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Interactive spatio-temporal exploration of massive time-Varying rectilinear scalar volumes based on a variable bit-rate sparse representation over learned dictionaries

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COMPUTERS & GRAPHICS-UK 2020年 88卷 45-56页

作者： Diaz, Jose Marton, Fabio Gobbetti, Enrico UVic UCC Digital Care Res Grp Vic Spain Ctr Adv Studies Res & Dev Sardinia CRS4 Visual Comp Grp Cagliari Italy

We introduce a novel approach for supporting fully interactive non-linear spatio-temporal exploration of massive time-varying rectilinear scalar volumes on commodity platforms. To do this, we decompose each frame into an octree of overlapping bricks. Each brick is further subdivided into smaller non-overlapping blocks compactly approximated by quantized variable-length sparse linear combinations of prototype blocks stored in a learned data-dependent dictionary. An efficient tolerance-driven learning and approximation process, capable of computing the tolerance required to achieve a given frame size, exploits coresets and an incremental dictionary refinement strategy to cope with datasets made of thousands of multi-gigavoxel frames. The compressed representation of each frame is stored in a GPU-friendly format that supports direct adaptive streaming to the GPU with spatial and temporal random access, view-frustum and transfer-function culling, and transient and local decompression interleaved with ray-casting. Our variable-rate codec provides high-quality approximations at very low bit-rates, while offering real-time decoding performance. Thus, the bandwidth provided by current commodity PCs proves sufficient to fully stream and render a working set of one gigavoxel per frame without relying on partial updates, thus avoiding any unwanted dynamic effects introduced by current incremental loading approaches. The quality and performance of our approach is demonstrated on massive time-varying datasets at the terascale. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Direct volume rendering Time-varying data Compression sparse coding Learned dictionary

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The GABA system regulates the sparse coding of odors in the mushroom bodies of Drosophila

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BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS 2013年第1期436卷 35-40页

作者： Lei, Zhengchang Chen, Ke Li, Hao Liu, He Ge, Aike Chinese Acad Sci Inst Neurosci Shanghai 200031 Peoples R China Chinese Acad Sci State Key Lab Neurosci Shanghai Inst Biol Sci Shanghai 200031 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Biophys State Key Lab Brain & Cognit Sci Beijing 100101 Peoples R China

In the mushroom bodies (MBs) of Drosophila, an analogue of the mammalian olfactory cortex, olfactory stimuli are sparsely encoded by Kenyon cells (KCs) that exhibit a high level of odor selectivity. sparse coding of olfactory stimuli has significant advantages for maximizing the discrimination power and storage capacity of MBs. The inhibitory gamma-aminobutyric acid (GABA) system is important for regulating information processing in MBs, but its specific role in the sparse coding of odors is unclear. In this study, we investigated the role of the GABA system in the sparse coding of odors using an in vivo calcium imaging strategy, which allowed us to measure the activity of the KC population at single cell resolution while the components of the GABA system were genetically manipulated. We found that the down-regulation of GABA(A) but not GABA(B) receptors in KCs reduced the sparseness of odor representations in the MB, as shown by an increase in the population response probability and decrease in the odor selectivity of single KCs. Furthermore, the down-regulation of GABA synthesis in a pair of large GABAergic neurons innervating the entire MB reduced the sparseness of odor representations in KCs. In conclusion, the sparse coding of odors in MBs is regulated by a pair of GABAergic neurons through the GABA(A) receptors on KCs, thus demonstrating a specific role of the inhibitory GABA system on information processing in the MB. (c) 2013 Elsevier Inc. All rights reserved.

关键词： Olfactory system sparse coding GABA receptor Mushroom body APL neuron Calcium imaging

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Secure OMP Computation Maintaining sparse Representations and Its Application to EtC Systems

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2020年第9期E103D卷 1988-1997页

作者： Nakachi, Takayuki Kiya, Hitoshi NTT Corp NTT Network Innovat Labs Yokosuka Kanagawa 2390847 Japan Tokyo Metropolitan Univ Hino Tokyo 1910065 Japan

In this paper, we propose a secure computation of sparse coding and its application to Encryption-then-Compression (EtC) systems. The proposed scheme introduces secure sparse coding that allows computation of an Orthogonal Matching Pursuit (OMP) algorithm in an encrypted domain. We prove theoretically that the proposed method estimates exactly the same sparse representations that the OMP algorithm for non-encrypted computation does. This means that there is no degradation of the sparse representation performance. Furthermore, the proposed method can control the sparsity without decoding the encrypted signals. Next, we propose an EtC system based on the secure sparse coding. The proposed secure EtC system can protect the private information of the original image contents while performing image compression. It provides the same rate-distortion performance as that of sparse coding without encryption, as demonstrated on both synthetic data and natural images.

关键词： sparse coding orthogonal matching pursuit (OMP) random unitary transform secure computation encryption-then-compression (EtC)

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Bionic vision system and its application in license plate recognition

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NATURAL COMPUTING 2020年第1期19卷 199-209页

作者： Yao, Zhenjie Yi, Weidong Rhinotech LLC. Beijing Beijing Peoples R China Beijing Univ Technol Beijing Adv Innovat Ctr Future Internet Technol Beijing Beijing Peoples R China Univ Chinese Acad Sciences Sch Elect Elect Commun Engn Beijing Beijing Peoples R China

Conventional computer vision systems detect object after super-resolution (SR) or image reconstruction of the whole image, which is not an economical manner. By imitating the visual system of human beings, we proposed the bionic vision system (BVS), which is mainly composed by three parts: object detection by visual attention model, object-oriented SR reconstruction and object recognition by convolutional neural networks. The visual attention model contains both bottom-up and top-down cues. The bottom-up cues integrate low-level features by the feature integration theory. An Adaboost detector imitates the top-down cues. sparse coding and compressed sensing reconstruction realize the object-oriented SR reconstruction. The BVS was validated on license plate recognition task. Both detection performance and SR reconstruction performance are tested. Besides of these, we also test the final recognition rate, all the experimental results are quite encouraging.

关键词： Bionic vision system Visual attention Super-resolution sparse coding Convolutional neural networks

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Multiple Batches of Motion History Images (MB-MHIs) for Multi-view Human Action Recognition

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第8期45卷 6109-6124页

作者： Naeem, Hajra Binte Murtaza, Fiza Yousaf, Muhammad Haroon Velastin, Sergio A. Univ Engn & Technol Dept Comp Engn Taxila 47080 Pakistan Univ Engn & Technol Natl Ctr Robot & Automat Swarm Robot Lab Taxila Pakistan Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Zebra Technol Corp London SE1 9LQ England Univ Carlos III Madrid Dept Comp Sci & Engn Colmenarejo 28270 Spain

The recognition of human actions recorded in a multi-camera environment faces the challenging issue of viewpoint variation. Multi-view methods employ videos from different views to generate a compact view-invariant representation of human actions. This paper proposes a novel multi-view human action recognition approach that uses multiple low-dimensional temporal templates and a reconstruction-based encoding scheme. The proposed approach is based upon the extraction of multiple 2D motion history images (MHIs) of human action videos over non-overlapping temporal windows, constructing multiple batches of motion history images (MB-MHIs). Then, two kinds of descriptions are computed for these MHIs batches based on (1) a deep residual network (ResNet) and (2) histogram of oriented gradients (HOG) to effectively quantify a change in gradient. ResNet descriptions are average pooled at each batch. HOG descriptions are processed independently at each batch to learn a class-based dictionary using a K-spectral value decomposition algorithm. Later, the sparse codes of feature descriptions are obtained using an orthogonal matching pursuit approach. These sparse codes are average pooled to extract encoded feature vectors. Then, encoded feature vectors at each batch are fused to form a final view-invariant feature representation. Finally, a linear support vector machine classifier is trained for action recognition. Experimental results are given on three versions of a multi-view dataset: MuHAVi-8, MuHAVi-14, and MuHAVi-uncut. The proposed approach shows promising results when tested for a novel camera. Results on deep features indicate that action representation by MB-MHIs is more view-invariant than single MHIs.

关键词： Human action recognition Motion history images sparse coding MuHAVi Multi-view

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Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data

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NEUROINFORMATICS 2020年第4期18卷 611-626页

作者： Timonidis, Nestor Bakker, Rembrandt Tiesinga, Paul Radboud Univ Nijmegen Donders Ctr Neurosci Neuroinformat Dept Heyendaalseweg 135 NL-6525 AJ Nijmegen Netherlands Julich Res Ctr Inst Neurosci & Med INM 6 Wilhelm Johnen Str D-52425 Julich Germany Julich Res Ctr Inst Adv Simulat IAS 6 Wilhelm Johnen Str D-52425 Julich Germany Julich Res Ctr JARA BRAIN Inst 1 Wilhelm Johnen Str D-52425 Julich Germany

Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes. To achieve our goal we used publicly available volumetric gene expression and connectivity data and we trained computational models to learn and predict cell-class and layer-specific axonal projections using gene expression data. Predictions were done in two ways, namely predicting projection strengths using the expression of individual genes and using the co-expression of genes organized in spatial modules, as well as predicting binary forms of projection. For predicting the strength of projections, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r(2) score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using a dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r(2) score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function. Taken together, we have demonstrated a prediction workflow that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases.

关键词： Spatial gene co-expression Connectomics Machine learning Predictive models Mouse brain Axonal projection Gene expression Gene ontology enrichment analysis Ridge regression Dictionary learning sparse coding ROC analysis Cellularly resolved connectome

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Prediction of Network Traffic Through Light-Weight Machine Learning

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2020年 1卷 1919-1933页

作者： Wang, Yitu Nakachi, Takayuki NTT Corp NTT Network Innovat Lab Yokosuka Kanagawa 2390847 Japan

The evolving nature of network traffic challenges existing learning-based models, as frequently re-training the hyper-parameters is required to adaptively learn and predict its behavior. Benefiting from discovering the sparsity and self-similarity of network traffic, it becomes possible to develop compact algorithms with high accuracy and low computational complexity. However, how to properly make use of these properties still remains to be explored. In this article, we establish a light-weight learning framework for network traffic prediction based on sparse representation, and try to take the full advantage of these properties to enhance the capability of tracking its highly evolving characteristics. Specifically, 1). The strict causality constraint makes it difficult to equip the conventional sparse representation with predictive capability. To solve this issue, we make use of the self-similarity and train the representative/predictive dictionaries in a joint manner, such that the query point is embedded in terms of a sparse combination of dictionary atoms, and jointly coded with its T + 1 time slot behind counterpart, which proves to be optimal in the concatenated representative/predictive feature space. Then, the query point can be estimated through iterative projection method. 2). The consideration of the sparsity constraint loosens the upper bound of the time averaged prediction error. To address this problem, we slightly modify the sparse representation-based prediction by adopting Lyapunov optimization, and try to minimize the time averaged prediction error. Finally, the simulation results verify the performance improvements.

关键词： Communication system traffic predictive models sparse coding Lyapunov optimization

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Biologically Inspired Classification of Microvessel Histopathology via sparse coding

Biologically Inspired Classification of Microvessel Histopat...

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6th International Conference on Advanced Computational Intelligence (ICACI)

作者： Wen, Quan Chen, Juan Liu, Wenhao Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Hangzhou Normal Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781467363433

Recently the sparse coding approaches have been successfully applied to solve the image classification problems. However, the classification of microvessel regions from histopathology still rely on the hand designed low-level features. In this paper, we propose a novel method to classify region of microvessel by applying sparse coding on biological signals. The Single-and Double-Opponent signals from human visual cortex are simulated to capture microvessel properties. The SIFT (Scale Invariant Feature Transform)) descriptors of these signals are encoded via sparse coding and classified by SVM (Support Vector Machine) with the linear spatial pyramid matching kernel. We have carried out extensive experiments on the classification of microvessel histopathology and the proposed method achieves satisfactory classification rates.

关键词： sparse coding Microvessels Visual Cortex signals Image classification histopathology LOW LEVEL Support Vector Network

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A spiking neural program for sensorimotor control during foraging in flying insects

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2020年第45期117卷 28412-28421页

作者： Rapp, Hannes Nawrot, Martin Paul Univ Cologne Inst Zool Computat Syst Neurosci D-50674 Cologne Germany

Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying neural circuits and computational principles, we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning, and motor control. We focus on cast and surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a spike-based plasticity rule, the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. We show that, without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short time scales generates cast-and-surge motor commands. Our generic systems approach predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. Our work successfully combines biological computational principles with spike-based machine learning. It shows how knowledge transfer from static to arbitrary complex dynamic conditions can be achieved by foraging insects and may serve as inspiration for agent-based machine learning.

关键词： mushroom body sparse coding navigation artificial intelligence spiking neural network

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A low-rank recovery dictionary learning method for spectrum data denoising

A low-rank recovery dictionary learning method for spectrum ...

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第33届中国过程控制会议

作者： Jiayi Yue Jingxuan Geng Yonggang Li Lijuan Lan Jie Han Chunhua Yang College of Automation Central South University

Spectral analysis is widely used in various detection activities,unfortunately,the detection accuracy is greatly reduced due to the interference of multiple noises in the spectrum data acquisition *** order to remove the mixed noise in the spectrum data and improve the detection accuracy,we propose a low-rank recovery dictionary learning ***,the method considers the effects of multiple noises of Gaussian noise and sparse noise,and establishes a dictionary learning model that can reduce these ***,the corresponding optimization method is designed to solve the ***,relevant experiments show the feasibility and superiority of the proposed spectrum data denoising method.

关键词： dictionary learning spectrum denoising sparse coding mixed noise

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