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Right Frontal Gamma Transcranial Alternating Current Stimulation Modulates Optimism Biases

Neuroscience Bulletin 2025年第1期41卷 172-176页

作者： Ziqing Yao Jinwen Wei Gan Huang Linling Li Zhen Liang Li Zhang Haiyan Wu Tifei Yuan Zhiguo Zhang Xiaoqing Hu Department of Psychology The State Key Laboratory of Brain and Cognitive SciencesThe University of Hong KongHong KongSARChina School of Biomedical Engineering Medical SchoolShenzhen UniversityShenzhen518060China Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen UniversityShenzhen518060China Centre for Cognitive and Brain Sciences and Department of Psychology University of MacaoMacao SARChina Shanghai Key Laboratory of Psychotic Disorders Brain Health InstituteNational Center for Mental DisordersShanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghai210109China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China Peng Cheng Laboratory Shenzhen518055China HKU-Shenzhen Institute of Research and Innovation Shenzhen518063China

Dear Editor,People are often optimistic when forecasting the future,such that they believe they are less likely to encounter adverse life *** phenomenon,known as optimism bias,emerges due to preferential encoding and consolidation of desirable over undesirable information[1,2],engaging frontal brain regions such as the anterior cingulate cortex and inferior frontal gyrus[1].An optimism bias is instrumental in supporting mental health:reduction or absence of optimism biases is associated with mood disorders such as depression,characterized by pessimistic thinking about the future[3].This relationship highlights the potential value of interventions that could enhance optimism bias[4].

关键词： ism such likely

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoke Zhu Danyang Li Xiaopan Chen Fumin Qi Fan Zhang Xiao-Yuan Jing School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Department of System Operation National Supercomuting Center in Shenzhen Shenzhen China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Facial image based kinship verification aims to decide whether there exists kinship between the given facial images. In practice, the cross-generation differences will cause adverse effects on kinship verification, which limits the performance. Therefore, how to mine the implied similarity from facial images with large cross-generation divergence is an important problem in kinship verification, which has not yet been well studied. In view of this, we propose a Similarity Mining via Implicit matching pattern LEarning (SMILE) approach for kinship verification. Specifically, SMILE mainly consists of two modules, including a Semi-coupled Multi-pattern Similarity Learning (SMSL) module and a Cross-Generation Feature Normalization (CGFN) module. The SMSL module is designed to learn multiple semi-coupled matching patterns for mining the implicit facial similarity information from different perspectives. The CGFN module aims to reduce the divergence between facial images of parent and child. Extensive experiments demonstrate that the proposed approach outperforms the existing state-of-the-art methods.

关键词： Pattern matching Similarity learning

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Design and Implementation of a 5G Security Testbed Based on Zero Trust Architecture

Design and Implementation of a 5G Security Testbed Based on ...

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International Conference on Information and Communication technology Convergence (ICTC)

作者： Mahnsuk Yoon Jihun Seo Jonghoon Lee Keuchul Cho Future Mobile Communication Research Center GERI(Gumi Electronics & Information Technology Research Institute) Gumi Korea Department of The Fourth Industrial Revolution & Transport Center for Connected & Automated Driving Research Korea Transport Institute Sejong Korea Cyber Security Research Division ETRI(Electronics and Telecommunications Research Institute) Daejeon Korea School of Computer Secience and Engineering Software Education Center Kyungpook National University Daegu Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

This paper presents the design and implementation of a 5G security testbed based on Zero Trust Architecture (ZTA). As 5G networks offer ultra-high speeds, low latency, and massive connectivity, ensuring their security has become a critical challenge, especially with the distributed nature and numerous connected devices. Traditional perimeter-based security models are no longer sufficient. Therefore, Zero Trust Architecture, which continuously verifies and monitors all resources and users regardless of their location, has been proposed as a solution. In this study, we propose a comprehensive security management system by integrating ZTA into a 5G testbed, focusing on real-time monitoring and dynamic policy enforcement using data collected from the Access and Mobility Management Function (AMF) and User Plane Function (UPF). The testbed's effectiveness is demonstrated through the deployment of Nokia's network equipment, allowing the collection and analysis of high-resolution traffic data, which aids in early threat detection and response. The proposed system enhances 5G network security and offers significant potential for application across various industries.

关键词： 5G mobile communication Security management Architecture Network security Real-time systems Threat assessment Zero Trust Security Low latency communication Monitoring

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Embedded propagation graph model for reflection and scattering and its millimeter-wave measurement-based evaluation

IEEE Open Journal of Antennas and Propagation

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IEEE Open Journal of Antennas and Propagation 2021年 2卷 191-202页

作者： Liu, Yuan Yin, Xuefeng Ye, Xiaokang He, Yongyu Lee, Juyul College of Electronics and Information Engineering Tongji University Shanghai China Guangdong Communications and Networks Institute Guangzhou China National Computer and Information Technology Practical Education Demonstration Center Tongji University Shanghai200092 China Telecommunication and Media Research Laboratory Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of

Propagation graph (PG) is a stochastic channel simulation method for scattering propagation. In this article, an embedded-PG (EPG) approach, an extension of conventional PG, is proposed to simulate reflection and scattering multi-path behaviors in wireless channels. In this method, multiple propagation paths are categorized into scattering-path, reflecting-path, and scattering-reflecting mixed paths among reflectors and scatterers. The matrix recursive formula of conventional PG modeling is used to calculate scattering-path, a recursive mathematical transformation is applied to adapt reflecting-path into the recursive formula, and an embedded graph method is used to decompose mixed-path into scattering effects and reflection effects. The proposed simulation approach is validated by comparison with conventional PG and measurement in 39 GHz millimeter-wave (mm-wave) time-variant corridor scenario. Power delay profiles (PDPs) and spatial consistency of multiple paths observed in concatenated-PDPs (CPDPs) obtained by EPG are more consistent with measurement than conventional PG, differences of mean delay and delay spread between simulations and measurement in typical snapshots are within 3 ns and 1.5 ns, respectively. © 2020 IEEE.

关键词： Millimeter waves

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Design and Analysis of Cyber Warfare Model with Intelligent Predictive Stochastic Networks for Attack-Defend Scenarios on Critical Infrastructures

SSRN

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SSRN 2023年

作者： Majeed, Khalid Masood, Zaheer Ghori, Muhammad Rizwan Raja, Muhammad Asif Zahoor Department of Electrical Engineering Capital University of Science and Technology Islamabad Pakistan School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan

World is occupied with spies, hackers, hidden digital weapon and in a virtual battlefield cyber warfare could turn every gadget into an armament. The purpose of this study is to develop a novel multi class differential system (Target network, Protected network, Inside attacker and Outside attacker) for cyber war model that imitate the cyber war scenarios on different attack surfaces in the presence of multifarious attack vectors and analysis is presented by stupendous knacks of intelligent predictive stochastic networks (IPSNs). Theoretical convergence analysis through attack free and attack invasion equilibrium in term of basic reproduction number R0 is presentedalong with the global stability of attack free equilibrium points using Lyapunov function. The reference solutions to study the dynamics of all four classes of cyber war model in air-gapped environment are also determined for sundry scenarios via variation in protection strategy parameter ρ, recovery strategy parameter Ε and rate of attack from an outside/inside attacker with implementation of Adams numerical method. The designed IPSNs is constructed by utilizing the multilayer feed-forward networks optimized with Levenberg-Marquardth method. The generated solution data is used in IPSNs as input/targets for train, validate and test samples to calculate the approximate predictive model for each class of nonlinear cyber war system. Exhaustive simulations are performed to determine the accuracy of the proposed model for a sophisticated advance persistent threats attacks, designed to target the critical industrial systems. The outcomes of IPSNs shows good close resemblance with the observed data, while analysis based on mean square error convergence, error histogram and regression index further verify the strength and worth of presented strategy. © 2023, The Authors. All rights reserved.

关键词： Critical infrastructures

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Optimization for Task Offloading and Downloading in UAV-Assisted MEC Systems with Aerial to Aerial Collaboration

Optimization for Task Offloading and Downloading in UAV-Assi...

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International Conference on computer Supported Cooperative Work in Design

作者： Jiawei Jiang Xiang Tian Yubing Han Chunyu Hu Bin Feng Jiguo Yu 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Information Science and Technology Taishan University Taian China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Big Data Institute Qilu University of Technology Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Owing to the easy deployment and mobile flexibility, Unmanned Aerial Vehicle (UAV) assisted Mobile Edge Computing (MEC) has been deemed as one potential technology for handling the computation-intensive tasks at terminal devices (TDs). In this work, a MEC architecture assisted by UAVs is designed which achieves efficient offloading, computing, and downloading for tasks from multiple TDs via aerial to aerial collaboration of two UAVs. In this architecture, the task offloading process contains two parts, i.e., the offloading from TDs to a mobile UAV which flies around TDs, and the offloading from the mobile UAV to a hovering UAV which hovers in the air. The computing tasks from TDs will be divided into three parts allocated to the TDs themselves, and both two UAVs. Upon completion of computation, the computation results are downloaded to the TDs. The optimization objective is to seek for an optimal task division strategy to attain the weighted total energy consumption minimization for all devices. Since the formulated optimization problem is not convex, we develop a two-step iteration algorithm which jointly optimizes computing frequency, task allocation volume, as well as UAV's trajectory based on the method of block coordinate descent. Simulation results confirm the effectiveness and performance advantages of the designed algorithm.

关键词： Energy consumption Simulation Collaboration computer architecture Autonomous aerial vehicles Minimization Resource management Jamming Trajectory optimization Radio spectrum management

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QoS-Aware and Cost-Efficient Dynamic Resource Allocation for Serverless ML Workflows

QoS-Aware and Cost-Efficient Dynamic Resource Allocation for...

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International Symposium on parallel and Distributed Processing (IPDPS)

作者： Hao Wu Junxiao Deng Hao Fan Shadi Ibrahim Song Wu Hai Jin 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 China Inria Univ. Rennes CNRS IRISA France

Machine Learning (ML) workflows are increasingly deployed on serverless computing platforms to benefit from their elasticity and fine-grain pricing. Proper resource allocation is crucial to achieve fast and cost-efficient execution of serverless ML workflows (specially for hyperparameter tuning and model training). Unfortunately, existing resource allocation methods are static, treat functions equally, and rely on offline prediction, which limit their efficiency. In this paper, we introduce CE-scaling – a Cost-Efficient autoscaling framework for serverless ML work-flows. During the hyperparameter tuning, CE-scaling partitions resources across stages according to their exact usage to minimize resource waste. Moreover, it incorporates an online prediction method to dynamically adjust resources during model training. We implement and evaluate CE-scaling on AWS Lambda using various ML models. Evaluation results show that compared to state-of-the-art static resource allocation methods, CE-scaling can reduce the job completion time and the monetary cost by up to 63% and 41% for hyperparameter tuning, respectively; and by up to 58% and 38% for model training.

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2.5 Million-Atom Ab Initio Electronic-Structure Simulation of Complex Metallic Heterostructures with DGDFT

2.5 Million-Atom Ab Initio Electronic-Structure Simulation o...

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Supercomputing Conference

作者： Wei Hu Hong An Zhuoqiang Guo Qingcai Jiang Xinming Qin Junshi Chen Weile Jia Chao Yang Zhaolong Luo Jielan Li Wentiao Wu Guangming Tan Dongning Jia Qinglin Lu Fangfang Liu Min Tian Fang Li Yeqi Huang Liyi Wang Sha Liu Jinlong Yang University of Science and Technology of China Hefei Anhui China Pilot National Laboratory for Marine Science and Technology (Qingdao) China Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Mathematical Sciences Peking University Beijing China Institute of Software Chinese Academy of Sciences Beijing China Qilu University of Technology Shandong Computer Science Center Jinan Shangdong China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781665454452

Over the past three decades, ab initio electronic structure calculations of large, complex and metallic systems are limited to tens of thousands of atoms in computational accuracy and efficiency on leadership supercomputers. We present a massively parallel discontinuous Galerkin density functional theory (DGDFT) implementation, which adopts adaptive local basis functions to discretize the Kohn-Sham equation, resulting in a block-sparse Hamiltonian matrix. A highly efficient pole expansion and selected inversion (PEXSI) sparse direct solver is implemented in DGDFT to achieve O(N 1.5 ) scaling for quasi two-dimensional systems. DGDFT allows us to compute the electronic structures of complex metallic heterostructures with 2.5 million atoms (17.2 million electrons) using 35.9 million cores on the new Sunway supercomputer. The peak performance of PEXSI can achieve 64 PFLOPS (~5% of theoretical peak), which is un-precedented for sparse direct solvers. This accomplishment paves the way for quantum mechanical simulations into mesoscopic scale for designing next-generation electronic devices.

关键词： Performance evaluation Analytical models Leadership Quantum computing Computational modeling Supercomputers Mathematical models

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A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals

A Non-Intrusive Neural Quality Assessment Model for Surface ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Cho-Yuan Lee Kuan-Chen Wang Kai-Chun Liu Yu-Te Wang Xugang Lu Ping-Cheng Yeh Yu Tsao School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan College of Information and Computer Sciences University of Massachusetts Amherst MA USA National Institute of Information and Communications Technology Tokyo Japan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

In practical scenarios involving the measurement of surface electromyography (sEMG) in muscles, particularly those areas near the heart, one of the primary sources of contamination is the presence of electrocardiogram (ECG) signals. To assess the quality of real-world sEMG data more effectively, this study proposes QASE-net, a new non-intrusive model that predicts the SNR of sEMG signals. QASE-net integrates a one-dimensional Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BLSTM) layer and attention mechanisms, following an end-to-end training strategy. Our experimental framework utilizes real-world sEMG and ECG data from two open-access databases, the Non-Invasive Adaptive Prosthetics Database and the MIT-BIH Normal Sinus Rhythm Database, respectively. The experimental results demonstrate the superiority of QASE-net over the baseline method, exhibiting significantly reduced prediction errors and notably higher linear correlations with the ground truth. These findings show the potential of QASE-net to substantially enhance the reliability and precision of sEMG quality assessment in practical applications.

关键词： Correlation Attention mechanisms Databases Surface contamination Electrocardiography Predictive models Electromyography Quality assessment Convolutional neural networks Signal to noise ratio

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Commercial Indian Bamboo Species Classification on matK DNA Barcode Sequences using Machine Learning Techniques with K-mer

Commercial Indian Bamboo Species Classification on matK DNA ...

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computer, Electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Ankush D. Sawarkar Deepti D. Shrimankar Lal Singh Anurag Agrahari Sagar Lachure Neeraj Dhanraj Bokde Department of Computer Science and Engineering Visvesvaraya National Institute of Technology (VNIT) Nagpur Maharashtra India Environmental Biotechnology And Genomics Division National Environmental and Engineering Research Institute (NEERI) Nagpur Maharashtra India Center for Quantitative Genetics and Genomics Aarhus University Aarhus Denmark

Bamboo, a grass, belongs to the Poaceae family, with 1642 species from 116 genera worldwide. It has exceptional physical, chemical, and mechanical qualities, which allow it to be employed in over a thousand different ways and contribute to a trade value of USD 2.76 billion. Bamboo is grown using rhizomes, tissue culture, or short branch cuttings without any other checks resulting in incorrect species identification and categorisation. Therefore, the classification or identification of these bamboo use its DNA barcode sequences with a K-mer based method, and machine learning (ML) is the most excellent strategy for resolving issues with the conventional or traditional categorisation of the species. A DNA barcode is a brief genetic signature that helps identify the species to which an organism belongs. It is possible to extract a useful feature from genome sequences using K-mer based approaches, which may then be used to increase comparison accuracy. In this research, we evaluate the classification performance of four supervised ML models on the DNA-barcode sequence of six Indian commercial bamboo species with a different K-mer combination. For this classification, we choose matK barcode region and supervised ML models such as Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM) and Gradient Boosting Machine (GBM). The results analysis of these models on the matK DNA sequence with different K-mers demonstrates that the classification capabilities of the GBM approaches are quite promising, and it has an accuracy of 95.3% on average.

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