Abstract: Described is a system for generating environmental features using deep reinforcement learning. The system receives a policy network architecture, initialization parameters, and a simulation environment that models a trajectory of a target system through a physical environment. Landmark features sampled from the policy network are initialized, and a trained policy network is generated by training the policy network using a reinforcement learning algorithm. A set of environmental features are generated using the trained policy network and displayed on a display device.

Abstract: A system for and method of, processing post-consumer and post-industrial waste streams, producing active ingredients for waste stream processing, processing aqueous waste streams, preparing and collecting a multi-purpose chemical precursor, removing phosphates, nitrates, heavy metals, and other contaminants from aqueous waste streams, collecting and processing a post-consumer and post-industrial product from aqueous waste streams, administering and positioning assets and processes associated with waste stream processing, and scheduling operations for sub-systems of the system.

Abstract: Described is a system for proving correctness properties of a neural network for providing estimates for point cloud data. The system receives as input a description of a neural network for generating estimates from a set of point cloud data. The description of the neural network is parsed to obtain a symbolic representation. Based on a combination of the symbolic representation and a set of analysis parameters, the system generates an analysis output indicating whether the neural network satisfies a correctness property in generating the estimates from the set of point cloud data. The analysis output is a mathematical proof artifact proving that the set of analysis parameters is satisfied, a list of one or more point clouds for which the set of analysis parameters is violated, or a report that progress could not be made by the analysis.

Abstract: Described is a system for training a neural network for estimating surface normals for use in operating an autonomous platform. The system uses a parallelizable k-nearest neighbor sorting algorithm to provide a patch of points, sampled from the point cloud data, as input to the neural network model. The points are transformed from Euclidean coordinates in a Euclidean space to spherical coordinates. A polar angle of a surface normal of the point cloud data is estimated in the spherical coordinates. The trained neural network model is utilized on the autonomous platform, and the estimate of the polar angle of the surface normal is used to guide operation of the autonomous platform within the environment.

Abstract: A system for and method of, processing post-consumer and post-industrial waste streams, producing active ingredients for waste stream processing, processing aqueous waste streams, preparing and collecting a multi-purpose chemical precursor, removing phosphates, nitrates, heavy metals, and other contaminants from aqueous waste streams, collecting and processing a post-consumer and post-industrial product from aqueous waste streams, administering and positioning assets and processes associated with waste stream processing, and scheduling operations for sub-systems of the system.

Abstract: An approach for tracking data stored in caches uses a Bloom filter to reduce the number of addresses that need to be tracked by a coherence directory. When a requested address is determined to not be currently tracked by either the coherence directory or the Bloom filter, tracking of the address is initiated in the Bloom filter, but not in the coherence directory. Initiating tracking of the address in the Bloom filter includes setting hash bits in the Bloom filter so that subsequent requests for the address will “hit” the Bloom filter. When a requested address is determined to be tracked by the coherence directory, the Bloom filter is not used to track the address.

Abstract: Antibody molecules that bind to CD73 are disclosed. The anti-CD73 antibody molecules can be used to treat, prevent and/or diagnose cancer.

Abstract: A system for and method of, processing post-consumer and post-industrial waste streams, producing active ingredients for waste stream processing, processing aqueous waste streams, preparing and collecting a multi-purpose chemical precursor, removing phosphates, nitrates, heavy metals, and other contaminants from aqueous waste streams, collecting and processing a post-consumer and post-industrial product from aqueous waste streams, administering and positioning assets and processes associated with waste stream processing, and scheduling operations for sub-systems of the system.

Abstract: A system for and method of, processing post-consumer and post-industrial waste streams, producing active ingredients for waste stream processing, processing aqueous waste streams, preparing and collecting a multi-purpose chemical precursor, removing phosphates, nitrates, heavy metals, and other contaminants from aqueous waste streams, collecting and processing a post-consumer and post-industrial product from aqueous waste streams, administering and positioning assets and processes associated with waste stream processing, and scheduling operations for sub-systems of the system.

Abstract: Described is a system for automatically generating images that satisfy specific image properties. Using a code parser component, a tensor expression intermediate representation (IR) of a deep neural network code is produced. A specification describing a set of image properties is parsed in a fixed formal syntax. The tensor expression IR and the specification is input into a rewriting and analysis engine. The rewriting and analysis engine queries an external solver to obtain pixel values satisfying the specification. When pixel values satisfying the specification can be found in a fixed time period, the rewriting and analysis engine combines the pixel values into an image that satisfies the specification and outputs the image.

Abstract: Methods and systems for managing attribute data for shipping and handling within a retail supply chain are disclosed. Item attributes of new items are recorded upon first receipt at a distribution center and are stored in a local database. This attribute data is synchronized to a centralized database. Data from the centralized database is pushed out to other distribution centers. The use of a centralized database of item attributes streamlines the process of inputting data for new items. This database is accessible by multiple systems with the supply chain to determine how the items should be processed. A pathfinding system utilizes item attribute data and facility data to determine a processing route for each item passing through a distribution facility.

Abstract: A system for and method of, processing post-consumer and post-industrial waste streams, producing active ingredients for waste stream processing, processing aqueous waste streams, preparing and collecting a multi-purpose chemical precursor, removing phosphates, nitrates, heavy metals, and other contaminants from aqueous waste streams, collecting and processing a post-consumer and post-industrial product from aqueous waste streams, administering and positioning assets and processes associated with waste stream processing, and scheduling operations for sub-systems of the system.

Abstract: The invention provides anti-CD39 antibody compositions and their use in treating cancer.

Abstract: Described is an attack system for generating perturbations of input signals in a recurrent neural network (RNN) based target system using a deep reinforcement learning agent to generate the perturbations. The attack system trains a reinforcement learning agent to determine a magnitude of a perturbation with which to attack the RNN based target system. A perturbed input sensor signal having the determined magnitude is generated and presented to the RNN based target system such that the RNN based target system produces an altered output in response to the perturbed input sensor signal. The system identifies a failure mode of the RNN based target system using the altered output.

Abstract: In some examples, a database system includes a storage medium to store a materialized view (MV) that includes data satisfying an MV condition. At least one processor is to receive a query including a query condition, determine that the query condition partially matches the MV condition, and access a part of the data in the MV to partially satisfy the query.

Abstract: Described is a control command system for generating control commands for a vehicle. The system receives a networked control system corresponding to a network of subsystems of sensors and actuators of the vehicle and data collected from sensors on the vehicle. Multiple subsystems are formed from the networked control system. A dynamic mode decomposition with control (DMDc) process is applied to each subsystem, yielding a linear approximation for each subsystem. The linear approximations are combined into a single linear approximation for the networked control system, and a linear control system approximating the networked control system is output. Control commands are generated for the vehicle based on the linear control system, which cause the vehicle to perform a vehicle operation.

Abstract: A method of calibrating a coordinate positioning machine is described. The machine is controlled into a pivot pose in which a target point associated with a moveable part of the machine and a pivot point associated with a fixed part of the machine are separated from one another by a known separation. An error value for that pose is determined based on the known separation and a separation expected for that pose from the existing model parameters of the machine. The machine is controlled into a plurality of different target poses, and for each target pose a separation between the target point and the pivot point is measured and an error value for that pose is determined based on the measured separation and a separation expected for that pose from the existing model parameters.

Abstract: In some examples, a system stores data in a logically disconnected data store. In response to a query for data in the data store, the system accesses metadata of objects stored in the data store, the metadata including information of a respective range of values of at least one clustering attribute in data contained in each respective object of the objects. The system partitions the objects across the plurality of processing engines based on the information of the respective ranges of values of the at least one clustering attribute in the data contained in the objects. The system assigns, based on the partitioning, the objects to respective processing engines of the plurality of processing engines.

Abstract: The present disclosure relates to anti-CD39 antibodies, and antigen binding portions thereof and their use in treating cancer.

Abstract: Apparatus and methods for training a machine learning algorithm (MLA) to control a first aircraft in an environment that comprises the first aircraft and a second aircraft are described. Training of the MLA can include: the MLA determining a first-aircraft action for the first aircraft to take within the environment; sending the first-aircraft action from the MLA; after sending the first-aircraft action, receiving an observation of the environment and a reward signal at the MLA, the observation including information about the environment after the first aircraft has taken the first-aircraft action and the second aircraft has taken a second-aircraft action, the reward signal indicating a score of performance of the first-aircraft action based on dynamic and kinematic properties of the second aircraft; and updating the MLA based on the observation of the environment and the reward signal.