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arXiv 2022年

作者： Tay, Fuyang Baydin, Andrey Katsutani, Fumiya Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

Optically probing materials in high magnetic fields can provide enlightening insight into field-modified electronic states and phases, while optically driving materials in high magnetic fields can induce novel nonequilibrium many-body dynamics of spin and charge carriers. While there are high-field magnets compatible with standard optical spectroscopy methods, they are generally bulky and have limited optical access, which prohibit performing state-of-the-art ultrafast and/or nonlinear optical experiments. The Rice Advanced Magnet with Broadband Optics (RAMBO), a unique 30-T pulsed mini-coil magnet system with direct optical access, has enabled previously challenging experiments using femtosecond optical pulses, including time-domain terahertz spectroscopy, in cutting-edge materials placed in strong magnetic fields. Here, we review recent experimental advances made possible by the first-generation RAMBO setup. After summarizing technological aspects of combining optical spectroscopic techniques with the mini-coil magnet, we describe results of magneto-optical studies of a wide variety of materials, providing new insight into the states and dynamics of four types of quasiparticles in solids – excitons, plasmons, magnons, and phonons – in high magnetic fields. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnets

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Two-step electronic response to magnetic ordering in a van der Waals ferromagnet

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Physical Review B 2024年第4期109卷 045416-045416页

作者： Han Wu Jian-Xin Zhu Lebing Chen Matthew W. Butcher Ziqin Yue Dongsheng Yuan Yu He Ji Seop Oh Bin Gao Jianwei Huang Shan Wu Cheng Gong Yucheng Guo Sung-Kwan Mo Jonathan Denlinger Donghui Lu Makoto Hashimoto Matthew B. Stone Alexander I. Kolesnikov Songxue Chi Junichiro Kono Andriy H. Nevidomskyy Robert J. Birgeneau Pengcheng Dai Ming Yi Department of Physics and Astronomy and Rice Center for Quantum Materials Rice University Houston Texas 77005 USA Theoretical Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Center for Integrated Nanotechnologies Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Physics University of California at Berkeley Berkeley California 94720 USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas 77005 USA Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA National Institute for Materials Science 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan Department of Applied Physics Yale University New Haven Connecticut 06511 USA Department of Electrical and Computer Engineering and Quantum Technology Center University of Maryland College Park Maryland 20742 USA Advanced Light Source Lawrence Berkeley National Laboratory Berkeley California 94720 USA Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo Park California 94025 USA Neutron Scattering Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Smalley-Curl Institute Rice University Houston Texas 77005 USA Department of Material Science and Nanoengineering Rice University Houston Texas 77005 USA Department of Materials Science and Engineering University of California Berkeley California 94720 USA

The two-dimensional material Cr2Ge2Te6 is a member of the class of insulating van der Waals (vdW) magnets. Here, using high resolution angle-resolved photoemission spectroscopy in a detailed temperature dependence study, we identify a clear response of the electronic structure to a dimensional crossover in the form of two distinct temperature scales marking onsets of modifications in the electronic structure. Specifically, we observe Te p-orbital-dominated bands to undergo changes at the Curie transition temperature TC while the Cr d-orbital-dominated bands begin evolving at a higher temperature scale. Combined with neutron scattering, density functional theory calculations, and Monte Carlo simulations, we find that the electronic system can be consistently understood to respond sequentially to the distinct temperatures at which in-plane and out-of-plane spin correlations exceed a characteristic length scale. Our findings reveal the sensitivity of the orbital-selective electronic structure for probing the dynamical evolution of local moment correlations in vdW insulating magnets.

关键词： Electronic structure Magnetic order Magnetic phase transitions Van der Waals systems Angle-resolved photoemission spectroscopy

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Beyond trees: classification with sparse pairwise dependencies

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 7728-7760页

作者： Yaniv Tenzer Amit Moscovich Mary Frances Dorn Boaz Nadler Clifford Spiegelman Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel Program in Applied and Computational Mathematics Princeton University Princeton NJ Los Alamos National Laboratory Los Alamos NM Department of Statistics Texas A&M University College Station TX

Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of variables, which may be crucial to attaining low classification errors. In this setting, the optimal classifier is linear in the log-transformed univariate and bivariate densities that correspond to the tree edges. In practice, observed data may not be well approximated by trees. Yet, motivated by the importance of pairwise dependencies for accurate classification, here we propose to approximate the optimal decision boundary by a sparse linear combination of the univariate and bivariate log-transformed densities. Our proposed approach is semi-parametric in nature: we non-parametrically estimate the univariate and bivariate densities, remove pairs of variables that are nearly independent using the Hilbert-Schmidt independence criterion, and finally construct a linear SVM using the retained log-transformed densities. We demonstrate on synthetic and real data sets, that our classifier, named SLB (sparse log-bivariate density), is competitive with other popular classification methods.

关键词： binary classification graphical model Bayesian network sparsity semi-parametric

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Terahertz Cavity Phonon Polaritons in the Deep-Strong Coupling Regime

Terahertz Cavity Phonon Polaritons in the Deep-Strong Coupli...

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International Conference on Infrared and Millimeter Waves

作者： Andrey Baydin Manukumara Manjappa Sobhan Subhra Mishra Hongjing Xu Jacques Doumani Fuyang Tay Dasom Kim Felix G. G. Hernandez Paulo H. O. Rappl Eduardo Abramof Ranjan Singh Junichiro Kono Department of Electrical and Computer Engineering Rice University Houston TX USA Smalley-Curl Institute Rice University Houston TX USA Department of Instrumentation and Applied Physics Indian Institute of Science Bangalore India Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Department of Physics and Astronomy Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Instituto de Física Universidade de São Paulo São Paulo SP Brazil Laboratório Associado de Sensores e Materiais Instituto Nacional de Pesquisas Espaciais São José dos ampos SP Brazil Center for Disruptive Photonic Technologies The Photonics Institute Nanyang Technological University Singapore Department of Material Science and NanoEngineering Rice University Houston TX USA

We have studied the resonant and nonperturbative coupling of transverse optical phonons in lead telluride with photons in small-mode-volume terahertz cavities, observing a giant vacuum Rabi splitting on the order of the uncoupled phonon and cavity frequencies. Our terahertz time-domain spectroscopy experimental data, systematically collected as a function of sample thickness, temperature, and cavity length, can be well reproduced by our electromagnetic simulations. These results demonstrate that this uniquely tunable platform is promising for realizing and understanding predicted cavity-vacuum-induced ferroelectric instabilities, as well as for exploring applications of light-matter coupling in the ultra-and deep-strong coupling regimes in quantum technology.

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Deep-Strong Coupling between Cavity Photons and Terahertz TO Phonons in PbTe

Deep-Strong Coupling between Cavity Photons and Terahertz TO...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Andrey Baydin Manukumara Manjappa Sobhan Subhra Mishra Hongjing Xu Fuyang Tay Dasom Kim Felix G. G. Hernandez Paulo H. O. Rappl Eduardo Abramof Ranjan Singh Junichiro Kono Department of Electrical and Computer Engineering Rice University Houston Texas USA Smalley-Curl Institute Rice University Houston Texas USA Instrumentation and Applied Physics Department Indian Institute of Science Bangalore India Center for Disruptive Photonic Technologies The Photonics Institute Nanyang Technological University Singapore Department of Physics and Astronomy Rice University Houston Texas USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas USA Instituto de Física Universidade de São Paulo São Paulo SP Brazil Laboratório Associado de Sensores e Materiais Instituto Nacional de Pesquisas Espaciais São José dos Campos SP Brazil Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Department of Material Science and NanoEngineering Rice University Houston Texas USA

We have investigated the resonant coupling of photons with TO phonons in lead telluride in small-mode-volume terahertz cavities, observing a giant vacuum Rabi splitting on the order of the bare cavity–phonon frequency.

ISBN: (纸本)9781957171258

关键词： Electric fields Numerical simulation Optical constants Photons Physical vapor deposition Terahertz spectroscopy

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vishakha D Bhandarkar Aruna Sri Rongali Mohamed Dawood Shamout Prof. Sachin Pund Gigih Forda Nama Shrishailappa Patil Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Computer engineering Vishwakarma Institute of Technology Pune

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way..

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Adaptive tikhonov strategies for stochastic ensemble Kalman inversion

arXiv

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arXiv 2021年

作者： Weissmann, Simon Chada, Neil K. Schillings, Claudia Tong, Xin T. Interdisciplinary Center for Scientific Computing University of Heidelberg Heidelberg69120 Germany Applied Mathematics and Computational Science Program King Abdullah University of Science and Technology Thuwal KSA 23955 Saudi Arabia Mannheim School of Computer Science and Mathematics University of Mannheim Mannheim68131 Germany Department of Mathematics National University of Singapore Singapore119077 Singapore

Ensemble Kalman inversion (EKI) is a derivative-free optimizer aimed at solving inverse problems, taking motivation from the celebrated ensemble Kalman filter. The purpose of this article is to consider the introduction of adaptive Tikhonov strategies for EKI. This work builds upon Tikhonov EKI (TEKI) which was proposed for a fixed regularization constant. By adaptively learning the regularization parameter, this procedure is known to improve the recovery of the underlying unknown. For the analysis, we consider a continuous-time setting where we extend known results such as well-posdeness and convergence of various loss functions, but with the addition of noisy observations. Furthermore, we allow a time-varying noise and regularization covariance in our presented convergence result which mimic adaptive regularization schemes. In turn we present three adaptive regularization schemes, which are highlighted from both the deterministic and Bayesian approaches for inverse problems, which include bilevel optimization, the MAP formulation and covariance learning. We numerically test these schemes and the theory on linear and nonlinear partial differential equations, where they outperform the non-adaptive TEKI and *** Codes 65M32, 60G35, 65C35, 70F17 Copyright © 2021, The Authors. All rights reserved.

关键词： Continuous time systems

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Learning shared neural manifolds from multi-subject FMRI data

arXiv

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arXiv 2021年

作者： Huang, Jessie Busch, Erica L. Wallenstein, Tom Gerasimiuk, Michal Benz, Andrew Lajoie, Guillaume Wolf, Guy Turk-Browne, Nicholas B. Krishnaswamy, Smita Department Of Computer Science Department Of Psychology Department Of Mathematics Department Of Genetics Program In Applied Mathematics Wu Tsai Institute Yale University United States Department Of Mathematics And Statistics Université De Montréal Canada Mila Quebec AI Institute Canada

Functional magnetic resonance imaging (fMRI) is a notoriously noisy measurement of brain activity because of the large variations between individuals, signals marred by environmental differences during collection, and spatiotemporal averaging required by the measurement resolution. In addition, the data is extremely high dimensional, with the space of the activity typically having much lower intrinsic dimension. In order to understand the connection between stimuli of interest and brain activity, and analyze differences and commonalities between subjects, it becomes important to learn a meaningful embedding of the data that denoises, and reveals its intrinsic structure. Specifically, we assume that while noise varies significantly between individuals, true responses to stimuli will share common, low-dimensional features between subjects which are jointly discoverable. Similar approaches have been exploited previously but they have mainly used linear methods such as PCA and shared response modeling (SRM). In contrast, we propose a neural network called MRMD-AE (manifold-regularized multiple decoder, autoencoder), that learns a common embedding from multiple subjects in an experiment while retaining the ability to decode to individual raw fMRI signals. We show that our learned common space represents an extensible manifold (where new points not seen during training can be mapped), improves the classification accuracy of stimulus features of unseen timepoints, as well as improves cross-subject translation of fMRI signals. We believe this framework can be used for many downstream applications such as guided brain-computer interface (BCI) training in the future. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Polarization entanglement enabled by orthogonally stacked van der Waals NbOCl2 crystals

arXiv

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arXiv 2024年

作者： Guo, Qiangbing Wu, Yun-Kun Zhang, Di Zhang, Qiuhong Guo, Guang-Can Alù, Andrea Ren, Xi-Feng Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Centre for Advanced 2D Materials Graphene Research Centre National University of Singapore Singapore Singapore CAS Key Laboratory of Quantum Information CAS Synergetic Innovation Center of Quantum Information & Quantum Physics University of Science and Technology of China Hefei China Hefei National Laboratory University of Science and Technology of China Hefei China Photonics Initiative Advanced Science Research Center City University of New York New York United States Physics Program Graduate Center City University of New York New York United States The Key Laboratory of Weak-Light Nonlinear Photonics Ministry of Education School of Physics TEDA Applied Physics Institute Nankai University Tianjin China School of Chemistry and Materials Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China

Polarization entanglement holds significant importance for photonic quantum technologies. Recently emerging subwavelength nonlinear quantum light sources, e.g., GaP and LiNbO3 thin films, benefiting from the relaxed phase-matching constraints and volume confinement, has shown intriguing properties, such as high-dimensional hyperentanglement and robust entanglement anti-degradation. Van der Waals (vdW) NbOCl2 crystal, renowned for its superior optical nonlinearities, has emerged as one of ideal candidates for ultrathin quantum light sources [Nature 613, 53 (2023)]. However, polarization-entanglement is inaccessible in NbOCl2 crystal due to its unfavorable nonlinear susceptibility tensor. Here, by leveraging the twist-stacking degree of freedom inherently in vdW systems, we showcase the preparation of tunable polarization entanglement and quantum Bell states. Our work not only provides a new and tunable polarization-entangled vdW photon-pair source, but also introduces a new knob in engineering the entanglement state of quantum light at the nanoscale. © 2024, CC BY-NC-ND.

关键词： Van der Waals forces

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Theoretical Modelling of Smart Meter Privacy Protection with Multi-meter Energy Routing

Theoretical Modelling of Smart Meter Privacy Protection with...

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IEEE International Conference on Smart Internet of Things (SmartIoT)

作者： Cankut Ergen Burhan Gulbahar Graduate School of Engineering and Science MSc Program of Computer Science Ozyegin University Istanbul Turkey Applied Research Center of Technology Products Ozyegin University Istanbul Turkey

ISBN: (数字)9781728165141

ISBN: (纸本)9781728165158

Potential of intrusion during smart meter data collection is an important problem for household privacy in next-generation smart homes. There are various privacy protection methods such as hiding the real usage with renewable batteries (RBs) and encrypted communication. There are privacy threats due to the detection of the energy transfer through RBs while encryption-based methods result in high computational complexity. In this study, a novel privacy improvement mechanism is proposed based on the routing of energy through multiple smart meters among multiple houses in a neighborhood. The optimization of privacy based on uniform randomization is theoretically modeled and some heuristic solutions are presented. In this method, energy transfer is randomly distributed among multiple smart meters, real usage of a house is buffered with the RB and smart meter indices are randomly selected. In numerical simulations, privacy improvement is shown by utilizing the random selection of smart meter indices for randomizing the energy routing and the buffers of RBs. Furthermore, energy privacy (EP) metric validating the simulation results is defined. Energy routing with multiple smart meters provides a practical and low complexity privacy protection mechanism for next-generation smart grid architectures.

关键词： Batteries Routing Smart meters Numerical models Eavesdropping Transmitters

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