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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Jung, Pawel S. Zakeri, M. Javad Ramaniuk, Aleksandr Blanco-Redondo, Andrea Hagan, David J. Dogariu, Aristide Christodoulides, Demetrios N. Assanto, Gaetano Krolikowski, Wieslaw Trippenbach, Marek CREOL The College of Optics and Photonics University of Central Florida OrlandoFL32816 United States Faculty of Physics Warsaw University of Technology Koszykowa 75 Warsaw00-662 Poland Faculty of Physics University of Warsaw Pasteura 5 Warsaw02-093 Poland Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States NooEL-Nonlinear Optics and OptoElectronics Lab University Roma Tre Rome00146 Italy Department of Quantum Science and Technologies Research School of Physics Australian National University CanberraACT2601 Australia

We discover and discuss the giant oscillations of stable soliton-molecules as induced by the violation of the action-reaction principle in optical systems ruled by competing nonlinear nonlocal interactions. © Opt... 详细信息

关键词： Solitons

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Machine-Learned Models for Power Magnetic Material Characteristics

arXiv

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arXiv 2023年

作者： Leszczyński, Pawel Kutorasiński, Kamil Szewczyk, Marcin Pawlowski, Jaroslaw Division of Power Apparatus Protection and Control Faculty of Electrical Engineering Electrical Power Engineering Institute Warsaw University of Technology Koszykowa 75 St. Warszawa00-662 Poland Department of Condensed Matter Physics Faculty of Physics and Applied Computer Science AGH University of Krakow Reymonta 19 St. Kraków30-059 Poland Institute of Theoretical Physics Wroclaw University of Science and Technology Wyb. Wyspiańskiego 27 St. Wroclaw50-370 Poland

We present a general framework for modeling power magnetic materials characteristics using deep neural networks. Magnetic materials represented by multidimensional characteristics (that mimic measurements) are used to train the neural autoencoder model in an unsupervised manner. The encoder is trying to predict the material parameters of a theoretical model, which is then used in a decoder part. The decoder, using the predicted parameters, reconstructs the input characteristics. The neural model is trained to capture a synthetically generated set of characteristics that can cover a broad range of material behaviors, leading to a model that can generalize on the underlying physics rather than just optimize the model parameters for a single measurement. After setting up the model, we prove its usefulness in the complex problem of modeling magnetic materials in the frequency and current (out-of-linear range) domains simultaneously, for which we use measured characteristics obtained for frequency up to 10 MHz and H-field up to saturation. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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computer-assisted validation of the existence of periodic orbits in the Brusselator system

arXiv

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arXiv 2023年

作者： Banaśkiewicz, Jakub Kalita, Piotr Zgliczyński, Piotr Faculty of Mathematics and Computer Science Jagiellonian University ul. Lojasiewicza 6 Kraków30-348 Poland AGH University of Science and Technology al. Mickiewicza 30 Kraków30-059 Poland

We investigate the Brusselator system with diffusion and Dirichlet boundary conditions on one dimensional space interval. Our proof demonstrates that, for certain parameter values, a periodic orbit exists. This proof is computer-assisted and rooted in the rigorous integration of partial differential equations. Additionally, we present the evidence of the occurrence of period-doubling bifurcation. © 2023, CC BY.

关键词： Boundary conditions

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Radial kinks in a Schwarzschild-like geometry

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Physical Review D 2024年第12期110卷 125025-125025页

作者： Jean-Guy Caputo Tomasz Dobrowolski Jacek Gatlik Panayotis G. Kevrekidis Laboratoire de Mathématiques INSA de Rouen Avenue de l’Université 76801 Saint-Etienne du Rouvray France Department of Computer Physics and Quantum Computing University of the National Education Commission in Krakow Podchora¸żych 2 30-084 Cracow Poland AGH University of Krakow Faculty of Physics and Applied Computer Science 30-059 Krakow Poland Department of Mathematics and Statistics University of Massachusetts Amherst Massachusetts 01003-4515 USA

We study the propagation of a domain wall (kink) of the ϕ4 model in a radially symmetric environment defined by a gravity source. This source introduces a Schwarzschild-like geometry. We introduce an effective model that accurately describes the dynamics of the kink center. This description works well even outside the perturbation region, i.e., even for large masses of the gravitating object. We observed that such a spherical domain wall surrounding a star-type object inevitably “collapses,” i.e., shrinks in radius toward the origin and offer an understanding of the latter phenomenology. The relevant analysis is presented for a circular domain wall and a spherical one.

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Robust Feature Engineering Techniques for Designing Motor Imagery-Based Bci Systems

SSRN

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SSRN 2025年

作者： Gardezi Ahmed, Syed Saim Khan, Mahnoor Bukhari, Syeda Muneeba Khan, Rizwan Ahmed Jawed, Soyiba Department of Biomedical Engineering Faculty of Engineering Salim Habib University Karachi Pakistan Department of Computer Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan Department of Physics Faculty of Applied Sciences Riphah International University Islamabad Pakistan Department of Biomedical Engineering Faculty of Engineering NED University Karachi Pakistan Department of Computer Science Faculty of Information Technology Salim Habib University Karachi Pakistan

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor and motor imagery tasks has been unveiled. Despite this, the productivity of the Machine Learning (ML) Models that were trained on this dataset was alarmingly deficient, and the evaluation framework seemed insufficient. To enhance outcomes, robust feature engineering (signal processing) methodologies are implemented. A collection of time domain, frequency domain, and wavelet-derived features was obtained from 16-channel EEG signals, and the Maximum Relevance Minimum Redundancy (MRMR) approach was employed to identify the four most significant features. For classification K Nearest Neighbors, Support Vector Machine, Decision Tree, and Naive Bayes models were implemented with these selected features, evaluating their effectiveness through metrics such as testing accuracy, precision, recall, and F1 Score. By leveraging SVM with a Gaussian Kernel, a remarkable maximum testing accuracy of 92.50% for motor activities and 95.48% for imagery activities is achieved. These results are notably more dependable and gratifying compared to the previous study, where the peak accuracy was recorded at 74.36%. This research work provide an in-depth analysis of the MI Limb EEG dataset and it will help in designing and developing simple, cost-effective and reliable BCI systems for neuro-rehabilitation. © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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Transmissive glucose concentration plasmonic Au sensor based on unclad optical fiber

Transmissive glucose concentration plasmonic Au sensor based...

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Bioengineering (ENBENG), Portuguese Meeting in

作者： Cristina Cunha Ana Sofia Assunção Catarina S. Monteiro Cátia Leitão João P. Mendes Susana Silva Orlando Frazão Susana Novais Department of Physics and Astronomy Faculty of Sciences of the University of Porto INESC TEC-Institute for Systems and Computer Engineering Technology and Science Porto Portugal Physics Department i3N University of Aveiro Aveiro portugal Faculty of Sciences of the University of Porto INESC TEC-Institute for Systems and Computer Engineering Technology and Science Porto Portugal CIQUP Chemistry Research Center Faculty of Sciences of the University of Porto INESC TEC-Institute for Systems and Computer Engineering Technology and Science Porto Portugal INESC TEC-Institute for Systems and Computer Engineering Technology and Science Porto Portugal

Using surface resonance (SPR) as a sensitivity enhancer, this work describes the development of a transmissive multimode optical fiber sensor with a gold (Au) thin film that measures glucose concentration. The fiber's cladding was initially removed, and an Au layer was then sputtered onto its surface to simultaneously excite SPR and reflect light, making the SPR sensor extremely sensitive to changes in the environment's refractive index. A range of glucose concentrations, from 0.0001 to 0.5000 g/ml, were tested on the sensor. A maximum sensitivity of 161.302 nm/(g/mL) was attained for the lowest glucose concentration, while the highest concentration yielded a sensitivity of 312.000 nm/(g/mL). The proposed sensor's compact size, high sensitivity, good stability and practicality make it a promising candidate for a range of applications, including detecting diabetes.

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Machine learning aided noise filtration and signal classification for CREDO experiment

Machine learning aided noise filtration and signal classific...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Bibrzycki, Lukasz Bar, Olaf Piekarczyk, Marcin Niedźwiecki, Michal Rzecki, Krzysztof Stuglik, Slawomir Homola, Piotr Bibrzycki, Lukasz Alvarez-Castillo, David Bar, Olaf Gora, Dariusz Homola, Piotr Kovács, Péter Niedźwiecki, Michal Piekarczyk, Marcin Rzecki, Krzysztof Stasielak, Jaroslaw Stuglik, Slawomir Sushchov, Oleksandr Tursunov, Arman Pedagogical University of Krakow Institute of Computer Science ul. Podchorażych Kraków30-084 Poland Cracow University of Technology Faculty of Computer Science and Telecommunications Kraków31-155 Poland AGH University of Science and Technology Kraków30-059 Poland Institute of Nuclear Physics Polish Academy of Sciences Kraków31-342 Poland Pedagogical University of Krakow Podchorażych 2 Kraków30-084 Poland Institute of Nuclear Physics Polish Academy of Sciences Walerego Eljasza Radzikowskiego 152 Kraków31-342 Poland Wigner Research Centre for Physics Konkoly-Thege Miklós út 29-33. BudapestH-1121 Hungary Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Kraków31-155 Poland AGH University of Science and Technology 30 Mickiewicza Ave. Kraków30-059 Poland Institute of Physics Silesian University in Opava Bezručovo nám 13 OpavaCZ-74601 Czech Republic

The wealth of smartphone data collected by the Cosmic Ray Extremely Distributed Observatory (CREDO) greatly surpasses the capabilities of manual analysis. So, efficient means of rejecting the non-cosmic-ray noise and identification of signals attributable to extensive air showers are necessary. To address these problems we discuss a Convolutional Neural Network-based method of artefact rejection and complementary method of particle identification based on common statistical classifiers as well as their ensemble extensions. These approaches are based on supervised learning, so we need to provide a representative subset of the CREDO dataset for training and validation. According to this approach over 2300 images were chosen and manually labeled by 5 judges. The images were split into spot, track, worm (collectively named signals) and artefact classes. Then the preprocessing consisting of luminance summation of RGB channels (grayscaling) and background removal by adaptive thresholding was performed. For purposes of artefact rejection the binary CNN-based classifier was proposed which is able to distinguish between artefacts and signals. The classifier was fed with input data in the form of Daubechies wavelet transformed images. In the case of cosmic ray signal classification, the well-known feature-based classifiers were considered. As feature descriptors, we used Zernike moments with additional feature related to total image luminance. For the problem of artefact rejection, we obtained an accuracy of 99%. For the 4-class signal classification, the best performing classifiers achieved a recognition rate of 88%. © Copyright owned by the author(s).

关键词： Cosmic rays

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Role of intercalated cobalt in the electronic structure of Co1/3NbS2

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Physical Review B 2022年第15期105卷 155114-155114页

作者： Petar Popčević Yuki Utsumi Izabela Biało Wojciech Tabis Mateusz A. Gala Marcin Rosmus Jacek J. Kolodziej Natalia Tomaszewska Mariusz Garb Helmuth Berger Ivo Batistić Neven Barišić László Forró Eduard Tutiš Institute of Physics Bijenička c. 46 10000 Zagreb Croatia Institute of Solid State Physics TU Wien 1040 Vienna Austria AGH University of Science and Technology Faculty of Physics and Applied Computer Science 30-059 Krakow Poland Solaris National synchrotron Radiation Centre Jagiellonian University Czerwone Maki 98 30-392 Krakow Poland Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Lojasiewicza 11 30-348 Krakow Poland Laboratory of Physics of Complex Matter École polytechnique fédérale de Lausanne 1015 Lausanne Switzerland Department of Physics Faculty of Science University of Zagreb Bijenička c. 32 10000 Zagreb Croatia Stavropoulos Center for Complex Quantum Matter University of Notre Dame Notre Dame Indiana 46556 USA

Co1/3NbS2 is the magnetic intercalate of 2H-NbS2 where electronic itinerant and magnetic properties strongly influence each other throughout the phase diagram. Here we report the angle-resolved photoelectron spectroscopy (ARPES) study in Co1/3NbS2. In agreement with previous reports, the observed electronic structure seemingly resembles the one of the parent material 2H-NbS2, with the shift in Fermi energy of 0.5 eV accounting for the charge transfer of approximately two electrons from each Co ion into the NbS2 layers. However, in addition, and in contrast to previous reports, we observe significant departures that cannot be explained by the rigid band shift accompanied by minor deformation of bands: First, entirely unrelated to the 2H-NbS2 electronic structure, a shallow electronic band is found crossing the Fermi level near the boundary of the first Brillouin zone of Co1/3NbS2. The evolution of the experimental spectra upon varying the incident photon energy suggests the Co origin of this band. Second, the Nb bonding band, found deeply submerged below the Fermi level at the Γ point, indicates that the interlayer hybridization is significantly amplified by intercalation, with Co magnetic ions probably acting as strong covalent bridges between NbS2 layers. The strong hybridization between orbitals that support the itinerant states and the orbitals hosting the local magnetic moments indicates the importance of strong electronic correlations, with the interlayer coupling playing an exquisite role.

关键词： Electronic structure Fermi surface Dichalcogenides Strongly correlated systems Angle-resolved photoemission spectroscopy DFT+U Density functional theory Wannier function methods

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Granular gases under resetting

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Physical Review E 2025年第1期111卷 015405-015405页

作者： Anna S. Bodrova Aleksei V. Chechkin Awadhesh Kumar Dubey Moscow Institute of Electronics and Mathematics HSE University Moscow 123458 Russia Institute of Physics and Astronomy University of Potsdam 14476 Potsdam Germany Faculty of Pure and Applied Mathematics Wroclaw University of Science and Technology Wyspianskiego 27 Wrocław 50-370 Poland Max Planck Institute of Microstructure Physics Weinberg 2 06120 Halle Germany Department of Pure and Applied Physics Guru Ghasidas Vishwavidyalaya Koni Bilaspur 495009 Chhattisgarh India

We investigate the granular temperatures in force-free granular gases under exponential resetting. When a resetting event occurs, the granular temperature attains its initial value, whereas it decreases because of the inelastic collisions between the resetting events. We develop a theory and perform computer simulations for granular gas cooling in the presence of Poissonian resetting events. We also investigate the probability density function to quantify the distribution of granular temperatures. Our theory may help us to understand the behavior of nonperiodically driven granular systems.

关键词： Probability density function

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Defending Against Digital Threats: Machine Learning Techniques for Cyber Persecution Detection

Defending Against Digital Threats: Machine Learning Techniqu...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： P. Chinnasamy Ramesh Kumar Ayyasamy Kesavan Krishnan Vijay Kumar Norazira Binti A Jalil Ajmeera Kiran Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Physics Graphic Era Hill University Dehardun Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

Cyber persecution has become a widespread problem on the social media. It has resulted in omissions such as suicide and sadness. Content regulation on social media sites is becoming increasingly important. The following work employs natural language processing and Machine Learning techniques to construct a cyber harassment detection model utilizing data from cyberbullying, hate speech tweets from Twitter, and personal assaults from Wikipedia. The optimal approach is investigated by two feature extraction methods and six classifiers (SVM, Random Forest, XGBoost, MLP, and Logistic Regression). This study summarizes the general procedure for detecting cyberbullying and, more crucially, the technique. During experimentation, SVM, Random Forest, and Logistic Regression performed well with an average of 90%. Our model has been created to detect offensive comments on social media websites.

关键词： Support vector machines Logistic regression Accuracy Hate speech Cyberbullying Natural language processing Regulation Internet Online services Random forests

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