Abstract: In various examples, systems and methods are disclosed relating to implementing language models for spatial sound synthesis. A system can identify a scene comprising at least one object or environmental feature. The system can determine, using a language model, a selection of an audio sample for the at least one object or environmental feature. The system can generate an updated scene including the audio sample associated with the at least one object or environmental feature.

Abstract: In various examples, a generalizable mobility model can receive a state and identifier of a robot, and generate an action for the robot based on the state and the identifier. The state can identify a position, environment, and navigation goal of the robot while the identifier can indicate a type of the robot. The generalizable mobility model can use the identifier to generate an action for the robot to reach the navigation goal from its position based on the type of the robot, such as a humanoid, quadruped, or wheeled robot. The generalizable mobility model can be a distilled combination of multiple robot type-specific models, and can use the identifier to mimic type-specific actions output by the multiple robot type-specific models to transmit to the robot to move the robot.

Abstract: In various examples, a system can include one or more processors to segment, using at least one machine learning model, a first representation of an object into a plurality of parts according to a hierarchy, where the hierarchy indicates groups of datapoints for at least one part of the plurality of parts based on a value. The one or more processors generate at least one second representation of the object based on the at least one of the part of the plurality of parts.

Abstract: In various examples, at least partial control of a vehicle may be transferred to a control system remote from the vehicle. Sensor data may be received from a sensor(s) of the vehicle and the sensor data may be encoded to generate encoded sensor data. The encoded sensor data may be transmitted to the control system for display on a virtual reality headset of the control system. Control data may be received by the vehicle and from the control system that may be representative of a control input(s) from the control system, and actuation by an actuation component(s) of the vehicle may be caused based on the control input.

Abstract: A horizontal patterned magnetic flux inductor system includes a magnetic inductor core that defines a longitudinal gap passing through the core volume in a vertical direction. The inductor core forms a three-dimensional flux path around the longitudinal gap whose projection onto the x-z plane forms a figure eight pattern with its two lobes oriented in the x direction. First and second electrical conductors are spaced apart in the x direction and pass through the core volume and through the longitudinal gap such that the inductor core causes inverse coupling between the conductors when electric current flows through the conductors in the same direction but causes positive coupling between the conductors when electric current flows through the conductors in opposite directions. Each of the electrical conductors has a front end exposed at the front side of the core volume and a back end exposed at the back side of the core volume.

Abstract: In various examples, a single generalizable mobility model can be applied to a variety of robot types. Actions output by the generalizable mobility model can be based on actions generated using imitation learning, and the actions can be refined per type of robot using residual reinforcement learning. A type-specific model can be updated per type of robot using residual reinforcement learning, and can be combined and distilled into the single generalizable model. Actions output by the type-specific model can be combined with actions generated using imitation learning, and results of the action on a simulated robot can update weights of one layer of the type-specific model. By building upon an imitation learning model and refining each type-specific model using residual reinforcement learning, the generalizable mobility model can improve data and time efficiency compared to continuous multi-type training while also avoiding joint multi-type training complexities.

Abstract: A vertical patterned magnetic flux inductor system includes a magnetic core volume that defines a longitudinal gap extending horizontally through the center of the core volume. Top and bottom members of the core volume have slots therein. The slots are arranged such that the top and bottom members define complementary S shaped profiles when viewed in the z direction. Longitudinal electrical conductors are disposed partially in the slots and slope from the bottom member of the core to the top member of the core. A first terminal end of each conductor is exposed at the top side of the core volume and a second terminal end of each conductor is exposed at the bottom side of the core volume.

Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images using one or more pixel weights determined based, at least in part, on one or more sub-pixel offset values.

Abstract: A request is received for a virtual asset of a virtual asset data store for inclusion in a virtual scene. The request includes first characteristic data including characteristics associated with the requested virtual asset. One or more virtual assets of the virtual asset data store are associated with tags obtained from an output of an artificial intelligence (AI) model. Tags are obtained for the requested virtual asset based on the first characteristic data and second characteristic data associated with an additional virtual asset of the virtual scene. A determination is made, based on tags for virtual assets of the virtual data store, of whether the virtual asset data store identifies a virtual asset that satisfies criteria with respect to the obtained tags for the requested asset. Upon determining that the criteria are satisfied, the virtual asset is provided for inclusion in the virtual scene in accordance with the request.

Abstract: Embodiments of the present disclosure relate to language instructed temporal localization in videos, and provide multimodal large language models (LLMs) for performing language instructed temporal localization in video, as well as methods for training and implementing such models. In contrast to conventional systems, models according to embodiments of the present disclosure are designed to answer “when?” questions, while simultaneously improving other relevant capabilities of multimodal LLMs.

Abstract: Various embodiments disclose a trans-inductor voltage regulator comprising a first group pairing of switching circuits that includes, a first group of switching circuits, a first compensating coupling inductor coupled to the first group of switching circuits, a second group of switching circuits, and a second compensating coupling inductor coupled to the second group of switching circuits, where the first compensating coupling inductor comprises a first winding of a compensating coupling transformer, and the second compensating coupling inductor is a second winding of the compensating coupling transformer.

Abstract: Apparatuses, systems, and techniques to animate objects in computer-generated graphics. In at least one embodiment, one or more neural networks are trained to identify one or more forces to be applied to one or more objects based, at least in part, on training data corresponding to two or more aspects of motion of the one or more objects.

Abstract: In various examples, a technique for performing spatial mapping and planning with large language models using knowledge graphs may include querying a temporal knowledge graph to predict a next step that a robotic system is to traverse in an environment, wherein the temporal knowledge graph comprises a graph representation of entities and relationships in the environment, wherein the entities and relationships in the graph representation are produced based on sensor data captured from the environment. The technique also may include receiving additional sensor data captured from the environment. The technique further may include generating, via a machine learning model, spatial and temporal data associated with the additional sensor data. The technique still further may include updating, via the machine learning model, the temporal knowledge graph to include representations of the spatial and temporal data, based on a similarity between the spatial and temporal data and the graph representation.

Abstract: A neural network is trained to recognize an object based on a first portion trained adversarially and a second portion trained non-adversarially. During adversarial training, the first portion encodes input to a set of latent variables, and the second portion reconstructs the input by decoding the variables. The first portion is trained based on an error signal generated from the decoded output of the second portion, whose configuration is kept fixed during the adversarial training.

Abstract: A first intermediate representation of a first portion of a source code implementing an application and a second intermediate representation of a second portion of the source code is received by a processing device. The first intermediate representation and the second intermediate representation is merged, at run-time, into a merged intermediate representation, wherein the first intermediate representation includes a reference to a function in the second intermediate representation. An execution flow transfer instruction within the merged intermediate representation is identified based on a run-time value of a parameter of the application. The execution flow transfer instruction references the function. A set of executable instructions implementing the function is identified within the merged intermediate representation. The execution flow transfer instruction is replaced with a copy of the set of executable instructions implementing the function.

Abstract: Apparatuses, systems, and techniques to allocate and/or assign registers. In at least one embodiment, registers are allocated based, at least in part, on a set of estimated performance metrics associated with transformations generated prior to a register allocation phase of compilation.

Abstract: A method performed by a server is provided. The method comprises sending copies of a set of parameters of a hyper network (HN) to at least one client device, receiving from each client device in the at least one client device, a corresponding set of updated parameters of the HN, and determining a next set of parameters of the HN based on the corresponding sets of updated parameters received from the at least one client device. Each client device generates the corresponding set of updated parameters based on a local model architecture of the client device.

Abstract: Apparatuses, systems, and techniques to identify objects in an image. In at least one embodiment, one or more neural networks identify the objects using pose information of the objects and visible properties of the objects.

Abstract: Apparatuses, systems, and techniques for neural network training and inference using multiple processor resources. In at least one embodiment, one or more neural networks are used to generate one or more second versions of one or more images based, at least in part, on a first version of the one or more images and a three-dimensional representation of the one or more first versions of the one or more images.

Abstract: Apparatuses, systems, and techniques to implement a cluster operating system that may control at least a portion of functionality provided by local operating systems executing on one or more computing systems within a distributed computing system. At least one embodiment pertains to methods of the distributed system operating system receiving a service request from an application and providing the service or causing the service to be provided to the application according to various novel techniques described herein.