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arXiv 2023年

作者： Liagkou, V. Nastou, P.E. Spirakis, P. Stamatiou, Y.C. Computer Technology Institute and Press - "Diophantus" University of Patras Campus 26504 Greece Department of Computer Science University of Liverpool UK and Computer Engineering and Informatics Department University of Patras 26504 Greece Department of Business Administration University of Patras 26504 Greece University of Ioannina Department of Informatics and Telecommunications Kostakioi Arta47100 Greece Department of Mathematics University of the Aegean Applied Mathematics and Mathematical Modeling Laboratory Samos Greece Center for Applied Optimization University of Florida Gainesville United States

In this paper we investigate phenomena of spontaneous emergence or purposeful formation of highly organized structures in networks of related agents. We show that the formation of large organized structures requires exponentially large, in the size of the structures, networks. Our approach is based on Kolmogorov, or descriptional, complexity of networks viewed as finite size strings. We apply this approach to the study of the emergence or formation of simple organized, hierarchical, structures based on Sierpinski Graphs and we prove a Ramsey type theorem that bounds the number of vertices in Kolmogorov random graphs that contain Sierpinski Graphs as subgraps. Moreover, we show that Sierpinski Graphs encompass close-knit relationships among their vertices that facilitate fast spread and learning of information when agents in their vertices are engaged in pairwise interactions modelled as two person games. Finally, we generalize our findings for any organized structure with succinct representations. Our work can be deployed, in particular, to study problems related to the security of networks by identifying conditions which enable or forbid the formation of sufficiently large insider-subnetworks with malicious common goal to overtake the network or cause disruption of its operation. © 2023, CC BY.

关键词： Graphic methods

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Enhanced Mini-YOLOv7: A AI Approach for Safety Helmet Detection in the Construction Industry

Enhanced Mini-YOLOv7: A AI Approach for Safety Helmet Detect...

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IEEE International WIE Conference on Electrical and computer engineering (WIECON-ECE)

作者： A. Alice Blessie R. S. Sankarasubramanian Joseph John S. Krishnapriya A. Bhuvanesh R. Keerthanadevi Department of Electronics and Communication Engineering Government College of Engineering Tirunelveli India Department of Mathematics PSG Institute of Technology and Applied Research Coimbatore India Department of Mechnanical Engineering Gregorian Institute of Technology Kottayam India Department of English Sri Eshwar College of Engineering Coimbatore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535483

Wearing safety helmets is an effective way to keep construction workers safe on the job. Workers often opt to remove their helmets due to discomfort or a lack of security knowledge, leaving them vulnerable to unsaid threats. A worker's risk of injury from falls, both physical and otherwise, increases when they do not wear a safety helmet. Because detecting whether workers are wearing safety helmets is an important aspect of managing safety on construction sites, a fast and accurate detector is required. However, standard manual monitors aren't particularly popular, and attaching sensors to a safety helmet is a laborious operation. With that in mind, this study presents EM-YOLOv7, a DL method for autonomous safety helmet recognition in real-time that has been developed for use on construction sites. A multi-scale non-local attention module (MSNA) is incorporated into the system to improve detection accuracy in complicated situations. This module enhances feature recovery by aggregating semantic context across multiple scales. Further, to improve detection performance and handle mutual occlusion, a loss function named Wise-IoUv3 (WIoUv3) is used. This method adjusts the loss depending on the overlap between the predicted and ground truth bounding boxes. To train and test the model, we will utilize the safety helmet-wearing (SHWD) dataset, which includes a wide variety of construction workers and whether or not they wore helmets. The goal of this technique is to provide a 99% accurate solution for real-time helmet recognition, which would greatly improve safety management and decrease the likelihood of accidents on construction sites.

关键词： Accuracy Semantics Safety management Helmets Real-time systems Safety Sensors Artificial intelligence Standards Construction industry

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Two-dimensional computer Modelling of Contaminant Migration in Unsaturated Porous Media with Nanoparticles

Two-dimensional Computer Modelling of Contaminant Migration ...

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International computer sciences and Information Technologies (CSIT)

作者： Viktor Zhukovskyy Nataliia Zhukovska Jan Perhac Department of Computer Science and Applied Mathematics The National University of Water and Environmental Engineering Rivne Ukraine Department of Computers and Informatics Technical University of Kosice Kosice Slovak Republic

ISBN: (纸本)9798350334326

The complicated physical problem of computer modeling pollutant migration in the zone of suspended water with nanoparticles to the catcher filter is discussed in this study. The brand-new mathematical model of nonlinearity in two dimensions is offered. The generalized Darcy-Clute laws for mass and moisture movement in various subregions of soil are taken into careful consideration in the model. The finite difference method was used to find a numerical solution to the boundary value problem and to provide an algorithm for computer implementation. Utilizing a newly created Nanosurface program solution, computer modeling was carried out along with the analysis of numerical experiments. The nanoparticle influence was demonstrated.

关键词： Nanoparticles Cloud computing Boundary value problems Computational modeling Soil Predictive models Mathematical models

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Towards End-to-End Structure Solutions from Information-Compromised Diffraction Data via Generative Deep Learning

arXiv

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arXiv 2023年

作者： Guo, Gabe Goldfeder, Judah Lan, Ling Ray, Aniv Yang, Albert Hanming Chen, Boyuan Billinge, Simon J.L. Lipson, Hod Columbia University Department of Computer Science New YorkNY10027 United States Columbia University Department of Applied Physics and Applied Mathematics New YorkNY10027 United States Duke University Department of Mechanical Engineering and Materials Science DurhamNC27708 United States Columbia University Department of Mechanical Engineering New YorkNY10027 United States

The revolution in materials in the past century was built on a knowledge of the atomic arrangements and the structure-property relationship. The sine qua non for obtaining quantitative structural information is single crystal crystallography. However, increasingly we need to solve structures in cases where the information content in our input signal is significantly degraded, for example, due to orientational averaging of grains, finite size effects due to nanostructure, and mixed signals due to sample heterogeneity. Understanding the structure property relationships in such situations is, if anything, more important and insightful, yet we do not have robust approaches for accomplishing it. In principle, machine learning (ML) and deep learning (DL) are promising approaches since they augment information in the degraded input signal with prior knowledge learned from large databases of already known structures. Here we present a novel ML approach, a variational query-based multi-branch deep neural network that has the promise to be a robust but general tool to address this problem end-to-end. We demonstrate the approach on computed powder x-ray diffraction (PXRD), along with partial chemical composition information, as input. We choose as a structural representation a modified electron density we call the Cartesian mapped electron density (CMED), that straightforwardly allows our ML model to learn material structures across different chemistries, symmetries and crystal systems. When evaluated on theoretically simulated data for the cubic and trigonal crystal systems, the system achieves up to 93.4% average similarity with the ground truth on unseen materials, both with known and partially-known chemical composition information, showing great promise for successful structure solution even from degraded and incomplete input data. The approach doesn’t presuppose a crystalline structure and the approach are readily extended to other situations such as nanomaterials and te

关键词： Carrier concentration

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Modelling and Economic Optimal Control for a Laboratory-scale Continuous Stirred Tank Reactor for Single-cell Protein Production

arXiv

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arXiv 2022年

作者： Nielsen, Marcus Krogh Dynesen, Jens Dragheim, Jess Christensen, Ib Jørgensen, Sten Bay Huusom, Jakob Kjøbsted Gernaey, Krist V. Jørgensen, John Bagterp Department of Applied Mathematics and Computer Science Denmark Department of Chemical and Biochemical Engineering the Technical University of Denmark Kgs. Lyngby Denmark Unibio A/S Roskilde Denmark

In this paper, we present a novel kinetic growth model for the micro-organism Methylococcus capsulatus (Bath) that couples growth and pH. We apply growth kinetics in a model for single-cell protein production in a laboratory-scale continuous stirred tank reactor inspired by a physical laboratory fermentor. The model contains a set of differential algebraic equations describing growth and pH-dynamics in the system. We present a method of simulation that ensures non-negativity in the state and algebraic variables. Additionally, we introduce linear scaling of the algebraic equations and variables for numerical stability in Newton’s method. Finally, we conduct a numerical experiment of economic optimal control for single-cell protein production in the laboratory-scale reactor. The numerical experiment shows non-trivial input profiles for biomass growth and pH tracking. Copyright © 2022, The Authors. All rights reserved.

关键词： Laboratories

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Mixed-mode oscillations in coupled FitzHugh-Nagumo oscillators: blowup analysis of cusped singularities

arXiv

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arXiv 2022年

作者： Kristiansen, Kristian Uldall Pedersen, Morten Gram Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby2800 Denmark Department of Information Engineering University of Padova Padova35131 Italy

In this paper, we use geometric singular perturbation theory and blowup, as our main technical tool, to study the mixed-mode oscillations (MMOs) that occur in two coupled FitzHugh-Nagumo units with symmetric and repulsive coupling. In particular, we demonstrate that the MMOs in this model are not due to generic folded singularities, but rather due to singularities at a cusp - not a fold - of the critical manifold. Using blowup, we determine the number of SAOs analytically, showing - as for the folded nodes - that they are determined by the Weber equation and the ratio of eigenvalues. We also show that the model undergoes a (symmetric) saddle-node bifurcation in the desingularized reduced problem, which - although resembling a folded saddle-node (type II) at this level - also occurs on a cusp, and not a fold. We demonstrate that this bifurcation is associated with the emergence of an invariant cylinder, the onset of SAOs, as well as SAOs of increasing amplitude. We relate our findings with numerical computations and find excellent *** Codes 34C15, 34D15, 37E17 Copyright © 2022, The Authors. All rights reserved.

关键词： Bifurcation (mathematics)

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An Optimal Transport Approach for Network Regression

arXiv

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arXiv 2024年

作者： Zalles, Alex G. Hung, Kai M. Finneran, Ann E. Beaudrot, Lydia Uribe, César A. Department of Computational Applied Mathematics and Operations Research Rice University HoustonTX77005 United States Department of Computer Science Rice University HoustonTX77005 United States Department of Biosciences Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States

We study the problem of network regression, where one is interested in how the topology of a network changes as a function of Euclidean covariates. We build upon recent developments in generalized regression models on metric spaces based on Fréchet means and propose a network regression method using the Wasserstein metric. We show that when representing graphs as multivariate Gaussian distributions, the network regression problem requires the computation of a Riemannian center of mass (i.e., Fréchet means). Fréchet means with non-negative weights translates into a barycenter problem and can be efficiently computed using fixed point iterations. Although the convergence guarantees of fixed-point iterations for the computation of Wasserstein affine averages remain an open problem, we provide evidence of convergence in a large number of synthetic and real-data scenarios. Extensive numerical results show that the proposed approach improves existing procedures by accurately accounting for graph size, topology, and sparsity in synthetic experiments. Additionally, real-world experiments using the proposed approach result in higher Coefficient of Determination (R2) values and lower mean squared prediction error (MSPE), cementing improved prediction capabilities in practice. Copyright © 2024, The Authors. All rights reserved.

关键词： Topology

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Quantum Analog of Landau-Lifshitz-Gilbert Dynamics

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Physical Review Letters 2024年第26期133卷 266704-266704页

作者： Yuefei Liu Ivan P. Miranda Lee Johnson Anders Bergman Anna Delin Danny Thonig Manuel Pereiro Olle Eriksson Vahid Azimi-Mousolou Department of Applied Physics School of Engineering Sciences KTH Royal Institute of Technology AlbaNova University Center SE-10691 Stockholm Sweden Department of Physics and Astronomy Uppsala University Box 516 SE-751 20 Uppsala Sweden Department of Physics and Electrical Engineering Linnaeus University SE-39231 Kalmar Sweden Swedish e-Science Research Center (SeRC) KTH Royal Institute of Technology SE-10044 Stockholm Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) KTH Royal Institute of Technology SE-10044 Stockholm Sweden School of Science and Technology Örebro University SE-701 82 Örebro Sweden WISE-Wallenberg Initiative Materials Science Uppsala University Box 516 SE-751 20 Uppsala Sweden Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan 81746-73441 Iran

The Landau-Lifshitz-Gilbert (LLG) and Landau-Lifshitz (LL) equations play an essential role for describing the dynamics of magnetization in solids. While a quantum analog of the LL dynamics has been proposed in [Phys. Rev. Lett. 110, 147201 (2013)], the corresponding quantum version of LLG remains unknown. Here, we propose such a quantum LLG equation that inherently conserves purity of the quantum state. We examine the quantum LLG dynamics of a dimer consisting of two interacting spin-12 particles. Our analysis reveals that, in the case of ferromagnetic coupling, the evolution of initially uncorrelated spins mirrors the classical LLG dynamics. However, in the antiferromagnetic scenario, we observe pronounced deviations from classical behavior, underscoring the unique dynamics of becoming a spinless state, which is nonlocally correlated. Moreover, when considering spins that are initially entangled, our study uncovers an unusual form of revival-type quantum correlation dynamics, which differs significantly from what is typically seen in open quantum systems.

关键词： Antiferromagnetism

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DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatial and Temporal Transformer Network

DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatia...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Shaokun Zhang Xinde Li Chuanfei Hu Jianping Xu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Science and Technology on Information Systems Engineering Laboratory Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Recent transformer-based methods for estimating 3D human pose have gained widespread attention, achieving state-of-the-art results. Previous methods have primarily focused on capturing motion patterns of the human body at a single scale or cascading multiple scales, such as joints, bones, and body-parts. However, they are difficult to simultaneously capture spatial-temporal motion patterns of the human body at different scales due to the complex motion patterns. To address this issue, we propose Dual-scale Spatial and Temporal transFormer (DSTFormer), which can concurrently explore the spatial dependencies and temporal motion patterns of human joints and bones. Additionally, we introduce a Gcn-Spatial Transformer Block (GSTB), which introduces Graph Convolutional Networks (GCN) into transformer to enhance the exploitation of local relationships and global information between adjacent joints or bones. Extensive experiments are conducted on the Human3.6M benchmark dataset, and superior results are reported when comparing to other state-of-the-art methods. More remarkably, our model achieves to-date the best published performance, with P1 errors of 37.9 mm and 15.6 mm, respectively.

关键词： Solid modeling Three-dimensional displays Mechatronics Graph convolutional networks Computational modeling Pose estimation Transformers Bones Joints Robots

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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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