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Optomechanically induced nonreciprocity in a three-mode optomechanical system

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Physical Review A 2018年第6期98卷 063845-063845页

作者： Xun-Wei Xu L. N. Song Qiang Zheng Z. H. Wang Yong Li Department of Applied Physics East China Jiaotong University Nanchang 330013 China Beijing Computational Science Research Center Beijing 100193 China School of Mathematics Guizhou Normal University Guiyang 550001 China Center for Quantum Sciences and School of Physics Northeast Normal University Changchun 130024 China

We propose to create optical nonreciprocity in a three-mode optomechanical system comprising one mechanical and two optical modes, where the mechanical mode is coupled with only one of the optical modes. The optical nonreciprocal response of the system is based on the nonlinearity induced by the optomechanical interaction. However, nonlinearity is a necessary but not a sufficient condition for observing nonreciprocity. Another necessary condition for nonreciprocal response of the system to a classical driving field is demonstrated analytically. The effects of the parameters on the nonreciprocal response of the system are discussed numerically. The three-mode optomechanical system provides a platform to realize nonreciprocity for strong optical signal fields.

关键词： Optical bistability Optomechanics

Transition pathways connecting crystals and quasicrystals

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arXiv 2020年

作者： Yin, Jianyuan Jiang, Kai Shi, An-Chang Zhang, Pingwen Zhang, Lei School of Mathematical Sciences Laboratory of Mathematics and Applied Mathematics Peking University Beijing100871 China School of Mathematics and Computational Science Xiangtan University Hunan 411105 China Department of Physics and Astronomy McMaster University HamiltonL8S 4M1 Canada Beijing International Center for Mathematical Research Center for Quantitative Biology Peking University Beijing100871 China

Due to structural incommensurability, the emergence of a quasicrystal from a crystalline phase represents a challenge to computational physics. Here the nucleation of quasicrystals is investigated by using an efficient computational method applied to a Landau free-energy functional. Specifically, transition pathways connecting different local minima of the Lifshitz-Petrich model are obtained by using the high-index saddle dynamics. Saddle points on these paths are identified as the critical nuclei of the 6-fold crystals and 12-fold quasicrystals. The results reveal that phase transitions between the crystalline and quasicrystalline phases could follow two possible pathways, corresponding to a one-stage phase transition and a two-stage phase transition involving a metastable lamellar quasicrystalline state, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Nucleation

Automated Characterization of cellular migration phenomena

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Automated Characterization of cellular migration phenomena

2005 IEEE computational Systems Bioinformatics Conference, Workshops and Poster Abstracts

作者： Beaudry, Christian Berens, Michael E. Doker, Tarek El Joy, Anna M. Karam, Lina J. Lacroix, Zoe Lutfi, Jad A. Motoru, Sai Renaut, Rosemary A. Rich, Ian J. Translational Genomics Research Institute Phoenix AZ United States Department of Electrical Engineering Machine Perception Laboratory Northern Arizona University United States Department of Electrical Engineering Image Video and Usability and Multi-D Signal Processing Labs. Arizona State University United States Department of Mechanical and Aerospace Engineering Scientific Data Management Lab. Arizona State University United States Department of Mathematics Computational Biosciences Program Arizona State University United States

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Dynamics and structure of monolayer polymer crystallites on graphene

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arXiv 2020年

作者： Gulde, Max Rissanou, Anastassia N. Harmandaris, Vagelis Müller, Marcus Schäfer, Sascha Ropers, Claus 4th Physical Institute - Solids and Nanostructures University of Göttingen Göttingen37077 Germany Institute of Applied and Computational Mathematics Foundation for Research and Technology Hellas HeraklionCrete71110 Greece Department of Mathematics and Applied Mathematics University of Crete HeraklionCrete71409 Greece Institute for Theoretical Physics University of Göttingen Göttingen37077 Germany Fraunhofer Institute for High-Speed Dynamics Ernst-Mach-Institut Freiburg79104 Germany

Graphene-based nanostructured systems and van-der-Waals heterostructures comprise a material class of growing technological and scientific importance. Joining materials with vastly different properties, polymer-graphene heterosystems promise diverse applications in surface- and nanotechnology, including photovoltaics or nanotribology. Fundamentally, molecular adsorbates are prototypical systems to study confinement-induced phase transitions exhibiting intricate dynamics, which require a comprehensive understanding of the dynamical and static properties on molecular time and length scales. Here, we investigate the dynamics and the structure of a single polyethylene chain on free-standing graphene by means of molecular dynamics simulations. In equilibrium, the adsorbed polymer is orientationally linked to the graphene as two-dimensional folded-chain crystallites or, at elevated temperatures, as a floating solid. The associated superstructure can be reversibly melted on a picosecond time scale upon quasi-instantaneous substrate heating, involving ultrafast heterogeneous melting via a transient floating phase. Our findings elucidate time-resolved molecular-scale ordering and disordering phenomena in individual polymers interacting with solids, yielding complementary information to collective friction and viscosity, and linking to recent experimental observables from ultrafast electron diffraction. We anticipate that the approach will help in resolving non-equilibrium phenomena of hybrid polymeric systems over a broad range of time and length scales. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphene

Orbital magnetization of insulating perovskite transition-metal oxides with a net ferromagnetic moment in the ground state

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Physical Review B 2014年第6期89卷 064428-064428页

作者： S. A. Nikolaev I. V. Solovyev Department of Theoretical Physics and Applied Mathematics Ural Federal University Mira str. 19 620002 Ekaterinburg Russia Computational Materials Science Unit National Institute for Materials Science 1-2-1 Sengen Tsukuba Ibaraki 305-0047 Japan

Modern theory of the orbital magnetization is applied to the series of prototype insulating perovskite transition metal oxides (orthorhombic YTiO3, LaMnO3, and YVO3, as well as monoclinic YVO3), carrying a net ferromagnetic (FM) moment in the ground state. For these purposes, we use an effective Hubbard-type model, derived from the first-principles electronic structure calculations and describing the behavior of magnetically active states near the Fermi level. The solution of this model in the mean-field Hartree-Fock approximation with the relativistic spin-orbit coupling typically gives us a distribution of the local orbital magnetic moments, which are related to the site-diagonal part of the density matrix D̂ by the “classical” expression μ0=−μBTr{L̂D̂}. These moments are usually well quenched by the crystal field. In this work, we evaluate “itinerant” corrections ΔM to the net FM moment, suggested by the modern theory. We show that these corrections are small and in most cases can be neglected. Nevertheless, the most interesting aspect of our analysis is that, even for these compounds, which are frequently regarded as prototype Mott insulators, the “itinerant” corrections reveal a strong k dependence in the reciprocal space, following the behavior of Chern invariants. Therefore, the small value of ΔM is the result of strong cancellation of relatively large contributions, coming from different parts of the Brillouin zone. We discuss details as well as possible implications of this cancellation, which depends on the crystal structure as well as the type of the magnetic ground state.

关键词： TRANSITION metals PEROVSKITE FERROMAGNETIC materials MAGNETIC moments FERMI level SPIN-orbit coupling CRYSTAL structure

Pairwise Learning for Autism Spectrum Disorder Imbalanced Classification

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Pairwise Learning for Autism Spectrum Disorder Imbalanced Cl...

IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)

作者： Shu Liu Qiang Wu Xin Yang Computational and Data Science PhD Program Middle Tennessee State University Murfreesboro TN USA Department of Mathematics University of Tennessee Knoxville TN USA Department of Computer Science Middle Tennessee State University Murfreesboro TN USA

ISBN: (数字)9798350396133

ISBN: (纸本)9798350396140

This paper performs a classification task on data obtained from the Autism Brain Imaging Data Exchange (ABIDE) repository. In real-world case analysis, the number of autism spectrum disorder (ASD) patients is much smaller than typically developed people. To address this issue, this paper proposes the utilization of pairwise robust support vector machine (PRSVM) algorithms to classify autism spectrum disorder (ASD) patients. In this project's experiments, the correlation matrix derived from functional magnetic resonance imaging (fMRI) data was employed as a classification feature. A comprehensive evaluation was conducted to compare the classification performance of PRSVM with various machine learning methods. The comparative analysis encompassed various aspects, including different data dimensions, imbalanced ratios, and sample sizes, providing valuable insights into the relative performance of the algorithms under different experimental conditions. The experimental results demonstrate that PRSVM can detect autistic patients more accurately when the data is imbalanced. Moreover, the results indicate that PRSVM outperforms or achieves comparable performance to other conventional classification methods in a variety of situations. Furthermore, our approach can be further improved by augmenting the training set with either exclusively normal person samples or by incorporating patient samples and normal people samples in a proportionate manner. This augmentation strategy holds promising application value, as it contributes to improving the performance and robustness of our method.

关键词： Training Support vector machines Neuroimaging Autism Machine learning algorithms Functional magnetic resonance imaging Vectors Robustness Classification algorithms Sustainable development

Inferring the Parameters of Ultrafast Lasers from Images with Surface Patterns

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Inferring the Parameters of Ultrafast Lasers from Images wit...

International Symposium on Image and Signal Processing and Analysis

作者： Anastasis Litsas Fotis Fraggelakis George D. Tsibidis Emmanuel Stratakis Yannis Pantazis Computer Science Department University of Crete Heraklion Greece Institute of Electronic Structure and Laser Foundation for Research and Technology - Hellas Heraklion Greece Institute of Applied and Computational Mathematics Foundation for Research and Technology - Hellas Heraklion Greece

In this paper, we explore the potential of deep learning techniques in the field of ultra-fast laser processing. More specifically, we trained convolutional neural networks on an in-house dataset with the aim of predicting the laser parameters from images which capture the morphological characteristics of the material surface after being laser fabricated. There are several challenges related with the collected dataset stemming from stochastic variations of the material and the laser, the sampling scheme and the overall low sample size. Therefore, to further test our models we additionally construct a synthetic dataset. The results on the synthetic dataset are almost perfect showing that the proposed predictive models have the capacity to learn the assigned tasks. As expected, the predictive performance decreases when the real dataset is utilized. Nevertheless, we show that both the accuracy in the classification task and the mean square error in the majority of the regression tasks are satisfactory (e.g., classification accuracy drops from 99.9% to 94.8%).

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Compact feature-aware hermite-style high-order surface reconstruction

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arXiv 2019年

作者： Li, Yipeng Zhao, Xinglin Ray, Navamita Jiao, Xiangmin Department of Applied Mathematics & Statistics Institute for Advanced Computational Science Stony Brook University United States Computer Computational & Statistical Sciences Los Alamos National Laboratory Los AlamosNM United States Google Inc. Mountain ViewCA United States

High-order surface reconstruction is an important technique for CAD-free, mesh-based geometric and physical modeling, and for high-order numerical methods for solving partial differential equations (PDEs) in engineering applications. In this paper, we introduce a novel method for accurate and robust reconstructions of piecewise smooth surfaces from a triangulated surface. Our proposed method extends the Continuous Moving Frames (CMF) and the Weighted Averaging of Local Fittings (WALF) methods (Engrg. Comput. 28 (2012)) in two main aspects. First, it utilizes a Hermite-style least squares approximation to achieve fourth and higher-order accuracy with compact support, even if the input mesh is relatively coarse. Second, it introduces an iterative feature-aware parameterization to ensure high-order accurate, G0 continuous reconstructions near sharp features. We present the theoretical framework of the method and compare it against CMF and WALF in terms of accuracy and stability. We also demonstrate that the use of the Hermite-style reconstruction in the solutions of PDEs using finite element methods (FEM), and show that quartic and sextic FEMs using the high-order reconstructed surfaces produce nearly identical results as using exact geometry while providing additional flexibility. Copyright © 2019, The Authors. All rights reserved.

关键词： Finite element method

ControlVAE: Tuning, analytical properties, and performance analysis

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arXiv 2020年

作者： Shao, Huajie Xiao, Zhisheng Yao, Shuochao Sun, Dachun Zhang, Aston Liu, Shengzhong Abdelzaher, Tarek Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Committee on Computational and Applied Mathematics University of Chicago Chicago60637 United States Amazon Web Services CA India

This paper reviews the novel concept of controllable variational autoencoder (ControlVAE), discusses its parameter tuning to meet application needs, derives its key analytic properties, and offers useful extensions and applications. ControlVAE is a new variational autoencoder (VAE) framework that combines the automatic control theory with the basic VAE to stabilize the KL-divergence of VAE models to a specified value. It leverages a non-linear PI controller, a variant of the proportional-integral-derivative (PID) control, to dynamically tune the weight of the KL-divergence term in the evidence lower bound (ELBO) using the output KL-divergence as feedback. This allows us to precisely control the KL-divergence to a desired value (set point), which is effective in avoiding posterior collapse and learning disentangled representations. In order to improve the ELBO over the regular VAE, we provide simplified theoretical analysis to inform setting the set point of KL-divergence for ControlVAE. We observe that compared to other methods that seek to balance the two terms in VAE’s objective, ControlVAE leads to better learning dynamics. In particular, it can achieve a good trade-off between reconstruction quality and KL-divergence. We evaluate the proposed method on three tasks: image generation, language modeling and disentangled representation learning. The results show that ControlVAE can achieve much better reconstruction quality than the other methods for comparable disentanglement. On the language modeling task, ControlVAE can avoid posterior collapse (KL vanishing) and improve the diversity of generated text. Moreover, our method can change the optimization trajectory, improving the ELBO and the reconstruction quality for image generation. Copyright © 2020, The Authors. All rights reserved.

关键词： Learning systems