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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

Sensor-based human activity recognition (HAR) is a widely explored research area focused on identifying human behaviors by analyzing sensor-generated data, particularly one-dimensional time series signals. Such HAR systems have many real-world applications, including elderly health monitoring and athlete performance evaluation. This paper presents Ens-DeepNet, a novel ensemble learning approach designed for recognizing complex human activities using data from wearable sensors. While deep neural networks have proven effective for HAR tasks, their performance can be further improved through ensemble strategies. To this end, we propose an ensemble architecture that combines five distinct deep learning models - convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU). These models are integrated using a majority voting scheme to boost classification accuracy. The proposed method is evaluated using the UTwente dataset, which includes sensor data collected from ten individuals via tri-axial accelerometers and gyroscopes. Experimental findings demonstrate that the ensemble model achieves an impressive accuracy of 99.28%, surpassing the performance of each standalone model, whose accuracies range from 92.77%to 97.34%. This performance gain is particularly notable in recognizing complex activities such as eating, drinking coffee, smoking, and conversing. © 2025 IEEE.

关键词： Deep neural networks

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Incorporating Clinical Guidelines through Adapting Multi-modal Large Language Model for Prostate Cancer PI-RADS Scoring

arXiv

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arXiv 2024年

作者： Zhang, Tiantian Lin, Manxi Guo, Hongda Zhang, Xiaofan Chiu, Ka Fung Peter Feragen, Aasa Dou, Qi Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark Department of Surgery The Chinese University of Hong Kong Hong Kong Shanghai Jiao Tong University Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China

The Prostate Imaging Reporting and Data System (PI-RADS) is pivotal in the diagnosis of clinically significant prostate cancer through MRI imaging. Current deep learning-based PI-RADS scoring methods often lack the incorporation of common PI-RADS clinical guideline (PICG) utilized by radiologists, potentially compromising scoring accuracy. This paper introduces a novel approach that adapts a multi-modal large language model (MLLM) to incorporate PICG into PI-RADS scoring model without additional annotations and network parameters. We present a designed two-stage fine-tuning process aiming at adapting a MLLM originally trained on natural images to the MRI images while effectively integrating the PICG. Specifically, in the first stage, we develop a domain adapter layer tailored for processing 3D MRI inputs and instruct the MLLM to differentiate MRI sequences. In the second stage, we translate PICG for guiding instructions from the model to generate PICG-guided image features. Through such a feature distillation step, we align the scoring network's features with the PICG-guided image features, which enables the model to effectively incorporate the PICG information. We develop our model on a public dataset and evaluate it on an in-house dataset. Experimental results demonstrate that our approach effectively improves the performance of current scoring networks. Code is available at: https://***/med-air/PICG2scoring. © 2024, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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Quantum Hamiltonian Descent for Non-smooth Optimization

arXiv

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arXiv 2025年

作者： Leng, Jiaqi Zheng, Yufan Jia, Zhiyuan Fan, Lei Zhao, Chaoyue Peng, Yuxiang Wu, Xiaodi Simons Institute for the Theory of Computing University of California Berkeley United States Department of Mathematics University of California Berkeley United States Department of Computer Science University of Maryland College Park United States Joint Center for Quantum Information and Computer Science University of Maryland United States Department of Applied Mathematics University of Washington United States Department of Engineering Technology University of Houston United States Department of Electrical and Computer Engineering University of Houston United States Department of Industrial and Systems Engineering University of Washington United States Artephi Computing Inc United States

Non-smooth optimization models play a fundamental role in various disciplines, including engineering, science, management, and finance. However, classical algorithms for solving such models often struggle with convergence speed, scalability, and parameter tuning, particularly in high-dimensional and non-convex settings. In this paper, we explore how quantum mechanics can be leveraged to overcome these limitations. Specifically, we investigate the theoretical properties of the Quantum Hamiltonian Descent (QHD) algorithm for non-smooth optimization in both continuous and discrete time. First, we propose continuous-time variants of the general QHD algorithm and establish their global convergence and convergence rate for non-smooth convex and strongly convex problems through a novel Lyapunov function design. Furthermore, we prove the finite-time global convergence of continuous-time QHD for non-smooth non-convex problems under mild conditions (i.e., locally Lipschitz). In addition, we propose discrete-time QHD, a fully digitized implementation of QHD via operator splitting (i.e., product formula). We find that discrete-time QHD exhibits similar convergence properties even with large time steps. Finally, numerical experiments validate our theoretical findings and demonstrate the computational advantages of QHD over classical non-smooth non-convex optimization algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Low Dimensional Invariant Embeddings for Universal Geometric Learning

arXiv

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arXiv 2022年

作者： Dym, Nadav Gortler, Steven J. Faculty of Mathematics Faculty of Computer Science Technion Haifa Israel School of Engineering and Applied Sciences Harvard University Cambridge United States

This paper studies separating invariants: mappings on D dimensional domains which are invariant to an appropriate group action, and which separate orbits. The motivation for this study comes from the usefulness of separating invariants in proving universality of equivariant neural network architectures. We observe that in several cases the cardinality of separating invariants proposed in the machine learning literature is much larger than the dimension D. As a result, the theoretical universal constructions based on these separating invariants is unrealistically large. Our goal in this paper is to resolve this issue. We show that when a continuous family of semi-algebraic separating invariants is available, separation can be obtained by randomly selecting 2D + 1 of these invariants. We apply this methodology to obtain an efficient scheme for computing separating invariants for several classical group actions which have been studied in the invariant learning literature. Examples include matrix multiplication actions on point clouds by permutations, rotations, and various other linear groups. Often the requirement of invariant separation is relaxed and only generic separation is required. In this case, we show that only D + 1 invariants are required. More importantly, generic invariants are often significantly easier to compute, as we illustrate by discussing generic and full separation for weighted graphs. Finally we outline an approach for proving that separating invariants can be constructed also when the random parameters have finite precision. Copyright © 2022, The Authors. All rights reserved.

关键词： Network architecture

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Two-Dimensional Thin Layer Convective Flow and Heat Transfer of a Dusty Fluid on a Nonlinear Stretching Sheet

引用

Multiscale science and engineering 2022年第3期4卷 111-118页

作者： Islam, Md. Robiul Ferdows, M. Misra, J. C. Murtaza, M. G. Research Group of Fluid Flow Modeling and Simulation Department of Applied Mathematics University of Dhaka Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Centre for Healthcare Science and Technology Indian Institute of Engineering Science and Technology Howrah India Department of Mathematics Comilla University Cumilla Bangladesh

In this study, steady two-dimensional boundary layer flow and heat transfer of a dusty fluid over a nonlinearly stretching sheet with variable thickness, $${u}_{w} (x)={U}_{0}{(x+b)}^{m}$$ has been considered. A varia...

In this study, steady two-dimensional boundary layer flow and heat transfer of a dusty fluid over a nonlinearly stretching sheet with variable thickness, $${u}_{w} (x)={U}_applied{(x+b)}^{m}$$ has been considered. A variable magnetic field is applied transversely to the direction of the flow. The general problem has been reduced to a system of coupled non-linear ordinary differential equations of two-dimensional boundary layer. By means of similarity transformation, the basic governing equations are transformed into a set of nonlinear ordinary differential equation and then are solved numerically by dsolve routine in MAPLE. Results of the present study have been duly validated by adopting an appropriate procedure. The influence of various parameters such as velocity power index, wall thickness parameter, magnetic parameter fluid-particle interaction parameter for velocity, fluid-particle interaction parameter for temperature, mass concentration of dust particle, Prandtl number, Eckert number has been investigated. The effects are then presented graphically both for dust phase and fluid phase. It is clear that flow field and other quantities of physical interest are significantly influenced by these parameters. Also the effects of the parameters on the skin-friction coefficients and rate of heat transfer in terms of the Nusselt number, which are of physical interest, are shown graphically/in tabular form. The study reveals that the impact of the parameters for the dusty fluid on Nusselt number is considerably higher than that for a clean fluid.

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Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport

Bilingual Lexicon Induction for Low-Resource Languages using...

引用

2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Marchisio, Kelly Saad-Eldin, Ali Duh, Kevin Priebe, Carey Koehn, Philipp Department of Computer Science Johns Hopkins University United States Department of Applied Mathematics and Statistics Johns Hopkins University United States Department of Biomedical Engineering Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States

Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. We improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optimal transport. The method is especially strong with low amounts of supervision. © 2022 Association for Computational Linguistics.

关键词： Natural language processing systems

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Efficient N-to-M Checkpointing Algorithm for Finite Element Simulations

arXiv

引用

arXiv 2024年

作者： Ham, David A. Hapla, Vaclav Knepley, Matthew G. Mitchell, Lawrence Sagiyama, Koki Department of Mathematics Imperial College London United Kingdom Department of Earth and Planetary Sciences ETH Zurich Department of Applied Mathematics FEECS at VSB-TU Ostrava Czech Republic Department of Computer Science and Engineering The University Buffalo United States NVIDIA Corporation Santa Clara United States

In this work, we introduce a new algorithm for N-to-M checkpointing in finite element simulations. This new algorithm allows efficient saving/loading of functions representing physical quantities associated with the mesh representing the physical domain. Specifically, the algorithm allows for using different numbers of parallel processes for saving and loading, allowing for restarting and post-processing on the process count appropriate to the given phase of the simulation and other conditions. For demonstration, we implemented this algorithm in PETSc, the Portable, Extensible Toolkit for Scientific Computation, and added a convenient high-level interface into Firedrake, a system for solving partial differential equations using finite element methods. We evaluated our new implementation by saving and loading data involving 8.2 billion finite element degrees of freedom using 8,192 parallel processes on ARCHER2, the UK National Supercomputing Service. © 2024, CC BY.

关键词： Degrees of freedom (mechanics)

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Regular perturbation solution of Couette flow(non-Newtonian)between two parallel porous plates: a numerical analysis with irreversibility

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applied mathematics and Mechanics(English Edition) 2021年第1期42卷 127-142页

作者： M.NAZEER M.I.KHAN S.KADRY Yuming CHU F.AHMAD W.ALI M.IRFAN M.SHAHEEN Department of Mathematics Institute of Arts and SciencesGovernment College UniversityChiniot CampusFaisalabad 35400Pakistan Department of Mathematics and Statistics Riphah International University I-14Islamabad 44000Pakistan Department of Mathematics and Computer Science Beirut Arab UniversityBeirut 11-5020Lebanon Department of Mathematics Huzhou UniversityHuzhou 313000Zhejiang ProvinceChina Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering Changsha University of Science&TechnologyChangsha 410114China Department of Applied Sciences National Textile UniversityFaisalabad 38000Pakistan Chair of Production Technology Faculty of Engineering TechnologyUniversity of TwenteEnschede 7500 AEThe Netherlands Department of Mathematics Riphah Inernational LniversityFaisalabad CampusFaiselabad 38000Pakistan

The unavailability of wasted energy due to the irreversibility in the process is called the entropy *** irreversible process is a process in which the entropy of the system is *** second law of thermodynamics is used to define whether the given system is reversible or ***,our focus is how to reduce the entropy of the system and maximize the capability of the *** are many methods for maximizing the capacity of heat *** constant pressure gradient or motion of the wall can be used to increase the heat transfer rate and minimize the *** objective of this study is to analyze the heat and mass transfer of an Eyring-Powell fluid in a porous *** this,we choose two different fluid models,namely,the plane and generalized Couette *** flow is generated in the channel due to a pressure gradient or with the moving of the upper *** present analysis shows the effects of the fluid parameters on the velocity,the temperature,the entropy generation,and the Bejan *** nonlinear boundary value problem of the flow problem is solved with the help of the regular perturbation *** validate the perturbation solution,a numerical solution is also obtained with the help of the built-in command NDSolve of MATHEMATICA *** velocity profile shows the shear thickening behavior via first-order Eyring-Powell *** is also observed that the profile of the Bejan number has a decreasing trend against the Brinkman ***ηi→0(i=1,2,3),the Eyring-Powell fluid is transformed into a Newtonian fluid.

关键词： Couette flow Eyring-Powell fluid entropy generation perturbation method Bejan number

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An Optimal Transport Approach for Network Regression

An Optimal Transport Approach for Network Regression

引用

Control Technology and Applications (CCTA),

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

ISBN: (数字)9798350370942

ISBN: (纸本)9798350370959

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 (R 2 ) values and lower mean squared prediction error (MSPE), cementing improved prediction capabilities in practice.

关键词： Accuracy Network topology Computational modeling Cement industry Predictive models Gaussian distribution Aerospace electronics

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Negative Binomial Regression Model for Predicting the Number of Tourists in National Parks of Thailand

Negative Binomial Regression Model for Predicting the Number...

引用

International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Sthaporn Thepsumritporn Anuchit Jitpattanakul Sakorn Mekruksavanich Wikanda Phaphan Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Computer Engineering School of Information and Communication University of Phayao Phayao Thailand Research Group in Statistical Learning and Inference

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This article examines the use of count data models to predict the number of tourists visiting Thailand’s national parks. The dataset, encompassing observations from 2016 to 2022 across 146 national parks, exhibits characteristics such as discreteness, over dispersion, and non-negative integer values. Two models— Poisson (POI), and Negative Binomial (NB)—were compared to identify the most effective approach for modeling and prediction. Model evaluation metrics, including the Akaike Information Criterion (AIC) and log-likelihood, were employed. Results demonstrate that the Negative Binomial regression model best captures the data’s overdispersion compared to the POI model. The NB model achieved the lowest AIC and highest log-likelihood, affirming its superior performance for this application. Our findings underscore the NB model’s utility in predicting tourism-related count data and suggest its broader applicability to other domains characterized by overdispersed count data.

关键词： Natural resources Measurement Analytical models Accuracy Predictive models Data models Planning Resource management Dispersion

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