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Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs

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arXiv 2021年

作者： Liang, Tianming Liu, Yang Liu, Xiaoyan Zhang, Hao Sharma, Gaurav Guo, Maozu The School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China The Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China The Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China

Label noise and long-tailed distributions are two major challenges in distantly supervised relation extraction. Recent studies have shown great progress on denoising, but paid little attention to the problem of long-tailed relations. In this paper, we introduce a constraint graph to model the dependencies between relation labels. On top of that, we further propose a novel constraint graph-based relation extraction framework(CGRE) to handle the two challenges simultaneously. CGRE employs graph convolution networks to propagate information from data-rich relation nodes to data-poor relation nodes, and thus boosts the representation learning of long-tailed relations. To further improve the noise immunity, a constraint-aware attention module is designed in CGRE to integrate the constraint information. Extensive experimental results indicate that CGRE achieves significant improvements over the previous methods for both denoising and long-tailed relation extraction. © 2021, CC BY.

关键词： Graphic methods

Real-Time Cell Sorting with Scalable In Situ FPGA-Accelerated Deep Learning

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arXiv 2025年

作者： Islam, Khayrul Forelli, Ryan F. Han, Jianzhong Bhadane, Deven Huang, Jian Agar, Joshua C. Tran, Nhan Ogrenci, Seda Liu, Yaling Department of Mechanical Engineering Lehigh University BethlehemPA18015 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Coriell Institute for Medical Research CamdenNJ United States Department of Computer Science Lehigh University BethlehemPA18015 United States Cooper Medical School of Rowan University CamdenNJ08103 United States Center for Metabolic Disease Research Temple University Lewis Katz School of Medicine PhiladelphiaPA19122 United States Department of Mechanical Engineering and Mechanics Drexel University PhiladelphiaPA19104 United States Real-time Processing Systems Division Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Bioengineering Lehigh University BethlehemPA18015 United States

Precise cell classification is essential in biomedical diagnostics and therapeutic monitoring, particularly for identifying diverse cell types involved in various diseases. Traditional cell classification methods such as flow cytometry depend on molecular labeling which is often costly, time-intensive, and can alter cell integrity. To overcome these limitations, we present a label-free machine learning framework for cell classification, designed for real-time sorting applications using bright-field microscopy images. This approach leverages a teacher-student model architecture enhanced by knowledge distillation, achieving high efficiency and scalability across different cell types. Demonstrated through a use case of classifying lymphocyte subsets, our framework accurately classifies T4, T8, and B cell types with a dataset of 80,000 preprocessed images, accessible via an open-source Python package for easy adaptation. Our teacher model attained 98% accuracy in differentiating T4 cells from B cells and 93% accuracy in zero-shot classification between T8 and B cells. Remarkably, our student model operates with only 0.02% of the teacher model’s parameters, enabling field-programmable gate array (FPGA) deployment. Our FPGA-accelerated student model achieves an ultra-low inference latency of just 14.5 µs and a complete cell detection-to-sorting trigger time of 24.7 µs, delivering 12x and 40x improvements over the previous state-of-the-art real-time cell analysis algorithm in inference and total latency, respectively, while preserving accuracy comparable to the teacher model. This framework provides a scalable, cost-effective solution for lymphocyte classification, as well as a new SOTA real-time cell sorting implementation for rapid identification of subsets using in situ deep learning on off-the-shelf computing hardware. © 2025, CC BY.

关键词： Deep learning

Improve the Performance of LSM-Tree based on Key-Value via Multithreading

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Improve the Performance of LSM-Tree based on Key-Value via M...

第二十届分布式计算及其应用国际学术研讨会

作者： Yuan Gao Ping Xie Wendi Hua Meng Lv Jiating Lu College of Computer of Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Key Laboratory of Internet of Things of Qinghai Province Academy of Plateau Science and Sustainability School of Physical and Electrical Engineering Qinghai Normal University

In the era of big data,key-value storage systems based on Log-Structure Merge tree(LSM-tree) are widely used in numerous ***-Tree is divided into two parts,one part is in the memory and the other part is on the hard *** writing data,first write to the memory,when the Immutable Memtable in the memory is full of data,then write to the disk to output a Sorted String Table(SSTable)***,the use of writing data from memory to disk is *** the Immutable Memtable writes data to the disk,it will block other *** article uses multiple Immutable Memtables and multi-threaded writing to solve the single-thread blocking *** running the benchmarks of LevelDB to analyze the experiment,the experimental results indicate a higher improvement.

关键词： Key-Value store Multi-Threading Write performance Read performance LSM-Tree Immutable Memtable

Syndrome Classification Based on Multi-Graph Attention Network

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Syndrome Classification Based on Multi-Graph Attention Netwo...

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Jishun Ma Weikai Lu Changen Zhou Shenghua Teng Zuoyong Li College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou China Research Base of Traditional Chinese Medicine Syndrome Fujian University of Traditional Chinese Medicine Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

Syndrome classification is an important step in Traditional Chinese Medicine (TCM) for diagnosis and treatment. In this paper, we propose a multi-graph attention network (MGAT) based method to simulate TCM doctors to infer the syndromes. Specifically, the complex relationships between symptoms and state elements are aggregated using graph attention networks (GAT) and syndromes are classified by a multilayer perceptron (MLP). To verify the effectiveness of the model, extensive experiments are conducted on the Treatise on Febrile Diseases dataset. The experimental results show that the proposed method outperforms several typical methods in terms of accuracy, precision, recall, and F1-score. The MGAT model has high accuracy in syndrome classification and has practical application value.

关键词： Computational modeling Education Medical services Multilayer perceptrons Feature extraction computer aided diagnosis Information technology

Structural Robustness of Complex Networks: A Survey of A Posteriori Measures

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arXiv 2023年

作者： Lou, Yang Wang, Lin Chen, Guanrong The Graduate School of Information Science and Technology Osaka University Osaka Suita565-0871 Japan The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various industrial and social networks against malicious attacks, which has various meanings in different research contexts and here it refers to the ability of a network to sustain its functionality when a fraction of the network fail to work due to attacks. The rapid development of complex networks research indicates special interest and great concern about the network robustness, which is essential for further analyzing and optimizing network structures towards engineering applications. This comprehensive survey distills the important findings and developments of network robustness research, focusing on the a posteriori structural robustness measures for single-layer static networks. Specifically, the a posteriori robustness measures are reviewed from four perspectives: 1) network functionality, including connectivity, controllability and communication ability, as well as their extensions;2) malicious attacks, including conventional and computation-based attack strategies;3) robustness estimation methods using either analytical approximation or machine learning-based prediction;4) network robustness optimization. Based on the existing measures, a practical threshold of network destruction is introduced, with the suggestion that network robustness should be measured only before reaching the threshold of destruction. Then, a posteriori and a priori measures are compared experimentally, revealing the advantages of the a posteriori measures. Finally, prospective research directions with respect to a posteriori robustness measures are recommended. © 2023, CC BY.

关键词： Complex networks

FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations

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arXiv 2022年

作者： Siew, Marie Sharma, Shikhar Li, Zekai Guo, Kun Xu, Chao Lorido-Botran, Tania Quek, Tony Q.S. Joe-Wong, Carlee The Information Systems and Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore The Electrical and Computer Engineering department Carnegie Mellon University PittsburghPA15213 United States The Shanghai Key Laboratory of Multidimensional Information Processing School of Communications and Electronics Engineering East China Normal University Shanghai200241 China The School of Information Engineering Northwest A&F University Yangling712100 China Roblox United States

In edge computing, users’ service profiles are migrated between edge servers due to user mobility. Reinforcement learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like AR/VR and real- time obstacle detection. These rare failures, being not adequately represented in historical training data, pose a challenge for data-driven RL algorithms. We introduce FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose FIRE-ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. FIRE considers delay, migration, failure, and backup placement costs across individual and shared service profiles. We prove FIRE-ImRE’s boundedness and convergence to optimality. Next, we introduce novel deep Q-learning (FIRE-ImDQL) and actor critic (FIRE-ImACRE) versions of our algorithm to enhance scalability. We extend our framework to accommodate users with varying risk tolerances of rare failure events. Through trace-driven experiments, we show that FIRE reduces edge computing costs compared to vanilla RL and the greedy baseline in the event of failures. © 2022, CC BY.

关键词： Mobile edge computing

Variational Distillation for Multi-View Learning

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arXiv 2022年

作者： Tian, Xudong Zhang, Zhizhong Wang, Cong Zhang, Wensheng Qu, Yanyun Ma, Lizhuang Wu, Zongze Xie, Yuan Tao, Dacheng School of Computer Science and Technology East China Normal University Shanghai200062 China The Distributed and Parallel Software Laboratory 2012 Labs Huawei Technologies Hangzhou China Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Information Science and Technology Xiamen University Fujian361005 China School of Computer Science and Techology East China Normal University Shanghai China The School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China College of Mechatronics and Control Engineering Shenzhen University Shenzhen China JD Exploer Academy China The University of Sydney Australia

Information Bottleneck (IB) based multi-view learning provides an information theoretic principle for seeking shared information contained in heterogeneous data descriptions. However, its great success is generally attributed to estimate the multivariate mutual information which is intractable when the network becomes complicated. Moreover, the representation learning tradeoff, i.e., prediction-compression and sufficiency-consistency tradeoff, makes the IB hard to satisfy both requirements simultaneously. In this paper, we design several variational information bottlenecks to exploit two key characteristics (i.e., sufficiency and consistency) for multi-view representation learning. Specifically, we propose a Multi-View Variational Distillation (MV2D) strategy to provide a scalable, flexible and analytical solution to fitting MI by giving arbitrary input of viewpoints but without explicitly estimating it. Under rigorously theoretical guarantee, our approach enables IB to grasp the intrinsic correlation between observations and semantic labels, producing predictive and compact representations naturally. Also, our information-theoretic constraint can effectively neutralize the sensitivity to heterogeneous data by eliminating both task-irrelevant and view-specific information, preventing both tradeoffs in multiple view cases. To verify our theoretically grounded strategies, we apply our approaches to various benchmarks under three different applications. Extensive experiments to quantitatively and qualitatively demonstrate the effectiveness of our approach against state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

Software Defect Prediction via Deep Belief Network

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Chinese Journal of Electronics 2019年第5期28卷 925-932页

作者： WEI Hua SHAN Chun HU Changzhen ZHANG Yu YU Xiao School of Computer Science and Technology Beijing Institute of Technology China Information Technology Security Evaluation Center Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology School of Electrical and Information Engineering and Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture State Key Laboratory in China for Geomechanics and Deep Underground Engineering (Beijing) China University of Mining and Technology School of Computer Science and Technology Shandong University of Technology

Defect distribution prediction is a meaningful topic because software defects are the fundamental cause of many attacks and data loss. Building accurate prediction models can help developers find bugs and prioritize their testing efforts. Previous researches focus on exploring different machine learning algorithms based on the features that encode the characteristics of *** problem of data redundancy exists in software defect data set, which has great influence on prediction *** propose a defect distribution prediction model(Deep belief network prediction model, DBNPM), a system for detecting whether a program module contains defects. The key insight of DBNPM is Deep belief network(DBN)technology, which is an effective deep learning technique in image processing and natural language processing,whose features are similar to defects in source *** results show that DBNPM can efficiently extract and process the data characteristics of source program and the performance is better than Support vector machine(SVM), Locally linear embedding SVM(LLE-SVM), and Neighborhood preserving embedding SVM(NPE-SVM).

关键词： Defect prediction Software security Deep belief network(DBN)

Novel Personalized Multimedia Recommendation Systems Using Tensor Singular-Value-Decomposition

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Novel Personalized Multimedia Recommendation Systems Using T...

IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Shih Yu Chang Hsiao-Chun Wu Kun Yan Xinjia Chen Yiyan Wu Department of Applied Data Science San Jose State University San Jose CA USA School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge LA USA Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin China Department of Information and Telecommunication Guilin University of Electronic Technology Guilin China Department of Engineering Technology Northwestern State University of Louisiana Natchitoches LA USA Communications Research Centre Canada Ottawa ON Canada

Nowadays, multimedia data are often produced by various sources, e.g., Internet of Things (IoT), social media, customer databases, etc. As tremendous multimedia data are produced every day, users or E-commerce customers cannot infer information from such data by themselves and therefore recommender systems have been developed to help users to select the products or services which better fit users’ preferences or requirements. Nonetheless, there exists few works on the incorporation of side information or multiple attributes about items into the design of a more robust recommender system. In this work, we propose a novel approach based on the third-order tensor singular-value-decomposition (T3-SVD) to design new personalized multimedia recommender systems (PMRSs) for internet users. A PMRS can dynamically adjust its recommendation strategy subject to a particular user’s on-line transaction behavior. To evaluate the effectiveness of our proposed PMRS based on T3-SVD, we compare the performances of our proposed new PMRS and two other existing tensor-based recommendation systems over realworld data in terms of normalized root-mean-square error (NRMSE). As a result, our proposed new PMRS greatly outperforms the other two existing systems.

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