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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Dong Xu Shuicheng Yan Lei Zhang Hong-Jiang Zhang Zhengkai Liu Heung-Yeung Shum MOE-Microsoft Key Laboratory of Multimedia Computing and Communication & Department of EEIS University of Science and Technology Hefei Anhui China Department of Information Engineering Chinese University of Hong Kong Sha Tin Hong Kong China Microsoft Research Asia Beijing China

ISBN: (纸本)0769523722

A representative subspace is significant for image analysis, while the corresponding techniques often suffer from the curse of dimensionality dilemma. In this paper, we propose a new algorithm, called concurrent subspaces analysis (CSA), to derive representative subspaces by encoding image objects as 2/sup nd/ or even higher order tensors. In CSA, an original higher dimensional tensor is transformed into a lower dimensional one using multiple concurrent subspaces that characterize the most representative information of different dimensions, respectively. Moreover, an efficient procedure is provided to learn these subspaces in an iterative manner. As analyzed in this paper, each sub-step of CSA takes the column vectors of the matrices, which are acquired from the k-mode unfolding of the tensors, as the new objects to be analyzed, thus the curse of dimensionality dilemma can be effectively avoided. The extensive experiments on the 3/sup rd/ order tensor data, simulated video sequences and Gabor filtered digital number image database show that CSA outperforms principal component analysis in terms of both reconstruction and classification capability.

关键词： Tensile stress Image sequence analysis Image analysis Algorithm design and analysis Image coding Analytical models Video sequences Digital filters Gabor filters Image databases

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Coupled kernel-based subspace learning

Coupled kernel-based subspace learning

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shuicheng Yan Dong Xu Lei Zhang Benyu Zhang HongJiang Zhang Department of Information Engineering Chinese University of Hong Kong Sha Tin Hong Kong China MOE-Microsoft Key Laboratory of Multimedia Computing and Communication & Department of EEIS University of Science and Technology Hefei Anhui China Microsoft Research Asia Beijing China

ISBN: (纸本)0769523722

It was prescriptive that an image matrix was transformed into a vector before the kernel-based subspace learning. In this paper, we take the kernel discriminant analysis (KDA) algorithm as an example to perform kernel analysis on 2D image matrices directly. First, each image matrix is decomposed as the product of two orthogonal matrices and a diagonal one by using singular value decomposition; then an image matrix is expanded to be of higher or even infinite dimensions by applying the kernel trick on the column vectors of the two orthogonal matrices; finally, two coupled discriminative kernel subspaces are iteratively learned for dimensionality reduction by optimizing the Fisher criterion measured by Frobenius norm. The derived algorithm, called coupled kernel discriminant analysis (CKDA), effectively utilizes the underlying spatial structure of objects and the discriminating information is encoded in two coupled kernel subspaces respectively. The experiments on real face databases compared with KDA and Fisherface validate the effectiveness of CKDA.

关键词： Kernel Matrix decomposition Algorithm design and analysis Principal component analysis Feature extraction Linear discriminant analysis Image analysis Performance analysis Asia Singular value decomposition

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An Approach for the Implementation of Personalization Recommendation of Text Information Retrieval With TextCC

An Approach for the Implementation of Personalization Recomm...

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2005 International Conference on Neural Networks and Brain(ICNN&B’05)

作者： Zhenya Zhang Shuguang Zhang Xufa Wang Shuangping Chen Hongmei Cheng MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China(USTC) Electronical Engineering & Information Science Department of USTC Statistics&Finance Department of USTC Computer Science Department of USTC Mathematics Department of Anhui Normal University

To implement personalization recommendation of text information retrieval(TR) based in user-focus,TextCC,a feed forward neural network for instantly classification is adopted to classify query results according to user-focuses of a user in the *** results show that the implementation of personalization recommendation with TextCC can lighten the user's burden induced by the work of filtering valid information from vast information in some degree while the time requirement of TR is satisfied very well.

关键词： Web An Approach for the Implementation of Personalization Recommendation of Text Information Retrieval With TextCC

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Squared Sine Adaptive Algorithm and Its Performance Analysis

TechRxiv

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TechRxiv 2023年

作者： Li, Yingsong Huang, Xinqi Zakharov, Yuriy Miao, Yongchun Huang, Zhixiang Li, Liping The College of Information and Communication Engineering Harbin Engineering University Harbin150001 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China The School of Physics Engineering and Technology University of York YorkYO10 5DD United Kingdom

We presents a squared sine functioned adaptive (SSA) algorithm for acoustic-echo-cancellation (AEC) applications in-presence-of impulsive noise. In the development of the SSA algorithm, a novel cost function is constructed by exerting a sliding window type squared sine function on the estimation error vector, which provides robustness in the impulsive-noise environments and speed-ups convergence when the input is colored signals. Theoretical models for predicting the mean-weight-behavior, transient excess-mean-square-error (EMSE) behavior, and tracking behavior are presented. Moreover, the optimal step size and minimum EMSE of the tracking performance are provided. The computation complexity of SSA algorithm has also been investigated. Numerical experiments demonstrate that the theoretical results match well with simulation results and show the superiority of the proposed SSA algorithm against known algorithms in AEC applications. © 2023, CC BY.

关键词： Adaptive filters

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Towards Accurate and Efficient Super Spreader Detection with Sketching

Towards Accurate and Efficient Super Spreader Detection with...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Shouyou Song Pu Wang Yangchun Li Nanyang Wang Wei Jiang Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education BUPT Beijing China Chinese Academy of Cyberspace Studies Beijing China School of Computer Science (National Pilot Software Engineering School) BUPT Beijing China School of Computer and Information Engineering Chuzhou University Anhui China

Super spreaders are the flow that have a large number of distinct connections (also called spread), which related with many threats to networks. Estimating flow spread is the crucial step in super spreader detection. However, existing methods cannot achieve flow spread estimation in terms of accurate, efficient, and reversible simultaneously. All these characteristics is highly required for high-speed network measurement. In this paper, we propose MorphSketch, a new data structure that estimates flow spread for super spreader detection with high accuracy, memory efficiency, high throughput and reversibility. MorphSketch combines hashing with sampling to process packets in order to improve throughput. It uses self-morph bitmap to record spread information, which can adaptively enlarge the upper bound of spread estimation under limited memory usage to ensure accuracy and memory efficiency. Moreover, MorphSketch can track candidate super spreader by comparing corresponding spread information, which realizes reversibility in super spreader detection. We perform a series of performance evaluations on real world traffic trace. Experiment results demonstrate that under same memory usage, the MorphSketch significantly outperforms existing work in terms of accuracy and efficiency.

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No-Reference Image Quality Assessment Based on Active Reasoning Module

No-Reference Image Quality Assessment Based on Active Reason...

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IEEE International Conference on Electronic Information and communication Technology (ICEICT)

作者： Junwei Qi Yuhao Deng Qingchun Wang Zhen Yang Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China College of Computer Science and Technology Harbin Engineering University Harbin China Shanghai Yanding Information Technology Co. Ltd. Shanghai China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

We present a no-reference image-quality - assessment algorithm based on active reasoning module. This algorithm has three modules: the feature extraction module, the active reasoning module, and the quality assessment module. The active reasoning module incorporates the generator component of the generative adversarial network and enhances its structure with the Res2Net architecture. By integrating this module into the backbone feature extraction network, we improve the receptive field of each convolutional layer, enabling the network to capture information at different scales of the image. To preserve the texture information of the image, we input the gradient map of the distorted image, the distorted image itself, and the image features generated by the generator into the quality assessment module, which has a multi-feature regression networks. This module establishes a mapping model from image-feature to image-quality scores. We conducted experimental analysis of this algorithm on three widely used public datasets, confirming the excellent performance and superiorities of the presented algorithm. The results validate the effectiveness of the designed algorithm and its ability to assess image quality accurately.

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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An efficient privacy-preserving compressive data gathering scheme in WSNs 1

引用

15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Xie, Kun Ning, Xueping Wang, Xin Wen, Jigang Liu, Xiaoxiao He, Shiming Zhang, Daqiang College of Computer Science and Electronics Engineering Hunan University Changsha410082 China The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommmunications Beijing100876 China Department of Electrical and Computer Engineering State University of New York at Stony Brook New York11790 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100080 China State Grid HuNan Electric Power Company Research Insitute Changsha410000 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China School of Software Engineering Tongji University Shanghai201804 China

ISBN: (数字)9783319271194

ISBN: (纸本)9783319271187

Due to the strict energy limitation and the common vulnerability of WSNs, providing efficient and security data gathering in WSNs becomes an essential problem. Compressive data gathering, which is based on the recent breakthroughs in compressive sensing theory, has been proposed as a viable approach for data gathering in WSNs at low communication overhead. Nevertheless, compressive data gathering is susceptible to various attacks due to the open wireless medium. To thwart traffic analysis/flow tracing and realize privacy preservation, this paper proposes a novel Efficient Privacy-Preserving Compressive Data Gathering Scheme which exploits homomorphic encryption functions in compressive data gathering. With homomorphic encryption on the compressive sensing encoded sensory reading messages, the proposed scheme offers two significant privacy-preserving features, message flow untraceability and message content confidentiality, for efficiently thwarting the traffic analysis attacks. Extensive performance evaluations and security analysis demonstrate the validity and efficiency of the proposed scheme. © Springer International Publishing Switzerland 2015.

关键词： Compressed sensing

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Spectroscopy-Based Food Internal Quality Evaluation with XGBoost Algorithm

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Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, APWeb-WAIM 2018

作者： Li, Lingling Li, Caihong Wu, Yuan Yang, Yi Zhang, Yixuan Zhang, Hairui Wu, Bin Liu, Li Xie, Rong School of Information Science and Engineering Lanzhou University Lanzhou730000 China Silk Road Economic Belt Research Center Lanzhou University Lanzhou730000 China Division of Biological Science University of California at San Diego La Jolla United States Communication and Network Center Lanzhou University Lanzhou730000 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400044 China School of Software Engineering Chongqing University Chongqing400044 China School of Computer Science Wuhan University Wuhan430072 China

ISBN: (纸本)9783030012977

In this paper, the combination of Near-Infrared (NIR) spectroscopy and a novel forecasting algorithm called XGBoost was proposed for food internal quality evaluation. First, the original NIR spectral data was preprocessed by Savitzky-Golay smoothing method to reduce the influence of noises. Secondly, the preprocessed spectra was submitted to PCA to extract essential information. Finally, the model was established by using the XGBoost algorithm. The performance of the proposed model was examined by comparing with different models including back propagation neural network (BPNN) and support vector regression (SVR). The results showed that the new proposed model outperformed other two models and this XGBoost-based tool was suitable for food internal quality control. © 2018, Springer Nature Switzerland AG.

关键词： Near infrared spectroscopy

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Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning

arXiv

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arXiv 2024年

作者： Xia, Tianle Ding, Liang Wan, Guojia Zhan, Yibing Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China The University of Sydney Australia JD Explore Academy China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Answering complex queries over incomplete knowledge graphs (KGs) is a challenging task. Most previous works have focused on learning entity/relation embeddings and simulating first-order logic operators with various neural networks. However, they are bottlenecked by the inability to share world knowledge to improve logical reasoning, thus resulting in suboptimal performance. In this paper, we propose a complex reasoning schema over KG upon large language models (LLMs), containing a curriculum-based logical-aware instruction tuning framework, named LACT. Specifically, we augment the arbitrary first-order logical queries via binary tree decomposition, to stimulate the reasoning capability of LLMs. To address the difficulty gap among different types of complex queries, we design a simple and flexible logic-aware curriculum learning framework. Experiments across widely used datasets demonstrate that LACT has substantial improvements (brings an average +5.5% MRR score) over advanced methods, achieving the new state-of-the-art. Copyright © 2024, The Authors. All rights reserved.

关键词： Binary trees

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