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20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

作者： Taoufik, Elmissaoui Cyril, Onyedeke Obinna Jonathan Chukwunwike, Agbo Angela, Obayi Adaora Nwangwu Emmanuel, C. University of Carthage Higher Institute of Applied Mathematics and Computer Science University of Kairouan Innov'Com Lab Sup'Com Tunisia University of Nigeria Department of Computer Science Nsukka Nigeria

ISBN: (纸本)9798350332568

The Remote Lab System (RLS) is a special combination between software and materials to give the chance for students to access remotely the laboratory equipment. With this system universities will be able to build a full online license and master's degree. Actually, the operational remote lab allows students access the equipment remotely. The number of beneficiaries of remote lab services continues to increase. The reservation process and the limit of the slot time given for each student became weaknesses for the RLS. In this paper, we propose and test a famous technique to share remotely the laboratory equipment between students at the same time. In fact, we test the Orthogonal Frequency Division Multiple Access (OFDMA) and exploit the good user separation done by the OFDMA technique, and also each users is assigned a frequency. The identification of each user or equipment lab will be based on the assigned frequency. Applying OFDMA to manage multi- user of our RLS allows us more than one student access our equipment lab. The system OFDMA allows students reused some equipment lab in multiple practical work or shared it between multiple-users. The limitation of our software and equipment lab present a hard barrier for us to test our solution for more than three users at the same time. In order to let our system stable we decide to give the possibility for only two users sharing the laboratory equipment at the same time. © 2023 IEEE.

关键词： Frequency division multiple access

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Dimensional Homogeneity in Classifying Second-Order Differential Invariant Systems for Four-Dimensional Lie Algebras

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International Journal of applied and Computational mathematics 2024年第6期10卷 1-44页

作者： Ayub, Muhammad Sultan, Zahida Mahomed, F.M. Ijaz, Saima Department of Mathematics COMSATS University Islamabad Abbottabad Campus Abbottabad Pakistan Department of Mathematics Azad Jammu and Kashmir University Muzaffarabad Pakistan DSI-NRF Centre of Excellence in Mathematical and Statistical Sciences School of Computer Science and Applied Mathematics University of the Witwatersrand Wits 2050 Johannesburg South Africa Department of Mathematics University of Engineering and Technology Lahore 54000 Pakistan

This research investigates a specific mathematical structure called a nonsingular differential invariant structure, which plays a crucial role in describing physical phenomena through differential equations. Focusing on a four-dimensional Lie algebra in (1+3)-dimensional space, this study classifies these structures. Here, a method is developed to identify second-order differential invariants and showed how they translate into systems of three second-order ordinary differential equations for the specific case of a four-dimensional Lie algebra, and these invariants are presented in the form of list in Table 1. To further refine the analysis, these systems are classified based on their “dimensional homogeneity structure,” which reveals their behavior when scaling all variables. This sub-classification described in Table 2, goes beyond the initial broad categorization, potentially linking these structures to specific physical applications. Novelty: To best of our knowledge, the classification of nonsingular differential invariant structure in (1+3)-dimensional space of four-dimensional real Lie algebras and association of dimensional homogeneity structure in classification scheme are new contributions in algebraic classification problems in literature and never reported before this. Such type of classification scheme is fruitful in analysis of many physical systems. In addition, the algebraic properties of these peculiar invariant systems including integrability, are analysed in a detailed manner both mathematically and from a mechanics viewpoint. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2024.

关键词： Classification Dimensional homogeneity Integrability Lie algebra Nonsingular differential invariants Systems of second-order ODEs

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Comparative Analysis of Radial Basis Functions and Cubic Splines for Data Imputation 10

Comparative Analysis of Radial Basis Functions and Cubic Spl...

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10th International Conference on Fuzzy Systems and Data Mining, FSDM 2024

作者： Kitchainukoon, Wisut Sangsuwan, Amornrat Pongchalee, Pornthip Chanthawara, Krittidej Department of Mathematics Faculty of Science and Technology Loei Rajabhat University Loei42000 Thailand Department of Mathematics Faculty of Education Loei Rajabhat University Loei42000 Thailand Department of Applied Mathematics and Statistics Faculty of Science and Liberal Arts Rajamangala University of Technology Isan Nakhon Ratchasima30000 Thailand Program of Mathematics Faculty of Science Ubon Ratchathani Rajabhat University Ubon Ratchathani34000 Thailand

ISBN: (纸本)9781643685694

This study evaluates the effectiveness of Radial Basis Function (RBF) approaches, specifically Gaussian and Multiquadric RBFs, compared to Cubic and Adaptive Splines for data imputation in time-series datasets. Three datasets-multiseasonal, non-stationary, and spikes and drops-were used, with varying data corruption levels to simulate real-world scenarios. Imputation performance was assessed using Root Mean Square Error (RMSE). The results indicate that spline-based methods, particularly Cubic and Adaptive Splines, consistently deliver lower RMSE values, showcasing superior robustness to data variability and high corruption levels compared to RBF methods. This comparative analysis offers insights into selecting appropriate techniques for effective data imputation in time-series analysis. © 2024 The Authors.

关键词： Splines

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Pain Classification Using Statistical Feature Extraction Using Machine Learning Approach: A Pilot Study 13

Pain Classification Using Statistical Feature Extraction Usi...

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13th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2023

作者： Sudimanto Trisetyarso, Agung Kartowisastro, Iman Herwidiana Budiharto, Widodo Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Department Binus Graduate Program Computer Science Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jl. K. H. Syahdan No. 9 Kemanggisan Palmerah Jakarta11480 Indonesia

ISBN: (纸本)9798350323184

Electrodermal activity (EDA) is a general term for all electrical phenomena occurring on the skin, both passive and active. EDA measurements are used by researchers to measure levels of stress, emotion, mental strain, and so on. Measuring human stress levels, emotions, and mental strain are generally associated with the skin conductance response. The function GSR sensor is not only used to read people's psychology but also can be used as a pain sensor used to read the degree of pain in the skin. This pilot study uses sample data from ***. The *** data is galvanic skin response sensor data. The output of this sensor is the conductivity value that occurs in the skin. The data obtained from *** will be extracted using the mean, standard deviation, maximum, minimum, RMS, skewness, and peak-to-peak characteristics. The extracted functions are selected using the forward selection method. The results of the feature selection are three features with an accuracy percentage greater than 50%, namely the mean feature, the RMS feature, and the skewness feature. The machine learning models used are bagged tree, SVM, and K-NN models. Of the three models used, the bagged tree model has the highest accuracy rate, at 98.05%, with an F1 score is 0.9807. The KNN model with k=10 has the lowest level of accuracy compared to other models, at 96.75%. © 2023 IEEE.

关键词： Extraction

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Mitigating Gradient Overlap in Deep Residual Networks with Gradient Normalization for Improved Non-Convex Optimization

Mitigating Gradient Overlap in Deep Residual Networks with G...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Yun, Juyoung Stony Brook University Department of Computer Science New York11794 United States Stony Brook University Department of Applied Mathematics and Statistics New York11794 United States

ISBN: (纸本)9798350362480

In deep learning, Residual Networks (ResNets) have proven effective in addressing the vanishing gradient problem, allowing for the successful training of very deep networks. However, skip connections in ResNets can lead to gradient overlap, where gradients from both the learned transformation and the skip connection combine, potentially resulting in overestimated gradients. This overestimation can cause inefficiencies in optimization, as some updates may overshoot optimal regions, affecting weight updates. To address this, we examine Z-score Normalization (ZNorm) as a technique to manage gradient overlap. ZNorm adjusts the gradient scale, standardizing gradients across layers and reducing the negative impact of overlapping gradients. Our experiments demonstrate that ZNorm improves training process, especially in non-convex optimization scenarios common in deep learning, where finding optimal solutions is challenging. These findings suggest that ZNorm can affect the gradient flow, enhancing performance in large-scale data processing where accuracy is critical. © 2024 IEEE.

关键词： Contrastive Learning

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The implicit bias of benign overfitting

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 5063-5102页

作者： Ohad Shamir Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel

The phenomenon of benign overfitting, where a predictor perfectly fits noisy training data while attaining near-optimal expected loss, has received much attention in recent years, but still remains not fully understood beyond well-specified linear regression setups. In this paper, we provide several new results on when one can or cannot expect benign over-fitting to occur, for both regression and classification tasks. We consider a prototypical and rather generic data model for benign overfitting of linear predictors, where an arbitrary input distribution of some fixed dimension k is concatenated with a high-dimensional distribution. For linear regression which is not necessarily well-specified, we show that the minimum-norm interpolating predictor (that standard training methods converge to) is biased towards an inconsistent solution in general, hence benign overfitting will generally not occur. Moreover, we show how this can be extended beyond standard linear regression, by an argument proving how the existence of benign overfitting on some regression problems precludes its existence on other regression problems. We then turn to classification problems, and show that the situation there is much more favorable. Specifically, we prove that the max-margin predictor (to which standard training methods are known to converge in direction) is asymptotically biased towards minimizing a weighted squared hinge loss. This allows us to reduce the question of benign overfitting in classification to the simpler question of whether this loss is a good surrogate for the misclassification error, and use it to show benign overfitting in some new settings.

关键词： benign overfitting interpolating predictors implicit bias surrogate losses

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Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution Microscopy

arXiv

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arXiv 2024年

作者： Sahel, Yair Ben Eldar, Yonina C. Department of Computer Science and Applied Mathematics Weizmann Institute of Science Israel

The use of fluorescent molecules to create long sequences of low-density, diffraction-limited images enables highly-precise molecule localization. However, this methodology requires lengthy imaging times, which limits the ability to view dynamic interactions of live cells on short time scales. Many techniques have been developed to reduce the number of frames needed for localization, from classic iterative optimization to deep neural networks. Particularly, deep algorithm unrolling utilizes both the structure of iterative sparse recovery algorithms and the performance gains of supervised deep learning. However, the robustness of this approach is highly dependant on having sufficient training data. In this paper we introduce deep unrolled self-supervised learning, which alleviates the need for such data by training a sequence-specific, model-based autoencoder that learns only from given measurements. Our proposed method exceeds the performance of its supervised counterparts, thus allowing for robust, dynamic imaging well below the diffraction limit without any labeled training samples. Furthermore, the suggested model-based autoencoder scheme can be utilized to enhance generalization in any sparse recovery framework, without the need for external training data. © 2024, CC BY-NC-ND.

关键词： Molecules

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Enhancing Solar Energy Production Forecasting Using Advanced Machine Learning and Deep Learning Techniques: A Comprehensive Study on the Impact of Meteorological Data

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computers, Materials & Continua 2024年第11期81卷 3147-3163页

作者： Nataliya Shakhovska Mykola Medykovskyi Oleksandr Gurbych Mykhailo Mamchur Mykhailo Melnyk Instutute of Computer Science and Information Technologies Lviv Polytechnic National UniversityLviv79013Ukraine Department of Applied Mathematics University of Agriculture in KrakowKrakow31-120Poland Blackthorn AI Ltd.LondonEC1V 2NXUK

The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability,reliability,and economic *** study explores advanced machine learning(ML)and deep learning(DL)techniques for predicting solar energy generation,emphasizing the significant impact of meteorological data.A comprehensive dataset,encompassing detailed weather conditions and solar energy metrics,was collected and preprocessed to improve model *** models were developed and trained with different preprocessing ***,three datasets were prepared.A novel hour-based prediction wrapper was introduced,utilizing external sunrise and sunset data to restrict predictions to daylight hours,thereby enhancing model performance.A cascaded stacking model incorporating association rules,weak predictors,and a modified stacking aggregation procedure was proposed,demonstrating enhanced generalization and reduced prediction *** indicated that models trained on raw data generally performed better than those on stripped *** Long Short-Term Memory(LSTM)with Inception layers’model was the most effective,achieving significant performance improvements through feature selection,data preprocessing,and innovative modeling *** study underscores the potential to combine detailed meteorological data with advanced ML and DL methods to improve the accuracy of solar energy forecasting,thereby optimizing energy management and planning.

关键词： Solar energy prediction machine learning deep learning

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Computing Smallest Convex Intersecting Polygons 30

Computing Smallest Convex Intersecting Polygons

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30th Annual European Symposium on Algorithms, ESA 2022

作者： Antoniadis, Antonios De Berg, Mark Kisfaludi-Bak, Sándor Skarlatos, Antonis Department for Applied Mathematics University of Twente Enschede Netherlands Department of Mathematics and Computer Science TU Eindhoven Netherlands Department of Computer Science Aalto University Espoo Finland Department of Computer Science Universiät Salzburg Austria

ISBN: (纸本)9783959772471

A polygon C is an intersecting polygon for a set O of objects in R2 if C intersects each object in O, where the polygon includes its interior. We study the problem of computing the minimum-perimeter intersecting polygon and the minimum-area convex intersecting polygon for a given set O of objects. We present an FPTAS for both problems for the case where O is a set of possibly intersecting convex polygons in the plane of total complexity n. Furthermore, we present an exact polynomial-time algorithm for the minimum-perimeter intersecting polygon for the case where O is a set of n possibly intersecting segments in the plane. So far, polynomial-time exact algorithms were only known for the minimum perimeter intersecting polygon of lines or of disjoint segments. © 2022 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Computational geometry

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Hamiltonian learning using machine-learning models trained with continuous measurements

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Physical Review applied 2024年第4期22卷 044080页

作者： Kris Tucker Amit Kiran Rege Conor Smith Claire Monteleoni Tameem Albash Department of Applied Mathematics Department of Computer Science Center for Quantum Information and Control Department of Electrical and Computer Engineering Paris France Center for Computing Research

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

关键词： Entanglement detection Nonlocality Quantum parameter estimation Machine learning Weak measurements

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