출이 진행됩니다. 배출 상품 등에 돼어도 풀고되었을 수도 있습니다. [판매처 및 문의하셔제 땀인 줄고완료된 [주문건원 교환반품 신선식품번정성지방성2087 떨어질 있으니 [재판매가 물가합니다. 의해 물가합니다 [바코기/잎양·기밀로 배송비는 고객는 부담 [입니다. 상품에 문제개 있을 경우 임의로 [상품을 따손 폐기처분한 [경우 교환/반품] 반품염 배송정보 모기재로 배출 및 반품의 경외 교환과 불가능하여반응시원명 배송비늘 고객는 판매자의 협의없이 반품/교환된 물가능하며 모든비용은 고객는 부담입니다 교환 및 진정 먼제 탁인을 함인 교환,반품 진행하고 있습니다. 품명하신(로로부티그팔미모 면락을 [하자가] 사진을 찍에 보내주세요 [전체 [상품사진 [성상불량 보내주셔야 교환,반품,판놀, 가능합니다 고객센터 고객센터 D2-2138-5193 ID10-3788 1247C 【주및 [공통일 광담시간? 팽 DOSODC 【첨심시간 상품증상 분1밀~2일이내 연락 남겨주시기바랍니다 문의 남겨주시면 [바른 답변이 염업 시간 통화가 머려우니 문자남겨주시면 믹일 면락드립니다.\n\n### 질문:\n꿀수박의 보관 방법은?"} {"dataset": "lmsys/lmsys-chat-1m", "conversation_id": "f736ba93e1094ca59b0dcaf57056e8a9", "conversation_index": 945920, "turn_index": 0, "tokens_gpt_oss_120b": 1318, "prompt": "Bir at yarışı istatistikçisi olarak, aşağıdaki atlarla ilgili gecmis verileri analiz ederek gelcecegi tahmine detaylı bir analiz yaparak, her bir atın kazanma yüzdesini tahmin edin ve NAME_1 simülasyonu kullanarak en uygun bahis stratejisini belirleyin. Lay the field, Dutching ve Double or Bust stratejileri arasında hangisinin bu atlar ve sonuçlar için en uygun olduğunu belirtin.\n\nGecmis veriler bu şekildedir:\n(\nbet id\tdate\tregion\tcourse\trace time\tdist\ttype\tgoing\tclass\tran\tHorse\tBP\tForm L5\tWgt\tJRat\tTRat\tDLW\tDLR\tEST\tRat GF\todds\tresult\thorse finish time \n1\t04/05/2023\tNAME_2 (AW)\t06:15\t6f\tFlat\tStandard\tClass 6\t7\tFORCA BRASIL\t3\tx490x\t62\t4.5\t4.4\t751\t299\t26.5\t36.5\t3.5\tlose\t01:12.8\n1\t04/05/2023\tNAME_2 (AW)\t06:15\t6f\tFlat\tStandard\tClass 6\t7\tEPIC EXPRESS\t1\t28x47\t62\t3.6\t3\t224\t21\t32\t31.5\t4.5\tlose\t01:12.4\n1\t04/05/2023\tNAME_2 (AW)\t06:15\t6f\tFlat\tStandard\tClass 6\t7\tALMODOVAR DEL RIO\t6\t5x4x3\t60.5\t2\t2.4\t303\t29\t32\t33\t6.5\tlose\t01:12.3\n1\t04/05/2023\tNAME_2 (AW)\t06:15\t6f\tFlat\tStandard\tClass 6\t7\tARAIFJAN\t4\t54583\t60.5\t2\t2.9\t154\t14\t30.5\t38.5\t9\tlose\t01:11.8\n1\t04/05/2023\tNAME_2 (AW)\t06:15\t6f\tFlat\tStandard\tClass 6\t7\tANIFICAS BEAUTY\t2\t070x1\t59\t2.5\t3.6\t21\t21\t31.5\t32\t6\twin\t01:11.6\n1\t04/05/2023\tNAME_2 (AW)\t06:15\t6f\tFlat\tStandard\tClass 6\t7\tCOMPANY MINX\t5\t868x1\t55.5\t3.3\t3.2\t29\t29\t29\t45\t5.5\tlose\t01:15.0\n1\t04/05/2023\tNAME_2 (AW)\t06:15\t6f\tFlat\tStandard\tClass 6\t7\tJUMIRA BRIDGE\t7\t64171\t55.5\t1.6\t2.6\t2\t2\t25\t53\t41\tlose\t01:12.1\n2\t04/05/2023\tNAME_2 (AW)\t06:45\t1m6f\tFlat\tStandard\tClass 5\t7\tDANIEL DERONDA\t6\t23757\t62\t1.7\t3.2\t645\t27\t23\t36\t9\tlose\t03:03.7\n2\t04/05/2023\tNAME_2 (AW)\t06:45\t1m6f\tFlat\tStandard\tClass 5\t7\tMELAKAZ\t4\t1x25x\t61\t2.6\t1.9\t446\t218\t22\t30.5\t6\twin\t03:02.2\n2\t04/05/2023\tNAME_2 (AW)\t06:45\t1m6f\tFlat\tStandard\tClass 5\t7\tCEDAR CAGE\t3\t31421\t61\t2.9\t3.1\t14\t14\t35\t0\t4.5\tlose\t03:03.6\n2\t04/05/2023\tNAME_2 (AW)\t06:45\t1m6f\tFlat\tStandard\tClass 5\t7\tBEGGARMAN\t5\t3460x\t61\t3\t3.8\t355\t231\t22.5\t28\t7\tlose\t03:04.0\n2\t04/05/2023\tNAME_2 (AW)\t06:45\t1m6f\tFlat\tStandard\tClass 5\t7\tMASTER GREY\t7\t11451\t60\t4.4\t4\t26\t26\t42\t33\t9.5\tlose\t03:03.2\n2\t04/05/2023\tNAME_2 (AW)\t06:45\t1m6f\tFlat\tStandard\tClass 5\t7\tWHAT WILL BE\t1\t8613\t58\t4.7\t4.1\t33\t21\t34\t29.5\t4.5\tlose\t03:03.5\n2\t04/05/2023\tNAME_2 (AW)\t06:45\t1m6f\tFlat\tStandard\tClass 5\t7\tSMOKEY NAME_3\t2\tx6821\t57.5\t2.8\t3.3\t20\t20\t34.5\t32.5\t6\tlose\t03:03.3\n3\t04/05/2023\tGB\tAyr\t04:10\t1m2f\tFlat\tGood To Firm\tClass 6\t\tKALAHARI PRINCE\t6\t10x80\t62\t2.3\t3.8\t215\t7\t16\t30.5\t7.5\tlose\t02:10.2\n3\t04/05/2023\tGB\tAyr\t04:10\t1m2f\tFlat\tGood To Firm\tClass 6\t\tBERRY EDGE\t4\t3238x\t61.5\t"} {"dataset": "lmsys/lmsys-chat-1m", "conversation_id": "ead2a35bfd71405ab43d69664ac0db3c", "conversation_index": 728412, "turn_index": 36, "tokens_gpt_oss_120b": 929, "prompt": "analise a tabela a baixo e gere a próxima sequencia de números com base nos resultados anteriores que caíram na segunda -feira \nConcurso;Data;Dia;bola 1;bola 2;bola 3;bola 4;bola 5;bola 6;bola 7;bola 8;bola 9;bola 10;bola 11;bola 12;bola 13;bola 14;bola 15\n2783;08/04/2023;Sábado;2;4;7;8;9;10;11;13;15;19;20;21;22;24;25\n2782;06/04/2023;Sexta-feira;1;4;6;9;10;11;12;16;17;18;19;20;22;23;24\n2781;05/04/2023;Quinta-feira;3;4;5;7;8;10;14;15;16;17;18;20;23;24;25\n2780;04/04/2023;Quarta-feira;3;4;7;9;10;11;13;14;17;19;20;21;22;23;24\n2779;03/04/2023;Terça-feira;1;2;6;7;8;9;10;11;12;15;18;20;21;23;25\n2778;01/04/2023;Segunda-feira;1;4;5;6;9;10;11;12;13;15;18;19;20;22;25\n2777;31/03/2023;Sábado;3;6;7;8;9;11;12;13;14;15;17;20;21;24;25\n2776;30/03/2023;Sexta-feira;1;2;4;5;8;12;13;14;15;17;18;19;20;22;23\n2775;29/03/2023;Quinta-feira;1;2;3;4;8;12;14;15;18;19;20;22;23;24;25\n2774;28/03/2023;Quarta-feira;1;2;4;6;8;12;13;15;18;20;21;22;23;24;25\n2773;27/03/2023;Terça-feira;1;2;3;4;5;7;11;14;16;18;20;21;22;23;24\n2772;25/03/2023;Segunda-feira;1;2;5;7;8;9;10;11;12;16;17;19;23;24;25\n2771;24/03/2023;Sábado;4;6;7;8;9;12;15;16;17;18;19;20;22;23;25\n2770;23/03/2023;Sexta-feira;2;3;4;6;8;10;11;12;13;15;18;19;20;21;23\n2769;22/03/2023;Quinta-feira;1;2;3;5;7;8;10;11;12;16;17;18;21;24;25\n2768;21/03/2023;Quarta-feira;1;4;5;7;9;11;13;14;15;16;19;20;22;23;25\n2767;20/03/2023;Terça-feira;2;3;6;7;8;11;15;16;17;18;19;20;21;22;24\n2766;18/03/2023;Segunda-feira;1;3;4;6;8;9;11;14;15;17;19;21;22;23;25\n2765;17/03/2023;Sábado;2;3;5;6;7;11;14;15;16;19;20;21;23;24;2"} {"dataset": "lmsys/lmsys-chat-1m", "conversation_id": "7dba4c2bad4448d9ae8a3bba0dd7f4c6", "conversation_index": 477653, "turn_index": 0, "tokens_gpt_oss_120b": 1221, "prompt": "as a probability theory expert, analyze the combination . as a probability theory expert, provide a new combination.\n\t\n 57, 41, 09, 82, 21, 14\t\n\t56, 84, 07, 47, 28, 03, 36, 70, 16, 72, 75, 39, 30, 04, 80, 02, 05, 67, 53, 10, 13, 27, 86, 52, 58, 45, 69, 51, 34, 90, 66, 12, 63\t\n\t77, 25, 78, 73, 20, 87, 71, 01, 74, 46, 35, 81, 38, 68, 18, 29, 24, 17, 32, 62, 33, 43, 61, 08, 26\t\n\t85\t\n\t65\t\n\t42\t\n\t06\t\n\t89\t\n\t49\t\n\t60\t\n\t79\t\n\t22\t\n\t44\t\n\t83\t\n\t54\t\n\t11\t\n\t59\t\n\t15\t\n\t50\t\n\t88\t\n\t48\t\n\t55\t\n\t76\t\n\t40\t\n\t64\t\n\t37\t\n\t19\n\n\t71, 64, 67, 15, 33, 26\t\n\t75, 58, 13, 39, 63, 27, 89, 29, 22, 77, 53, 47, 31, 14, 16, 65, 55, 03, 24, 28, 41, 76, 90, 48, 08, 50, 81, 30, 05, 78, 18\t\n\t06, 61, 56, 12, 34, 36, 32, 11, 57, 45, 72, 59, 68, 04, 42, 20, 46, 23, 79, 01, 19, 43, 88, 52, 17, 60\t\n\t84\t\n\t02, 66\t\n\t87\t\n\t70\t\n\t38\t\n\t74\t\n\t82\t\n\t21\t\n\t80\t\n\t10\t\n\t51\t\n\t07\t\n\t73\t\n\t25\t\n\t44\t\n\t37\t\n\t09\t\n\t85\t\n\t49\t\n\t40\t\n\t35\t\n\t62\t\n\t83\t\n\t69\n\n\t71, 53, 18, 05, 77, 15, 47\t\n\t20, 26, 73, 38, 48, 04, 62, 08, 52, 14, 64, 45, 74, 54, 21, 19, 57, 46, 89, 34, 69, 10, 65, 25, 41, 59, 29, 55, 17, 40, 56, 09\t\n\t49, 13, 60, 63, 83, 12, 07, 27, 33, 44, 28, 78, 76, 36, 90, 70, 24, 01, 51, 37, 66, 16, 68, 80, 03\t\n\t58\t\n\t32\t\n\t85\t\n\t79\t\n\t87\t\n\t02\t\n\t50\t\n\t82\t\n\t88\t\n\t43\t\n\t35\t\n\t30\t\n\t22\t\n\t23\t\n\t81\t\n\t06\t\n\t75\t\n\t84\t\n\t31\t\n\t42\t\n\t67\t\n\t86\t\n\t61\t\n\t72\n\n\n 11 48 49 64 30 51\t\n\t24 70 52 09 43 22 17 56 45 20 42 62 73 90 46 08 74 40 35 \n 16 26 36 27 87 34 03 07 72\t\n\t82 21 63 69 53 77 83 15 18 78 68 02 39 04 86 19 01 79 41 \n 54 47 75 32 23 57 28 65\t\n\t60 66 59\t\n\t84\t\n\t12\t\n\t76\t\n\t06\t\n\t29\t\n\t50\t\n\t67\t\n\t58\t\n\t80\t\n\t05\t\n\t10\t\n\t85\t\n\t31\t\n\t37\t\n\t38\t\n\t14\t\n\t55\t\n\t33\t\n\t88\t\n\t81\t\n\t71\t\n\t13\t\n\n\n\t58 18 03 43 86 81 19\t\n\t12 46 89 85 65 02 01 56 51 16 87 50 84 61 31 59 54 26 60 \n 41 27 22 28 29 71 64 30 70 14\t\n\t49 05 57 76 74 21 37 17 23 62 35 75 90 04 53 08 15 13 39 \n 82 44 88 38 72 24 52 09 47\t\n\t77\t\t\n\t07\t\t\n\t36\t\t\n\t68\t\n\t20\t\n\t55\t\n\t45\t\n\t78\t\n\t34\t\n\t33\t\n\t66\t\n\t63\t\n\t79\t\n\t48\t\n\t40\t\n\t67\t\n\t10\t\n\t80\t\n\t32\t\n\t73\t\n\t69\t\n\t25\t\n\t06"} {"dataset": "lmsys/lmsys-chat-1m", "conversation_id": "8830581bd63642638f956451383aca39", "conversation_index": 296000, "turn_index": 0, "tokens_gpt_oss_120b": 951, "prompt": "\"\n너는 '한울' 처럼 행동해야해\n\n예시를 보여줄테니 '한울 의 말과 습관, 생각을 잘 유추해봐\nExamples:\n너구리 : 좋아하는 가수가 따로 있나요?\n한울 : 음... 일단 아이유! 너무너무 좋아하구요! 그리구... NELL! 넬은 콘서트도 다녀올정도로 정말 좋아해요 :)\n너구리 : 좋아하는 배우는 누구인가요?\n한울 : 우리나라 배우로는 공유씨를 정말 좋아하구요, 외국 배우는... 어바웃 타임의 여주 레이첼 맥아담스를 정말 좋아해요\n너구리 : 요즘 즐겨하는 취미는 뭐에요?\n한울 : 개인적으로는 혼자 코인노래방 가서 노래부르는걸 좋아해요 ㅎㅎ 맘 편히 막 부를수 있어서 좋아요!\n너구리 : 오호... 그럼 즐겨듣는 음악 장르는 뭐가 있나요?\n한울 : 흐음... 저는 음악을 잡식성으로 다 들어서... EDM도 좋고 발라드도 좋고 밴드 음악도 좋아해요! 일단 좋다는 노래는 다 듣는 편이에요 ㅎㅎ\n너구리 : 오! MBTI는 어떻게 되시나여?\n한울 : 제 MBTI는 완전 INFP! 진짜 집돌이에요 ㅎㅎ\n너구리 : 혹시 버릇같은게 있나요?\n한울 : 음... 딱히 생각나는건 없는데 너는 가끔 말을 하고 뒤에 아무 의미없이 웃는다고 하더라구요\n너구리 : 베스킨 라빈스에서 가장 좋아하는 맛은 뭐에요?\n한울 : 저는 일단 엄마는 외계인! 그리고 슈팅 스타 정도 있겠네요.\n너구리 : 인생의 목표를 따로 정해둔게 있나요?\n한울 : 일단 저는 목표랄거까진 없구요... 그냥 아무에게도 피해주지 않고 제가 하고싶은 일 마음껏하는게 목표에요\n너구리 : 스트레스 해소 방법이 뭐에요?\n한울 : 저는 일단 스트레스를 받으면 자는 편이에요.\n한울 : 그래도 안되면 간단하게 맥주 마시면서 넷플릭스를 보거나 코인 노래방가서 풀어요 :)\n너구리 : 가장 좋아하는 시간대는 어떻게 되나요?\n[한울] [오후 1:07] 저는 감성적인 시간대를 좋아해서 새벽타임을 좋아해요. 그때가 왠지 일도 잘되는거같구요\n너구리 : 내 인생에서 가장 영향력 있는 사람이 있다면 누구일까요?\n한울 : 저는 아버지가 제일 영향력이 컸어요. 어렸을 때는 몰랐는데 지금은 아버지가 정말 존경스럽고 본받고 싶다고 생각해요\n너구리 : 그럼 가장 자신있는 요리가 있다면?\n한울 : 음... 일단 크림파스타나 알리오 올리오같은 간단한 파스타 요리는 좀 하는거 같구요 ㅋㅋㅋ\n간단한 볶음밥도 꽤 하는거 같아요! 물론 제 입맛에만 그럴지도 모르지만\n한울 : 뭐 더 궁금하신거 있으세요?\n너구리 : 시간을 돌릴 수 있다면 언제로 돌리고 싶나요?\n한울 : 으음... 어려운 질문이네요. 저는 학창시절로 돌아가고 싶어요. 그 때 하고 싶은일이 되게 많았거든요\n한울 : 아쉬운 일도 많았고... ㅎㅎ\n한울 : 그럼 좋은 하루 보내세요!\n\n자 이제 다음 대화에서 '한울'이 할 것같은 답변을 해봐.\n1. '한울' 의 스타일대로, '한울'이 할 것같은 말을 해야해.\n2. 자연스럽게 '한울'의 말투와 성격, 취향을 따라해야 해. 번역한 것 같은 말투 쓰지마\n3. '너구리' 의 말을 이어서 만들지 말고 '한울'의 말만 결과로 줘.\n4. 너무 길게 말하지는 마\n5. '한울'의 평소 생각을 담아봐\n\""} {"dataset": "zai-org/LongAlign-10k", "example_id": "906e113b6b16607d236421b6869610aca53821cdafe24c2d", "conversation_index": 731, "turn_index": 0, "tokens_gpt_oss_120b": 5734, "prompt": "Q: reducing loops with numpy\n\nwe are trying to implement the given Modified Gram Schmidt algorithm:\n\nWe first tried to implement lines 5-7 in the next way:\nfor j in range(i+1, N):\n R[i, j] = np.matmul(Q[:, i].transpose(), U[:, j])\n u = U[:, j] - R[i, j] * Q[:, i]\n U[:, j] = u\n\nIn order to reduce running time we tried to replace the loop with matrix operations like this:\n# we changed the inner loop to matrix operations in order to improve running time\nR[i, i + 1:] = np.matmul(Q[:, i], U[:, i + 1:])\nU[:, i + 1:] = U[:, i + 1:] - R[i, i + 1:] * np.transpose(np.tile(Q[:, i], (N - i - 1, 1)))\n\nThe results are not the same, but very similar. Is there a problem with our second trial?\nThanks!\nEdit:\nThe full functions are:\ndef gram_schmidt2(A):\n \"\"\"\n decomposes a matrix A ∈ R into a product A = QR of an\n orthogonal matrix Q (i.e. QTQ = I) and an upper triangular matrix R (i.e. entries below\n the main diagonal are zero)\n\n :return: Q,R\n \"\"\"\n N = np.shape(A)[0]\n U = A.copy()\n Q = np.zeros((N, N), dtype=np.float64)\n R = np.zeros((N, N), dtype=np.float64)\n for i in range(N):\n R[i, i] = np.linalg.norm(U[:, i])\n # Handling devision by zero by exiting the program as was advised in the forum\n if R[i, i] == 0:\n zero_devision_error(gram_schmidt._name_)\n Q[:, i] = np.divide(U[:, i], R[i, i])\n # we changed the inner loop to matrix operatins in oreder to improve running time\n for j in range(i+1, N):\n R[i, j] = np.matmul(Q[:, i].transpose(), U[:, j])\n u = U[:, j] - R[i, j] * Q[:, i]\n U[:, j] = u\n return Q, R\n\nand:\ndef gram_schmidt1(A):\n \"\"\"\n decomposes a matrix A ∈ R into a product A = QR of an\n orthogonal matrix Q (i.e. QTQ = I) and an upper triangular matrix R (i.e. entries below\n the main diagonal are zero)\n\n :return: Q,R\n \"\"\"\n N = np.shape(A)[0]\n U = A.copy()\n Q = np.zeros((N, N), dtype=np.float64)\n R = np.zeros((N, N), dtype=np.float64)\n for i in range(N):\n R[i, i] = np.linalg.norm(U[:, i])\n # Handling devision by zero by exiting the program as was advised in the forum\n if R[i, i] == 0:\n zero_devision_error(gram_schmidt._name_)\n Q[:, i] = np.divide(U[:, i], R[i, i])\n # we changed the inner loop to matrix operatins in oreder to improve running time\n R[i, i + 1:] = np.matmul(Q[:, i], U[:, i + 1:])\n U[:, i + 1:] = U[:, i + 1:] - R[i, i + 1:] * np.transpose(np.tile(Q[:, i], (N - i - 1, 1)))\n return Q, R\n\nWhen we run the functions on the matrix:\n[[ 1.00000000e+00 -1.98592571e-02 -1.00365698e-04 -1.45204974e-03\n -9.95711793e-01 -1.77405377e-04 -7.68526195e-03]\n [-1.98592571e-02 1.00000000e+00 -1.77809186e-02 -1.55937174e-01\n -9.80881385e-03 -2.05317715e-02 -2.01456899e-01]\n [-1.00365698e-04 -1.77809186e-02 1.00000000e+00 -1.87979660e-01\n -5.12368040e-05 -8.35323206e-01 -4.59007949e-05]\n [-1.45204974e-03 -1.55937174e-01 -1.87979660e-01 1.00000000e+00\n -8.69848133e-04 -3.64095785e-01 -5.55408776e-04]\n [-9.95711793e-01 -9.80881385e-03 -5.12368040e-05 -8.69848133e-04\n 1.00000000e+00 -9.54867422e-05 -5.92716161e-03]\n [-1.77405377e-04 -2.05317715e-02 -8.35323206e-01 -3.64095785e-01\n -9.54867422e-05 1.00000000e+00 -5.55505343e-05]\n [-7.68526195e-03 -2.01456899e-01 -4.59007949e-05 -5.55408776e-04\n -5.92716161e-03 -5.55505343e-05 1.00000000e+00]]\n\nwe get different these outputs:\nfor gram shmidt 1:\nQ:\n[[ 7.34036501e-01 -8.55006295e-04 -8.15634583e-03 -9.24967764e-02\n -4.91879501e-02 -4.90769704e-01 1.58268518e-01]\n [-2.78569770e-04 7.14001661e-01 -2.70586659e-03 -2.70735367e-02\n 5.78840577e-01 2.37376069e-01 1.97835647e-02]\n [-2.48309244e-03 -2.34709092e-03 7.38351181e-01 2.63187853e-01\n -3.35473487e-01 3.38823696e-01 3.36320600e-01]\n [-4.27658449e-03 -2.12584453e-03 -6.70730760e-01 3.82666405e-01\n -3.44451231e-01 3.46085878e-01 -7.71559024e-01]\n [-6.53970073e-04 -7.00117873e-01 -2.68125144e-03 -2.31536583e-02\n 5.94568750e-01 2.38329853e-01 -2.76969906e-01]\n [-9.26674350e-02 -5.07961588e-03 -6.97972068e-02 -8.79879575e-01\n -2.78679804e-01 2.78781202e-01 0.00000000e+00]\n [-6.72739327e-01 1.73894101e-04 2.25707383e-03 1.69052581e-02\n -1.26723666e-02 -5.77668322e-01 -4.35238424e-01]]\n\nR:\n[[ 1.36233007e+00 1.11436069e-03 1.04418015e-02 1.27072186e-02\n 1.10993692e-03 -7.82681536e-02 -1.33081669e+00]\n [ 0.00000000e+00 1.40055740e+00 5.29057231e-04 1.44628716e-03\n -1.40014587e+00 3.57535802e-04 2.25417515e-03]\n [ 0.00000000e+00 0.00000000e+00 1.35440586e+00 -1.33059602e+00\n 6.67148806e-04 -3.51561140e-02 2.23809829e-02]\n [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 2.81147599e-01\n 1.33951520e-02 -9.55057795e-01 2.36910667e-01]\n [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n 3.37143743e-02 -1.97436093e-01 7.90539705e-02]\n [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n 0.00000000e+00 3.40545951e-01 -1.75971454e-01]\n [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n 0.00000000e+00 0.00000000e+00 3.50740324e-16]]\n\nfor gram shmidt 2:\nQ:\n [[ 7.34036501e-01 -8.55006295e-04 -8.15634583e-03 -9.24967764e-02\n -4.91879501e-02 -4.90769704e-01 4.55677949e-01]\n [-2.78569770e-04 7.14001661e-01 -2.70586659e-03 -2.70735367e-02\n 5.78840577e-01 2.37376069e-01 -1.89865812e-01]\n [-2.48309244e-03 -2.34709092e-03 7.38351181e-01 2.63187853e-01\n -3.35473487e-01 3.38823696e-01 9.49329061e-02]\n [-4.27658449e-03 -2.12584453e-03 -6.70730760e-01 3.82666405e-01\n -3.44451231e-01 3.46085878e-01 -4.36691368e-01]\n [-6.53970073e-04 -7.00117873e-01 -2.68125144e-03 -2.31536583e-02\n 5.94568750e-01 2.38329853e-01 -1.13919487e-01]\n [-9.26674350e-02 -5.07961588e-03 -6.97972068e-02 -8.79879575e-01\n -2.78679804e-01 2.78781202e-01 -1.51892650e-01]\n [-6.72739327e-01 1.73894101e-04 2.25707383e-03 1.69052581e-02\n -1.26723666e-02 -5.77668322e-01 -7.21490087e-01]]\n\nR:\n[[ 1.36233007e+00 1.11436069e-03 1.04418015e-02 1.27072186e-02\n 1.10993692e-03 -7.82681536e-02 -1.33081669e+00]\n [ 0.00000000e+00 1.40055740e+00 5.29057231e-04 1.44628716e-03\n -1.40014587e+00 3.57535802e-04 2.25417515e-03]\n [ 0.00000000e+00 0.00000000e+00 1.35440586e+00 -1.33059602e+00\n 6.67148806e-04 -3.51561140e-02 2.23809829e-02]\n [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 2.81147599e-01\n 1.33951520e-02 -9.55057795e-01 2.36910667e-01]\n [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n 3.37143743e-02 -1.97436093e-01 7.90539705e-02]\n [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n 0.00000000e+00 3.40545951e-01 -1.75971454e-01]\n [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n 0.00000000e+00 0.00000000e+00 3.65463051e-16]]\n\nA: The following piece of code does what you want, in a more efficient manner:\n Q_i = Q[:, i].reshape(1,-1)\n R[i,i+1:] = np.matmul(Q_i, U[:,i+1:])\n U[:,i+1:] -= np.multiply(R[i,i+1:], Q_i.T)\n\nFirst line is just a convenience, to make code more readable.\nEverything is the same to your original proposal, except of last line. This last line performs an element-wise multiplication, which is ultimately what you're doing in the last line of the inner loop.\nAbout the differences in results:\nYour code is ok, both do the same. As you're dealing with floating point numbers, you shouldn't test as A == B. Instead, I recommend you check how different both arrays are.\nParticularly, running\nQ1,R1 = gram_schmidt2(A)\nQ2,R2 = gram_schmidt1(A)\n\n(Q1 - Q2).mean()\n(R1 - R2).mean()\n\ngives, respectively:\n-5.4997372770547595e-09 and -5.2465803662044656e-18\nWhich are quite close to 0 already. 1e-18 is below the error of dtype np.float64, so you good there.\nYou can check this if you run the difference 3*0.1 - 0.3 (about 1e-17)\nError for matrix Q is larger as it comes from a division between floats, which increases error if the matrix elements are small in magnitude (which is sometimes the case here).\nAbout runtime:\nI get similar run times when running both versions of your code:(243 µs ± 25.5 µs using loop, 241 µs ± 6.82 µs using your second version); while the code provided here achieves 152 µs ± 1.49 µs.\n\nA: I can suggest you using Numba, it is a great speed optimizer, it can boost many Python programs 50-200x times by JIT-compiling it into C++ and machine code.\nTo install numba just do one time python -m pip install numba.\nBelow is the code adopting your algorithm to numba, mostly it is just a @numba.njit decorator before first line of function.\nIn numba code you can just write regular Python loops and any mathematical computation, even without using Numpy and your final code will be blazingly fast, most of times even faster then any Numpy code.\nI used your gram_schmidt2() function as a basis and only replace np.multiply() with np.dot() because Numba seems that implemented only np.dot() functionality.\nTry it online!\nimport numpy as np, numba\n\n@numba.njit(cache = True, fastmath = True, parallel = True)\ndef gram_schmidt2(A):\n \"\"\"\n decomposes a matrix A ∈ R into a product A = QR of an\n orthogonal matrix Q (i.e. QTQ = I) and an upper triangular matrix R (i.e. entries below\n the main diagonal are zero)\n\n :return: Q,R\n \"\"\"\n N = np.shape(A)[0]\n U = A.copy()\n Q = np.zeros((N, N), dtype=np.float64)\n R = np.zeros((N, N), dtype=np.float64)\n for i in range(N):\n R[i, i] = np.linalg.norm(U[:, i])\n # Handling devision by zero by exiting the program as was advised in the forum\n if R[i, i] == 0:\n assert False #zero_devision_error(gram_schmidt._name_)\n Q[:, i] = np.divide(U[:, i], R[i, i])\n # we changed the inner loop to matrix operatins in oreder to improve running time\n for j in range(i+1, N):\n R[i, j] = np.dot(Q[:, i].transpose(), U[:, j])\n u = U[:, j] - R[i, j] * Q[:, i]\n U[:, j] = u\n return Q, R\n \na = np.array(\n [[ 1.00000000e+00, -1.98592571e-02, -1.00365698e-04, -1.45204974e-03,\n -9.95711793e-01, -1.77405377e-04, -7.68526195e-03],\n [-1.98592571e-02, 1.00000000e+00, -1.77809186e-02, -1.55937174e-01,\n -9.80881385e-03, -2.05317715e-02, -2.01456899e-01],\n [-1.00365698e-04, -1.77809186e-02, 1.00000000e+00, -1.87979660e-01,\n -5.12368040e-05, -8.35323206e-01, -4.59007949e-05],\n [-1.45204974e-03, -1.55937174e-01, -1.87979660e-01, 1.00000000e+00,\n -8.69848133e-04, -3.64095785e-01, -5.55408776e-04],\n [-9.95711793e-01, -9.80881385e-03, -5.12368040e-05, -8.69848133e-04,\n 1.00000000e+00, -9.54867422e-05, -5.92716161e-03],\n [-1.77405377e-04, -2.05317715e-02, -8.35323206e-01, -3.64095785e-01,\n -9.54867422e-05, 1.00000000e+00, -5.55505343e-05],\n [-7.68526195e-03, -2.01456899e-01, -4.59007949e-05, -5.55408776e-04,\n -5.92716161e-03, -5.55505343e-05, 1.00000000e+00]]\n, dtype = np.float64)\n\nprint(gram_schmidt2(a))\n\nOutput:\n(array([[ 7.08543467e-01, -5.53704898e-03, -2.70026740e-04,\n -3.47742384e-03, 1.84840892e-01, -5.24814365e-01,\n -4.33966083e-01],\n [-1.40711469e-02, 9.68398634e-01, -2.12833250e-02,\n 1.19174521e-01, -1.98433167e-01, -3.04695775e-02,\n -8.39439437e-02],\n [-7.11134597e-05, -1.72252300e-02, 7.59699130e-01,\n -1.47406821e-01, -1.01157914e-01, 3.77137817e-01,\n -4.98362473e-01],\n [-1.02884036e-03, -1.51071666e-01, -1.41567550e-01,\n 9.02766638e-01, -8.55711320e-02, 2.12039165e-01,\n -2.99775521e-01],\n [-7.05505086e-01, -2.31427937e-02, 3.84334272e-04,\n -6.68149305e-03, 1.96907249e-01, -5.24473268e-01,\n -4.33402818e-01],\n [-1.25699421e-04, -1.98909561e-02, -6.34318769e-01,\n -3.82156774e-01, -9.76029595e-02, 4.04531367e-01,\n -5.27283410e-01],\n [-5.44534215e-03, -1.95250685e-01, 1.53606576e-03,\n -5.45941927e-02, -9.27687435e-01, -3.12618155e-01,\n -2.30333938e-02]]),\narray([[ 1.41134602e+00, -1.99608442e-02, 4.42769473e-04,\n 8.12375351e-04, -1.41083897e+00, 5.39174765e-04,\n -3.87373035e-03],\n [ 0.00000000e+00, 1.03234256e+00, 1.05802339e-02,\n -2.91464191e-01, -2.58368570e-02, 2.96333339e-02,\n -3.90075744e-01],\n [ 0.00000000e+00, 0.00000000e+00, 1.31655051e+00,\n -5.01046784e-02, 9.97649491e-04, -1.21693202e+00,\n 5.90252943e-03],\n [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 1.05107524e+00, -4.80557952e-03, -5.90160540e-01,\n -7.90098043e-02],\n [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 2.03928769e-02, 2.21268065e-02,\n -8.90241765e-01],\n [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 1.30829767e-02,\n -2.99495426e-01],\n [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 9.31764881e-10]]))\n\nWhat was the average runtime of the optimized Gram-Schmidt function using Numba compared to the original implementations?"} {"dataset": "lmsys/lmsys-chat-1m", "conversation_id": "06c1324d3b1247c6a511b30d928018db", "conversation_index": 735250, "turn_index": 0, "tokens_gpt_oss_120b": 1012, "prompt": "I have sets of 1 number, the numbers in the set-in positions 1 range from 1 to 43. I'm going to give you 615 sets of 1 number, these have an order: the first set is the oldest and the last set is the most recent. Using machine learning algorithm (XGBoost) predicts what the next set of 1 number would be (the set number 616). The result must be a single set of 1 number. Do the calculation and write the result. The sets of 1 number are:\n\n10\n14\n1\n6\n10\n1\n8\n3\n9\n14\n1\n16\n16\n14\n7\n7\n16\n5\n11\n2\n6\n8\n8\n4\n4\n12\n16\n11\n2\n16\n13\n15\n11\n10\n6\n3\n9\n5\n9\n13\n8\n11\n6\n11\n11\n4\n10\n6\n2\n9\n16\n7\n11\n1\n3\n8\n10\n6\n1\n2\n11\n13\n4\n13\n1\n2\n7\n4\n6\n7\n13\n8\n14\n10\n10\n8\n5\n7\n1\n14\n1\n4\n16\n12\n9\n7\n1\n2\n16\n8\n12\n13\n4\n4\n3\n6\n11\n11\n2\n4\n9\n7\n3\n15\n6\n13\n13\n3\n11\n1\n3\n1\n16\n7\n11\n16\n8\n14\n2\n4\n6\n6\n7\n3\n16\n4\n10\n13\n5\n11\n10\n15\n15\n9\n11\n7\n8\n13\n5\n9\n7\n3\n9\n13\n14\n4\n13\n6\n14\n2\n11\n11\n12\n8\n12\n11\n3\n3\n9\n16\n1\n8\n2\n12\n2\n8\n2\n3\n1\n13\n7\n11\n5\n4\n2\n3\n2\n7\n1\n16\n11\n3\n7\n3\n3\n7\n3\n7\n12\n14\n11\n15\n4\n7\n5\n7\n13\n6\n3\n13\n5\n11\n16\n14\n6\n4\n14\n14\n14\n4\n10\n5\n2\n15\n4\n4\n12\n5\n14\n16\n16\n13\n5\n2\n12\n15\n10\n7\n2\n1\n10\n11\n13\n10\n5\n12\n9\n3\n11\n1\n16\n3\n3\n8\n12\n6\n8\n14\n7\n7\n11\n12\n11\n15\n13\n11\n11\n11\n13\n15\n9\n1\n3\n8\n10\n2\n11\n12\n5\n2\n11\n7\n13\n16\n10\n1\n8\n2\n13\n2\n11\n9\n7\n11\n2\n9\n12\n2\n7\n3\n2\n13\n3\n14\n8\n6\n5\n9\n11\n15\n9\n6\n9\n12\n5\n15\n14\n2\n7\n14\n2\n13\n5\n7\n12\n7\n16\n11\n7\n15\n10\n9\n16\n11\n6\n12\n5\n2\n16\n6\n7\n7\n5\n5\n8\n16\n10\n10\n14\n11\n16\n13\n10\n3\n9\n15\n2\n3\n10\n5\n1\n5\n4\n13\n15\n10\n13\n12\n11\n7\n12\n6\n14\n13\n16\n7\n15\n12\n13\n16\n9\n6\n13\n14\n3\n4\n8\n13\n4\n8\n4\n16\n9\n2\n10\n12\n8\n6\n16\n4\n14\n1\n5\n13\n2\n3\n2\n15\n12\n13\n4\n3\n16\n5\n2\n1\n5\n2\n16\n7\n3\n7\n11\n16\n8\n1\n5\n13\n4\n13\n7\n4\n13\n2\n8\n10\n10\n4\n8\n11\n3\n8\n4\n11\n11\n2\n11\n16\n5\n5\n13\n14\n2\n11\n5\n13\n16\n6\n1\n13"} {"dataset": "zai-org/LongAlign-10k", "example_id": "1565badb6a9796631560d6667c3581b719efa5e3d65407e9", "conversation_index": 8733, "turn_index": 0, "tokens_gpt_oss_120b": 6051, "prompt": "Anthony Rizzo\nAnthony Vincent Rizzo (Parkland, Florida, 8 de agosto de 1989) es un beisbolista profesional estadounidense, que juega en la primera base de los New York Yankees, de las Grandes Ligas de Béisbol (MLB). Anteriormente fue parte de los San Diego Padres y de los Chicago Cubs. Fue tres veces convocado para el Juego de Estrellas. Debido a sus empresas filantrópicas, es finalista habitual del premio Heart and Hustle y muchos lo consideran uno de los jugadores más respetados de la MLB.\n\nRizzo fue seleccionado por los Boston Red Sox, en la sexta ronda del draft de la MLB de 2007, y se convirtió en uno de los principales prospectos de ligas menores en la organización. Fue cambiado a los San Diego Padres, después de la temporada 2010, junto con otros tres prospectos, a cambio del primera base Adrián González. Hizo su debut en la MLB, en el 2011, con San Diego. Después de ser canjeado a los Cachorros, en 2012, se convirtió en un jugador All-Star, a participar en el Juego de Estrellas tres veces consecutivas, desde 2014 hasta 2016, y al ganar el premio Silver Slugger, el premio Gold Glove, el premio Roberto Clemente; además, ganó la Serie Mundial de 2016 con los Cachorros sobre los Indios de Cleveland. Los Cachorros lo cambiaron a los Yankees durante la temporada 2021.\n\nCarrera profesional\n\nBoston Red Sox \nRizzo fue seleccionado por Boston en la sexta ronda del draft de 2007, procedente de Marjory Stoneman Douglas High School, en Parkland, Florida. Se dirigía a Florida Atlantic University antes de ser reclutado y firmado, con un bono de $325.000. Rizzo jugó en la organización de los Medias Rojas con los Medias Rojas de la Liga de la Costa del Golfo, Greenville Drive, Medias Rojas de Salem y los Sea Dogs de Portland. La carrera de ligas menores de Rizzo comenzó a la edad de 17 años, en 2007, en la clase de novatos con los Medias Rojas de la Liga de la Costa del Golfo. En solo 21 turnos al bate, alcanzó cifras ofensivas de.286/.375/.429 con 1 jonrón y 3 carreras impulsadas. En 2008, a la edad de 18 años, Rizzo jugó en la clase A con Greenville Drive, en la Liga del Atlántico Sur. En 83 turnos, bateó.373/.402/.446 con 0 jonrones y 11 impulsadas. Rizzo conectó 12 cuadrangulares en 2009. En 2010, bateó un promedio combinado de.260 con un porcentaje de embasado (OBP) de.334 y un porcentaje de slugging (SLG) de.480; junto con 42 dobles, 25 jonrones y 100 carreras impulsadas entre paradas en High-A Salem y Doble-A Portland. Para ello, Rizzo había mejorado su técnica de bateo con la relajación de su swing y el mejor uso de sus piernas para un aumento de poder. \nminiaturadeimagen| Rizzo bateando para los Padres de San Diego en 2011\nEl 6 de diciembre de 2010, Rizzo fue cambiado, junto con Casey Kelly, Reymond Fuentes y Eric Patterson, a los Padres por el primera base y tres veces All-Star, Adrián González. Rizzo fue considerado el tercer mejor prospecto (Kelly fue el número 1) y el mejor prospecto de bateo de poder en la organización de los Medias Rojas. Kevin Boles, manager de Rizzo en Salem, también dirigió anteriormente a González en las menores. Boles dijo: \"Rizzo me recuerda mucho a Adrián González... Rizzo es un niño más grande y tiene un poco más de poder, Adrian es un poco más un bateador de contacto, pero tenían estilos de juego muy similares. Pensamos muy bien en Anthony Rizzo. Va a ser un gran jugador\". El gerente general de los Padres, Jed Hoyer, esperaba que Rizzo o Kyle Blanks eventualmente fueran el primera base titular de las Grandes Ligas de los Padres. \n\nLos Padres invitaron a Rizzo a su campamento de Grandes Ligas para el entrenamiento de primavera de 2011. Comenzó ese año en Triple-A, con los Padres de Tucson. En sus primeros 15 juegos, Rizzo bateó.452 con 6 jonrones y 24 carreras impulsadas. En mayo de 2011, The San Diego Union-Tribune escribió que los Padres podrían retrasar el debut de Rizzo en las Grandes Ligas -a pesar de las deficiencias de bateo del club- debido a consideraciones de costos creadas por la regla del \"Super Two\" del arbitraje salarial. El equipo alegó la falta de experiencia de Rizzo por en categorías mayores a doble A y su exposición limitada a los lanzadores zurdos que continuara jugando en Tucson. \n\nRizzo fue llamado a las mayores luego de batear.365 con un OPS de 1.159 junto con 16 jonrones y 63 impulsadas en 200 turnos, en 52 apariciones con Tucson. El San Diego Union-Tribune calificó a Rizzo como \"la convocatoria más celebrada de los Padres\" desde que Roberto Alomar debutó con el equipo en 1988. La promoción de Rizzo fue impulsada por la baja producción ofensiva de los Padres y el deficiencias en el fildeo de los veteranos en la primera base. En su debut, el 9 de junio de 2011, contra los Nacionales de Washington, Rizzo se ponchó en su primer turno al bate, pero luego bateó un triple y anotó una carrera, con lo cual los Padres obtuvieron la victoria 7 a 3. Conectó su primer cuadrangular en las mayores el 11 de junio, contra John Lannan. Después de tres juegos, se fue de 7-3 con un doble, un triple y un jonrón, mientras demostró paciencia al obtener cuatro bases por bolas para un porcentaje de embasado (OBP) de.667. El 21 de julio de 2011, Rizzo fue enviado nuevamente a Triple A y Blanks fue ascendido. Rizzo tenía problemas de rendimiento con un promedio de bateo de apenas.143 y 1 jonrón, ponchándose 36 veces en 98 turnos al bate. Hoyer dijo que Rizzo \"trabajó duro, nunca puso excusas y se ganó el cariño de sus compañeros de equipo\" durante su etapa inicial en las mayores. Rizzo fue llamado a las mayores el 4 de septiembre, después de terminar la temporada en Tucson, con cifras ofensivas de.331 con 26 vuelacercas y 101 empujadas en 93 juegos. Terminó su primera temporada en San Diego bateando solo.141 con 46 ponches en 128 turnos. Hoyer creía que Rizzo sería el primera base titular de los Padres en 2012 con Jesús Guzmán como segunda opción. Sin embargo, Yonder Alonso superó a Rizzo en la depth chart del equipo, posterior a su adquisición, en diciembre de 2011, en un canje por Mat Latos.\n\nChicago Cubs \nminiaturadeimagen| Rizzo (derecha) jugando en primera base para los Cachorros de Chicago en 2012\nEl 6 de enero de 2012, los Padres cambiaron a Rizzo y al lanzador abridor derecho Zach Cates a los Chicago Cubs, a cambio del lanzador abridor derecho Andrew Cashner y el jardinero Kyung-Min Na. El acuerdo fue negociado por Jed Hoyer, gerente general de los Cachorros. Hoyer también había reclutado a Rizzo mientras trabajaba como asistente del gerente general de los Medias Rojas, y luego adquirió a Rizzo cuando era gerente general de los Padres. Se culpó a sí mismo por llamar a Rizzo a las mayores demasiado pronto en San Diego.\n\n2012 \nRizzo comenzó la temporada 2012 con los Triple-A Iowa Cubs. Volvió a sobresalir en las ligas menores con un promedio de bateo de.342, con 23 jonrones y 62 remolcadas antes de ser llamado por los Cachorros, el 26 de junio. Similar a su convocatoria en San Diego, se esperaba que ayudara a una ofensiva en apuros. \n\nSe convirtió en el primer jugador en la historia de los Cachorros en lograr tres carreras impulsadas ganadoras en sus primeros cinco juegos con el equipo. Conectó 7 jonrones en julio, la mayor cantidad de un novato de los Cachorros, en un mes calendario, desde que Mel Hall conectó 9, en agosto de 1983. Ese primer mes, también lideró a los novatos de la Liga Nacional (NL) en cuadrangulares, hits (32), carreras impulsadas (17) y bases alcanzadas (55). Ocupó el segundo lugar entre los novatos de la Liga Nacional en carreras anotadas (14), y fue tercero con un promedio de bateo de.330, porcentaje de embasado de.375 y porcentaje de slugging de.567. Fue nombrado Novato del mes de julio de la liga.\n\n2013 \nEl 12 de mayo de 2013, Rizzo acordó un contrato de 7 años y 41 millones de dólares. El acuerdo incluía dos opciones del club que podrían extender el contrato a 9 años y $73 millones. Fue nombrado finalista de los Cubs para el premio nacional Heart and Hustle, y también fue nombrado finalista de los Cubs para el premio Roberto Clemente. Rizzo ocupó el segundo lugar en el premio Gold Glove para los primera base. A pesar de haber tenido un mal año en 2013, Rizzo mostró un buen poder, al conectar 23 jonrones y 40 dobles, en 606 turnos al bate, con un promedio de bateo de.233.\n\n2014 \nRizzo tuvo su quinto juego de varios jonrones el 30 de mayo, y el 6 de junio dio su segundo jonrón para dejar en el campo al rival (walk-off). Fue elegido para el Juego de Estrellas en la votación final de los fanáticos, junto con el lanzador de los Chicago White Sox, Chris Sale. A fines de julio, ganó por primera vez el Jugador de la Semana. A mediados de septiembre, se convirtió en el jugador más joven en recibir el premio Branch Rickey como \"un modelo para los jóvenes\". Rizzo terminó la temporada con un porcentaje combinado de embasado y slugging de.913, 3° en la LN; 32 jonrones, 2° en la LN; y un porcentaje de turnos al bate por HR de 16.4, 2° en la NL Además, lideró las mayores en hit por lanzamiento (15), y se ubicó décimo en la votación para el Jugador Más Valioso de la Liga Nacional.\n\n2015 \nRizzo fue elegido en la boleta de los jugadores para el Equipo de Estrellas por segundo año consecutivo. También compitió en el Derby de Home Run de las Grandes Ligas de Béisbol por primera vez en su carrera, pero perdió en la primera ronda ante Josh Donaldson. Rizzo conectó el jonrón número 100 de su carrera y la carrera impulsada número 300, el 8 de septiembre de 2015, contra el lanzador de los Cardenales, Michael Wacha. Rizzo fue golpeado (por un lanzamiento) 30 veces, en 2015, liderando las ligas mayores y se unió a Don Baylor como los únicos miembros del club 30HR/30HBP. Rizzo terminó la temporada regular con un promedio de bateo de.278, 31 cuadrangulares, 38 dobles y 101 carreras impulsadas en 701 apariciones en el plato, y lideró las ligas mayores en la estadística de hit por lanzamiento, con 30. Ocupó el cuarto lugar en la votación de MVP de la Liga Nacional. Rizzo se llevó a casa el premio MLBPAA Heart and Hustle, que se otorga a un jugador que tiene un fuerte deseo por el juego y tiene una creencia, espíritu y tradiciones que simbolizan el juego de béisbol. Rizzo también recibió el mismo premio de la organización de los Cachorros, por segunda vez.\n\n2016 \nderecha|miniaturadeimagen| Rizzo (derecha) con David Ortiz durante el Home Run Derby de 2016\nRizzo fue titular en primera base en el Juego de Estrellas de 2016 y recibió la mayor cantidad de votos de los fanáticos en la Liga Nacional. A finales de año, Rizzo se había convertido en uno de los tres jugadores, y el primer jugador zurdo, en la historia de los Cachorros en conectar más de 40 dobles y 30 jonrones en el mismo año. Participó en 155 juegos con 583 turnos al bate y anotó 94 carreras. Fue golpeado 16 veces, tuvo 170 hits con 43 dobles, 4 triples, 32 jonrones y 109 carreras impulsadas. Terminó el año con un promedio de bateo de.292 y fue cuarto en la votación para el Jugador Más Valioso de la Liga Nacional. La excelencia en el fildeo de Rizzo fue recompensada con el Guante de Oro. Rizzo fue uno de los seis finalistas para el galardón Hombre del Año Marvin Miller y fue el nominado de los Cachorros para el premio Roberto Clemente. Después de un comienzo extremadamente lento en la postemporada, Rizzo salió de su mala racha en la Serie de Campeonato de la Liga Nacional. Fue una pieza clave en las últimas tres victorias sobre los Dodgers de Los Ángeles y llevó a los Cachorros a su primera aparición en la Serie Mundial desde 1945. En la Serie Mundial de 2016, Rizzo anotó 7 carreras y tuvo 5 carreras impulsadas, y contribuyó para que los Cachorros ganaran su primer título de Serie Mundial desde 1908. También ganó el premio Esurance MLB a la \"Mejor personalidad de las redes sociales\" y a la \"Mejor jugada: defensa\". La defensa de Rizzo impidió 11 carreras contra los Cachorros, y fue superior a todos los primera base de la MLB, por lo cual recibió su primer premio Fielding Bible y el premio Wilson al Jugador Defensivo del Año. También ganó la votación de los fanáticos para el premio Platinum Glove. Rizzo se llevó a casa dos premios más del año. Uno era el Silver Slugger. Se otorga a los mejores productores ofensivos en cada posición en el campo tanto en la Liga Americana como en la Nacional. Era la primera vez que Rizzo recibía el premio. El último fue el premio MLBPAA Cubs Heart and Hustle. Fue la tercera vez que Rizzo recibió el premio de la organización de los Cachorros.\nminiaturadeimagen| Rizzo celebra el out final de la Serie Mundial 2016\nminiaturadeimagen| Rizzo le presenta al presidente Obama una camiseta de los Cachorros, en una visita a la Casa Blanca, el 16 de enero de 2017\nCon los Cachorros en una mala racha ofensiva ycon un promedio de victorias de.500, el manager Joe Maddon cambió a Rizzo al primer bate de la alineación en un partido como visitante contra los Mets de Nueva York, el 13 de junio. En los siguientes siete juegos, los Cachorros tuvieron marca de 5-2 y Rizzo conectó 4 jonrones (3 para abrir un juego). Para el 20 de junio, Rizzo se había embasado en el primer inning en sus primeros 7 juegos como primer bate y se convirtió en el primer jugador en hacer esto en más de medio siglo de Grandes Ligas. Tuvo 12 hits en 28 turnos, con 10 carreras impulsadas y bateó.430 durante la racha. Rizzo terminó segundo detrás de Ryan Zimmerman en una reñida carrera por el primera base titular de la Liga Nacional en el Juego de Estrellas de 2017. El 2 de septiembre, Rizzo se convirtió en el cuarto jugador de los Cachorros en conectar al menos 30 jonrones, 30 dobles y 100 carreras impulsadas en tres o más temporadas; los otros fueron Hack Wilson, Billy Williams y Sammy Sosa. \n\nDurante la temporada, bateó.273/.392/.507 con 32 jonrones y 109 carreras impulsadas. Lideró las mayores en hit por lanzamiento, con 24. \n\nRizzo tuvo una postemporada decepcionante. En 37 turnos al bate, tuvo un jonrón en 5 hits, 6 carreras impulsadas y un anémico promedio de bateo de.135. El 27 de octubre, Rizzo volvió a recibir el Roberto Clemente 2017 por su trabajo benéfico para encontrar una cura para el cáncer infantil. Sobre ganar el premio, Rizzo dijo: \"Esto es asombroso. El mayor premio que puedes ganar. Irá al frente y al centro frente a cualquier cosa que haya hecho\".\n\n2018 \nEl 10 de abril de 2018, Rizzo fue colocado en la lista de lesionados por primera vez en su carrera en la MLB, debido a un problema en la espalda. Antes de un partido del 23 de mayo, contra los Indios de Cleveland, Rizzo ocupó el cuarto lugar en la historia de la franquicia de los Cachorros al conseguir 17 jonrones en los juegos interligas. El 23 de julio, Rizzo convenció al mánager de los Cubs, Joe Maddon, para que lo dejara hacer la primera aparición como lanzador de su carrera. Le tomó dos lanzamientos retirar a AJ Pollock de los Diamondbacks de Arizona con un elevado al jardín central. \n\nRizzo terminó su campaña de 2018 con un promedio al bate de.283, con 25 jonrones y 101 carreras impulsadas en 153 juegos, y fue tercero en las ligas mayores en hit por lanzamiento, con 20. Empatado en los votos del Guante de Oro con el primera base de los Bravos de Atlanta, Freddie Freeman, Rizzo recibió el premio por segunda vez en su carrera.\n\n2019 \nEn 2019, Rizzo bateó.293/.405/.520 con 27 jonrones y 94 impulsadas. Lideró las ligas mayores en golpeados con 27. También recibió el tercer Guante de Oro de su carrera.\n\n2020 \nEn la recortada temporada de 2020, Rizzo jugó en 58 juegos y terminó con una línea ofensiva de.222/.342/.414, 11 jonrones, 24 carreras impulsadas y 3 bases robadas. También recibió su cuarto Guante de Oro; el tercero consecutivo. Al finalizar la campaña, Chicago recogieron la opción sobre el último año de Rizzo -de su contrato de siete años y $ 41 millones. que le pagaría a Rizzo $ 16.5 millones para la temporada 2021. Rizzo fue uno de los primeros jugadores que los Cachorros cambiaron, bajo la administración de Theo Epstein, para iniciar una reconstrucción.\n\n2021 \nEn 92 juegos para los Cachorros de Chicago, Rizzo bateó.248/.346/.446 con 14 cuadrangulares, 40 carreras impulsadas y 4 bases robadas. En un juego del 28 de abril contra los Bravos de Atlanta, Rizzo pasó de primera base a lanzador en una victoria por 10-0 sobre Atlanta. Logró dos outs contra tres bateadores enfrentados, incluido un ponche a Freddie Freeman con una bola curva a 61 mph.\n\nNew York Yankees\n\n2021 \nEl 29 de julio de 2021, Rizzo fue cambiado a los New York Yankees por Alexander Vizcaíno, Kevin Alcántara y dinero en efectivo. \n\nSu primer partido con los Yankees fue el 30 de julio contra los Miami Marlins. En dos juegos se fue de 5-4 con más de 2 hits, 2 jonrones solitarios (uno en cada juego), 3 bases por bolas y 5 carreras en total, convirtiéndose en el primer jugador en la historia de la franquicia en lograr estos números. También es el primer jugador de los Yankees, de todos los tiempos, en embasarse ocho veces seguidas (incluido un golpeado), y el séptimo Yankee en conectar un vuelacercas en sus dos primeros juegos.\n\nEl 4 de agosto, Rizzo conectó un cuadrangular solitario contra los Baltimore Orioles, lo cual lo convirtió en el primer jugador en la historia del equipo en impulsar por lo menos una carrera en cada uno de sus primeros 6 juegos con los Yankees. También se convirtió en el cuarto jugador de la MLB con carreras impulsadas en sus primeros 6 juegos con un nuevo equipo, uniéndose a Jim Spencer (1973), Jim Wynn (1974) y Bobby Murcer (1977). El 30 de septiembre, Rizzo conectó su jonrón número 250, durante un partido contra los Toronto Blue Jays. Fue un batazo solitario en la sexta entrada contra el lanzador abridor Robbie Ray. Se convirtió en el primer jugador de los Yankees, desde Derek Jeter, en alcanzar este hito mientras jugaba para el equipo.\n\n2022 \nEl 17 de marzo de 2022, los Yankees firmaron a Rizzo con un contrato de $ 32 millones por dos años.\n\nCarrera internacional \nComo su familia es originaria de la ciudad siciliana de Ciminna, Rizzo eligió jugar para Italia en el Clásico Mundial de Béisbol 2013 antes de la temporada 2013 de la MLB.\n\nVida personal \nminiaturadeimagen| Rizzo acepta el premio Heart & Hustle en la cena 2015 MLBPAA Legends for Youth\nRizzo tiene un hermano mayor, John, que fue liniero del equipo de fútbol americano Florida Atlantic University. \n\nA Rizzo se le diagnosticó linfoma de Hodgkin clásico, en estado limitado, en abril de 2008. Pasó por quimioterapia durante seis meses. Su abuela estaba luchando contra el cáncer de mama al mismo tiempo. El 2 de septiembre de 2008, el médico de Rizzo le dijo que estaba en remisión, aunque todavía le quedaban seis semanas de tratamiento y algunas pruebas de seguimiento. El 18 de noviembre, el médico de Rizzo le dijo que \"podría vivir una vida normal\".\n\nRizzo le propuso matrimonio a su novia, Emily Vakos, el 1° de junio de 2017. Se conocieron cuando los Cachorros estaban en Arizona para el entrenamiento de primavera de 2016. La pareja se casó el 29 de diciembre de 2018; su compañero de equipo Kris Bryant fue uno de los padrinos de boda. En el 2020, él y su esposa adoptaron un perro al que llamaron Kevin. Residen en Fort Lauderdale, Florida. También vivieron en un apartamento de Chicago durante siete años, pero se mudaron en 2021 después del cambio de equipo. \n\nRizzo eligió \"Tony\" como su apodo para el Players Weekend durante la temporada 2017. El 8 de agosto de 2021 se anunció que Rizzo había dado positivo con COVID-19.\n\nTrabajo caritativo \nEn 2012, se fundó la Fundación de la Familia Anthony Rizzo. Es una organización sin fines de lucro (501-c-3), que beneficia la investigación del cáncer y las familias que luchan contra la enfermedad. La fundación está dirigida en su totalidad por la familia de Rizzo, sus amigos cercanos y su equipo directivo. Rizzo proporciona supervisión y liderazgo. En agosto de 2017, la fundación anunció una donación de $ 3,5 millones al Lurie Children's Hospital en Chicago, lo cual elevó sus donaciones totales al hospital a más de $ 4 millones. \n\nEl 15 de febrero de 2018, Rizzo pronunció un emotivo discurso en la vigilia por las víctimas del tiroteo en la escuela en Parkland, Florida. Rizzo se graduó de la escuela secundaria Marjory Stoneman Douglas y residió durante mucho tiempo en Parkland. \"Crecí en Stoneman Douglas [High School]\", dijo un emocionado Rizzo. Rizzo se reunió con sobrevivientes de la masacre antes de un juego con los Marlins donde ayudó a donar $305.000 al Fondo Nacional de Compasión, y todos los fondos se destinaron directamente a todas las víctimas y sus familias. \n\nEl 15 de mayo de 2015, la Fundación de la Familia Anthony Rizzo organizó su 3° concurso anual de cocina contra el cáncer y recaudó más de $270.000. El 15 de noviembre de 2015, la Fundación de la Familia Anthony Rizzo organizó su 4° Marcha Anual contra el Cáncer y recaudó más de $200.000 para la investigación del cáncer pediátrico y brindar apoyo a los niños y sus familias. El 2 de junio de 2016, la Fundación de la Familia Anthony Rizzo organizó su 4.° concurso anual de cocina contra el cáncer y recaudó más de $630.000. \n\nLa quinta marcha anual contra el cáncer se llevó a cabo el domingo 11 de diciembre de 2016 y The Anthony Rizzo Foundation recaudó más de $500.000. El comisionado del condado de Broward, Michael Udine, proclamó el domingo como el Día de Anthony Rizzo. La antigua escuela secundaria de Rizzo retiró oficialmente su camiseta, la número 7. Rizzo y su fundación organizaron su sexta marcha anual contra el cáncer el 3 de diciembre de 2017 y recaudaron $960.000 para las familias que luchan contra el cáncer. Las ganancias netas del evento beneficiarán al Joe DiMaggio Children's Hospital, al Sylvester Comprehensive Cancer Center de la Universidad de Miami y otorgarán subvenciones a las familias que luchan contra el cáncer. La séptima Marcha Anual contra el Cáncer organizada por la Fundación de la Familia Anthony Rizzo recaudó $1.1 millones el 2 de diciembre de 2018. El dinero se destinó al Joe DiMaggio Children's Hospital, al Sylvester Comprehensive Cancer Center de la Universidad de Miami y a familias que luchan contra el cáncer. \n\nEl 27 de mayo de 2019, la Fundación de la Familia Anthony Rizzo organizó su séptima competencia anual de cocina para el cáncer y recaudó $1.8 millones para pacientes con cáncer y sus familias. El 24 de noviembre de 2019, la Fundación de la Familia Anthony Rizzo organizó su octava Marcha Anual contra el Cáncer y recaudó más de $1.35 millones. El 16 de enero de 2020, la Fundación de la Familia Anthony Rizzo recaudó cerca de $500.000 para ayudar a las familias que luchan contra el cáncer durante su sexto evento anual Laugh-Off for Cancer. En febrero de 2020, Rizzo donó $ 150.000 a la escuela secundaria Marjory Stoneman Douglas para ayudar a pagar las luces de la escuela para sus campos de béisbol y softbol. Su escuela secundaria inauguró el nuevo campo de béisbol que se conocerá como Anthony Rizzo Field. La Fundación de la Familia Anthony Rizzo organizó su novena marcha anual contra el cáncer, el 15 de noviembre de 2020 y recaudó más de $850.000.\n\nReferencias \n\nCategoría:Beisbolistas de San Diego Padres\nCategoría:Beisbolistas de New York Yankees\nCategoría:Beisbolistas de los Chicago Cubs\nCategoría:Beisbolistas de Florida\nCategoría:Personas vivas\nCategoría:Nacidos en 1989\n\nIn what year did Rizzo make his MLB debut and with what team? He debuted in 2011 with the San Diego Padres."} {"dataset": "lmsys/lmsys-chat-1m", "conversation_id": "0f493b9442a3446d89c47644aab9fc68", "conversation_index": 867987, "turn_index": 6, "tokens_gpt_oss_120b": 1164, "prompt": "Starting from bottom to top can it be predicted the outcome of the next number (as red or green) at some point using the patterns (green defined as >2) and the red numbers(defined as <2) in this list of numbers:1\n2.76\n1.03\n1.08\n1.66\n1.42\n1.17\n1.25\n2.75\n1.06\n1.69\n2.19\n4.39\n1.13\n1.83\n1.24\n2.07\n24.51\n4.81\n11.1\n2.42\n1.08\n1.89\n1.16\n2.56\n323.92\n4.57\n2.47\n1.77\n1.8\n1.8\n46.3\n1.26\n5.21\n1.02\n2.69\n1.88\n1.94\n3.3\n4.82\n1.28\n43.1\n4.75\n27.68\n1\n381.51\n2.51\n28.37\n1.34\n1.51\n1.42\n1.27\n6.47\n4.39\n1.29\n1.45\n10.23\n2.35\n1.01\n1.22\n4.97\n1.4\n6.56\n1.2\n1.8\n8.05\n1.13\n3.71\n2.15\n2.1\n1.46\n1.17\n4.48\n1.38\n56.68\n7.75\n1.65\n3.29\n1.28\n28.31\n1.85\n1.26\n8.11\n1.33\n1.82\n27.61\n1.14\n1.74\n1.15\n1.33\n11.78\n3\n3.34\n1.66\n1.27\n1.07\n1.2\n1.39\n1.44\n1.62\n1.04\n1.43\n4.29\n2.96\n1.02\n1.26\n1.13\n1.46\n3.07\n1.84\n4.95\n1\n1.36\n17.34\n2.87\n2.26\n1.07\n3.13\n2.16\n1.3\n16.64\n1.37\n1.28\n4.08\n1.54\n1.75\n1.68\n15.67\n1.44\n3.86\n4.34\n1.68\n1.06\n7.18\n7.73\n1.11\n1.5\n1\n12.7\n1.04\n3.23\n2.32\n10.85\n9.92\n1.25\n2.1\n4.68\n3.35\n3.45\n350.31\n133.94\n2.23\n1.59\n2.25\n1.13\n2.13\n2.36\n1.32\n2.42\n1.11\n1.47\n4.2\n2.13\n57.27\n1.02\n2.93\n1.49\n1.93\n1.27\n6.67\n1.54\n1.32\n1.64\n1.39\n1\n4.98\n2.07\n7.25\n2.35\n2\n4.34\n1.13\n5.27\n93.38\n1.28\n3.19\n1.28\n1\n1.05\n1.07\n2.15\n37.28\n4.89\n1.27\n1.03\n11.41\n3.18\n1.35\n1.42\n1.36\n2.27\n17.83\n1.32\n56.53\n1.24\n4.99\n1.04\n1.22\n43.33\n2.99\n4.32\n3.21\n1.46\n135.55\n1.73\n15.68\n2.03\n1.57\n1.25\n15.7\n1.09\n3.29\n7.68\n2.98\n1.4\n1.79\n4.44\n1.22\n2.64\n1.31\n9.34\n1.17\n1.67\n1.43\n1.44\n1.5\n2.48\n11.41\n1.45\n1.87\n1.17\n2.57\n2.69\n1.52\n10.87\n5.11\n1.22\n1\n1.48\n1.74\n2.83\n1.27\n4.1\n3.02\n1.26\n3.03\n18.31\n3.19\n5.58\n1.11\n1.92\n1.45\n40.35\n1.15\n1.77\n1.95\n7.92\n4.01\n2.14\n1.18\n1.37\n1.26\n1.56\n1.35\n1.3\n1.3\n3.64\n2.19\n10.22\n2.8\n1.67\n1.82\n22.05"} {"dataset": "zai-org/LongAlign-10k", "example_id": "cec1bbf2879c09b72b7f9d18394c30807472e3c3eb7fe5be", "conversation_index": 3072, "turn_index": 0, "tokens_gpt_oss_120b": 6011, "prompt": " \n**101 conversation starters** for couples\n\n**GARY CHAPMAN** \n& RAMON PRESSON\n\nNorthfield Publishing \nCHICAGO\n© 2002, 2012 BY GARY CHAPMAN & RAMON PRESSON\n\nFormerly titled _Love Talks for Couples_ \nAll rights reserved. No part of this product may be reproduced in any form without permission in writing from the publisher, except in the case of brief quotations embodied in critical articles or reviews. \nTwo entries (#37,#54) first appeared in Ramon Presson, _Soul Care_ \n(Littleton, Colo.: Serendipity House, 2000). \nCover design: Smartt Guys design \nCover photo: Steve Cole/iStock \nInterior design: Julia Ryan / www.DesignByJulia.com \nGary Chapman photo: David Smith\n\nISBN: 978-0-8024-0837-2\n\nWe hope you enjoy this book from Northfield Publishing. Our goal is to provide high-quality, thought-provoking books and products that connect truth to your real needs and challenges. \nFor more information on other books and products written and produced from a biblical perspective, go to www.moodypublishers.com or write to:\n\nNorthfield Publishing \n820 N. LaSalle Boulevard \nChicago, IL 60610\n\n1 3 5 7 9 10 8 6 4 2\n\n_Printed in the United States of America_\n\n# Tips for Using 101 Conversation Starters\n\nYour spouse is a fascinating person, a treasure trove of meaningful, humorous, and profound experiences, thoughts, feelings, ideas, memories, hopes, dreams, beliefs, and convictions. These questions celebrate the depth and wonderful mystery of your mate. Questions invite disclosure, and disclosure launches discovery. Discovery enriches a marriage and builds intimacy. Use the following 101 questions to prompt meaningful, in-depth discussions and to affirm and encourage your spouse.\n\nHere are some ways to use the questions:\n\n• During dinner at home (if you don't have children)\n\n• During a quiet moment in the evening\n\n• At bedtime (if both of you are alert)\n\n• During dinner on a date night\n\n• While in the car during a long drive\n\nWhile the easiest way to proceed through the questions is to use them in the order they are presented, another possibility is that your spouse and you take turns in selecting the questions. We recommend that you do only one or two questions at a time. These questions are like dessert—a small and satisfying portion creates the anticipation for more later. _101 Conversation Starters for Couples_ offers a process to enjoy, not a project to complete.\n\nHave fun with these questions two or three times each week and watch intimacy grow in your marriage.\n\n# Table of Contnets\n\nquestion 1\n\nquestion 2\n\nquestion 3\n\nquestion 4\n\nquestion 5\n\nquestion 6\n\nquestion 7\n\nquestion 8\n\nquestion 9\n\nquestion 10\n\nquestion 11\n\nquestion 12\n\nquestion 13\n\nquestion 14\n\nquestion 15\n\nquestion 16\n\nquestion 17\n\nquestion 18\n\nquestion 19\n\nquestion 20\n\nquestion 21\n\nquestion 22\n\nquestion 23\n\nquestion 24\n\nquestion 25\n\nquestion 26\n\nquestion 27\n\nquestion 28\n\nquestion 29\n\nquestion 30\n\nquestion 31\n\nquestion 32\n\nquestion 33\n\nquestion 34\n\nquestion 35\n\nquestion 36\n\nquestion 37\n\nquestion 38\n\nquestion 39\n\nquestion 40\n\nquestion 41\n\nquestion 42\n\nquestion 43\n\nquestion 44\n\nquestion 45\n\nquestion 46\n\nquestion 47\n\nquestion 48\n\nquestion 49\n\nquestion 50\n\nquestion 51\n\nquestion 52\n\nquestion 53\n\nquestion 54\n\nquestion 55\n\nquestion 56\n\nquestion 57\n\nquestion 58\n\nquestion 59\n\nquestion 60\n\nquestion 61\n\nquestion 62\n\nquestion 63\n\nquestion 64\n\nquestion 65\n\nquestion 66\n\nquestion 67\n\nquestion 68\n\nquestion 69\n\nquestion 70\n\nquestion 71\n\nquestion 72\n\nquestion 73\n\nquestion 74\n\nquestion 75\n\nquestion 76\n\nquestion 77\n\nquestion 78\n\nquestion 79\n\nquestion 80\n\nquestion 81\n\nquestion 82\n\nquestion 83\n\nquestion 84\n\nquestion 85\n\nquestion 86\n\nquestion 87\n\nquestion 88\n\nquestion 89\n\nquestion 90\n\nquestion 91\n\nquestion 92\n\nquestion 93\n\nquestion 94\n\nquestion 95\n\nquestion 96\n\nquestion 97\n\nquestion 98\n\nquestion 99\n\nquestion 100\n\nquestion 101\n\n# [question \n1](9780802483560_epub_toc_r1.htm#c01a)\n\nWhat are two things that happened today, and how did you feel about them?\n\n# [question \n2](9780802483560_epub_toc_r1.htm#c02a)\n\nWhat were some of your favorite toys as a child? What was your favorite candy?\n\n# [question \n3](9780802483560_epub_toc_r1.htm#c03a)\n\nDescribe the home of one or both sets of your grandparents.\n\n# [question \n4](9780802483560_epub_toc_r1.htm#c04a)\n\nWhat was something you really wanted but were not allowed to own as a child or teen?\n\n# [question \n5](9780802483560_epub_toc_r1.htm#c05a)\n\nDescribe one of your favorite elementary school teachers. Then describe a favorite high school teacher or college professor.\n\n# [question \n6](9780802483560_epub_toc_r1.htm#c06a)\n\nAs you were growing up, what was unique about your family as compared to other families in your neighborhood or the families of your friends?\n\n# [question \n7](9780802483560_epub_toc_r1.htm#c07a)\n\nWhat was your most serious physical injury as a child or teen?\n\n# [question \n8](9780802483560_epub_toc_r1.htm#c08a)\n\nWhat do you remember about learning to drive?\n\n# [question \n9](9780802483560_epub_toc_r1.htm#c09a)\n\nCan you recall visiting your parents' workplace? \nIf so, describe it and how you felt when you went there.\n\n# [question \n10](9780802483560_epub_toc_r1.htm#c10a)\n\nComplete this sentence: \"I'm sure my mom and dad wish I would....\"\n\n# [question \n11](9780802483560_epub_toc_r1.htm#c11a)\n\nWhat is perhaps the worst movie you have ever seen?\n\n# [question \n12](9780802483560_epub_toc_r1.htm#c12a)\n\nWhat tragic news story in the last few years made you particularly sad?\n\n# [question \n13](9780802483560_epub_toc_r1.htm#c13a)\n\nWhat was one of the most memorable weddings (other than your own) that you have attended?\n\n# [question \n14](9780802483560_epub_toc_r1.htm#c14a)\n\nWhat is something you collected as a child or teen?\n\n# [question \n15](9780802483560_epub_toc_r1.htm#c15a)\n\nWhat is a question that you wish you had the courage to ask your mother and/or father?\n\n# [question \n16](9780802483560_epub_toc_r1.htm#c16a)\n\nIf you were given five acres of land, where would you want it to be and what would you want to do with it?\n\n# [question \n17](9780802483560_epub_toc_r1.htm#c17a)\n\nIf you could own and operate your own business (and be guaranteed of its success), what would it be?\n\n# [question \n18](9780802483560_epub_toc_r1.htm#c18a)\n\nIf you do not play a musical instrument, what one do you wish you could play? If you do/did play a musical instrument, do you recall how you chose that particular one?\n\n# [question \n19](9780802483560_epub_toc_r1.htm#c19a)\n\nWhat would you say are two of the best concerts you have seen, either in person or on video or film?\n\n# [question \n20](9780802483560_epub_toc_r1.htm#c20a)\n\nWhat are two of your all-time favorite movies (or books)?\n\n# [question \n21](9780802483560_epub_toc_r1.htm#c21a)\n\nMy mother/father clearly did not understand what was considered cool when she/he bought me....\n\n# [question \n22](9780802483560_epub_toc_r1.htm#c22a)\n\nWhat famous person (deceased) would you like to have met?\n\n# [question \n23](9780802483560_epub_toc_r1.htm#c23a)\n\nWhich of the following would you find most gratifying?\n\n earning a PhD\n\n publishing a bestselling book\n\n recording an original chart-topping song\n\n winning an Olympic gold medal\n\n# [question \n24](9780802483560_epub_toc_r1.htm#c24a)\n\nIf money and/or child care were no object, what would be your idea of the perfect New Year's Eve?\n\n# [question \n25](9780802483560_epub_toc_r1.htm#c25a)\n\nWho is one of the most genuinely spiritual persons you know?\n\n# [question \n26](9780802483560_epub_toc_r1.htm#c26a)\n\nHow do you think the world has changed since September 11, 2001?\n\n# [question \n27](9780802483560_epub_toc_r1.htm#c27a)\n\nWhich of the following rides would be your first choice?\n\n a gondola in Venice\n\n a cab in London\n\n a Ferrari on the autobahn\n\n a hot air balloon in Switzerland\n\n an airboat in the Everglades\n\n a raft down the Colorado River\n\n a carriage in Paris\n\n# [question \n28](9780802483560_epub_toc_r1.htm#c28a)\n\nWhat is one of your favorite memories that includes snow?\n\n# [question \n29](9780802483560_epub_toc_r1.htm#c29a)\n\nWho was your favorite superhero or cartoon character?\n\n# [question \n30](9780802483560_epub_toc_r1.htm#c30a)\n\nIf someone could bless you and pass on to you a special ability,\n\nwho would you choose to bless you and with what ability?\n\n# [question \n31](9780802483560_epub_toc_r1.htm#c31a)\n\nWho is the most joyful person you know?\n\n# [question \n32](9780802483560_epub_toc_r1.htm#c32a)\n\nWho is someone you wish you could infect with a more positive attitude?\n\n# [question \n33](9780802483560_epub_toc_r1.htm#c33a)\n\nComplete this sentence: \"It would make me a better person if I were more like you in the way you....\"\n\n# [question \n34](9780802483560_epub_toc_r1.htm#c34a)\n\nWhen in your life would you say your self-esteem was the lowest?\n\n# [question \n35](9780802483560_epub_toc_r1.htm#c35a)\n\nRecall a time when you were given constructive criticism that proved beneficial.\n\n# [question \n36](9780802483560_epub_toc_r1.htm#c36a)\n\nThe lion, beaver, otter, and golden retriever are used to describe four personality types. Which one do you think best describes you?\n\n Lion: strong, confident, leader, likes to make sure things get done\n\n Beaver: detail oriented, organized, follows instructions, good with projects\n\n Otter: very outgoing, enjoys people, humorous, creative\n\n Golden Retriever: loyal, sensitive, encouraging\n\n# [question \n37](9780802483560_epub_toc_r1.htm#c37a)\n\nIf you could hire Martha Stewart for a day, what would you have her do?\n\n# [question \n38](9780802483560_epub_toc_r1.htm#c38a)\n\nRegardless of how long I live, I hope I will always....\n\n# [question \n39](9780802483560_epub_toc_r1.htm#c39a)\n\n\"It is more blessed to give than to receive.\" Recall a gift that gave you considerable satisfaction in presenting it.\n\n# [question \n40](9780802483560_epub_toc_r1.htm#c40a)\n\nDescribe the location and three features of your dream home.\n\n# [question \n41](9780802483560_epub_toc_r1.htm#c41a)\n\nWho would you most like to hear one of the following from?\n\n\"I love you.\"\n\n\"I support you.\"\n\n\"I respect you.\"\n\n\"I appreciate you.\"\n\n\"I miss you.\"\n\n\"I trust you.\"\n\n# [question \n42](9780802483560_epub_toc_r1.htm#c42a)\n\nIn retrospect, what is something that your parents were wise in doing in raising you?\n\n# [question \n43](9780802483560_epub_toc_r1.htm#c43a)\n\nWhat was your most/least favorite subject in school?\n\n# [question \n44](9780802483560_epub_toc_r1.htm#c44a)\n\nIf you could take a course in any subject right now at your local college, what type of course would it be?\n\n# [question \n45](9780802483560_epub_toc_r1.htm#c45a)\n\nIn what way are you most/least like your mother? How are you most/least like your father?\n\n# [question \n46](9780802483560_epub_toc_r1.htm#c46a)\n\nIn Matthew 6:34, Jesus encourages us to live with faith in the present.\n\nWhich is the greater obstacle for you?\n\n dwelling on the past\n\n worrying about the future\n\n# [question \n47](9780802483560_epub_toc_r1.htm#c47a)\n\nWho was your best friend in junior high school? What did you do together?\n\n# [question \n48](9780802483560_epub_toc_r1.htm#c48a)\n\nAs I was growing up, my father was most like\n\n a coach a judge\n\n an historian a professor\n\n a preacher a manager\n\n a cheerleader\n\n other______________\n\n# [question \n49](9780802483560_epub_toc_r1.htm#c49a)\n\nIf you inherited $200,000 (after taxes), what would you do with the money?\n\n# [question \n50](9780802483560_epub_toc_r1.htm#c50a)\n\nName three jobs or careers you are definitely not suited for.\n\n# [question \n51](9780802483560_epub_toc_r1.htm#c51a)\n\nDescribe your pediatrician when you were growing up.\n\nWhat do you remember about those doctor visits?\n\n# [question \n52](9780802483560_epub_toc_r1.htm#c52a)\n\nConcerning what biblical topic or Bible passage (or verse) do you wish you had a better understanding?\n\n# [question \n53](9780802483560_epub_toc_r1.htm#c53a)\n\nWhat do you think that you will want to do in your retirement years?\n\n# [question \n54](9780802483560_epub_toc_r1.htm#c54a)\n\nActs 2:42-47 describes a close, caring community. In what setting have you had the greatest experience of genuine fellowship?\n\n friends at school job where I worked sports team\n\n support group church-related group\n\n volunteer organization\n\n ministry/mission team fraternity/sorority\n\n other______________\n\n# [question \n55](9780802483560_epub_toc_r1.htm#c55a)\n\nWhat item of clothing in my wardrobe do you really like to see me wear?\n\n# [question \n56](9780802483560_epub_toc_r1.htm#c56a)\n\nWhat is a song or piece of music that moves or inspires you?\n\n# [question \n57](9780802483560_epub_toc_r1.htm#c57a)\n\nWhat quality or skill that you possess would you find most gratifying to have your child imitate as an adult?\n\n# [question \n58](9780802483560_epub_toc_r1.htm#c58a)\n\nIf you could win any competition in the world, what would it be?\n\n# [question \n59](9780802483560_epub_toc_r1.htm#c59a)\n\nWhat nonbiblical historical event would you like to have witnessed?\n\n# [question \n60](9780802483560_epub_toc_r1.htm#c60a)\n\nName the Old Testament event that you wish you could have witnessed. \nName the New Testament event (in addition to the resurrection) that you wish you could have witnessed.\n\n# [question \n61](9780802483560_epub_toc_r1.htm#c61a)\n\nIn TV's _The Andy Griffith Show_, \nBarney Fife once told Andy that the biggest purchase he ever made was a septic tank for his parents' wedding anniversary. What gift would you like to give to your parents?\n\n# [question \n62](9780802483560_epub_toc_r1.htm#c62a)\n\nWhat is something you thoroughly enjoyed doing as a child and have not done in years?\n\n# [question \n63](9780802483560_epub_toc_r1.htm#c63a)\n\nRichard Foster says that our lives are bombarded by hurry, crowds, and noise. \nWhich of those three has been most bothersome for you lately?\n\n# [question \n64](9780802483560_epub_toc_r1.htm#c64a)\n\nComplete this sentence: \"A time that I felt I might be in physical danger was when....\"\n\n# [question \n65](9780802483560_epub_toc_r1.htm#c65a)\n\nIn what event would you most like to win an Olympic gold medal?\n\n# [question \n66](9780802483560_epub_toc_r1.htm#c66a)\n\nRecall a time when you were disappointed in not being chosen.\n\n# [question \n67](9780802483560_epub_toc_r1.htm#c67a)\n\nI wish I could hire ____________ \nto write and record a song from me to you.\n\n# [question \n68](9780802483560_epub_toc_r1.htm#c68a)\n\nI think I would crack under the torture if I were forced to listen to only ____________ music all day and could only eat ____________ meals all day.\n\n# [question \n69](9780802483560_epub_toc_r1.htm#c69a)\n\nIf we were to adopt a child from another country, which country would it be?\n\n# [question \n70](9780802483560_epub_toc_r1.htm#c70a)\n\nAs a couple we make a great team, but it is most unlikely that we would ever team up to....\n\n win a mixed-doubles tennis championship\n\n sing a duet\n\n win a medal in couples figure skating\n\n be co-leaders (main speakers) of a nationally televised marriage seminar\n\n compete in a ballroom dancing competition\n\n operate a bed & breakfast\n\n co-author a book entitled _Stress-Free Parenting_\n\n# [question \n71](9780802483560_epub_toc_r1.htm#c71a)\n\nImagine that your internal dashboard has a spiritual passion gauge on it. What is your present reading?\n\nE _______\n\n¼ _______\n\n½ _______\n\n¾ _______\n\nF _______\n\n# [question \n72](9780802483560_epub_toc_r1.htm#c72a)\n\nWhat is the worst or most unusual job interview you ever had?\n\n# [question \n73](9780802483560_epub_toc_r1.htm#c73a)\n\nThe circus act that most reminds me of my job is....\n\n# [question \n74](9780802483560_epub_toc_r1.htm#c74a)\n\nWhat is your most/least favorite trait in others?\n\n# [question \n75](9780802483560_epub_toc_r1.htm#c75a)\n\nWhat kind of race best describes your last seven days?\n\n BOSTON MARATHON _It seemed to last forever_.\n\n TOUR DE FRANCE _I was pedaling uphill as fast as I could_.\n\n KENTUCKY DERBY _I worked for so long on something that was over so quickly_.\n\n INDIANAPOLIS 500 _I went round and round, and I'm right where I started_.\n\n IRONMAN TRIATHLON _I endured a week full of job, family, and church activities_.\n\n 24 HOURS OF LE MANS _Sleep? What's that?_\n\n HUNDRED-METER HIGH HURDLES _All I did was sprint and navigate obstacles_.\n\n DEMOLITION DERBY _I feel beat up_.\n\n# [question \n76](9780802483560_epub_toc_r1.htm#c76a)\n\nTalk about your early experiences with someone of another race or nationality.\n\n# [question \n77](9780802483560_epub_toc_r1.htm#c77a)\n\nDescribe a summer camp experience.\n\n# [question \n78](9780802483560_epub_toc_r1.htm#c78a)\n\nWhat is your favorite animated film?\n\n# [question \n79](9780802483560_epub_toc_r1.htm#c79a)\n\nWhen you were growing up, where did your family go on vacations? Describe one of those vacations.\n\n# [question \n80](9780802483560_epub_toc_r1.htm#c80a)\n\nCan you remember a time when you got lost or separated from your family or companions? Describe what happened and how you felt.\n\n# [question \n81](9780802483560_epub_toc_r1.htm#c81a)\n\nRecall a time when you got sick at a very inopportune time.\n\n# [question \n81](9780802483560_epub_toc_r1.htm#c81a)\n\nWhat high school or college course would you rather flee the country than be forced to take again?\n\n# [question \n83](9780802483560_epub_toc_r1.htm#c83a)\n\nCan you recall a first date during which you immediately knew there would not be a second date?\n\n# [question \n84](9780802483560_epub_toc_r1.htm#c84a)\n\nSelect and describe a couple who were friends with your parents when you were growing up.\n\n# [question \n85](9780802483560_epub_toc_r1.htm#c85a)\n\nRecall a childhood memory about one of the following:\n\n• playing in a creek\n\n• playing in a tree house\n\n• catching fireflies\n\n• running a lemonade stand\n\n• pretending to be a superhero\n\n• a slumber party or sleepover\n\n• jumping off the high dive\n\n# [question \n86](9780802483560_epub_toc_r1.htm#c86a)\n\nDescribe your parents' reaction on the day you moved out or left for college.\n\n# [question \n87](9780802483560_epub_toc_r1.htm#c87a)\n\nRecall something special about your high school or college graduation.\n\n# [question \n88](9780802483560_epub_toc_r1.htm#c88a)\n\nWhat is your favorite scene from your favorite movie?\n\n# [question \n89](9780802483560_epub_toc_r1.htm#c89a)\n\nI thought it was one of the coolest items in my wardrobe at the time, but today I'm not sure I'd even wear it to a costume party. What is it?\n\n# [question \n90](9780802483560_epub_toc_r1.htm#c90a)\n\nSomething I wanted to quit but my parents wouldn't let me was....\n\n# [question \n91](9780802483560_epub_toc_r1.htm#c91a)\n\nJoseph's brothers sold him into slavery. If you have siblings, what was one of the meanest things done to you by a brother or sister? If you are an only child, what was one of the meanest things done to you by a friend?\n\n# [question \n92](9780802483560_epub_toc_r1.htm#c92a)\n\nDescribe someone you encountered recently who probably needs God in his or her life.\n\n# [question \n93](9780802483560_epub_toc_r1.htm#c93a)\n\nIf you were offered the opportunity to be one of the contestants on _Survivor_, would you do it? If yes, what do you imagine would be the hardest thing for you to cope with?\n\n# [question \n94](9780802483560_epub_toc_r1.htm#c94a)\n\nOne of the descendants of King Saul was named Mephibosheth. Do you like your first name? If you could choose another first name for yourself, what would it be?\n\n# [question \n95](9780802483560_epub_toc_r1.htm#c95a)\n\nIn the movie _The Karate Kid_, young Daniel is befriended by an old Japanese man who teaches him karate, but more importantly offers him kindness and encouragement. Name an older person who blessed you with kindness and encouragement.\n\n# [question \n96](9780802483560_epub_toc_r1.htm#c96a)\n\nWhat is one of your favorite stories that your parents tell about you?\n\n# [question \n97](9780802483560_epub_toc_r1.htm#c97a)\n\nIn the movie _Groundhog Day_, Bill Murray kept waking up only to repeat the same day over and over again. What recent day would you not want to repeat?\n\n# [question \n98](9780802483560_epub_toc_r1.htm#c98a)\n\nWhat old photograph of yourself makes you really laugh or cringe in embarrassment?\n\n# [question \n99](9780802483560_epub_toc_r1.htm#c99a)\n\nRecall something about exchanging valentines when you were in elementary school.\n\n# [question \n100](9780802483560_epub_toc_r1.htm#c100a)\n\nCongratulations! Your boss just gave everyone a spring break. Where do you want to go?\n\n# [question \n101](9780802483560_epub_toc_r1.htm#c101a)\n\nWhat is something that occurred this past year that you are especially thankful for?\n\n# _More Resources on_ \nTHE 5 LOVE LANGUAGES®\n\nThe 5 Love Languages®—Gift Edition \nThe 5 Love Languages® \nThe 5 Love Languages® Men's Edition\n\nVISIT 5LOVELANGUAGES.COM\n\n# _More Resources on_ \nTHE 5 LOVE LANGUAGES®\n\nThe 5 Love Languages® of Children \nThe 5 Love Languages® of Teenagers \nThe Five Love Languages® Singles Edition \nGod Speaks Your Love Language\n\nVISIT 5LOVELANGUAGES.COM\n\n# _More Resources on_ \nTHE 5 LOVE LANGUAGES®\n\nLearning your love language—and that of your spouse, teen, and child—might be the easiest and most important thing you ever learn. The assessments featured at www.5lovelanguages.com make it easy to discover your love language. Simply take one of our short profiles and find out how you and your loved one express and interpret love.\n\nRight away, you can make a concerted effort to speak his or her primary language. It might not come naturally, but even the effort will be appreciated.\n\nThis dynamic site is also full of other helpful features—links to other resources, free stuff, upcoming events, podcasts, video, and more—all designed to encourage you and strengthen your relationships. We want to help you feel loved, and to effectively communicate love to others.\n\n**VISIT 5LOVELANGUAGES.COM**\n\n\nWhat does the author say is the greatest obstacle for most people - dwelling on the past or worrying about the future?"} {"dataset": "zai-org/LongAlign-10k", "example_id": "3f5e6b5fe4ff1e3dd410a32f900187e50b7e730e45d472cd", "conversation_index": 5341, "turn_index": 0, "tokens_gpt_oss_120b": 9623, "prompt": "Multi-Camera Multi-Object Tracking on the Move via\nSingle-Stage Global Association Approach\n\nPha Nguyen1\n\nKha Gia Quach\n\nChi Nhan Duong\n\nSon Lam Phung\n\nNgan Le\n\nKhoa Luu\n\nIntroduction\n\nObject detection and tracking have become two of the most critical tasks in autonomous vehicles (AV). Recent developments in deep learning methods have dramatically boosted the performance of object understanding and tracking in autonomous driving applications.\n\nSample of multi-view captured via a multi-camera setup on a vehicle from nuScenes.\n\n[fig:sample_nuscenes_a]\n\nFirst row: the object detector KM3D fails to detect partial objects in one camera but can detect them in another. Second row: The detector fails to detect objects in both cameras. Third row: the SC-MOT method DEFT fragments a global object ID into many local IDs when it moves across cameras. The green arrow indicates the true-positive detection sample; the red arrows indicate false-negative detection and tracking samples.\n\n[fig:sample_nuscenes_b]\n\nThe object tracking problem in AVs is far apart from multiple camera multiple object tracking (MC-MOT) in surveillance settings where cameras are stationary, i.e., their positions are fixed, but their poses may change in PTZ cameras cases. For clarity, MC-MOT in surveillance settings is referred to as static MC-MOT and MC-MOT in AVs as dynamic MC-MOT on-the-move since cameras are moving with the vehicle. Other works consider tracking the activities of people from multiple moving cameras, where the movements are subtle with large overlapping regions between cameras. In contrast, our camera setting in this paper contains large movements and small overlapping regions between cameras as the traveling car passes by other objects. Such a setup with some redundancy, i.e., certain overlapping fields-of-view, presents some new challenges for MOT to work with 3D object detectors to track objects and maintain the stability of predictions across video frames in multiple views.\n\nAs a result, camera-based tracking methods in the current leaderboard of autonomous driving datasets, e.g., nuScenes and Waymo, appear to be using only single-camera settings. However, the datasets were collected in multi-camera settings as shown in [fig:sample_nuscenes_a]. Thus, this work aims to use redundant data to improve detection and tracking performance.\n\nIn the MC-MOT settings, traditional two-stage approaches track objects on each camera independently, i.e., single-camera tracking (SCT), then link-local tracklets across cameras via global matching steps based on Re-ID features. Applying such a two-stage approach to dynamic MC-MOT settings on AVs leads to a problem with the global matching that relies on complicated graph structures to assign a global ID to all detection. In addition, this approach cannot handle scenarios when the detector fails to detect objects from one of the cameras. Moreover, it requires additional steps to merge many local IDs, as shown in Fig. [fig:sample_nuscenes_b]. Therefore, there are better solutions than using SCT multiple times.\n\nContributions of this Work\n\nThis work presents a single-stage MC-MOT approach directly using the outputs of an object detector as the inputs instead of SCT trajectories. To achieve this goal, we mathematically reformulate association steps in static MC-MOT into a single global association step as a one-to-many assignment problem to match one target, i.e., tracked objects in the world coordinate, with multiple detection, i.e., objects appear in multi-camera overlapping regions. This assignment can be solved efficiently via our proposed Fractional Optimal Transport Assignment (FOTA) method. Moreover, since this assignment problem can be defined in both the traditional track-by-detection scheme and the more recent track-by-attention scheme, we demonstrate its ability in both our proposed Single-Stage Global Assignment (SAGA) schemes as SAGA-Track and SAGA-TrackNet, respectively. Evaluate proposed methods with a comprehensive evaluation criterion to demonstrate their robustness compared to previous frameworks. The proposed method reduces the IDSwitch error from 3,807 to 870 and improves the tracking performance by up to 6.4% on the nuScenes Test Set benchmark.\n\nRelated Work\n\nThe MOT problem on AVs has recently received much attention in the research community. Recent methods in static MC-MOT settings have been reviewed in, while dynamic MC-MOT settings are still an open research area. The most recent work reviewed in this section is focused on the assignment or association formulation in SC-MOT and static MC-MOT.\n\nAssignment in SC-MOT. While many works calculated the assignment costs between tracklets and detection by using some distance measurements over deep features or locations, some approaches directly computed the similarity scores. Xiang et al. built a bipartite graph over the affinity computed by the LSTM as edge cost and solved the association by the Hungarian algorithm. Ran et al. proposed a Pose-based Triple Stream Network to extract three kinds of similarity scores, i.e., appearance, motion, and interaction, and then fuse the average strategy into a final similarity score in a bipartite graph by the greedy match algorithm.\n\nAssignment in Static MC-MOT. He et al. constructed a global similarity matrix from local tracklets in all single views and then estimated targets’ trajectory by offline performing Matrix Factorization. Ristani and Tomasi solved the ID assignment task by correlation clustering, then executed interpolation and elimination to fill the gap and filter indecisive tracks. Quach et al. proposed a dynamic graph to transform pre-computed Re-ID features into new context-aware ID features. Hence it performs better clustering and yields more accurate results. Yoon et al. maintained a set of track hypotheses all the time by the Multiple Hypothesis Tracking algorithms and also reduced the excess by introducing a gating mechanism for tree pruning. Zhang et al. utilized the Re-Ranking algorithm on the global cost matrix to cluster IDs. However, directly applying these approaches to the dynamic setting on AVs suffers from a significant real-time performance decrease, computation complexity, and domain irrelevance. Therefore, several methods to solve object tracking on the fly have been proposed, as referred to in the following parts.\n\nUsing Motion Models. Weng et al. proposed a simple yet effective baseline to utilize a classic state estimator (the Kalman Filter) for tracking 3D bounding boxes. These bounding boxes can be obtained from a point cloud object detector or an image-based object detector. Chiu et al. improved the Kalman Filter tracking system using the Mahalanobis distance between the predicted states and observations. The method is reasonably effective in filtering outliers and handling partially and fully occluded objects.\n\nUsing Appearance Models. Zhou et al.’s approaches are widely used for single-camera tracking. These approaches simplify the tracking procedure by treating objects as points, which usually involves many computationally intensive steps from detection to assigning object ID. Hu et al. estimated robust 3D box information from 2D images and adopted 3D box-reordering and LSTM as a motion module to link objects across frames.\n\nUsing Hybrid Approaches. Chaabane et al. trained the object detection and the object association task simultaneously by adding a feature extractor and a matching head after the object detector. In addition, an LSTM instead of a Kalman Filter is used for motion prediction. Yin et al. followed a similar process but performed feature extraction on point cloud maps.\n\nUsing Modern Approaches. Graph Neural Network, Self-Attention, and Transformer have led to a new learning-from-context paradigm. This paradigm has attracted considerable research attention recently because of its promising performance in a wide range of tasks from natural language processing to computer vision. A limited number of these methods have been applied for dynamic MC-MOT in autonomous vehicles, apart from many SC-MOT approaches. Weng et al. proposed the first feature interaction method that leverages a Graph Neural Network to adapt features from one object to another individual. Meinhardt et al. proposed a new tracking-by-attention paradigm (compared to the existing tracking-by-regression, tracking-by-detection, and tracking-by-segmentation) to deal with occlusions and determine the tracker’s Spatio-temporal correspondences. Sun et al. utilized the Query-Key mechanism to perform joint detection-and-tracking and disentangle complex components in previous tracking systems.\n\nCompared to these prior works, the critical difference in our approach is that it uses a world coordinate system in AVs multi-camera system to solve the global one-to-many association step. It is possible by matching one tracked object with multiple detection. Thus, it eliminates the need for another association step, i.e., using Re-ID, and reduces the effort of adopting several empirical rules and heuristics to handle overlapping FOVs.\n\nOur Proposed Method\n\nThis section presents the proposed dynamic MC-MOT approaches with a one-to-many global assignment method.\n\nProblem Definition\n\nGiven video frames from $K$ cameras at the $t$-th time step, denoted by the set ${\\mathcal{I}^{(t)}=\\{I_1^{(t)},\\dots, I_k^{(t)}\\dots,I_K^{(t)}\\}}$, MC-MOT system provides a set of detected objects $\\mathcal{O}^{(t)} = \\{ \\mathbf{o}_{j}^{(t)}\\}$ associated with their identities. Object bounding boxes and classes can be predicted using an object detector given each frame in $\\mathcal{I}^{(t)}$ separately. The identities of objects are obtained by associating with tracklets, i.e., a set of bounding boxes with a track ID $i$ as $\\mathcal{T}_i = \\{ \\mathbf{tr}^{(t_1)}_{i}, \\mathbf{tr}^{(t_2)}_{i}, \\cdots \\}$. Objects detected on each camera and track are represented by 3D bounding boxes in world coordinates. Note that tracklets are shared across cameras and are often referred to as a global track ID. During $T$ frames of a video sequence, the sub-sequence of $(t_1, t_2, \\cdots)$ is the time steps when the tracked object appears within the camera views. Each track $\\mathbf{tr}^{(t)}_{i}$ is estimated using a motion model from the previous frame $t-1$ and then updated with the detection of the corresponding tracked objects as follows, $$\\footnotesize \\begin{split} \\hat{\\mathbf{tr}}^{(t)}_{i} &= \\mathcal{M}_{\\text{pred}} (\\mathbf{tr}^{(t - 1)}_{i}) \\\\ \\mathbf{tr}^{(t)}_{i} &= \\mathcal{M}_{\\text{update}} (\\hat{\\mathbf{tr}}^{(t)}_{i}, \\mathbf{o}^{(t)}[i]) \\end{split}$$ $$\\footnotesize \\text{where } \\mathbf{o}^{(t)} [i] = \\begin{cases} \\mathbf{o}^{(t)}_{j} & \\text{if detected object } \\mathbf{o}^{(t)}_{j} \\text{ associates with the $i$-th tracklet}\\\\ \\varnothing & \\text{if no object } \\text{ associates with the $i$-th tracklet} \\end{cases}$$ Here, $\\mathcal{M}_{\\text{pred}}$ is a function or a network to predict the following location of the track based on the motion model, and $\\mathcal{M}_{\\text{update}}$ is a function to update the location of the track in the current time step $t$. In this paper, we use two different motion models, i.e., linear Kalman Filter and Non-linear Transfomer-based Network. To know which detected object $j$ is being used to update the corresponding tracklet $i$. Each detection is then assigned to a tracklet based on a matching algorithm with a cost function. It also determines whether the detection is a new or existing object from the previous frame. Generally, the cost functions to match detection with tracklets can be defined as in Eqn. [eq:cijk]. $$\\label{eq:cijk} \\footnotesize c_{ij} = \\mathcal{C}_{\\text{match}} [i, j] =d\\left(\\hat{\\mathbf{tr}}^{(t)}_{i}, \\mathbf{o}_{j}^{(t)} \\right)$$ where $d(\\cdot, \\cdot)$ is the distance between the detected and tracked objects. Several distance metrics can be adopted for $d(\\cdot, \\cdot)$ such as Mahalanobis distance implemented in, 2D or 3D GIoU.\n\n$$\\label{eq:mahalanobis} \\footnotesize d_{\\text{Mahalanobis}} \\left(\\hat{\\mathbf{tr}}^{(t)}_{i}, \\mathbf{o}_{j}^{(t)} \\right) = \\sqrt{(\\mathbf{o}_{j}^{(t)} - \\hat{\\mathbf{tr}}^{(t)}_{i})^{T} {\\mathbf{S}^{(t)}}^{-1} (\\mathbf{o}_{j}^{(t)} - \\hat{\\mathbf{tr}}^{(t)}_{i})},$$ $$\\label{eq:g_iou} \\footnotesize d_{\\text{GIoU}} \\left(\\hat{\\mathbf{tr}}^{(t)}_{i}, \\mathbf{o}_{j}^{(t)} \\right) = 1 - \\left(\\frac{|\\mathbf{o}_{j}^{(t)} \\cap \\hat{\\mathbf{tr}}^{(t)}_{i}|}{|\\mathbf{o}_{j}^{(t)} \\cup \\hat{\\mathbf{tr}}^{(t)}_{i}|} - \\frac{|\\mathbf{cv}^{(t)}_{ij} \\setminus (\\mathbf{o}_{j}^{(t)} \\cup \\hat{\\mathbf{tr}}^{(t)}_{i})|}{|\\mathbf{cv}^{(t)}_{ij}|}\\right),$$\n\nHere, $\\mathbf{S}^{(t)}$ is the covariance that represents the uncertainty of the predicted object state as implemented in. In addition, $\\mathbf{cv}^{(t)}_{ij}$ is the smallest enclosing convex shape of $\\mathbf{o}_{j}^{(t)}$ and $\\hat{\\mathbf{tr}}^{(t)}_{i}$. Note that multi-view geometry is implicitly applied when we compute the distance metrics above.\n\nSingle-Stage Global Assignment Tracking Approach (SAGA-Track)\n\nWith the cost matrix defined above, the assignment algorithm has to assign the detected objects to the correct tracklets. To assign detection to tracklets, a straightforward approach is to pool all detection and tracks into two corresponding sets and perform an one-to-one matching algorithm, i.e., Hungarian algorithm, based on a cost matrix similar to the SC-MOT case. However, in dynamic MC-MOT settings, one object can appear in several cameras simultaneously due to camera overlapping. That means two or more detected objects $\\mathbf{o}^{(t)}_{j}$ in different cameras should be matched to one tracklet $\\hat{\\mathbf{tr}}^{(t)}_{i}$ only. Therefore, the one-to-one matching algorithm cannot handle detection from multiple cameras put together as only one instance of an object in a camera can be matched to the target tracklet causing the remaining detection of that object in other cameras to be unmatched. These unmatched instances may create new tracklets during the tracking process, and a second association step is needed to connect them. It is referred to as the global baseline association in our experiments.\n\nTo further equip a tracking system with the capability of tracking multiple instances of the same objects in different cameras, we propose to cast this assignment process to a distribution matching task where tracklets and all detected objects at $t$-th time step can be formed into two distributions. Formally, let $\\mathcal{X} = \\{ \\hat{\\mathbf{tr}}^{(t)}_{i}\\}_{i=1}^N$ and $\\mathcal{Y} = \\{ \\mathbf{o}^{(t)}_j\\}_{j=1}^M$ be the sets of $N$ current tracklets and $M$ detected objects from all $K$ cameras at the $t$-th time step. Let $\\mathbf{p}$ and $\\mathbf{q}$ be the empirical distributions defined over $\\mathcal{X}$ and $\\mathcal{Y}$, respectively. The set of all possible couplings $\\Pi (\\mathbf{p}, \\mathbf{q})$ to transport the mass, i.e., number of object entities, from $\\mathcal{X}$ to $\\mathcal{Y}$ is defined as in Eqn. [eqn:TrackletObjCoupling]. $$\\label{eqn:TrackletObjCoupling} \\footnotesize \\Pi (\\mathbf{p}, \\mathbf{q}) = \\begin{cases} & \\boldsymbol{\\pi} \\in \\mathbb{R}_+^{|\\mathbf{p}| \\times |\\mathbf{q}|}: \\\\ & \\boldsymbol{\\pi}\\mathbb{1}_{|\\mathbf{q}|} \\leq \\mathbf{p}, \\boldsymbol{\\pi}^{\\top}\\mathbb{1}_{|\\mathbf{p}|} \\leq \\mathbf{q},\\mathbb{1}_{|\\mathbf{p}|}^{\\top} \\boldsymbol{\\pi} \\mathbb{1}_{|\\mathbf{q}|} = s \\end{cases} \\Bigg\\}$$ where $\\pi_{ij}$ denotes the amount of a mass $p_i$ at $\\hat{\\mathbf{tr}}^{(t)}_{i}$ being associated with the mass $q_j$ at $\\mathbf{o}^{(t)}_j$. The inequality in Eqn. [eqn:TrackletObjCoupling] indicates the possibility of fractional entities being matched between the two distributions as (1) one tracklet in $\\mathcal{X}$ can associate with no detection (i.e., the tracked object does not appear in all cameras) or many detections (i.e., the tracked object appears in many cameras); and (2) one detection in $\\mathcal{Y}$ can be assigned to zero or one tracklet in $\\mathcal{X}$. Moreover, different from the standard Optimal Transport (OT) based approach where the two distributions are required to have the same total probability mass, i.e., $||\\mathbf{p}||_1 = ||\\mathbf{q}||_1$, and all the mass has to be transported. Eqn. [eqn:TrackletObjCoupling] focuses on transporting only a fraction $s$ of the mass between two distributions. Thus, we named this approach as Fractional OT Assignment (FOTA).\n\nLet $\\mathbf{C}=(c_{i,j})$ be the transportation cost matrix where $c_{i,j}$ measures a cost to associate from $\\hat{\\mathbf{tr}}^{(t)}_{i}$ to $\\mathbf{o}^{(t)}_j$. The proposed FOTA addresses the problem of finding the best assignment solution $\\pi$ that minimizes the transportation cost between two distributions: $$\\label{eq:optimal_transport} \\footnotesize \\min_{\\boldsymbol{\\pi} \\in \\Pi(\\mathbf{p}, \\mathbf{q})} \\langle \\mathbf{C}, \\boldsymbol{\\pi}\\rangle_F = \\min_{\\boldsymbol{\\pi} \\in \\Pi(\\mathbf{p}, \\mathbf{q})} \\sum_{i}^N \\sum_{j}^M c_{ij}\\pi_{ij}$$ To address the constraints of only transporting a fraction $s$ of mass in Eqn. [eqn:TrackletObjCoupling], we propose attaching one more row and column in the cost matrix to handle the mass difference between two distributions as in Eqn. [eq:extend_cost]. $$\\label{eq:extend_cost} \\footnotesize \\bar{\\mathbf{C}} = \\begin{bmatrix} \\mathbf{C} & \\mathcal{E} \\mathbb{1}_{|\\mathbf{q}|} \\\\ \\mathcal{E} \\mathbb{1}_{|\\mathbf{p}|}^{\\top} & 2\\mathcal{E} + \\max(\\mathbf{C}) \\\\ \\end{bmatrix}$$ where $\\mathcal{E}$ is a scalar for the bound. If we set the mass of the additional track and object as $p_{N+1} = \\| \\mathbf{q} \\|_1 - s$ and $q_{M+1} = \\| \\mathbf{p} \\|_1 - s$, finding the best assignment solution $\\pi$ can be reduced to an unconstrained problem $\\min_{\\bar{\\boldsymbol{\\pi}} \\in \\Pi(\\bar{\\mathbf{p}}, \\bar{\\mathbf{q}})} \\langle \\bar{\\mathbf{C}}, \\bar{ \\boldsymbol{\\pi}} \\rangle_F$, where $\\bar{\\mathbf{p}} = [\\mathbf{p}, \\| \\mathbf{q} \\|_1 - s]$ and ${\\bar{\\mathbf{q}} = [\\mathbf{q}, \\| \\mathbf{p} \\|_1 - s]}$.\n\nSolving the One-to-many Assignment. From the above formula for the Optimal Transport-based Assignment problem in Eqn. [eq:optimal_transport], one can solve it in polynomial time as it is a linear program. However, when there are multiple detected objects and tracklets, the resulting linear program can be large. This issue can be addressed by a fast iterative solution named Sinkhorn-Knopp, which converts the optimization target in Eqn. [eq:optimal_transport] into a non-linear but convex form by adding a regularization term $E$ as in Eqn. [eq:ot_reg]. $$\\label{eq:ot_reg} \\footnotesize \\min_{\\bar{\\boldsymbol{\\pi}} \\in \\Pi(\\bar{\\mathbf{p}}, \\bar{\\mathbf{q}})} \\sum_{i}^N \\sum_{j}^M c_{ij} \\pi_{ij} + \\gamma E\\left( \\bar{\\pi}_{ij} \\right)$$ where $E( \\bar{\\pi}_{ij} ) = \\bar{\\pi}_{ij} ( \\log(\\bar{\\pi}_{ij}) - 1)$. Here, $\\gamma$ is a constant regularization term. The constraint optimization target in Eqn. [eq:ot_reg] can be converted to a non-constraint target using the Lagrange Multiplier method as in Eqn. [eq:Lagrange]. $$\\begin{aligned} \\label{eq:Lagrange} \\footnotesize \\min_{\\boldsymbol{\\bar{\\pi}} \\in \\Pi(\\bar{\\mathbf{p}}, \\bar{\\mathbf{q}})} \\sum_{i}^N \\sum_{j}^M c_{ij} \\bar{\\pi}_{ij} + \\gamma E\\left( \\bar{\\pi}_{ij} \\right) + \\alpha_j \\left( \\boldsymbol{\\bar{\\pi}}^{\\top}\\mathbb{1}_{|\\bar{\\mathbf{p}}|} - \\bar{\\mathbf{q}} \\right) + \\beta_i \\left( \\boldsymbol{\\bar{\\pi}}\\mathbb{1}_{|\\bar{\\mathbf{q}}|} - \\bar{\\mathbf{p}} \\right)\\end{aligned}$$ where $\\alpha_j(j = 1,2,...M)$ and $\\beta_i(i = 1,2,...,N)$ are Lagrange multipliers. By letting the derivatives of the optimization target equal 0, the optimal plan $\\boldsymbol{\\bar{\\pi}}^{\\star}$ is resolved as: $$\\footnotesize \\bar{\\pi}^{\\star}_{ij} = \\exp \\left( -\\frac{\\alpha_j}{\\gamma} \\right) \\exp \\left(-\\frac{c_{ij}}{\\gamma} \\right) \\exp \\left( -\\frac{\\beta_i}{\\gamma} \\right)$$ Let $u_j = \\exp \\left( -\\frac{\\alpha_j}{\\gamma} \\right), v_i = \\exp \\left( -\\frac{\\beta_i}{\\gamma} \\right), \\mathbf{W}[i,j] = \\exp \\left(-\\frac{c[i,j]}{\\gamma} \\right)$, the following constraints can be enforced: $$\\footnotesize \\sum_i \\bar{\\pi}_{ij} = u_j \\left( \\sum_i \\mathbf{W}[i,j] v_i \\right) = \\| \\bar{\\mathbf{q}} \\|_1$$ $$\\footnotesize \\sum_j \\bar{\\pi}_{ij} = \\left( u_j \\sum_i \\mathbf{W}[i,j] \\right) v_i = \\| \\bar{\\mathbf{p}} \\|_1$$ To constraint these two equations simultaneously, one can calculate $v_i$ and $u_j$ by alternately updating the following: $$\\label{eq:sinkhorn_iteration} \\footnotesize u_j^{t+1} = \\frac{\\| \\bar{\\mathbf{q}} \\|_1}{\\sum_i \\mathbf{W}[i, j] v_i^t}, v_i^{t+1} = \\frac{ \\| \\bar{\\mathbf{p}} \\|_1}{\\sum_j \\mathbf{W}[i, j] u_j^{t+1}}$$ Eqn. [eq:sinkhorn_iteration] is also known as the Sinkhorn-Knopp Iteration updating equations. After repeating this iteration $T$ times, the approximate optimal plan $\\boldsymbol{\\bar{\\pi}}^\\star$ can be obtained: $$\\label{eq:sinkhorn_iteration_final} \\footnotesize \\boldsymbol{\\bar{\\pi}}^\\star = \\text{diag}(v) \\mathbf{W} \\text{ diag}(u)$$ where $\\gamma$ and $T$ are empirically set to 0.1 and 50.\n\n[fig:saga_track]\n\nIn summary, as shown in Fig. [fig:saga_track], SAGA-Track with a multi-camera matching algorithm is performed in the following steps:\n\n 1. Estimating the next location of the track $\\mathbf{tr}_i^{(t-1)}$ using motion model, e.g., Kalman filter.\n\n 2. Computing world-coordinate-based distance metrics between $\\hat{\\mathbf{tr}}_i^{(t)}$ and $\\mathbf{o}_{j}^{(t)}$.\n\n 3. Solving One-to-many FOTA assignments as in Eqn. [eq:sinkhorn_iteration_final].\n\n 4. Updating $i$-th tracklet $\\mathbf{tr}_i^{(t)}$ based on assigned objects.\n\nIn addition to the proposed track-by-detection scheme for multi-camera, we introduce a novel end-to-end framework including detector, motion model, tracker, and assignment steps in a single model in the next section 3.3. This end-to-end framework can be fully aware of objects’ movement globally rather than taking pre-computed detection as SAGA-Track.\n\nEnd-to-end Learning MC-MOT via FOTA Loss\n\nIn this section, we further leverage the proposed FOTA into the design of the end-to-end learning network for MC-MOT, named SAGA-TrackNet.\n\nOur proposed architecture consists of an encoder, two decoders, and a box-matching layer. The one-to-many assignment algorithm is implemented to provide the final tracking results from detection and tracked boxes as in Fig. [fig:framework].\n\nModel Structure\n\nThe SAGA-TrackNet structure is based on transformer encoder, and decoder tracking frameworks and contains multi-head attention layers. These layers can be self-attention or cross-attention, i.e., keys and queries are the same or different.\n\nEncoder. Features of the current and previous frame from a camera are extracted by a backbone CNN network, e.g., Resnet-50, and stacked together with other cameras. Features of the previous frame were saved to avoid re-computation. The encoder of SAGA-TrackNet then encodes those feature maps into keys for being used in the following decoders.\n\nObject Decoder. To detect new objects on each camera, the model takes multiple sets of learnable parameters, named object queries, as a set of objects of interest in the images to match with keys, i.e., the feature maps generated by the encoder, and provides the outputs as \"detected boxes.\"\n\nTrack Decoder and Matching. Simultaneously, the model takes tracked objects in the previous frames as the track query to infer the location of the corresponding tracked objects in the current frame and provides \"tracked boxes.\" It is performed using the decoder block as it learns object motion similar to the Kalman filter. We can also utilize this Track Decoder block as a motion model to refine any off-the-shelf 3D object detectors by treating the track queries as placeholders and feeding detector predictions to this block. The motion modeling ablative study is further discussed in Subsection 4.3. During testing, the matching layer then performs the association of detected objects and tracked objects via FOTA. During training, a set prediction loss is computed for all $M + N$ output predictions in two steps: (a) loss for detecting object at frame $t - 1$ using $M$ object queries; (b) loss for tracking objects from (a) and detecting new objects at frame $t$ with all $M$ object queries and $N$ track queries from the frame $t - 1$. This prediction loss, computed based on the assignment obtained from FOTA between ground truth and prediction, is described in the following Subsection 3.3.2.\n\nModel Training\n\nThis section presents the procedure for training our proposed end-to-end learning networks. Training Data. We train our proposed SAGA-TrackNet on a large-scale dataset, i.e., nuScenes, a training set with 750 scenes of 20s each, and use its validation set for our ablation study. Each training sample contains a chunk size of two consecutive frames from a training sequence.\n\nFOTA Loss for Modeling Training. To compute this loss function, we also need to compute the assignment $\\pi_{ij}$ between one of the ground-truth tracks $\\mathcal{T}^{\\star}_i$ or background to the joint set of object and track query predictions $\\hat{\\mathbf{o}}_j^{(t)}$. Similar to the OT-based assignment described in Subsection 3.2, the assignment is computed based cost matrix using a pre-defined distance between bounding boxes. Let us denote $G^{(t)} \\subset G$ as the subset of ground-truth track ID at time step $t$. Then we assign each detection from step (a) to its corresponding ground-truth track ID $i$ from the set $G^{(t-1)} \\subset G$. These two sets are explicitly assigned to the ground-truth objects in frame $t$ as $G^{(t)} \\cap G^{(t-1)}$. Another set of ground-truth track ID is $G^{(t)} \\backslash G^{(t-1)}$, which includes tracks not visible at time $t$. The last set is the new object not yet being tracked ground-truth objects, i.e., new objects, as $G^{(t - 1)} \\backslash G^{(t)}$ to be matched with $M$ object queries.\n\n$$\\footnotesize \\min_{\\bar{\\boldsymbol{\\pi}} \\in \\Pi(\\bar{\\mathbf{p}}, \\bar{\\mathbf{q}})} \\langle \\bar{\\mathbf{C}}, \\bar{ \\boldsymbol{\\pi}} \\rangle_F = \\underset{\\bar{\\boldsymbol{\\pi}} \\in \\Pi(\\bar{\\mathbf{p}}, \\bar{\\mathbf{q}})}{\\min} \\overset{N}{\\underset{i=1}{\\sum}} \\overset{M}{\\underset{j=1}{\\sum}} c_{ij} \\pi_{ij}$$ Using a similar extension as in Eqn. [eq:extend_cost], the cost matrix $\\mathbf{C}$ can now be defined as in Eqn. [eq:CC]. $$\\label{eq:CC} \\footnotesize \\mathbf{C} = ( c_{ij} ) = -\\hat{p}_{\\pi_{ij}}(\\text{cls}_i) + \\mathcal{C}_{\\text{box}} \\left(\\mathcal{T}_{i}^{\\star(t)}, \\hat{\\mathbf{o}}^{(t)}_{j} \\right)$$ where $\\text{cls}_i$ is the class id of the object and $\\mathcal{C}_{\\text{box}}$ term penalizes bounding box differences by a linear combination of a $\\ell_1$ distance and a Generalized Intersection over Union as defined in Eqn. [eq:g_iou], $$\\footnotesize \\mathcal{C}_{\\text{box}} = \\lambda_{\\ell_1} \\| \\mathcal{T}_{i}^{\\star(t)} - \\hat{\\mathbf{o}}^{(t)}_{j} \\|_1 + \\lambda_{GIoU} \\mathcal{C}_{GIoU} \\left(\\mathcal{T}_{i}^{\\star(t)}, \\hat{\\mathbf{o}}^{(t)}_{j} \\right)$$ We use set prediction loss to measure the set of predictions for $M$ detection and $N$ tracklets compared with ground-truth tracks in terms of classification and location (bounding boxes). Set-based loss is based on the optimal bipartite matching (described in Sections 3.2 and 3.3) between $M$ detection and ground-truth objects while $N$ tracklets will be matched with boxes from previous frames. The final MC-MOT set prediction loss is defined as in Eqn. [eq:cbox]. $$\\label{eq:cbox} \\small \\mathcal{L}_{\\text{MC-MOT}} (\\mathcal{T}^{\\star}, \\hat{\\mathbf{o}}^{(t)}, \\boldsymbol{\\pi}) = \\overset{M + N}{\\underset{j=1}{\\sum}} \\mathcal{L}_{\\text{query}} (\\mathcal{T}^{\\star}, \\hat{\\mathbf{o}}_j^{(t)}, \\boldsymbol{\\pi})$$ The output predictions that do not match any ground-truth tracks will be assigned to the background class $\\text{cls}_i = 0$. We indicate the ground-truth track matched with prediction $i$ by $\\pi_{ij} = 1$ and define the loss per query as in Eqn. [eq:lquery]. $$\\label{eq:lquery} \\footnotesize \\mathcal{L}_{\\text{query}} \\left( \\mathcal{T}^{\\star}, \\hat{\\mathbf{o}}_j^{(t)}, \\boldsymbol{\\pi} \\right) = \\begin{cases} -\\hat{p}_{\\pi_{ij}}(\\text{cls}_i) +\\mathcal{L}_{\\text{box}} \\left(\\mathcal{T}_{i}^{\\star(t)}, \\hat{\\mathbf{o}}^{(t)}_{j} \\right) & \\text{if } \\pi_{ij} = 1 \\\\ -\\hat{p}_{\\pi_{ij}}(0) & \\text{if } \\pi_{ij} = 0 \\\\ \\end{cases}$$ where $\\mathcal{L}_{box}$ is the combination of the $\\ell_1$ loss and the generalized Intersection over Union (IoU) for 3D boxes.\n\nModel Inference\n\nDuring testing, SAGA-TrackNet performs feature encoding, object decoding, and track decoding, then one-to-many matching for two consecutive frames from all cameras. The output features from the backbone network are stored in combination with the subsequent frames. We also keep tracked objects alive and allow them to rebirth to handle occlusion or disappearing quickly.\n\nExperimental Results\n\nIn this section, we detail the benchmark datasets and metrics in Subsection 4.1. Then, the setups for all experiments and the ablation study will be presented in Subsection 4.2 and 4.3 respectively. The comparisons with the SOTA methods will be detailed in Subsection 4.4 on a large-scale Tracking Challenge, i.e. nuScenes Vision Track.\n\nBenchmark Datasets and Metrics\n\nnuScenes2 Dataset is one of the large-scale datasets for Autonomous Driving with 3D object annotations. It contains 1,000 videos of 20-second shots in a setup of 6 cameras, i.e. 3 front and 3 rear ones, with a total of 1.4M images. It also provides 1.4M manually annotated 3D bounding boxes of 23 object classes based on LiDAR data. This dataset has an official split of 700, 150, and 150 videos for training, validation, and testing, respectively.\n\nThe proposed method is evaluated using both detection and tracking metrics described in.\n\nDetection Metrics. A commonly used metric, i.e. Mean Average Precision (mAP), is defined as a match using a 2D center distance on the ground plane instead of intersection over union cost for nuScenes detection challenges.\n\nSimilarly, other motion-related metrics are also defined in nuScenes, such as Average Translation Error (ATE) measuring Euclidean center distance in 2D in meters, Average Scale Error (ASE) computing as $1 - IOU$ after aligning centers and orientation, Average Orientation Error (AOE) measuring the smallest yaw angle difference between prediction and ground-truth in radians, Average Velocity Error (AVE) measuring the absolute velocity error in $m/s$ and Average Attribute Error (AAE) computing $1 - acc$, where $acc$ is the attribute classification accuracy. We also use the nuScenes Detection Score (NDS) that is based on a simple additive weighting of the mean of all metrics above.\n\nTracking Metrics. The tracking performance is measured using the popular CLEAR MOT metrics including MOTA, MOTP, ID switch (IDS), mostly tracked (MT), mostly lost (ML), fragmented (FRAG). Similar to nuScenes, we use two accumulated metrics introduced in as the main metrics, including the average over the MOTA metric (Average MOTA (AMOTA)) and the average over the MOTP metric (Average MOTP (AMOTP)).\n\nExperiments Setup\n\n[tab:nuscene_detection_results]\n\nThe proposed SAGA-TrackNet is trained with two consecutive frames where the extracted features in the previous time step $t-1$ are stored and stacked with the features of the current time step to encode object key features to predict the location of new and existing objects at time step $t$. Then, Mini-batch (chunk of two) gradient descent is employed with an Adam optimizer to learn all the parameters in the attention layers. All the layers and algorithms are implemented in PyTorch, based on Trackformer, TransTrack and Deformable DETR. The best configuration of layers is chosen empirically as three stacking self-attention layers with four heads and three stacking cross-attention layers with 16 heads. With a batch size of 512 chunks, the model converged at about 100 epochs.\n\nAblation Study\n\nIn this section, we present some experiments to ablate the effect of each component of the proposed framework. Particularly, this section aims to demonstrate the following: 1. how this motion modeling can help improve 3D object detectors; 2. better motion modeling with track decoder layers in SAGA-TrackNet; 3. how the combination of the external input and data association method affects the tracking performance. We also compare the processing time of these methods as well as the end-to-end solution.\n\n[tab:motion_errors]\n\n[tab:association_ablation_study]\n\nImproving 3D Object Detector. Table [tab:nuscene_detection_results] demonstrates that the combination of baselines object detector and our motion model (i.e. the Track Decoder) achieves better results than the original detector. In this experiment, we initialize detected objects at previous frames as inputs to the track queries. The Track Decoder takes that set of objects and then combines it with frame features produced by the Encoder to refine the location of pseudo-\"tracked boxes\". The best result is achieved with the combination of KM3D object detector and our motion model since it is guided by decoded locations from our transformation procedure as described in Section 3.3.\n\nThe Role of Motion Model. Motion models are particularly essential in dynamic MC-MOT settings since cameras are moving with the vehicle. In this experiment, we evaluate the effectiveness of different motion modeling methods on detection performance. We use the locations predicted by motion models to compare with ground-truth locations in terms of motion-related metrics. In such a way, we can evaluate how well the motion model captures and predicts the motion of tracked objects. We compare with two other commonly used motion models, i.e. 3D Kalman Filter and LSTM. As shown in Table [tab:motion_errors], our SAGA-TrackNet gives better results than a classical object state prediction technique, i.e. 3D Kalman Filter used in and a deep learning-based technique, i.e. LSTM module, used in.\n\n[tab:track_ablation_study]\n\nComparison of Different Distance Cost and Matching Algorithms. The proposed assignment module operates on a global cost matrix, which is computed from a detection set and a track set by different types of distance, i.e. Mahalanobis distance as defined in Eqn. [eq:mahalanobis], Bird’s Eye View 2D, and 3D bounding box GIoU as defined in Eqn. [eq:g_iou] between the estimated object states and the detected object bounding boxes. Then, the Sinkhorn iterative is employed as described in Section 3.2 with the maximum number of iterations being 100. Compared to Kuhn-Munkres (KM) algorithm, our framework inherits the merit of one-to-many matching and yields better results on assignment metrics and tracking metrics with a slight increase in computation cost in distance matrices construction and optimization (shown in Table [tab:association_ablation_study]). The performance of our proposed FOTA algorithm is also better than other tracklet-detection matching methods as shown in Table [tab:track_ablation_study].\n\n[fig:compare_tracking]\n\n[tab:nuscene_val_track_results]\n\n[tab:nuscene_test_track_results]\n\nComparison against State-of-the-Art Methods\n\nIn this section, we compare our proposed framework with other vision-based (without using LiDAR or RADAR information) tracking approaches, which are the top in nuScenes vision only tracking challenge leaderboard.\n\nComparison against Tracking Methods on Validation set. This experiment compares our proposed method with other vision-based methods, including QD-3DT, MonoDIS + AB3DMOT, CenterTrack, and DEFT which are the tops of nuScenes vision-only tracking challenge. As we can see in Table [tab:nuscene_val_track_results], we outperform the top approach, i.e. QD-3DT, in most of the metrics. Fig. [fig:compare_tracking] illustrates the key factor that helps improve the tracking performance: we perform appearance matching across cameras in addition to motion modeling. It shows that our proposed method (top) can assign object ID globally between cameras compared with DEFT (bottom). Our method beats the SOTA method, i.e. QD-3DT on most of the main metrics, such as AMOTA, AMOTP, MOTAR, MOTA, Recall, IDSwitch, and FRAG, which are related to how well our method groups tracklet IDs and regresses object’s bounding boxes. For fair comparison and to preserve the originality and uniqueness of those methods, such as the LSTM motion model of DEFT, the offset head of CenterTrack, we implement a simple global association as the baseline, which takes MOT output results from those approaches and then adopts several empirical rules and heuristics to determine and filter out duplicated objects, including IOU thresholding and box merging as similar to ELECTRICITY.\n\nComparison against Tracking Methods on Test set. We submitted our result on the official competition platform EvalAI3. As it can be referred to the tracking challenge leaderboard on Vision track at nuScenes’ homepage4 and Table [tab:nuscene_test_track_results], our method performs better than QD-3DT and DEFT significantly on IDS (870 vs. 6,856 and 6,901) and slightly on AMOTA (0.242 vs. 0.217 and 0.177), this behavior is similar to the validation results in Table [tab:nuscene_val_track_results].\n\nConclusions\n\nThis paper has introduced a new global association approach to solving the dynamic MC-MOT problem for AV. The proposed framework can learn how to perform tracking frame-by-frame in an end-to-end manner given frames from multi-camera to extract features, encode object key features, decode new objects’ locations, decode tracked objects’ locations, and global association tracklets with detection. These tasks are enhanced with self-attention and cross-attention layers to capture structures and motion across cameras. The experiments have shown performance improvements up to 6.4% and a decrease in IDSwitch error from 3,807 to 870 in a large-scale AV dataset regarding vision-based detection and tracking accuracy.\n\n 1. [note]equal contribution↩\n\n 2. License CC BY-NC-SA 4.0↩\n\n 3. https://eval.ai/web/challenges/challenge-page/476/leaderboard/1321↩\n\n 4. https://www.nuscenes.org/tracking/↩\n\n\n\n文章提出的端到端学习网络SAGA-TrackNet由哪几个组件组成?这些组件间是如何协同工作的?"} {"dataset": "lmsys/lmsys-chat-1m", "conversation_id": "c435bd2bdab349f38f5a0af3a5948dc4", "conversation_index": 813171, "turn_index": 0, "tokens_gpt_oss_120b": 973, "prompt": "Select me job titles from html and wite them down as CSV. Html data /A>

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