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arXiv 2020年

作者： Banluesombatkul, Nannapas Ouppaphan, Pichayoot Leelaarporn, Pitshaporn Lakhan, Payongkit Chaitusaney, Busarakum Jaimchariyatam, Nattapong Chuangsuwanich, Ekapol Chen, Wei Phan, Huy Dilokthanakul, Nat Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand Department of Otolaryngology Faculty of Medicine Chulalongkorn University Thailand Excellence Center for Sleep Disorders King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand Division of Pulmonary and Critical Care Medicine Faculty of Medicine Chulalongkorn University Thailand Head of Excellence Center for Sleep Disorders King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand The Computer Engineering Department Chulalongkorn University Bangkok Thailand The Center for Intelligent Medical Electronics Department of Electronic Engineering School of Information Science and Technology Fudan University Shanghai200433 China The School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficulties can be posed in replacing the clinicians with the automatic system due to the differences in many aspects found in individual bio-signals, causing the inconsistency in the performance of the model on every incoming individual. Thus, we aim to explore the feasibility of using a novel approach, capable of assisting the clinicians and lessening the workload. We propose the transfer learning framework, entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subjects (source code is available at https://***/IoBTVISTEC/MetaSleepLearner). The framework was demonstrated to require the labelling of only a few sleep epochs by the clinicians and allow the remainder to be handled by the system. Layer-wise Relevance Propagation (LRP) was also applied to understand the learning course of our approach. In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4% to 17.7% improvement with statistical difference in the mean of both approaches. The illustration of the model interpretation after the adaptation to each subject also confirmed that the performance was directed towards reasonable learning. MetaSleepLearner outperformed the conventional approaches as a result from the fine-tuning using the recordings of both healthy subjects and patients. This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording. © 2020, CC BY-N

关键词： Convolutional neural networks

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Roadmap on Data-Centric Materials science

arXiv

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arXiv 2024年

作者： Bauer, Stefan Benner, Peter Bereau, Tristan Blum, Volker Boley, Mario Carbogno, Christian Catlow, C.A. Richard Dehm, Gerhard Eibl, Sebastian Ernstorfer, Ralph Fekete, Ádám Foppa, Lucas Fratzl, Peter Freysoldt, Christoph Gault, Baptiste Ghiringhelli, Luca M. Giri, Sajal K. Gladyshev, Anton Goyal, Pawan Hattrick-Simpers, Jason Kabalan, Lara Karpov, Petr Khorrami, Mohammad S. Koch, Christoph Kokott, Sebastian Kosch, Thomas Kowalec, Igor Kremer, Kurt Leitherer, Andreas Li, Yue Liebscher, Christian H. Logsdail, Andrew J. Lu, Zhongwei Luong, Felix Marek, Andreas Merz, Florian Mianroodi, Jaber R. Neugebauer, Jörg Pei, Zongrui Purcell, Thomas A.R. Raabe, Dierk Rampp, Markus Rossi, Mariana Rost, Jan-Michael Saal, James Saalmann, Ulf Sasidhar, Kasturi Narasimha Saxena, Alaukik Sbailò, Luigi Scheidgen, Markus Schloz, Marcel Schmidt, Daniel F. Teshuva, Simon Trunschke, Annette Wei, Ye Weikum, Gerhard Xian, R. Patrick Yao, Yi Yin, Junqi Zhao, Meng Scheffler, Matthias School of Computation Information and Technology Technical University of Munich & Helmholtz AI Munich Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Institute for Theoretical Physics Heidelberg University Heidelberg Germany Thomas Lord Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States Monash University Department of Data Science and AI Melbourne Australia The NOMAD Laboratory The FHI of the Max-Planck-Gesellschaft IRIS-Adlershof of the Humboldt-Universität zu Berlin Berlin Germany School of Chemistry Cardiff University Cardiff United Kingdom Max-Planck-Institut für Eisenforschung GmbH Düsseldorf Germany Max Planck Computing and Data Facility Garching Germany Institute for Optics and Atomic Physics Technical University of Berlin Berlin Germany Physics Department and IRIS-Adlershof Humboldt Universität zu Berlin Berlin Germany Max Planck Institute of Colloids and Interfaces Potsdam Germany Department of Materials Imperial College London United Kingdom Department of Materials Science and Engineering Friedrich-Alexander Universität Erlangen-Nürnberg Germany Department of Chemistry Northwestern University Illinois United States Humboldt-Universität zu Berlin Department of Physics Berlin Germany Department of Materials Science and Engineering University of Toronto Toronto Canada STFC Hartree Centre Daresbury Laboratory Daresbury Warrington United Kingdom Molecular Simulations from First Principles e.V. Berlin Germany Humboldt-Universität zu Berlin Department of Computer Science Berlin Germany Max Planck Institute for Polymer Research Mainz Germany ICFO-Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Barcelona Spain Lenovo HPC Innovation Center Stuttgart Germany New York University New York United States University of Arizona Biochemistry Department Arizona United States Max Planck Institute for the Structure an

science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research. © 2024, CC BY.

关键词： Data Analytics

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Scanned single-electron probe inside a silicon electronic device

arXiv

引用

arXiv 2020年

作者： Ng, Kevin S.H. Voisin, Benoit Johnson, Brett C. McCallum, Jeffrey C. Salfi, Joe Rogge, Sven Centre for Quantum Computation and Communication Technology School of Physics University of New South Wales SydneyNSW2052 Australia Physikalisches Institut Center for Integrated Quantum Science and Technology Universität Stuttgart Pfaffenwaldring 57 Stuttgart70569 Germany Centre for Quantum Computation and Communication Technology School of Physics University of Melbourne MelbourneVIC3010 Australia Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Solid-state devices can be fabricated at the atomic scale, with applications ranging from classical logic to current standards and quantum technologies. While it is very desirable to probe these devices and the quantum states they host at the atomic scale, typical methods rely on long-ranged capacitive interactions, making this difficult. Here we probe a silicon electronic device at the atomic scale using a localized electronic quantum dot induced directly within the device at a desired location, using the biased tip of a low-temperature scanning tunneling microscope. We demonstrate control over short-ranged tunnel coupling interactions of the quantum dot with the device's source reservoir using sub-nm position control of the tip, and the quantum dot energy level using a voltage applied to the device's gate reservoir. Despite the ∼1 nm proximity of the quantum dot to the metallic tip, we find the gate provides sufficient capacitance to enable a high degree of electric control. Combined with atomic scale imaging, we use the quantum dot to probe applied electric fields and charge in individual defects in the device. This capability is expected to aid in the understanding of atomic-scale devices and the quantum states realized in them. Copyright © 2020, The Authors. All rights reserved.

关键词： Semiconductor quantum dots

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Master equations and quantum trajectories for squeezed wave packets

arXiv

引用

arXiv 2021年

作者： Gross, Jonathan A. Baragiola, Ben Stace, T.M. Combes, Joshua Center for Quantum Information and Control University of New Mexico AlbuquerqueNM87131-0001 United States Institut Quantique Université de Sherbrooke SherbrookeQCJ1K 2R1 Canada Centre for Quantum Computation & Communication Technology School of Science RMIT University MelbourneVIC3000 Australia ARC Centre for Engineered Quantum System Department of Physics University of Queensland BrisbaneQLD4072 Australia Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO80309 United States

The interaction between matter and squeezed light has mostly been treated within the approximation that the field correlation time is small. Methods for treating squeezed light with more general correlations currently involve explicitly modeling the systems producing the light. We develop a general purpose input-output theory for a particular form of narrowband squeezed light-a squeezed wave-packet mode-that only cares about the statistics of the squeezed field and the shape of the wave packet. This formalism allows us to derive the input-output relations and the master equation. We also consider detecting the scattered field using photon counting and homodyne measurements which necessitates the derivation of the stochastic master equation. The non Markovian nature of the field manifests itself in the master equation as a coupled hierarchy of equations. We illustrate these with consequences for the decay and resonance fluorescence of two-level atoms in the presence of such fields. Copyright © 2021, The Authors. All rights reserved.

关键词： Wave packets

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Specific targeting of brain endothelial cells using enhancer AAV vectors

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Neuron 2025年第10期113卷 1562-1578.e6页

作者： Velazquez-Rivera, Eric Dey, Oyshi Kim, Nayoon S. Cao, Wenhao Ye, Qiao Gao, Pan Thai, Andy Nguyen, Jason K. Zhang, Hai Ting, Jonathan T. Gopi, M. Ren, Bing Holmes, Todd C. Xu, Xiangmin Department of Anatomy and Neurobiology School of Medicine University of California Irvine Irvine 92697 CA United States Department of Computer Science University of California Irvine Irvine 92697 CA United States Human Cell Types Program Allen Institute for Brain Science Seattle 98109 WA United States Department of Cellular and Molecular Medicine School of Medicine University of California San Diego La Jolla 92093 CA United States Department of Physiology and Biophysics School of Medicine University of California Irvine Irvine 92697 CA United States Department of Biomedical Engineering University of California Irvine Irvine 92697 CA United States Department of Microbiology and Molecular Genetics University of California Irvine Irvine 92697 CA United States Center for Neural Circuit Mapping (CNCM) University of California Irvine Irvine 92697 CA United States

Brain endothelial cells (BECs) in brain vasculature are critical structural and functional components of the blood brain barrier (BBB). Adeno-associated virus (AAV) capsids have previously been genetically engineered to confer specificity to endothelial cells, but these capsids show limited endothelial cell specificity that varies by delivery conditions. We developed a set of new BEC-enhancer AAV vectors that specifically target BECs based on the cis-regulatory elements identified from single-cell epigenetic datasets. Ex vivo and in vivo characterization of BEC-enhancer AAVs in wild-type, Ai9 reporter, and Alzheimer's disease model mouse brains show their utility for high transduction selectivity of the BECs with little off-target transduction in the liver. Our BEC-enhancer AAVs target the brain vasculature by systemic administration and can be minimally invasive in terms of the route of administration. They are useful new tools for delivering genetic payloads specifically to BECs for normal and diseased brain studies. © 2025 The Authors

关键词： AAV Alzheimer's endothelial cells enhancer gene therapy genetic access recombinase

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computational Thinking for STEM Teacher Leadership Training at Louisiana State University

Computational Thinking for STEM Teacher Leadership Training ...

引用

Research in Equity and Sustained Participation in engineering, Computing, and Technology (RESPECT)

作者： Fernando Alegre Juana Moreno Trent Dawson Egbeyong E. Tanjong David H. Kirshner Gordon A. Cain Center for STEM Literacy Louisiana State University Baton Rouge LA U.S.A. Physics & Astronomy and Center for Computation & Technology Louisiana State University Baton Rouge LA U.S.A. School of Education Louisiana State University Baton Rouge LA U.S.A. Computer Science & Engineering Louisiana State University Baton Rouge LA U.S.A.

ISBN: (数字)9781728171722

ISBN: (纸本)9781728171739

We developed and piloted a new course titled computer science Teaching Methods (CSM) in the fall semester of 2019. This course was based on materials developed from a previous program that trained high school teachers in computational thinking and programming through LSU's Cain center. Pedagogical content knowledge informed the design of this course. Also, data gathered from teacher and instructor interaction at multiple sites during the summer STEM professional development program contributed to the courses' design. The CS Methods course targeted undergraduate computer science majors who were considering a career in teaching or who were interested in CS pedagogy. We encountered several challenges recruiting and retaining students and found that computer science students attracted to teaching careers do not fall into the stereotype of most computer science majors. Participation of women was higher than the average undergraduate CS courses. A disconnect appeared between the pedagogical practices promoted for teaching computing at the high school level and those being practiced at the college level. After learning about the 5E pedagogical model for teaching computing, students expressed interest in the potential of using more student-centered instruction, not only for high school instruction, but also for their own college courses. An area of disconnect also emerged in the programming formats, as all the students were comfortable with Java but all were unfamiliar with popular block-based programming platforms, such as Scratch. The transition from the CS curriculum taught in high schools to what follows in college needs to be smoother.

关键词： Programming profession computational modeling Training STEM Encoding Planning Interviews

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Perspectives on adaptive dynamical systems

arXiv

引用

arXiv 2023年

作者： Sawicki, Jakub Berner, Rico Loos, Sarah A.M. Anvari, Mehrnaz Bader, Rolf Barfuss, Wolfram Botta, Nicola Brede, Nuria Franović, Igor Gauthier, Daniel J. Goldt, Sebastian Hajizadeh, Aida Hövel, Philipp Karin, Omer Lorenz-Spreen, Philipp Miehl, Christoph Mölter, Jan Olmi, Simona Schöll, Eckehard Seif, Alireza Tass, Peter A. Volpe, Giovanni Yanchuk, Serhiy Kurths, Jürgen Potsdam Institute for Climate Impact Research Telegrafenberg Potsdam14473 Germany Fachhochschule Nordwestschweiz FHNW Leonhardsstrasse 6 Basel4009 Switzerland Department of Physics Humboldt-Universität zu Berlin Newtonstraße 15 Berlin12489 Germany DAMTP University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt-Augustin53757 Germany Institute of Systematic Musicology University of Hamburg Germany Transdisciplinary Research Area: Sustainable Futures University of Bonn Bonn53113 Germany University of Bonn Bonn53113 Germany Department of Computer Science and Engineering Chalmers University of Technology Göteborg412 96 Sweden Department of Computer Science University of Potsdam An der Bahn 2 Potsdam14476 Germany Scientific Computing Laboratory Center for the Study of Complex Systems Institute of Physics Belgrade University of Belgrade Pregrevica 118 Belgrade11080 Serbia The Ohio State University Department of Physics and Electrical and Computer Engineering 191 West Woodruff Ave ColumbusOH43210 United States ResCon Technologies LLC 1275 Kinnear Rd. Suite 239 ColumbusOH43212 United States Trieste Italy Research Group Comparative Neuroscience Leibniz Institute for Neurobiology Magdeburg Germany Theoretical Physics Saarland University Saarbrücken66123 Germany Center for Biophysics Saarland University Saarbrücken66123 Germany Department of Mathematics Imperial College London United Kingdom Center for Adaptive Rationality Max Planck Institute for Human Development Lentzeallee 94 Berlin14195 Germany Max Planck Institute for Brain Research Max-von-Laue-Straße 4 Frankfurt am Main60438 Germany Technical University of Munich School of Life Sciences Alte Akademie 8 Freising85354 Germany Department of Mathematics School of Computation Information and Technology Technical University of Munich Boltzmannstraße 3 Garching bei München8574

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems like the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges, and give perspectives on future research directions, looking to inspire interdisciplinary approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamical systems

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The ideal wavelength for daylight free-space quantum key distribution

APL Quantum

引用

APL Quantum 2024年第1期1卷

作者： Mostafa Abasifard Chanaprom Cholsuk Roberto G. Pousa Anand Kumar Ashkan Zand Thomas Riel Daniel K. L. Oi Tobias Vogl Abbe Center of Photonics Institute of Applied Physics Friedrich Schiller University Jena 07745 Jena Germany Department of Computer Engineering School of Computation Information and Technology Technical University of Munich 80333 Munich Germany Computational Nonlinear and Quantum Optics SUPA Department of Physics University of Strathclyde Glasgow G4 0NG United Kingdom University of Applied Sciences Wiener Neustadt 2700 Wiener Neustadt Austria Walton Institute for Information and Communication Systems Science South East Technological University Waterford X91 P20H Ireland

Quantum key distribution (QKD) has matured in recent years from laboratory proof-of-principle demonstrations to commercially available systems. One of the major bottlenecks is the limited communication distance in fiber networks due to the exponential signal damping. To bridge intercontinental distances, low Earth orbit satellites transmitting quantum signals over the atmosphere can be used. These free-space links, however, can only operate during the night, as the sunlight otherwise saturates the detectors used to measure the quantum states. For applying QKD in a global quantum internet with continuous availability and high data rates, operation during daylight is required. In this work, we model a satellite-to-ground quantum channel for different quantum light sources to identify the optimal wavelength for free-space QKD under ambient conditions. Daylight quantum communication is possible within the Fraunhofer lines or in the near-infrared spectrum, where the intrinsic background from the sun is comparably low. The highest annual secret key length considering the finite key effect is achievable at the Hα Fraunhofer line. More importantly, we provide the fundamental model that can be adapted, in general, to any other specific link scenario taking into account the required modifications. We also propose a true single-photon source based on a color center in hexagonal boron nitride coupled to a microresonator that can implement such a scheme. Our results can also be applied in roof-to-roof scenarios and are, therefore, relevant for near-future quantum networks.

关键词：

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Direct Cation Exchange in Monolayer MoS2 via Recombination-Enhanced Migration

引用

Physical Review Letters 2019年第10期122卷 106101-106101页

作者： Shi-Ze Yang Weiwei Sun Yu-Yang Zhang (张余洋) Yongji Gong Mark P. Oxley Andrew R. Lupini Pulickel M. Ajayan Matthew F. Chisholm Sokrates T. Pantelides Wu Zhou (周武) Center for Nanophase Materials Science Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Physics and Astronomy and Department of Electrical Engineering and Computer Science Vanderbilt University Nashville Tennessee 37235 USA School of Physical Sciences and CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing 100049 China School of Materials Science and Engineering Beihang University Beijing 100191 China Department of Materials Science and NanoEngineering Rice University Houston Texas 77005 USA

In addition to their unique optical and electronic properties, two-dimensional materials provide opportunities to directly observe atomic-scale defect dynamics. Here we use scanning transmission electron microscopy to observe substitutional Re impurities in monolayer MoS2 undergo direct exchanges with neighboring Mo atoms in the lattice. Density-functional-theory calculations find that the energy barrier for direct exchange, a process that has only been studied as a diffusion mechanism in bulk materials, is too large for either thermal activation or energy directly transferred from the electron beam. The presence of multiple sulfur vacancies next to the exchanged Re-Mo pair, as observed by electron microscopy, does not lower the energy barrier sufficiently to account for the observed atomic exchange. Instead, the calculations find that a Re dopant and surrounding sulfur vacancies introduce an ever-changing set of deep levels in the energy gap. We propose that these levels mediate an “explosive” recombination-enhanced migration via multiple electron-hole recombination events. As a proof of concept, we also show that Re-Mo direct exchange can be triggered via controlled creation of sulfur vacancies. The present experimental and theoretical findings lay a fundamental framework towards manipulating single substitutional dopants in two-dimensional materials.

关键词： Defects Diffusion Dopants Transition metal dichalcogenides Density functional theory Scanning transmission electron microscopy

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AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline

arXiv

引用

arXiv 2019年

作者： Perrault, Andrew Fang, Fei Sinha, Arunesh Tambe, Milind The Center for Research on Computation and Society The John A. Paulson School of Engineering and Applied Science Harvard University United States The Institute for Software Research The School of Computer Science Carnegie Mellon University United States The School of Information Systems Singapore Management University Singapore Center for Research on Computation and Society Harvard University United States AI for Social Good Google Research India India

With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. In pursuit of this goal of AI for Social Impact, we as AI researchers must go beyond improvements in computational methodology;it is important to step out in the field to demonstrate social impact. To this end, we focus on the problems of public safety and security, wildlife conservation, and public health in low-resource communities, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. We present case studies from our deployments around the world as well as lessons learned that we hope are of use to researchers who are interested in AI for Social Impact. In pushing this research agenda, we believe AI can indeed play an important role in fighting social injustice and improving society. Copyright © 2019, The Authors. All rights reserved.

关键词： Multi agent systems

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