Abstract: The present disclosure generally relates to systems and methods for generating subdivision boundaries based on extracted subdivision information from parcel data. In some embodiments, the subdivision boundary system can classify, extract, and standardize information from parcel data to generate “cleaned” parcel data. This cleaned parcel data can be used to generate subdivision boundaries that merge parcels that are likely to belong to the same subdivision within the same boundary. In some embodiments, the subdivision boundary system can complete and correct subdivision boundaries by refilling missing subdivision names. In some embodiments, a boundary system can access the subdivision information (e.g., subdivision names) stored in the subdivision data store and other information (e.g., map information, geographical information) to generate a boundary corresponding to a subdivision.

Abstract: A method and a system for coordinated control of a UAV swarm based on communication information completion are provided. The method includes: obtaining an information weight based on a local observation and information obtained from other UAVs through an information-level weighting network; obtaining weighted information of each UAV, and generating a global state through an adaptive generation network; obtaining a global action-value function by integrating a local action-value function of each UAV through a mixing network based on the global state and the local action-value function of the UAV; in a distributed execution phase, obtaining, by each UAV, the local action-value function of the UAV based on the local observation and the information weight through a Transformer-based decoder, and performing coordinated decision-making through a policy network.

Abstract: Provided is a collaborative exploration system for an unknown space based on a heterogeneous air-ground robot, comprising: an unmanned ground vehicle (UGV) and a plurality of unmanned aerial vehicles (UAVs) loaded on the UGV; wherein the UGV is equipped with a first perception module and a first computing platform configured for constructing a fine-grained map of an environment; each of the plurality of UAVs is equipped with a second perception module and a second computing platform configure for constructing a lightweight map of the environment; and a wireless communication module is disposed in the UGV and each of the plurality of UAVs, and the UGV and the plurality of UAVs communicate via a semi-centralized self-organizing communication mode; when the system performs cooperative exploration for the unknown space, the plurality of UAVs explore unknown regions around the UGV with the UGV as a center; based on the second perception module and the second computing platform of each of the plurality of UAVs, the

Abstract: The present invention discloses a multi-UAV coordinated control method based on a dynamic directed graph communication structure. In a distributed execution phase, based on local observation values and historical hidden states, a communication structure between UAVs is dynamically adjusted through a multi-key gated communication network using an adjacency trajectory matrix. Local action value functions are calculated and the hidden states are updated based on an updated adjacency trajectory matrix and pre-communication information, and a final control decision is generated and executed. In a centralized training phase, the adjacency trajectory matrix and the local observation value at each moment are recorded to generate a global state, and a global action value function is generated using a mixing network, and network parameters are then updated through rewards. A trained graph collapse network and the mixing network are deployed to each UAV to achieve distributed execution.

Abstract: Provided in the present disclosure are a method and apparatus for determining a position of a client, and a processor. The method includes: a first acquisition step: acquiring at least one piece of first Channel State Information (CSI) from a first communication link for an Access Point (AP) to communicate with the client; a second acquisition step: when a receiving antenna of the AP meets a preset condition, acquiring at least one piece of second CSI from a second communication link for the AP to communicate with the client; a processing step: processing the first CSI to obtain a first phase difference, and processing the second CSI to obtain a second phase difference; and a determination step: determining a position of the client according to the first phase difference and the second phase difference. By respectively acquiring two different groups of CSI, the limitation that a spacing of the AP antenna is limited to half a signal wavelength is lifted.

Abstract: Methods, apparatus, and processor-readable storage media for generating image-based avatars using multi-modal artificial intelligence techniques are provided herein. An example computer-implemented method includes encoding one or more image-related features by processing one or more portions of input image data using at least one multi-channel image encoder; encoding one or more audio-related features by processing one or more portions of input audio data using at least one audio encoder; encoding one or more text-related features by processing one or more portions of input text data using at least one text encoder; and generating at least one image-based avatar by processing, using at least one vision encoder, at least a portion of the one or more image-related features and one or more of at least a portion of the one or more audio-related features and at least a portion of the one or more text-related features.

Abstract: Implementations of the present disclosure relate to a method, an electronic device, and a computer program product for managing an inference process. Here, the inference process is implemented based on a machine learning model. A method includes: determining, based on a computational graph defining the machine learning model, dependency relationships between a set of functions for implementing the inference process; acquiring, in at least one edge device located in an edge computing network, a set of computing units available to execute the inference process; selecting at least one computing unit for executing the set of functions from the set of computing units; and causing the at least one computing unit to execute the set of functions based on the dependency relationships. With example implementations of the present disclosure, the inference process is implemented by making use of a variety of computing units in the edge computing network, thereby improving performance.

Abstract: Methods, apparatus, and processor-readable storage media for generating 3D images using machine learning and generative artificial intelligence are provided herein. An example computer-implemented method includes obtaining at least one 2D image; determining one or more features of the at least one 2D image by processing at least a portion of the at least one 2D image using a first machine learning technique; generating multiple 3D visualizations associated with the at least a portion of the at least one 2D image by processing at least a portion of the one or more features using one or more generative artificial intelligence techniques; selecting at least one of the multiple 3D visualizations by processing at least a portion of the multiple 3D visualizations using a second machine learning technique different from the first machine learning technique; and performing one or more automated actions based on the at least one selected 3D visualization.

Abstract: The present invention pertains to a thermoelectric material which exhibits a ZT of ?0.3 and which is exposed to an external magnetic field of 0.01 T-2 T resulting in a ZT of ?1.3 at a temperature of ?300 K. The present invention pertains to thermoelectric materials at a temperature of ?300 K under an applied external magnetic field of 0.01 T-2 T, in which the material includes: a three-dimensional topological insulator, or topological semimetal having a carrier mobility of ?104 cm2/Vs at 20K and a carrier concentration of 1017-1020/cm3, and an effective mass?0.04 free electron mass, and a Fermi energy of ?100 meV. The invention further relates to methods of making such thermoelectric material at a low magnetic field of less than 2 T.

Abstract: The present disclosure relates to a method for interaction operation control of a deformable object based on a vision-tactile-language-action multimodal model, including: encoding a visual image, tactile data, and language data for the deformable object to obtain a visual feature, a tactile feature, and a language feature, performing cross-modal feature alignment processing on the visual feature, the tactile feature, and the language feature to obtain a multimodal fusion feature, inputting the multimodal fusion feature into a large model for environment understanding, adopting a planning manner of ‘thinking-decision’ to iteratively perform action planning and execution, and repeating the above operation steps until an interaction operation task of the deformable object is completed.

Abstract: The present disclosure relates to a training method of a large model for wire harness operation of a humanoid robot based on a meta-action, which includes: constructing wire-harness-operation meta-actions based on human wire harness operations; constructing a wire-harness-operation meta-action dataset; training the large model for the wire harness operation of the humanoid robot using reinforcement learning based on the wire-harness-operation meta-action dataset; acquiring input data, outputting joint-motor parameters using the trained large model for the wire harness operation of the humanoid robot and updating the input data in real time based on the joint-motor parameters; acquiring a basic strategy and acquiring a residual strategy based on the basic strategy; and performing the wire harness operation of the humanoid robot based on the basic strategy and the residual strategy.

Abstract: Provided is a real-time control method for robot manipulation actions based on collaboration between a large model and a small model.

Abstract: A processing system and method for executing arithmetic and conversion operations involving 16-bit brain floating-point (BF16)-formatted data are described. An instruction specifying either an arithmetic or conversion operation and a first data element in BF16 data format are received. For arithmetic operations, the exponents of the data elements are aligned, and a result is generated using the aligned exponents. For conversion operations, the mantissa of the BF16 data element is scaled based on its exponent, and the element is converted to a second data format, such as FP32 or a reduced-precision format, using precision-aware scaling and rounding. The result is stored in operations registers, such as for additional processing.

Abstract: Techniques are disclosed for transposing and loading 6-bit floating-point matrix data into operations registers of a processing unit. A matrix comprising 6-bit floating-point data elements is received from memory, in which the matrix is stored in either a row-major or column-major layout. A matrix transposition loading operation is performed during the loading process, rearranging the matrix elements into a transposed layout (e.g., converting column-major to row-major). The transposed matrix is stored in operations registers for use in parallel processing tasks. The process may include caching partially stored data elements from memory and combining them with subsequently retrieved data to complete the transposition.

Abstract: Systems and techniques for providing co-issue of instructions utilize a scheduler associated with a compute unit to select one double-precision (i.e., 64-bit) instruction and one single-precision (i.e., 32-bit) instruction for issue to and execution at a compute unit. Each compute unit includes or is associated with one or more pairs of double-precision arithmetic logic units (ALUs) and single-precision ALUs. The selected double-precision and single-precision instructions are associated with different threads or waves such that no dependency can exist between the two instructions. The selected single-precision instruction may perform address calculations or other memory tasks while the double-precision instruction may perform data computations that may be required for, e.g., matrix multiplication, or other machine learning functionality.

Abstract: Systems and techniques for providing mixed-precision matrix multiplication in multi-chiplet processors recognize different precision formats of matrices to be multiplied based on, e.g., parameters provided with instructions or start and end memory locations of the matrices. A plurality of different multiplication chains are provided for different formats such that mixed-precision matrix multiplication can be performed using multiplication chains configured to handle multiplication of different precision formats. The multiplication chains are automatically selected based on the precision formats of the matrices to be multiplied, enabling programmers to utilize the chains without having to directly access the individual multiplication chains.

Abstract: One or more cache lines are configured to store a lookup table (LUT) having a plurality of floating-point numbers that are represented by a first number of bits. One or more multiplexers are connected to the cache line(s). The multiplexer(s) is/are configured to select one of the floating-point numbers based on an integer index having a second number of bits that is less than the first number of bits. The integer index is a quantized representation of the selected one of the floating-point numbers. In some cases, a memory is configured to store a matrix of integer indices that are generated by quantizing floating-point numbers that represent data associated with a machine learning algorithm. The integer index is provided by the matrix of the integer indices.

Abstract: An accelerator unit (AU) including vector registers and one or more processor cores is configured to schedule instructions for execution that share one or more matrix block. To this end, the AU maintains tracking entries for the hardware buffers of one or more of these processor cores with each tracking entry indicating vector register addresses associated with the corresponding matrix block loaded into the hardware buffer. When the AU schedules an instruction indicating a matrix multiplication operation for execution, the AU compares the vector register addresses indicated in the instruction to the tracking entries of the hardware buffers. In response to the vector register addresses in the instruction matching a tracking entry, the AU suppresses a read request to the vector registers and uses data from a corresponding hardware buffer to perform the matrix multiplication operation.

Abstract: The present invention relates to a vision-language large model-based multi-robot collaborative navigation method and system. The method includes steps of: according to a task instruction input by a user, combined with a semantic map constructed by each robot, allocating subtask instructions to robots after parsing by a large language model; acquiring, by each robot, an environmental image in real time, according to the allocated subtask instruction, parsing and predicting a next action using a vision-language navigation large model, and executing a corresponding action and updating a state by the robot; and monitoring a robot state and checking task progress in real time through a human-machine interface and adjusting a task or handling an exception during execution when needed by an operator, and dynamically adjusting, by the robot, an execution strategy according to feedback information to optimize navigation for task completion.

Abstract: The present invention relates to a robot action generation method and system combining general and specialized models, where the method includes: constructing the general model and the specialized model, pre-training the general model, performing parameter fine-tuning on a pre-trained general model, and training the specialized model based on a fine-tuned general model; acquiring a task instruction and real-time visual information, inputting the task instruction and real-time visual information into the fine-tuned general model, and outputting an action sequence and a task latent feature; and acquiring real-time point cloud perception data, and inputting the real-time point cloud perception data, together with the action sequence and the task latent feature, into a trained specialized model, and outputting continuous robot actions. Compared with the prior art, the present invention improves the speed of robot action generation and enhances the generalization of robot action generation.