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arXiv 2023年

作者： Qu, Eric Zou, Dongmian The Department of Electrical Engineering and Computer Science University of California BerkeleyCA94720 United States CMCS DSRC Division of Natural and Applied Sciences Duke Kunshan University Jiangsu Kunshan215316 China

Learning representations according to the underlying geometry is of vital importance for non-Euclidean data. Studies have revealed that the hyperbolic space can effectively embed hierarchical or tree-like data. In particular, the few past years have witnessed a rapid development of hyperbolic neural networks. However, it is challenging to learn good hyperbolic representations since common Euclidean neural operations, such as convolution, do not extend to the hyperbolic space. Most hyperbolic neural networks do not embrace the convolution operation and ignore local patterns. Others either only use non-hyperbolic convolution, or miss essential properties such as equivariance to permutation. We propose HKConv, a novel trainable hyperbolic convolution which first correlates trainable local hyperbolic features with fixed kernel points placed in the hyperbolic space, then aggregates the output features within a local neighborhood. HKConv not only expressively learns local features according to the hyperbolic geometry, but also enjoys equivariance to permutation of hyperbolic points and invariance to parallel transport of a local neighborhood. We show that neural networks with HKConv layers advance state-of-the-art in various tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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A statistical model of FBMC-OQAM signals for predicting spectral regrowth

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International Journal of Electronics Letters 2021年第3期9卷 330-341页

作者： Yan, Siyuan Yang, Xianzhen Li, Xiao Jedynak, Bruno Li, Fu Department of Electrical and Computer Engineering Portland State University Portland OR United States Department of Mathematics and Statistics Portland State University Portland OR United States 1930 SW 4th AVE #500 Portland 97201 OR United States

In this article, we proved that the filter bank multi-carrier (FBMC) with offset quadrature amplitude modulation (OQAM) signals asymptotically converges to a white Gaussian random process as the number of subcarriers increase to infinity. Hence, a band-limited flat spectrum model, with a large number of subcarriers, is proposed to simplify the prediction of the spectral regrowth of amplified FBMC-OQAM signals in terms of the output intermodulation products. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： FBMC-OQAM Gaussian intermodulation products power amplifier spectrum regrowth

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Heteroclinic cycling and extinction in May-Leonard models with demographic stochasticity

arXiv

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arXiv 2021年

作者： Barendregt, Nicholas W. Thomas, Peter J. Department of Applied Mathematics University of Colorado Boulder BoulderCO United States Department of Mathematics Applied Mathematics and Statistics Department of Biology Department of Cognitive Science Department of Data and Computer Science Department of Electrical Computer and Systems Engineering Case Western Reserve University ClevelandOH United States

May and Leonard (SIAM J. Appl. Math 1975) introduced a three-species Lotka-Volterra type population model that exhibits heteroclinic cycling. Rather than producing a periodic limit cycle, the trajectory takes longer and longer to complete each "cycle", passing closer and closer to unstable fixed points in which one population dominates and the others approach zero. Aperiodic heteroclinic dynamics have subsequently been studied in ecological systems (side-blotched lizards;colicinogenic E. coli), in the immune system, in neural information processing models ("winnerless competition"), and in models of neural central pattern generators. Yet as May and Leonard observed "Biologically, the behavior (produced by the model) is nonsense. Once it is conceded that the variables represent animals, and therefore cannot fall below unity, it is clear that the system will, after a few cycles, converge on some single population, extinguishing the other two." Here, we explore different ways of introducing discrete stochastic dynamics based on May and Leonard’s ODE model, with application to ecological population dynamics, and to a neuromotor central pattern generator system. We study examples of several quantitatively distinct asymptotic behaviors, including total extinction of all species, extinction to a single species, and persistent cyclic dominance with finite mean cycle length. © 2021, CC BY.

关键词： Stochastic systems

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An atomic frequency comb memory in rare-earth doped thin-film lithium niobate

An atomic frequency comb memory in rare-earth doped thin-fil...

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CLEO: QELS_Fundamental Science, QELS 2022

作者： Dutta, Subhojit Zhao, Yuqi Saha, Uday Farfurnik, Demitry Goldschmidt, Elizabeth A. Waks, Edo Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics Joint Quantum Institute University of Maryland College ParkMD20742 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States US Army Research Laboratory AdelphiMD20783 United States

We demonstrate compact chip-integrated atomic frequency comb storage in rare-earth doped thin-film lithium niobate. Our optical memory exhibits a broad storage bandwidth exceeding 100 MHz, and optical storage time of ... 详细信息

ISBN: (纸本)9781557528209

关键词： Rare earths

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DeepTrace: Learning to Optimize Contact Tracing in Epidemic Networks with Graph Neural Networks

arXiv

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arXiv 2022年

作者： Tan, Chee Wei Yu, Pei-Duo Chen, Siya Poor, H. Vincent Nanyang Technological University Nanyang Ave. Singapore Singapore Department of Applied Mathematics Chung Yuan Christian University Taiwan Department of Computer Science City University of Hong Kong Hong Kong Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Digital contact tracing aims to curb epidemics by identifying and mitigating public health emergencies through technology. Backward contact tracing, which tracks the sources of infection, proved crucial in places like Japan for identifying COVID-19 infections from superspreading events. This paper presents a novel perspective on digital contact tracing by modeling it as an online graph exploration problem, framing forward and backward tracing strategies as maximum-likelihood estimation tasks that leverage iterative sampling of epidemic network data. The challenge lies in the combinatorial complexity and rapid spread of infections. We introduce DeepTrace, an algorithm based on a Graph Neural Network that iteratively updates its estimations as new contact tracing data is collected, learning to optimize the maximum likelihood estimation by utilizing topological features to accelerate learning and improve convergence. The contact tracing process combines either BFS or DFS to expand the network and trace the infection source, ensuring efficient real-time exploration. Additionally, the GNN model is fine-tuned through a two-phase approach: pre-training with synthetic networks to approximate likelihood probabilities and fine-tuning with high-quality data to refine the model. Using COVID-19 variant data, we illustrate that DeepTrace surpasses current methods in identifying superspreaders, providing a robust basis for a scalable digital contact tracing strategy. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

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Human-Aware Navigation in Crowded Environments Using Adaptive Proxemic Area and Group Detection

Human-Aware Navigation in Crowded Environments Using Adaptiv...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Carlos Medina-Sánchez Simon Janzon Matteo Zella Jesús Capitán Pedro J. Marrón Faculty of Business Administration and Economics Institute for Computer Science and Business Information Systems (ICB) University of Duisburg-Essen Essen Germany Faculty of Electrical Engineering and Computer Science University of Applied Sciences Niederrhein Krefeld Germany Department of System Engineering and Automation University of Seville Seville Spain

Navigation is an essential task for social robots. However, certain rules must be followed to allow them to move without causing distraction or discomfort to people. Considering that the context surrounding robots and persons affects the expected behavior, this work defines a social area around a person that adapts to the real situation. In addition, the social context of a person is extended to identify groups of people, which the robot should take into account while navigating. With this understanding of the surrounding of the robot together with the ability to predict the trajectory of individuals as well as groups, the proposed solution not only effectively addresses collision avoidance while promoting socially acceptable behavior but also outperforms the majority of recent works in terms of accuracy. Furthermore, a dedicated policy is introduced to react to social navigation conflicts. The evaluation performed in a simulated environment shows that the computation of our proposed solution is at least 8 times faster than the best state-of-the-art approach while preserving comparable social conduct. Also, the results of realistic experiments performed using Gazebo and a real robot are reported.

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Temporal Imaging using Integrated Electro-Optic Time Lens on Thin Film Lithium Niobate

Temporal Imaging using Integrated Electro-Optic Time Lens on...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Clayton Cheung Zaijun Chen Mian Zhang Marko Loncar Mengjie Yu Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles California USA HyperLight Cambridge MA USA John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA USA

We realize a temporal magnifying and 2f system using a lithium-niobate-chip-based electro-optic time lens. We achieve a temporal magnification factor up to 7.25 and Fourier transform operation of the input pulses with... 详细信息

ISBN: (纸本)9781957171258

关键词： Fourier transforms Lithium niobate Mode locking Phase modulation Quantum information processing Time lenses

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Convolutional Bi-LSTM Based Human Gait Recognition Using Video Sequences

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computers, Materials & Continua 2021年第8期68卷 2693-2709页

作者： Javaria Amin Muhammad Almas Anjum Muhammad Sharif Seifedine Kadry Yunyoung Nam ShuiHua Wang University of Wah Wah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan COMSATS University Islamabad Wah CampusWah CanttPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics University of LeicesterLeicesterUK

Recognition of human gait is a difficult assignment,particularly for unobtrusive surveillance in a video and human identification from a large ***,a method is proposed for the classification and recognition of different types of human *** proposed approach is consisting of two *** phase I,the new model is proposed named convolutional bidirectional long short-term memory(Conv-BiLSTM)to classify the video frames of human *** this model,features are derived through convolutional neural network(CNN)named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal *** phase II,the YOLOv2-squeezeNet model is designed,where deep features are extricated using the fireconcat-02 layer and fed/passed to the tinyYOLOv2 model for recognized/localized the human gaits with predicted *** proposed method achieved up to 90%correct prediction scores on CASIA-A,CASIA-B,and the CASIA-C benchmark *** proposed method achieved better/improved prediction scores as compared to the recent existing works.

关键词： Bi-LSTM YOLOv2 open neural network resNet-18 gait squeezeNet

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Intelligent Bayesian regularization-based solution predictive procedure for hybrid nanoparticles of AA7072-AA7075 oxide movement across a porous medium

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ZAMM Zeitschrift fur Angewandte Mathematik und Mechanik 2023年第10期103卷

作者： Awan, Saeed Ehsan Ali, Faizan Awais, Muhammad Shoaib, Muhammad Raja, Muhammad Asif Zahoor Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Pakistan Department of Mathematics COMSATS University Islamabad Attock Pakistan Yuan Ze University Taoyuan City Taiwan Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan

The research community has shown great interest for investigation in the nanofluids models involving Aluminum Alloys AA7072 and AA7072 +AA7075 due to their advantageous impact on heat transfer, physical and mechanical characteristic exploiting in broad engineering applications such as manufacturing of spacecraft, aircraft parts and building testing. The hybrid nanomaterial AA7072-AA7075 based fluidic system is investigated in this paper using an artificial neural network with Bayesian regularization scheme (ANNs-BRS). The derived partial differential equations (PDEs) are transformed into ordinary differential equations system ODEs and obtained the reference datasets for the estimated solution dynamics of hybrid nanofluidic system. For prominent parameters, the influence of flow on the temperature distribution and velocity plot are investigated. The performance on 80% training samples, 5% testing and 15% validation dataset for ANNs-BRS is well-established in terms of error histogram plots, regression analysis, and MSE based statistics. The results for entropy generation, Eckert number Ec, magnetic interaction parameter M, suction parameter S, and heat generation parameter Q are also discussed. The results show that the Eckert number Ec has the effect of slowing down the rate of heat transfer while increasing the temperature and increases in suction parameter causes decrease in temperature while increase in temperature profile due to enhancement of suction parameter. © 2023 Wiley-VCH GmbH.

关键词： Ordinary differential equations

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CONVERGENCE AND COMPLEXITY OF BLOCK MAJORIZATION-MINIMIZATION FOR CONSTRAINED BLOCK-RIEMANNIAN OPTIMIZATION

arXiv

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arXiv 2023年

作者： Li, Yuchen Balzano, Laura Needell, Deanna Lyu, Hanbaek Department of Mathematics University of Wisconsin-Madison 480 Lincoln Dr. MadisonWI53706 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Department of Mathematics University of California Los AngelesCA90025 United States

Block majorization-minimization (BMM) is a simple iterative algorithm for nonconvex optimization that sequentially minimizes a majorizing surrogate of the objective function in each block coordinate while the other block coordinates are held fixed. We consider a family of BMM algorithms for minimizing smooth nonconvex objectives, where each parameter block is constrained within a subset of a Riemannian manifold. We establish that this algorithm converges asymptotically to the set of stationary points, and attains an ϵ-stationary point within O͂(Ε−2) iterations. In particular, the assumptions for our complexity results are completely Euclidean when the underlying manifold is a product of Euclidean or Stiefel manifolds, although our analysis makes explicit use of the Riemannian geometry. Our general analysis applies to a wide range of algorithms with Riemannian constraints: Riemannian MM, block projected gradient descent, optimistic likelihood estimation, geodesically constrained subspace tracking, robust PCA, and Riemannian CP-dictionary-learning. We experimentally validate that our algorithm converges faster than standard Euclidean algorithms applied to the Riemannian setting. © 2023, CC BY.

关键词： Constrained optimization

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