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arXiv 2021年

作者： Yahiaoui, Riad Chase, Zizwe A. Kyaw, Chan Tay, Fuyang Baydin, Andrey Noe, G. Timothy Song, Junyeob Kono, Junichiro Agrawal, Amit Bamba, Motoaki Searles, Thomas A. Department of Physics and Astronomy Howard University WashingtonDC20059 United States Department of Electrical and Computer Engineering Rice University HoustonTX70005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Physical Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States Maryland NanoCenter University of Maryland College ParkMD20742 United States The Hakubi Center for Advanced Research Kyoto University Kyoto606-8502 Japan Department of Physics I Kyoto University Kyoto606-8502 Japan Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States

A system of N two-level atoms cooperatively interacting with a photonic field can be described as a single giant atom coupled to the field with interaction strength ∝ √N. This enhancement, known as Dicke cooperativity in quantum optics, has recently become an indispensable element in quantum information technology based on strong light-matter coupling. Here, we extend the coupling beyond the standard light-matter interaction paradigm, emulating Dicke cooperativity in a terahertz metasurface with N meta-atoms. Cooperative enhancement manifested in the form of matter-matter coupling, through the hybridization of localized surface plasmon resonance in individual meta-atoms and surface lattice resonance due to the periodic array of the meta-atoms. By varying the lattice constant of the array, we observe a clear anticrossing behavior, a signature of strong coupling. Furthermore, through engineering of the capacitive split-gap in the meta-atoms, the coupling rate was cooperatively enhanced into the ultrastrong coupling regime by a factor of √N. This room-temperature technology serves as a convenient quantum emulator of the dynamics of a qubit with a giant dipole moment coherently driven by a single bosonic field. Copyright © 2021, The Authors. All rights reserved.

关键词： Atoms

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Magnetic anisotropy reversal driven by structural symmetry-breaking in monolayer α-RuCl3

arXiv

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arXiv 2022年

作者： Yang, Bowen Goh, Yin Min Sung, Suk Hyun Ye, Gaihua Biswas, Sananda Kaib, David A.S. Dhakal, Ramesh Yan, Shaohua Li, Chenghe Jiang, Shengwei Chen, Fangchu Lei, Hechang He, Rui Valentí, Roser Winter, Stephen M. Hovden, Robert Tsen, Adam W. Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics University of Michigan Ann ArborMI48109 United States Department of Materials Science and Engineering University of Michigan Ann ArborMI48109 United States Department of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States Institut für Theoretische Physik Goethe-Universität Frankfurt Max-von-Laue-Strasse 1 Frankfurt am Main60438 Germany Department of Physics Center for Functional Materials Wake Forest University Winston-SalemNC27109 United States Department of Physics Beijing Key Laboratory of Opto-electronic Functional Materials & Micro-nano Devices Renmin University of China Beijing100872 China Department of Physics Cornell University IthacaNY United States School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Chemistry University of Waterloo WaterlooONN2L 3G1 Canada

The Kitaev model is a celebrated spin-½ model on the two-dimensional (2D) honeycomb lattice with bond-dependent Ising interactions1, which features a highly entangled quantum spin liquid (QSL) ground state, fractionalized Majorana excitations, and a series of magnetic-field-induced quantum phase transitions1-7. The search for materials realizing the Kitaev model has been an ongoing challenge for over a decade and may potentially lead to applications in fault-tolerant topological quantum computing8. Yet, the unavoidable presence of non-Kitaev interactions (Heisenberg, off-diagonal, next-nearest neighbor, etc.) almost always drives the ground state away from the QSL phase, and a careful tuning of the exchange parameters is needed9-12. α-RuCl3 is a layered van der Waals material that is a particularly promising candidate to realize Kitaev physics13-15. Although the ground state is zigzag (ZZ) antiferromagnetic (AFM), this ordering can be suppressed with the application of an ~ 6-8T in-plane magnetic field. The presence of a half-integer thermal quantum Hall effect has been reported in this intermediate phase at low temperature16,17, while an unusual continuum of magnetic excitations can be seen even without magnetic field that persists far above the Néel temperature (TN ~ 7-8K)18,19. Both observations hint at α-RuCl3 being in proximity to a QSL, making it a current subject of intense scrutiny. Yet from a theoretical point of view, a QSL induced by in-plane field generally cannot be accounted for, as most calculations for α-RuCl3 show Kitaev phases more broadly emerging from an out-of-plane field6,20-23. Due to the strong "easy-plane" magnetic anisotropy of bulk crystals, however, prohibitively high fields above 30T are required to access such states24-26. The strong coupling between the spin, charge, and lattice degrees of freedom in α-RuCl3 presents an exciting opportunity to tune its magnetic interactions via external perturbations. Although pressure27-29, strain30,

关键词： Ground state

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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A Collection of Software Engineering Challenges for Big Data System Development

A Collection of Software Engineering Challenges for Big Data...

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Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

作者： Oliver Hummel Holger Eichelberger Andreas Giloj Dominik Werle Klaus Schmid Faculty of Computer Science Mannheim University of Applied Sciences Software Systems Engineering University of Hildesheim Institute for Program Structures and Data Organization Karlsruhe Institute of Technology

ISBN: (纸本)9781538673843

In recent years, the development of systems for processing and analyzing large amounts of data (so-called Big Data) has become an important sub-discipline of software engineering. However, to date there exits no comprehensive summary of the specific idiosyncrasies and challenges that the development of Big Data systems imposes on software engineers. With this paper, we aim to provide a first step towards filling this gap based on our collective experience from industry and academic projects as well as from consulting and initial literature reviews. The main contribution of our work is a concise summary of 26 challenges in engineering Big Data systems, collected and consolidated by means of a systematic identification process. The aim is to make practitioners more aware of common challenges and to offer researchers a solid baseline for identifying novel software engineering research directions.

关键词： Big Data Software Software engineering computer architecture Data analysis Data privacy Systematics

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Visualizing structure and transitions in high-dimensional biological data (vol 54, pg 781, 2019)

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NATURE BIOTECHNOLOGY 2020年第1期38卷 108-108页

作者： Moon, Kevin R. van Dijk, David Wang, Zheng Gigante, Scott Burkhardt, Daniel B. Chen, William S. Yim, Kristina van den Elzen, Antonia Hirn, Matthew J. Coifman, Ronald R. Ivanova, Natalia B. Wolf, Guy Krishnaswamy, Smita Department of Mathematics and Statistics Utah State University Logan USA Cardiovascular Research Center section Cardiology Department of Internal Medicine Yale University New Haven USA Department of Computer Science Yale University New Haven USA School of Basic Medicine Qingdao University Qingdao China Yale Stem Cell Center Department of Genetics Yale University New Haven USA Computational Biology and Bioinformatics Program Yale University New Haven USA Department of Genetics Yale University New Haven USA Department of Computational Mathematics Science and Engineering Michigan State University East Lansing USA Department of Mathematics Michigan State University East Lansing USA Applied Mathematics Program Yale University New Haven USA Department of Genetics Center for Molecular Medicine University of Georgia Athens USA Department of Mathematics and Statistics Université de Montréal Montréal Canada Mila—Quebec Artificial Intelligence Institute Montréal Canada

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Classification of datasets with imputed missing values: does imputation quality matter?

arXiv

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arXiv 2022年

作者： Shadbahr, Tolou Roberts, Michael Stanczuk, Jan Gilbey, Julian Teare, Philip Dittmer, Sören Thorpe, Matthew Torné, Ramon Viñas Sala, Evis Lió, Pietro Patel, Mishal Rudd, James H.F. Mirtti, Tuomas Rannikko, Antti Sakari Aston, John A.D. Tang, Jing Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Preller, Jacobus Stanczuk, Jan Babar, Judith Sánchez, Lorena Escudero Wassin, Marcel Holzer, Markus Walton, Nicholas Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Department of Mathematics University of Manchester Manchester United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pathology University of Helsinki Helsinki University Hospital Finland iCAN-Digital Precision Cancer Medicine Flagship Helsinki Finland Department of Urology University of Helsinki Helsinki University Hospital Helsinki Finland Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Faculty of Mathematics University of Vienna Austria Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete, imputed, samples. The focus of the machine learning researcher is then to optimise the downstream classification performance. In this study, we highlight that it is imperative to consider the quality of the imputation. We demonstrate how the commonly used measures for assessing quality are flawed and propose a new class of discrepancy scores which focus on how well the method recreates the overall distribution of the data. To conclude, we highlight the compromised interpretability of classifier models trained using poorly imputed data. All code and data used in this paper are also released publicly at [inserted upon publication]. © 2022, CC BY.

关键词： Classification (of information)

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Plane-wave analysis of a hyperbolic system of equations with relaxation in Rd

arXiv

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arXiv 2018年

作者： de Hoop, Maarten V. Liu, Jian-Guo Markowich, Peter A. Ussembayev, Nail S. Department of Computational and Applied Mathematics Rice University HoustonTX77005 United States Department of Mathematics Department of Physics Duke University DurhamNC27708 United States Applied Mathematics and Computer Science Program CEMSE Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Faculty of Mathematics University of Vienna ViennaA-1090 Austria Applied Mathematics and Computer Science Program CEMSE Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

We consider a multi-dimensional scalar wave equation with memory corresponding to the viscoelastic material described by a generalized Zener model. We deduce that this relaxation system is an example of a non-strictly hyperbolic system satisfying Majda's block structure condition. Well-posedness of the associated Cauchy problem is established by showing that the symbol of the spatial derivatives is uniformly diagonalizable with real eigenvalues. A long-time stability result is obtained by plane-wave analysis when the memory term allows for dissipation of energy.35L40 (Primary) 74D05, 35B35 (Secondary) Copyright © 2018, The Authors. All rights reserved.

关键词： Viscoelasticity

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Geometry based data generation 18

Geometry based data generation

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Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Ofir Lindenbaum Jay S. Stanley, III Guy Wolf Smita Krishnaswamy Applied Mathematics Program Yale University New Haven CT Computational Biology & Bioinformatics Program Yale University New Haven CT Departments of Genetics & Computer Science Yale University New Haven CT

We propose a new type of generative model for high-dimensional data that learns a manifold geometry of the data, rather than density, and can generate points evenly along this manifold. This is in contrast to existing generative models that represent data density, and are strongly affected by noise and other artifacts of data collection. We demonstrate how this approach corrects sampling biases and artifacts, thus improves several downstream data analysis tasks, such as clustering and classification. Finally, we demonstrate that this approach is especially useful in biology where, despite the advent of single-cell technologies, rare subpopulations and gene-interaction relationships are affected by biased sampling. We show that SUGAR can generate hypothetical populations, and it is able to reveal intrinsic patterns and mutual-information relationships between genes on a single-cell RNA sequencing dataset of hematopoiesis.

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Metatranscriptomics uncovers diurnal functional shifts in bacterial transgenes with profound metabolic effects

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Cell Host & Microbe 2025年

作者： Stephany Flores Ramos Nicole Siguenza Wuling Zhong Ipsita Mohanty Amulya Lingaraju R. Alexander Richter Smruthi Karthikeyan April L. Lukowski Qiyun Zhu Wilhan D.G. Nunes Jasmine Zemlin Zhenjiang Zech Xu Jeff Hasty Pieter C. Dorrestein Satchidananda Panda Rob Knight Amir Zarrinpar Division of Gastroenterology University of California San Diego La Jolla CA USA Biomedical Sciences Graduate Program University of California San Diego La Jolla CA USA Department of Pediatrics University of California San Diego La Jolla CA USA Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA Division of Engineering and Applied Science California Institute of Technology Pasadena CA USA Scripps Institution of Oceanography University of California San Diego La Jolla CA USA School of Life Sciences Arizona State University Tempe AZ USA Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA Collaborative Mass Spectrometry Innovation Center Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA Key Laboratory of Food Science and Technology Nanchang University Nanchang China Shu Chien-Gene Lay Department of Bioengineering University of California San Diego La Jolla CA USA Center for Microbiome Innovation University of California San Diego La Jolla CA USA Synthetic Biology Institute University of California San Diego La Jolla CA USA Salk Institute for Biological Studies La Jolla CA USA Department of Computer Science and Engineering University of California San Diego La Jolla CA USA Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA USA Moores Cancer Center University of California San Diego La Jolla CA USA Division of Gastroenterology Jennifer Moreno Department of Veterans Affairs Medical Center La Jolla CA USA Institute of Diabetes and Metabolic Health University of California San Diego La Jolla CA USA

Diurnal rhythmicity in the gut maintains gut integrity, circadian rhythms, and metabolic homeostasis. However, existing studies focus on microbial composition rather than transcriptional activity. To understand microbial functional dynamics, we characterize diurnal fluctuations in the mouse cecal metatranscriptome and metagenome under high-fat diet and time-restricted feeding (TRF). We show that metatranscriptomics uncovers TRF-induced time-dependent microbial functional shifts that are undetectable with metagenomics alone. We also found bile salt hydrolase ( bsh ) from Dubosiella newyorkensis exhibits diurnal expression in the TRF group. Engineering this bsh , along with other candidates, into a native E. coli chassis reveals distinct differences in deconjugation and amidation activities, underscoring functional specificity. In vivo , a D. newyorkensis bsh improves insulin sensitivity, glucose tolerance, and body composition, suggesting a direct role in TRF metabolic benefits. This study highlights how coupling metatranscriptomics with engineered bacterial systems is a powerful approach for uncovering time-dependent bacterial functions related to health and disease.

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Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

arXiv

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arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

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