Abstract: A method for coordinated control of multiple UVs based on an environment-aligned LLM is provided, including: obtaining, by each of the UVs, a local observation; transmitting, for each of the UVs, the local observation to a prompt system, and obtaining, based on the local observation, a policy prompt and a communication prompt for each of the UVs; executing a centralized training phase, including: obtaining a global state; generating, for each of the UVs, an executable action sequence and an action execution probability of each action in the executable action sequence; obtaining, for each of the UVs, a next action, executing the next action, and obtaining a reward value; and inputting the global state and the reward value into a critic network, and optimizing parameters of a policy network; executing a distributed execution phase, including: for each of the UVs, obtaining an action probability distribution function; and obtaining the next action.

Abstract: A computer system and associated processes are disclosed for grouping similar real estate properties into contiguous neighborhoods, and for generating neighborhood-specific models capable of estimating property values within their respective neighborhoods. A clustering component uses various sources of property-level data to group properties based on measures of property similarity. For example, the clustering component may measure property similarity based on how frequently specific properties are designated as comparable in appraisal reports. A model generator uses a machine learning process to determine, for specific neighborhoods, correlations between property attributes and values, and uses these correlations to generate the neighborhood specific models.

Abstract: A method for constructing a structural semantic map under an underground weak-light and low-texture environment is provided. The method includes: fusing traditional methods with a parameter line detection and verification model of structural semantics of a Transformer; and establishing a geometric primitive half-plane search method guided by the direction of structural information. The method also includes establishing a neighborhood greedy expansion algorithm based on a geometric primitive model; and optimizing geometric primitive poses and boundaries one by one. The method further includes fusing a point cloud map with structural information, and establishing a semantic map with geometric structure primitives.

Abstract: Disclosed are preparation for an ethinylestradiol-cyclodextrin complex and use thereof. The ethinylestradiol-cyclodextrin complex utilizes the properties of cyclodextrin that the inner cavity is hydrophobic and the outer surface is hydrophilic to increase the water solubility of the ethinylestradiol-cyclodextrin complex that is also an ethinylestradiol drug, so that the solubility of ethinylestradiol is greatly increased and the bioavailability of water-insoluble ethinylestradiol is improved, thereby providing the possibility for changing the administration mode and homogeneous injection of ethinylestradiol.

Abstract: A method in an illustrative embodiment includes blocking cells with predetermined attributes in a first table by performing semantic segmentation on the first table, to obtain a blocked first table. The method further includes converting the blocked first table to a first text. The method further includes generating a second text in a form of natural language based on an understanding of the first text. The method further includes generating a second table corresponding to the first table based on the second text. In this way, the cells containing sensitive information can be identified and blocked, thereby retaining other useful information of the table while protecting privacy. Moreover, using the text in a form of natural language as an intermediate processing result can increase the transparency of table generation, lower the risk of data distortion, and improve the accuracy and consistency of synthetic data while protecting data privacy.

Abstract: A display substrate includes a substrate and a display region, an isolation region, a peripheral region and a hole on the substrate. The display region surrounds the hole, the isolation region is between the display region and the peripheral region, and the peripheral region surrounds and adjoins the hole. The isolation region has an isolation column and a first inorganic structure. The isolation column is at a side of the substrate, and the first inorganic structure is at a side of the isolation column away from the substrate. The peripheral region has a structure layer on the substrate and including a first organic structure and a second inorganic structure along a direction perpendicular to the substrate. The first inorganic structure, the second inorganic structure and the first organic structure all are made of an insulation material. The first inorganic structure and the second inorganic structure encapsulate the display substrate.

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.