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Signatures of electronic nematicity in (111) LaAlO3/SrTiO3 interfaces

Physical Review B 2018年第4期97卷 041408(R)-041408(R)页

作者： S. Davis Z. Huang K. Han Ariando T. Venkatesan V. Chandrasekhar Graduate Program in Applied Physics and Department of Physics and Astronomy Northwestern University 2145 Sheridan Road Evanston Illinois 60208 USA NUSNNI-Nanocore National University of Singapore 117411 Singapore Department of Physics National University of Singapore 117551 Singapore NUS Graduate School for Integrative Sciences & Engineering National University of Singapore 117456 Singapore Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore Department of Material Science and Engineering National University of Singapore 117575 Singapore

The two-dimensional conducting gas (2DCG) that forms at the interface between LaAlO3 (LAO) and SrTiO3 (STO) has been widely studied due to the multitude of in situ tunable phenomena that exist at the interface. Recently it has been shown that nearly every property of the 2DEG that forms at the interface of (111) oriented LAO/STO is strongly anisotropic with respect to the in-plane crystal direction. This in-plane rotational symmetry breaking points to the existence of an electronic nematic phase at the interface that can be modified by an in situ electrostatic back-gate potential. Here we show that the onset temperature of the anisotropy in the longitudinal resistance is T≈22K, which does not match up with any known structural transition, and coincides with the onset of anisotropy in the Hall response of the system. Furthermore, below 22 K, charge transport is activated in nature with different activation energies along the two in-plane crystal directions. Such a response implies that the band edges along the two directions are different and provides further evidence of an electronic nematic state at the interface.

关键词： electrical properties Hall effect Nematic order Transport phenomena Hall bar Resistivity measurements Transport techniques

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Deep learning on image denoising: An overview

arXiv

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arXiv 2019年

作者： Tian, Chunwei Fei, Lunke Zheng, Wenxian Xu, Yong Zuo, Wangmeng Lin, Chia-Wen Bio-Computing Research Center Harbin Institute of Technology Shenzhen Shenzhen Guangdong518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen Guangdong518055 China School of Computers Guangdong University of Technology Guangzhou Guangdong510006 China Tsinghua Shenzhen International Graduate School Shenzhen Guangdong518055 China Peng Cheng Laboratory Shenzhen Guangdong518055 China School of Computer Science and Technology Harbin Institute of Technology Harbin Heilongjiang150001 China Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan

Deep learning techniques have obtained much attention in image denoising. However, deep learning methods of different types deal with the noise have enormous differences. Specifically, discriminative learning based on deep learning can well address the Gaussian noise. Optimization model methods based on deep learning have good effect on estimating of the real noise. So far, there are little related researches to summarize different deep learning techniques for image denoising. In this paper, we make such a comparative study of different deep techniques in image denoising. We first classify the (1) deep convolutional neural networks (CNNs) for additive white noisy images, (2) deep CNNs for real noisy images, (3) deep CNNs for blind denoising and (4) deep CNNs for hybrid noisy images, which is the combination of noisy, blurred and low-resolution images. Then, we analyze the motivations and principles of deep learning methods of different types. Next, we compare and verify the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analysis. Finally, we point out some potential challenges and directions of future research. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Author Correction: Pathway to a land-neutral expansion of Brazilian renewable fuel production

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Nature communications 2022年第1期13卷 3450页

作者： Luis Ramirez Camargo Gabriel Castro Katharina Gruber Jessica Jewell Michael Klingler Olga Turkovska Elisabeth Wetterlund Johannes Schmidt Institute for Sustainable Economic Development University of Natural Resources and Life Sciences Vienna Austria. l.e.ramirezcamargo@uu.nl. Electric Vehicle and Energy Research Group (EVERGI) Mobility Logistics and Automotive Technology Research Centre (MOBI) Department of Electrical Engineering and Energy Technology Vrije Universiteit Brussel Brussels Belgium. l.e.ramirezcamargo@uu.nl. Copernicus Institute of Sustainable Development Utrecht University Utrecht The Netherlands. l.e.ramirezcamargo@uu.nl. Institute for Sustainable Economic Development University of Natural Resources and Life Sciences Vienna Austria. Energy Planning Program Graduate School of Engineering Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil. Department of Space Earth and Environment Chalmers University of Technology Gothenburg Sweden. Center for Climate and Energy Transformations and Department of Geography University of Bergen Bergen Norway. International Institute for Applied Systems Analysis (IIASA) Laxenburg Austria. Department of Geography University of Innsbruck Innsbruck Austria. Energy Engineering Division of Energy Science Luleå University of Technology Luleå Sweden. Institute for Sustainable Economic Development University of Natural Resources and Life Sciences Vienna Austria. johannes.schmidt@boku.ac.at.

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Retracted: LUC Algorithm in Visual Cryptography

Retracted: LUC Algorithm in Visual Cryptography

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2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, computer science, Information Systems, and Information Technology, WMA-Mathcomtech 2018

作者： Achmad Daengs, G.S. Ratnadewi Alpha, Christian Dio Napitupulu, Darmawan Nurdiyanto, Heri Kurniasih, Nuning Hasibuan, Abdurrozaq Daengs, Achmad Manurung, Rosida Tiurma Mulyaningsih, Indrya Rahim, Robbi Sugianto, Castaka Agus Abdullah, Dahlan Department of Electrical Engineering Universitas Kristen Maranatha Bandung Indonesia Research Center for Quality System and Testing Technology Indonesian Institute of Sciences Banten Indonesia STMIK Dharma Wacana Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Universitas Islam Sumatera Utara Indonesia University of 45 Surabaya Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Bandung Indonesia Department of Education Institut Agama Islam Negeri Syekh Nurjati Cirebon Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia Politeknik TEDC Bandung Indonesia Department of Informatics Universitas Malikussaleh Aceh Utara Indonesia

Nowadays, messaging technology in digital data form more often used and not less messages that confidentially wanted. Then it should be modified so that can be understood only by the sender and the intended recipients. All of this system can be realization by using cryptography. LUC algorithms are introduced by Smith and Lennon in 1993. LUC algorithm is an algorithm that based on the use of Lucas sequence (specific arithmetic operations derived from Lucas row) that rarely used to enhance the security of the messages. In this research, LUC algorithm is combined with visual cryptography to process the encryption and the description of a colored image. Four different images were used in the trial here. The performance of the system is assessed using Structural similarity (SSIM) which has an assessment similar to human eye. If the image quality is the same as the original image, then the SSIM value is one. Whereas if the quality of decryption image is very different from the original image then the value of SSIM is zero. © Published under licence by IOP Publishing Ltd.

关键词： Information systems

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Light‐Emitting Transistors: Multioperation‐Mode Light‐Emitting Field‐Effect Transistors Based on van der Waals Heterostructure (Adv. Mater. 43/2020)

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Advanced Materials 2020年第43期32卷

作者： Junyoung Kwon June‐Chul Shin Huije Ryu Jae Yoon Lee Dongjea Seo Kenji Watanabe Takashi Taniguchi Young Duck Kim James Hone Chul‐Ho Lee Gwan‐Hyoung Lee Department of Materials Science and Engineering Yonsei University Seoul 03722 Korea Department of Materials Science and Engineering Seoul National University Seoul 08826 Korea KU‐KIST Graduate School of Converging Science and Technology Korea University Seoul 02841 Korea Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota 55455 USA Research Center for Functional Materials National Institute for Materials Science 1‐1 Namiki Tsukuba 305‐0044 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science 1‐1 Namiki Tsukuba 305‐0044 Japan Department of Physics Kyung Hee University Seoul 02447 Korea Department of Mechanical Engineering Columbia University New York NY 10027 USA Research Institute of Advanced Materials (RIAM) Institute of Engineering Research Institute of Applied Physics Seoul National University Seoul 08826 Korea

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The complete mitogenome of Bagrid catfishChrysichthys nigrodigitatus(Siluriformes: Claroteidae)

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Mitochondrial DNA Part B 2018年第2期3卷 1239-1240页

作者： Nack-Keun Kim Fantong Zealous Gietbong Sapto Andriyono Ah Ran Kim Hyun-Woo Kim Interdisciplinary Program of Biomedical Mechanical and Electrical Engineering Pukyong National University Busan Republic of Korea Department of Marine Biology Pukyong National University Busan Republic of KoreaKOICA-PKNU International Graduate Program of Fisheries Science Graduate School of Global Fisheries Pukyong National University Busan Republic of Korea Interdisciplinary Program of Biomedical Mechanical and Electrical Engineering Pukyong National University Busan Republic of KoreaDepartment of Marine Fisheries and Marine Faculty Universitas Airlangga C Campus Jl. Mulyorejo Surabaya East Java Indonesia Department of Marine Biology Pukyong National University Busan Republic of Korea Interdisciplinary Program of Biomedical Mechanical and Electrical Engineering Pukyong National University Busan Republic of KoreaDepartment of Marine Biology Pukyong National University Busan Republic of Korea

We here report the complete mitochondrial genome ofChrysichthys nigrodigitatus, which is 16,514 bp in length. Mitogenome ofC. nigrodigitatusshowed the conserved 13 protein-coding genes, 22 tRNA genes, two rRNA genes, and two noncoding regions including the light-strand replication origin (OL) and a putative control region (CR). All tRNA genes were predicted to fold into the typical cloverleaf secondary structures with the typical base-pairing except for tRNA-Ser(AGC). Phylogenetic analysis with currently known complete mitogenome sequences in Siluriformes showed thatC. nigrodigitatusis most closely related toAuchenoglanis occidentalisforming a family Claroteidae cluster.

关键词： Chrysichthys nigrodigitatus mitochondrial genome catfish Cameroon Africa

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Learning from learning machines: A new generation of AI technology to meet the needs of science

arXiv

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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

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Abstracts

引用

JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

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Erratum to: Search for single top-quark production via flavour-changing neutral currents at 8 TeV with the ATLAS detector

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The European Physical Journal C 2022年第1期82卷 1-15页

作者： Aad, G. Abbott, B. Abdallah, J. Abdinov, O. Aben, R. Abolins, M. AbouZeid, O. S. Abramowicz, H. Abreu, H. Abreu, R. Abulaiti, Y. Acharya, B. S. Adamczyk, L. Adams, D. L. Adelman, J. Adomeit, S. Adye, T. Affolder, A. A. Agatonovic-Jovin, T. Agricola, J. Aguilar-Saavedra, J. A. Ahlen, S. P. Ahmadov, F. Aielli, G. Akerstedt, H. Åkesson, T. P. A. Akimov, A. V. Alberghi, G. L. Albert, J. Albrand, S. Alconada Verzini, M. J. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexander, G. Alexopoulos, T. Alhroob, M. Alimonti, G. Alio, L. Alison, J. Alkire, S. P. Allbrooke, B. M. M. Allport, P. P. Aloisio, A. Alonso, A. Alonso, F. Alpigiani, C. Altheimer, A. Alvarez Gonzalez, B. Álvarez Piqueras, D. Alviggi, M. G. Amadio, B. T. Amako, K. Amaral Coutinho, Y. Amelung, C. Amidei, D. Amor Dos Santos, S. P. Amorim, A. Amoroso, S. Amram, N. Amundsen, G. Anastopoulos, C. Ancu, L. S. Andari, N. Andeen, T. Anders, C. F. Anders, G. Anders, J. K. Anderson, K. J. Andreazza, A. Andrei, V. Angelidakis, S. Angelozzi, I. Anger, P. Angerami, A. Anghinolfi, F. Anisenkov, A. V. Anjos, N. Annovi, A. Antonelli, M. Antonov, A. Antos, J. Anulli, F. Aoki, M. Aperio Bella, L. Arabidze, G. Arai, Y. Araque, J. P. Arce, A. T. H. Arduh, F. A. Arguin, J-F. Argyropoulos, S. Arik, M. Armbruster, A. J. Arnaez, O. Arnal, V. Arnold, H. Arratia, M. Arslan, O. Artamonov, A. Artoni, G. Asai, S. Asbah, N. Ashkenazi, A. Åsman, B. Asquith, L. Assamagan, K. Astalos, R. Atkinson, M. Atlay, N. B. Augsten, K. Aurousseau, M. Avolio, G. Axen, B. Ayoub, M. K. Azuelos, G. Baak, M. A. Baas, A. E. Baca, M. J. Bacci, C. Bachacou, H. Bachas, K. Backes, M. Backhaus, M. Bagiacchi, P. Bagnaia, P. Bai, Y. Bain, T. Baines, J. T. Baker, O. K. Baldin, E. M. Balek, P. Balestri, T. Balli, F. Banas, E. Banerjee, Sw. Bannoura, A. A. E. Bansil, H. S. Barak, L. Barberio, E. L. Barberis, D. Barbero, M. Barillari, T. Barisonzi, M. Barklow, T. Barlow, N. Barnes, S. L. Barnett, B. M. Barnett, R. M. Barnovska, Z. Baroncelli, A. Barone, G. Barr, A. J. Barreiro, F. Barreiro Guimarães da Costa, J. Bartol CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman USA Institute of Physics Academia Sinica Taipei Taiwan Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan Nikhef National Institute for Subatomic Physics and University of Amsterdam Amsterdam The Netherlands Department of Physics and Astronomy Michigan State University East Lansing USA Department of Physics University of Toronto Toronto Canada Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion: Israel Institute of Technology Haifa Israel Center for High Energy Physics University of Oregon Eugene USA Department of Physics Stockholm University Stockholm Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London UK AGH University of Science and Technology Faculty of Physics and Applied Computer Science Kraków Poland Physics Department Brookhaven National Laboratory Upton USA Department of Physics Northern Illinois University De Kalb USA Fakultät für Physik Ludwig-Maximilians-Universität München Munich Germany Particle Physics Department Rutherford Appleton Laboratory Didcot UK Oliver Lodge Laboratory University of Liverpool Liverpool UK Institute of Physics University of Belgrade Belgrade Serbia II Physikalisches Institut Georg-August-Universität Göttingen Germany Laboratório de Instrumentação e Física Experimental de Partículas-LIP Lisbon Portugal Departamento de Fisica Teorica y del Cosmos and CAFPE Universidad de Granada Granada Spain Department of Physics Boston University Boston USA Joint Institute for Nuclear Research JINR Dubna Dubna Russia INFN Sezione di Roma Tor Vergata Rome Italy Dipartimento di Fisica Università di Roma Tor Vergata Rome Italy Fysiska institutionen Lunds uni

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An Adaptive Large Neighborhood Search with Learning Automata for the Unrelated Parallel Machine Scheduling Problem

An Adaptive Large Neighborhood Search with Learning Automata...

引用

2017 IEEE Congress on Evolutionary Computation, CEC 2017

作者： Cota, Luciano Perdigao Guimaraes, Frederico Gadelha De Oliveira, Fernando B. Souza, Marcone J. Freitas Graduate Program in Electrical Engineering Federal University of Minas Gerais Belo Horizonte Brazil Department of Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Department of Computer and Systems Universidade Federal de Ouro Preto João Monlevade Brazil Department of Computer Science Universidade Federal de Ouro Preto Brazil

ISBN: (纸本)9781509046010

This work deals with the Unrelated Parallel Machine Scheduling Problem with Setup Times, with the objective of minimizing the makespan. It is proposed an Adaptive Large Neighborhood Search (ALNS) metaheuristic using Learning Automata (LA) to adapt the probabilities of using removal and insertion heuristics and methods. A computable function in the LA updates the probability vector for selecting the actions, corresponding to six removal and six insertion methods. We also propose a new insertion method based on Hungarian algorithm, which is applied to solve subproblems optimally. Computational experiments are performed to verify the performance of the proposed method. A set of instances available in the literature with problems up to 150 jobs and 10 machines is employed in the experiments. The proposed LA-ALNS is compared against three other algorithms from the literature. The results suggest that our algorithm has better performance in most of cases (88%) under the defined conditions of experiments. Statistical tests also suggest that LA-ALNS is better than the other algorithms from the literature. The proposed method is able to automatically choose the most suitable heuristics for the instance of the problem, through adaptation and learning in the Learning Automata. © 2017 IEEE.

关键词： Heuristic methods

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