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Conformational Aspects in the Formation of Structures of the Backbone of Polypeptide Chains in Proteins: the Relationship between Conformational Stability/Lability and β-Turns

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Biophysics (Russian Federation) 2023年第1期68卷 1-5页

作者： Torshin, I. Yu. Filatov, I.V. Batyanovskii, A.V. Smirnov, K.V. Anashkina, A.A. Esipova, N.G. Tumanyan, V.G. Federal Research Center “Computer Science and Control” Moscow 119333 Russian Federation Moscow Institute of Physics and Technology Moscow oblast Dolgoprudny 141700 Russian Federation Institute of Biophysics and Cell Engineering National Academy of Sciences of Belarus Minsk 220072 Belarus Engelhardt Institute of Molecular Biology Russian Academy of Sciences Moscow 119991 Russian Federation

Abstract: To assess the nature of the relationship between the integral conformational stability of tetrapeptides and the main types of β-turns (which are also tetrapeptides), spectrum diagrams, the asymmetry of the distribution of conformationally stable and unstable tetrapeptides have been calculated. It has been shown that β-turns of types I', II, and II' consist mainly of conformationally labile peptides;this is consistent with the context-predetermined nature of their structure. Since, as we have shown earlier, in this case the conformation is imposed by external conditions (specifically, the closure of the cycle), the prevalence of conformation-labile peptides facilitates the formation of the structure due to external factors. The type I β-turn is an exception, since peptides with different conformational lability are distributed fairly even in it. It can be assumed that the formation of the type I β-turn is not contextually determined. © 2023, Pleiades Publishing, Inc.

关键词： conformational analysis conformational stable/labile segments of protein local protein structure molecular dynamics statistical analysis β-turns

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Physical Safety and Cyber Security Analysis of Multi-Agent Systems:A Survey of Recent Advances

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 319-333页

作者： Dan Zhang Gang Feng Yang Shi Dipti Srinivasan the Research Center of Automation and Artificial Intelligence Zhejiang University of TechnologyHangzhou310023 Key Laboratory of Advanced Control and Optimization for Chemical Processes(East China University of Science and Technology) Ministry of EducationShanghai 200237China the Department of Biomedical Engineering City University of Hong KongHong KongChina the Department of Mechanical Engineering University of VictoriaCanada the Department of Electrical and Computer Engineering National University of SingaporeSingapore

Multi-agent systems(MASs)are typically composed of multiple smart entities with independent sensing,communication,computing,and decision-making ***,MASs have a wide range of applications in smart grids,smart manufacturing,sensor networks,and intelligent transportation *** of the MASs are often coordinated through information interaction among agents,which is one of the most important factors affecting coordination and cooperation ***,unexpected physical faults and cyber attacks on a single agent may spread to other agents via information interaction very quickly,and thus could lead to severe degradation of the whole system performance and even destruction of *** paper is concerned with the safety/security analysis and synthesis of MASs arising from physical faults and cyber attacks,and our goal is to present a comprehensive survey on recent results on fault estimation,detection,diagnosis and fault-tolerant control of MASs,and cyber attack detection and secure control of MASs subject to two typical cyber ***,the paper concludes with some potential future research topics on the security issues of MASs.

关键词： Consensus deception attack deny-of-service(DoS)attack fault detection fault estimation fault tolerant control multiagent systems

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UniDetector: Towards Universal Object Detection With Heterogeneous Supervision

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2024年第7期47卷 5205-5222页

作者： Zhenyu Wang Yali Li Xi Chen Ser-Nam Lim Antonio Torralba Hengshuang Zhao Shengjin Wang Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology (BNRist) Beijing China Department of Computer Science The University of Hong Kong Hong Kong Meta AI Boston MA USA Massachusetts Institute of Technology Cambridge MA USA

In this paper, we formally address universal object detection, which aims to detect every category in every scene. The dependence on human annotations, the limited visual information, and the novel categories in open world severely restrict the universality of detectors. We propose UniDetector, a universal object detector that recognizes enormous categories in the open world. The critical points for UniDetector are: 1) it leverages images of multiple sources and heterogeneous label spaces in training through image-text alignment, which guarantees sufficient information for universal representations. 2) it involves heterogeneous supervision training, which alleviates the dependence on the limited fully-labeled images. 3) it generalizes to open world easily while keeping the balance between seen and unseen classes. 4) it further promotes generalizing to novel categories through our proposed decoupling training manner and probability calibration. These contributions allow UniDetector to detect over 7 k categories, the largest measurable size so far, with only about 500 classes participating in training. Our UniDetector behaves the strong zero-shot ability on large-vocabulary datasets - it surpasses supervised baselines by more than 5% without seeing any corresponding images. On 13 detection datasets with various scenes, UniDetector also achieves state-of-the-art performance with only a 3% amount of training data.

关键词： Training Detectors Object detection Vocabulary Proposals Task analysis Annotations

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Multilingual Parameter-Sharing Adapters: A Method for Optimizing Low-Resource Neural Machine Translation

Multilingual Parameter-Sharing Adapters: A Method for Optimi...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yunlong Zhang Nan Chen Yonghe Wang Xiangdong Su Feilong Bao School of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China National and Local Joint Engineering Research Center of Mongolian Information Processing Technology Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Adapter-based Multilingual Neural Machine Translation (MNMT) has become a significant approach in low-resource language translation by mitigating data imbalances between high-resource and low-resource language pairs and reducing training costs. However, existing adapter-based methods lack generalization in cross-lingual settings, particularly under low-resource conditions, where their scalability is limited. Additionally, current methods often introduce independent adapter modules for each language, leading to a linear increase in model parameters with the number of languages. To address these challenges, we propose a multilingual parameter-sharing adapter approach. Moreover, we introduce a neural architecture search (NAS)-based strategy to improve translation performance. Experimental results demonstrate that the multilingual parameter-sharing adapter exhibits competitive performance on both low-resource and high-resource datasets. The multilingual parameter-sharing adapter method has only 400K trainable parameters, which is 20× lower than the parameters of the traditional adapter method.

关键词： Training Degradation Adaptation models Translation Scalability Neural machine translation Signal processing Multilingual Neural architecture search Speech processing

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Advances in solar energy harvesting integrated by van der Waals graphene heterojunctions

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RSC Advances 2023年第44期13卷 31273-31291页

作者： Le, Top Khac Mai, The-Hung Iqbal, Muhammad Aamir Vernardou, Dimitra Dao, Van-Duong Ponnusamy, Vinoth Kumar Rout, Chandra Sekhar Pham, Phuong V. Faculty of Materials Science and Technology University of Science Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Department of Physics National Sun Yat-sen University Kaohsiung80424 Taiwan School of Materials Science and Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering School of Engineering Hellenic Mediterranean University Heraklion71410 Greece Faculty of Biotechnology Chemistry and Environmental Engineering Phenikaa University Hanoi100000 Viet Nam Department of Medicinal and Applied Chemistry and Research Center for Precision Environmental Medicine Kaohsiung Medical University Kaohsiung807 Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung807 Taiwan Department of Chemistry National Sun Yat-sen University Kaohsiung80424 Taiwan Centre for Nano and Material Sciences Jain University Bangalore562112 India

Graphene has garnered increasing attention for solar energy harvesting owing to its unique features. However, limitations hinder its widespread adoption in solar energy harvesting, comprising the band gapless in the molecular orbital of graphene lattice, its vulnerability to oxidation in oxidative environments, and specific toxic properties that require careful consideration during development. Beyond current challenges, researchers have explored doping graphene with ionic liquids to raise the lifespan of solar cells (SCs). Additionally, they have paid attention to optimizing graphene/Si Schottky junction or Schottky barrier SCs by enhancing the conductivity and work function of graphene, improving silicon's reflectivity, and addressing passivation issues at the surface/interface of graphene/Si, resulting in significant advancements in their power conversion efficiency. Increasing the functional area of graphene-based SCs and designing efficient grid electrodes are also crucial for enhancing carrier collection efficiency. Flaws and contaminants present at the interface between graphene and silicon pose significant challenges. Despite the progress of graphene/Si-based photovoltaic cells still needs to catch up to the efficiency achieved by commercially available Si p-n junction SCs. The low Schottky barrier height, design-related challenges associated with transfer techniques, and high lateral resistivity of graphene contribute to this performance gap. To maximize the effectiveness and robustness of graphene/Si-based photovoltaic cells, appropriate interlayers have been utilized to tune the interface and modulate graphene's functionality. This mini-review will address ongoing research and development endeavors using van der Waals graphene heterojunctions, aiming to overcome the existing limitations and unlock graphene's full potential in solar energy harvesting and smart storage systems. © 2023 The Royal Society of Chemistry.

关键词： Graphene

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Identity-preserving 3D Facial Completion under Skull Constraints

Identity-preserving 3D Facial Completion under Skull Constra...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Longtao Yu Junli Zhao Fuqing Duan Chenlei Lv Dantong Li Zhenkuan Pan Mingquan Zhou College of Computer Science and Technology Qingdao University School of Artificial Intelligence Beijing Normal University College of Computer Science and Software Engineering Shenzhen University School of Computer Science and Informations Cardiff University School of Information Science and Technology Northwest University National and Local Joint Engineering Research Center for Cultural Heritage Digitization Northwest University

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

3D face shape completion is a necessary pre-process for various facial applications as they are often mutilated due to the acquiring environment or occlusion. However, it is challenging to ensure identity consistency when completing faces with large missing regions. Therefore, we introduce craniofacial information to supervise the completion of face shapes. Firstly, a novel dual encoder-decoder structure for face depth image inpainting is constructed by combining dilated convolution and the coherent semantic attention mechanism, guaranteed to generate smooth results with complete semantic information even in the presence of large missing regions in the face model. Then, we innovatively design a craniofacial superimposition module to determine the probability that the inpainted face and corresponding skull come from the same person, constraining the inpainting network to learn identity consistency information. Finally, extensive experimental results show that our method can effectively complete 3D face shapes containing large arbitrary missing regions while guaranteeing identity consistency.

关键词： Biometrics Three-dimensional displays Attention mechanisms Shape Convolution Semantics Faces

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A SUPERVISED INFORMATION ENHANCED MULTI-GRANULARITY CONTRASTIVE LEARNING FRAMEWORK FOR EEG BASED EMOTION RECOGNITION

arXiv

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arXiv 2024年

作者： Li, Xiang Song, Jian Zhao, Zhigang Wang, Chunxiao Song, Dawei Hu, Bin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Institute of Engineering Medicine Beijing Institute of Technology Beijing China

This study introduces a novel Supervised Info-enhanced Contrastive Learning framework for EEG based Emotion Recognition (SI-CLEER). SI-CLEER employs multi-granularity contrastive learning to create robust EEG contextual representations, potentially improving emotion recognition effectiveness. Unlike existing methods solely guided by classification loss, we propose a joint learning model combining self-supervised contrastive learning loss and supervised classification loss. This model optimizes both loss functions, capturing subtle EEG signal differences specific to emotion detection. Extensive experiments demonstrate SI-CLEER's robustness and superior accuracy on the SEED dataset compared to state-of-the-art methods. Furthermore, we analyze electrode performance, highlighting the significance of central frontal and temporal brain region EEGs in emotion detection. This study offers an universally applicable approach with potential benefits for diverse EEG classification tasks. © 2024, CC0.

关键词： Emotion Recognition

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A novel offloading strategy for multi-user optimization in blockchain-enabled Mobile Edge Computing networks for improved Internet of Things performance

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computers and Electrical engineering 2024年第PartA期119卷

作者： Rahmani, Amir Masoud Tanveer, Jawad Gharehchopogh, Farhad Soleimanian Rajabi, Shakiba Hosseinzadeh, Mehdi Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Science and Engineering Sejong University Seoul05006 Korea Republic of Department of Computer Engineering Urmia Branch Islamic Azad University Urmia Iran Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam

As blockchain technology merges with Mobile Edge Computing (MEC) and the Internet of Things (IoT), we encounter increasing challenges such as high energy consumption and latency due to the network's scale and complexity. Therefore, optimizing these communications and effectively managing computational task offloading is crucial, with data security being an essential concern. This paper studies these issues by examining networks that already operate within blockchain environments, where the complex process of transaction validations intensifies the demands on the system. We propose a new solution through the A3C-AHP offloading framework, which integrates the Asynchronous Advantage Actor-Critic (A3C) algorithm with the Analytic Hierarchy Process (AHP). This combination optimizes task offloading for multiple IoT users in MEC settings. The A3C algorithm improves decision-making by allowing agents to learn and adjust policies simultaneously, while AHP helps systematically evaluate and prioritize different network elements. Our framework adeptly manages the trade-offs between energy use, latency, and security when implemented in a blockchain context. Simulation results show that the A3C-AHP model notably enhances performance, cutting energy consumption by approximately 15.4 %, computational overhead by 49.5 %, latency by about 9.1 %, and operational costs by 8 % for networks with 100 users, highlighting its effectiveness and practical relevance. © 2024

关键词： Analytic hierarchy process

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Enabling Efficient Large Recommendation Model Training with Near CXL Memory Processing

Enabling Efficient Large Recommendation Model Training with ...

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Annual International Symposium on computer Architecture, ISCA

作者： Haifeng Liu Long Zheng Yu Huang Jingyi Zhou Chaoqiang Liu Runze Wang Xiaofei Liaot Hai Jinf Jingling Xue National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab Huazhong University of Science and Technology China School of Computer Science and Engineering University of New South Wales Australia Zhejiang Lab Hangzhou China

ISBN: (数字)9798350326581

ISBN: (纸本)9798350326598

Personalized recommendation systems have become one of the most important Internet services nowadays. A critical challenge of training and deploying the recommendation models is their high memory capacity and bandwidth demands, with the embedding layers occupying hundreds of GBs to TBs of storage. The advent of memory disaggregation technology and Compute Express Link (CXL) provides a promising solution for memory capacity scaling. However, relocating memory-intensive embedding layers to CXL memory incurs noticeable performance degradation due to its limited transmission bandwidth, which is significantly lower than the host memory bandwidth. To address this, we introduce ReCXL, a CXL memory disaggregation system that utilizes near-memory processing for scalable, efficient recommendation model training. ReCXL features a unified, hardwareefficient NMP architecture that processes the entire embedding training within CXL memory, minimizing data transfers over the bandwidth-limited CXL and enhancing internal bandwidth. To further improve the performance, ReCXL incorporates softwarehardware co-optimizations, including sophisticated dependencyfree prefetching and fine-grained update scheduling, to maximize hardware utilization. Evaluation results show that ReCXL outperforms the CPU-GPU baseline and the naïve CXL memory by $7.1 \times \sim 10.6 \times(9.4 \times$ on average) and $12.7 \times \sim 31.3 \times(22.6 \times$ on average), respectively.

关键词： Training Degradation Scalability Prefetching Memory management Web and internet services Bandwidth

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MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network

arXiv

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arXiv 2024年

作者： Liu, Yu-Tung Wang, Kuan-Chen Chao, Rong Siniscalchi, Sabato Marco Yeh, Ping-Cheng Tsao, Yu Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan University of Palermo Italy Research Center for Information Technology Innovation Academic Sinica Taiwan

Surface electromyography (sEMG) recordings can be contaminated by electrocardiogram (ECG) signals when the monitored muscle is closed to the heart. Traditional signal processing-based approaches, such as high-pass filtering and template subtraction, have been used to remove ECG interference but are often limited in their effectiveness. Recently, neural network-based methods have shown greater promise for sEMG denoising, but they still struggle to balance both efficiency and effectiveness. In this study, we introduce MSEMG, a novel system that integrates the Mamba state space model with a convolutional neural network to serve as a lightweight sEMG denoising model. We evaluated MSEMG using sEMG data from the Non-Invasive Adaptive Prosthetics database and ECG signals from the MIT-BIH Normal Sinus Rhythm Database. The results show that MSEMG outperforms existing methods, generating higher-quality sEMG signals using fewer parameters. © 2024, CC BY.

关键词： High pass filters

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