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Cooperative Spectrum Sensing Based on Centralized Double Threshold in MCN

China Communications 2020年第5期17卷 235-242页

作者： Hai Huang Junsheng Mu Xiaojun Jing School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of EducationBeijing 100876China

Cooperative spectrum sensing appears popular currently due to its ability to solve the issue of hidden terminal and improve detection performance in Cognitive Radio Networks. Meanwhile, double threshold based energy detector has attracted much attention for its low computational complexity and superior performance. Motivated by this, a cooperative spectrum sensing scheme is proposed in this paper based on centralized double threshold in the maritime communication networks(MCN), where the energy value of received signal in each cognitive node is forwarded to the fusion center for final decision based on double thresholds. Additionally, the proposed scheme is further optimized for the decisions that the energy is within the scope of maximum threshold and minimum threshold. Simulation experiments verify the performance of the proposed method.

关键词： cooperative spectrum sensing double threshold maritime communication network

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EALink: An Efficient and Accurate Pre-trained Framework for Issue-Commit Link Recovery

arXiv

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arXiv 2023年

作者： Zhang, Chenyuan Wang, Yanlin Wei, Zhao Xu, Yong Wang, Juhong Li, Hui Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China School of Software Engineering Sun Yat-sen University China Tencent China

Issue-commit links, as a type of software traceability links, play a vital role in various software development and maintenance tasks. However, they are typically deficient, as developers often forget or fail to create tags when making commits. Existing studies have deployed deep learning techniques, including pre-trained models, to improve automatic issue-commit link recovery. Despite their promising performance, we argue that previous approaches have four main problems, hindering them from recovering links in large software projects. To overcome these problems, we propose an efficient and accurate pre-trained framework called EALink for issue-commit link recovery. EALink requires much fewer model parameters than existing pre-trained methods, bringing efficient training and recovery. Moreover, we design various techniques to improve the recovery accuracy of EALink. We construct a large-scale dataset and conduct extensive experiments to demonstrate the power of EALink. Results show that EALink outperforms the state-of-the-art methods by a large margin (15.23%-408.65%) on various evaluation metrics. Meanwhile, its training and inference overhead is orders of magnitude lower than existing methods. We provide our implementation and data at https://***/KDEGroup/EALink. Copyright © 2023, The Authors. All rights reserved.

关键词： Recovery

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ExplSched: Maximizing Deep Learning Cluster Efficiency for Exploratory Jobs

ExplSched: Maximizing Deep Learning Cluster Efficiency for E...

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IEEE International Conference on Cluster computing

作者： Hongliang Li Hairui Zhao Zhewen Xu Xiang Li Haixiao Xu College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun China High Performance Computing Center Jilin University China

Resource management for Deep Learning (DL) clusters is essential for system efficiency and model training quality. Existing schedulers provided by DL frameworks are mostly adaptations from traditional HPC clusters and usually work on jobs’ makespan, assuming that DL training jobs finish completely. Unfortunately, it is reported that a fair amount of training jobs are exploratory jobs and often finish unsuccessfully (over 30%) in production clusters. This is due to the distinct characteristic of Deep Neural Network (DNN) training that it is an exploratory process of frequent user interventions, such as adjusting model structures, tuning hyperparameters, and exploring feature validity. Existing DL cluster schedulers using offline algorithms are not suitable for exploratory jobs when unexpected early terminations can cause noticeable resource waste. Moreover, DL training jobs are iterative and usually yield diminishing returns as they progress. Equally allocating resource among training iterations is not efficient, especially when dealing with exploratory jobs where it can worsen the degradation of system efficiency. The fundamental goal of a DL training job is to gain model quality improvement, usually indicated by the loss reduction (job profit) of a DNN model. This paper introduces a novel scheduling problem for exploratory jobs that seeks to maximize the overall training profit of a DL cluster. We propose ExplSched, an online scheduling solution based on the primal-dual framework, resulting in a competitive ratio of 2α that belongs to O(ln n). It uses a resource price function that emphasizes the importance of job profit to resource consumption ratio to make quick resource allocation decisions. Experimental results show that ExplSched achieved an average system utility improvement of 87.28% compared with other related work.

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Joint DNN Partition and Thread Allocation Optimization for Energy-Harvesting MEC Systems

Joint DNN Partition and Thread Allocation Optimization for E...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Shi, Yizhou Li, Liying Zeng, Yue Cong, Peijin Zhou, Junlong Nanjing University National Key Laboratory for Novel Software Technology Nanjing210023 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China

ISBN: (纸本)9783982674100

Deep neural networks (DNNs) have demonstrated exceptional performance, leading to diverse applications across various mobile devices (MDs). Considering factors like portability and environmental sustainability, an increasing number of MDs are adopting energy harvesting (EH) techniques for power supply. However, the computational intensity of DNNs presents significant challenges for their deployment on these resource-constrained devices. Existing approaches often employ DNN partition or offloading to mitigate the time and energy consumption associated with running DNNs on MDs. Nonetheless, existing methods frequently fall short in accurately modeling the execution time of DNNs, and do not consider to use thread allocation for further latency and energy consumption optimization. To solve these problems, we propose a dynamic DNN partition and thread allocation method to optimize the latency and energy consumption of running DNNs on EH-enabled MDs. Specifically, we first investigate the relationship between DNN inference latency and allocated threads and establish an accurate DNN latency prediction model. Based on the prediction model, a DRL-based DNN partition (DDP) algorithm is designed to find the optimal partitions for DNNs. A thread allocation (TA) algorithm is proposed to reduce the inference latency. Experimental results from our test-bed platform demonstrate that compared to four benchmarking methods, our scheme can reduce DNN inference latency and energy consumption by up to 37.3% and 38.5%. © 2025 EDAA.

关键词： Energy utilization

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Implementing a modified Smith predictor using chemical reaction networks and its application to protein translation 4

Implementing a modified Smith predictor using chemical react...

引用

4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Xiao, Yijun Lv, Hui Wang, Xing'an School of Software Engineering Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Liaoning Dalian116622 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Liaoning Shenyang110004 China College of Environmental and Chemical Engineering Dalian University Liaoning Dalian116622 China Dalian Chivy Biotechnology Co. Ltd Liaoning Dalian116622 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In this article, a special attention is paid to the biochemical controller synthesis for time delay systems and try to implement the well-established Smith predictor approach in the context of biochemical systems. Then, chemical reaction networks (CRNs) are adopted to construct a modified Smith predictor scheme (integrating Smith predictor and feedback compensation controllers) for the first time. Taking a delayed protein translation model as the background, the CRNs-based proposed scheme has access to a method that can solve the effect of co-translated mRNA decay in protein translation. In addition, considering that the decay of mRNA affects mRNA stability and protein production, the co-translated mRNA degradation is treated as an interference input of the protein translation process. Our results show that the impact of a disturbance input (mRNA degradation) is restrained by the modified control strategy. The CRNs-based modified Smith predictor makes the protein translation process more robust and achieves protein output quickly and stably. © 2022 IEEE.

关键词： Time delay

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A Point-Wise Isogeometric Contact Analysis with Interpolatory Nurbs Basis

SSRN

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SSRN 2024年

作者： Peng, Xuan Zeng, Zheng Wu, Shengchuan Yu, Peng Lian, Haojie Department of Mechanical Engineering Suzhou University of Science and Technology Suzhou215009 China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong University Chengdu610031 China College of Civil Engineering and Architecture Key Laboratory of Disaster Prevention and Structural Safety Ministry of Education Guangxi Key Laboratory of Disaster Prevention and Structural Safety Scientific Research Center of Engineering Mechanics Guangxi University Nanning530004 China Key Laboratory of In-situ Property-improving Mining Ministry of Education Taiyuan University of Technology Taiyuan030024 China Suzhou Asia-pacific Intelligent Transmission Technology Co. Ltd Suzhou215132 China

The point-to-segment approach is appealing for contact analysis due to its efficiency. In this work, formulations of a point-wise isogeometric contact analysis method for large-deformation bodies are presented. The approach is based on the transformed non-uniform rational basis spline (NURBS) basis, which possesses an interpolation property at the corresponding collocation points at the slave contact boundary. The physical meaning of the contact forces at the collocation points (or control points because of the interpolation property) is clear. The idea is realized under the framework of two-dimensional frictionless contact with penalty regularization. Numerical examples including the contact patch test, Hertzian problem, and iron sliding contact revealed the robustness and accuracy of the proposed method for contact analysis. © 2024, The Authors. All rights reserved.

关键词： Interpolation

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Reverse Multi-Choice Dialogue Commonsense Inference with Graph-of-Thought

arXiv

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arXiv 2023年

作者： Zheng, Li Fei, Hao Li, Fei Li, Bobo Liao, Lizi Ji, Donghong Teng, Chong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China National University of Singapore Singapore Singapore Management University Singapore

With the proliferation of dialogic data across the Internet, the Dialogue Commonsense Multi-choice Question Answering (DC-MCQ) task has emerged as a response to the challenge of comprehending user queries and intentions. Although prevailing methodologies exhibit effectiveness in addressing single-choice questions, they encounter difficulties in handling multi-choice queries due to the heightened intricacy and informational density. In this paper, inspired by the human cognitive process of progressively excluding options, we propose a three-step Reverse Exclusion Graph-of-Thought (ReX-GoT) framework, including Option Exclusion, Error Analysis, and Combine Information. Specifically, our ReX-GoT mimics human reasoning by gradually excluding irrelevant options and learning the reasons for option errors to choose the optimal path of the GoT and ultimately infer the correct answer. By progressively integrating intricate clues, our method effectively reduces the difficulty of multi-choice reasoning and provides a novel solution for DC-MCQ. Extensive experiments on the CICERO and CICEROv2 datasets validate the significant improvement of our approach on DC-MCQ task. On zero-shot setting, our model outperform the best baseline by 17.67% in terms of F1 score for the multi-choice task. Most strikingly, our GPT3.5-based ReX-GoT framework achieves a remarkable 39.44% increase in F1 score. Our code is available at: https://***/ZhengL00/ReX-GoT. Copyright © 2023, The Authors. All rights reserved.

关键词： HTTP

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Structural repurposing of SGLT2 inhibitor empagliflozin for strengthening anti-heart failure activity with lower glycosuria

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Acta Pharmaceutica Sinica B 2023年第4期13卷 1671-1685页

作者： Yixiang Xu Chao Zhang Kai Jiang Xinchun Yang Feng Chen Zhiyang Cheng Jinlong Zhao Jiaxing Cheng Xiaokang Li Xin Chen Luoyifan Zhou Hao Duan Yunyuan Huang Yaozu Xiang Jian Li State Key Laboratory of Bioreactor Engineering Shanghai Frontiers Science Center of Optogenetic Techniques for Cell MetabolismFrontiers Science Center for Materiobiology and Dynamic ChemistryShanghai Key Laboratory of New Drug DesignSchool of PharmacyEast China University of Science and TechnologyShanghai 200237China Shanghai East Hospital School of Life Sciences and TechnologyTongji UniversityShanghai 200092China East China University of Science and Technology-Tengbai Pharmaceutical Innovative Drugs Joint Research Institute Zhuhai Tengbai Pharmaceutical Co.Ltd.Zhuhai 519000China Hubei Key Laboratory of Genetic Regulation and Integrative Biology School of Life SciencesCentral China Normal UniversityWuhan 430079China Yunnan Key Laboratory of Screening and Research on Anti-pathogenic Plant Resources from West Yunnan College of PharmacyDali UniversityDali 671000China Clinical Medicine Scientific and Technical Innovation Center Shanghai Tenth People's HospitalTongji University School of MedicineShanghai 200092China Key Laboratory of Tropical Biological Resources of Ministry of Education College of PharmacyHainan UniversityHaikou 570228China

Sodium-glucose cotransporter 2(SGLT2)inhibitors have been reapproved for heart failure(HF)therapy in patients with and without ***,the initial glucose-lowering indication of SGLT2i has impeded their uses in cardiovascular clinical practice.A challenge of SGLT2i then becomes how to separate their anti-HF activity from glucose-lowering *** address this issue,we conducted structural repurposing of EMPA,a representative SGLT2 inhibitor,to strengthen anti-HF activity and reduce the SGLT2-inhibitory activity according to structural basis of inhibition of *** to EMPA,the optimal derivative JX01,which was produced by methylation of C2—OH of the glucose ring,exhibited weaker SGLT2-inhibitory activity(IC_(50)>100 nmol/L),and lower glycosuria and glucose-lowering side-effect,better NHE1-inhibitory activity and cardioprotective effect in HF ***,JX01 showed good safety profiles in respect of single-dose/repeat-dose toxicity and hERG activity,and good pharmacokinetic properties in both mouse and rat ***,the present study provided a paradigm of drug repurposing to discover novel anti-HF drugs,and indirectly demonstrated that SGLT2-independent molecular mechanisms play an important role in cardioprotective effects of SGLT2 inhibitors.

关键词： Heart failure SGLT2 inhibitor Empagliflozin Structural repurposing NHE1(sodium-hydrogen exchanger 1)

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EALink: An Efficient and Accurate Pre-Trained Framework for Issue-Commit Link Recovery

EALink: An Efficient and Accurate Pre-Trained Framework for ...

引用

IEEE International Conference on Automated Software engineering (ASE)

作者： Chenyuan Zhang Yanlin Wang Zhao Wei Yong Xu Juhong Wang Hui Li Rongrong Ji Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China School of Software Engineering Sun Yat-sen University China Tencent China

Issue-commit links, as a type of software traceability links, play a vital role in various software development and maintenance tasks. However, they are typically deficient, as developers often forget or fail to create tags when making commits. Existing studies have deployed deep learning techniques, including pretrained models, to improve automatic issue-commit link recovery. Despite their promising performance, we argue that previous approaches have four main problems, hindering them from recovering links in large software projects. To overcome these problems, we propose an efficient and accurate pre-trained framework called EALink for issue-commit link recovery. EALink requires much fewer model parameters than existing pre-trained methods, bringing efficient training and recovery. Moreover, we design various techniques to improve the recovery accuracy of EALink. We construct a large-scale dataset and conduct extensive experiments to demonstrate the power of EALink. Results show that EALink outperforms the state-of-the-art methods by a large margin (15.23%-408.65%) on various evaluation metrics. Meanwhile, its training and inference overhead is orders of magnitude lower than existing methods. We provide our implementation and data at https://***/KDEGroup/EALink.

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A Novel Collaborative Group Construction Method Based on Learner Community Interaction Characteristics

A Novel Collaborative Group Construction Method Based on Lea...

引用

Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Guangxu Wang Peng Li Zongyang Xie Yue Xu School of Computer Science Shaanxi Normal University Xi'an China Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xian China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi'an China Xi'an Key Laboratory of Cultural Tourism Resources Development and Utilization Xi'an China College of Computer Science and Engineering Northwest Normal University Lanzhou China Xi'an Branch of Chaoxing Group Co. Ltd Xi'an China

ISBN: (数字)9798331522285

ISBN: (纸本)9798331522292

With the rapid development of Internet technology, collaborative learning has become a new learning method, which has attracted many people's attention. However, in the current practical application of collaborative learning, almost all collaborative groups are divided randomly by seat, student ID, or other methods. The method of dividing collaborative groups is not scientific enough, and the ultimate learning efficiency could have been significantly improved. Therefore, this article proposes a method for constructing collaborative groups based on learner interaction features and validates its application and the effectiveness of this division method.

关键词： Seminars Learning systems Federated learning Collaboration Internet Information technology

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