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Dimensionality-driven metal to Mott insulator transition in two-dimensional 1T-TaSe2

National science Review 2024年第3期11卷 108-118页

作者： Ning Tian Zhe Huang Bo Gyu Jang Shuaifei Guo Ya-Jun Yan Jingjing Gao Yijun Yu Jinwoong Hwang Cenyao Tang Meixiao Wang Xuan Luo Yu Ping Sun Zhongkai Liu Dong-Lai Feng Xianhui Chen Sung-Kwan Mo Minjae Kim Young-Woo Son Dawei Shen Wei Ruan Yuanbo Zhang State Key Laboratory of Surface Physics New Cornerstone Science Laboratory and Department of PhysicsFudan University Shanghai Qi Zhi Institute Shanghai Research Center for Quantum Sciences Institute for Nanoelectronic Devices and Quantum Computing Fudan University Zhangjiang Fudan International Innovation Center Fudan University State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology(SIMIT)Chinese Academy of Sciences School of Physical Science and Technology ShanghaiTech University Korea Institute for Advanced Study School of Emerging Technology and Department of Physics University of Science and Technology of China Advanced Light Source Lawrence Berkeley National Laboratory ShanghaiTech Laboratory for Topological Physics Key Laboratory of Materials Physics Institute of Solid State Physics Hefei Institutes of Physical ScienceChinese Academy of Sciences High Magnetic Field Laboratory Hefei Institutes of Physical Science Chinese Academy of Sciences Collaborative Innovation Centre of Advanced Microstructures Nanjing University Department of Physics University of Science and Technology of China and Key Laboratory of Strongly Coupled Quantum Matter Physics Chinese Academy of Sciences National Synchrotron Radiation Laboratory University of Science and Technology of China

Two-dimensional materials represent a major frontier for research into exotic many-body quantum phenomena. In the extreme two-dimensional limit, electron-electron interaction often dominates over other electronic energy scales, leading to strongly correlated effects such as quantum spin liquid and unconventional *** dominance is conventionally attributed to the lack of electron screening in the third dimension. Here, we discover an intriguing metal to Mott insulator transition in 1T-TaSe2that defies conventional wisdom. Specifically, we find that dimensionality crossover, instead of reduced screening, drives the transition in atomically thin 1T-TaSe2. A dispersive band crossing the Fermi level is found to be responsible for the bulk metallicity in the material. Reducing the dimensionality,however, effectively quenches the kinetic energy of these initially itinerant electrons, and drives the material into a Mott insulating *** dimensionality-driven metal to Mott insulator transition resolves the long-standing dichotomy between metallic bulk and insulating surface of 1T-TaSe2. Our work further reveals a new pathway for modulating two-dimensional materials that enables exploring strongly correlated systems across uncharted parameter space.

关键词： two-dimensional materials 1T-TaSe2 dimensionality crossover metal to Mott insulator transition

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VAction: A Lightweight and Integrated VR Training System for Authentic Film-Shooting Experience 25

VAction: A Lightweight and Integrated VR Training System for...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Wang, Shaocong Qu, Che Yu, Minjing Zhou, Chao Wang, Yuntao Wen, Yu-Hui Shi, Yuanchun Liu, Yong-Jin Department of Computer Science and Technology Tsinghua University Beijing China Film & TV Technology Department Beijing Film Academy Beijing China College of Intelligence and Computing Tianjin University Tianjin China Human Computer Interaction Technology Intelligent Information Processing Laboratory Institute of Software Chinese Acadamy of Sciences Beijing China School of Computer Science and Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400713941

The film industry exerts significant economic and cultural influence, and its rapid development is contingent upon the expertise of industry professionals, underscoring the critical importance of film-shooting education. However, this process typically necessitates multiple practice in complex professional venues using expensive equipment, presenting a significant obstacle for ordinary learners who struggle to access such training environments. Despite VR technology has already shown its potential in education, existing research has not addressed the crucial learning component of replicating the shooting process. Moreover, the limited functionality of traditional controllers hinder the fulfillment of the educational requirements. Therefore, we developed VAction VR system, combining high-fidelity virtual environments with a custom-designed controller to simulate the real-world camera operation experience. The system's lightweight design ensures cost-effective and efficient deployment. Experiment results demonstrated that VAction significantly outperforms traditional methods in both practice effectiveness and user experience, indicating its potential and usefulness in film-shooting education. © 2025 Copyright held by the owner/author(s).

关键词： Personnel training

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Grid Search Hyperparameters Tuning with Supervised Machine Learning for Awngi Language Named Entity Recognition 2

Grid Search Hyperparameters Tuning with Supervised Machine L...

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2nd International Conference Computational and Characterization Techniques in Engineering and sciences, IC3TES 2024

作者： Ayalew, Amogne Andualem Bitew, Gizachew Salau, Ayodeji Olalekan Tegegne, Melaku Lake Gupta, Shashi Kant Israr, Mohammad Tegegne, Tesfa Assegie, Tsehay Admassu Mizan-Tepi University School of Computing and Informatics Department of Information Technology Tepi Ethiopia Mizan Tepi University School of Computing Computer Science Department Mizan-Tepi Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Injibara University College of Engineering and Technology Department of Information Technology Injibara Ethiopia Eudoxia Research University Department of Computer Science and Engineering United States Maryam Abacha American University of Nigeria Hotoro Gra Kano State Nigeria Bahir Dar University Faculty of Computing Bahir Dar Institute of Technology Bahir Dara Ethiopia Injibara University College of Engineering and Technology Department of Computer Science Injibara Ethiopia

ISBN: (纸本)9798350364699

Named entity recognition has emerged as a critical step in recognizing, classifying, and extracting the most significant information from unstructured text without human intervention. It is used in information retrieval, conversation systems, machine translation, data mining, and information extraction. However, the Awngi language lacks a NER system due to inadequate datasets. This study combines feature extraction techniques with supervised machine learning algorithms, including support vector machines, Naïve Bayes, and conditional random fields with a grid search algorithm parameter optimization. These supervised machine-learning models often have various hyperparameters that must be tuned to optimize the model's performance by automatically selecting the parameter. Furthermore, the dataset used for training and testing consists of 20,193 annotated Awngi tokens, collected from Amhara Media Corporation, Awngi Telegram, and Facebook pages. The proposed SVM with grid search algorithm approach provided a promising result in ANER with an accuracy of 92% compared with Naïve Bayes, conditional random field, and decision-tree supervised machine learning models. Finally, the experiment results show that the proposed parameter tuning-based model can detect and categorize Awingi-named entities and outperforms the performance accuracy without parameter adjustment. © 2024 IEEE.

关键词： Support vector machines

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JetVision: A Low-Cost Real-Time Depth Estimation System using Jetson computing Platform 9

JetVision: A Low-Cost Real-Time Depth Estimation System usin...

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9th IEEE International Conference on Electronics, computing and Communication Technologies, CONECCT 2023

作者： Mandal, Siddhanta Cholachgudda, Kartik E. Chamakura, Lohith V. Biradar, Rajashekhar C. Devanagavi, Geetha D. Reva University School of Computer Science and Engineering Bengaluru India Reva University School of Electronics and Communication Engineering Bengaluru India Reva University School of Computing and Information Technology Bengaluru India

ISBN: (纸本)9798350334395

Stereo vision is an important technique in computer vision that enables depth information to be extracted from a pair of cameras. Depth estimation has numerous applications across various fields. Some of the notable applications include autonomous vehicles, augmented reality (AR) and virtual reality (VR), robotics, 3D reconstruction, surveillance and security and medical imaging. In this research, the authors develop and test a low-cost real-time stereo vision system, JetVision, that uses two Raspberry Pi cameras, a Jetson computing platform and CUDA programming. The algorithm developed for JetVision is designed to efficiently analyze the differences between the images captured by each camera to estimate the depth of objects in the scene using Semi-Global Matching (SGM) method. To achieve real-time performance, the algorithm is implemented using CUDA programming, which enables efficient parallel processing on Jetson's GPU. The algorithm is tested on a dataset of real-world images captured by JetVision and is compared its result with high-grade Intel® RealSenseTM depth camera and ground truth depth information. The results demonstrate that JetVision is capable of accurately estimating depth information in real-time with an FPS ranging between 15 to 28. This research contributes to the field of computer vision by demonstrating the potential of stereo vision algorithms in real-world applications using cost-effective hardware and efficient parallel processing. © 2023 IEEE.

关键词： Stereo vision

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An Effective Fire Detection Algorithm Based on Improved YOLOv7 24

An Effective Fire Detection Algorithm Based on Improved YOLO...

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6th International Conference on Control and Computer Vision, ICCCV 2024

作者： Shen, Jing Wang, Xue Alghazzawi, Daniyal Aldhaheri, Sahar Business School Hubei University Hubei Wuhan430062 China College of Automation and Electronic Engineering Qingdao University of Science and Technology Shandong Qingdao266061 China Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

ISBN: (纸本)9798400718045

In the critical domain of fire detection, essential for safeguarding public safety and industrial assets, traditional methods have struggled with issues like low accuracy and slow response times, compromising real-time detection capabilities. Our response to this challenge is the development of a novel algorithm built upon the enhanced YOLOv7 framework, which integrates the CBS module with the cutting-edge ConvNeXtBlock to form the CN-B network module—a design that allows for effective longitudinal and transverse normalization, expanding the receptive field to capture a more comprehensive set of global information without the penalty of increased memory usage. This is further bolstered by the introduction of the SIoU loss function, which dramatically improves model convergence speed, ensuring a swift and accurate learning process. Rigorous testing on the BoWFire dataset has proven the algorithm’s superior performance, boasting an average fire detection accuracy of 89.74% and improving detection speed by 2.9 milliseconds per image, outperforming the original YOLOv7 network by a substantial margin and demonstrating a 2.1% increase in precision. Comparative experiments with eight leading networks validate our model’s adherence to the stringent requirements of real-time and accurate fire detection, positioning our improved YOLOv7 network as a significant advancement in the field of fire detection technology. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Fires

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Classification of SEMG Signal Based Hand Movements by Using Support Vector Machine 6

Classification of SEMG Signal Based Hand Movements by Using ...

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6th IEEE Symposium on Computers and Informatics, ISCI 2024

作者： Zahra, Syeda Rabail Ismail, Sadia Ali, Muhammad Danish Khan, Muhammad Amir Darus, Mohamad Yusof Mazhar, Tahseen Gomal University Institute of Computing & Information Technology Dera Ismail Khan Pakistan University Islamabad Computer Science Comsats Abbottabad Pakistan Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Mathematics Selangor Shah Alam40450 Malaysia Virtual University of Pakistan Department of Computer Science Pakistan

ISBN: (纸本)9798350353853

This paper explores the application of Surface Electromyography (SEMG) for hand movement classification using machine learning techniques. SEMG signals play a crucial role in sports medicine, rehabilitation, prosthesis control, and medical diagnosis. The study addresses a multi-class problem, extending the dataset with limited prior exploration. Nine features from SEMG signal's time domain are extracted, including Maximum Amplitude, Root Mean Square, Slope Sign Change, Variance, Simple Square Integral, Zero Crossing, Waveform Length, and Willison Amplitude. Linear Discriminant Analysis is employed for dimensionality reduction as a feature selection technique. The proposed algorithm achieves an overall classification accuracy of 72% for the classification of six different hand movements performed by five healthy subjects using Support Vector Machines. This research contributes to the advancement of SEMG-based hand movement classification in various practical applications. © 2024 IEEE.

关键词： Dimensionality reduction

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Practical fixed-time adaptive fuzzy control of uncertain nonlinear systems with time-varying asymmetric constraints: a unified barrier function based approach

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Frontiers of information technology & Electronic Engineering 2024年第9期25卷 1282-1294页

作者： Zixuan HUANG Huanqing WANG Ben NIU Xudong ZHAO Adil M.AHMAD College of Engineering Bohai UniversityJinzhou 121013China College of Mathematical Science Bohai UniversityJinzhou 121013China School of Information Science and Engineering Shandong Normal UniversityJinan 250014China Faculty of Electronic Information and Engineering Dalian University of TechnologyDalian 116024China Communication Systems and Networks Research Group Department of Information TechnologyFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia

A practical fixed-time adaptive fuzzy control strategy is investigated for uncertain nonlinear systems with time-varying asymmetric constraints and input quantization. To overcome the difficulties of designing controllers under the state constraints, a unified barrier function approach is employed to construct a coordinate transformation that maps the original constrained system to an equivalent unconstrained one, thus relaxing the time-varying asymmetric constraints upon system states and avoiding the feasibility check condition typically required in the traditional barrier Lyapunov function based control approach. Meanwhile, the “explosion of complexity” problem in the traditional backstepping approach arising from repeatedly derivatives of virtual controllers is solved by using the command filter method. It is verified via the fixed-time Lyapunov stability criterion that the system output can track a desired signal within a small error range in a predetermined time, and that all system states remain in the constraint range. Finally, two simulation examples are offered to demonstrate the effectiveness of the proposed strategy.

关键词： Unified barrier function Time-varying asymmetric state constraints Fuzzy logic systems Fixed-time control Command filter

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MCDS: An Effective Multi-UAV Collaborative Decision-Making System in Mobile Edge computing Networks

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IEEE Internet of Things Journal 2025年

作者： Xiong, Naixue Zhong, Weiyu Chen, Yuxiang He, Dacheng Li, Yuhui Chen, Linshu Liang, Wei Sanya Research Institute of Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology China Sanya Research Institute of Hunan University of Science and Technology Hunan University School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Information Science and Engineering China The Hong Kong Polytechnic University Department of Computing Hong Kong

Mobile Edge computing (MEC) pushes computing resources from the network center to the network edge to provide efficient and reliable computing services. However, due to the mobility and diversity of mobile users (MUs), edge servers (ESs) are likely to be overloaded, which leads to a rapid decline in the quality of service provided by the MEC system. This paper introduces unmanned aerial vehicles (UAVs) to solve the problem of ES overload. This paper presents an effective Multi-UAV Collaborative Decision-Making System (MCDS) tailored to optimize task offloading and resource allocation in MEC networks. The proposed system seeks to reduce task computing delays and energy consumption while guaranteeing timely task completion and compliance with resource constraints. We design a distributed two-stage optimization algorithm to jointly optimize the task offloading decision and resource allocation of the collaborative computing system (UAVs, ESs, and MUs). To address the flight cooperation problem of multi-UAVs, we also proposed a task-aware scheduling algorithm for multi-UAVs. In addition, we conducted a large number of simulation experiments. Experimental results show that our two-stage optimization scheme is lower than other benchmark algorithms in terms of task completion delay, energy consumption and total cost of objective function. Specifically, compared with seven baseline algorithms under three different system setting scenarios, our algorithm reduces the total cost by an average of 38.79%, the energy consumption by an average of 40.70%, and the delay by an average of 15.67%. © 2014 IEEE.

关键词： Drones

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Comfort Shoulder (Rhomboid Major) Muscles Chair Identifier System 6

Comfort Shoulder (Rhomboid Major) Muscles Chair Identifier S...

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6th International Conference on Communications, information System and Computer Engineering, CISCE 2024

作者： Moshayedi, Ata Jahangir Yu, Yuexin Savoji, Kimia Andani, Mehran Emadi School of Information Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou China School of Computing Clemson University Biomedical Data Science and Informatics ClemsonSC United States University of Verona Department of Neurosciences Biomedicine and Movement Sciences Verona Italy

ISBN: (纸本)9798350353174

Nowadays, Sitting is one of the frequent activities of humans. particularly with the rise of office work, which is leading to an increase in the sitting periods for many individuals. Research highlights the importance of studying sitting posture to enhance seat quality, correct sitting methods, and prevent various health issues like neck and shoulder strains, etc. This article focuses on designing an IoT-based system that enables web-based data visualization without specialized software that can enable distance sampling. Through temperature sensors (DS18b20 and SHT11) and an FSR sensor, the system can identify distinct patterns in the main rhomboid muscles (the most important muscles of humans). The results show a significant relationship between individuals' body mass (BMI) and the observed muscle patterns, highlighting the importance of this correlation for researchers and industry designers alike. © 2024 IEEE.

关键词： Seats

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Cooperative Self-Scheduling Routing Approach Based on Energy Efficient Optimal Link Stability Routing Allocation for Improving QoS-WSN

Cooperative Self-Scheduling Routing Approach Based on Energy...

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2024 Advances in science and Engineering technology International Conferences, ASET 2024

作者： Radhika, G. Radhika, N. Paul, Sujni Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India Computer Information Science Higher Colleges of Technology Dubai Men's Campus Academic City Dubai United Arab Emirates

ISBN: (纸本)9798350344134

The development of Wireless Sensor Networks (WSN) is broad research in recent communication approaches. Due to the increasing demand for service, the quality is insufficient to meet the requirements to take energy efficient network. This leads to high energy losses, traffic, improper scheduling, and poor network performance. Selecting a higher traffic route introduces higher packet drop and reduces the throughput performance. Analyzing the traffic in the existing process is complex and doesn't provide Quality of Service (QoS). So data analysis is essential based on reinforcement learning with an intelligent machine learning approach. To resolve this problem, we propose a Cooperative self-scheduling routing approach (CS2RA) based on energy Efficient Optimal Link stability routing allocation (EOLSRA) designed to improve the quality of service in Wireless sensor networks. The dynamic network topology medium is constructed with Neighbor Stable Link Connectivity Based Routing. With the Probability support, the traffic density approximation rate (PTDAR) is estimated by improving energy stabilization. By intending an optimistic Q-learning network model based on the energy-consuming parameters using an adaptive Petal spider ant colony cluster (PSACC) is constructed for link stability for route optimization. Then Cooperative stabilized proactive Energy-aware cluster routing protocol (CSPECR) was intended to make self-balanced congestion routing with the support of the Lookup energy constraint duty cycle (LECDC) to improve the lifetime of the network. © 2024 IEEE.

关键词： Cooperative communication

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