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Epitaxial growth of large-area and highly crystalline anisotropic ReSe2 atomic layer

Nano Research 2017年第8期10卷 2732-2742页

作者： Fangfang Cui Xiaobo Li Qingliang Feng Jianbo Yin Lin Zhou Dongyan Liu Kaiqiang Liu Xuexia He Xing Liang Shengzhong Liu Zhibin Lei Zonghuai Liu Hailin Peng Jin Zhang Jing Kong Hua Xu Key Laboratory of Applied Surface and Colloid Chemistry Ministry of Education Shaanxi Key Laboratory for Advanced Energy Devices Shaanxi Engineering Lab for Advanced Energy Technology School of Materials Science and Engineering Shaanxi Normal University Xi'an 710119 China Center for Nanochemistry Beijing National Laboratory for Molecular Sciences Key Laboratory for the Physics and Chemistry of Nanodevices College of Chemistry and Molecular Engineering Peking University Beijing 100871 China Department of Electrical Engineering and Computer Sciences Massachusetts Institute of Technology Cambridge MA 02139 USA School of Chemistry and Chemical Engineering Shaanxi Normal University Xi' an 710119 China

The anisotropic two-dimensional （2D） layered material rhenium disulfide （ReSe2） has attracted considerable attention because of its unusual properties and promising applications in electronic and optoelectronic devices. However, because of its low lattice symmetry and interlayer decoupling, anisotropic growth and out-of-plane growth occur easily, yielding thick flakes, dendritic structure, or flower-like structure. In this stud34 we demonstrated a bottom-up method for the controlled and scalable synthesis of ReSe2 by van der Waals epitaxy. To achieve controllable growth, a micro-reactor with a confined reaction space was constructed by stacking two mica substrates in the chemical vapor deposition system. Within the confined reaction space, the nucleation density and growth rate of ReSe2 were significantly reduced, favoring the large-area synthesis of ReSe2 with a uniform monolayer thickness. The morphological evolution of ReSe2 with growth temperature indicated that the anisotropic growth was suppressed at a low growth temperature （〈600 ℃）. Field-effect transistors employing the grown ReSe2 exhibited p-type conduction with a current ON/OFF ratio up to 10s and a hole carrier mobility of 0.98 cm^2/（V·s）. Furthermore, the ReSe2 device exhibited an outstanding photoresponse to near-infrared light, with responsivity up to 8.4 and 5.1 A/W for 850- and 940-nm light, respectively. This work not only promotes the large-scale application of ReSe2 in high-performance electronic devices but also clarifies the growth mechanism of low-lattice symmetry 2D materials.

关键词： rhenium diselenide （ReSe2） epitaxial growth high crystal quality anisotropy optoelectronics

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SARRC: Secure Auditing and Re-signing of Revoked Customer Chunks by Cloud Using Regression Method

SARRC: Secure Auditing and Re-signing of Revoked Customer Ch...

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Information Processing (ICINPRO), International Conference on

作者： C M Geeta Usharani R G Shreyas Raju S Raghavendra Rajkumar Buyya K R Venugopal S S Iyengar L M Patnaik Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Cloud Computing and Distributed Systems (CLOUDS) Lab The University of Melbourne Australia Bangalore University Bengaluru India Department of Computer Science and Engineering Florida International University USA INSA National Institute of Advanced Studies Indian Institute of Science Campus Bangalore India

ISBN: (数字)9781538677964

ISBN: (纸本)9781538677971

The demand of deploying information has enormously increased within the last decade. Numerous distributed computing service suppliers have emerged (for eg., Microsoft Azure, Dropbox) in order to satisfy the requirements for information repository and high performance computation. The customers using the cloud repository services can conveniently arrange as a cluster and distribute information among themselves. Information proprietor computes the signatures for every chunk and deploys in the distributed server in order to allow the public verifier to perform public integrity verification on the information stored on the cloud server. In Panda scheme [1], by using the proxy re-signatures, Cloud Service Provider (CSP) verifies and re-signs the revoked customer chunks in favor of the existing customers. The malicious CSP might use the Resign key deliberately to transform the signature of one customer to another. Apart from this, conspiracy amidst the mischievous cloud server and the repudiated customer reveals the private key information of the customers present in the cluster. We propose Secure Auditing and Re-signing of Revoked Customer Chunks by Cloud Using Regression Method. Re-key computed by the information proprietor using regression method is highly secure and the mischievious cloud cannot detect the private information of the customers in the cluster. Our mechanism is collusion resistant, reduces computation cost of re-sign key by information proprietor and in addition CSP securely performs auditing and re-signing of repudiated customer chunks.

关键词： Servers Cloud computing Transforms Resistance computer science Authentication

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Author Correction: 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 2024年第10期21卷 1959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

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Adiabatic frequency conversion in epsilon near zero materials – It is all about group velocity

arXiv

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arXiv 2019年

作者： Khurgin, Jacob B. Clerici, Matteo Bruno, Vincenzo Caspani, Lucia DeVault, Clayton Kim, Jongbum Shaltout, Amr Boltasseva, Alexandra Shalaev, Vladimir M. Ferrera, Marcello Faccio, Daniele Kinsey, Nathaniel Dept. of Electrical and Computer Engineering Johns Hopkins University Baltimore21218 United States School of Engineering University of Glasgow GlasgowG12 8LT United Kingdom School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom Institute of Photonics Department of Physics University of Strathclyde GlasgowG1 1RD United Kingdom Dept. of Physics and Astronomy Purdue University West LafayetteIN47907 United States School of Electrical and Computer Engineering West LafayetteIN47907 United States Geballe Lab for Advanced Materials Stanford University StanfordCA94305 United States Institute of Photonics and Quantum Sciences Heriot-Watt University SUPA EdinburghEH14 4AS United Kingdom Dept. of Electrical and Computer Engineering Virginia Commonwealth University RichmondVA23284 United States

We investigate adiabatic frequency conversion using epsilon near zero (ENZ) materials. We show that, while the maximum frequency conversion for a given change of permittivity does not exhibit an increase in the vicinity of the Re[Εr]=0 condition, the change in permittivity can be achieved in a shorter length and, if the pump is also in the ENZ region, at a lower pump intensity. This slow propagation effect makes the conversion efficiency in the ENZ material comparable to that in micro-resonators and other structured slow light schemes, but unlike the latter, no nanofabrication is required for ENZ materials which constitutes their major advantage over alternative frequency conversion approaches. Our results, supported by experimental measurements, indicate that transparent metal oxides operating near the ENZ point are good candidates for future frequency conversion schemes. Copyright © 2019, The Authors. All rights reserved.

关键词： Slow light

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Design and Evaluation of Vivado HLS-Based Compressive Sensing for ECG Signal Analysis

Design and Evaluation of Vivado HLS-Based Compressive Sensin...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Oussama Kerdjidj Naeem Ramzan Abbes Amira Khalida Ghanem Fatima Chouireb Lab Telecommunications Signals and Systems University of Amar Telidji-Laghouat School of Engineering and Computing University of the West of Scotland Scotland Paisley Department of Computer Science and Engineering Qatar University Qatar Wireless Communication Telecom Centre for Development of Advanced Technologies Algeria

ISBN: (纸本)9781538675199

This paper proposes an efficient implementation of the compressed sensing technique. Specifically, we present an architecture for implementing the Matching pursuit algorithm using the Vivado High-Level Synthesis Tool. The proposed architecture consists of compressing and recovering the electrocardiogram signal on a hardware. For the healthcare application, there is an obvious need to get the algorithm implementation up and running in real-time. The proposed implementation is applied on two different blocks sizes of the input data. Our results suggest that this difference in the size of input data produces consistent performance at the speed of execution. Specifically, according to our simulation, the blocks size of 256 samples achieve the speed by 8X as compare of a block of 2000 samples. Therefore, a calculation time of 2000 samples was 598ms and the computing time of 256 samples was 8.61ms and the necessary time to compute 2000 samples using a block of input 256 data was 67.2ms, which brings back to use a small vector to expect the best performance in terms of speed and area consumption. The hardware implementation results were compared with software implementation and the error between these two implementations was negligible of an order of 10 (-15) .

关键词： Matching pursuit algorithms Electrocardiography Greedy algorithms Dictionaries Tools Hardware Compressed sensing

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Peptide Programmed Hydrogels: Peptide Programmed Hydrogels as Safe Sanctuary Microenvironments for Cell Transplantation (Adv. Funct. Mater. 9/2020)

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advanced Functional Materials 2020年第9期30卷

作者： Yi Wang Xuefei He Kiara F. Bruggeman Bishakhdatta Gayen Antonio Tricoli Woei Ming Lee Richard J. Williams David R. Nisbet Laboratory of Advanced Biomaterials Research School of Electrical and Energy Engineering Australian National University Canberra ACT 2601 Australia Applied Optics Lab Research School of Electrical and Energy Engineering College of Engineering and Computer Science Australian National University Canberra ACT 2601 Australia Department of Mechanical Engineering University of Melbourne Parkville VIC 3010 Australia Nanotechnology Research Laboratory Research School of Electrical and Energy Engineering Australian National University Canberra ACT 2601 Australia Centre for Molecular and Medical Research School of Medicine Deakin University Warun Ponds VIC 3216 Australia

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NTIRE 2020 challenge on perceptual extreme super-resolution: Methods and results

arXiv

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arXiv 2020年

作者： Zhang, Kai Gu, Shuhang Timofte, Radu Shang, Taizhang Dai, Qiuju Zhu, Shengchen Yang, Tong Guo, Yandong Jo, Younghyun Yang, Sejong Kim, Seon Joo Zha, Lin Jiang, Jiande Gao, Xinbo Lu, Wen Liu, Jing Yoon, Kwangjin Jeon, Taegyun Akita, Kazutoshi Ooba, Takeru Ukita, Norimichi Luo, Zhipeng Yao, Yuehan Xu, Zhenyu He, Dongliang Wu, Wenhao Ding, Yukang Li, Chao Li, Fu Wen, Shilei Li, Jianwei Yang, Fuzhi Yang, Huan Fu, Jianlong Kim, Byung-Hoon Baek, JaeHyun Ye, Jong Chul Fan, Yuchen Huang, Thomas S. Lee, Junyeop Lee, Bokyeung Min, Jungki Kim, Gwantae Lee, Kanghyu Park, Jaihyun Mykhailych, Mykola Zhong, Haoyu Shi, Yukai Yang, Xiaojun Yang, Zhijing Lin, Liang Zhao, Tongtong Peng, Jinjia Wang, Huibing Jin, Zhi Wu, Jiahao Chen, Yifu Shang, Chenming Zhang, Huanrong Min, Jeongki Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Computer Vision Lab ETH Zurich Switzerland OPPO Research Yonsei University Korea Republic of Facebook United States Xidian University China China SI Analytics Co. Ltd. 441 Expo-ro Yuseong-gu Daejeon34051 Korea Republic of Japan Co. Ltd China Baidu Inc China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Peking University China State Key Laboratory of Digital Publishing Technology Founder Group Shanghai Jiao Tong University China Microsoft Research Beijing China Korea Republic of Amazon Web Services University of Illinois at Urbana-Champaign United States Korea University Korea Republic of *** Ltd Guangdong University of Technology China Sun Yat-sen University China Dalian Maritime University China School of Intelligent Systems Engineering Sun Yat-sen University China College of Engineering Trivandrum India

This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor ×16 based on a set of prior examples of low and corresponding high resolution images. The goal is to obtain a network design capable to produce high resolution results with the best perceptual quality and similar to the ground truth. The track had 280 registered participants, and 19 teams submitted the final results. They gauge the state-of-the-art in single image super-resolution. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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A Pipeline for Distributed Segmentation of Teravoxel Tomography Datasets

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2018年第SUPPL 2期24卷 164-165页

作者： Tondravi M. Scullin W. Du M. Vescovi R. Andrade V. Jacobsen C. Kording K.P. Gürsoy D. Dyer E. Chemistry of Life Processes Institute Northwestern University Evanston United States Argonne Leadership Computing Facility Argonne National Lab Lemont United States Dept. Materials Science Northwestern University Evanston United States Advanced Photon Source Argonne National Lab Lemont United States Dept. Physics & Astronomy Northwestern University Evanston United States Dept. Bioengineering and Neuroscience University of Pennsylvania Philadelphia United States Dept. Electrical Engineering and Computer Sci. Northwestern University Evanston United States Dept. Biomedical Engineering Georgia Institute of Technology Atlanta United States

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Designing ecosystems of intelligence from first principles

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Collective Intelligence 2024年第1期3卷

作者： Karl J Friston Maxwell JD Ramstead Alex B Kiefer Alexander Tschantz Christopher L Buckley Mahault Albarracin Riddhi J Pitliya Conor Heins Brennan Klein Beren Millidge Dalton AR Sakthivadivel Toby St Clere Smithe Magnus Koudahl Safae Essafi Tremblay Capm Petersen Kaiser Fung Jason G Fox Steven Swanson Dan Mapes Gabriel René VERSES Research Lab Los Angeles CA USA (KJF MJDR ABK AT CLB MA RJP CH BK BM DARS TSCS MK SET CP KF JGF SS DM GR) Wellcome Centre for Human Neuroimaging University College London London UK (KJF MJDR) Department of Philosophy Monash University Melbourne VIC Australia (ABK) Department of Informatics University of Sussex Brighton UK (CLB) Department of Computer Science Université du Québec à Montréal Montréal QC Canada (MA) Department of Experimental Psychology University of Oxford Oxford UK (RJP TSCS) Department of Collective Behaviour Max Planck Institute of Animal Behavior Konstanz Germany (CH) Department of Biology University of Konstanz Konstanz Germany (CH) Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany (CH) Network Science Institute Northeastern University Boston MA USA (BK) Brain Network Dynamics Unit University of Oxford Oxford UK (BM) Department of Mathematics Stony Brook University New York NY USA (DARS) Department of Physics and Astronomy Stony Brook University New York NY USA (DARS) Department of Biomedical Engineering Stony Brook University New York NY USA (DARS) Topos Institute Berkeley CA USA (TSCS) Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands (MK) Department of Philosophy Université du Québec à Montréal Montréal QC Canada (SET)

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants—what we call “shared intelligence.” This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one’s sensed world—also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales, that is, inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent’s generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing—leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first—and key—step towards such an ecology.

关键词： Active inference free energy principle artificial intelligence belief updating belief propagation

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