198 results about "Information compression" patented technology

The invention discloses a multi-modal multi-scale retrieval enhancement generation method, system and device applied to external knowledge questions and answers and a medium. The method comprises question perception, multi-modal multi-scale query fusion coding, dense recall and answer generation. Analyzing a key query phrase from the question through a fine-tuned instruction language model, and accurately positioning a region of interest corresponding to the phrase in an image by using an open set visual positioning model; multi-source information is compressed and distilled into an optimal query vector through a deep fusion network integrating multi-head self-attention and an information bottleneck theory; executing a maximum inner product search to recall related knowledge; guiding the large language model to synthesize all information to generate a final answer; the system, the equipment and the medium directly perform feature fusion in the vector space based on the method, so that challenges such as information loss and cascading errors caused by a traditional normal form can be effectively dealt with, high correlation and high accuracy of retrieval knowledge are ensured, and accurate and reliable image-text questions and answers are realized.

The invention discloses a three-dimensional point cloud geometric information compression method based on implicit neural representation, and the method comprises the steps: constructing a trunk structure of an implicit neural network through a plurality of sine representation network layers which are connected in series, and introducing a variable-scale position coding mechanism on this basis, the method enables a network to obtain higher geometric reduction precision while keeping a compression ratio, and comprises the following steps: (1) inputting space coordinates of divided voxels into a position coding module with adjustable scale parameters; (2) feeding a coding result into an implicit neural network constructed by a network layer based on sine representation, and outputting the occupancy probability of the voxel through an activation function; (3) in a training stage, the model continuously optimizes parameters, so that the output probability distribution is highly consistent with a real occupied label; (4) after model training is completed, a method of combining an AdaRound second-order quantization optimization strategy and quantization perception training is introduced, network weight is finely adjusted, and quantization errors are reduced; (5) in a reasoning stage, judging whether the voxel is occupied or not according to a preset threshold value, and when the prediction probability exceeds the threshold value, regarding the voxel as occupied; and (6) all voxels judged to be occupied are aggregated, and reconstruction of the geometric structure of the point cloud is completed.

The invention discloses a federated learning image classification method based on time extension gradient cumulative average. Each client uses a local data set to train a local model to obtain gradient information of a current communication round, and uploads the gradient information to the central server after compression; the client continues to perform local training in a time period from the time when the client starts to upload the compression gradient information of the current communication round to the time when the server completes the compression gradient information aggregation of the current communication round; the central server side aggregates the compression gradient information to obtain average compression gradient information of the current communication round, and issues the average compression gradient information to the client side to update a local model of the (t + beta) th communication round; and repeating the steps until the training is completed, and inputting the to-be-detected image into the local model to obtain an image classification result. The method shows excellent performance under various data sets and different compression levels, has higher convergence speed and model precision, and solves the problem of low communication efficiency caused by network delay and bandwidth limitation in image classification of federated learning.

The invention provides a multi-modal large-model long-sequence information compression retrieval method, and relates to the technical field of data processing, and the method comprises the following steps: receiving multi-modal long-sequence original data of a target patient; all modal data in the multi-modal long sequence original data are uniformly mapped to the same hidden space through a pre-trained multi-modal large model, and a unified semantic representation sequence of semantic association between modals is obtained; performing time sequence feature analysis on the unified semantic representation sequence, regarding feature points in the semantic representation as a spatial point set, determining boundary distribution by constructing convex hulls of feature clusters, identifying key feature clusters in the semantic representation, and determining a key time sequence interval based on a convex hull distribution mode of the key feature clusters. According to the method, the problems of weak cross-modal semantic association, easy loss of key time sequence information and low compression and retrieval efficiency in the existing multi-modal long sequence data retrieval are solved.

For example, a processor may be configured to generate compressed radar information by compressing radar values in a plurality of data bins of at least one radar processing dimension, the at least one radar processing dimension including a range dimension. For example, the processor may be configured to generate the compressed radar information by quantizing a plurality of normalized values corresponding to the radar values in the plurality of data bins. For example, a normalized value corresponding to a radar value in a data bin may be based on a normalization of the radar value with respect to a plurality of radar values in the data bin. For example, the processor may be configured to store the compressed radar information in a memory.

The invention provides a sparse adaptation circuit and a network-on-chip sparse data processing method, the sparse adaptation circuit is embedded between a network interface unit (NIU) and a router of an NoC system, and the sparse adaptation circuit comprises an input processing module, a compression engine and an output processing module; the sparse detection unit is used for performing real-time zero value detection on the data stream received from the network interface unit NIU to generate sparse identification information; the compression engine is coupled with the sparse detection unit and is used for carrying out dynamic compression coding on the data stream based on the sparse identification information; and the output interface is used for sending the processed data flow to a router in a format conforming to the network-on-chip protocol. In the network-on-chip, a hardware method is adopted to identify a data packet containing a large number of zero values in AI calculation and compress the data packet into a data packet only containing valid data, so that the problems of low transmission efficiency, waste of power consumption and the like caused by the fact that on-chip bandwidth is occupied by invalid data are avoided.

The various implementations described herein include methods and systems for encoding video. In one aspect, a method includes receiving a mesh with polygons representing a surface of an object; detecting a first symmetric region in the mesh that includes a first symmetry line to divide the first symmetric region into a first partition and a second partition. The method includes recursively determining whether one of the first partition or the second partition includes a second symmetric region until no symmetric region is detected in both the first partition and the second partition. The method includes in response to detecting the second symmetric region within one of the first partition or the second partition: determining a second symmetry line within the second symmetric region to divide the first or the second partition into a third sub-partition and a fourth sub-partition; and compressing information of the third sub-partition, the second symmetry line and the first symmetry line into a bitstream.

The invention provides a portrait animation generation method and system based on a reversible encoder and dynamic optimization, and relates to the technical field of video portrait reconstruction, high-precision portrait animation generation is realized through multi-module collaborative innovation, multi-scale appearance features are extracted by using a VAE encoder, identity vectors extracted by ArcFace and global appearance embedding of CLIP are combined, and the portrait animation generation method and system based on the reversible encoder and dynamic optimization are realized. Constructing a multi-modal characteristic system; a high-precision attitude estimation algorithm is adopted to extract fine joint points, and an error between a generated attitude and an original attitude is calibrated in real time through a dynamic optimization strategy, so that the attitude driving accuracy is remarkably improved. A reversible residual block is designed to realize lossless two-way transmission of features, and the problem of information compression of a traditional encoder is avoided; a high-frequency detail injection mechanism is introduced, high-frequency features such as facial textures are extracted through a pre-training model and fused with shallow features, an attention mechanism guided by identity features is introduced based on the generation process of a diffusion model, and the consistency of character identities in the generation process is ensured.

The invention belongs to the technical field of landslide evaluation, and particularly relates to a modeling method of a landslide susceptibility evaluation coupling model fusing an auto-encoder and a feedforward neural network, and the method comprises the steps: obtaining slope unit data and historical landslide data, carrying out the preprocessing, extracting landslide-related evaluation factors, carrying out the correlation test and correlation analysis, and obtaining a landslide susceptibility evaluation coupling model. Screening out evaluation factors for subsequent evaluation; constructing a landslide data set, balancing samples by adopting a GPU acceleration oversampling technology, and dividing a training set and a test set; constructing a landslide susceptibility evaluation coupling model based on the auto-encoder and the feedforward neural network; training the landslide susceptibility evaluation coupling model by using the training set and the test set to obtain optimal parameters; and outputting a landslide susceptibility distribution result by using the trained landslide susceptibility evaluation coupling model. According to the method, feature dimension reduction and redundant information compression can be effectively realized, and the evaluation precision and generalization ability of the model are improved.

The invention discloses a method and a system for protecting privacy of source codes in memory error detection, belongs to the technical field of computer software security, and particularly relates to a technology for protecting confidentiality of the source codes in a third-party memory error analysis process of software. According to the method, a debugging information minimization processing system DIREDUCER is constructed, a selective deletion and type minimization technology is utilized, on the premise that complete source codes are not exposed, necessary debugging information used for memory error detection is effectively reserved, and dual guarantee of source code privacy and memory error detection effects is achieved. According to the method, the debugging information in the non-stripping binary file is compressed to be within 10% of the original volume, and the recognition capability of an analysis tool on the problems such as memory leak, buffer overflow and overhanging pointers is not remarkably reduced. Experimental results show that the method has good adaptability and expandability in actual deployment, is suitable for various debugging tools and binary analysis frameworks, and has wide application prospects and practical values.

A user equipment (UE) includes a transceiver and a processor configured to transmit, via the transceiver to a network, UE capability information that corresponds with performance monitoring for artificial intelligence (AI) model-based channel state information (CSI) compression at the UE. In accordance with the UE capability information, the processor is configured to receive, via the transceiver and from the network, configuration and activation instructions for performing the AI model-based CSI compression and a network configuration on assisted information for the performance monitoring. The processor is configured to monitor performance of the AI model-based CSI compression in accordance with the received UAI configuration, and based on the monitored performance of the AI model-based CSI compression, transmit, via the transceiver to the network, a preference of the UE for the AI model-based CSI compression.

The invention relates to a method for realizing semantic chain retrieval enhanced generation processing for knowledge questions and answers of long documents. The method mainly comprises two parts of knowledge base construction and user knowledge questions and answers. User knowledge questions and answers are constructed based on a knowledge base, and knowledge base construction relates to long document preprocessing, semantic chain type storage, information compression and knowledge extraction and storage. When a user performs knowledge questions and answers, a retrieval vector is formed through fine-grained semantic coding, and then related document vectors and knowledge maps are matched in a knowledge base through vector similarity calculation. If the length of the document exceeds the context scale of the model, document information after static information compression is indexed through information compression, whether dynamic compression is carried out or not is evaluated, and document fragments related to tasks are returned through dynamic compression. Otherwise, directly returning the related original document as the background knowledge of the generator. And the model generator generates an answer based on the related document information / information after information compression, the knowledge graph information, the user question and a model prompt.

A hierarchical personalized federal learning method based on cross-client prototype clustering relates to the field of artificial intelligence and distributed machine learning, and comprises the following steps: a client maps a local sample to a semantic prototype space for information compression, and generates a category prototype matrix; the server receives the prototype matrix uploaded by each client, dynamically clusters and aggregates the prototype matrix, constructs a multi-level knowledge structure including a cluster-level expert model and a global semantic prototype, and maintains the global semantic prototype through index moving average; the client receives the cluster-level expert model, the global model parameters and the global semantic prototype from the server, and carries out a new round of local training on the basis; the client integrates local data and multi-level knowledge issued by the server to optimize three types of loss updating models; through loop iteration, the local model of the client gradually realizes personalized adaptation and global alignment. According to the method, the communication efficiency, the privacy protection safety, the model convergence speed and the generalization ability are improved.

A method implemented in a wireless transmit and receive unit (WTRU) may include receiving one or more reference signals; a first codebook-based precoding matrix is selected based on the one or more reference signals, and a second precoding matrix is determined based on the one or more reference signals. The method may include determining a differential precoding matrix based on the first codebook-based precoding matrix and the second precoding matrix. The method may include determining a compressed differential precoding matrix based on the differential precoding matrix. The method may include transmitting feedback information indicating the compressed differential precoding matrix.

The application belongs to the field of biomedicine and particularly relates to a TB-level biomedical three-dimensional image data large data format archiving method, which comprises the following steps: calculating TB-level image sequence slice information, dividing the image sequence into a plurality of three-dimensional local blocks; quickly reading the three-dimensional local blocks in a serial reading and parallel decoding mode; cutting each three-dimensional local block to obtain a plurality of three-dimensional small blocks; compressing each three-dimensional small block by using the information of the z-axis continuous change of the three-dimensional small block; continuously writing the compressed data into a corresponding container to obtain 0-level large data format data; further downsampling, compressing and continuously writing the local blocks into a corresponding 1-level large data format container; and obtaining 2-level large data format data in the same way. The application directly and quickly reads the local blocks, compresses and makes TB-level biomedical three-dimensional image large data format by using the z-axis continuous information, has the advantages of low cost, strong practicability, simple operation and the like, and can be calculated in parallel to further accelerate the archiving.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first wireless communication device may receive configuration information associated with uplink data compression, the uplink data compression being associated with at least one of multiple compression dictionaries or multiple compression buffers. The first wireless communication device may compress, in accordance with the configuration information, uplink data to generate compressed uplink data via at least one of: one or more compression dictionaries of the multiple compression dictionaries, or a compression buffer of the multiple compression buffers, and the one or more compression dictionaries or the compression buffer being selected in association with one or more traffic parameters of the uplink data. The first wireless communication device may transmit an uplink communication that includes the compressed uplink data. Numerous other aspects are described.

This invention discloses a translation method and system based on structured segmentation and contextual memory, comprising: performing multi-dimensional vectorization representation of the long text to be translated to establish a full-text semantic feature mapping space; adaptively decomposing the long text to be translated to obtain a tree-like hierarchical structure; compressing key information of semantic units, extracting core semantic feature vectors, and storing them in a globally dynamically updated contextual memory pool; generating an enhanced prompt information sequence based on the core semantic feature vectors and the hierarchical attributes of the current semantic unit; inputting the enhanced prompt information sequence into a large-scale language model to generate a candidate translation sequence; performing boundary verification and smoothing on adjacent candidate translation sequences to output a complete structured translation. This invention improves the automation level of long text translation by systematically optimizing the processes of text deconstruction, contextual memory, constraint generation, and global verification, ensuring the structural integrity, logical coherence, and semantic fidelity of the translation.

Disclosed in the present application are a data transmission method and apparatus. The method comprises: a transmitter device generating and transmitting first signaling, which comprises information of a base matrix, indication information of the base matrix, information of a compression coefficient matrix, and indication information of the compression coefficient matrix, wherein the base matrix comprises at least one base vector, the base vector corresponds to a first vector in an original data matrix, second vectors in the original data matrix other than the first vector form a non-base matrix, and the base matrix performs data compression on the non-base matrix to obtain the compression coefficient matrix; and a receiver device receiving the first signaling, and obtaining the original data matrix on the basis of the first signaling. The present application provides a method for reducing the overheads of dictionary data transmission during dictionary compression, and proposes how to perform standardized transmission on data and information generated during the process of the method, thereby ensuring the reliability of a data decompression process.

The invention provides a laser radar fault detection method based on machine learning, and relates to the field of fault detection. The invention provides a LiDAR-FID model which comprises an integrated mask module, a thermal bottleneck mechanism module and a diagnosis module, the integrated mask module is used for retaining effective features of data, the thermal bottleneck mechanism module is used for compressing redundant information of multi-source data, and the diagnosis module realizes detection of laser radar faults. All the modules cooperate with one another to achieve intelligent detection of laser radar faults.

The present disclosure provides an information compression system that is capable of achieving higher compression efficiency. A data acquisition section acquires data. A generation section (segmentation section and integration section) determines each object depicted by the data and a sense of each object, and according to results of the determination, generates compression target data by converting values of elements in the data to identification information indicating each object and the sense of each object. A data storage section generates compressed data by compressing the compression target data. This makes it possible to convert highly random element values to slightly random identification information and compress the resulting converted information while reducing the amount of information. Consequently, the compression ratio can be increased.

The disclosure describes a method of evaluating performance of a two-sided model used for performing CSI compression. The method includes receiving, by a network, from a UE, a compressed channel Ht, a second eigenvector EVt+1 corresponding to a channel Ht+1, and a first similarity score determined between a first eigenvector EVt and the second eigenvector EVt+1. A channel Ht is reconstructed by processing the compressed channel Ht using a decoder part of the two-sided AI / ML model. Eigen decomposition of the channel Ht is performed to obtain an eigenvector (EV)t. A second similarity score is determined between the eigenvectors (EV)t and EVt+1. Alternatively, the first similarity score is determined between an estimated channel H and a codebook based precoder W, and a second similarity score is determined between a reconstructed channel H and the codebook based precoder W.

This invention relates to the field of artificial intelligence technology and discloses a method, apparatus, computer device, and computer-readable storage medium for classifying graph structure data. The method includes: acquiring input data to be classified; converting the data to be classified into target graph structure data; inputting the target graph structure data into a graph neural network to obtain a classification result for the data to be classified; wherein the graph neural network includes an input module, an information compression module based on structural entropy, and a prediction module; the information compression module based on structural entropy is used to calculate the structural entropy of each node and generate node weights corresponding to each node, and compress the target graph structure data according to the node weights; the structural entropy is calculated based on the normalized connection probability between the node and its neighboring nodes; and the classification result is output. Through the above method, this invention improves the predictive performance of the model and provides a more causal explanatory ability for the structure.

The invention discloses an image recompression method and device, electronic equipment and a storage medium, and relates to the technical field of image coding, and the method comprises the steps: extracting metadata and an original DCT coefficient from an original image file, and the original image file is in a JPEG format; and determining a target deep learning model based on the metadata, processing the original DCT coefficient according to the target deep learning model, and generating a feature bit stream and a prediction image. And based on the prediction image, performing residual calculation on the original image file and the feature bit stream to obtain a residual bit stream. And combining and packaging the predicted image and the residual bit stream to obtain an image recompression file. By calculating the residual error between the predicted image and the original image, the focus of information compression is transferred from total image information to extremely small error information, so that the image can be reconstructed losslessly and completely at a decoding end according to a small amount of data and differential information, and the storage and transmission cost is effectively reduced while the image information completeness is ensured.

Methods and network nodes for exclusive beam pairing for multi-user multiple input multiple output (MU-MIMO) are disclosed A method in a network node comprises transferring (S1401) to another network node, B, (1303) channel state information (CSI) compressed such that for a Wireless Device (WD), it only identifies a set of directions or beams containing a significant amount of power. A method in a network node B, comprises receiving (S1501), from the network node A, CSI compressed such that for the WD, it only identifies a set of directions or beams containing a significant amount of power.

The invention discloses an information processing method and device, equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a data set containing a plurality of texts; for each text in the data set, marking the text through a first mark and a second mark to obtain a marked data set; wherein the second mark is also used for representing to-be-compressed information of the text; and training a preset model according to the marked data set, and reasoning an input text by using the trained model. According to the method, the original text does not need to be split and compressed, and the to-be-compressed information of the original text is defined and expressed by introducing the special mark, so that the model is endowed with the capability of compressing the context information, meanwhile, the calling frequency of the model is reduced, and the reasoning efficiency of the model is greatly improved.

Methods, systems, and devices for techniques for logical-to-physical information compression are described. In some cases, a memory system may compress a logical-to-physical (L2P) mapping to expand the quantity of physical addresses mapped by the L2P mapping. For example, if a set of consecutive entries of an uncompressed L2P mapping includes consecutive physical addresses, the memory system may compress the consecutive entries into a single entry which includes a starting physical address of the consecutive physical addresses. Additionally, the memory system may include an indication of a starting logical address corresponding to the starting physical address in the compressed entry. To identify a physical address within the compressed entry, the memory system may determine an offset between a logical address corresponding to the physical address and the starting physical address using the indication, and may apply the offset to the starting physical address to determine the physical address.

A method performed by a terminal according to at least one of embodiments disclosed in the present specification may comprise: acquiring information on predicted channel state information (CSI) and information on compressed CSI on the basis of an artificial intelligence / machine learning (AI / ML) model supporting both CSI compression and CSI prediction; and transmitting a CSI report on the basis of the information on the predicted CSI and the information on the compressed CSI, wherein on the basis that the performance of the AI / ML model is less than a threshold, the terminal adjusts past CSIs used as an input data set of the CSI compression before changing at least one of a configuration for the CSI compression and a configuration for the CSI prediction of the AI / ML model.

The communication method comprises the steps that first communication equipment generates compression information of first data and sends the compression information to second communication equipment, the compression information comprises information of a first object, the first object is an object in a first grid chart, and the first grid chart corresponds to the first data, the information of the first object comprises type information of the first object and coordinate information of a grid reference point corresponding to the first object, and the type information of the first object indicates the type of the first object. The grid reference point corresponding to the first object can be used for representing the shape of the first object, that is, the shape feature of the object in the grid chart is considered in the generation process of the compression information, and the compression efficiency of the first data can be improved.

A bus control unit includes: a compression execution unit, configured to compress sparse data by using a compression algorithm, to obtain compression data and compression attribute information; and a compression control unit, configured to generate, according to an instruction format of a communication bus, a data signal carrying the compression attribute information and the compression data, and send the data signal through the communication bus, where the compression attribute information is used for decompressing the compression data to obtain the sparse data.

The present application discloses calibrating one or more UE antenna ports using a network node virtual port to compress channel estimation information into beamformed channel estimation information. In some aspects, one of the UE or the network node may transmit the beamformed channel estimation information to the other of the UE or the network node. The UE antenna ports may then be calibrated in accordance with the beamformed channel estimation information.