Abstract: An apparatus comprising a memory to store an observed trajectory of a pedestrian, the observed trajectory comprising a plurality of observed locations of the pedestrian over a first plurality of timesteps; and a processor to generate a predicted trajectory of the pedestrian, the predicted trajectory comprising a plurality of predicted locations of the pedestrian over the first plurality of timesteps and over a second plurality of timesteps occurring after the first plurality of timesteps; determine a likelihood of the predicted trajectory based on a comparison of the plurality of predicted locations of the pedestrian over the first plurality of timesteps and the plurality of observed locations of the pedestrian over the first plurality of timesteps; and responsive to the determined likelihood of the predicted trajectory, provide information associated with the predicted trajectory to a vehicle to warn the vehicle of a potential collision with the pedestrian.

Abstract: Systems and techniques for locating a radio transmission source by scene reconstruction are described herein. A multipath radio signal—comprised of two or more multipath components—may be received at an antenna array. A collection of angle-of-arrival (AoA) values may be created from the multipath components. Thus, each AoA in the collection corresponds to one of the multipath components. An optical image of the environment may be obtained and possible reflective surfaces for the multipath components identified in the optical image. A set of paths may be created in a model of the environment by backtracking from the antenna array along the AoAs in the collection of AoA values. Here, the backtracking simulates reflections of the ray off of the surfaces identified in the optical image. A transmitter of the multipath radio signal may then be located via the set of paths.

Abstract: Systems and methods for determining a configuration for a microarchitecture are described herein. An example system includes a proposal generator to generate a first candidate configuration of parameters for the microarchitecture, a machine learning model to process the first candidate configuration of parameters to output estimated performance indicators for the microarchitecture, an uncertainty checker to determine whether the estimated performance indicators are reliable, and a performance checker. In response to a determination that the estimated performance indicators are reliable, the performance checker is to determine whether the estimated performance indicators have improved toward a target. Further, if the estimated performance indicators have improved, the performance checker is to store the first candidate configuration of parameters in a memory as a potential solution for a microarchitecture without performing a full simulation on the first candidate configuration of parameters.

Abstract: Methods, apparatus, systems and articles of manufacture for providing query selection systems are disclosed. An example query selection system includes a processor to: analyze a graph database; identify respective ones of objects associated with the graph database; obtain properties associated with the objects; identify common properties present in the respective ones of the objects; in response to determining the common properties present in the identified objects, output a list of the common properties corresponding to the respective ones of the objects; generate a table for the common properties and the respective ones of the objects; and populate the table with the common properties and the respective ones of the objects from the graph database to establish a relational database. The system further includes a machine learning model classifier to enable relational database query searching via the relational database.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for object manipulation via action sequence optimization. An example method disclosed herein includes determining an initial state of a scene, generating a first action phase sequence to transform the initial state of the scene to a solution state of the scene by selecting a plurality of action phases based on action phase probabilities, determining whether a first simulated outcome of executing the first action phase sequence satisfies an acceptability criterion and, when the first simulated outcome does not satisfy the acceptability criterion, calculating a first cost function output based on a difference between the first simulated outcome and the solution state of the scene, the first cost function output utilized to generate updated action phase probabilities.

Abstract: Technologies for management of data layers in a heterogeneous geographic information system (GIS) map are disclosed. A compute device may maintain a GIS database that includes geo-quads that represent physical locations of various scales. Data layers and layer tracks may be dynamically added to the GIS database at different scales, allowing for an extensible framework that enables a mechanism for integrating additional functionality. In the illustrative embodiment, a graph database is used to store the GIS database, allowing for a flexible structure. In some embodiments, entries in layer tracks may include binary large objects that may have properties and associated methods, allowing for application-specific functionality.

Abstract: An autonomous object modeler includes a modeling table, a controllable arm, a depth camera attached to the controllable arm, and a controller to control operation of the modeling table, the controllable arm, and the depth camera. The modeling table may be movable and includes a mass sensor to produce mass sensor data indicative of a mass of an object positioned on the modeling table. The controllable arm includes a force-torque sensor to produce force-torque sensor data indicative of an inertia of the object while the object is moved by the controllable arm. The controller is configured to control operation of the controllable arm to reposition the object on the modeling table to generate three-dimensional models of the object. The three-dimensional models include the mass data and the inertia data.

Abstract: Methods, apparatus, systems and articles of manufacture to provide machine programmed creative support to a user are disclosed. An example apparatus include an artificial intelligence architecture to be trained based on previous inputs of the user; a processor to: implement a first machine learning model based on the trained artificial intelligence architecture; and predict a first action based on a current state of a computer program using the first machine learning model; implement a second machine learning model based on the trained artificial intelligence architecture; and predict a second action based on the current state of the computer program using the second machine learning model; and a controller to select a state based on the action that results in a state that is more divergent from the current state of the computer program.

Abstract: Methods and apparatus to facilitate generation of database queries are disclosed. An example apparatus includes a generator to generate a global importance tensor. The global importance tensor based on a knowledge graph representative of information stored in a database. The knowledge graph includes objects and connections between the objects. The global importance tensor includes importance values for different types of the connections between the objects. The example apparatus further includes an importance adaptation analyzer to generate a session importance tensor based on the global importance tensor and a user query, and a user interface to provide a suggested query to a user based on the session importance tensor.

Abstract: Methods and apparatus to automatically generate code for graphical user interfaces are disclosed. An example apparatus includes a textual description analyzer to encode a user-provided textual description of a GUI design using a first neural network. The example apparatus further includes a DSL statement generator to generate a DSL statement with a second neural network. The DSL statement is to define a visual element of the GUI design. The DSL statement is generated based on at least one of the encoded textual description or a user-provided image representative of the GUI design. The example apparatus further includes a rendering tool to render a mockup of the GUI design based on the DSL statement.

Abstract: Apparatus, systems, articles of manufacture, and methods to generate acceptability criteria for autonomous systems plans are disclosed. An example apparatus includes a data compiler to compile data generated by the autonomous system into an autonomous system task dataset, a data encoder to encode the dataset for input into a rule distillation neural network architecture, a model trainer to train the rule distillation neural network architecture, an adaptor to adapt the trained rule distillation neural network architecture to a new input data domain using the autonomous system task dataset, a verifier to generate formally verified acceptability criteria, and an inferer to evaluate a control command, the evaluation resulting in an acceptance or rejection of the command.

Abstract: Apparatus, systems, methods, and articles of manufacture for automatic robot perception programming by imitation learning are disclosed. An example apparatus includes a percept mapper to identify a first percept and a second percept from data gathered from a demonstration of a task and an entropy encoder to calculate a first saliency of the first percept and a second saliency of the second percept. The example apparatus also includes a trajectory mapper to map a trajectory based on the first percept and the second percept, the first percept skewed based on the first saliency, the second percept skewed based on the second saliency. In addition, the example apparatus includes a probabilistic encoder to determine a plurality of variations of the trajectory and create a collection of trajectories including the trajectory and the variations of the trajectory.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve spatial-temporal data management. An example apparatus includes a hypervoxel data structure generator to generate a root hexatree data structure having sixteen hypernodes, an octree manager to improve a spatiotemporal data access efficiency by generating a first degree of symmetry in the root hexatree, the octree manager to assign a first portion of the hypernodes to a positive temporal subspace and to assign a second portion of the hypernodes to a negative temporal subspace, and a quadtree manager to improve the spatiotemporal data access efficiency by generating a second degree of symmetry in the root hexatree, the quadtree manager to assign respective hypernodes of the positive temporal subspace and the negative temporal subspace to respective positive and negative spatial subspaces.

Abstract: Various systems and methods for updating a current probability density function. The PDF includes a list of objects within a location. First information is received from a first remote device at a first position within the location. The first information includes a vehicle identification, a first PDF of the objects. Second information is received from a second remote device at a second position within the location. The second information includes a vehicle identification, a second PDF of the objects. A first rank for the first PDF is determined based on locations of the objects in the first PDF compared to locations of the objects in the current PDF. A second rank for the second PDF is determined. The first PDF and the second PDF are combined using the first rank and the second rank into a combined PDF. The current PDF based on the combined PDF.

Abstract: Systems, apparatuses and methods may provide for technology that obtains a neural network output, which estimates a difference between a first measured output of a sensor and a simulated output of the sensor. The technology may also add the difference to the simulated output of the sensor. In one example, the neural network output includes mean displacement data and parametrically controllable covariance data. Additionally, the technology may subtract a point-wise difference from a second measurement output of the sensor.

Abstract: An example includes obtaining first sensor data from a first sensor and second sensor data from a second sensor, the first sensor of a first sensor type different than a second sensor type of the second sensor; generating first encoded sensor data based on the first sensor data and second encoded sensor data based on the second sensor data; generating a contextual fused sensor data representation of the first and second sensor data based on the first and second encoded sensor data; generating first and second reconstructed sensor data based on the contextual fused sensor data representation; determining a deviation estimation based on the first and second reconstructed sensor data, the deviation estimation representative of a deviation between: (a) the first reconstructed sensor data, and (b) the first sensor data; and detecting an anomaly in the deviation estimation, the anomaly indicative of an error associated with the first sensor.

Abstract: Various systems and methods for implementing intelligent traffic management for vehicle platoons are described herein. A road controller system includes A road controller system comprising: a data store to store an active traffic policy; a processor subsystem to: determine a speed or platoon size of a vehicle platoon traveling on an area controlled by the road controller system; and determine a change to the speed or platoon size of the vehicle platoon, the change based on the active traffic policy; and a transceiver to transmit a control message to the vehicle platoon to implement the change to the speed or platoon size of the vehicle platoon.

Abstract: In one example a management system for an autonomous vehicle, comprises a first image sensor to collect first image data in a first geographic region proximate the autonomous vehicle and a second image sensor to collect second image data in a second geographic region proximate the first geographic region and a controller communicatively coupled to the first image sensor and the second image sensor and comprising processing circuitry to collect the first image data from the first image sensor and second image data from the second image sensor, generate a first reliability index for the first image sensor and a second reliability index for the second image sensor, and determine a correlation between the first image data and the second image data. Other examples may be described.

Abstract: An example system for generating a three dimensional (3D) model includes a receiver to receive a single two dimensional (2D) image of an object to be modeled. The system includes a segment extractor to extract a binary segment, a textured segment, and a segment characterization based on the single 2D image. The system further includes a skeleton cue extractor to generate a medial-axis transform (MAT) approximation based on the binary segment and the segment characterization and extract a skeleton cue and a regression cue from the MAT approximation. The system also includes a contour generator to generate a contour based on the binary segment and the regression cue. The system can also further include a 3D model generator to generate a 3D model based on the contour and the skeleton cue.

Abstract: An autonomous object modeler includes a modeling table, a controllable arm, a depth camera attached to the controllable arm, and a controller to control operation of the modeling table, the controllable arm, and the depth camera. The modeling table may be movable and includes a mass sensor to produce mass sensor data indicative of a mass of an object positioned on the modeling table. The controllable arm includes a force-torque sensor to produce force-torque sensor data indicative of an inertia of the object while the object is moved by the controllable arm. The controller is configured to control operation of the controllable arm to reposition the object on the modeling table to generate three-dimensional models of the object. The three-dimensional models include the mass data and the inertia data.