67 results about "Tensor representation" patented technology

The invention discloses a quick and precise high-throughput drug screening system based on deep learning. The drug screening system comprises a picture preprocessing module and a neural network module, wherein the picture preprocessing module comprises a channel merging module and a picture standardization module; the channel merging module merges different cell single-color channel pictures intothe multi-channel picture representation, wherein the merged picture tensors are represented as [H,W,C]; the picture standardization module standardizes the input multi-channel picture data into the tensor representation of [70,70,C]; the neural network module is connected with the picture standardization module, the input data of the neural network module is standardized picture tensors, and finally the trained neural network is subjected to final prediction classification prediction. The drug screening system DeepScreen based on deep learning has the advantages of being high in throughput, precise, efficient, fast, convenient, low in cost and resistant to interference, and has a worth-concerning practical and application prospect.

A set of input images are acquired sequentially as image tensors. A low-tubal rank tensor and a sparse tensor are initialized using the image tensor, wherein the low-tubal rank tensor is a tensor product of a low-rank spanning tensor basis and corresponding tensor coefficients, and for each image, updating iteratively the image tensor, the tensor coefficients, and the sparse tensor using the image tensor and the low-rank spanning basis from a previous iteration. The spanning tensor basis is updated using the tensor coefficients, the sparse tensor, and the low rank tubal tensor, wherein the low rank tubal tensor represents a set of output images and the sparse tensor representing a set of sparse images.

A method including processing social network data to establish a tensor model of the social network data, the tensor model having at least an order of four. The tensor model is decomposed into a plurality of principal factors. A summary tensor is synthesized from a subset of the plurality of principal factors. The summary tensor represents a plurality of relationships among a plurality of entities in the tensor model. A synthesis of relationships is formed and stored. At least one parameter is identified using one of the summary tensor and a single principal factor in the subset. The at least one parameter is selected from the group consisting of: a correlation among the plurality of entities, a similarity between two of the plurality of entities, and a time-based trend of changes in the synthesis of relationships. The at least one parameter is communicated.

In an exemplary embodiment, some of the main aspects of the present invention are the following: (i) Data model: We introduce tensor streams to deal with large collections of multi-aspect streams; and (ii) Algorithmic framework: We propose window-based tensor analysis (WTA) to effectively extract core patterns from tensor streams. The tensor representation is related to data cube in On-Line Analytical Processing (OLAP). However, our present invention focuses on constructing simple summaries for each window, rather than merely organizing the data to produce simple aggregates along each aspect or combination of aspects.

The invention discloses a hyperspectral image target detection method based on tensor matched subspace, which relates to a hyperspectral image target detection method and aims at solving problems that the existing hyperspectral image target detection precision is low and the space information utilization rate is low. The method comprises specific steps: 1, signal representation models for a target and a background under tensor representation are built; 2, four-order tensor matrixes for the target and the background are built respectively; 3, an orthogonal projection matrix in three background directions and three target directions of space X, space Y and a spectrum in the four-order tensor matrixes for the target and the background is solved; 4, a to-be-detected signal is mapped to target sample projection subspace and background sample projection subspace obtained in the third step; and 5, whether the to-be-detected signal is a detection target is judged, if a generalized likelihood ratio detection model value under the tensor representation is larger than or equal to eta, the to-be-detected signal is the detection target, or otherwise, the to-be-detected signal is the background target, wherein eta is a threshold. The method of the invention is used in the image detection field.

A tensor processing technique includes: accessing a tensor, wherein the tensor: represents interconnections of nodes across one or more dimensions, comprises a plurality of matrices, and forms a plurality of vectors across at least one of the one or more dimensions; applying Fourier Transform on the tensor to obtain a plurality of harmonic matrices; performing singular value decompositions (SVDs) on the plurality of harmonic matrices to obtain a plurality of corresponding SVD results; reducing the plurality of corresponding SVD results, including selecting one or more dominant components in the plurality of corresponding SVD results to obtain one or more reduced results; and performing Inverse Fourier Transform on the one or more reduced results to obtain a de-noised tensor.