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An efficient quadratic programming relaxation based algorithm for large-scale MIMO detection

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arXiv 2020年

作者： Zhao, Ping-Fan Li, Qing-Na Chen, Wei-Kun Liu, Ya-Feng School of Mathematics and Statistics Beijing Institute of Technology Beijing China School of Mathematics and Statistics Beijing Key Laboratory on MCAACI Beijing Institute of Technology Beijing China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

Multiple-input multiple-output (MIMO) detection is a fundamental problem in wireless communications and it is strongly NP-hard in general. Massive MIMO has been recognized as a key technology in the fifth generation (5G) and beyond communication networks, which on one hand can significantly improve the communication performance, and on the other hand poses new challenges of solving the corresponding optimization problems due to the large problem size. While various efficient algorithms such as semidefinite relaxation (SDR) based approaches have been proposed for solving the small-scale MIMO detection problem, they are not suitable to solve the large-scale MIMO detection problem due to their high computational complexities. In this paper, we propose an efficient sparse quadratic programming (SQP) relaxation based algorithm for solving the large-scale MIMO detection problem. In particular, we first reformulate the MIMO detection problem as an SQP problem. By dropping the sparse constraint, the resulting relaxation problem shares the same global minimizer with the SQP problem. In sharp contrast to the SDRs for the MIMO detection problem, our relaxation does not contain any (positive semidefinite) matrix variable and the numbers of variables and constraints in our relaxation are significantly less than those in the SDRs, which makes it particularly suitable for the large-scale problem. Then we propose a projected Newton based quadratic penalty method to solve the relaxation problem, which is guaranteed to converge to the vector of transmitted signals under reasonable conditions. By extensive numerical experiments, when applied to solve small-scale problems, the proposed algorithm is demonstrated to be competitive with the state-of-the-art approaches in terms of detection accuracy and solution efficiency;when applied to solve large-scale problems, the proposed algorithm achieves better detection performance than a recently proposed generalized power *** Codes 90C2

关键词： Quadratic programming

Interface Faceting-Defaceting Mediated by Disconnections

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arXiv 2022年

作者： Qiu, Caihao Salvalaglio, Marco Srolovitz, David J. Han, Jian Department of Materials Science and Engineering City University of Hong Kong Hong Kong Institute of Scientific Computing TU Dresden Dresden01062 Germany Dresden Center for Computational Materials Science TU Dresden Dresden01062 Germany Department of Mechanical Engineering The University of Hong Kong Pokfulam Road Hong Kong Shenzhen China

An intrinsic feature of nearly all internal interfaces in crystalline systems (homo- and hetero-phase) is the presence of disconnections (topological line defects constrained to the interface that have both step and dislocation character). Disconnections play a major role in determining interface thermodynamics and kinetics. We demonstrate that elastic interactions between disconnections lead to a thermodynamic, first-order, finite-temperature, faceting-defaceting transition, in agreement with experiments. These elastic interactions strongly modify equilibrium interface morphologies (compared with those solely determined by anisotropic surface energy) as well as the kinetics and morphologies of migrating interfaces. We demonstrate these phenomena through numerical simulations based upon a general, continuum disconnection-based model for interface thermodynamics and kinetics applied to embedded particles/grains, steady-state interface migration geometries, and nominally flat interfaces. Copyright © 2022, The Authors. All rights reserved.

关键词： Thermodynamics

On the Power of Gradual Network Alignment Using Dual-Perception Similarities

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arXiv 2022年

作者： Park, Jin-Duk Tran, Cong Shin, Won-Yong Cao, Xin The School of Mathematics and Computing Computational Science and Engineering Yonsei University Seoul03722 Korea Republic of The Faculty of Information Technology Posts and Telecommunications Institute of Technology Hanoi100000 Viet Nam Pohang37673 Korea Republic of The School of Computer Science and Engineering The University of New South Wales Sydney2052 Australia

Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes. Our study is motivated by the fact that, since most of existing NA methods have attempted to discover all node pairs at once, they do not harness information enriched through interim discovery of node correspondences to more accurately find the next correspondences during the node matching. To tackle this challenge, we propose Grad-Align, a new NA method that gradually discovers node pairs by making full use of node pairs exhibiting strong consistency, which are easy to be discovered in the early stage of gradual matching. Specifically, Grad-Align first generates node embeddings of the two networks based on graph neural networks along with our layer-wise reconstruction loss, a loss built upon capturing the first-order and higher-order neighborhood structures. Then, nodes are gradually aligned by computing dual-perception similarity measures including the multi-layer embedding similarity as well as the Tversky similarity, an asymmetric set similarity using the Tversky index applicable to networks with different scales. Additionally, we incorporate an edge augmentation module into Grad-Align to reinforce the structural consistency. Through comprehensive experiments using real-world and synthetic datasets, we empirically demonstrate that Grad-Align consistently outperforms state-of-the-art NA methods. © 2022, CC BY.

关键词： Embeddings

First passage times as a measure of hysteresis in stochastic gene regulatory circuits

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IFAC-PapersOnLine 2022年第18期55卷 50-55页

作者： Manuel Pájaro Irene Otero-Muras Antonio A. Alonso Department of Mathematics and CITIC research center University of A Coruña Campus Elviña s/n 15071 A Coruña Computational Synthetic Biology Group I2SYSBIO Institute for Integrative Systems Biology (CSIC-UV) 46980 València Bioprocess Engineering Group IIM-CSIC Spanish National Council for Scientific Research 36208 Vigo

In the context of phenotype switching and cell fate determination, numerousexperimental studies report hysteresis, despite the fact that the (forward) Chemical Master Equation governing the inherently stochastic underlying gene regulatory networks has a unique steady state (precluding memory effects and hysteresis). In previous works, we demonstrate thathysteresis is a transient phenomenon in systems far from the thermodynamic limit, using the convergence rates of the partial integro-differential equation associated to the forward master equation governing the stochastic process. Here, we make use of the backward master equationto quantify hysteresis and irreversibility based on First Passage Times.. First, we derive the backward master equation for a gene regulatory network with protein production in bursts. Solving this equation, we obtain the probability distributions of the first times to reach some fixed final state from one starting state. The mean first passage time provides a measure to quantify how hysteresis and irreversibility in gene regulation at the singlecell level are transient effects that vanish at steady state. In addition, we provide a theoretical basis that reconciles phenotype coexistence and prevalence far from the thermodynamic limit. In fact, we substitute the notion of pseudo-potential (the so-called Waddington landscape) by a time evolving landscape built upon the Chemical Master Equation (CME) in which phenotypes,rather than prevail, persist with different intensities.

关键词： Gene regulation cell decision-making hysteresis Chemical Master Equation stochastic dynamics bistability First Passage Times

Gravitational wave surrogate model for spinning, intermediate mass ratio binaries based on perturbation theory and numerical relativity

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Physical Review D 2024年第12期110卷 124069-124069页

作者： Katie Rink Ritesh Bachhar Tousif Islam Nur E. M. Rifat Kevin González-Quesada Scott E. Field Gaurav Khanna Scott A. Hughes Vijay Varma Department of Physics and Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth Massachusetts 02747 USA Department of Physics and Weinberg Institute for Theoretical Physics University of Texas at Austin Austin Texas 78712 USA Department of Physics and Center for Computational Research East Hall University of Rhode Island Kingston Rhode Island 02881 USA Kavli Institute for Theoretical Physics University of California Santa Barbara Kohn Hall Lagoon Road Santa Barbara California 93106 USA Kavli Institute for Theoretical Physics University of California Santa Barbara Santa Barbara California 93106 USA Department of Physics University of Virginia 1827 University Avenue Charlottesville Virginia 22903 USA Department of Physics and Center for Education and Research in Cosmology and Astrophysics (CERCA) Case Western Reserve University Cleveland Ohio 44106 USA Department of Mathematics and Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth Massachusetts 02747 USA Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

We present BHPTNRSur2dq1e3, a reduced order surrogate model of gravitational waves emitted from binary black hole (BBH) systems in the comparable to large mass ratio regime with aligned spin (χ1) on the heavier mass (m1). We trained this model on waveform data generated from point particle black hole perturbation theory (ppBHPT) with mass ratios varying from 3≤q≤1000 and spins from −0.8≤χ1≤0.8. The waveforms are 13,500 m1 long and include all ℓ≤4 spin-weighted spherical harmonic modes except the (4,1) and m=0 modes. We find that, for binaries with χ1≲−0.5, retrograde quasinormal modes are significantly excited, thereby complicating the modeling process. To overcome this issue, we introduce a domain decomposition approach to model the inspiral and merger-ringdown portion of the signal separately. The resulting model can faithfully reproduce ppBHPT waveforms with a median time-domain mismatch error of 8×10−5. We then calibrate our model with numerical relativity (NR) data in the comparable mass regime (3≤q≤10). By comparing with spin-aligned BBH NR simulations at q=15, we find that the dominant quadrupolar (subdominant) modes agree to better than ≈10−3 (≈10−2) when using a time-domain mismatch error, where the largest source of calibration error comes from the transition-to-plunge and ringdown approximations of perturbation theory. Mismatch errors are below ≈10−2 for systems with mass ratios between 6≤q≤15 and typically get smaller at larger mass ratio. Our two models—both the ppBHPT waveform model and the NR-calibrated ppBHPT model—will be publicly available through gwsurrogate and the black hole perturbation toolkit packages.

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Characterizing Faculty Online Learning Community Interactions Using Social Network Analysis

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arXiv 2024年

作者： Bolger, Emily Nwobi, Marius Caballero, Marcos D. Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Biochemistry and Molecular Biology Michigan State University East LansingMI48824 United States Department of Physics and Astronomy CREATE for STEM Institute Michigan State University East LansingMI48824 United States Department of Physics Center for Computing in Science Education University of Oslo Oslo0315 Norway

Expanding on other work in the Physics Education Community, we apply Social Network Analysis to a Faculty Online Learning Community focused on facilitating the integration of computation into physics courses. The Partnership for Integration of Computation into Undergraduate Physics (PICUP) uses Slack as a mechanism for continued communication beyond face-to-face meetings. Through our analysis, we use networks to represent the Slack channels, identify lurkers, and use metrics to quantify pariticipation. Additionally, we use randomization techniques to understand how similar our metric values are to networks of similar size and distribution. We highlight the need for using the appropriate randomization model and assumption checking. We characterize participation amongst users in the network and provide potential reasonings for their actions. Copyright © 2024, The Authors. All rights reserved.

关键词： Social networking (online)

Comparison of propagation models and forward calculation methods on cellular, tissue and organ scale atrial electrophysiology

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arXiv 2022年

作者： Nagela, Claudia Espinosa, Cristian Barrios Gillette, Karli Gsell, Matthias A.F. Sánchez, Jorge Plank, Gernot Dössel, Olaf Loewe, Axel Institute of Biomedical Engineering Karlsruhe Institute of Technology Kaiserstr. 12 Karlsruhe76131 Germany Division of Biophysics Medical University of Graz Neue Stiftingtalstrasse 6/D04 GrazA-8010 Austria Institute of Mathematics and Scientific Computing University of Graz NAWI Graz Heinrichstrasse 36 GrazA-8010 Austria

Objective: The bidomain model and the finite element method are an established standard to mathematically describe cardiac electrophysiology, but are both suboptimal choices for fast and large-scale simulations due to high computational costs. We investigate to what extent simplified approaches for propagation models (monodomain, reaction-eikonal and eikonal) and forward calculation (boundary element and infinite volume conductor) deliver markedly accelerated, yet physiologically accurate simulation results in atrial electrophysiology. Methods: We compared action potential durations, local activation times (LATs), and electrocardiograms (ECGs) for sinus rhythm simulations on healthy and fibrotically infiltrated atrial models. Results: All simplified model solutions yielded LATs and P waves in accurate accordance with the bidomain results. Only for the eikonal model with precomputed action potential templates shifted in time to derive transmembrane voltages, repolarization behavior notably deviated from the bidomain results. ECGs calculated with the boundary element method were characterized by correlation coefficients >0.9 compared to the finite element method. The infinite volume conductor method led to lower correlation coefficients caused predominantly by systematic overestimations of P wave amplitudes in the precordial leads. Conclusion: Our results demonstrate that the eikonal model yields accurate LATs and combined with the boundary element method precise ECGs compared to markedly more expensive full bidomain simulations. However, for an accurate representation of atrial repolarization dynamics, diffusion terms must be accounted for in simplified models. Significance: Simulations of atrial LATs and ECGs can be notably accelerated to clinically feasible time frames at high accuracy by resorting to the eikonal and boundary element methods. © 2022, CC BY-NC-ND.

关键词： Finite element method

A Jacobi Collocation Method for the Fractional Ginzburg-Landau Differential Equation

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Advances in Applied mathematics and Mechanics 2020年第1期12卷 57-86页

作者： Yin Yang Jianyong Tao Shangyou Zhang Petr V.Sivtsev Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationSchool of Mathematics and Computational ScienceXiangtan UniversityXiangtanHunan 411105China Department of Mathematical Sciences University of DelawareNewarkDE 19716USA International Scientific and Research Laboratory of Multiscale Model Reduction and High Performance Computing Ammosov North Eastern UniversityKulakovskogo677013YakutskRussia

In this paper,we design a collocation method to solve the fractional Ginzburg-Landau equation.A Jacobi collocation method is developed and implemented in two ***,we space-discretize the equation by the Jacobi-Gauss-Lobatto collocation(JGLC)method in one-and two-dimensional *** equation is then converted to a system of ordinary differential equations(ODEs)with the time variable based on *** second step applies the Jacobi-Gauss-Radau collocation(JGRC)method for the time ***,we give a theoretical proof of convergence of this Jacobi collocation method and some numerical results showing the proposed scheme is an effective and high-precision algorithm.

关键词： The fractional Ginzburg-Landau equation Jacobi collocation method convergence

Cross-Platform Multimodal Frogeye Leaf Spot Recognition and Classification: Advancing with Cutting-Edge CNN Technologies and Transfer Learning in Diverse Plant Species

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Cross-Platform Multimodal Frogeye Leaf Spot Recognition and ...

International Conference on Circuit, Power and computing Technologies (ICCPCT)

作者： G. Mohan R Usharani Vatsala Tomar Arthi A Suniti Kumar Kuriyal K.P. Yuvaraj Department of Mathematics K.S.Rangasamy College of Technology Tamil Nadu India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Botany School of Life Science & Technology IIMT University Meerut UP India Department of Information Technology Karpagam College of Engineering Coimbatore India Department of Botany Pt.L.M.S.Sridev Suman Uttarakhand University Campus Rishikesh India Department of Mechanical Engineering Sri Krishna College of Engineering and Technology Coimbatore India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Frogeye Leaf Spot, a common agricultural disease, poses a significant threat to crop yields. Using multimodal data from different platforms successfully has been a hurdle for existing research in leaf spot detection. Using cutting-edge convolutional neural network (CNN) and transfer learning techniques, this research presents a Cross-Platform Multimodal Recognition system to solve this issue. With a recall of 0.88, F1-score of 0.90, accuracy of 0.91, and precision of 0.92, the suggested method demonstrates exceptional performance. Our accuracy is 6% higher, F1-score is 8% higher, and recall is 9% higher than the preceding state-of-the-art, according to comparisons with previous studies. Improved Frogeye Leaf Spot detection on many platforms is a direct result of our method's success in combining picture and sensor data. This study improves farmers' ability to identify diseases and provides a solid answer for precision agriculture. Our Cross-Platform Multimodal Recognition system has shown to be effective in enhancing agricultural disease recognition, and the shown advances highlight its potential influence on crop management tactics.

关键词： Precision agriculture Accuracy Plants (biology) Transfer learning Neural networks Solids Stability analysis