28 results about "Tree code" patented technology

The invention discloses a sewage high-risk pollutant screening and identification method based on fragmentation tree pre-training, and the method comprises the steps: constructing a fragmentation tree pre-training data set according to the second-level mass spectrum data of known high-risk pollutants, so as to carry out the self-supervision pre-training of a graph neural network encoder, and obtaining a fragmentation tree editor; the method comprises the following steps: acquiring secondary mass spectrum data of suspected high-risk pollutant related compounds in a to-be-detected sewage sample, constructing a fragmentation tree set, constructing a high-risk pollutant screening model based on a fragmentation tree encoder, performing high-risk pollutant signal screening on the fragmentation tree set to obtain a high-risk candidate fragmentation tree set, and performing high-risk pollutant signal screening on the high-risk candidate fragmentation tree set. And generating a candidate molecule set of each high-risk candidate fragmentation tree, and identifying a target molecular structure and a matching score of the high-risk candidate fragmentation tree from the candidate molecule set. According to the invention, rapid screening and priority ranking of high-risk pollutants in sewage are realized, and candidate output of structure identification is further provided on the basis of screening.

An example device includes a memory and one or more processors. The one or more processors are configured to determine whether to use single-tree coding for a first codec tree unit (CTU), determine a first transform unit (TU) based on a first luma sample transform block (TB) and first two corresponding chroma sample TBs based on the first CTU using the single-tree coding. The one or more processors are further configured to determine whether to use dual-tree coding for a second CTU, determine a second TU based on (I) a second luma sample TB or (II) a second set of two chroma sample TBs based on the second CTU using the dual-tree coding. The first TU includes a syntax structure for transforming the first TB samples, and the second TU includes a syntax structure for transforming the second TB samples.

The application discloses a sewage high-risk pollutant screening and identification method based on fragmentation tree pre-training, comprising the following steps: constructing a fragmentation tree pre-training data set according to secondary mass spectrum data of known high-risk pollutants, pre-training a graph neural network encoder in a self-supervised manner to obtain a fragmentation tree editor, obtaining secondary mass spectrum data of suspected high-risk pollutant-related compounds in a sewage sample to be tested, constructing a fragmentation tree set, constructing a high-risk pollutant screening model based on the fragmentation tree editor, screening high-risk pollutant signals from the fragmentation tree set to obtain a high-risk candidate fragmentation tree set, generating a candidate molecule set of each high-risk candidate fragmentation tree, and identifying a target molecular structure and a matching score of the high-risk candidate fragmentation tree from the candidate molecule set. The application realizes rapid screening and priority sorting of high-risk pollutants in sewage, and further provides a candidate output of structure identification on the basis of screening.

The embodiment of the application provides a kind of based on syntax semantic fusion sentiment tuple extraction method and system, it is related to natural language processing technical field.The method can be after obtaining input text, using structure perception syntax encoder calculates the encoding embedding data of input text, again through bidirectional cross attention fusion, obtains the fusion representation of syntax representation and semantic representation depth interaction.Based on double similarity comparison learning framework, the unified representation of fusion representation is calculated, and candidate example is retrieved according to the unified representation.The method can be based on the structure perception graph converter of dependency tree coding to generate encoding embedding data, and through the syntax and semantic fusion realized by bidirectional cross attention mechanism, and carry out double similarity comparison learning, for the structured task of large language model Provide explicit syntax guidance, can simultaneously integrate syntax and semantic information, correctly identify complete sentiment tuple, improve the output precision of sentiment tuple extraction result.

Post training compression of 3DGS data is agnostic to training in a traditional signal compression perspective. Gaussian parameters are treated as signals. Pre-processing and transform coding techniques are used to compress the signals effectively. Firstly, lossless / lossy compression is performed on 3DGS geometry (positions, scales, rotations) using a point cloud coding-based (e.g., G-PCC, GeS) framework. Positions are compressed using occupancy tree coding. Scales and rotations are encoded as attributes using transform coding. The widely used block-based graph Fourier transform (GFT) is used to compress the attributes (base colors, spherical harmonic coefficients and opacities). In addition, a graph construction strategy is used for 3DGS data that computes the edge weights based on similarity (or dissimilarity) between the 3D Gaussian distributions using KL-divergence. Alternatively, positions can be encoded using occupancy tree (e.g., G-PCC, GeS) or AI-based PCC methods, and any subset of Gaussian parameters or the transformed coefficients of Gaussian parameters can be mapped into 2D frames and encoded by video coders.

Adaptive dual tree coding may be optimized, for example, to implement a trade-off between compression efficiency and encoder complexity. For example, adaptive dual tree / single tree representation in inter slices may be conditioned on characteristics of the slice / picture, e.g., slice type, temporal layer id, and / or temporal depth of the slice being coded / decoded. Use of block-vector guided convolutional cross-component model (BVG-CCCM) may be conditioned on disabling adaptive dual tree and / or characteristics of the slice being encoded / decoded. Use of multi-type tree (MTT) split modes early termination may be conditioned on disabling of adaptive dual tree. In examples, a device (e.g., decoder / encoder) may determine a hierarchical parameter associated with a slice. The device may determine whether to enable a dual tree mode for the slice based on the hierarchical parameter for the slice. The device may decode the slice based on the determination of whether to enable the dual tree mode for the slice.

The invention relates to the field of fault tree analysis, in particular to a fault tree cut set solving method based on artificial intelligence. The method comprises the following steps: coding a fault tree into a structure matrix in a similar adjacent matrix form, and expressing an event and a cut set in a binary matrix form; searching the structure matrix to find out an analyzable intermediate event; analyzing each currently analyzable intermediate event through logic gate operation; performing a simplification operation on the analyzed intermediate event cut set, and only reserving a minimum cut set; after all parsable events searched in the round are parsed, rows and columns corresponding to parsed events in the middle event area of the structure matrix are deleted, and the next round of search is continued; and repeating the operation until the top event is analyzed. According to the method, the cut set is solved by adopting the matrix Boolean algebraic method, the fault tree analysis cut set simplification agent model is built based on the neural network, and the cut set is simplified in the solving process, so that the calculation efficiency of the minimum cut set is improved.

A point cloud encoding method, decoding method, encoding device, and decoding device are disclosed. The point cloud encoding method includes calculating a distance from a first point to a parent point of the first point as a first distance; determining a point in un-encoded points closest to the first point as a second point, and calculating residual values on N coordinate components from the second point to the first point; encoding absolute values of the residual values on the N coordinate components from the second point to the first point; encoding signs of the residual values of the second point based on the residual values of the second point and the first distance. The present disclosure utilizes the geometry position relationship between points in a point cloud to encode the absolute values of residual values and the feasible signs of residual values, thereby improving the encoding efficiency of residual values. At the same time, by optimizing the encoding efficiency of residual values, the performance of point cloud prediction tree encoding is improved.

The application provides a data transmission method, device, user equipment, base station and system. The method is applied to user equipment in the coverage of a base station. There are multiple groups of user equipment in the coverage of the base station. The method comprises obtaining uplink resource information corresponding to a group to which the user equipment belongs. The uplink resource information comprises the frequency of L subcarriers used for uplink communication and the position information of n OFDM symbols corresponding to the group on a single subcarrier. The tree code encoder is used to encode the to-be-sent data and the check data, so as to obtain L groups of encoded data. Each group of encoded data in the L groups of encoded data is compressed into a code word with a length of n by using a compression matrix. The L groups of code words are subjected to OFDM modulation. After modulation, each group of code words with the length of n occupies one subcarrier in the L subcarriers, and each code word corresponds to one of the n OFDM symbols on the occupied subcarrier. The modulated data is sent. The uplink communication without random access can be realized.

The invention discloses a general manuscript checking method and device based on a central knowledge base. The general manuscript checking method comprises the following steps: constructing the central knowledge base; obtaining a to-be-examined manuscript, and constructing an abstract syntax tree of the to-be-examined manuscript; extracting key information in the abstract syntax tree through a dynamic syntax tree encoder to obtain key words; determining target knowledge base data of which the key words have core contradictions with a central knowledge base; determining a corresponding target similarity score based on the target knowledge base data and a central knowledge base; if the target similarity score is smaller than a preset threshold value, performing general review on the to-be-reviewed manuscript to obtain a first review result, a second review result and a third review result; and obtaining a target review result based on the first review result, the second review result and the third review result. The method does not need to depend on manpower, and can adapt to diversified context scenes, so that the checking accuracy is improved, and the application range is wide.

The invention discloses a zero-sample Chinese character recognition method based on radical structure tree graph level representation, and belongs to the technical field of Chinese character recognition. The method comprises the following steps: establishing a Chinese character database and constructing a directed radical structure tree of each Chinese character from top to bottom; adding direction position codes to nodes in the tree to distinguish the spatial relationship of brother nodes; inputting the radical structure tree with the direction and position codes into a GraphSAGE-based tree encoder to obtain normalized vector representation of the Chinese characters; extracting vector representation of the sample image by using ResNet; aligning the Chinese character vector and the image vector through comparative learning; and finally, zero sample recognition is realized by calculating the similarity between the to-be-recognized image and all candidate Chinese character vectors, and the highest similarity is selected as a recognition result. According to the method, the structure perception capability is improved through the directed tree, fine-grained spatial differences are perceived through directional coding, and the recognition capability of the model on unseen Chinese characters is effectively improved.

The invention belongs to the technical field of image processing, and provides a brain tumor image segmentation method based on lightweight multi-modality, and the main scheme is as follows: obtaining a multi-modality brain nuclear magnetic resonance image data set, and constructing a multi-modality data fusion feature extraction network based on a binary tree structure; the feature extraction network comprises tree-shaped coding layers with the same number as the modals; constructing a local feature extractor for the brain nuclear magnetic resonance image of each modal, and performing feature extraction; constructing a multi-modal fusion unit for feature extraction results of two adjacent modals, and performing inter-modal feature fusion; generating jump connection parameters for each tree coding layer, performing up-sampling on outputs of all the tree coding layers, and then performing connection fusion operation by using the jump connection parameters; constructing a brain tumor image segmentation network, and obtaining a segmentation result of the brain tumor image; and training, verifying and testing the two networks by using a multi-modal brain nuclear magnetic resonance image data set.

The invention provides a privacy protection decision tree coding method based on CKKS homomorphic encryption, and the method comprises the steps: reconstructing a mapping mechanism between integer input and a ciphertext polynomial, building an accurate coefficient mapping model on the premise of maintaining the integrity of a CKKS homomorphic operation framework, and effectively solving the problem of function adaptation of a privacy protection decision tree XCMPPDTE; by fully utilizing the plaintext space of CKKS complex field coding, the real / virtual part separation mode of the traditional complex field coding is improved into integer field total space coding, so that the number of plaintext slots corresponds to the polynomial dimension n by 1: 1, and the data coding capacity is improved by 100% compared with the original scheme.

The application discloses a point cloud encoding method, a point cloud decoding method and a terminal, and belongs to the technical field of point cloud processing. The point cloud encoding method comprises the following steps: obtaining a first identification parameter of a first target point cloud to be encoded; and performing an encoding operation on the first target point cloud based on the first identification parameter. The encoding operation comprises at least one of the following: in the case that the first identification parameter is used to represent parallel encoding, performing geometric encoding and attribute prediction encoding on the first target point cloud in parallel to obtain an encoding result of the first target point cloud; and performing geometric prediction encoding on at least part of the to-be-encoded points of the first target point cloud. In the embodiment of the application, the geometric encoding and the attribute prediction encoding are performed on the first target point cloud in parallel based on the first identification parameter of the first target point cloud. By performing the geometric prediction encoding on at least part of the to-be-encoded points of the first target point cloud, the multi-tree encoding on all the to-be-encoded points of the first target point cloud is avoided.

The invention discloses a multi-input gating structure model (TC-MIGSM for short) based on tree coding, which is used for the risk tracing of biological risk factors of codling moths. According to the method, risk source dealers are traced through a multi-input gating structure (MIGSM for short, and the problem of data structural deficiency caused by equipment limitation in a codling moth circulation chain is solved. The traditional interpolation method cannot process the deficiency, so that the invention provides a tree coding algorithm to extract missing data features and effectively cope with the structural deficiency. Besides, internal and mutual characteristics of each dealer in the same circulation link are extracted through an auto encoder model (AE for short) and are input into the MIGSM, so that accurate risk traceability is realized. An experiment proves that TC-MIGSM is superior to other models in accuracy by tracing 208 batches of apples transported from Gansu to Beijing.

The invention discloses a linear multi-threshold classification method based on a Huffman tree, and the method comprises the steps: obtaining a correlation coefficient matrix according to multi-class data in a collected historical data set, and obtaining the average intensity of each class of data in a multi-feature dimension, so as to obtain the probability of each data class; according to the probability, coding is carried out through a Huffman tree, the coding length is inversely proportional to the probability, and classification containers are divided according to the coding length; and according to the divergence matrix of the data in the classification containers, in combination with the divergence matrix between the classification containers, obtaining a feature vector so as to obtain a regression mean value of the classification containers, and determining a classification threshold between the classification containers so as to perform classification by using the data collected in real time. According to the method, classification containers are dynamically divided based on Huffman tree coding, a multi-classification task is optimized into O (logK) subtasks, and in combination with divergence matrix driven feature vector optimization and dynamic threshold selection, the model complexity is reduced, and meanwhile, the recognition precision in a class imbalance scene is improved.

The invention provides a distributed drainage basin model multi-flux parallel computing method. The method comprises the following steps: S1, performing multi-way tree coding on mutually independent computing units according to parallel features of a distributed drainage basin model; s2, creating a parallel computing system according to the total process number, wherein the parallel computing system comprises a master control process which is globally and uniquely responsible for task allocation regulation and control, a plurality of computing processes which are responsible for physical processes and a plurality of transfer processes which are responsible for data transmission among computing units; s3, the main control process sequentially distributes the calculation tasks of all the units to the calculation process according to the multi-way tree codes; s4, the calculation process receives the task and receives upstream data from the transfer process, and calculation is started after unpacking; s5, after the calculation process task is completed, a result is packaged and transmitted to the transfer process; and S6, repeating the steps S3-S5 until all the calculation units are calculated. According to the method, the multi-flux parallel computing method for any distributed drainage basin model discrete in a tree structure is realized.

The present invention discloses an adaptive Huffman coding system and method. The adaptive Huffman coding system includes: a data acquisition module for acquiring data to be encoded and a Huffman binary tree encoding module. The data acquisition module is used to acquire data to be encoded; the Huffman binary tree encoding module is used to construct a Huffman binary tree based on the data to be encoded acquired by the data acquisition module. Each node of the Huffman binary tree is indexed with memory. When constructing the Huffman binary tree, the memory index value of the target node is calculated based on the unique path from the root node to the target node. The adaptive Huffman coding system and method proposed by the present invention can efficiently access the encoding data structure on hardware and optimize the encoding storage space, while providing the flexibility of configurable memory.

The embodiment of the invention provides an emotion tuple extraction method and system based on syntax and semantic fusion, and relates to the technical field of natural language processing. According to the method, after an input text is obtained, coding embedded data of the input text can be calculated by using a structure perception syntactic encoder, and then syntactic representation and semantic representation are deeply interacted through bidirectional cross attention fusion to obtain fusion representation. And calculating a unified representation of the fusion representation based on a dual similarity contrast learning framework, and retrieving candidate examples according to the unified representation. According to the method, coding embedded data can be generated based on a structure perception graph converter of dependency tree coding, explicit syntactic guidance is provided for a structured task of a large language model through syntactic and semantic fusion achieved through a bidirectional cross attention mechanism and double-similarity comparative learning, syntactic and semantic information can be integrated at the same time, and the method has the advantages of being high in practicability and easy to popularize. The complete emotion tuple is correctly recognized, and the output precision of the extraction result of the emotion tuple is improved.

The application discloses an optimization method for long video generation based on tree coding, comprising: constructing a video frame sequence and obtaining frame features; clustering frame features and obtaining correlation scores between key frames and video queries; calculating the number of high correlation clusters and outputting corresponding frame feature sets and correlation scores; taking the key frames of all high correlation clusters as the first layer nodes of a video feature tree, clustering the high correlation clusters into w sub-clusters respectively, constructing a second layer based on the key frames of the sub-clusters, and taking the correlation score of the second layer nodes as the correlation score of the first layer nodes minus one; constructing each layer according to the above method until the correlation score of the last layer node is one; a large language model optimizes inference results based on the output of the speculation sampling principle and the draft model; and inputting the optimized inference results and the video frame sequence into a preset video generation model to obtain an answer video. The application improves inference accuracy and efficiency through adaptive query and hierarchical long video understanding.

The invention discloses a platform data trusted sharing method based on a block chain and zero knowledge proof, and relates to the technical field of data security sharing, the method comprises the following steps: when data enters a sharing platform, executing structure analysis processing on data records to form a field set, extracting field structure parameters, and constructing a structure tree node sequence; calculating a field structure difference quantity Sdif, a structure hierarchical expansion quantity Sh and a structure connection stability quantity Sc, generating a structure consistency index Cstr, constructing a structure tree coding sequence, generating a structure commitment value through Hash operation, and writing the structure commitment value into the block chain for data registration; when a data sharing request occurs, structure analysis processing is executed again on to-be-shared data, a structure consistency index and a structure commitment value are calculated again, consistency judgment is carried out through a structure consistency difference quantity and a structure code difference quantity, comprehensive structure consistency judgment is executed according to a judgment result, and data sharing is allowed when structures are consistent. The sharing request is terminated upon a change in structure.

The application discloses a point cloud encoding method, a decoding method, a point cloud encoding device and a decoding device. The point cloud encoding method comprises the following steps: calculating the distance from a first vertex to the parent node of the first vertex as a first distance; determining the point closest to the first vertex among the uncoded vertices as a second vertex, and calculating the residual value of the second vertex on N coordinate components of the first vertex; encoding the absolute value of the residual value of the second vertex on N coordinate components of the first vertex; and encoding the sign of the residual value of the second vertex according to the residual value of the second vertex and the first distance. The application encodes the feasible sign bit of the absolute value of the residual value and the residual value by using the geometric position relationship between the vertices in the point cloud, improves the encoding efficiency of the residual value, and improves the performance of the point cloud prediction tree encoding by optimizing the efficiency of the residual value encoding.

The application belongs to the technical field of image processing, and proposes a brain tumor image segmentation method based on a lightweight multi-modal, the main scheme is: acquiring a multi-modal brain magnetic resonance image dataset, and constructing a multi-modal data fusion feature extraction network based on a binary tree structure, the feature extraction network includes a tree coding layer consistent with the number of modes; constructing a local feature extractor for each mode of brain magnetic resonance image, and performing feature extraction; constructing a multi-modal fusion unit for the feature extraction results of two adjacent modes, and performing inter-modal feature fusion; generating a skip connection parameter for each tree coding layer, and performing up-sampling on the output of all tree coding layers before utilizing the skip connection parameter for splicing and fusion operation; constructing a brain tumor image segmentation network, and obtaining the segmentation result of the brain tumor image; training, verifying and testing the two networks by using the multi-modal brain magnetic resonance image dataset.

This invention discloses an automatic annotation generation method based on multimodal code representation. It employs multiple encoders to learn the source code representation, including a graph encoder to holistically represent the API context graph, a tree encoder to represent the abstract syntax tree, and a field encoder to represent the token sequence, thereby learning a holistic and comprehensive code representation. Unlike existing annotation generation techniques based on encoders and decoders, this invention models the code from a holistic perspective, focusing on API usage and control flow information. Furthermore, it designs a feature fusion method to combine multiple intermediate encoding results, which are then fed into a joint decoder to generate annotations word by word. This invention can effectively generate high-quality annotations for specified code snippets, thereby promoting software maintenance and significantly improving software productivity.

Post training compression of 3DGS data is agnostic to training in a traditional signal compression perspective. Gaussian parameters are treated as signals. Pre-processing and transform coding techniques are used to compress the signals effectively. Firstly, lossless / lossy compression is performed on 3DGS geometry (positions, scales, rotations) using a point cloud coding-based (e.g., G-PCC, GeS) framework. Positions are compressed using occupancy tree coding. Scales and rotations are encoded as attributes using transform coding. The widely used block-based graph Fourier transform (GFT) is used to compress the attributes (base colors, spherical harmonic coefficients and opacities). In addition, a graph construction strategy is used for 3DGS data that computes the edge weights based on similarity (or dissimilarity) between the 3D Gaussian distributions using KL-divergence. Alternatively, positions can be encoded using occupancy tree (e.g., G-PCC, GeS) or AI-based PCC methods, and any subset of Gaussian parameters or the transformed coefficients of Gaussian parameters can be mapped into 2D frames and encoded by video coders.

To provide a method for decoding an encoded tree unit of an image frame from a video bit stream into an encoded unit of the encoded tree.SOLUTION: In a method for decoding an encoded unit from a bit stream, the encoded unit is divided from an encoded tree unit of an image by the use of a tree structure, and the encoded unit can have a luma component and a chroma component, which includes a Cb component and a Cr component. The method includes: a first decoding step of decoding a luma conversion skip flag for the luma component from the bit stream when the encoded unit has the luma component; a second decoding step of decoding a first chroma conversion skip flag for the Cb component and a second chroma conversion skip flag for the Cr component from the bit stream when the encoded unit has the chroma component; and a determination step of determining an LFNST index for LFNST processing.SELECTED DRAWING: Figure 16

The application belongs to the technical field of video coding, and particularly relates to a sample adaptive offset mode decision method and device, a computer readable storage medium and a terminal device. In the method, an optimal non-parametric fusion offset mode can be selected from various non-parametric fusion offset modes according to the division depth of a tree coding unit, and then an optimal offset mode can be selected from the optimal non-parametric fusion offset mode and a parametric fusion offset mode. Through the method, the optimal non-parametric fusion offset mode can be selected according to the division depth of the tree coding unit, the decision time in the non-parametric fusion offset mode is saved, the efficiency of the sample adaptive offset mode decision method is improved, and the method has strong usability and practicality.

A mechanism for processing video data implemented by a video coding apparatus is disclosed. The mechanism determines usage of one or more of a separate tree coding and a partitioning beyond a minimum size threshold for a video unit. The mechanism also disables a cross-component coding tool for the video unit based on the determination. A conversion between a visual media data and a bitstream is performed in accordance with the coding tool being disabled.