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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Yuhui Li Wei Liang Kun Xie Dafang Zhang Songyou Xie KuanChing Li College of Computer Science and Electronic Engineering Hunan University Changsha China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

Network operation and maintenance rely heavily on network traffic monitoring. Due to the measurement overhead reduction, lack of measurement infrastructure, and unexpected transmission error, network traffic monitoring systems suffer from incomplete observed data and high data sparsity problems. Recent studies model missing data recovery as a tensor completion task and show good performance. Although promising, the current tensor completion models adopted in network traffic data recovery lack an effective and efficient retraining scheme to adapt to newly arrived data while retaining historical information. To solve the problem, we propose LightNestle, a novel sequential tensor completion scheme based on meta-learning, which designs (1) an expressive neural network to transfer spatial knowledge from previous embeddings to current embeddings; (2) an attention-based module to transfer temporal patterns into current embeddings in linear complexity; and (3) meta-learning-based algorithms to iteratively recover missing data and update transfer modules to catch up with learned knowledge. We conduct extensive experiments on two real-world network traffic datasets to assess our performance. Results show that our proposed methods achieve both fast retraining and high recovery accuracy.

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Review Neural Networks about Image Transformation Based on IGC Learning Framework with Annotated Information

arXiv

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arXiv 2022年

作者： Yan, Yuanjie Yang, Suorong Wang, Yan Zhao, Jian Shen, Furao The National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210046 China The School of Electronic Science and Engineering Nanjing University Nanjing210046 China The National Key Laboratory for Novel Software Technology Nanjing University China

Image transformation, a class of vision and graphics problems whose goal is to learn the mapping between an input image and an output image, develops rapidly in the context of deep neural networks. In computer Vision (CV), many problems can be regarded as the image transformation task, e.g., semantic segmentation and style transfer. These works have different topics and motivations, making the image transformation task flourishing. Some surveys only review the research on style transfer or image-to-image translation, all of which are just a branch of image transformation. However, none of the surveys summarize those works together in a unified framework to our best knowledge. This paper proposes a novel learning framework including Independent learning, Guided learning, and Cooperative learning, called the IGC learning framework. The image transformation we discuss mainly involves the general image-to-image translation and style transfer about deep neural networks. From the perspective of this framework, we review those subtasks and give a unified interpretation of various scenarios. We categorize related subtasks about the image transformation according to similar development trends. Furthermore, experiments have been performed to verify the effectiveness of IGC learning. Finally, new research directions and open problems are discussed for future research. © 2022, CC0.

关键词： Deep neural networks

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Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys

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Zoological Research 2023年第5期44卷 967-980页

作者： Chuxi Li Zifan Xiao Yerong Li Zhinan Chen Xun Ji Yiqun Liu Shufei Feng Zhen Zhang Kaiming Zhang Jianfeng Feng Trevor W.Robbins Shisheng Xiong Yongchang Chen Xiao Xiao School of Information Science and Technology Micro Nano System Center Fudan UniversityShanghai 200433China Department of Anesthesiology Huashan HospitalKey Laboratory of Computational Neuroscience and Brain-Inspired IntelligenceMinistry of EducationBehavioral and Cognitive Neuroscience CenterInstitute of Science and Technology for Brain-Inspired IntelligenceMOE Frontiers Center for Brain ScienceFudan UniversityShanghai 200433China Kuang Yaming Honors School Nanjing UniversityNanjingJiangsu 210023China Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China State Key Laboratory of Primate Biomedical Research Institute of Primate Translational MedicineKunming University of Science and TechnologyKunmingYunnan 650500China New Vision World LLC. Aliso ViejoCalifornia 92656USA Behavioural and Clinical Neuroscience Institute University of CambridgeCambridgeCB21TNUK

Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and ***,action recognition currently used in non-human primate(NHP)research relies heavily on intense manual labor and lacks standardized *** this work,we established two standard benchmark datasets of NHPs in the laboratory:Monkeyin Lab(Mi L),which includes 13 categories of actions and postures,and MiL2D,which includes sequences of two-dimensional(2D)skeleton ***,based on recent methodological advances in deep learning and skeleton visualization,we introduced the Monkey Monitor Kit(Mon Kit)toolbox for automatic action recognition,posture estimation,and identification of fine motor activity in *** the datasets and Mon Kit,we evaluated the daily behaviors of wild-type cynomolgus monkeys within their home cages and experimental environments and compared these observations with the behaviors exhibited by cynomolgus monkeys possessing mutations in the MECP2 gene as a disease model of Rett syndrome(RTT).Mon Kit was used to assess motor function,stereotyped behaviors,and depressive phenotypes,with the outcomes compared with human manual *** Kit established consistent criteria for identifying behavior in NHPs with high accuracy and efficiency,thus providing a novel and comprehensive tool for assessing phenotypic behavior in monkeys.

关键词： Action recognition Fine motor identification Two-stream deep model 2D skeleton Non-human primates Rett syndrome

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Inner-approximate Reachability Computation via Zonotopic Boundary Analysis

arXiv

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arXiv 2024年

作者： Ren, Dejin Liang, Zhen Wu, Chenyu Ding, Jianqiang Wu, Taoran Xue, Bai Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China College of Computer Science and Technology National University of Defense Technology Changsha China Department of Electrical Engineering and Automation Aalto University Espoo Finland

Inner-approximate reachability analysis involves calculating subsets of reachable sets, known as inner-approximations. This analysis is crucial in the fields of dynamic systems analysis and control theory as it provides a reliable estimation of the set of states that a system can reach from given initial states at a specific time instant. In this paper, we study the inner-approximate reachability analysis problem based on the set-boundary reachability method for systems modelled by ordinary differential equations, in which the computed inner-approximations are represented with zonotopes. The set-boundary reachability method computes an inner-approximation by excluding states reached from the initial set’s boundary. The effectiveness of this method is highly dependent on the efficient extraction of the exact boundary of the initial set. To address this, we propose methods leveraging boundary and tiling matrices that can efficiently extract and refine the exact boundary of the initial set represented by zonotopes. Additionally, we enhance the exclusion strategy by contracting the outer-approximations in a flexible way, which allows for the computation of less conservative inner-approximations. To evaluate the proposed method, we compare it with state-of-the-art methods against a series of benchmarks. The numerical results demonstrate that our method is not only efficient but also accurate in computing inner-approximations. Copyright © 2024, The Authors. All rights reserved.

关键词： Ordinary differential equations

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Distributed Quantum Simulation

arXiv

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arXiv 2024年

作者： Feng, Tianfeng Xu, Jue Yu, Wenjun Ye, Zekun Yao, Penghui Zhao, Qi QICI Quantum Information and Computation Initiative Department of Computer Science School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong State Key Laboratory for Novel Software Technology Nanjing University Hefei National Laboratory Nanjing 210023 China Hefei230088 China

Quantum simulation is a promising pathway toward practical quantum advantage by simulating large-scale quantum systems. In this work, we propose communication-efficient distributed quantum simulation protocols by exploring three quantum simulation algorithms, including the product formula, the truncated Taylor series, and the processing of quantum signals over a quantum network. Our protocols are further shown to be optimal by deriving a lower bound on the quantum communication complexity for distributed quantum simulations with respect to evolution time and the number of distributed quantum processing units. Additionally, our distributed techniques go beyond quantum simulation and are applied to distributed versions of Grover’s algorithms and quantum phase estimation. Our work not only paves the way for achieving a practical quantum advantage by scalable quantum simulation but also enlightens the design of more general distributed architectures across various physical systems for quantum computation. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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RAT: A Refactoring-Aware Traceability Model for Bug Localization

RAT: A Refactoring-Aware Traceability Model for Bug Localiza...

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International Conference on software Engineering (ICSE)

作者： Feifei Niu Wesley K. G. Assunção LiGuo Huang Christoph Mayr-Dorn Jidong Ge Bin Luo Alexander Egyed State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science and Engineering Southern Methodist University Dallas Texas USA

A large number of bug reports are created during the evolution of a software system. Locating the source code files that need to be changed in order to fix these bugs is a challenging task. Information retrieval-based bug localization techniques do so by correlating bug reports with historical information about the source code (e.g., previously resolved bug reports, commit logs). These techniques have shown to be efficient and easy to use. However, one flaw that is nearly omnipresent in all these techniques is that they ignore code refactorings. Code refactorings are common during software system evolution, but from the perspective of typical version control systems, they break the code history. For example, a class when renamed then appears as two separate classes with separate histories. Obviously, this is a problem that affects any technique that leverages code history. This paper proposes a refactoring-aware traceability model to keep track of the code evolution history. With this model, we reconstruct the code history by analyzing the impact of code refactorings to correctly stitch together what would otherwise be a fragmented history. To demonstrate that a refactoring aware history is indeed beneficial, we investigated three widely adopted bug localization techniques that make use of code history, which are important components in existing approaches. Our evaluation on 11 open source projects shows that taking code refactorings into account significantly improves the results of these bug localization techniques without significant changes to the techniques themselves. The more refactorings are used in a project, the stronger the benefit we observed. Based on our findings, we believe that much of the state of the art leveraging code history should benefit from our work.

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2D-3D Feature Co-Embedding Network with Sparse Annotation for 3D Medical Image Segmentation

2D-3D Feature Co-Embedding Network with Sparse Annotation fo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Mingzhu Guo, Shuyu Wang, Ziwei Jiao, Jianhang Bai, Tian Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China Jilin University College of Software Changchun China The Second Norman Bethune Hospital Jilin University Department of Orthopedics Changchun China

ISBN: (纸本)9798350386226

Supervised methods on 3D medical image segmentation need large amounts of annotated data, but annotating is time-consuming. Also, existing 3D segmentation methods capture more global structural information but overlook local detailed features, which negatively impacts the segmentation of small tissues. In this paper, we propose a novel weakly-supervised 2D-3D Feature Co-Embedding Network (2D-3D CoENet) that includes 2D and 3D encoding layers, simultaneously extracting 2D local detailed and 3D global structural features. To reduce annotation costs, we use fewer labeled slices as ground truth and pseudo-labels are generated by 2D-3D CoENet for other slices. Additionally, multi-view learning is introduced to capture more 2D local detailed information, and a Multi-view Semantic Consistency loss (MSC loss) is proposed to constrain features from multiple perspectives. To further enhance the local detailed texture features, we propose an Edge Enhancement Module (EEM) in the 3D segmentation network to enhance the edge detail features. Our experimental results on the SKI10 dataset and OAI ZIB dataset demonstrate that our method outperforms the SOTA weakly-supervised segmentation methods. Moreover, our approach achieves results that are comparable to the fully-supervised upper bound results. © 2024 IEEE.

关键词： Medical imaging

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V-PETL Bench: A Unified Visual Parameter-Efficient Transfer Learning Benchmark 38

V-PETL Bench: A Unified Visual Parameter-Efficient Transfer ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Xin, Yi Luo, Siqi Liu, Xuyang Du, Yuntao Zhou, Haodi Cheng, Xinyu Lee, Christina Du, Junlong Wang, Haozhe Chen, Mingcai Liu, Ting Hu, Guimin Wan, Zhongwei Zhang, Rongchao Li, Aoxue Yi, Mingyang Liu, Xiaohong State Key Laboratory for Novel Software Technology Nanjing University China Shanghai Jiao Tong University China Sichuan University China BIGAI China MIT United States Cerebras United States Tencent China Hong Kong University of Science and Technology Hong Kong NUDT China University of Copenhagen Denmark Ohio State University United States Peking University China Huawei Noah's Ark Lab Canada Renmin University of China China

Parameter-efficient transfer learning (PETL) methods show promise in adapting a pre-trained model to various downstream tasks while training only a few parameters. In the computer vision (CV) domain, numerous PETL algorithms have been proposed, but their direct employment or comparison remains inconvenient. To address this challenge, we construct a Unified Visual PETL Benchmark (V-PETL Bench) for the CV domain by selecting 30 diverse, challenging, and comprehensive datasets from image recognition, video action recognition, and dense prediction tasks. On these datasets, we systematically evaluate 25 dominant PETL algorithms and open-source a modular and extensible codebase for fair evaluation of these algorithms. V-PETL Bench runs on NVIDIA A800 GPUs and requires approximately 310 GPU days. We release all the benchmark, making it more efficient and friendly to researchers. Additionally, V-PETL Bench will be continuously updated for new PETL algorithms and CV tasks. © 2024 Neural information processing systems foundation. All rights reserved.

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A Survey on Task Partitioning and Scheduling for Vehicular Edge Computing

A Survey on Task Partitioning and Scheduling for Vehicular E...

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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Jing Huang Wenyu Wu Weihong Huang Yufeng Xiao Li Lisi Jinxi Sun School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China The Bilingual Experimental School Attached to HNSDFZ Changsha China

Vehicle edge computing (VEC) has become an important research field in recent years. In VEC, computation offloading moves computationally intensive tasks from resource-constrained devices to the network edge, it provides service closer to the end-users. By processing tasks with abundant idle resources at the network edge, low-latency demands for some tasks can be met. However, the mobility and uncertainty of vehicles pose significant challenges to vehicle computation offloading. This paper focuses on the decision-making process of vehicle computation offloading, specifically task partitioning and scheduling decisions. This paper summarizes some hot problems and solutions, including latency optimization, reliability optimization, energy efficiency optimization, cost optimization, and mobility support. This study will help researchers discover important features of vehicle computation offloading and find the most suitable scheme to solve the vehicle offloading problem in different scenarios.

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A transient model integrating the nanoconfinement effect and pore structure characteristics of oil transport through nanopores

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Petroleum science 2023年第6期20卷 3461-3477页

作者： Cheng Cao Bin Chang Zhao Yang Chao Gao School of Mathematics and Computer Science Shaanxi University of TechnologyHanzhong723001ShaanxiChina State Key Laboratory of Continental Dynamics Department of GeologyNorthwest UniversityXi'an710069ShaanxiChina Shaanxi Polytechnic Institute Sch Civil EngnXianyang712000ShaanxiChina Research Institute of Shanxi Yanchang Petroleum(Group)Co.Ltd Xi'an710075ShaanxiChina

Understanding the integrated transport behavior of oil in shale nanopores is critical to efficient shale oil development. In this paper, based on the time-dependent Poiseuille flow momentum equation, we present a novel transient model to describe oil transport in unsteady and steady states. The model incorporates the effect of the critical shift density, apparent viscosity, slip length, and alkane property, as well as pore tortuosity and surface roughness. We evaluated our model through a comparison with other models, experiments, and molecular dynamics simulations. The results show that the development rates of the volume flows of C_(6)–C_(12) alkane confined in inorganic nanopores and C_(12) alkane confined in organic nanopores were faster than that of the corresponding bulk alkane. In addition, the critical drift density positively promoted the volume flow development rate in the unsteady state and negatively inhibited the mass flow rate in the steady state. This effect was clearest in pores with a smaller radius and lower-energy wall and in alkane with shorter chain lengths. Furthermore, both the nanoconfinement effect and pore structure determined whether the volume flow enhancement rate was greater than or less than 1. The rate increased or decreased with time and was controlled mainly by the nanoconfinement effect. Moreover, as the wall energy increased, the flow inhibition effect increased;as the carbon number of alkane increased, the flow promotion effect increased. The results indicate that the proposed model can accurately describe oil transport in shale nanopores.

关键词： Nanopores Transient transport Nanoconfinement effect Tortuosity Roughness Allkane properties

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