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arXiv 2020年

作者： Abdollahramezani, Sajjad Hemmatyar, Omid Taghinejad, Hossein Krasnok, Alex Kiarashinejad, Yashar Zandehshahvar, Mohammadreza Alù, Andrea Adibi, Ali School of Electrical and Computer Engineering Georgia Institute of Technology 778 Atlantic Drive NW AtlantaGA30332-0250 United States Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Nanophotonics has garnered intensive attention due to its unique capabilities in molding the flow of light in the subwavelength regime. Metasurfaces (MSs) and photonic integrated circuits (PICs) enable the realization of mass-producible, cost-effective, and highly efficient flat optical components for imaging, sensing, and communications. In order to enable nanophotonics with multi-purpose functionalities, chalcogenide phase-change materials (PCMs) have been introduced as a promising platform for tunable and reconfigurable nanophotonic frameworks. Integration of non-volatile chalcogenide PCMs with unique properties such as drastic optical contrasts, fast switching speeds, and long-term stability grants substantial reconfiguration to the more conventional static nanophotonic platforms. In this review, we discuss state-of-the-art developments as well as emerging trends in tunable MSs and PICs using chalcogenide PCMs. We outline the unique material properties, structural transformation, electro-optic, and thermo-optic effects of well-established classes of chalcogenide PCMs. The emerging deep learning-based approaches for the optimization of reconfigurable MSs and the analysis of light-matter interactions are also discussed. The review is concluded by discussing existing challenges in the realization of adjustable nanophotonics and a perspective on the possible developments in this promising area. Copyright © 2020, The Authors. All rights reserved.

关键词： Phase change materials

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Crowdsourcing annotation system of object counting dataset for deep learning algorithm 2

Crowdsourcing annotation system of object counting dataset f...

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2nd International Conference on Eco Engineering Development 2018, ICEED 2018

作者： Cenggoro, Tjeng Wawan Tanzil, Fidelson Aslamiah, Ayu Hidayah Karuppiah, Ettikan Kandasamy Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia NVIDIA Corporation Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Deep Learning is currently the state-of-the-art technique for various computer Vision tasks, including object counting. Despite of its high performance, Deep Learning requires a gigantic amount of training data to show its best result. Getting this massive data in reasonable time requires a proper strategy such as crowdsourcing. However, in case of object counting, we found no crowdsourcing system able to effectively collect necessary data. To tackle this problem, we develop a crowdsourcing system to annotate image for object counting dataset. This system is also equipped with validation system to ensure the quality of collected dataset. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Crowdsourcing

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Transfer Learning from Chest X-Ray Pre-trained Convolutional Neural Network for Learning Mammogram Data 3

Transfer Learning from Chest X-Ray Pre-trained Convolutional...

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3rd International Conference on computer science and Computational Intelligence, ICCSCI 2018

作者： Pardamean, Bens Cenggoro, Tjeng Wawan Rahutomo, Reza Budiarto, Arif Karuppiah, Ettikan Kandasamy Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia NVIDIA Corporation United States

Breast cancer is one of the deadliest cancer for female nowadays. Despite of the rapid advancement in medical image analysis with the rise of deep learning, development of breast cancer detection system is limited due to relatively small size of the publicly available mammogram dataset. In this paper, we discover an effective configuration for transfer learning from Chest X-Ray pre-trained Convolutional Neural Network to overcome the small-size mammogram dataset problem. We found that the best configuration achieve 90.38% validation accuracy for modified. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Deep learning

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To and fro in the archipelago: Repeated inter-island dispersal and New Guinea's orogeny affect diversification of Delias, the world's largest butterfly genus

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Molecular Phylogenetics and Evolution 2024年 194卷 108022页

作者： Liang, Weijun Nunes, Renato Leong, Jing V. Carvalho, Ana Paula S. Müller, Chris J. Braby, Michael F. Pequin, Olivier Hoshizaki, Sugihiko Morinaka, Sadaharu Peggie, Djunijanti Badon, Jade Aster T. Mohagan, Alma B. Beaver, Ethan Hsu, Yu-Feng Inayoshi, Yutaka Monastyrskii, Alexander Vlasanek, Petr Toussaint, Emmanuel F.A. Benítez, Hugo A. Kawahara, Akito Y. Pierce, Naomi E. Lohman, David J. Department of Biology City College of New York City University of New York United States PhD Program in Biology Graduate Center City University of New York New York NY United States Biology Centre of the Czech Academy of Sciences Branisovska 31 Ceske Budejovice Czech Republic Faculty of Science Department of Zoology University of South Bohemia Ceske Budejovice Czech Republic McGuire Center for Lepidoptera and Biodiversity Florida Museum of Natural History University of Florida Gainesville FL United States Australian Museum Sydney NSW Australia Division of Ecology and Evolution Research School of Biology The Australian National University Acton ACT Australia Australian National Insect Collection Canberra ACT Australia 5 Avenue du Port Sibouliere Dinard France Department of Agricultural and Environmental Biology Graduate School of Agricultural and Life Sciences The University of Tokyo Yayoi Tokyo Bunkyo-ku Japan Saitama Study Center The Open University of Japan Ômiya-ku Japan Museum Zoologicum Bogoriense Research Center for Biosystematics and Evolution National Research and Innovation Agency Cibinong-Bogor Indonesia Animal Biology Division Institute of Biological Sciences University of the Philippines Los Baños Laguna Philippines Department of Biology College of Arts and Sciences and Center for Biodiversity Research & Extension in Mindanao Central Mindanao University Musuan Maramag Bukidnon Philippines College of Life Science National Taiwan Normal University Taipei Taiwan Sritana Condominium 2 96/173 Huay Kaeo Rd. T. Suthep A. Muang Chiang Mai Thailand Vietnam National Museum of Nature Vietnam Academy of Science and Technology Cau Giay Hanoi Viet Nam T.G. Masaryk Water Research Institute Prague Czech Republic Department of Entomology Natural History Museum of Geneva Geneva Switzerland Laboratorio de Ecología y Morfometría Evolutiva Centro de Investigación de Estudios Avanzados del Maule Universidad Católica del Maule Talca Chile Entomolo

The world's largest butterfly genus Delias, commonly known as Jezebels, comprises ca. 251 species found throughout Asia, Australia, and Melanesia. Most species are endemic to islands in the Indo-Australian Archipelago or to New Guinea and nearby islands in Melanesia, and many species are restricted to montane habitats over 1200 m. We inferred an extensively sampled and well-supported molecular phylogeny of the group to better understand the spatial and temporal dimensions of its diversification. The remarkable diversity of Delias evolved in just ca. 15–16 Myr (crown age). The most recent common ancestor of a clade with most of the species dispersed out of New Guinea ca. 14 Mya, but at least six subsequently diverging lineages dispersed back to the island. Diversification was associated with frequent dispersal of lineages among the islands of the Indo-Australian Archipelago, and the divergence of sister taxa on a single landmass was rare and occurred only on the largest islands, most notably on New Guinea. We conclude that frequent inter-island dispersal during the Neogene—likely facilitated by frequent sea level change—sparked much diversification during that period. Many extant New Guinea lineages started diversifying 5 Mya, suggesting that orogeny facilitated their diversification. Our results largely agree with the most recently proposed species group classification system, and we use our large taxon sample to extend this system to all described species. Finally, we summarize recent insights to speculate how wing pattern evolution, mimicry, and sexual selection might also contribute to these butterflies’ rapid speciation and diversification. © 2024 Elsevier Inc.

关键词： Aposematism Biogeographic stochastic mapping Divergence dating Indo-Australian archipelago Lepidoptera Sequence capture

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Training of Physical Neural Networks

arXiv

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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Phase transitions in the frustrated Ising ladder with stoquastic and nonstoquastic catalysts

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Physical Review Research 2021年第4期3卷 043013-043013页

作者： Kabuki Takada Shigetoshi Sota Seiji Yunoki Bibek Pokharel Hidetoshi Nishimori Daniel A. Lidar Department of Physics Tokyo Institute of Technology Yokohama Kanagawa 226-8503 Japan Computational Materials Science Research Team RIKEN Center for Computational Science (R-CCS) Kobe Hyogo 650-0047 Japan Computational Condensed Matter Physics Laboratory RIKEN Cluster for Pioneering Research (CPR) Wako Saitama 351-0198 Japan Computational Quantum Matter Research Team RIKEN Center for Emergent Matter Science (CEMS) Wako Saitama 351-0198 Japan Department of Physics University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science & Technology University of Southern California Los Angeles California 90089 USA Institute of Innovative Research Tokyo Institute of Technology Yokohama Kanagawa 226-8503 Japan Graduate School of Information Sciences Tohoku University Sendai Miyagi 980-8579 Japan RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) Wako Saitama 351-0198 Japan Department of Electrical & Computer Engineering and Department of Chemistry University of Southern California Los Angeles California 90089 USA

The role of nonstoquasticity in the field of quantum annealing and adiabatic quantum computing is an actively debated topic. We study a strongly-frustrated quasi-one-dimensional quantum Ising model on a two-leg ladder to elucidate how a first-order phase transition with a topological origin is affected by interactions of the ±XX-type. Such interactions are sometimes known as stoquastic (negative sign) and nonstoquastic (positive sign) “catalysts”. Carrying out a symmetry-preserving real-space renormalization group analysis and extensive density-matrix renormalization group computations, we show that the phase diagrams obtained by these two methods are in qualitative agreement with each other and reveal that the first-order quantum phase transition of a topological nature remains stable against the introduction of both XX-type catalysts. This is the first study of the effects of nonstoquasticity on a first-order phase transition between topologically distinct phases. Our results indicate that nonstoquastic catalysts are generally insufficient for removing topological obstacles in quantum annealing and adiabatic quantum computing.

关键词： Adiabatic quantum optimization Phase transitions by order Quantum spin models Density matrix renormalization group Renormalization group

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Miniaturized Microarray-Format Digital ELISA Enabled by Lithographic Protein Patterning

SSRN

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SSRN 2023年

作者： Stephens, Andrew D. Song, Yujing McClellan, Brandon L. Su, Shiuan-Haur Xu, Sonnet Chen, Kevin Castro, Maria G. Singer, Benjamin H. Kurabayashi, Katsuo Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States Department of Neurosurgery University of Michigan Medical School Ann ArborMI48109 United States Department of Cell and Developmental Biology University of Michigan Medical School Ann ArborMI48109 United States Graduate Program in Immunology University of Michigan Medical School Ann ArborMI48109 United States Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Rogel Cancer Center University of Michigan Ann ArborMI48109 United States Department of Internal Medicine Division of Pulmonary and Critical Care Medicine University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI48109 United States

The search for reliable protein biomarker candidates is critical for early disease detection and treatment. However, current immunoassay technologies are failing to meet increasing demands for sensitivity and multiplexing. Here, the authors have created a highly sensitive protein microarray using the principle of single-molecule counting for signal amplification, capable of simultaneously detecting a panel of cancer biomarkers at sub-pg/mL levels. To enable this amplification strategy, the authors introduce a novel method of protein patterning using photolithography to subdivide addressable arrays of capture antibody spots into hundreds of thousands of individual microwells. This allows for the total sensor area to be miniaturized, increasing the total possible multiplex capacity. With the immunoassay realized on a standard 75x25 mm form factor glass substrate, sample volume consumption is minimized to © 2023, The Authors. All rights reserved.

关键词： Biomarkers

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Observation of the Magnonic Dicke Superradiant Phase Transition

arXiv

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arXiv 2024年

作者： Kim, Dasom Dasgupta, Sohail Ma, Xiaoxuan Park, Joong-Mok Wei, Hao-Tian Luo, Liang Doumani, Jacques Li, Xinwei Yang, Wanting Cheng, Di Kim, Richard H.J. Everitt, Henry O. Kimura, Shojiro Nojiri, Hiroyuki Wang, Jigang Cao, Shixun Bamba, Motoaki Hazzard, Kaden R.A. Kono, Junichiro Applied Physics Graduate Program Smalley–Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Ames National Laboratory AmesIA50011 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Physics International Center of Quantum and Molecular Structures Materials Genome Institute Shanghai University Shanghai200444 China Department of Physics National University of Singapore 117551 Singapore Department of Physics and Astronomy Iowa State University AmesIA50011 United States Smalley–Curl Institute Rice University HoustonTX77005 United States DEVCOM Army Research Laboratory-South HoustonTX77005 United States Institute for Materials Research Tohoku University Sendai980-8577 Japan Department of Physics Yokohama National University Yokohama240-8501 Japan Department of Physics and Astronomy University of California DavisCA95616 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

Two-level atoms coupled with single-mode cavity photons are predicted to exhibit a quantum phase transition when the coupling strength exceeds a critical value, entering a phase in which atomic polarization and photonic field are finite even at zero temperature and without external driving. However, this phenomenon, the superradiant phase transition (SRPT), is forbidden by a no-go theorem due to the existence of the diamagnetic term in the Hamiltonian. Here, we present spectroscopic evidence for a magnonic SRPT in ErFeO3, where the role of the photonic mode (two-level atoms) in the photonic SRPT is played by an Fe3+ magnon mode (Er3+ spins). The absence of the diamagnetic term in the Fe3+–Er3+ exchange coupling ensures that the no-go theorem does not apply. Terahertz and gigahertz magnetospectroscopy experiments revealed the signatures of the SRPT – a kink and a softening, respectively, of two spin–magnon hybridized modes at the critical point. Copyright © 2024, The Authors. All rights reserved.

关键词： Phase transitions

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Development of a Selective Wet-Chemical Etchant for 3D Structuring of Silicon via Nonlinear Laser Lithography

arXiv

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arXiv 2023年

作者： Borra, Mona Zolfaghari Radfar, Behrad Nasser, Hisham Çolakoğlu, Tahir Tokel, Onur Turnalı, Ahmet Demirtaş, Merve Ustunel, Hande Toffoli, Daniele Ömer İlday, F. Turan, Raşit Pavlov, Ihor Bek, Alpan ODTÜ-GÜNAM Middle East Technical University Ankara06800 Turkey Ankara06800 Turkey Department of Electronics and Nanoengineering Aalto University Tietotie 3 EspooFI-02150 Finland Department of Physics Engineering Ankara University Ankara06100 Turkey Department of Physics Bilkent University Ankara06800 Turkey UNAM National Nanotechnology Research Center Institute of Materials Science and Nanotechnology Bilkent University Ankara06800 Turkey Department of Electrical and Electronics Engineering Bilkent University Ankara06800 Turkey Department of Electrical and Computer Engineering Boston University 8 Saint Mary’s Street BostonMA02215 United States Ankara06800 Turkey Network Technologies Department TUBITAK ULAKBIM Ankara06800 Turkey Department of Chemical and Pharmaceutical Sciences Trieste University Trieste Italy IOM-CNR Istituto Officina dei Materiali Trieste34149 Italy Micro and Nanotechnology Graduate Program of Natural and Applied Sciences Middle East Technical Turkey

Recently-demonstrated high-quality three-dimensional (3D) subsurface laser processing inside crystalline silicon (c-Si) wafers opens a door to a wide range of novel applications in multidisciplinary research areas. Using this technique, a novel maskless micro-pillars with precise control on the surface reflection and coverage are successfully fabricated by etching the laser processed region of c-Si wafer. To achieve this, a particular selective wet chemical etching is developed to follow subsurface laser processing of c-Si to reveal the desired 3D structures with smooth surfaces. Here, we report the development of a novel chromium-free chemical etching recipe based on copper nitrate, which yields substantially smooth surfaces at high etch rate and selectivity on the both laser-processed Si surface and subsurface, i.e., without significant etching of the unmodified Si. Our results show that the etch rate and surface morphology are interrelated and strongly influenced by the composition of the adopted etching solution. After an extensive compositional study performed at room temperature, we identify an etchant with a selectivity of over 1600 times for laser-modified Si with respect to unmodified Si. We also support our findings using density functional theory calculations of HF and Cu adsorption energies, indicating significant diversity on the c-Si and laser-modified surfaces. Copyright © 2023, The Authors. All rights reserved.

关键词： Morphology

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Acoustic Supercoupling in a Zero-Compressibility Waveguide

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Research 2019年第1期2019卷 1086-1095页

作者： H.Esfahlani M.S.Byrne M.McDermott A.Alù Photonics Initiative Advanced Science Research CenterCity University of New YorkNew York CityNY 10031USA Department of Electrical and Computer Engineering Te University of Texas at AustinAustinTX 78712USA Electrical Engineering Institute Ecole Polytechnique Fed´erale de Lausanne(EPFL)CH-1015 LausanneSwitzerland Naval Surface Warfare Center Carderock Division West BethesdaMD 20817USA Physics Program Graduate CenterCity University of New YorkNew York CityNY 10026USA Department of Electrical Engineering City College of New YorkNew York CityNY 10031USA

Funneling acoustic waves through largely mismatched channels is of fundamental importance to tailor and transmit sound for a variety of *** electromagnetics,zero-permittivity metamaterials have been used to enhance the coupling of energy in and out of ultranarrow channels,based on a phenomenon known as *** metamaterial channels can support total transmission and complete phase uniformity,independent of the channel length,despite being geometrically mismatched with their input and output *** the feld of acoustics,this phenomenon is challenging to achieve,since it requires zero-density metamaterials,typically realized with waveguides periodically loaded with membranes or *** to electromagnetics,the additional challenge is due to the fact that conventional acoustic waveguides do not support a cut-of for the dominant mode of propagation,and therefore zero-index can be achieved only based on a collective resonance of the loading *** we propose and experimentally realize acoustic supercoupling in a dual regime,using a compressibility-nearzero acoustic *** than engineering the channel with subwavelength inclusions,we operate at the cut-of of a higherorder acoustic mode,demonstrating the realization and efcient excitation of a zero-compressibility waveguide with efective sof *** experimentally verify strong transmission through a largely mismatched channel and uniform phase distribution,independent of the channel *** results open interesting pathways towards the realization of extreme acoustic parameters and their implementation in relevant applications,such as ultrasound imaging,acoustic transduction and sensing,nondestructive evaluation,and sound communications.

关键词： waveguide coupling realization

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