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arXiv 2025年

作者： Su, Yifan Lv, B.Q. Zong, Alfred Müller, Aaron Chattopadhyay, Sambuddha Dolgirev, Pavel E. Singh, Anisha G. Straquadine, Joshua A.W. Choi, Dongsung Azoury, Doron Mogi, Masataka Fisher, Ian R. Demler, Eugene Gedik, Nuh Massachusetts Institute of Technology Department of Physics CambridgeMA02139 United States Tsung-Dao Lee Institute School of Physics and Astronomy Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University Shanghai200240 China Departments of Physics and of Applied Physics Stanford University StanfordCA94305 United States SIMES SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Institute for Theoretical Physics ETH Zürich Zürich8093 Switzerland Lyman Laboratory Department of Physics Harvard University CambridgeMA02138 United States Geballe Laboratory for Advanced Materials Stanford University StanfordCA94305 United States Department of Applied Physics Stanford University StanfordCA94305 United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States Department of Applied Physics University of Tokyo Bunkyo-ku Tokyo113-8656 Japan

Understanding the origin of phase transitions and the interactions between distinct phases remains a central task in condensed matter physics. Charge density wave (CDW) systems provide an ideal platform for investigating these phenomena. While the dominant CDW phases in many materials can be explained through Fermi surface nesting or electron-phonon interactions, certain CDW phase transitions remain poorly understood, challenging conventional paradigms. One notable example is rare-earth tritelluride ErTe3, which hosts two competing CDW orders. While the dominant CDW phase fits within the electron-phonon coupling framework, the formation mechanism of the subdominant CDW remains enigmatic. In this study, we combine time- and angle-resolved photoemission spectroscopy and time-dependent Ginzburg-Landau theory to establish a time-domain approach for probing phase transitions in solid-state systems. By analyzing the distinct recovery dynamics of the two CDW orders in ErTe3 following light excitation, we reveal a novel nucleation and growth mechanism that likely drives the secondary CDW phase transition. This work not only uncovers a previously unknown CDW formation mechanism in rare-earth tritellurides but also introduces a non-equilibrium framework for understanding phase transitions and phase competition in quantum materials. Copyright © 2025, The Authors. All rights reserved.

关键词： Charge density waves

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Author Correction: A wireless haptic interface for programmable patterns of touch across large areas of the skin

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Nature Electronics 2022年 621页

作者： Yei Hwan Jung Jaeman Lim Jae-Young Yoo Abraham Vázquez-Guardado Jae-Hwan Kim Jin-Tae Kim Haiwen Luan Minsu Park Hee-Sup Shin Chun-Ju Su Robert Schloen Jacob Trueb Jan-Kai Chang Da Som Yang Yoonseok Park Hanjun Ryu Hong-Joon Yoon Geumbee Lee Hyoyeong Jeong Jong Uk Kim John A. Rogers Raudel Avila Yonggang Huang Aadeel Akhtar Jesse Cornman Tae-il Kim Department of Electronic Engineering Hanyang University Seoul Republic of Korea Querrey Simpson Institute for Bioelectronics Northwestern University Evanston IL USA Department of Mechanical Engineering Northwestern University Evanston IL USA Department of Materials Science and Engineering Northwestern University Evanston IL USA Departments of Biomedical Engineering Neurological Surgery Chemistry and Electrical and Computer Engineering Northwestern University Evanston IL USA Department of Civil and Environmental Engineering Northwestern University Evanston IL USA PSYONIC Champaign IL USA School of Chemical Engineering Sungkyunkwan University (SKKU) Suwon Republic of Korea

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Calibration of flux crosstalk in large-scale flux-tunable superconducting quantum circuits

arXiv

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arXiv 2021年

作者： Dai, Xi Tennant, D.M. Trappen, R. Martinez, A.J. Melanson, D. Yurtalan, M.A. Tang, Y. Novikov, S. Grover, J.A. Disseler, S.M. Basham, J.I. Das, R. Kim, D.K. Melville, A.J. Niedzielski, B.M. Weber, S.J. Yoder, J.L. Lidar, D.A. Lupascu, A. Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Northrop Grumman Corporation LinthicumMD21090 United States MIT Lincoln Laboratory 244 Wood Street LexingtonMA02421 United States Departments of Electrical & Computer Engineering Chemistry and Physics Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Waterloo Institute for Nanotechnology University of Waterloo WaterlooONN2L 3G1 Canada

Magnetic flux tunability is an essential feature in most approaches to quantum computing based on superconducting qubits. Independent control of the fluxes in multiple loops is hampered by crosstalk. Calibrating flux crosstalk becomes a challenging task when the circuit elements interact strongly. We present a novel approach to flux crosstalk calibration, which is circuit model independent and relies on an iterative process to gradually improve calibration accuracy. This method allows us to reduce errors due to the inductive coupling between loops. The calibration procedure is automated and implemented on devices consisting of tunable flux qubits and couplers with up to 27 control loops. We devise a method to characterize the calibration error, which is used to show that the errors of the measured crosstalk coefficients are all below 0.17%. Copyright © 2021, The Authors. All rights reserved.

关键词： Crosstalk

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Unaligned Sequence Similarity Search Using Deep Learning

Unaligned Sequence Similarity Search Using Deep Learning

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IEEE International Conference on Bioinformatics and Biomedicine

作者： James K. Senter Taylor M. Royalty Andrew D. Steen Amir Sadovnik Department of Electrical Engineering and Computer Science University of Tennessee - Knoxville Departments of Microbiology and Earth and Planetary Sciences University of Tennessee - Knoxville

ISBN: (纸本)9781728118680

Gene annotation has traditionally required direct comparison of DNA sequences between an unknown gene and a database of known ones using string comparison methods. However, these methods do not provide useful information when a gene does not have a close match in the database. In addition, each comparison can be costly when the database is large since it requires alignments and a series of string comparisons. In this work we propose a novel approach: using recurrent neural networks to embed DNA or amino-acid sequences in a low-dimensional space in which distances correlate with functional similarity. This embedding space overcomes both shortcomings of the method of aligning sequences and comparing homology. First, it allows us to obtain information about genes which do not have exact matches by measuring their similarity to other ones in the database. If our database is labeled this can provide labels for a query gene as is done in traditional methods. However, even if the database is unlabeled it allows us to find clusters and infer some characteristics of the gene population. In addition, each comparison is much faster than traditional methods since the distance metric is reduced to the Euclidean distance, and thus efficient approximate nearest neighbor algorithms can be used to find the best match. We present results showing the advantage of our algorithm. More specifically we show how our embedding can be useful for both classification tasks when our labels are known, and clustering tasks where our sequences belong to classes which have not been seen before.

关键词： Gene annotation Comparative Genomics Deep Learning Gene Annotation Database comparative genomics embedding distance metric Euclidean distance Genes recurrent neural nets

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arXiv

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arXiv 2023年

作者： Bilgic, Berkin Costagli, Mauro Chan, Kwok-Shing Duyn, Jeff Langkammer, Christian Lee, Jongho Li, Xu Liu, Chunlei Marques, José P. Milovic, Carlos Robinson, Simon Schweser, Ferdinand Shmueli, Karin Spincemaille, Pascal Straub, Sina van Zijl, Peter Wang, Yi Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School CharlestownMA United States University of Genoa Genoa Italy Laboratory of Medical Physics and Magnetic Resonance IRCCS Stella Maris Pisa Italy Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherlands Advanced MRI Section NINDS National Institutes of Health BethesdaMD United States Department of Neurology Medical University of Graz Graz Austria Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States F.M. Kirby Research Center for Functional Brain Imaging Kennedy Krieger Institute BaltimoreMD United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Helen Wills Neuroscience Institute University of California BerkeleyCA United States Pontificia Universidad Catolica de Valparaiso Valparaiso Chile High Field MR Centre Department of Biomedical Imaging and Image-Guided Therapy Medical University of Vienna Austria Buffalo Neuroimaging Analysis Center Department of Neurology Jacobs School of Medicine and Biomedical Sciences the University at Buffalo BuffaloNY United States Center for Biomedical Imaging Clinical and Translational Science Institute the University at Buffalo BuffaloNY United States Department of Medical Physics and Biomedical Engineering University College London London United Kingdom MRI Research Institute Department of Radiology Weill Cornell Medicine New YorkNY United States Department of Radiology Mayo Clinic JacksonvilleFL United States MRI Research Institute Departments of Radiology and Biomedical Engineering Cornell University New YorkNY United States

This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of whole brain as a region of interest in the analysis, and QSM results should be reported with – as a minimum – the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in data acquisition, analysis, and reporting. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Nemato-elasticity of hybrid molecular-colloidal liquid crystals

arXiv

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arXiv 2021年

作者： Senyuk, B. Mundoor, H. Smalyukh, I.I. Wensink, H.H. Department of Physics University of Colorado BoulderCO United States Department of Electrical Computer and Energy Engineering Materials Science and Engineering Program and Soft Materials Research Center University of Colorado BoulderCO United States Chemical Physics Program Departments of Chemistry and Physics University of Colorado BoulderCO United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado BoulderCO80309 United States Laboratoire de Physique des Solides Université Paris-Saclay CNRS UMR 8502 Orsay91405 France

Colloidal rods immersed in a thermotropic liquid-crystalline solvent are at the basis of so-called hybrid liquid crystals which are characterized by tunable nematic fluidity with symmetries ranging from conventional uniaxial nematic or anti-nematic to orthorhombic [Mundoor et al., science 360, 768 (2018)]. We provide a theoretical analysis of the elastic moduli of such systems by considering interactions between the individual rods with the embedding solvent through surface-anchoring forces, as well as steric and electrostatic interactions between the rods themselves. For uniaxial systems the presence of colloidal rods generates a marked increase of the splay elasticity which we found to be in quantitative agreement with experimental measurements. For orthorhombic hybrid liquid crystals we provide estimates of all twelve elastic moduli and show that only a small subset of those elastic constants play a relevant role in describing the nemato-elastic properties. The complexity and possibilities related to identifying the elastic moduli in experiments are briefly discussed. The results are expected to be helpful in stimulating future studies on defect-dressed "topological" colloids and the analysis of boundary-driven phenomena where the non-trivial director topology generated by biaxial nematics plays a key role. © 2021, CC BY-NC-SA.

关键词： Elasticity

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1002 Heart Rate Variability During Sleep and Future Risk of Parkinson’s Disease in Community-dwelling Older Men

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Sleep 2024年第SUPPLEMENT_1期47卷 A429–A430页

作者： Milton, Sasha Sun, Haoqi Kaffashi, Farhad Loparo, Kenneth Stone, Katie Yaffe, Kristine Westover, M Brandon Leng, Yue Department of Psychiatry and Behavioral Sciences University of California San Francisco Department of Neurology Beth Israel Deaconess Medical center Department of Electrical Engineering and Computer Science Case Western Reserve University Research Institute California Pacific Medical Center Departments of Psychiatry and Behavioral Sciences Neurology and Epidemiology University of California San Francisco Department of Neurology Beth Israel Deaconess Medical School Department of Psychiatry and Behavioral Sciences University of California San Francisco

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Frequency dependence of near-surface oceanic kinetic energy from drifter observations and global high-resolution models

arXiv

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arXiv 2022年

作者： Arbic, Brian K. Elipot, Shane Brasch, Jonathan M. Menemenlis, Dimitris Ponte, Aurélien L. Shriver, Jay F. Yu, Xiaolong Zaron, Edward D. Alford, Matthew H. Buijsman, Maarten C. Abernathey, Ryan Garcia, Daniel Guan, Lingxiao Martin, Paige E. Nelson, Arin D. Department of Earth and Environmental Sciences University of Michigan Ann ArborMI United States Rosenstiel School of Marine and Atmospheric Science University of Miami MiamiFL United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Ifremer Université de Brest CNRS IRD Laboratoire d'Océanographie Physique et Spatiale IUEM Brest France Naval Research Laboratory Stennis Space Center Mississippi United States College of Earth Ocean and Atmospheric Sciences Oregon State University CorvallisOR United States Scripps Institution of Oceanography University of California La Jolla San DiegoCA United States School of Ocean Science and Engineering University of Southern Mississippi Stennis Space Center MS United States Lamont-Doherty Earth Observatory Columbia University New York CityNY United States Department of Physics University of Michigan Ann ArborMI United States Graduate School of Oceanography University of Rhode Island KingstonRI United States

The geographical variability, frequency content, and vertical structure of near-surface oceanic kinetic energy (KE) are important for air-sea interaction, marine ecosystems, operational oceanography, pollutant tracking, and interpreting remotely sensed velocity measurements. Here, KE in high-resolution global simulations (HYbrid Coordinate ocean Model;HYCOM, and Massachusetts Institute of Technology general circulation model;MITgcm), at the sea surface (0 m) and 15 m, are respectively compared with KE from undrogued and drogued surface drifters. Global maps and zonal averages are computed for low-frequency ( © 2022, CC BY.

关键词： Kinetic energy

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Plasmonic bacteria on a nanoporous mirror via hydrodynamic trapping for rapid identification of waterborne pathogens

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Light(science & Applications) 2018年第1期7卷 359-367页

作者： Keumrai Whang Jong-Hwan Lee Yonghee Shin Wooju Lee Young Wan Kim Dongchoul Kim Luke P.Lee Taewook Kang Department of Chemical and Biomolecular Engineering Sogang UniversitySeoul 04107Korea Berkeley Sensor and Actuator Center Departments of BioengineeringElectrical Engineering and Computer ScienceBiophysics Graduate ProgramUniversity of CaliforniaBerkeleyBerkeleyCA 94720USA Department of Mechanical Engineering Sogang UniversitySeoul 04107Korea

A rapid,precise method for identifying waterborne pathogens is critically needed for effective disinfection and better ***,conventional methods,such as culture-based counting,generally suffer from slow detection times and low ***,we developed a rapid detection method for tracing waterborne pathogens by an innovative optofluidic platform,a plasmonic bacteria on a nanoporous mirror,that allows effective hydrodynamic cell trapping,enrichment of pathogens,and optical signal *** designed and simulated the integrated optofluidic platform to maximize the enrichment of the bacteria and to align bacteria on the nanopores and plasmonic mirror via hydrodynamic cell *** nanoparticles are self-assembled to form antenna arrays on the surface of bacteria,such as Escherichia coli and Pseudomonas aeruginosa,by replacing citrate with hydroxylamine hydrochloride in order to amplify the signal of the plasmonic optical *** to the synergistic contributions of focused light via the nanopore geometry,self-assembled nanoplasmonic optical antennas on the surface of bacteria,and plasmonic mirror,we obtain a sensitivity of detecting *** as low as 102 cells/ml via surface-enhanced Raman *** believe that our label-free strategy via an integrated optofluidic platform will pave the way for the rapid,precise identification of various pathogens.

关键词： mirror trapping precise

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sbi reloaded: a toolkit for simulation-based inference workflows

arXiv

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arXiv 2024年

作者： Boelts, Jan Deistler, Michael Gloeckler, Manuel Tejero-Cantero, Álvaro Lueckmann, Jan-Matthis Moss, Guy Steinbach, Peter Moreau, Thomas Muratore, Fabio Linhart, Julia Durkan, Conor Vetter, Julius Miller, Benjamin Kurt Herold, Maternus Ziaeemehr, Abolfazl Pals, Matthijs Gruner, Theo Bischoff, Sebastian Krouglova, Anastasia N. Gao, Richard Lappalainen, Janne K. Mucsányi, Bálint Pei, Felix Schulz, Auguste Stefanidi, Zinovia Rodrigues, Pedro L.C. Schröder, Cornelius Zaid, Faried Abu Beck, Jonas Kapoor, Jaivardhan Greenberg, David S. Gonçalves, Pedro J. Macke, Jakob H. Machine Learning in Science University of Tübingen Germany Tübingen AI Center Germany TransferLab AppliedAI Institute for Europe Germany ML Colab Cluster ML in Science University of Tübingen Germany Google Research United States Helmholtz-Zentrum Dresden-Rossendorf Germany Université Paris-Saclay INRIA CEA Palaiseau France Robert Bosch GmbH Germany School of Informatics University of Edinburgh United Kingdom University of Amsterdam Netherlands Research and Innovation Center BMW Group Germany Institute for Applied Mathematics and Scientific Computing University of the Bundeswehr Munich Germany Aix Marseille INSERM INS France TU Darmstadt Hessian.AI Germany University Hospital Tübingen M3 Research Center Germany Faculty of Science KU Leuven B-3000 Belgium Imec Belgium Methods of Machine Learning University of Tübingen Germany Neuroscience Institute Carnegie Mellon University United States Université Grenoble Alpes INRIA CNRS Grenoble INP LJK France Hertie Institute for AI in Brain Health University of Tübingen Germany Institute of Coastal Systems - Analysis and Modeling Helmholtz AI Germany Departments of Computer Science Electrical Engineering KU Leuven Belgium Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended sbi, a PyTorch-based package4 that implements Bayesian SBI algorithms based on neural networks. The sbi toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the sbi toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data. © 2024, CC BY-SA.

关键词： Neural networks

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