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International Conference on networking and network Applications (NaNA)

作者： Yanping Chen Cong Gao Weihui Yang Zhongmin Wang Zhong Yu Hong Xia School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Communication and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9781728126296

ISBN: (纸本)9781728126302

With the blooming development of Industry 4.0, the management of industrial processes has drawn a lot of research attention. We employed the Gaussian process regression model to predict the key indicators of an industrial process. To facilitate the description of the data characteristics in an industrial process, the kernel function is reconstructed with respect to different kinds of data. We propose a novel structure based on the Gaussian process regression model which incorporates the previously predicted values into the input data of subsequent prediction. A series of contrastive experiments are conducted on the Tennessee Eastman process simulator. Experimental results show that the proposed method possesses a higher prediction accuracy than several notable prediction methods.

关键词： Ground penetrating radar Kernel Predictive models Gaussian processes data models Biological neural networks Training

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Improved Upper Bound on the network Function Computing Capacity

Improved Upper Bound on the Network Function Computing Capac...

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作者： Guang, Xuan Yeung, Raymond W. Yang, Shenghao Li, Congduan School of Mathematical Sciences and LPMC Nankai University Tianjin300071 China Institute of Network Coding Chinese University of Hong Kong Hong Kong Hong Kong Department of Information Engineering Chinese University of Hong Kong Hong Kong Hong Kong School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510006 China

The problem of network function computation over a directed acyclic network is investigated in this paper. In such a network, a sink node desires to compute with zero error a target function, of which the inputs are generated at multiple source nodes. The edges in the network are assumed to be error-free and have limited capacity. The nodes in the network are assumed to have unbounded computing capability and be able to perform network coding. The computing rate of a network code that can compute the target function over the network is the average number of times that the target function is computed with zero error for one use of the network. In this paper, we obtain an improved upper bound on the computing capacity, which is applicable to arbitrary target functions and arbitrary network topologies. This improved upper bound not only is an enhancement of the previous upper bounds but also is the first tight upper bound on the computing capacity for computing an arithmetic sum over a certain non-tree network, which has been widely studied in the literature. We also introduce a multi-dimensional array approach that facilitates evaluation of the improved upper bound. Furthermore, we apply this bound to the problem of computing a vector-linear function over a network. With this bound, we are not only able to enhance a previous result on computing a vector-linear function over a network but also simplify the proof significantly. Finally, we prove that for computing the binary maximum function over the reverse butterfly network, our improved upper bound is not achievable. This result establishes that in general our improved upper bound is non-achievable, but whether it is asymptotically achievable or not remains open. © 1963-2012 IEEE.

关键词： network coding

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Dynamic-K recommendation with personalized decision boundary 23rd

Dynamic-K recommendation with personalized decision boundary

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23rd China conference on Information Retrieval, CCIR 2017

作者： Gao, Yan Guo, Jiafeng Lan, Yanyan Liao, Huaming CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319686981

In this paper, we investigate the recommendation task in the most common scenario with implicit feedback (e.g., clicks, purchases). State-of-the-art methods in this direction usually cast the problem as to learn a personalized ranking on a set of items (e.g., webpages, products). The top-N results are then provided to users as recommendations, where the N is usually a fixed number pre-defined by the system according to some heuristic criteria (e.g., page size, screen size). There is one major assumption underlying this fixed-number recommendation scheme, i.e., there are always sufficient relevant items to users’ preferences. Unfortunately, this assumption may not always hold in real-world scenarios. In some applications, there might be very limited candidate items to recommend, and some users may have very high relevance requirement in recommendation. In this way, even the top-1 ranked item may not be relevant to a user’s preference. Therefore, we argue that it is critical to provide a dynamic-K recommendation, where the K should be different with respect to the candidate item set and the target user. We formulate this dynamic-K recommendation task as a joint learning problem with both ranking and classification objectives. The ranking objective is the same as existing methods, i.e., to create a ranking list of items according to users’ interests. The classification objective is unique in this work, which aims to learn a personalized decision boundary to differentiate the relevant items from irrelevant items. Based on these ideas, we extend two state-of-the-art ranking-based recommendation methods, i.e., BPRMF and HRM, to the corresponding dynamic-K versions, namely DK-BPRMF and DK-HRM. Our experimental results on two datasets show that the dynamic-K models are more effective than the original fixed-N recommendation methods. © Springer International Publishing AG 2017.

关键词： Websites

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Beyond precision: A study on recall of initial retrieval with neural representations

arXiv

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arXiv 2018年

作者： Xiao, Yan Guo, Jiafeng Fan, Yixing Lan, Yanyan Xu, Jun Cheng, Xueqi University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Vocabulary mismatch is a central problem in information retrieval (IR), i.e., the relevant documents may not contain the same (symbolic) terms of the query. Recently, neural representations have shown great success in capturing semantic relatedness, leading to new possibilities to alleviate the vocabulary mismatch problem in IR. However, most existing efforts in this direction have been devoted to the re-ranking stage. That is to leverage neural representations to help re-rank a set of candidate documents, which are typically obtained from an initial retrieval stage based on some symbolic index and search scheme (e.g., BM25 over the inverted index). This naturally raises a question: If the relevant documents have not been found in the initial retrieval stage due to vocabulary mismatch, there would be no chance to re-rank them to the top positions later. Therefore, in this paper, we study the problem how to employ neural representations to improve the recall of relevant documents in the initial retrieval stage. Specifically, to meet the efficiency requirement of the initial stage, we introduce a neural index for the neural representations of documents, and propose two hybrid search schemes based on both neural and symbolic indices, namely the parallel search scheme and the sequential search scheme. Our experiments show that both hybrid index and search schemes can improve the recall of the initial retrieval stage with small overhead. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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Generalized Association and Deep Understanding Method for Big Video data Processing

Generalized Association and Deep Understanding Method for Bi...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Lin Xu Wei Wu Wanghan Lv Dong Wang Science and Technology in Information System Engineering Lab Nanjing Research institute of Electronic Engineering Nanjing China School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9781728152103

In Big data Era, it is becoming more and more important to timely and efficient processing of massive videos, and mining of the value information contained in them. This paper studies chaotic compressed sensing theory and uses Long-term and short-term memory networks (LSTM) algorithm to generate a summary of the content of video behavior track. It has already to apply the proposed method in target recognition and tracking, and the recognition rate achieves more than 86%.

关键词： Big data compressed sensing data compression data mining video coding big data Compressed sensing data compression Video coding data mining video data Target recognition

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A rich ranking model based on the matthew effect optimization 7th

A rich ranking model based on the matthew effect optimizatio...

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7th International Conference on Computational data and Social networks, CSoNet 2018

作者： Li, Jinzhong Liu, Guanjun Department of Computer Science and Technology College of Electronic and Information Engineering Jinggangshan University Ji’an343009 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu610054 China Department of Computer Science and Technology College of Electronic and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9783030046477

Most existing approaches of learning to rank treat the effectiveness of each query equally which results in a relatively lower ratio of queries with high effectiveness (i.e. rich queries) in the produced ranking model. Such ranking models need to be further optimized to increase the number of rich queries. In this paper, queries with different effectiveness are distinguished, and the queries with higher effectiveness are given higher weights. We modify the gradient in the LambdaMART algorithm based on a new perspective of Matthew effect to highlight the optimization of the rich queries and to produce the rich ranking model, and we present a consistency theorem for the modified optimization objective. Based on the effectiveness evaluation criteria for information retrieval, we introduce the Gini coefficient, mean-variance and quantity statistics to measure the performances of the ranking models. Experimental results show that the ranking models produced by the gradient-modified LambdaMART algorithm based on Matthew effect exhibit a stronger Matthew effect compared to the original LambdaMART algorithm. © Springer Nature Switzerland AG 2018.

关键词： Artificial intelligence

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Target-specificity of different amyrin subunits in impeding HCV influx mechanism inside the human cells considering the quantum tunnel profiles and molecular strings of the CD81 receptor: a combined in silico and in vivo study

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In silico pharmacology 2023年第1期11卷 8页

作者： Anika Jabin Mohammad Fahim Uddin Salauddin Al Azad Ashfaque Rahman Fawzia Tabassum Pritthy Sarker A K M Helal Morshed Samiur Rahman Fatima Fairuz Raisa Musfiqur Rahman Sakib Abeer Hasan Olive Tabassum Islam Ramisha Tahsin Shahlaa Zernaz Ahmed Partha Biswas Mst Umme Habiba Mahbuba Siddiquy Maryam Jafary Department of Biochemistry and Microbiology North South University Dhaka 1229 Bangladesh. College of Material Science and Engineering Zhejiang Sci-Tech University Hangzhou 310018 Zhejiang People's Republic of China. Key Laboratory of Industrial Biotechnology Ministry of Education School of Biotechnology Jiangnan University Wuxi 214122 Jiangsu Province People's Republic of China. Department of Genetic Engineering and Biotechnology Shahjalal University of Science and Technology Sylhet 3114 Bangladesh. Pathology and Pathophysiology Major Academy of Medical Science Zhengzhou University Zhengzhou City 450001 Henan Province People's Republic of China. Department of Electrical and Electronic Engineering Brac University Dhaka 1212 Bangladesh. Department of Pharmacy Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj 8100 Bangladesh. Department of Pharmacy East West University Dhaka 1212 Bangladesh. Department of Computer Science and Engineering East West University Dhaka 1212 Bangladesh. Department of Pharmaceutical Sciences North South University Dhaka 1229 Bangladesh. Department of Genetic Engineering and Biotechnology Jashore University of Science and Technology Jashore 7408 Bangladesh. Data Science Research Unit RPG Interface Lab Jashore 7400 Bangladesh. State Key Laboratory of Food Science and Technology Jiangnan University Wuxi 214122 Jiangsu Province People's Republic of China. Division of Food Safety and Hygiene Department of Environmental Health Engineering School of Public Health Tehran University of Medical Sciences Tehran *** Iran.

HCV is a hepatotropic RNA virus recognized for its frequent virulence and fatality worldwide. Despite many vaccine development programs underway, researchers are on a quest for natural bioactive compounds due to their multivalent efficiencies against viral infections, considering which the current research aimed to figure out the target-specificity and therapeutic potentiality of α, β, and δ subunits of amyrin, as novel bioactive components against the HCV influx mechanism. Initially, the novelty of amyrin subunits was conducted from 203 pharmacophores, comparing their in-silico pharmacokinetic and pharmacodynamic profiles. Besides, the best active site of CD81 was determined following the quantum tunneling algorithm. The molecular dynamic simulation was conducted (100 ns) following the molecular docking steps to reveal the parameters- RMSD (Å); Cα; RMSF (Å); MolSA (Å); Rg (nm); PSA (Å); SASA (Å), and the MM-GBSA dG binding scores. Besides, molecular strings of CD81, along with the co-expressed genes, were classified, as responsible for encoding CD81-mediated protein clusters during HCV infection, resulting in the potentiality of amyrins as targeted prophylactics in HCV infection. Finally, in vivo profiling of the oxidative stress marker, liver-specific enzymes, and antioxidant markers was conducted in the DMN-induced mice model, where -amyrin scored the most significant values in all aspects.

关键词： CD81 receptor Co-expressed genes behind HCV invasion DMN-induced hepatic fibrosis HCV infection In-silico pharmacokinetics and pharmacodynamics Target-specificity of amyrins

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The QUasi-Affine TRansformation Evolution (QUATRE) Algorithm: An Overview 4th

The QUasi-Affine TRansformation Evolution (QUATRE) Algorithm...

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4th Euro-China Conference on Intelligent data Analysis and Applications, ECC 2017

作者： Meng, Zhenyu Pan, Jeng-Shyang Li, Xiaoqing Department of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783319685267

QUasi-Affine TRansformation Evolution (QUATRE) algorithm is a new simple but powerful stochastic optimization algorithm proposed recently. The QUATRE algorithm aims to tackle the representational/positional bias inborn with DE algorithm and secures an overall better performance on commonly used Conference of Evolutionary Computation (CEC) benchmark functions. Recently, several QUATRE variants have been already proposed since its inception in 2016 and performed very well on many benchmark functions. In this paper, we mainly have a brief overview of all these proposed QUATRE variants first and then make simple contrasts between these QUATRE variants and several state-of-the-art DE variants under CEC2013 test suites for real-parameter single objective optimization benchmark functions. Experiment results show that the movement trajectory of individuals in the QUATRE structure is much more efficient than DE structure on most of the tested benchmark functions. © 2018, Springer International Publishing AG.

关键词： Global optimization

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Wide Band-Gap Polymer Donors Functionalized with Unconventional Carbamate Side Chains for Polymer Solar Cells

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Angewandte Chemie 2022年第50期134卷

作者： Jie Tang Chentong Liao Yuwei Duan Xiaopeng Xu Min Deng Liyang Yu Ruipeng Li Qiang Peng School of Chemical Engineering College of Chemistry and State Key Laboratory of Polymer Materials Engineering Sichuan University Chengdu 610065 P. R. China Contribution: Conceptualization (equal) Data curation (lead) Formal analysis (lead) Methodology (lead) Contribution: Data curation (equal) Formal analysis (equal) Methodology (equal) Key Laboratory of Applied Surface and Colloid Chemistry of Ministry of Education Shaanxi Key Laboratory for Advanced Energy Devices Shaanxi Engineering Lab for Advanced Energy Technology School of Materials Science & Engineering Shaanxi Normal University Xi'an 710119 P. R. China Contribution: Data curation (supporting) Formal analysis (supporting) Contribution: Conceptualization (supporting) Data curation (supporting) Formal analysis (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) Methodology (supporting) Contribution: Conceptualization (supporting) Formal analysis (supporting) Investigation (supporting) Supervision (supporting) Writing - original draft (lead) Writing - review & editing (supporting) National Synchrotron Light Source II Brookhaven National Lab Suffolk Upton NY 11973 USA Contribution: Conceptualization (lead) Project administration (lead) Supervision (lead) Validation (lead) Writing - review & editing (lead)

Side-chain engineering with heteroatoms is not only effective in tuning frontier molecular orbitals, but also possible for forming secondary bonds which can be utilized to planarize the molecular backbone, hence, improving the photon absorption as well as charge-transport abilities of polymer solar-cell (PSC) materials. Herein, two types of unconventional side chains, namely carboxylate and carbamate, containing various heteroatoms are introduced to the thiophene bridges in high performance benzodithiophene (BDT) based donor polymers to from the novel polymers PTzTz-C and PTzTz-N, respectively. In these polymers, non-covalent O⋅⋅⋅S and N⋅⋅⋅H interactions induce a high tendency to aggregation. In a ternary-blend PSC with PTzTz-N added to the high-performance D18 : BTP-eC9 blend, complimentary absorption and improved thin-film morphology were observed with a top power conversion efficiency of 18.76 %, which is an improvement of almost 5 % over the D18 : BTP-eC9 binary blends.

关键词： Carbamate Conformation Locks Donor Polymer Solar Cells

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stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs

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Nature communications 2025年第1期16卷 5317页

作者： Chunman Zuo Junjie Xia Yupeng Xu Ying Xu Pingting Gao Jing Zhang Yan Wang Luonan Chen School of Life Sciences Sun Yat-sen University Guangzhou China. zuochm@***. Institute of Artificial Intelligence Donghua University Shanghai China. zuochm@***. Shanghai Collaborative Innovation Center of Endoscopy Fudan University Shanghai China. zuochm@***. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China. zuochm@***. Institute of Artificial Intelligence Donghua University Shanghai China. System Biology Lab for Metabolic Reprogramming Department of Human Genetics and Cell Biology School of Medicine Southern University of Science and Technology Shenzhen China. Shanghai Collaborative Innovation Center of Endoscopy Fudan University Shanghai China. Department of Pathology Changzheng Hospital Secondary Military Medical University Shanghai China. College of Computer Science and Technology Jilin University Changchun China. School of Mathematical Sciences Shanghai Jiao Tong University Shanghai China. lnchen@***. School of AI Shanghai Jiao Tong University Shanghai China. lnchen@***. Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai China. lnchen@***. Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Chinese Academy of Sciences Hangzhou China. lnchen@***. West China Biomedical Big Data Center Med-X Center for Informatics West China Hospital Sichuan University Chengdu China. lnchen@***.

Spatial multi-slice multi-omics (SMSMO) integration has transformed our understanding of cellular niches, particularly in tumors. However, challenges like data scale and diversity, disease heterogeneity, and limited sample population size, impede the derivation of clinical insights. Here, we propose stClinic, a dynamic graph model that integrates SMSMO and phenotype data to uncover clinically relevant niches. stClinic aggregates information from evolving neighboring nodes with similar-profiles across slices, aided by a Mixture-of-Gaussians prior on latent features. Furthermore, stClinic directly links niches to clinical manifestations by characterizing each slice with attention-based geometric statistical measures, relative to the population. In cancer studies, stClinic uses survival time to assess niche malignancy, identifying aggressive niches enriched with tumor-associated macrophages, alongside favorable prognostic niches abundant in B and plasma cells. Additionally, stClinic identifies a niche abundant in SPP1+ MTRNR2L12+ myeloid cells and cancer-associated fibroblasts driving colorectal cancer cell adaptation and invasion in healthy liver tissue. These findings are supported by independent functional and clinical data. Notably, stClinic excels in label annotation through zero-shot learning and facilitates multi-omics integration by relying on other tools for latent feature initialization.

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