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Perovskite-type YRh3B with multiple types of nodal point and nodal line states

Physical Review B 2021年第24期103卷 245126-245126页

作者： Feng Zhou Chaoxi Cui Jianhua Wang Minquan Kuang Tie Yang Zhi-Ming Yu Xiaotian Wang Gang Zhang Zhenxiang Cheng School of Physical Science and Technology Southwest University Chongqing 400715 China Key Lab of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) Beijing Key Lab of Nanophotonics & Ultrafine Optoelectronic Systems and School of Physics Beijing Institute of Technology Beijing 100081 China Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) 138632 Singapore Institute for Superconducting and Electronic Materials (ISEM) University of Wollongong Wollongong 2500 Australia

Experimentally synthesized perovskite-type YRh3B with a Pm3¯m type structure was proposed as a topological material (TM) via first-principles calculations and the low-energy k·p effective Hamiltonian, which has a quadratic contact triple point (QCTP) at point Γ and six pairs of open nodal lines (NLs) of the hybrid type. Clear surface states observed in the surface spectrum confirmed the topological states. When spin-orbit coupling was considered, the QCTP at Γ transferred to the quadratic-type Dirac nodal point (NP). Under 1% tetragonal strained lattice constants, YRh3B hosted richer topological states, including a quadratic-type twofold degenerate NP, six pairs of open NLs of the hybrid type, and two closed NLs of type I and hybrid type. Moreover, it was proved that the NLs of YRh3B at its strained lattice constants contain all types of band-crossing points (BCPs) (i.e., type I, type II, and critical type). Such rich types of NP and NL states in one compound make it potentially applicable for multifunctional electronic devices as well as an appropriate platform to study entanglement among topological states.

关键词： Density functional theory First-principles calculations Tight-binding model k dot p method

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Lightdet: A Lightweight and Accurate Object Detection Network

Lightdet: A Lightweight and Accurate Object Detection Networ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Qiankun Tang Jie Li Zhiping Shi Yu Hu Research Center for Intelligent Computing Systems State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing Advanced Innovation Center for Imaging Theory and Technology Capital Normal University

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

The extensive computational burden limits the usage of accurate but complex object detectors in resource-bounded scenarios. In this paper, we present a lightweight object detector, named LightDet, to address this dilemma. We design a lightweight backbone that is able to capture rich low-level features by the proposed Detail-Preserving Module. To effectively aggregate bottom and top-down features, we introduce an efficient Feature-Preserving and Refinement Module. A lightweight prediction head is employed to further reduce the entire network complexity. Experimental results show that our LightDet achieves 75.5% mAP on PASCAL VOC 2007 at the speed of 250 FPS and 24.0% mAP on MS COCO dataset.

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Exploring spatial-temporal multi-frequency analysis for high-fidelity and temporal-consistency video prediction

arXiv

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arXiv 2020年

作者： Jin, Beibei Hu, Yu Tang, Qiankun Niu, Jingyu Shi, Zhiping Han, Yinhe Li, Xiaowei Research Center for Intelligent Computing Systems State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Capital Normal University

Video prediction is a pixel-wise dense prediction task to infer future frames based on past frames. Missing appearance details and motion blur are still two major problems for current predictive models, which lead to image distortion and temporal inconsistency. In this paper, we point out the necessity of exploring multi-frequency analysis to deal with the two problems. Inspired by the frequency band decomposition characteristic of Human Vision System (HVS), we propose a video prediction network based on multi-level wavelet analysis to deal with spatial and temporal information in a unified manner. Specifically, multi-level spatial discrete wavelet transform decomposes each video frame into anisotropic sub-bands with multiple frequencies, helping to enrich structural information and reserve fine details. On the other hand, multi-level temporal discrete wavelet transform which operates on time axis decomposes the frame sequence into sub-band groups of different frequencies to accurately capture multi-frequency motions under fixed frame rate. Extensive experiments on diverse datasets demonstrate that our model shows significant improvements on fidelity and temporal consistency over the state-of-the-art works. Copyright © 2020, The Authors. All rights reserved.

关键词： Discrete wavelet transforms

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Exploring Spatial-Temporal Multi-Frequency Analysis for High-Fidelity and Temporal-Consistency Video Prediction

Exploring Spatial-Temporal Multi-Frequency Analysis for High...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Beibei Jin Yu Hu Qiankun Tang Jingyu Niu Zhiping Shi Yinhe Han Xiaowei Li Research Center for Intelligent Computing Systems State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Capital Normal University

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Video prediction is a pixel-wise dense prediction task to infer future frames based on past frames. Missing appearance details and motion blur are still two major problems for current models, leading to image distortion and temporal inconsistency. We point out the necessity of exploring multi-frequency analysis to deal with the two problems. Inspired by the frequency band decomposition characteristic of Human Vision System (HVS), we propose a video prediction network based on multi-level wavelet analysis to uniformly deal with spatial and temporal information. Specifically, multi-level spatial discrete wavelet transform decomposes each video frame into anisotropic sub-bands with multiple frequencies, helping to enrich structural information and reserve fine details. On the other hand, multilevel temporal discrete wavelet transform which operates on time axis decomposes the frame sequence into sub-band groups of different frequencies to accurately capture multifrequency motions under a fixed frame rate. Extensive experiments on diverse datasets demonstrate that our model shows significant improvements on fidelity and temporal consistency over the state-of-the-art works. Source code and videos are available at https://***/Bei-Jin/STMFANet.

关键词： Discrete wavelet transforms Predictive models Wavelet analysis Streaming media Time-frequency analysis

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Symbiotic evolution of photonics and artificial intelligence: a comprehensive review

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Advanced Photonics 2025年第2期 8-85页

作者： Fu Feng Dewang Huo Ziyang Zhang Yijie Lou Shengyao Wang Zhijuan Gu Dong-Sheng Liu Xinhui Duan Daqian Wang Xiaowei Liu Ji Qi Shaoliang Yu Qingyang Du Guangyong Chen Cuicui Lu Yu Yu Xifeng Ren Xiaocong Yuan Zhejiang Lab Research Center for Frontier Fundamental Studies Westlake Institute for Optoelectronics Zhejiang Key Laboratory of 3D Micro/Nano Fabrication and Characterization Beijing Institute of Technology School of Physics Center for Interdisciplinary Science of Optical Quantum and NEMS Integration Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements of Ministry of Education Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics University of Science and Technology of China CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence in Quantum Information and Quantum Physics Zhejiang Lab Research Center for Life Sciences Computing

The rapid advancement of artificial intelligence(AI) has significantly impacted photonics, creating a symbiotic relationship that accelerates the development and applications of both fields. From the perspective of AI aiding photonics, deep-learning methods and various intelligent algorithms have been developed for designing complex photonic structures, where traditional design approaches fall short. AI's capability to process and analyze large data sets has enabled the discovery of novel materials, such as for photovoltaics,leading to enhanced light absorption and efficiency. AI is also significantly transforming the field of optical imaging with improved performance. In addition, AI-driven techniques have revolutionized optical communication systems by optimizing signal processing and enhancing the bandwidth and reliability of data transmission. Conversely, the contribution of photonics to AI is equally profound. Photonic technologies offer unparalleled advantages in the development of AI hardware, providing solutions to overcome the bottlenecks of electronic systems. The implementation of photonic neural networks, leveraging the high speed and parallelism of optical computing, demonstrates significant improvements in the processing speed and energy efficiency of AI computations. Furthermore, advancements in optical sensors and imaging technologies not only enrich AI applications with high-quality data but also expand the capabilities of AI in fields such as autonomous vehicles and medical imaging. We provide comprehensive knowledge and a detailed analysis of the current state of the art, addressing both challenges and opportunities at the intersection of AI and photonics. The multifaceted interactions between AI and photonics will be explored, illustrating how AI has become an indispensable tool in the development of photonics and how photonics, in turn,facilitates advancements in AI. Through a collection of case studies and examples, we underscore the potential

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Inverse learning of black-box aggregator for robust Nash equilibrium

arXiv

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arXiv 2024年

作者： Chen, Guanpu Xu, Gehui He, Fengxiang Tao, Dacheng Parisini, Thomas Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Digital Futures Stockholm100 44 Sweden Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom College of Computing & Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an aggregative game given a known aggregator. When the coupling parameters are affected by uncertainty, robust NE need to be computed. We consider a scenario where players' weight in the aggregator is unknown, making the aggregator kind of "a black box". We pursue a suitable learning approach to estimate the unknown aggregator by proposing an inverse variational inequality-based relationship. We then utilize the counterpart to reconstruct the game and obtain first-order conditions for robust NE in the worst case. Furthermore, we characterize the generalization property of the learning methodology via an upper bound on the violation probability. Simulation experiments show the effectiveness of the proposed inverse learning approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Bibliographic Exploration of Application of Machine Learning and Artificial Intelligence in Solar Energy

Bibliographic Exploration of Application of Machine Learning...

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Science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Samuel-Soma M. Ajibade Faizah Mohammed Bashir Yakubu Aminu Dodo Oluwadare Joshua Oyebode Rex V. Culpable Limic M. De La Calzada Anthonia Oluwatosin Adediran Temiloluwa Iyanuoluwa Ajibade Dept of Computing and Information Systems Sunway University Selangor Malaysia Dept of Comp. Engineering Istanbul Ticaret Univ Turkey Department of Decoration and Interior Design University of Hail Hail Kingdom of Saudi Arabia Architectural Engineering Department Najran University Najran Kingdom of Saudi Arabia Civil and Environmental Engineering Afe Babalola University Ado-Ekiti Ekiti State Nigeria College of Technology Cebu Technological University Cebu Philippines College of Engineering Cebu Technological University Cebu Philippines Faculty of Architecture and Urban Design Federal University of Uberlandia Brazil Dept of Business Management Scholar School System Birmingham United Kingdom

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Solar energy could mitigate global warming and climate change. Solar energy faces economic, environmental, and technical challenges. Machine learning solves these technical issues. Despite several studies, machine learning in photovoltaics and solar energy is understudied. This study examines publishing patterns and bibliometrics to critically evaluate machine learning applications in photovoltaics and solar energy research. Scopus uses PRISMA. International publishing, citations, and collaboration are high. The Chinese Ministry of Education employs famous scholars like G. E. Georghiou and Haibo Ma. China is most active due to funding schemes like the National Natural Science Foundation and the National Key Research and Development Programme. This study examines publication patterns by country, institution, and funding organisation from 2014 to 2022, spanning topic categories and indicators. Examining author-keyword data to group publishing themes and identify influential journals. Increasing understanding of machine learning applications in photovoltaics and solar energy research. This project will examine the potential for significant development and the hurdles that must be overcome to leverage Cognitive computing's benefits in cancer and tumour research. In response to the rising amount of malware, phishing, and intrusion attacks on global energy and grid infrastructure, photovoltaic and solar energy system cybersecurity may be studied.

关键词： Photovoltaic systems Phishing Solar energy Machine learning Market research Malware Sustainable development

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Solving the multiplication problem of a large language model system using a graph-based method

arXiv

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arXiv 2023年

作者： Tuncer, Turker Dogan, Sengul Baygin, Mehmet Barua, Prabal Datta Hafeez-Baig, Abdul Tan, Ru-San Chakraborty, Subrata Acharya, U. Rajendra Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey Department of Computer Engineering Faculty of Engineering and Architecture Erzurum Technical University Erzurum Turkey University of Southern Queensland Australia School of Management and Enterprise University of Southern Queensland ToowoombaQLD Australia Department of Cardiology National Heart Centre Singapore Singapore169609 Singapore Duke-NUS Medical School Singapore169857 Singapore School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2351 Australia Center for Advanced Modelling and Geospatial Information Systems Faculty of Engineering and IT University of Technology Sydney SydneyNSW2007 Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

The generative pre-trained transformer (GPT)-based chatbot software ChatGPT possesses excellent natural language processing capabilities but is inadequate for solving arithmetic problems, especially multiplication. Its GPT structure uses a computational graph for multiplication, which has limited accuracy beyond simple multiplication operations. We developed a graph-based multiplication algorithm that emulated human-like numerical operations by incorporating a ×10k operator, where k represents the maximum power to base 10 of the larger of two input numbers. Our proposed algorithm attained 100% accuracy for 1,000,000 large number multiplication tasks, effectively solving the multiplication challenge of GPT-based and other large language models. Our work highlights the importance of blending simple human insights into the design of artificial intelligence algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Blending

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Cloud-based remote control framework for unmanned surface vehicles ⁎

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IFAC-PapersOnLine 2020年第2期53卷 14564-14569页

作者： Zhao Wang Shaolong Yang Xianbo Xiang Antonio Vasilijević Nikola Mišković Ðula Nađ School of Naval Architecture and Ocean Engineering Huazhong University of Science and Technology Wuhan 430074 China Hubei Key Laboratory of Naval Architecture and Ocean Engineering Hydrodynamics (HUST) Wuhan 430074 PR China Laboratory for Underwater Systems and Technologies Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

This paper proposes a cloud-based mission control framework for a fleet of unmanned vehicles. The framework enables easy remote access to fleet operations regardless of operator location. By leveraging cloud-based technologies the framework accomplishes scalable monitoring, remote control, data acquisition and sharing. While the front-end is applicable across mobile robotic systems, the back-end presented in this paper provides integration with the Robot Operating System (ROS); thus, enabling integration of various marine robotic agents based on the same robot framework. The proposed framework operation is demonstrated on the H2OmniX unmanned surface vehicles during trials in Biograd na Moru, Croatia.

关键词： remote control distributed control cloud-based control Robot Operating System

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The Hitchhiker's Guide to Fused Twins: A Review of Access to Digital Twins in situ in Smart Cities

arXiv

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arXiv 2022年

作者： Grübel, Jascha Thrash, Tyler Aguilar, Leonel Gath-Morad, Michal Chatain, Julia Sumner, Robert W. Hölscher, Christoph Schinazi, Victor R. Cognitive Science ETH Zurich Switzerland Game Technology Center ETH Zurich Switzerland Visual Computing Group Harvard University United States Center for Sustainable Future Mobility ETH Zurich Switzerland Geoinformation Engineering ETH Zurich Switzerland Department of Biology Saint Louis University United States Data Science Systems and Services Group ETH Zurich Switzerland Department of Architecture University of Cambridge United Kingdom Bartlett School of Architecture University College London United Kingdom Professorship for Learning Sciences and Higher Education ETH Zurich Switzerland Department of Psychology Bond University Australia

Smart Cities already surround us, and yet they are still incomprehensibly far from directly impacting everyday life. While current Smart Cities are often inaccessible, the experience of everyday citizens may be enhanced with a combination of the emerging technologies Digital Twins (DTs) and Situated Analytics. DTs represent their Physical Twin (PT) in the real world via models, simulations, (remotely) sensed data, context awareness, and interactions. However, interaction requires appropriate interfaces to address the complexity of the city. Ultimately, leveraging the potential of Smart Cities requires going beyond assembling the DT to be comprehensive and accessible. Situated Analytics allows for the anchoring of city information in its spatial context. We advance the concept of embedding the DT into the PT through Situated Analytics to form Fused Twins (FTs). This fusion allows access to data in the location that it is generated in an embodied context that can make the data more understandable. Prototypes of FTs are rapidly emerging from different domains, but Smart Cities represent the context with the most potential for FTs in the future. This paper reviews DTs, Situated Analytics, and Smart Cities as the foundations of FTs. Regarding DTs, we define five components (Physical, Data, Analytical, Virtual, and Connection environments) that we relate to several cognates (i.e., similar but different terms) from existing literature. Regarding Situated Analytics, we review the effects of user embodiment on cognition and cognitive load. Finally, we classify existing partial examples of FTs from the literature and address their construction from Augmented Reality, Geographic Information systems, Building/City Information Models, and DTs and provide an overview of future directions. © 2022, CC BY.

关键词： Sensor networks

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