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Hyperbolic Sound Propagation over Nonlocal Acoustic Metasurfaces

Physical Review Letters 2019年第24期123卷 244303-244303页

作者： Li Quan Andrea Alù Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10016 USA Department of Electrical Engineering City College of The City University of New York New York 10031 USA

Hyperbolic metasurfaces, supporting extreme anisotropy of the surface impedance tensor, have recently been explored in nanophotonic systems for robust diffractionless propagation over a surface, offering interesting opportunities for subdiffraction imaging and enhanced Purcell emission. In acoustics, due to the longitudinal nature of sound transport in fluids, these phenomena are forbidden by symmetry, requiring the acoustic surface impedance to be inherently isotropic. Here we show that nonlocalities produced by strong coupling between neighboring impedance elements enable extreme anisotropic responses for sound traveling over a surface, supporting negative phase and energy velocities, as well as hyperbolic propagation for acoustic surface waves.

关键词： Acoustics Hyperbolic metamaterials Metamaterials

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Passive Acoustic Metasurface with Unitary Reflection Based on Nonlocality

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Physical Review Applied 2019年第5期11卷 054077-054077页

作者： Li Quan Andrea Alù Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10026 USA Department of Electrical Engineering City College of New York New York New York 10031 USA

Metasurfaces have introduced large flexibility in manipulating the impinging wavefront for light and sound by locally engineering the reflection and transmission coefficients based on generalized Snell’s laws. Local phenomena in each unit cell, however, are fundamentally limited in the level of efficiency with which anomalous wavefront transformations can be achieved. Here, we explore acoustic metasurfaces with suitably engineered nonlocality, obtained by coupling neighboring cells. We demonstrate that nonlocal passive metastructures can overcome the limitations of local designs, and mimic balanced gain and loss distributions, enabling unitary efficiency for extreme beam steering.

关键词： Acoustic metamaterials Acoustic wave phenomena Beam control Beam optics Light propagation, transmission & absorption

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Global priority areas for ecosystem restoration (vol 586, pg 724, 2022)

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NATURE 2022年第7926期609卷 E7-E7页

作者： Strassburg, Bernardo B. N. Iribarrem, Alvaro Beyer, Hawthorne L. Cordeiro, Carlos Leandro Crouzeilles, Renato Jakovac, Catarina C. Braga Junqueira, Andre Lacerda, Eduardo Latawiec, Agnieszka E. Balmford, Andrew Brooks, Thomas M. Butchart, Stuart H. M. Chazdon, Robin L. Erb, Karl-Heinz Brancalion, Pedro Buchanan, Graeme Cooper, David Diaz, Sandra Donald, Paul F. Kapos, Valerie Leclere, David Miles, Lera Obersteiner, Michael Plutzar, Christoph de M. Scaramuzza, Carlos Alberto Scarano, Fabio R. Visconti, Piero Rio Conservation and Sustainability Science Centre Department of Geography and the Environment Pontifical Catholic University Rio de Janeiro Brazil International Institute for Sustainability Rio de Janeiro Brazil Programa de Pós Graduacão em Ecologia Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Botanical Garden Research Institute of Rio de Janeiro Rio de Janeiro Brazil Forest Ecology and Management Group Wageningen University Wageningen The Netherlands Institut de Ciència i Tecnologia Ambientals Universitat Autònoma de Barcelona Barcelona Spain Department of Geography Fluminense Federal University Niterói Brazil Department of Production Engineering Logistics and Applied Computer Science Faculty of Production and Power Engineering University of Agriculture in Kraków Kraków Poland School of Environmental Sciences University of East Anglia Norwich UK Department of Ecology and Evolutionary Biology University of Connecticut Storrs CT USA World Resources Institute Global Restoration Initiative Washington DC USA Tropical Forests and People Research Centre University of the Sunshine Coast Sunshine Coast Queensland Australia School of Biological Sciences University of Queensland St Lucia Queensland Australia Department of Zoology University of Cambridge Cambridge UK BirdLife International Cambridge UK RSPB Centre for Conservation Science Royal Society for the Protection of Birds Edinburgh UK International Union for Conservation of Nature (IUCN) Gland Switzerland World Agroforestry Center (ICRAF) University of the Philippines Los Baños The Philippines Institute for Marine and Antarctic Studies University of Tasmania Hobart Tasmania Australia Institute of Social Ecology University of Natural Resources and Life Sciences Vienna Vienna Austria Division of Conservation Biology Vegetation Ecology and Landscape Ecology University of Vienna Vienna Austria Department of Forest Sciences ‘Luiz de Queiroz’ College of Agriculture University of São Pau

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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An approach to improve online sequential extreme learning machines using restricted Boltzmann machines

An approach to improve online sequential extreme learning ma...

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International Joint Conference on Neural Networks

作者： Andre G. C. Pacheco Renato A. Krohling Graduate program in computer science PPGI Federal University of Espirito Santo UFES Av. Fernando Ferrari 514 Vitoria Brazil Graduate program in computer science PPGI and Production engineering department Federal University of Espirito Santo UFES Av. Fernando Ferrari 514 Vitoria Brazil

The extreme learning machine (ELM) is an analytical algorithm developed to train single-hidden layer feedforward neural network (SLFN). Although the ELM provides a good generalization and a very fast training phase, two shortcomings can be pointed in this approach: it assigns its input weights randomly and it is not able to handle sequential learning. Thereby, the online sequential extreme learning machine (OS-ELM) was proposed in order to tackle the lack of sequential learning on ELMs. Nonetheless, the OS-ELM still assigns the input weights randomly. Thus, in this work, we proposed an approach based on a restricted Boltzmann machine (RBM) to improve the OS-ELM by determining its input weights throughout the training phase. We applied our method to several well-known benchmarks and the results show that the proposed approach is able to improve the OS-ELM for most them.

关键词： Training Training data Neurons Standards Feedforward neural networks Arrays

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Magnetless Circulators with Harmonic Rejection Based on N-Way Cyclic-Symmetric Time-Varying Networks

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Physical Review Applied 2019年第2期12卷 024046-024046页

作者： Ahmed Kord Harish Krishnaswamy Andrea Alù Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Department of Electrical Engineering Columbia University New York New York 10027 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10026 USA Department of Electrical Engineering City College of New York New York New York 10031 USA

Magnetfree circulators are crucial devices to enable full-duplex communications with the goal of enhancing the spectral efficiency of wireless radios compared to currently deployed half-duplex systems. Nevertheless, implementations proposed to date at radio- and millimeter-wave frequencies have been based on spatiotemporally modulating a discrete number of nonlinear elements, such as varactors or switched capacitors, which, despite achieving strong nonreciprocity, suffer from spurious emission in adjacent channels and limited power handling. These problems jeopardize the practical use of these components in many applications, including wireless communications and quantum computing, and make them less attractive compared to harmonicfree magnetic devices. We address these problems by introducing N-way circulators, which consist of N spatiotemporally modulated angular-momentum (STM AM) biased units connected either in series or in parallel and modulated through a rotating phase pattern. We show that these circuits, for a sufficiently large N, effectively impart an overall linear and continuous angular-momentum bias, which significantly reduces spurious emission and increases the overall power handling. This, in turn, strengthens the argument of magnetless circulators compared to their magnetic counterparts, and opens exciting venues to address the realistic needs of practical systems.

关键词： Electromagnetism Metamaterials Photonics Quantum information with hybrid systems Devices T-symmetry

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Identification of Pre-University Factors that Affect the Initial Motivation of Students in Computing programs: A multi-institutional case study 48

Identification of Pre-University Factors that Affect the Ini...

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48th Frontiers in Education Conference, FIE 2018

作者： Schoeffel, Pablo Wazlawick, Raul Sidnei Ramos, Vinicius Faria Culmant Vahldick, Adilson Souza, Marcelo De Graduate Program in Computer Science Federal University of Santa Catarina Florianópolis Brazil Department of Information and Communication Technologies Federal University of Santa Catarina Araranguá Brazil Departament of Software Engineering University of Santa Catarina State Ibirama Brazil

ISBN: (纸本)9781538611739

This Research to Practice Paper presents the results of the evaluation of pre-university factors that impact the initial motivation of undergraduate students in computing. Although there are studies in the literature that have investigated some previous factors, this paper replicates a previous work that aims to consolidate several pre-university factors and, as the main differential, uses the AMS (Academic Motivation Scale), a scale already consolidated in the literature to measure students' initial motivation, and evaluate the relation between motivation and candidate factors. We applied a questionnaire to 159 students from different computing programs in ten universities, which evaluates 20 factors divided into 4 groups: personal and demographic data, taste and knowledge of the program and area, computing experience, and school performance. To evaluate the correlation between factors and motivation, we used Spearman's coefficient, t-student test, and ANOVA to evaluate the correlation between factors and motivation. As main results, we found significant variation in the initial motivation according to following factors: taste for programming and technology, knowledge about the undergraduate program content, correct perception about computing professionals, knowledge and experience in computer programming, and general school performance. © 2018 IEEE.

关键词： Motivation

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Corrigendum to “Terrestrial and groundwater storage characteristics and their quantification in the Chitral (Pakistan) and Kabul (Afghanistan) river basins using GRACE/GRACE-FO satellite data”

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Groundwater for Sustainable Development 2023年 23卷

作者： Aqil Tariq Shoaib Ali Iqra Basit Ahsan Jamil Nizom Farmonov Behnam Khorrami Muhammad Mohsin Khan Samira Sadri Muhammad Yousuf Jat Baloch Fakhrul Islam Muhammad Bilawal Junaid Wesam Atef Hatamleh Ubaid-ur-Rehman Janjua Department of Wildlife Fisheries and Aquaculture College of Forest Resources Mississippi State University 775 Stone Boulevard 39762-9690 MS USA State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430079 China School of Water Conservancy & Civil Engineering Northeast Agricultural University Harbin Heilongjiang 150030 China Department of Irrigation and Drainage Faculty of Agricultural Engineering and Technology University of Agriculture Faisalabad Faisalabad 38000 Pakistan Remote Sensing GIS and Climate Research Lab (National Center of GIS and Space Application) Centre for Remote Sensing University of the Punjab Lahore 54590 Pakistan Department of Plant and Environmental Sciences New Mexico State University 3170S Espina Street Las Cruces NM 88003 USA Department of Geoinformatics Physical and Environmental Geography University of Szeged Egyetem utca 2 Szeged 6722 Hungary Department of GIS The Graduate School of Applied and Natural Sciences Dokuz Eylul University 35220 Izmir Turkey Department of Agricultural Engineering Muhammad Nawaz Shareef University of Agriculture Multan Multan Pakistan Department of Water Resources Engineering Shahid Chamran University of Ahvaz Ahvaz *** Iran College of New Energy and Environment Jilin University Changchun 130021 China Department of Geology Khushal Khan Khattak University Karak Pakistan Department of Plant Production College of Food and Agriculture King Saud University Riyadh Saudi Arabia Department of Computer Science College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh 11543 Saudi Arabia Department of Geography and Geospatial Sciences Geospatial Sciences Center of Excellence South Dakota State University Brookings South Dakota USA

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Evaluation of an Instrument to Identify Factors that Impact the Motivation and Engagement of Undergraduate Students in Computing 48

Evaluation of an Instrument to Identify Factors that Impact ...

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48th Frontiers in Education Conference, FIE 2018

ISBN: (纸本)9781538611739

This Research Paper presents the evaluation of an instrument to identify the impact of motivation and engagement factors in undergraduate students in computing. Although researches indicate a direct impact of motivation and engagement on student performance and retention, few studies have been found that address which factors are relevant in this process. The instrument is a questionnaire based on the compilation of several works of the literature containing 48 items divided into 6 groups: personal and demographic data, general perception about motivation, perception about the university, student behavior, perception about program and perception about classes/teachers. The questionnaire evaluation is based on a case study with 112 undergraduate students in Software engineering. As a result, we found that the questionnaire can be considered reliable (Cronbach's alpha = 8904). Considering the validity of constructs, we found an acceptable degree of correlation between the most pair of items in each group (averaging 63%). We also found that the item-total correlation coefficient was only not adequate for one factor group, indicating satisfactory correlation for all other items. Finally, we found that the number of factors is coherent, but there are several items from different groups strongly correlated, indicating the need for a reorganization. © 2018 IEEE.

关键词： Students

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Exciton Localization of High-Quality ZnO/Mg_xZn_1-xO Multiple Quantum Wells on Si (111) with a Y₂O₃ Buffer Layer (vol 1, pg 3829, 2018)

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ACS APPLIED NANO MATERIALS 2018年第10期1卷 5958-5958页

作者： Liu, Wei-Rein Huang, Wei-Lun Wu, Yung-Chi Lai, Liang-Hsun Hsu, Chia-Hung Hsieh, Wen-Feng Chiang, Tsung-Hung Wan, H. W. Hong, M. Kao, J. National Synchrotron Radiation Research Center Hsinchu 30076 Taiwan Program for Science and Technology of Accelerator Light Source National Chiao Tung University Hsinchu 30010 Taiwan Department of Photonics College of Electrical and Computer Engineering National Chiao Tung University Hsinchu 30010 Taiwan Department of Photonics College of Electrical and Computer Engineering National Chiao Tung University Hsinchu 30010 Taiwan Department of Materials Science & Engineering National Tsing Hua University Hsinchu 30013 Taiwan Graduate Institute of Applied Physics and Department of Physics National Taiwan University Taipei 10617 Taiwan Department of Physics National Tsing Hua University Hsinchu 30013 Taiwan

We report the structural and optical properties of ten-period ZnO/MgxZn1–xO multiple quantum wells (MQWs) prepared on the most widely used semiconductor material, Si. The introduction of a nanometer thick high-k Y2O3transition layer between Si (111) substrate and a ZnO buffer layer significantly improves the structural perfection of the MQWs grown on top of it. The high structural quality of the ZnO/MgxZn1–xO MQWs is evidenced by the appearance of pronounced high order satellite peaks in X-ray crystal truncation rods; high resolution cross-sectional transmission electron microscopy images also confirmed the regularly arranged well and barrier layers. When the well width is less than ∼2.7 nm, the quantum-confined Stark effect in MQWs can be negligible. Not only the increasing exciton-binding energy but also reducing exciton–phonon coupling determined in temperature-dependent photoluminescence spectra indicate quantum-size effect. Our results demonstrate that ZnO/MgxZn1–xO MQWs integrated on Si have great potential in UV optoelectronic device applications.

关键词： Excitons Phonons Oxides Binding energy Layers

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