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Roadmap on Deep Learning for Microscopy

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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

Integrated silicon photonic true-time delay beam-former for wide-band phased-array antenna

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Integrated silicon photonic true-time delay beam-former for ...

Electronic Components and Technology Conference (ECTC)

作者： Stephen Anderson A. Begović A. Chen Z. R. Huang W. Zhou G. Sun Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA Army Research Laboratory Combat Capabilities Development Command Adelphi MD USA

ISBN: (数字)9781728161808

ISBN: (纸本)9781728161815

Conventional electronically-steerable phased-array antenna systems suffer from increasingly complicated control schemes to allow for narrowband RF steering. A phased array antenna architecture was previously presented which provides for wideband steering using a simplified control scheme requiring only one analog control parameter for steering along one dimension. A cascaded architecture of slow-light-based true-time delay gratings within an silicon photonic integrated circuit (PIC) enable the C-band optical carrier wavelength to serve as this control parameter for an X-band radio-frequency signal. Fabrication of this beamformer PIC was carried out using the American Institute for Manufacturing (AIM) Photonics foundry. Thermal sensitivity of the slow-light gratings and directional couplers on the PIC were investigated, and packaging solutions were evaluated, including integrated doped-silicon heaters to compensate for fabrication variations. A homodyne phase-tracking technique is employed to further characterize the slow-light performance of the beamformer, allowing for phase-sensitive measurements near the band-edge of the slow-light gratings.

关键词： Heating systems Couplings Gratings Directional couplers Silicon Delays Optical waveguides

Retraction notice to "SqueezeNet for the forecasting of the energy demand using a combined version of the sewing training-based optimization algorithm" [Heliyon 9 (2023) e16827]

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Heliyon 2025年第4期11卷 e42826页

作者： Noradin Ghadimi Elnazossadat Yasoubi Ehsan Akbari Mohammad Hosein Sabzalian Hamzah Ali Alkhazaleh Mojtaba Ghadamyari Young Researchers and Elite Club Ardabil Branch Islamic Azad University Ardabil Iran. College of Technical Engineering The Islamic University Najaf Iraq. Department of Electrical Engineering West Tehran Branch Islamic Azad University Tehran Iran. Department of Electrical Engineering Mazandaran University of Science and Technology Babol Iran. Smart Grid Laboratory (LabREI) Department of Systems and Energy School of Electrical and Computer Engineering (FEEC) University of Campinas (UNICAMP) Campinas Brazil. College of Engineering and IT University of Dubai Academic City 14143 Dubai United Arab Emirates. Department of Electrical Engineering Shahid Beheshti University 48512 Tehran Tehran Iran.

[This retracts the article DOI: 10.1016/***.2023.e16827.].

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SGD-Net: Efficient model-based deep learning with theoretical guarantees

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arXiv 2021年

作者： Liu, Jiaming Sun, Yu Gan, Weijie Xu, Xiaojian Wohlberg, Brendt Kamilov, Ulugbek S. Department of Electrical & Systems Engineering Washington University in St. Louis St. LouisMO63130 United States Department of Computer Science & Engineering Washington University in St. Louis St. LouisMO63130 United States Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States

Deep unfolding networks have recently gained popularity in the context of solving imaging inverse problems. However, the computational and memory complexity of data-consistency layers within traditional deep unfolding networks scales with the number of measurements, limiting their applicability to large-scale imaging inverse problems. We propose SGD-Net as a new methodology for improving the efficiency of deep unfolding through stochastic approximations of the data-consistency layers. Our theoretical analysis shows that SGD-Net can be trained to approximate batch deep unfolding networks to an arbitrary precision. Our numerical results on intensity diffraction tomography and sparse-view computed tomography show that SGD-Net can match the performance of the batch network at a fraction of training and testing complexity. Copyright © 2021, The Authors. All rights reserved.

关键词： computerized tomography

Accessing Provenance Records in Semantic Web Services

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Accessing Provenance Records in Semantic Web Services

International Conference on Semantic Computing

作者： Omar Ochoa Miralda Rodney Nicholas Del Rio Department of Electrical Computer Software & Systems Engineering Embry-Riddle Aeronautical University Daytona Beach FL USA Air Force Research Laboratory Rome NY USA

ISBN: (数字)9781728163321

ISBN: (纸本)9781728163338

Provenance on the Semantic Web provides information about the creation, publication and access of resources. Provenance and provenance metadata records from a resource are used to determine trust, to integrate diverse information about the resource and to determine how to credit originators when reusing information. Web services make resources available over the internet by using standardized communication services to automatically exchange data between applications and various systems. The World Wide Web consortium (W3C) proposed the Provenance Access and Query (PROV-AQ) that recommends how to represent and exchange provenance information about resources though a standardized communication protocol. Integrating semantics into web-based service descriptions enables agents to more quickly assemble, discover, and reuse arbitrary web services and the information retained within. The addition of provenance information as part of a Semantic Web service would further increase the reuse and discovery of information and trust on the Web when determining which services to employ in each application. In this paper, the provenance of a resource is tracked using a semantic version control tool with a query interface. The underlying data is made available through a web service using provenance access and query to associate the service to its provenance records. Furthermore, the implications of integrating provenance into a Semantic Web service are discussed. Findings from the literature review and developing a Semantic Web service that exposes provenance records, indicate few differences with a Semantic Web service without attaching provenance records.

关键词： Web services Semantic Web Semantics Ontologies Protocols Standards Tools

Graph-Based File Dispatching Protocol With D2D-Aided UAV-NOMA Communications in Large-Scale Networks

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Graph-Based File Dispatching Protocol With D2D-Aided UAV-NOM...

IEEE Conference on Wireless Communications and Networking

作者： Baoji Wang Rongqing Zhang Chen Chen Xiang Cheng Liuqing Yan State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing China school of Software Engineering Tongji University Shanghai China Department of Electrical & Computer Engineering Colorado State University CO USA

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

Unmanned aerial vehicle (UAV)-assisted communications are expected to become an important part of the next generation mobile communication systems, due to the high mobility of the UAVs. Non-orthogonal multiple access (NOMA) is regarded as a rosy technology in the fifth generation (5G) mobile communication systems, since it can effectively improve the spectral efficiency. In this paper, we combine the advantages of the UAV-assisted communications and NOMA, and propose a device-to-device (D2D)-enhanced UAV-NOMA network architecture, in which D2D is introduced to increase the file dispatching efficiency. Resource reuse based on spatial reuse is also allowed to further improve the spectral efficiency. Then, we propose a graph-based file dispatching (GFD) protocol to control the interference and minimize the UAV-assisted file dispatching mission time. Simulation results verify the advantages of our proposed D2D-enhanced UAV-NOMA network architecture and the efficiency of our designed GFD protocol.

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Impasse Surface of Differential-Algebraic Power System Models: An Interpretation Based on Admittance Matrices

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arXiv 2020年

作者： Song, Yue Hill, David J. Liu, Tao Zhang, Xinran The State Key Laboratory of Intelligent Autonomous Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong The Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia The School of Automation Science and Electrical Engineering Beihang University Beijing China

The impasse surface is an important concept in the differential-algebraic equation (DAE) model of power systems, which is associated with short-term voltage collapse. This paper establishes a necessary condition for a system trajectory hitting the impasse surface. The condition is in terms of admittance matrices regarding the power network, generators and loads, which specifies the pattern of interaction between those system components that can induce voltage collapse. It applies to generic DAE models featuring high-order synchronous generators, static loads, induction motor loads and lossy power networks. We also identify a class of static load parameters that prevent power systems from hitting the impasse surface;this proves a conjecture made by Hiskens that has been unsolved for decades. Moreover, the obtained results lead to an early indicator of voltage collapse and a novel viewpoint that inductive compensation to the power network has a positive effect on preventing short-term voltage collapse, which are verified via numerical simulations. Copyright © 2020, The Authors. All rights reserved.

关键词： Differential equations

Broken poloidal symmetry and plasmonic eigenmodes on a torus

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Physical Review B 2020年第11期101卷 115426-115426页

作者： Tharindu Warnakula Sarath D. Gunapala Mark I. Stockman Malin Premaratne Advanced Computing and Simulation Laboratory (AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton Vic 3800 Australia Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 USA Department of Physics and Astronomy Georgia State University Atlanta Georgia 30303 USA

We study the poloidal modes (modes whose variations are strictly along the direction of the torus axis) of toroidal nanoparticles. We show that the modes may be understood in terms of the symmetry breaking that occurs when an infinite cylinder is folded to form a torus. This symmetry breaking results in the splitting of the transverse cylinder modes into two distinct sets of modes on a torus. One set of these modes was known to exist already. We show the existence of the second set and also analytically derive the surface charge structure for that set of modes. We also consolidate recent advances made in studying the modal structure on torus particles and complete our understanding of the plasmonic modes of a torus.

关键词： Electromagnetic field calculations Plasmonics Surface plasmons Nanoparticles

Vector Symbolic Architectures as a Computing Framework for Emerging Hardware

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arXiv 2021年

作者： Kleyko, Denis Davies, Mike Frady, E. Paxon Kanerva, Pentti Kent, Spencer J. Olshausen, Bruno A. Osipov, Evgeny Rabaey, Jan M. Rachkovskij, Dmitri A. Rahimi, Abbas Sommer, Friedrich T. The Redwood Center for Theoretical Neuroscience The University of California BerkeleyCA94720 United States The Intelligent Systems Lab Research Institutes of Sweden Kista16440 Sweden The Neuromorphic Computing Laboratory Intel Labs Santa ClaraCA95054 United States The Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå97187 Sweden The Department of Electrical Engineering and Computer Sciences The University of California BerkeleyCA94720 United States International Research and Training Center for Information Technologies and Systems Kyiv03680 Ukraine IBM Research – Zurich Rüschlikon8803 Switzerland

This article reviews recent progress in the development of the computing framework Vector Symbolic Architectures (also known as Hyperdimensional Computing). This framework is well suited for implementation in stochastic, emerging hardware and it naturally expresses the types of cognitive operations required for Artificial Intelligence (AI). We demonstrate in this article that the field-like algebraic structure of Vector Symbolic Architectures offers simple but powerful operations on high-dimensional vectors that can support all data structures and manipulations relevant to modern computing. In addition, we illustrate the distinguishing feature of Vector Symbolic Architectures, "computing in superposition," which sets it apart from conventional computing. It also opens the door to efficient solutions to the difficult combinatorial search problems inherent in AI applications. We sketch ways of demonstrating that Vector Symbolic Architectures are computationally universal. We see them acting as a framework for computing with distributed representations that can play a role of an abstraction layer for emerging computing hardware. This article serves as a reference for computer architects by illustrating the philosophy behind Vector Symbolic Architectures, techniques of distributed computing with them, and their relevance to emerging computing hardware, such as neuromorphic computing. Copyright © 2021, The Authors. All rights reserved.

关键词： Vectors