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arXiv 2022年

作者： Chen, Jialin Chen, Ruoxi Gong, Zheng Hu, Hao Yang, Yi Zhang, Xinyan Wang, Chan Kaminer, Ido Chen, Hongsheng Zhang, Baile Lin, Xiao Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJUHangzhou Global Scientific and Technological Innovation Center College of Information Science & Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center The Electromagnetics Academy Zhejiang University Zhejiang University Haining314400 China Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa32000 Israel School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Department of Physics University of Hong Kong 999077 Hong Kong Key Lab of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Zhejiang University Jinhua321099 China Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing312000 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore637371 Singapore

When a charged particle penetrates through an optical interface, photon emissions emerge — a phenomenon known as transition radiation. Being paramount to fundamental physics, transition radiation has enabled many applications from high-energy particle identification to novel light sources. A rule of thumb in transition radiation is that the radiation intensity generally decreases with the particle velocity v;as a result, low-energy particles are not favored in practice. Here we find that there exist situations where transition radiation from particles with extremely low velocities (e.g. v/c −3) exhibits comparable intensity as that from high-energy particles (e.g. v/c = 0. 999), where c is light speed in free space. The comparable radiation intensity implies an extremely high photon extraction efficiency from low-energy particles, up to eight orders of magnitude larger than that from high-energy particles. This exotic phenomenon of low-velocity-favored transition radiation originates from the excitation of Ferrell-Berreman modes in epsilon-near-zero materials. Our findings may provide a promising route towards the design of integrated light sources based on low-energy electrons and specialized detectors for beyond-standard-model particles. © 2022, CC BY.

关键词： Light sources

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Localized heat diffusion in topological thermal materials

arXiv

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arXiv 2021年

作者： Qi, Minghong Wang, Dong Cao, Pei-Chao Zhu, Xue-Feng Qiu, Cheng-Wei Chen, Hongsheng Li, Ying Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy Zhejiang University Zhejiang University Haining314400 China School of Physics Innovation Institute Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Various unusual behaviors of artificial materials are governed by their topological properties, among which the edge state at the boundary of a photonic or phononic lattice has been captivated as a popular notion. However, this remarkable bulk-boundary correspondence and the related phenomena are missing in thermal materials. One reason is that heat diffusion is described in a non-Hermitian framework because of its dissipative nature. The other is that the relevant temperature field is mostly composed of modes that extend over wide ranges, making it difficult to be rendered within the tight-binding theory as commonly employed in wave physics. Here, we overcome the above challenges and perform systematic studies on heat diffusion in thermal lattices. Based on a continuum model, we introduce a state vector to link the Zak phase with the existence of the edge state, and thereby analytically prove the thermal bulk-boundary correspondence. We experimentally demonstrate the predicted edge states with a topologically protected and localized heat dissipation capacity. Our finding sets up a solid foundation to explore the topology in novel heat transfer manipulations. © 2021, CC BY.

关键词： Topology

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Perspectives on adaptive dynamical systems

arXiv

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arXiv 2023年

作者： Sawicki, Jakub Berner, Rico Loos, Sarah A.M. Anvari, Mehrnaz Bader, Rolf Barfuss, Wolfram Botta, Nicola Brede, Nuria Franović, Igor Gauthier, Daniel J. Goldt, Sebastian Hajizadeh, Aida Hövel, Philipp Karin, Omer Lorenz-Spreen, Philipp Miehl, Christoph Mölter, Jan Olmi, Simona Schöll, Eckehard Seif, Alireza Tass, Peter A. Volpe, Giovanni Yanchuk, Serhiy Kurths, Jürgen Potsdam Institute for Climate Impact Research Telegrafenberg Potsdam14473 Germany Fachhochschule Nordwestschweiz FHNW Leonhardsstrasse 6 Basel4009 Switzerland Department of Physics Humboldt-Universität zu Berlin Newtonstraße 15 Berlin12489 Germany DAMTP University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt-Augustin53757 Germany Institute of Systematic Musicology University of Hamburg Germany Transdisciplinary Research Area: Sustainable Futures University of Bonn Bonn53113 Germany University of Bonn Bonn53113 Germany Department of Computer Science and Engineering Chalmers University of Technology Göteborg412 96 Sweden Department of Computer Science University of Potsdam An der Bahn 2 Potsdam14476 Germany Scientific Computing Laboratory Center for the Study of Complex Systems Institute of Physics Belgrade University of Belgrade Pregrevica 118 Belgrade11080 Serbia The Ohio State University Department of Physics and Electrical and Computer Engineering 191 West Woodruff Ave ColumbusOH43210 United States ResCon Technologies LLC 1275 Kinnear Rd. Suite 239 ColumbusOH43212 United States Trieste Italy Research Group Comparative Neuroscience Leibniz Institute for Neurobiology Magdeburg Germany Theoretical Physics Saarland University Saarbrücken66123 Germany Center for Biophysics Saarland University Saarbrücken66123 Germany Department of Mathematics Imperial College London United Kingdom Center for Adaptive Rationality Max Planck Institute for Human Development Lentzeallee 94 Berlin14195 Germany Max Planck Institute for Brain Research Max-von-Laue-Straße 4 Frankfurt am Main60438 Germany Technical University of Munich School of Life Sciences Alte Akademie 8 Freising85354 Germany Department of Mathematics School of Computation Information and Technology Technical University of Munich Boltzmannstraße 3 Garching bei München8574

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems like the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges, and give perspectives on future research directions, looking to inspire interdisciplinary approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamical systems

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The vulnerability of cyber-physical system under stealthy attacks

arXiv

引用

arXiv 2020年

作者： Sui, Tianju Mo, Yilin Marelli, Damián Sun, Ximing Fu, Minyue School of Control Science and Engineering Dalian University of Technology Dalian China Department of Automation Tsinghua University Beijing China School of Automation Guangdong University of Technology Guangzhou China French Argentine International Center for Information and Systems Sciences National Scientific and Technical Research Council Argentina School of Electrical Engineering and Computer Science University of Newcastle CallaghanNSW2308 Australia School of Automation Guandong University of Technology China

In this paper, we study the impact of stealthy attacks on the Cyber-Physical System (CPS) modeled as a stochastic linear system. An attack is characterised by a malicious injection into the system through input, output or both, and it is called stealthy (resp. strictly stealthy) if it produces bounded changes (resp. no changes) in the detection residue. Correspondingly, a CPS is called vulnerable (resp. strictly vulnerable) if it can be destabilized by a stealthy attack (resp. strictly stealthy attack). We provide necessary and sufficient conditions for the vulnerability and strictly vulnerability. For the invulnerable case, we also provide a performance bound for the difference between healthy and attacked system. Numerical examples are provided to illustrate the theoretical results. Copyright © 2020, The Authors. All rights reserved.

关键词： Embedded systems

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Exploring explainable selection to control abstract generation

arXiv

引用

arXiv 2020年

作者： Haonan, Wang Yang, Gao Yu, Bai Lapata, Mirella Heyan, Huang School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Institute for Language Cognition and Computation School of Informatics University of Edinburgh 10 Crichton Street EdinburghEH8 9AB United Kingdom Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications

It is a big challenge to model long-range input for document summarization. In this paper, we target using a select and generate paradigm to enhance the capability of selecting explainable contents (i.e., interpret the selection given its semantics, novelty, relevance) and then guiding to control the abstract generation. Specifically, a new designed pair-wise extractor is proposed to capture the sentence pair interactions and its centrality. Furthermore, the generator is hybrid with the selected content and is jointly integrated with a pointer distribution that is derived from a sentence deployment's attention. The abstract generation can be controlled by an explainable mask matrix that determines to what extent the content can be included in the summary. Encoders are adaptable with both Transformerbased and BERT-based configurations. Overall, both results based on ROUGE metrics and human evaluation gain outperformance over several state-of-The-Art models on two benchmark CNN/DailyMail and NYT datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Abstracting

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Spectroscopic signatures of edge states in hexagonal boron nitride

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Nano Research 2019年第7期12卷 1663-1667页

作者： Chuang Gao Lei Tao Yu-Yang Zhang Shixuan Du Sokrates T. Pantelides Juan Carlos Idrobo Wu Zhou Hong-Jun Gao School of Physical Sciences and CAS Key Laboratory of Vacuum Physics University of Chinese Academy of SciencesBeijing 100049China Institute of Physics Chinese Academy of SciencesBeijing 100190China Department of Physics and Astronomy and Department of Electrical Engineering and Computer Science Vanderbilt UniversityNashvilleTN 37235USA CAS Centre for Excellence in Topological Quantum Computation University of Chinese Academy of SciencesBeijing 100049China Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA

We use Z-contrast imaging and atomically resolved electron energy-loss spectroscopy on an aberration-corrected scanning transmission electron microscope to investigate the local electronic states of boron atoms at different edge structures in monolayer and bilayer *** find that edges with bonding unsaturated sp2 boron atoms have a unique spectroscopic signature with a prominent pre-peak at - 190.2 eV in the B K-edge fine ***-principles calculations reveal that the observed pre-peak arises from excitations to the in-plane lowest-energy empty sp2 boron dangling bonds at the B-terminated *** spectroscopic signature can serve as a fingerprint to explore new edge structures in h-BN.

关键词： h-BN edge structure spectroscopic signatures electron energy-loss near edge structure

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Impact of thermal radiation on fractional viscoelastic nanofluid flow with joint heat mass transfer using a neural network based approach

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Journal of Radiation Research and Applied sciences 2025年第3期18卷

作者： Shajar Abbas Taoufik Saidani Muhammad Ramzan S. Saleem Nashwan Adnan Othman Dilsora Abduvalieva Faiza Benabdallahi Ilyas Khan Centre for Advanced Studies in Pure and Applied Mathematics Bahauddin Zakariya University Multan Pakistan AS (Ayub Shah) Institute of Mathematics and Research Centre Multan Pakistan Center for Scientific Research and Entrepreneurship Northern Border University 73213 Arar Saudi Arabia Department of Mathematics College of Science King Khalid University Abha 61413 Saudi Arabia Center for Artificial Intelligence (CAI) King Khalid University Abha 61421 Saudi Arabia Department of Computer Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Computer Engineering Al-Kitab University Altun Kupri Iraq Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor Avenue 27 Tashkent 100070 Uzbekistan Department of Industrial and Systems Engineering College of Engineering Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Mathematical Sciences Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Mathematics College of Science Al-Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Mathematics in Applied Sciences and Engineering Research Group Scientific Research Center Al-Ayen University Nasiriyah 64001 Iraq

This study aims to enhance the predictive modeling of free convection flow in viscoelastic nanofluids influenced by thermal radiation, concentrating especially on improving the understanding of heat and mass transfer behavior. To achieve this, a novel computational framework is developed by incorporating the Prabhakar fractional operator, it successfully records memory effects and non-local behavior of-ten neglected in classical models. The governing fractional partial differential equations are solved using Levenberg-Marquardt backpropagation algorithm-trained artificial neural networks. Training data are generated analytically through the Laplace transform, with a 70 %30 % training-validation split. The proposed model demonstrates high predictive accuracy, achieving a mean squared error below 10 −4 . Sensitivity analysis reveals that the fractional parameter reduces velocity fluid, while concentration and temperature respond strongly to chemical reactions and thermal conditions. The main contribution of this work lies in combining fractional calculus with ANN-based optimization to provide a reliable, efficient tool for modeling and enhancing heat transfer in nanofluid-based engineering systems.

关键词： Thermal radiation Viscoelasticity Fractional operator Method of the laplace transform Chemical reaction

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Statistical approach to detection of attacks for stochastic cyber-physical systems

arXiv

引用

arXiv 2020年

作者： Marelli, Damián Sui, Tianju Fu, Minyue Lu, Renquan School of Automation Guangdong University of Technology Guangzhou China French Argentine International Center for Information and Systems Sciences National Scientific and Technical Research Council Argentina School of Control Science and Engineering Dalian University of Technology Dalian China School of Electrical Engineering and Computer Science University of Newcastle CallaghanNSW2308 Australia School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guandong University of Technology Guangzhou China

We study the problem of detecting an attack on a stochastic cyber-physical system. We aim to treat the problem in its most general form. We start by introducing the notion of asymptotically detectable attacks, as those attacks introducing changes to the system’s output statistics which persist asymptotically. We then provide a necessary and sufficient condition for asymptotic detectability. This condition preserves generality as it holds under no restrictive assumption on the system and attacking scheme. To show the importance of this condition, we apply it to detect certain attacking schemes which are undetectable using simple statistics. Our necessary and sufficient condition naturally leads to an algorithm which gives a confidence level for attack detection. We present simulation results to illustrate the performance of this algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

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Enhance the Mammogram Images for Both Segmentation and Feature Extraction Using Wavelet Transform

Enhance the Mammogram Images for Both Segmentation and Featu...

引用

2019 International Conference on Advanced science and engineering, ICOASE 2019

作者： Zebari, Dilovan Asaad Haron, Habibollah Zeebaree, Subhi R.M. Zeebaree, Diyar Qader Faculty of Engineering School of Computing University Teknologi Malaysia Johor Malaysia University Teknologi Malaysia Faculty of Computing Department of Computer Science Johor Malaysia IT Dept. Technical College of Informatics Akre Duhok Scientific Research and Development Center Nawroz University Duhok Iraq

ISBN: (纸本)9781538693438

Breast cancer (BC) is a main killer disease for women and men. It can be cured and controlled only if it is detected at its early detection. BC initial identification can be realized by the help of computer support identification approaches. From the detailed study on previous researches, it is found that, there is no system producing high accuracy because of one or more reasons. Absence of effective preprocessing is the discussed reason that obstructs the detection accuracy of computer-Aided diagnosis (CAD) method. Noise removal and contrast enhancement are the two types of preprocessing. There is no system performs the preprocessing on mammogram image. This work is an attempt to develop an enhanced preprocessing method for CAD of breast cancer by incorporating suitable noise reduction and contrast enhancement methods in the conventional CAD system. Contrast enhancement after noise reduction double enhances the mammogram image and the proposed methods MSE value for the mammogram image mdb072 has been 1.44% reduced. Reduction in MSE increases the PSNR to 0.16%. Many mammogram images have been tested and the result shows that, increase in contrast, decrease in mean square error and increase in peak signal to noise ratio when comparing to existing methods. © 2019 IEEE.

关键词： Wavelet transforms

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