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arXiv 2021年

作者： Wang, Xiong Ye, Jiancheng Lui, John C.S. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Network Technology Lab Hong Kong Research Center Huawei Technologies Co. Ltd. Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Mobile edge computing facilitates users to offload computation tasks to edge servers for meeting their stringent delay requirements. Previous works mainly explore task offloading when system-side information is given (e.g., server processing speed, cellular data rate), or centralized offloading under system uncertainty. But both generally fall short to handle task placement involving many coexisting users in a dynamic and uncertain environment. In this paper, we develop a multi-user offloading framework considering unknown yet stochastic system-side information to enable a decentralized user-initiated service placement. Specifically, we formulate the dynamic task placement as an online multi-user multi-armed bandit process, and propose a decentralized epoch based offloading (DEBO) to optimize user rewards which are subjected under network delay. We show that DEBO can deduce the optimal user-server assignment, thereby achieving a close-to-optimal service performance and tight O(log T) offloading regret. Moreover, we generalize DEBO to various common scenarios such as unknown reward gap, dynamic entering or leaving of clients, and fair reward distribution, while further exploring when users' offloaded tasks require heterogeneous computing resources. Particularly, we accomplish a sub-linear regret for each of these instances. Real measurements based evaluations corroborate the superiority of our offloading schemes over state-of-the-art approaches in optimizing delay-sensitive rewards. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

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A Deep Learning Model Incorporating Knowledge Representation Vectors and Its Application in Diabetes Prediction

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Disease Markers 2022年第1期2022卷 7593750页

作者： Xu, He Zheng, Qunli Zhu, Jingshu Xie, Zuoling Cheng, Haitao Li, Peng Ji, Yimu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Jiangsu Province Nanjing 210023 China Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing 210023 China Nanjing Center of HPC China Nanjing 210023 China Jiangsu Research Engineering of HPC and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China Department of Endocrinology Zhongda Hospital Southeast University Nanjing 210009 China

The deep learning methods for various disease prediction tasks have become very effective and even surpass human experts. However, the lack of interpretability and medical expertise limits its clinical application. This paper combines knowledge representation learning and deep learning methods, and a disease prediction model is constructed. The model initially constructs the relationship graph between the physical indicator and the test value based on the normal range of human physical examination index. And the human physical examination index for testing value by knowledge representation learning model is encoded. Then, the patient physical examination data is represented as a vector and input into a deep learning model built with self-attention mechanism and convolutional neural network to implement disease prediction. The experimental results show that the model which is used in diabetes prediction yields an accuracy of 97.18% and the recall of 87.55%, which outperforms other machine learning methods (e.g., lasso, ridge, support vector machine, random forest, and XGBoost). Compared with the best performing random forest method, the recall is increased by 5.34%, respectively. Therefore, it can be concluded that the application of medical knowledge into deep learning through knowledge representation learning can be used in diabetes prediction for the purpose of early detection and assisting diagnosis. © 2022 He Xu et al.

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Robust and Fast Measure of Information via Low-rank Representation

arXiv

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arXiv 2022年

作者： Dong, Yuxin Gong, Tieliang Yu, Shujian Chen, Hong Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Ministry of Education Xi’an710049 China Machine Learning Group UiT - The Arctic University of Norway Norway College of Science Huazhong Agriculture University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China

The matrix-based Rényi’s entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution. This intriguing property makes it widely applied in statistical inference and machine learning tasks. However, this information theoretical quantity is not robust against noise in the data, and is computationally prohibitive in large-scale applications. To address these issues, we propose a novel measure of information, termed low-rank matrix-based Rényi’s entropy, based on low-rank representations of infinitely divisible kernel matrices. The proposed entropy functional inherits the specialty of of the original definition to directly quantify information from data, but enjoys additional advantages including robustness and effective calculation. Specifically, our low-rank variant is more sensitive to informative perturbations induced by changes in underlying distributions, while being insensitive to uninformative ones caused by noises. Moreover, low-rank Rényi’s entropy can be efficiently approximated by random projection and Lanczos iteration techniques, reducing the overall complexity from O(n3) to O(n2s) or even O(ns2), where n is the number of data samples and s n. We conduct large-scale experiments to evaluate the effectiveness of this new information measure, demonstrating superior results compared to matrix-based Rényi’s entropy in terms of both performance and computational efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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Incorporating edge sharpening and covariance attention for named entity recognition

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Neurocomputing 2025年 643卷

作者： Yang, Caiwei Chen, Yanping Yu, Shuai Huang, Ruizhang Qin, Yongbin Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guizhou Guiyang550025 China State Key Laboratory of Public Big Data Guizhou university Guizhou Guiyang550025 China College of Computer Science and Technology Guizhou University Guiyang550025 China

Named Entity Recognition (NER) is a key application in the field of Artificial Intelligence and Natural Language Processing, which automatically identifies and categorizes entities in text by intelligent algorithms. In NER, all spans of a sentence can be organized into a two-dimensional representation. The semantic plane has the advantage to represent the semantic structure of a sentence and to learn the interaction between spans. One of an important phenomenon of this representation is that neighboring elements of the semantic plane are spans denoted to overlapped subsequences in a sentence. Because they share the same contextual features and semantic dependencies, it is difficult to distinguish true entities from the backgrounds. Therefore, refining span representations and building the semantic dependency between spans is helpful for the entity recognition task. In this paper, we propose an Edge Sharpening and Covariance Attention (ES&CA) model to support recognizing named entities from the semantic plane representation. The edge sharpening (ES) module adopts a differential convolution to sharpen the semantic gradients in the semantic plane, which has the ability to gather semantic information from neighborhoods. In the covariance attention (CA) module, the covariance between spans are applied to weight the attention of spans relevant to task-relevant learning objective. Establishing semantic relationships across spans is a highly successful approach. The ES&CA model is assessed on five public datasets for nested and flattened named entity recognition. The evaluation results demonstrate the effectiveness of our strategy in distinguishing entity spans from the backgrounds, hence significantly enhancing the final performance. © 2025 Elsevier B.V.

关键词： Natural language processing systems

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NEAR-INSTANTANEOUS ATMOSPHERIC RETRIEVALS AND MODEL COMPARISON WITH FASTER

arXiv

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arXiv 2025年

作者： Lueber, Anna Karchev, Konstantin Fisher, Chloe Heim, Matthias Trotta, Roberto Heng, Kevin Faculty of Physics Ludwig Maximilian University Scheinerstrasse 1 Bavaria MunichD-81679 Germany Center for Space and Habitability University of Bern Gesellschaftsstrasse 6 BernCH-3012 Switzerland via Bonomea 265 Trieste34136 Italy Department of Physics University of Oxford Keble Road OxfordOX1 3RH United Kingdom Astrophysics Group Physics Department Blackett Lab Imperial College London Prince Consort Road LondonSW7 2AZ United Kingdom INFN – National Institute for Nuclear Physics Via Valerio 2 Trieste34127 Italy Italian Research Center on High-Performance Computing Big Data and Quantum Computing Italy ARTORG Center for Biomedical Engineering Research University of Bern Murtenstrasse 50 BernCH-3008 Switzerland University College London Department of Physics & Astronomy Gower St LondonWC1E 6BT United Kingdom Astronomy & Astrophysics Group Department of Physics University of Warwick CoventryCV4 7AL United Kingdom

In the era of the James Webb Space Telescope (JWST), the dramatic improvement in the spectra of exoplanetary atmospheres demands a corresponding leap forward in our ability to analyze them: atmospheric retrievals need to be performed on thousands of spectra, applying to each large ensembles of models (that explore atmospheric chemistry, thermal profiles and cloud models) to identify the best one(s). In this limit, traditional Bayesian inference methods such as nested sampling become prohibitively expensive. We introduce FASTER (Fast Amortized Simulation-based Transiting Exoplanet Retrieval), a neural-network based method for performing atmospheric retrieval and Bayesian model comparison at a fraction of the computational cost of classical techniques. We demonstrate that the marginal posterior distributions of all parameters within a model as well as the posterior probabilities of the models we consider match those computed using nested sampling both on mock spectra, and for the real NIRSpec PRISM spectrum of WASP-39b. The true power of the FASTER framework comes from its amortized nature, which allows the trained networks to perform practically instantaneous Bayesian inference and model comparison over ensembles of spectra – real or simulated – at minimal additional computational cost. This offers valuable insight into the expected results of model comparison (e.g., distinguishing cloudy from cloud-free and isothermal from non-isothermal models), as well as their dependence on the underlying parameters, which is computationally unfeasible with nested sampling. This approach will constitute as large a leap in spectral analysis as the original retrieval methods based on Markov Chain Monte Carlo have proven to be. © 2025, CC BY.

关键词： Fog

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RAGRAPH: a general retrieval-augmented graph learning framework 24

RAGRAPH: a general retrieval-augmented graph learning framew...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xinke Jiang Rihong Qiu Yongxin Xu Wentao Zhang Yichen Zhu Ruizhe Zhang Yuchen Fang Xu Chu Junfeng Zhao Yasha Wang Key Laboratory of High Confidence Software Technologies (Peking University) School of Computer Science Peking University China University of Electronic Science and Technology of China Key Laboratory of High Confidence Software Technologies (Peking University) School of Computer Science Peking University China and Center on Frontiers of Computing Studies Peking University Beijing China and Peking University Information Technology Institute Tianjin Binhai China Key Laboratory of High Confidence Software Technologies (Peking University) School of Computer Science Peking University China and Big Data Technology Research Center Nanhu Laboratory Jiaxing China Key Laboratory of High Confidence Software Technologies (Peking University) School of Computer Science Peking University China and Peking University Information Technology Institute Tianjin Binhai China

ISBN: (纸本)9798331314385

Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances. In this paper, we introduce a novel framework called General Retrieval-Augmented Graph Learning (RAGRAPH), which brings external graph data into the general graph foundation model to improve model generalization on unseen scenarios. On the top of our framework is a toy graph vector library that we established, which captures key attributes, such as features and task-specific label information. During inference, the RAGRAPH adeptly retrieves similar toy graphs based on key similarities in downstream tasks, integrating the retrieved data to enrich the learning context via the message-passing prompting mechanism. Our extensive experimental evaluations demonstrate that RAGRAPH significantly outperforms state-of-the-art graph learning methods in multiple tasks such as node classification, link prediction, and graph classification across both dynamic and static datasets. Furthermore, extensive testing confirms that RAGRAPH consistently maintains high performance without the need for task-specific fine-tuning, highlighting its adaptability, robustness, and broad applicability.

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Superpage-Friendly Page Table Design for Hybrid Memory Systems 6th

Superpage-Friendly Page Table Design for Hybrid Memory Syste...

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6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020

作者： Wang, Xiaoyuan Liu, Haikun Liao, Xiaofei Jin, Hai National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan430074 China Service Computing Technology and System Lab Huazhong University of Science and Technology Wuhan430074 China Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811579806

Page migration has long been adopted in hybrid memory systems comprising dynamic random access memory (DRAM) and non-volatile memories (NVMs), to improve the system performance and energy efficiency. However, page migration introduces some side effects, such as more translation lookaside buffer (TLB) misses, breaking memory contiguity, and extra memory accesses due to page table updating. In this paper, we propose superpage-friendly page table called SuperPT to reduce the performance overhead of serving TLB misses. By leveraging a virtual hashed page table and a hybrid DRAM allocator, SuperPT performs address translations in a flexible and efficient way while still remaining the contiguity within the migrated pages. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Energy efficiency

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Enhanced Particle Swarm Optimization for Workflow Scheduling in Clouds

Enhanced Particle Swarm Optimization for Workflow Scheduling...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Chang Lu Dayu Feng Jie Zhu Haiping Huang Nanjing University of Posts & Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China

ISBN: (纸本)9781665426565

As a NP-hard problem, it is always baffling to figure out a scheduling strategy to arrange the interconnected tasks of a workflow on the infinite number of resources in the cloud environment so that the workflow can be addressed efficiently and robustly. This paper focuses on scheduling the workflow’s tasks on the cloud resources with less rental cost of resources while the whole schedule length (makespan) will not exceed the given deadline. As one of the most popular evolutionary algorithms, particle swarm optimization (PSO) has been successfully applied for the workflow scheduling problem. Inspired by the idea of multiple groups and the distributed parallel computing, we develop an enhanced PSO algorithm for the workflow scheduling problem in clouds. Besides, a pretreatment strategy is adopted to simplify the workflow’s structure. The experimental results demonstrate that our proposal has good performance on improving the algorithm’s searching ability and finding better solutions.

关键词： Schedules Costs Processor scheduling NP-hard problem Parallel processing Scheduling Proposals

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A parameter adaptive differential evolution based on depth information

A parameter adaptive differential evolution based on depth i...

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作者： Meng, Zhenyu Yang, Cheng Meng, Fanjia Chen, Yuxin Lin, Fang Institute of Artificial Intelligence Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou China Guanzhuang Central Primary School of Zhangqiu District Jinan China

Differential Evolution (DE) was an easy-coding and efficient stochastic algorithm for global optimization, and the whole optimization process simulates biological evolution. Superior individuals of the population that were suitable for the environment were retained during the evolution, and consequently the tolerable solutions could be obtained in the end. Despite the excellent performance of DE algorithm, there were still some shortcomings. For example, the general performance of DE depended largely on mutation strategy and control parameters, how to design the appropriate control parameters and mutation strategy were difficult tasks. Here a novel DE variant was proposed to overcome these shortcomings. By incorporating the depth information of previous generations of populations, a better diversity of trial vector candidates could be secured during the evolution process. Moreover, the thought that successful parameters should be retained to guide the update of themselves during the evolution was also incorporated into the novel algorithm. The optimization performance of the new proposed DE variant was verified under CEC 2013 test suit containing 28 benchmarks, and the results showed its competitiveness with several state-of-the-art DE variants. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Global optimization

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Improving the lifestyle behavior of type 2 diabetes mellitus patients using a mobile application

Improving the lifestyle behavior of type 2 diabetes mellitus...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Andreia Pinto João Viana Diogo Martinho Vítor Crista José Miguel Diniz Joana Reis João Matos David Greer Goreti Marreiros Alberto Freitas Department of Community Medicine Information and Health Decision Sciences (MEDCIDS) Faculty of Medicine Center for Health Technology and Services Research (CINTESIS) University of Porto Porto Portugal Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Institute of Engineering Polytechnic of Porto Porto Portugal Health Data Science PhD Program Faculty of Medicine Center for Health Technology and Services Research (CINTESIS) University of Porto Lisboa Portugal CLOO - Behavioral Insights Unit Faculty of Psychology CICPSI - Research Center for Psychological Science University of Lisbon Lisboa Portugal Behavioral Insights Unit CLOO Porto Portugal Behavioral Insights Unit CLOO Lisboa Portugal

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

Type 2 diabetes is an increasingly prevalent disease and patients do not always manage the disease properly. Therefore, creating tools that help diabetics self-manage their condition over time is of the utmost importance. Technological tools that include features meeting this population's needs, with an integrated personalized feedback system, in an environment of gamified incentives, may be the way for developing a sustained app's usage. This work aims to present a still in development, novel approach to managing diabetes type 2 with a focus on data analysis from user-inputs, gamification, and personalized coaching, with an accessible user interface. Thus, we will present some functionalities, in particular the theoretical and practical concept that is behind this development, namely the Transtheoretical model of behavior change to create different profiles, to provide customized feedback according to the user's behavioral stage, and also to ascertain if it induces any improvement at the behavioral level.

关键词： Computers data analysis Computational modeling User interfaces Diabetes Behavioral sciences Mobile applications

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