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Bending and Base-Stacking Interactions in Double-Stranded DNA

Physical Review Letters 1999年第22期82卷 4560-4560页

作者： Zhou Haijun Zhang Yang Ou-Yang Zhong-can Institute of Theoretical Physics Academia Sinica P.O. Box 2735 Beijing 100080 China State Key Laboratory of Scientific and Engineering Computing Beijing 100080 China Center for Advanced Study Tsinghua University Beijing 100084 China

An elastic model for double-stranded polymers is constructed to study the recently observed DNA entropic elasticity, cooperative extensibility, and supercoiling property. With the introduction of a new structural parameter (the folding angle ϕ), bending deformations of sugar-phosphate backbones, steric effects of nucleotide base pairs, and base-stacking interactions are considered. The comprehensive agreements between theory and experiments both on torsionally relaxed DNA and on negatively supercoiled DNA strongly indicate that base-stacking interactions, although short-ranged in nature, dominate the elasticity of DNA and, hence, are of vital biological significance.

关键词： .parameter negatively extensibility supercoiling backbones relaxed supercoiled dsDNA Ou-Yang torsionally base-stacking folding angle steric effects nucleotide base

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Performance evaluation of packet discard schemes in ATM switches in heterogeneous traffic environment

Performance evaluation of packet discard schemes in ATM swit...

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IEEE International Conference on ATM

作者： Z. Jing H. Sun L. Li National Key Laboratory of Optical Fiber Communication University of Electronic Science and Technology Chengdu Sichuan China Center for Advanced Computing and Communications Department of Electrical and Computer Engineering Duke University Durham NC USA

In this paper, we study the performance of different packet discard schemes in ATM switches in a heterogeneous traffic environment. The throughput, fairness and delay characteristics of different packet discard schemes are studied in the environment of heterogeneous traffic and in the environment of fixed and time-varying available bandwidth. Through performance comparison, we show that the queuing system with EPD (early packet discard) algorithm gives higher goodput to the sources near it, and the sources far away from the system have lower goodput. It is necessary to integrate the RED (random early detection) algorithm with the EPD algorithm and the RED+EPD algorithm can provide relatively fair goodput to heterogeneous traffic. An ATM switch with the RED+EPD algorithm can provide higher throughput, better fairness and smaller delay than the EPD algorithm, so it can support the traffic with delay requirement.

关键词： Asynchronous transfer mode Packet switching Switches Traffic control Communication switching Delay Throughput Telecommunication traffic TCPIP Optical packet switching

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Uniform Graph Pre-training and Prompting for Transferable Recommendation

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ACM Transactions on Information Systems 1000年

作者： Qing Yu Lixin Zou Xiangyang Luo Xiangyu Zhao Chenliang Li Wuhan University China State Key Lab of Mathematical Engineering and Advanced Computing China City University of Hong Kong China

Recently, the paradigm of pre-training and fine-tuning has achieved impressive performance owing to their ability to transfer general knowledge from pre-trained domain to target domain. Meanwhile, Graph Neural Networks (GNNs) have gained prominence in recommender systems. However, there is a lack of unified pre-training and fine-tuning paradigms in graph-based recommendation systems. Applying pre-training and fine-tuning in graph-based recommendation is challenging due to the unique characteristics of recommendation data, including the non-uniform representation, negative transfer effects, and skewed data distributions. To overcome these challenges, we introduce ProRec (Pre-training and prompting Recommendation), a novel model that synergizes uniform graph pre-training with prompt-tuning for recommendation systems. Specifically, to address the challenge of inconsistent features across different recommendation datasets, ProRec constructs unified input features at the subgraph level and uses a Graph Auto-Encoder for pre-training, laying the foundation for uniform knowledge transfer from the pre-trained domain to the downstream domain. Additionally, ProRec employs prompt-tuning during the fine-tuning phase, which, in a parameter-efficient manner, enhancing the generalization of pre-trained knowledge to downstream tasks and thereby reducing negative transfer effects. Furthermore, a cross-layer contrastive learning strategy is adopted to eliminate uneven data distribution, promoting more evenly distributed and informative representations. Finally, extensive benchmark comparisons have demonstrated that ProRec outperforms the latest state-of-the-art methods. The source code necessary for replication is available at https://***/Code2Q/ProRec.

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Adaptive Alignment Contrastive Learning of Degradation Prediction for Blind Image Super-Resolution

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ACM Transactions on Multimedia computing, Communications, and Applications 1000年

作者： Xianhong Wen Sheng Ren Bin Hu Xiangyuan Zhu Tianyu Chen Kehua Guo School of Computer Science and Engineering Central South University China School of Computer and Electrical Engineering Hunan University of Arts and Science China School of Computer and Communication Engineering Changsha University of Science and Technology China Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police Academy China School of Computing Engineering and Mathematical Sciences La Trobe University Australia School of Computer Science and Engineering Central South University China and Furong Laboratory Central South University China

Blind super-resolution (BSR) is entering a new era focused on diverse and complex applications, where the trade-off between generalization and performance prevents models from performing as they should. Model performance decreases when trained on multiple degraded images due to the inter-class and intra-class imbalances in degradation prediction, which consists of degradation sampling and estimation. The inter-class imbalance in degradation estimation causes inaccurate estimates, leading to severe artifacts in images. The intra-class imbalance in degradation sampling causes a long-tail problem, leading to model collapse and satisfactory results only in specific applications. To tackle these challenges, we propose adaptive alignment contrastive learning (AACL), which includes adaptive degradation sampling (ADS) and \(\sigma\)-alignment. ADS utilizes nonlinear sampling by weighting the parameters of the degradation process for training uniformly degraded images, avoiding the long-tail problem. \(\sigma\)-alignment controls the standard deviation among positive samples, we identify a subset with small degraded distance, which aids contrastive learning in extracting representations more effectively. We extend AACL to several CNN-based and Transformer-based methods by coming up with a 6\(\times\)6 fair architecture with degradation representation fusion block (DRFB) and degradation representation fusion group (DRFG). DRFB and DRFG are designed for degradation representation fusion and image reconstruction, respectively. We evaluate on 6 types of degradation, and the improvement experiments on synthesized images show that our method balances performance and generalization, and is applicable to networks with different architectures. The comparison experiments show that our improved methods achieve promising results compared to SOTA methods. Code is available at: https://***/para999/AACL.

关键词： Blind super-resolution contrastive learning degradation prediction

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Nanomaterials for Sensors and Sustainable Energy Volume 2 1

Nanomaterials for Sensors and Sustainable Energy Volume 2

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丛书名： Materials Horizons: From Nature to Nanomaterials

1000年

作者： Sadia Ameen Shaheer M. Akhtar Ing Kong

ISBN: (数字)9789819606641

ISBN: (纸本)9789819606634;9789819606665

This book offers readers an extensive insight into advanced nanomaterials and their applications in the realm of energy generation and utilization. It places a specific emphasis on emerging nanomaterials and their diverse applications within energy-related sectors and sensor technology. The primary focus is on comprehending how these materials enhance the performance of energy storage devices and sensors. Furthermore, the book provides a comprehensive overview of the significant challenges faced in these areas and presents a forward looking perspective. Comprising ten chapters, the book extensively explores the applications of nanomaterials in energy-related fields, specifically in solar cells, supercapacitors, sensors, and more. It caters to the needs of budding researchers involved in the synthesis of nanomaterials and post-graduate students interested in delving into the intricacies of nanomaterials.

关键词： Nanoscale Science and Technology Materials Science, general Analytical Chemistry Renewable and Green Energy Nanotechnology

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Multi-Agent Reinforcement Learning based Edge Content Caching for Connected Autonomous Vehicles in IoV

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xiaolong Xu Linjie Gu Muhammad Bilal Maqbool Khan Yiping Wen Guoqiang Liu Yuan Yuan School of Software Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing University of Information Science and Technology China Changwang School of Honors Nanjing University of Information Science and Technology China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Korea Department of IT and Computer Science Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology Pakistan School of Computer Science and Engineering Hunan University of Science and Technology China School of Software Nanjing University of Information Science and Technology China School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Zhongguancun Laboratory China

Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.

关键词： Content Caching Reinforcement Learning Connected Autonomous Driving

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Transactions on Data Hiding and Multimedia Security IX 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Yun Q. Shi Feng Liu Weiqi Yan

ISBN: (数字)9783642550461

ISBN: (纸本)9783642550454

Since the mid 1990s, data hiding has been proposed as an enabling technology for securing multimedia communication and is now used in various applications including broadcast monitoring, movie fingerprinting, steganography, video indexing and retrieval and image authentication. Data hiding and cryptographic techniques are often combined to complement each other, thus triggering the development of a new research field of multimedia security. Besides, two related disciplines, steganalysis and data forensics, are increasingly attracting researchers and becoming another new research field of multimedia security. This journal, LNCS Transactions on Data Hiding and Multimedia Security, aims to be a forum for all researchers in these emerging fields, publishing both original and archival research results. The seven papers included in this special issue were carefully reviewed and selected from 21 submissions. They address the challenges faced by the emerging area of visual cryptography and provide the readers with an overview of the state of the art in this field of research.

关键词： Cryptology Systems and Data Security Coding and Information Theory Image Processing and Computer Vision

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Secure Collaborative Learning for Self-Adaptive Systems on Connected Autonomous Vehicles

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xiaotong Wu Yuwen Liu Xiaoxiao Chi Rong Jiang Xiaokang Zhou Wajid Rafique Maqbool Khan Alibaba Business School Hangzhou Normal University China College of Computer Science and Technology China University of Petroleum (East China) China Department of Computing Macquarie University Australia Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics China Faculty of Business Data Science Kansai University Japan and RIKEN Center for Advanced Intelligence Project Japan Department of Electrical and Software Engineering University of Calgary Canada Software Competence Center Hagenberg Austria

As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile, machine learning (ML) has been applied to enhance analysis and plan functions of MAPE so that self-adaptive systems have optimal adaption to changing conditions. However, most of ML-based approaches don’t utilize CAVs’ connectivity to collaboratively generate an optimal learner for MAPE, because of sensor data threatened by gradient leakage attack (GLA). In this article, we first design an intelligent architecture for MAPE-based self-adaptive systems on Web 3.0-based CAVs, in which a collaborative machine learner supports the capabilities of managing systems. Then, we observe by practical experiments that importance sampling of sparse vector technique (SVT) approaches cannot defend GLA well. Next, we propose a fine-grained SVT approach to secure the learner in MAPE-based self-adaptive systems, that uses layer and gradient sampling to select uniform and important gradients. At last, extensive experiments show that our private learner spends a slight utility cost for MAPE (e.g., \(0.77\%\) decrease in accuracy) defending GLA and outperforms the typical SVT approaches in terms of defense (increased by \(10\%\sim 14\%\) attack success rate) and utility (decreased by \(1.29\%\) accuracy loss).

关键词： collaborative learning optimal adaption vehicle application data security self-adaptive system

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Web-Age Information Management 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Feifei Li Guoliang Li Seung-won Hwang Bin Yao Zhenjie Zhang

ISBN: (数字)9783319080109

ISBN: (纸本)9783319080093

This book constitutes the refereed proceedings of the 15th International Conference on Web-Age Information Management, WAIM 2014, held in Macau, China, in June 2014. The 48 revised full papers presented together with 35 short papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on information retrieval; recommender systems; query processing and optimization; data mining; data and information quality; information extraction; mobile and pervasive computing; stream, time-series; security and privacy; semantic web; cloud computing; new hardware; crowdsourcing; social computing.

关键词： Information Storage and Retrieval Information Systems Applications (incl. Internet) Data Mining and Knowledge Discovery Database Management Artificial Intelligence

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Distributed Computation Offloading and Power Control for UAV-Enabled Internet of Medical Things

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ACM Transactions on Internet Technology 1000年

作者： Jiakun Gao Xiaolong Xu Lianyong Qi Wanchun Dou Xiaoyu Xia Xiaokang Zhou School of Software Nanjing University of Information Science and Technology NanJing China School of Software Nanjing University of Information Science and Technology NanJing China and Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) Nanjing University of Information Science and Technology Nanjing China College of Computer Science and Technology China University of Petroleum (East China) QingDao China and State Key Laboratory for Novel Software Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Technology RMIT University Victoria Australia Faculty of Data Science Shiga University Hikone Japan

The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT services, Mobile Edge computing (MEC) has emerged as an indispensable technology in smart health. Benefiting from the cost-effectiveness of deployment, unmanned aerial vehicles (UAVs) equipped with MEC servers in Non-Orthogonal Multiple Access (NOMA) have emerged as a promising solution for providing smart health services in proximity to medical devices (MDs). However, the escalating number of MDs and the limited availability of communication resources of UAVs give rise to a significant increase in transmission latency. Moreover, due to the limited communication range of UAVs, the geographically-distributed MDs lead to workload imbalance of UAVs, which deteriorates the service response delay. To this end, this paper proposes a UAV-enabled Distributed computation Offloading and Power control method with Multi-Agent, named DOPMA, for NOMA-based IoMT environment. Specifically, this paper introduces computation and transmission queue models to analyze the dynamic characteristics of task execution latency and energy consumption. Moreover, a credit assignment scheme-based reward function is designed considering both system-level rewards and rewards tailored to each MD, and an improved multi-agent deep deterministic policy gradient algorithm is developed to derive offloading and power control decisions independently. Extensive simulations demonstrate that the proposed method outperforms existing schemes, achieving \(7.1\%\) reduction in energy consumption and \(16\%\) decrease in average delay.

关键词： Internet of Medical Things (IoMT) Mobile Edge computing (MEC) Unmanned Aerial Vehicles (UAV) Non-Orthogonal Multiple Access (NOMA)

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