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Doublet Thermal Metadevice

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Physical Review Applied 2019年第4期11卷 044021-044021页

作者： Jiaxin Li Ying Li Tianlong Li Wuyi Wang Longqiu Li Cheng-Wei Qiu State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 150001 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Department of Analytical Physical and Colloidal Chemistry Institute of Pharmacy Sechenov University Moscow 119991 Russia

Thermal metamaterials have served as a practical way to manipulate heat flow thanks to their judiciously designed structures, which achieve various functionalities. However, the connection and interplay between various possible structural configurations and their functions’ switchability have long been neglected and this is usually perceived as “one configuration, one function.” Here, we propose a doublet thermal metadevice in which we utilize the local configuration of two unequal conductivity phases to manipulate global temperature distribution. Based on effective medium theory, we apply the phase interchange identity to show that the functionalities of transparency, cloaking, concentrating, and rotating can be unified. The doublet thermal metadevice is capable of realizing tunable multifunctions by simply adjusting the angular displacement of specific sublayers of the assembly. The functional diversity of the device can be further extended in two ways. One way is to use a secondary transformation and the other way is to alter the shape parameter of the unit phase. This work provides a different framework to understand and design thermal metamaterials, which might be extended to other disciplines such as optics, electromagnetics, and acoustics.

关键词： Heat transfer Thermal conductivity Energy applications Metamaterials

STARdom: An architecture for trusted and secure human-centered manufacturing systems

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arXiv 2021年

作者： Rožanec, Jože M. Zajec, Patrik Kenda, Klemen Mladenić, Dunja Cauli, Nino Soldatos, John Papamartzivanos, Dimitrios Alonso, Rubén Veliou, Entso Sofianidis, Georgios Menesidou, Sofia Anna Theodoropoulos, Spyros Kyriazis, Dimosthenis Fortuna, Blaž Novalija, Inna Giannetsos, Thanassis Recupero, Diego Reforgiato Jožef Stefan International Postgraduate School Jamova 39 Ljubljana1000 Slovenia Jožef Stefan Institute Jamova 39 Ljubljana1000 Slovenia Qlector d.o.o. Rovšnikova 7 Ljubljana1000 Slovenia Ubitech Ltd Digital Security & Trusted Computing Group Athens Greece Department of Informatics and Computer Engineering University of West Attica Agiou Spyridonos Street Egaleo Athens12243 Greece R2M Solution Srl Pavia Italy Department of Computer Science University of Cagliari Cagliari Italy Department of Digital Systems University of Piraeus Piraeus Greece Department of Electrical and Computer Engineering National Technical University of Athens Athens Greece INTRASOFT International 19.5 KM Markopoulou Ave. PeaniaGR 19002 Greece

There is a lack of a single architecture specification that addresses the needs of trusted and secure Artificial Intelligence systems with humans in the loop, such as human-centered manufacturing systems at the core of the evolution towards Industry 5.0. To realize this, we propose an architecture that integrates forecasts, Explainable Artificial Intelligence, supports collecting users' feedback and uses Active Learning and Simulated Reality to enhance forecasts and provide decision-making recommendations. The architecture security is addressed as a general concern. We align the proposed architecture with the Big Data Value Association Reference Architecture Model. We tailor it for the domain of demand forecasting and validate it on a real-world case study. Copyright © 2021, The Authors. All rights reserved.

关键词： Industry 4.0

Author Correction: Super-resolution multicolor fluorescence microscopy enabled by an apochromatic super-oscillatory lens with extended depth-of-focus

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Nature communications 2024年第1期15卷 1510页

作者： Wenli Li Pei He Dangyuan Lei Yulong Fan Yangtao Du Bo Gao Zhiqin Chu Longqiu Li Kaipeng Liu Chengxu An Weizheng Yuan Yiting Yu Ningbo Institute of Northwestern Polytechnical University College of Mechanical Engineering Northwestern Polytechnical University Xi'an 710072 China. Key Laboratory of Micro/Nano Systems for Aerospace (Ministry of Education) Northwestern Polytechnical University Xi'an 710072 China. Shaanxi Province Key Laboratory of Micro and Nano Electro-Mechanical Systems Northwestern Polytechnical University Xi'an 710072 China. Department of Materials Science and Engineering City University of Hong Kong Hong Kong 999077 China. dangylei@cityu.edu.hk. Department of Materials Science and Engineering City University of Hong Kong Hong Kong 999077 China. The Institute of AI and Robotics Fudan University Shanghai 200433 China. Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an 710119 China. Department of Electrical and Electronic Engineering Joint Appointment with School of Biomedical Sciences The University of Hong Kong Hong Kong 999077 China. State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin Heilongjiang 150001 China. Ningbo Institute of Northwestern Polytechnical University College of Mechanical Engineering Northwestern Polytechnical University Xi'an 710072 China. yyt@***. Key Laboratory of Micro/Nano Systems for Aerospace (Ministry of Education) Northwestern Polytechnical University Xi'an 710072 China. yyt@***. Shaanxi Province Key Laboratory of Micro and Nano Electro-Mechanical Systems Northwestern Polytechnical University Xi'an 710072 China. yyt@***.

Augmentation of the reconstruction performance of Fuzzy C-Means with an optimized fuzzification factor vector

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arXiv 2020年

作者： Xu, Kaijie Pedrycz, Witold Li, Zhiwu School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Department of remote sensing science and technology School of Electronic Engineering Xidian University Xi’an710071 China Systems Research Institute Polish Academy of Sciences Warsaw Poland Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Institute of Systems Engineering Macau University of Science and Technology 999078 China

Information granules have been considered to be the fundamental constructs of Granular Computing (GrC). As a useful unsupervised learning technique, Fuzzy C-Means (FCM) is one of the most frequently used methods to construct information granules. The FCM-based granulation-degranulation mechanism plays a pivotal role in GrC. In this paper, to enhance the quality of the degranulation (reconstruction) process, we augment the FCM-based degranulation mechanism by introducing a vector of fuzzification factors (fuzzification factor vector) and setting up an adjustment mechanism to modify the prototypes and the partition matrix. The design is regarded as an optimization problem, which is guided by a reconstruction criterion. In the proposed scheme, the initial partition matrix and prototypes are generated by the FCM. Then a fuzzification factor vector is introduced to form an appropriate fuzzification factor for each cluster to build up an adjustment scheme of modifying the prototypes and the partition matrix. With the supervised learning mode of the granulation-degranulation process, we construct a composite objective function of the fuzzification factor vector, the prototypes and the partition matrix. Subsequently, the particle swarm optimization (PSO) is employed to optimize the fuzzification factor vector to refine the prototypes and develop the optimal partition matrix. Finally, the reconstruction performance of the FCM algorithm is enhanced. We offer a thorough analysis of the developed scheme. In particular, we show that the classical FCM algorithm forms a special case of the proposed scheme. Experiments completed for both synthetic and publicly available datasets show that the proposed approach outperforms the generic data reconstruction approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Granulation

Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

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Genome biology 2025年第1期26卷 31页

作者： Jacob Matthew Schreiber Carles A Boix Jin Wook Lee Hongyang Li Yuanfang Guan Chun-Chieh Chang Jen-Chien Chang Alex Hawkins-Hooker Bernhard Schölkopf Gabriele Schweikert Mateo Rojas Carulla Arif Canakoglu Francesco Guzzo Luca Nanni Marco Masseroli Mark James Carman Pietro Pinoli Chenyang Hong Kevin Y Yip Jefrey P Spence Sanjit Singh Batra Yun S Song Shaun Mahony Zheng Zhang Wuwei Tan Yang Shen Yuanfei Sun Minyi Shi Jessika Adrian Richard S Sandstrom Nina P Farrell Jessica M Halow Kristen Lee Lixia Jiang Xinqiong Yang Charles B Epstein J Seth Strattan Bradley E Bernstein Michael P Snyder Manolis Kellis William S Noble Anshul Bharat Kundaje Department of Genetics Stanford University Stanford CA USA. jmschreiber91@***. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA. Department of Genetics Stanford University Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Research and Development DeepSeq.AI San Francisco CA USA. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Empirical Inference Max Planck Institute for Intelligent Systems Stuttgart Germany. School of Life Sciences University of Dundee Dundee UK. Department of Electronics Information and Bioengineering Politecnico Di Milano Milan Italy. Department of Computational Biology University of Lausanne Lausanne Switzerland. Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong. Sanford Burnham Prebys Medical Discovery Institute San Diego CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Statistics University of California Berkeley Berkeley CA USA. Department of Biochemistry & Molecular Biology Center for Eukaryotic Gene Regulation Pennsylvania State University University Park PA USA. Department of Statistics Pennsylvania State University University Park PA USA. Department of Electrical and Computer Engineering Texas A&M University College Station TX USA. Altius Institute Seattle WA USA. Epigenomics Program The Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Computer Science Stanford University Stanford CA USA.

Searching for GEMS: Discovery and Characterization of Two Brown Dwarfs Around M Dwarfs∗

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arXiv 2025年

作者： Larsen, Alexander Swaby, Tera N. Kobulnicky, Henry A. Cañas, Caleb I. Kanodia, Shubham Libby-Roberts, Jessica Monson, Andrew Gupta, Arvind Cochran, William Mahadevan, Suvrath Bender, Chad Diddams, Scott A. Halverson, Samuel Lin, Andrea S.J. Moe, Maxwell Ninan, Joe Robertson, Paul Roy, Arpita Schwab, Christian Stefansson, Gudmundur Department of Physics & Astronomy University of Wyoming LaramieWY82070 United States NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States Earth and Planets Laboratory Carnegie Institution for Science 5241 Broad Branch Road NW WashingtonDC20015 United States Department of Astronomy & Astrophysics The Pennsylvania State University 525 Davey Laboratory University ParkPA16802 United States Center for Exoplanets and Habitable Worlds The Pennsylvania State University 525 Davey Laboratory University ParkPA16802 United States Steward Observatory The University of Arizona 933 N. Cherry Avenue TucsonAZ85721 United States U.S. National Science Foundation National Optical-Infrared Astronomy Research Laboratory 950 N. Cherry Ave. TucsonAZ85719 United States McDonald Observatory Department of Astronomy The University of Texas Austin United States Center for Planetary Systems Habitability The University of Texas Austin United States Electrical Computer & Energy Engineering University of Colorado 1111 Engineering Dr. BoulderCO80309 United States Department of Physics University of Colorado 2000 Colorado Avenue BoulderCO80309 United States Jet Propulsion Laboratory 4800 Oak Grove Drive PasadenaCA91109 United States Department of Astronomy California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Homi Bhabha Road Colaba Mumbai400005 India Department of Physics & Astronomy The University of California Irvine IrvineCA92697 United States Astrophysics & Space Institute Schmidt Sciences New YorkNY10011 United States School of Mathematical and Physical Sciences Macquarie University Balaclava Road North RydeNSW2109 Australia Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands

Brown dwarfs bridge the gap between stars and planets, providing valuable insight into both planetary and stellar formation mechanisms. Yet the census of transiting brown dwarf companions, in particular around M dwarf stars, remains incomplete. We report the discovery of two transiting brown dwarfs around low-mass hosts using a combination of space- and ground-based photometry along with near-infrared radial velocities. We characterize TOI-5389Ab (68.0+2−2.22 MJ) and TOI-5610b (40.4+1−1.00 MJ), two moderately massive brown dwarfs orbiting early M dwarf hosts (Teff = 3569±59 K and 3618±59 K, respectively). For TOI-5389Ab, the best fitting parameters are period P = 10.40046 ± 0.00002 days, radius RBD = 0.824+0−0.033031 RJ, and low eccentricity e = 0.0962+0−0.00270046. In particular, this constitutes one of the most extreme substellar-stellar companion-to-host mass ratios of q = 0.150. For TOI-5610b, the best fitting parameters are period P = 7.95346 ± 0.00002 days, radius RBD = 0.887+0−0.031031 RJ, and moderate eccentricity e = 0.354+0−0.011012. Both targets are expected to have shallow but potentially observable secondary transits: 500 ppm in Johnson K band for both. A statistical analysis of M-dwarf/BD systems reveals for the first time that those at short orbital periods (P J BD J companions (q © 2025, CC0.

关键词： Stars

Noise-enabled species recovery in the aftermath of a tipping point

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Physical Review E 2020年第1期101卷 012206-012206页

作者： Yu Meng Junjie Jiang Celso Grebogi Ying-Cheng Lai Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

The beneficial role of noise in promoting species coexistence and preventing extinction has been recognized in theoretical ecology, but previous studies were mostly concerned with low-dimensional systems. We investigate the interplay between noise and nonlinear dynamics in real-world complex mutualistic networks with a focus on species recovery in the aftermath of a tipping point. Particularly, as a critical parameter such as the mutualistic interaction strength passes through a tipping point, the system collapses and approaches an extinction state through a dramatic reduction in the species populations to near-zero values. We demonstrate the striking effect of noise: when the direction of parameter change is reversed through the tipping point, noise enables species recovery which otherwise would not be possible. We uncover an algebraic scaling law between the noise amplitude and the parameter distance from the tipping point to the recovery point and provide a physical understanding through analyzing the nonlinear dynamics based on an effective, reduced-dimension model. Noise, in the form of small population fluctuations, can thus play a positive role in protecting high-dimensional, complex ecological networks.

关键词： Bifurcations Dynamics of networks Noise Population dynamics Ecological networks

A leadfield-free optimization framework for transcranially applied electric currents

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computers in Biology and Medicine 2025年 195卷

作者： Konstantin Weise Kristoffer H. Madsen Torge Worbs Thomas R. Knösche Anders Korshøj Axel Thielscher Department of Clinical Medicine Aarhus University Aarhus Denmark Methods and Development Group “Brain Networks” Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Leipzig University of Applied Sciences (HTWK) Institute for Electrical Power Engineering Leipzig Germany Technical University of Denmark Section for Cognitive Systems Department of Applied Mathematics and Computer Science Kongens Lyngby Denmark Danish Research Centre for Magnetic Resonance Department of Radiology and Nuclear Medicine Copenhagen University Hospital Amager and Hvidovre Hvidovre Denmark Technical University of Denmark Section for Magnetic Resonance Department of Health Technology Kongens Lyngby Denmark Department of Neurosurgery Aarhus University Hospital Aarhus Denmark

Background Transcranial electrical Stimulation (TES), Temporal Interference Stimulation (TIS), Electroconvulsive Therapy (ECT) and Tumor Treating Fields (TTFields) are based on the application of electric current patterns to the brain. Objective The optimal electrode positions, shapes and alignments for generating a desired current pattern in the brain vary between persons due to anatomical variability. The aim is to develop a flexible and efficient computational approach to determine individually optimal montages based on electric field simulations. Methods We propose a leadfield-free optimization framework that allows the electrodes to be placed freely on the head surface. It is designed for the optimization of montages with a low to moderate number of spatially extended electrodes or electrode arrays. Spatial overlaps are systematically prevented during optimization, enabling arbitrary electrode shapes and configurations. The approach supports maximizing the field intensity in target region-of-interests (ROI) and optimizing for a desired focality-intensity tradeoff. Results We demonstrate montage optimization for standard two-electrode TES, focal center-surround TES, TIS, ECT and TTFields. Comparisons against reference simulations are used to validate the performance of the algorithm. The system requirements are kept moderate, allowing the optimization to run on regular notebooks and promoting its use in basic and clinical research. Conclusion(s) The new framework complements existing optimization methods that require small electrodes, a predetermined discretization of the electrode positions on the scalp and work best for multi-channel systems. It strongly extends the possibilities to optimize electrode montages towards application-specific aims and supports researchers in discovering innovative stimulation schemes. The framework is available in SimNIBS.

关键词： Electroconvulsive therapy Montage optimization Temporal interference stimulation Transcranial electric stimulation Tumor treating fields

From multi-modal property dataset to robot-centric conceptual knowledge about household objects

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arXiv 2019年

作者： Thosar, Madhura Mueller, Christian A. Jaeger, Georg Schleiss, Johannes Pulugu, Narender Chennaboina, Ravi Mallikarjun Jeevangekar, Sai Vivek Birk, Andreas Pfingsthorn, Max Zug, Sebastian Institute for Intelligent Cooperating Systems Faculty of Computer Science Otto-von-Guericke University Magdeburg Germany Robotics Computer Science & Electrical Engineering Department Jacobs University Bremen Germany Institute for Computer Science Technische Universität Bergakademie Freiberg Germany OFFIS Institute for Information Technology Oldenburg Germany

Tool-use applications in robotics require conceptual knowledge about objects for informed decision making and object interactions. State-of-the-art methods employ hand-crafted symbolic knowledge which is defined from a human perspective and grounded into sensory data afterwards. However, due to different sensing and acting capabilities of robots, their conceptual understanding of objects must be generated from a robot's perspective entirely, which asks for robot-centric conceptual knowledge about objects. With this goal in mind, this article motivates that such knowledge should be based on physical and functional properties of objects. Consequently, a selection of ten properties is defined and corresponding extraction methods are proposed. This multi-modal property extraction forms the basis on which our second contribution, a robot-centric knowledge generation is build on. It employs unsupervised clustering methods to transform numerical property data into symbols, and Bivariate Joint Frequency Distributions and Sample Proportion to generate conceptual knowledge about objects using the robot-centric symbols. A preliminary implementation of the proposed framework is employed to acquire a dataset comprising physical and functional property data of 110 houshold objects. This Robot-Centric dataSet (RoCS) is used to evaluate the framework regarding the property extraction methods, the semantics of the considered properties within the dataset and its usefulness in real-world applications such as tool substitution. Copyright © 2019, The Authors. All rights reserved.

关键词： Decision making