检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Liu, Yixin Li, Zhao Pan, Shirui Gong, Chen Zhou, Chuan Karypis, George The Department of Data Science and AI Faculty of Information Technology Monash University ClaytonVIC3800 Australia The Alibaba Group Hangzhou310000 China The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Department of Computing The Hong Kong Polytechnic University Hong Kong The Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100093 China The Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States

Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomaly detection in their learning objective and fail to scale to large networks due to the full graph training mechanism. To overcome these limitations, in this paper, we present a novel Contrastive self-supervised Learning framework for Anomaly detection on attributed networks (CoLA for abbreviation). Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way. Meanwhile, a well-designed graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure and measure the agreement of each instance pairs with its outputted scores. The multi-round predicted scores by the contrastive learning model are further used to evaluate the abnormality of each node with statistical estimation. In this way, the learning model is trained by a specific anomaly detection-aware target. Furthermore, since the input of the graph neural network module is batches of instance pairs instead of the full network, our framework can adapt to large networks flexibly. Experimental results show that our proposed framework outperforms the state-of-the-art baseline methods on all seven benchmark datasets. © 2021, CC BY.

关键词： Anomaly detection

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TFormer: A Transmission-Friendly ViT Model for IoT Devices

arXiv

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arXiv 2023年

作者： Lu, Zhichao Ding, Chuntao Juefei-Xu, Felix Boddeti, Vishnu Naresh Wang, Shangguang Yang, Yun School of Software Engineering Sun Yat-sen University Zhuhai519082 China School of Computer and Information Technology Beijing Jiaotong University Beijing China Meta AI New YorkNY10001 United States New York University New YorkNY10012 United States Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Computing Technologies Swinburne University of Technology Melbourne Australia

Deploying high-performance vision transformer (ViT) models on ubiquitous Internet of Things (IoT) devices to provide high-quality vision services will revolutionize the way we live, work, and interact with the world. Due to the contradiction between the limited resources of IoT devices and resource-intensive ViT models, the use of cloud servers to assist ViT model training has become mainstream. However, due to the larger number of parameters and floating-point operations (FLOPs) of the existing ViT models, the model parameters transmitted by cloud servers are large and difficult to run on resource-constrained IoT devices. To this end, this paper proposes a transmission-friendly ViT model, TFormer, for deployment on resource-constrained IoT devices with the assistance of a cloud server. The high performance and small number of model parameters and FLOPs of TFormer are attributed to the proposed hybrid layer and the proposed partially connected feed-forward network (PCS-FFN). The hybrid layer consists of nonlearnable modules and a pointwise convolution, which can obtain multitype and multiscale features with only a few parameters and FLOPs to improve the TFormer performance. The PCS-FFN adopts group convolution to reduce the number of parameters. The key idea of this paper is to propose TFormer with few model parameters and FLOPs to facilitate applications running on resource-constrained IoT devices to benefit from the high performance of the ViT models. Experimental results on the ImageNet-1K, MS COCO, and ADE20K datasets for image classification, object detection, and semantic segmentation tasks demonstrate that the proposed model outperforms other state-of-the-art models. Specifically, TFormer-S achieves 5% higher accuracy on ImageNet-1K than ResNet18 with 1.4× fewer parameters and FLOPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Task Offloading with Multi-Tier computing Resources in Next Generation Wireless Networks

arXiv

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arXiv 2022年

作者： Wang, Kunlun Jin, Jiong Yang, Yang Zhang, Tao Nallanathan, Arumugam Tellambura, Chintha Jabbari, Bijan The Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China The School of Communication and Electronic Engineering East China Normal University Shanghai200241 China The School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia Terminus Group Beijing100027 China ShanghaiTech University Shanghai201210 China Peng Cheng Laboratory Shenzhen518055 China United States The School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom The Department of Electrical and Computer Engineering University of Alberta Canada The Department of Electrical and Computer Engineering George Mason University FairfaxVA United States

With the development of next-generation wireless networks, the Internet of Things (IoT) is evolving towards the intelligent IoT (iIoT), where intelligent applications usually have stringent delay and jitter requirements. In order to provide low-latency services to heterogeneous users in the emerging iIoT, multi-tier computing was proposed by effectively combining edge computing and fog computing. More specifically, multi-tier computing systems compensate for cloud computing through task offloading and dispersing computing tasks to multi-tier nodes along the continuum from the cloud to things. In this paper, we investigate key techniques and directions for wireless communications and resource allocation approaches to enable task offloading in multi-tier computing systems. A multi-tier computing model, with its main functionality and optimization methods, is presented in details. We hope that this paper will serve as a valuable reference and guide to the theoretical, algorithmic, and systematic opportunities of multi-tier computing towards next-generation wireless networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Resource allocation

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HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding

arXiv

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arXiv 2022年

作者： Wang, Shi Tang, Daniel Zhang, Luchen Li, Huilin Han, Ding Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China TruX Luxembourg National Computer Network Emergency Response Technical Team Coordination Center of China China Department of Civil Engineering Technical University of Denmark Lyngby2800 Denmark Huazhong University of Science and Technology China

International Classification of Diseases (ICD) is a set of classification codes for medical records. Automated ICD coding, which assigns unique International Classification of Diseases codes with each medical record, is widely used recently for its efficiency and error-prone avoidance. However, there are challenges that remain such as heterogeneity, label unbalance, and complex relationships between ICD codes. In this work, we proposed a novel Bidirectional Hierarchy Framework(HieNet) to address the challenges. Specifically, a personalized PageRank routine is developed to capture the co-relation of codes, a bidirectional hierarchy passage encoder to capture the codes’ hierarchical representations, and a progressive predicting method is then proposed to narrow down the semantic searching space of prediction. We validate our method on two widely used datasets. Experimental results on two authoritative public datasets demonstrate that our proposed method boosts the state-of-the-art performance by a large margin. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Optimal resource allocation for flexible-grid entanglement distribution networks

arXiv

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arXiv 2022年

作者： Alnas, Jude Alshowkan, Muneer Rao, Nageswara S.V. Peters, Nicholas A. Lukens, Joseph M. Department of Electrical and Computer Engineering University of Alabama 245 7th Avenue TuscaloosaAL35406 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States Advanced Computing Methods Section Oak Ridge National Laboratory Oak RidgeTN37831 United States

We use a genetic algorithm (GA) as a design aid for determining the optimal provisioning of entangled photon spectrum in flex-grid quantum networks with arbitrary numbers of channels and users. After introducing a general model for entanglement distribution based on frequency-polarization hyperentangled biphotons, we derive upper bounds on fidelity and entangled bit rate for networks comprising one-to-one user connections. Simple conditions based on user detector quality and link efficiencies are found that determine whether entanglement is possible. We successfully apply a GA to find optimal resource allocations in four different representative network scenarios and validate features of our model experimentally in a quantum local area network in deployed fiber. Our results show promise for the rapid design of large-scale entanglement distribution networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Genetic algorithms

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Generative Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges

arXiv

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arXiv 2025年

作者： Zhang, Yuan Zhang, Xinfeng Qi, Xiaoming Wu, Xinyu Chen, Feng Yang, Guanyu Fu, Huazhu Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications [Southeast University Ministry of Education Nanjing China School of Biomedical Engineering Tsinghua University Beijing China Department of Biomedical Engineering Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore School of Information Science and Engineering Southeast University Nanjing China Department of Biostatistics Center for Global Health School of Public Health Nanjing Medical University Nanjing China Institute of High-Performance Computing Agency for Science Technology and Research Singapore

Generative modeling has emerged as a promising direction in computational pathology, offering capabilities such as data-efficient learning, synthetic data augmentation, and multimodal representation across diverse diagnostic tasks. This review provides a comprehensive synthesis of recent progress in the field, organized into four key domains: image generation, text generation, multimodal image–text generation, and other generative applications, including spatial simulation and molecular inference. By analyzing over 150 representative studies, we trace the evolution of generative architectures—from early generative adversarial networks to recent advances in diffusion models and foundation models with generative capabilities. We further examine the datasets and evaluation protocols commonly used in this domain and highlight ongoing limitations, including challenges in generating high-fidelity whole slide images, clinical interpretability, and concerns related to the ethical and legal implications of synthetic data. The review concludes with a discussion of open challenges and prospective research directions, with an emphasis on developing unified, multimodal, and clinically deployable generative systems. This work aims to provide a foundational reference for researchers and practitioners developing and applying generative models in computational pathology. © 2025, CC BY-NC-ND.

关键词： Generative adversarial networks

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Stripe charge order driven manipulation of Majorana bound states in 2M-WS2 topological superconductor

arXiv

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arXiv 2023年

作者： Fan, Xuemin Sun, Xiaoqi Zhu, Penghao Fang, Yuqiang Ju, Yongkang Yuan, Yonghao Huang, Fuqiang Hughes, Taylor L. Tang, Peizhe Xue, Qi-Kun Li, Wei State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Collaborative Innovation Center of Quantum Matter Beijing100084 China Frontier Science Center for Quantum Information Beijing100084 China Institute for Condensed Matter Physics Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States State Key Laboratory of High Performance Ceramics and Superfine Microstructure Shanghai Institute of Ceramics Chinese Academy of Science Shanghai200050 China State Key Laboratory of Rare Earth Materials Chemistry and Applications College of Chemistry and Molecular Engineering Peking University Beijing100871 China School of Materials Science and Engineering Beihang University Beijing100191 China Max Planck Institute for the Structure and Dynamics of Matter Center for Free-Electron Laser Science Hamburg22761 Germany Beijing Academy of Quantum Information Sciences Beijing100193 China Southern University of Science and Technology Shenzhen518055 China

Majorana bound states (MBSs) are building blocks for topological quantum computing. They can be generated via the combination of electronic topology and superconductivity. To achieve logic operations via Majorana braiding, positional control of the MBS must be established. To this end, exotic co-existing phases or collective modes in an intrinsic topological superconductor can provide a tuning knob to manipulate the MBS. Here we report the observation of a striped surface charge order coexisting with superconductivity and its controllable tuning of the MBS in the topological superconductor 2M-WS2 using low-temperature scanning tunneling microscopy. By applying an out-of-plane magnetic field, we observe that MBS is absent in vortices in the region with strong stripe order. This is in contrast to adjacent underlaying layers without charge order where vortex-bound MBSs are observed. Via theoretical simulations, we show that the surface stripe order does not destroy the bulk topology, but it can effectively modify the spatial distribution of MBS, i.e., it pushes them downward away from the 2M-WS2 surface. Our findings demonstrate that the interplay of charge order and topological superconductivity can be used to manipulate the positions of the MBS, and to explore of new states of matter. © 2023, CC BY-NC-ND.

关键词： Tungsten compounds

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Improving performance for simulating complex fluids on massively parallel computers by component loop-unrolling and communication hiding 22

Improving performance for simulating complex fluids on massi...

引用

22nd IEEE International Conference on high performance computing and Communications, 18th IEEE International Conference on Smart City and 6th IEEE International Conference on Data science and Systems, HPCC-SmartCity-DSS 2020

作者： Guo, Xiao-Wei Li, Chao Li, Wei Cao, Yu Liu, Yi Zhao, Ran Zhang, Sen Yang, Canqun Institute for Quantum Information State Key Laboratory of High Performance Computing Colloege of Computer National University of Defense Technology Changsha Hunan China Colloege of Meteorology and Oceanology National University of Defense Technology Changsha Hunan China Press and Communication Center of the Pla Technical Support Office of Network Department Beijing China

ISBN: (纸本)9781728176499

Due to the complex geometry and physical models of real-world engineering applications, the parallel performance of the mainstream computational fluid dynamics(CFD) codes is unsatisfactory. For complex fluids, an extra stress tensor governed by constitutive equations including nine components brings much more amount of computations. This paper focused on optimizing the most compute-intensive part of a simulation for complex fluids: The iterative linear solver for solving multicomponent equations. Based on the most widely used opensource CFD code OpenFOAM, we unrolled the component loops and replaced the blocking collective MPI calls to non-blocking communications. After operation rescheduling between the loops, the collective communications could be partly overlapped by the computations. Taking the preconditioned conjugate gradient (PCG) algorithm for instance, we presented the complete loop unrolled algorithm for solving multi-component equations. The numerical experiments showed 8.0%29.0% simulation time reduction for a demonstrative case with 2 million cells on 642048 cores. It is worth noting that the approach proposed in this paper is a high-level scheduling algorithm and could be used in combination with other intra-component optimization algorithms, *** pipelined CG methods. © 2020 IEEE.

关键词： Constitutive equations

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Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

arXiv

引用

arXiv 2022年

作者： Feng, Xin-Ru Li, Heng-Chao Wang, Rui Du, Qian Jia, Xiuping Plaza, Antonio School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS39762 United States School of Engineering and Information Technology University of New South Wales CanberraACT2612 Australia Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politécnica University of Extremadura Cáceres10071 Spain

Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing. Taking the NMF model as a baseline, we show how to improve NMF by utilizing the main properties of HSIs (e.g., spectral, spatial, and structural information). We categorize three important development directions including constrained NMF, structured NMF, and generalized NMF. Furthermore, several experiments are conducted to illustrate the effectiveness of associated algorithms. Finally, we conclude the article with possible future directions with the purposes of providing guidelines and inspiration to promote the development of hyperspectral unmixing. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud computing: A Review and Future Directions

arXiv

引用

arXiv 2021年

作者： Zhou, Guangyao Tian, Wenhong Buyya, Rajkumar Xue, Ruini Song, Liang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Laboratory Department of Computing and Information Systems The University of Melbourne Melbourne Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Energy Internet Research Institute Tsinghua University Chengdu China

With the acceleration of the Internet in Web 2.0, Cloud computing is a new paradigm to offer dynamic, reliable and elastic computing services. Efficient scheduling of resources or optimal allocation of requests is one of the prominent issues in emerging Cloud computing. Considering the growing complexity of Cloud computing, future Cloud systems will require more effective resource management methods. In some complex scenarios with difficulties in directly evaluating the performance of scheduling solutions, classic algorithms (such as heuristics and meta-heuristics) will fail to obtain an effective scheme. Deep reinforcement learning (DRL) is a novel method to solve scheduling problems. Due to the combination of deep learning (DL) and reinforcement learning (RL), DRL has achieved considerable performance in current studies. To focus on this direction and analyze the application prospect of DRL in Cloud scheduling, we provide a comprehensive review for DRL-based methods in resource scheduling of Cloud computing. Through the theoretical formulation of scheduling and analysis of RL frameworks, we discuss the advantages of DRL-based methods in Cloud scheduling. We also highlight different challenges and discuss the future directions existing in the DRL-based Cloud scheduling. © 2021, CC BY.

关键词： Deep reinforcement learning

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