39 results about "Scalar Value" patented technology

An improved system implements Fitness Gradient Consensus including hash distance and bucket consensus variations within a digital blockchain by calculating the highest fitness value competing blocks to resolve conflicts and allocate the rewards associated with building new blocks. The consensus system applies conflict resolution formulas to incentivize block-building nodes to share blocks generated, as it completes construction, to improve chances of a reward, resulting in enhanced speed and security of blockchain.The hash distance consensus utilizes a hash distance scalar value as part of its fitness metric, and the bucket consensus assigns tokens to buckets and calculates an aggregate value of the assigned tokens. A trust-but-verify variant increases transactional throughput and reduces linearity and computational constraints.The system also utilizes novel record types, such as token genesis, transfer, transaction, trade order, settlement, proposition, determination, and pattern linkage records to facilitate the automation of financial, commercial and legal processes.

An improved system implements Fitness Gradient Consensus including hash distance and bucket consensus variations within a digital blockchain by calculating the highest fitness value competing blocks to resolve conflicts and allocate the rewards associated with building new blocks. The consensus system applies conflict resolution formulas to incentivize block-building nodes to share blocks generated, as it completes construction, to improve chances of a reward, resulting in enhanced speed and security of blockchain.The hash distance consensus utilizes a hash distance scalar value as part of its fitness metric, and the bucket consensus assigns tokens to buckets and calculates an aggregate value of the assigned tokens. A trust-but-verify variant increases transactional throughput and reduces linearity and computational constraints.The system also utilizes novel record types, such as token genesis, transfer, transaction, trade order, settlement, proposition, determination, and pattern linkage records to facilitate the automation of financial, commercial and legal processes.

The invention discloses a GRU-based vehicle-mounted CAN intrusion detection method and system, a storage medium and a computer system. According to the method, an automatic encoder (AE) is introduced to deepen the understanding of a model on input sequence characteristics, a sliding window is used for selecting batch CAN data to be preprocessed to obtain 13-dimensional time sequence data, and a scalar value within the range of [0, 1] is obtained through processing of the encoder, a GRU, a decoder, a full connection layer and a sigmoid activation function and used for classification of abnormal data. The Conv1D is used as a hidden layer, and compared with two-dimensional convolution, the one-dimensional convolution parameter quantity is smaller, and the calculation is simpler and more convenient. An attack message and a normal message can be completely distinguished, the precision and the accuracy rate reach 100%, and the precision in Fuzz detection is 0.9983; compared with the prior art, the method has high accuracy and reliability in the aspect of intrusion behavior detection, can effectively identify most intrusion events, and can keep a relatively low overall error rate, so that good balance between safety and availability is realized.

The invention discloses an efficient simultaneous interpretation method based on an expert routing threshold, and relates to the field of voice processing, the method is based on a classical Transform architecture model, an expert routing strategy model based on the expert routing threshold is constructed, and multi-language streaming translation is realized, the model comprises a streaming voice encoder, and the streaming voice encoder adopts a hybrid design and is connected with the expert routing threshold. The block-by-block autoregression block is composed of an autoregression block and a non-autoregression block; the text decoder is used for simultaneously processing the complete offline voice and the randomly truncated voice prefix to generate a hidden state; the routing threshold module is realized by a feedforward network and projects the final hidden state into a scalar value to determine an expert weight; and the hybrid expert post-processing module shares a language model head with the text decoder, and predicts a target translation sequence in combination with prefix information and global information. According to the method, a mixed expert threshold scheme is adopted to learn the strategy, the self-learning ability of the neural network is fully played, good effects are achieved in streaming translation and streaming TTS, and the method can be used for generating more streaming sequences.

Embodiments compress image data. According to an embodiment, analog image data comprising an array of pixel exposure values representing an image is received and the analog image data is convolved with at least one programmable kernel to produce an array of scalar values. The array of scalar values are quantized to generate a quantized feature map. The quantized feature map is a compressed representation of the image relative to the analog image data received.

The invention discloses a quadratic programming task solving method and device based on a neural network, and the method comprises the steps: obtaining a quadratic programming task, determining a constraint type based on the quadratic programming task, and constructing a quadratic programming problem based on the constraint type; constructing a residual vector based on a quadratic programming problem, and constructing a scalar value gain based on the residual vector; constructing a gradient neural network model with fixed time convergence based on scalar value gain, a quadratic programming problem and an excitation function; node updating of the gradient neural network model is defined in a negative gradient flow form; and iteratively updating the gradient neural network model based on the scalar value gain until a preset condition is met, and outputting a target parameter of the quadratic programming task. According to the method, the quadratic programming problems are respectively constructed for quadratic programming tasks of different constraint types, so that the residual vectors are respectively constructed based on the constraint types, the gradient neural network model with fixed time convergence is constructed by means of the excitation function, and the solving efficiency of the quadratic programming tasks is improved.

A computer-implemented method suitable for evaluating a progressive spectacle lens and a corresponding computer are provided. Measurement data indicative of at least one of color or transmittance along a plurality of measurement points across at least one line of a spectacle lens is received. The method further includes calculating scalar values representing differences between measurement data of adjacent or overlapping sets of measurement points of the plurality of measurement points. The spectacle lenses are then evaluated based on the scalar values.

The invention provides a storage method and device for converting an XML (Extensible Markup Language) semi-structured file into a relation table. The method covers four insertion scenes, namely insertion in the front of nodes of the same level, insertion between the nodes of the same level and insertion in the absence of child nodes. Selecting front and back reference vectors according to a scene, and respectively calculating the sum of horizontal and vertical coordinates to obtain two scalar values; and after comparison, generating a new start or stop vector according to a result, and distributing the new start or stop vector to a new node. According to the method, XML documents are efficiently managed, storage occupation is reduced, query performance is improved, local vector updating during dynamic insertion is supported, global recoding is avoided, and performance loss is reduced.

The present invention discloses a GPU-accelerated adaptive sampling volume rendering method for non-uniform cuboid meshes. First, based on data bounding box rendering and GPU multi-threaded computing, a set of sampling rays for non-uniform cuboid meshes is generated. Subsequently, the intersection points of each ray with the mesh cell faces are calculated and the coordinate positions of the adaptive sampling points are determined. Then, the non-uniform cuboid mesh data is stored in the 3D texture memory of the GPU in a normalized manner, and texture hardware queries are performed using the weighted and corrected adaptive sampling point coordinates to obtain the trilinear interpolation result scalar values at the sampling points. Finally, the colors and transparencies corresponding to the sampling points are calculated according to the transfer function, and the Under operator in Alpha blending is used to achieve the correct color synthesis of all sampling points on the ray, and finally a visualization result image is formed. Experimental results show that the present invention can effectively improve the computational efficiency of Ray Casting volume rendering for non-uniform cuboid meshes and accelerate the visualization analysis and calculation process of non-uniform cuboid meshes.

A device multiplies a first public key by a first scalar value, generating an intermediate result. The first public key corresponds to a point on an elliptic curve of order n, n is an integer, and the first scalar value is equal to n / m where in is a largest prime integer factor of n. The device determines whether the intermediate result is equal to a value corresponding to a point O at infinity on the elliptic curve. In response to the determining indicating the intermediate result is different from the value corresponding to the point O, the device multiplies the intermediate result by a second scalar, generating a shared secret value. The device performs one or more processing operations using the generated shared secret value. Otherwise, the device may initiate error processing without generating the shared secret value.

A method (1000) for producing an estimated head-related (HR) filter, formula (I), that consists of a set of S HR filter sections formula (II) for s=1 to S. The method includes obtaining (s1002) an alpha matrix (e.g., an N×K matrix), wherein the alpha matrix consists of S sections, where each one of the sections of the alpha matrix corresponds to a different one of the S HR filter sections (e.g., the first section of the alpha matrix corresponds to formula (III), the second section of the alpha matrix corresponds to formula (IV), etc.), each section of the alpha matrix consists of N sub-vectors (N>1, e.g., N=8), and each sub-vector comprises a number of scalar values. The method further includes separately computing (s1004) each one of the S HR filter sections, wherein, for at least a certain one of the S HR filter sections, formula (II), the step of computing formula (II) comprises using not more than a predetermined number, qs, of the sub-vectors within the section of the alpha matrix corresponding to formula (II) to compute formula (II), where qs is less than N.

Techniques for mitigating readout error for quantum expectation are presented. Calibration component applies first random Pauli gates to qubits at first output of first circuit prior to first readout measurements of the qubits. Estimation component applies second random Pauli gates to qubits at second output of second circuit prior to second readout measurements of the qubits, and generates an error-mitigated readout determination based on first random Pauli gates applied to qubits at first circuit output and second random Pauli gates applied to qubits at second circuit output. Calibration component determines calibration data based on first readout measurements. Estimation component determines estimation data based on second readout measurements. Estimation component determines normalization scalar value based on the calibration data, determines estimation scalar value based on the estimation data, and determines the error-mitigated readout determination associated with a circuit of interest based on the normalization scalar value and estimation scalar value.

An information processing apparatus includes: a latent representation calculation unit that calculates a latent representation representing a feature amount regarding a prediction target event from processing target data including the feature amount; monotonic neural networks that are modeled to output a scalar value in accordance with a monotonically increasing function defined by the latent representation calculated by the latent representation calculation unit and a clock time; and a function estimation unit that estimates at least one of a hazard function and a survival function on the basis of the scalar value output from the monotonic neural networks.

The invention relates to the technical field of navigation testing, and discloses an optimization method and system for adjusting an anti-interference gain of a navigation receiver, so as to avoid invalid frequent switching. The method comprises the following steps: calibrating an interference intensity threshold X and a weight matrix B of the switching gain of the navigation receiver, the weight matrix B being an M-order square matrix, and M being an integer greater than or equal to 3; segmenting the obtained group of M * M * N continuous sampling values of the external interference intensity according to a time sequence, and obtaining the median of each segment based on bubble sorting, N being the number of an odd number of sampling points greater than or equal to 5 in each segment; combining results obtained by subtracting the median of each segment from X into an M-order square matrix A according to a time sequence in an element arrangement mode from left to right and from top to bottom; performing non-filling convolution operation on the square matrix A and the square matrix B to obtain a scalar value C; judging whether the scalar value C is greater than a trigger threshold value or not, and if so, triggering a gain switching operation; otherwise, the gain switching operation is not triggered.

The application provides an image processing method and a processing method and device of an image processing model, relates to the field of artificial intelligence, and particularly relates to the field of computer vision. The image processing method comprises the following steps: generating a scalar map based on the difference between the distribution of reconstruction errors and the distribution of noise between a first image and a second image, the scalar map comprising one or more regions, and the scalar value corresponding to each region in the one or more regions being used for indicating the quality of one or more image blocks in the second image. The application can intuitively reflect the quality of the image, and is beneficial to guaranteeing the effect of image processing.

The invention discloses a vector operation mapping optimization system, method, equipment and program product, and the system comprises a mapping configuration module which is used for determining the multiplication of a plurality of groups of vectors and scalar quantities corresponding to a vector operation batch processing instruction when the vector operation batch processing instruction is received, and obtaining the length of the vector in the multiplication; when the length of the vector is smaller than a preset vector length threshold value, determining a mapping numerical value of each processing unit based on a preset parallel mapping strategy, generating parallel mapping configuration information and sending the parallel mapping configuration information to the reconfigurable computing array module; and the reconfigurable computing array module is used for mapping a plurality of vector values and scalar values in the at least two groups of multiplication operations to the processing unit array based on the parallel mapping configuration information so as to execute the at least two groups of multiplication operations in parallel based on the mapped processing unit array. According to the method, the parallel optimization of the vector and scalar multiplication operation can be realized based on the coarse-grained reconfigurable computing architecture, the operation execution efficiency can be improved, and the group delay of the operation execution can be optimized.

A computer-implemented system for graph-based analysis of institutional succession coverage, the system comprising the following: a storage unit that stores structured data records, including • a role matrix consisting of role identifier values, organizational unit identifiers, hierarchy level parameters, strategic impact parameters, operational impact parameters, replacement difficulty parameters, disruption risk parameters, and role dependency identifiers; • a person attribute matrix comprising person identifiers linked to role identifiers and generative attribute parameters, including parameters for contribution to the legacy system, parameters for mentoring contribution, parameters for innovation contribution, parameters for contribution to the community, and parameters for institutional alignment; • an interaction event matrix comprising relational interaction data records, including originating person identifier, targeting person identifier, interaction type identifier, interaction frequency parameters, interaction intensity parameters, interaction start timestamp, interaction end timestamp, and knowledge criticality parameters; and • a risk parameter matrix that includes person role risk parameters including exit probability parameters, proximity to retirement parameters, contractual risk parameters and external market risk parameters; a matrix preprocessing unit connected to the storage unit and configured to convert heterogeneous parameter scales stored in the person attribute matrix into normalized scalar values ​​within a predefined reference interval; a vector generation unit coupled to the matrix preprocessing unit and configured to create a multidimensional generative vector for each person identifier by arranging the normalized scalar values ​​of the generative attribute parameters into an ordered numeric vector; a graph construction unit coupled to the interaction event matrix and configured to generate an institutional interaction graph represented by an adjacency structure in which the nodes correspond to person identifiers and the edges to relational interaction datasets weighted by the interaction frequency parameter, the interaction intensity parameter, and the knowledge criticality parameter; a unit coupled to and configured with the role matrix for calculating role criticality, which calculates a role criticality index for each role identifier using a weighted aggregation of the strategic impact parameter, the operational impact parameter, the difficulty of replacement parameter, and the disruption risk parameter stored in the role matrix; a network metric calculation unit coupled with the graph construction unit and the vector generation unit, configured to calculate node relevance indicators, including graph degree values, mediation values, and proximity values ​​derived from the adjacency structure, and subsequently combines the node relevance indicators with the multidimensional generative vectors to generate generative centrality values ​​associated with the nodes; and A successor selection control unit coupled with the role criticality calculation unit, the network metric calculation unit, and the risk parameter matrix is ​​configured to identify successor candidate sets for roles with role criticality index values ​​exceeding a threshold by applying coverage constraints defined in a coverage parameter matrix, including minimum successor number parameters and readiness level constraints.

A method for producing an estimated head-related (HR) filter, ĥ′, that consists of a set of S HR filter sections ĥ′s for s=1 to S. The method includes obtaining an alpha matrix (e.g., an N×K matrix), wherein the alpha matrix consists of S sections, where each one of the sections of the alpha matrix corresponds to a different one of the S HR filter sections (e.g., the first section of the alpha matrix corresponds to ĥ′1, the second section of the alpha matrix corresponds to ĥ′2, etc.), each section of the alpha matrix consists of N sub-vectors (N>1, e.g., N=8), and each sub-vector comprises a number of scalar values. The method further includes separately computing each one of the S HR filter sections, wherein, for at least a certain one of the S HR filter sections, ĥ′s, the step of computing ĥ′s comprises using not more than a predetermined number, qs, of the sub-vectors within the section of the alpha matrix corresponding to ĥ′s to compute ĥ′s, where qs is less than N.

Approaches presented herein provide for the use of implicit functions to simulate differentiable object elasticity. An implicit continuous function, such as a signed distance function (SDF), can be used to approximate the surface of an object by providing scalar values from a set of vertices of a regular grid in which the object representation is to be generated. Interpolation can be applied to determine an approximate surface location and shape within each boundary cell. A trained neural network, such as a multilayer perceptron (MLP), can be used to determine appropriate quadrature points that fall within the volume of the object. A finite element analysis can integrate over these quadrature points, using both continuous and discrete settings, as a basis for performing efficient differentiable elasticity simulations including the deformable object.

The present application discloses a method, apparatus, device and medium for designing a transfer function based on vision and frequency, which relates to the field of visualization technology, and includes: projecting each scalar value sub-interval of the volume data in the form of scatter points onto a first preset two-dimensional space to obtain a frequency feature image; traversing the midpoints of each scalar value sub-interval in the frequency feature image to extract the contour image of the isosurface image of the corresponding isovalue point; calculating the similarity index between any two adjacent contour images and projecting it onto a second preset two-dimensional space in the form of scatter points to obtain a visual feature image; performing point distribution processing on a one-dimensional transfer function based on the frequency feature image, the visual feature image, and key isovalues that meet the preset isovalue conditions to obtain a target transfer function. In this way, the present application separately processes the frequency feature, visual feature and key isovalue of the scalar value sub-interval of the volume data, which can enhance the feature recognition and improve the design efficiency and quality of the transfer function.

The invention provides a multi-scalar multiplication calculation method. Point sequences and scalar sequences involved in multi-scalar multiplication are subjected to one-to-one para-position multiplication. The method is applied to a target GPU, a plurality of thread blocks are deployed in the target GPU, and each thread block is responsible for one sub-sequence in a plurality of sub-sequences divided by a point sequence. In the execution process of the method, any first thread block determines a sub-bucket corresponding to each point in the allocated first sub-sequence, and for each sub-bucket, the sum of the number of points falling into the sub-bucket in the first sub-sequence is determined. All the thread blocks are based on a distributed shared memory mechanism, different counters are aggregated for the sub-buckets corresponding to the same scalar value and from different thread blocks, and the global offset of all the sub-buckets of all the thread blocks in the target global memory is obtained. And according to the global offset, the points are stored in a global memory, and calculation operation is executed based on the bucket dividing result, so that a calculation result of multi-scalar multiplication is obtained. Global atomic operation is not needed, and acceleration is achieved.

This application provides an intelligent answering method, apparatus, device, and medium based on interactive follow-up questioning, applicable to the financial or healthcare fields. The method includes: receiving a question to be answered and generating multiple candidate answers; calculating the overall uncertainty scalar value of all candidate answers based on a preset uncertainty identification model, and determining whether the uncertainty scalar value is lower than a preset threshold; if not lower, determining the uncertainty type based on the preset uncertainty identification model; generating a target follow-up question based on the question to be answered, multiple candidate answers, and uncertainty type based on a preset follow-up question generation model, and sending it to a client; receiving the target answer returned by the client and updating the question to be answered until the overall uncertainty scalar value of the regenerated candidate answers is lower than the preset threshold, then selecting the optimal answer from among the multiple regenerated candidate answers according to a preset answer selection rule and sending it to the client. This addresses the problem of model overconfidence.

Provided is a method for securing against side channel attacks. An elliptic curve cryptographic process comprises a multiple points multiplication operation using predetermined scalar values, Pi being points of an elliptic curve over a finite field defined by parameters (F, E, G, N) together with the point addition law where F is a field over which is defined the curve, E is an equation of the curve, G is a base point in E over F and N is the order of the base point G. The method comprises generating (S1) a masking value iRand, multiplicatively masking (S2) each predetermined scalar value di with said generated masking value iRand to obtain masked scalars di′, computing (S3) a masked multiple points multiplication operation result, and obtaining (S4) said multiple points multiplication operation result R by unmasking said masked multiple points multiplication operation result R′.

An information processing device according to one embodiment comprises: an acquisition unit that acquires observation data which is composed of the number of data points and input value data at each of the data points; an inverse M-matrix processing unit that, on the basis of the observation data, selects, for a matrix which is calculated from the data points and the input value data at any data point, a non-negative scalar value and a positive definite kernel function in which all off-diagonal elements of an inverse matrix of the matrix becomes negative or zero; an estimation model learning unit that learns a parameter of an estimation model by solving a minimization problem using the observation data, the positive definite kernel function, and the non-negative scalar value; and an output control unit that outputs the positive definite kernel function, the non-negative scalar value, the learned parameter, and the input value data.

Encoding methods and apparatus for packing single-channel data into a multi-channel container (e.g., MP4, TIFF or JPEG container) to at least achieve good utilization of container data capacity. In some examples, a method of encoding includes converting a plurality of scalar values of a received data stream to a corresponding plurality of n-dimensional values, the converting being performed using a mapper; assigning each of the n-dimensional values as a pixel value to a respective pixel of the virtual image frame, where n is an integer greater than one; and compressing the virtual image frame according to the type of the image data container. The mapper is configured to map scalar values to corresponding n-dimensional values based on a relationship represented by an n-dimensional curve or by a plurality of 2n-way tree partitions of an n-dimensional space.

The invention discloses a multi-dimensional customer behavior analysis and commodity recommendation system, relates to the technical field of commodity pushing, solves the problem that customer multi-dimensional feature analysis is not comprehensive, performs point position calibration on behavior data on the basis of a multi-dimensional space coordinate system, and dynamically adjusts a sorting rule and a dimension weight so as to improve the commodity recommendation efficiency. Quantization and visualization of behavior characteristics are achieved, the behavior gravity center of a user under a specific category is accurately locked, a more refined input signal is provided for recommendation, complex behavior characteristics are converted into computable scalar values through spatial vector decomposition and gradual dimensionality reduction (for example, reduction from a four-dimensional space to a two-dimensional space), and the user experience is improved. According to the method, model efficiency reduction caused by dimension disasters is avoided, a high-dimensional behavior mode can be compressed into a single comprehensive feature value, the subsequent clustering efficiency is improved, a high-frequency behavior mode is locked through clustering analysis, and the matching degree of pushed commodities and potential demands of users is improved to 85% or above from 60% of a traditional scheme.

A computer-implemented method suitable for gradient spectacle lens evaluation and a corresponding computer are provided. Measurement data indicating at least one of a color or a transmission of a plurality of measurement points along at least one line across the spectacle lens is received. The method further includes calculating scalar values representing a difference of the measurement data of adjacent or overlapping measurement point groups of the plurality of measurement points. The spectacle lens is then evaluated based on the scalar values.

The invention relates to a method for analyzing image information (134, 234) using specified scalar values ​​(119, 129, 219, 229), comprising the method steps of: a.) detecting a first image (134) of an object (132, 232) or a scene (130) and detecting at least one first scalar sensor value (129) associated with the object (132) or the scene (130), b.) detecting a second image (234) of the object (132, 232) or the scene (130) and detecting at least one first scalar sensor value (129) associated with the object (132, 232) or the scene (130), c.) introducing the first image (134) and the at least one first scalar sensor value (129) as a consistent representation into a first data structure (159), and introducing the second image (234) and the at least one second scalar sensor value (229) as a consistent representation into a second data structure (259), d.) comparing the first data structure (159) and the second data structure (259), and e.) outputting information if the comparison results in a difference that corresponds to a predefined or predefined standard.

The application discloses a prefix sum and calculation device, method, chip, board card and electronic equipment, and relates to the technical field of prefix sum and calculation, in particular to a prefix sum and calculation device, method, chip, board card and electronic equipment.The device comprises a data segmentation unit, which is used for grouping vector data to be processed according to a preset segmentation granularity, splicing each vector obtained by grouping in sequence to form a matrix to be processed; a matrix operation unit, which is used for batch calculating prefix sums of each row data in the matrix to be processed to obtain an output matrix; a vector operation unit, which is used for performing addition operation on each row data in the output matrix and a corresponding scalar value to obtain a result vector corresponding to each row data, wherein the corresponding scalar value is equal to the accumulated value of the last data of each row before the row; and a data splicing unit, which is used for splicing each result vector in sequence to obtain a prefix sum vector corresponding to the vector data to be processed.The application can fully utilize the matrix operation resources and vector operation resources inside a chip, improve the overall processing efficiency of prefix sum calculation while ensuring the calculation accuracy.