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arXiv 2024年

作者： Gardezi, Syed Saim Jawed, Soyiba Khan, Mahnoor Bukhari, Muneeba Khan, Rizwan Ahmed 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 School of Mathematics and Computer Science Institute of Business Administration 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 Naïve 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 provides 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. © 2024, CC BY-NC-ND.

关键词： Support vector machines

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Multiplicity fluctuations and rapidity correlations in ultracentral proton-nucleus collisions

arXiv

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arXiv 2024年

作者： Samanta, Rupam Ollitrault, Jean-Yves AGH University of Science and Technology Faculty of Physics and Applied Computer Science aleja Mickiewicza 30 Cracow30-059 Poland Université Paris Saclay CNRS CEA Institut de physique théorique Gif-sur-Yvette91191 France

A collision between a proton and a heavy nucleus at ultrarelativistic energy creates particles whose rapidity distribution is asymmetric, with more particles emitted in the direction of the nucleus than in the direction of the proton. This asymmetry becomes more pronounced as the centrality estimator, defined from the energy deposited in a calorimeter, increases. We argue that for high-multiplicity collisions, the variation of the impact parameter plays a negligible role, and that the fluctuations of the multiplicity and of the centrality estimator are dominated by quantum fluctuations, whose probability distribution can be well approximated by a correlated gamma distribution. We show that this simple model reproduces existing data, and we make quantitative predictions for collisions in the 0−0.1% and 0−0.01% centrality windows. We argue that by repeating the same analysis with a different centrality estimator, one can obtain direct information about the rapidity decorrelation in particle production. Copyright © 2024, The Authors. All rights reserved.

关键词： Hadrons

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Mitigating Saturation in Fuzzy-Flip-Flop Neural Networks Trained with Memetic PSO Algorithm

Mitigating Saturation in Fuzzy-Flip-Flop Neural Networks Tra...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Piotr A. Kowalski Tomasz Sloczynski Faculty of Physics and Applied Computer Science AGH University of Krakow al. Mickiewicza 30 Kraków Poland Systems Research Institute Polish Academy of Sciences ul. Newelska 6 Warsaw Poland

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

This paper investigates the efficacy of learning algorithms for training Fuzzy Flip-Flop Neural Networks, focusing on Particle Swarm Optimization (PSO) and memetic PSO variants. The study explores their performance across diverse datasets, including Breast Cancer Wisconsin, Seeds, and Glass. Results demonstrate that memetic PSO variants, consistently outperform other algorithms in terms of classification accuracy and reduced saturation levels. The examination of different crossover strategies within the memetic PSO framework provides insights into their impact on learning performance. Overall, this research highlights the superiority of memetic PSO algorithms in optimising Fuzzy Flip-Flop Neural Networks for classification tasks with complex datasets, suggesting their potential for broader applications.

关键词： Memetics Training Numerical analysis Neural networks Glass Classification algorithms Task analysis

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Tensor network simulation of the quantum Kibble-Zurek quench from the Mott to the superfluid phase in the two-dimensional Bose-Hubbard model

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Physical Review B 2023年第14期107卷 144510-144510页

作者： Jacek Dziarmaga Jakub M. Mazur Faculty of Physics Astronomy and Applied Computer Science Institute of Theoretical Physics Jagiellonian University ul. Łojasiewicza 11 30-348 Kraków Poland Mark Kac Center for Complex Systems Research Jagiellonian University ul. Łojasiewicza 11 30-348 Kraków Poland

Quantum simulations of the Bose-Hubbard model (BHM) at commensurate filling can follow spreading of correlations after a sudden quench for times long enough to estimate their propagation velocities. In this work we perform tensor network simulation of the quantum Kibble-Zurek (KZ) ramp from the Mott towards the superfluid phase in the square lattice BHM and demonstrate that even relatively short ramp/quench times allow one to test the power laws predicted by the KZ mechanism (KZM). They can be verified for the correlation length and the excitation energy but the most reliable test is based on the KZM scaling hypothesis for the single-particle correlation function: scaled correlation functions for different quench times evaluated at the same scaled time collapse to the same scaling function of the scaled distance. The scaling of the space and time variables is done according to the KZ power laws.

关键词： Bose-Einstein condensates Mott insulators Quantum many-body systems Superfluids Ultracold gases Bose-Hubbard model Tensor network methods

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CONSUMER TRANSACTIONS SIMULATION THROUGH GENERATIVE ADVERSARIAL NETWORKS UNDER STOCKS CONSTRAINTS IN LARGE-SCALE RETAIL

arXiv

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arXiv 2024年

作者： Tkachuk, Sergiy Lukasik, Szymon Wróblewska, Anna Systems Research Institute Polish Academy of Sciences ul. Newelska 6 Warsaw01-447 Poland Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland Faculty of Physics and Applied Computer Science AGH University of Science and Technology al. Mickiewicza 30 Kraków30-059 Poland

In the rapidly evolving domain of large-scale retail data systems, envisioning and simulating future consumer transactions has become a crucial area of interest. It offers significant potential to fortify demand forecasting and fine-tune inventory management. This paper presents an innovative application of Generative Adversarial Networks (GANs) to generate synthetic retail transaction data, specifically focusing on a novel system architecture that combines consumer behavior modeling with stock-keeping unit (SKU) availability constraints to address real-world assortment optimization challenges. We diverge from conventional methodologies by integrating SKU data into our GAN architecture and using more sophisticated embedding methods (e.g., hyper-graphs). This design choice enables our system to generate not only simulated consumer purchase behaviors but also reflects the dynamic interplay between consumer behavior and SKU availability – an aspect often overlooked, among others, because of data scarcity in legacy retail simulation models. Our GAN model generates transactions under stock constraints, pioneering a resourceful experimental system with practical implications for real-world retail operation and strategy. Preliminary results demonstrate enhanced realism in simulated transactions measured by comparing generated items with real ones using methods employed earlier in related studies. This underscores the potential for more accurate predictive modeling. © 2024, CC BY-NC-ND.

关键词： Generative adversarial networks

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Quantum versus semi-classical signatures of correlated triple ionization in Dalitz plots

arXiv

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arXiv 2024年

作者： Efimov, Dmitry K. Katsoulis, Georgios P. Rozpętkowski, Tymoteusz Chwalowski, Sergiusz Emmanouilidou, Agapi Prauzner-Bechcicki, Jakub S. Institute of Theoretical Physics Faculty of Fundamental Problems of Technology Wroclaw University of Science and Technology Wroclaw50-370 Poland Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Jagiellonian University in Kraków Faculty of Physics Astronomy and Applied Computer Science Marian Smoluchowski Institute of Physics Lojasiewicza 11 Krakow30-348 Poland

We investigate correlated three-electron escape in Ne when driven by an intense, infrared laser field. We do so by employing a reduced-dimensionality quantum-mechanical model and two three-dimensional semi-classical models. One semi-classical model is a recently developed one that accounts with effective coulomb potentials for the interaction between two bound electrons (ECBB) while it fully accounts for all other interactions. The other semi-classical model is the Heisenberg one, which effectively accounts for the interaction of each electron with the core via a soft-core potential. We identify and compare the signatures of correlated three-electron escape for both quantum and semi-classical models on Dalitz plots and find a better agreement between the quantum and the ECBB model. We also show that a central "spot" on the Dalitz plots is reproduced by all models. Using the ECBB model we associate this "spot" with the direct triple ionization pathway and argue this to be the case also for the quantum model. Devising a simple classical model that accounts for the direct pathway of triple ionization, we show that the width of this spot in the Dalitz plots solely depends on the time of tunnel-ionization. © 2024, CC BY-NC-ND.

关键词： Coulomb interactions

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Enhancement and anisotropy of electron Landé factor due to spin-orbit interaction in semiconductor nanowires

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Physical Review B 2024年第8期109卷 085411-085411页

作者： Julian Czarnecki Andrea Bertoni Guido Goldoni Paweł Wójcik AGH University of Krakow Faculty of Physics and Applied Computer Science al. A. Mickiewicza 30 30-059 Krakow Poland CNR-NANO S3 Istituto Nanoscienze Via Campi 213/a 41125 Modena Italy Department of Physics Informatics and Mathematics University of Modena and Reggio Emilia via Campi 213/a 41125 Modena Italy

We investigate the effective Landé factor in semiconductor nanowires with strong Rashba spin-orbit coupling. Using the k·p theory and the envelope function approach we derive a conduction band Hamiltonian where g* is explicitly related to the spin-orbit coupling constants αR. Our model includes orbital effects from the Rashba spin-orbit term, leading to a significant enhancement of the effective Landé factor which is naturally anisotropic. For nanowires based on the low-gap, high spin-orbit coupled material InSb, we investigate the anisotropy of the effective Landé factor with respect to the magnetic field direction, exposing a twofold symmetry for the bottom gate architecture. The anisotropy results from the competition between the localization of the envelope function and the spin polarization of the electronic state, both determined by the magnetic field direction.

关键词： Spin-orbit coupling Nanowires Gyromagnetic ratio k dot p method

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Honeycomb antidot artificial lattice as a prototypical correlated Dirac fermion system

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Physical Review B 2023年第23期108卷 235119-235119页

作者： A. Biborski M. Zegrodnik P. Wójcik M. P. Nowak Academic Centre for Materials and Nanotechnology AGH University of Krakow Al. Mickiewicza 30 30-059 Krakow Poland Faculty of Physics and Applied Computer Science AGH University of Krakow Al. Mickiewicza 30 30-059 Krakow Poland

We study theoretically the electronic properties of the artificial quantum dot honeycomb lattice defined in a two-dimensional electron gas, focusing on the possibility of achieving a regime in which electronic correlations play a dominant role. At first, we establish a noninteracting model compatible with recently studied experimentally devices. According to the values of the obtained electron-electron interaction integrals, we postulate that the inclusion of inherent electron-gas self-screening is indispensable to reconstruct the experimental observations. Applying the Thomas-Fermi type of screening, we show that the radius of the antidot is crucial to achieve a correlated state in which phenomena like antiferromagnetic ordering and interaction-induced insulating state appear. We estimate the conditions for which the electronically correlated state in an artificial honeycomb lattice can be realized.

关键词： Antiferromagnetism Electronic structure Flat bands Bloch wave theory Electron-correlation calculations Free-electron model Hubbard model Materials modeling Tight-binding model Trial wave functions Variational approach Variational wave functional methods Wannier function methods

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Automatic Story Generation Based on Graph Model Using Godot Engine 19th

Automatic Story Generation Based on Graph Model Using Godot ...

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19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020

作者： Grabska-Gradzińska, Iwona Nowak, Leszek Grabska, Ewa Department of Games Technology Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland Department of Information Technologies Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland Department of Design and Computer Graphics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland

ISBN: (纸本)9783030614003

This paper deals with storytelling aspects of computer games. A new approach to automatic story generation based on graph modelling is proposed. Godot Engine for visualization of output stories is used. In our research the plot is represented by a specific graph called the layered graph composed of story building blocks, namely layer of locations, layer of characters, layer of items, layer of narration and plot dependencies. An appropriate set of graph rules (productions) stored in JSON format is a generative tool for the building blocks. Graph rules are internal representation of actions which can be taken by the hero of the story. A sequence of the rules from start to end can describe the whole gameplay of one player or can be generated in automatic way as a possible gameplay. All such sequences of the rules create the space of all potential gameplays. Every rule corresponds to a certain animation made in Godot Engine. It allows to visualize every possible story, represented by sequence of rules, by seamless animation. Given a rule, animation is based on graphic assets corresponding to the rule nodes of the layered graph. © 2020, Springer Nature Switzerland AG.

关键词： Animation

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Parametric activation functions modelling fuzzy connectives for better explainability of neural models 20

Parametric activation functions modelling fuzzy connectives ...

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20th IEEE Jubilee International Symposium on Intelligent Systems and Informatics, SISY 2022

作者： Pusztahazi, Luca Sara Csiszar, Gabor Gashler, Michael S. Csiszar, Orsolya Physiological Control Research Center Obuda University Budapest Hungary Applied Informatics and Applied Mathematics Doctoral School Obuda University Budapest Hungary Institute of Materials Physics University of Stuttgart Stuttgart Germany SupplyPike Fayetteville Arkansas72703 United States Faculty of Electrical Engineering and Computer Science Aalen University Aalen Germany

ISBN: (纸本)9781665489881

In this work, a neuro-fuzzy hybrid deep learning model is presented for finding human-readable relationships between input features with the help of nilpotent fuzzy logic and multi-criteria decision making (MCDM). In the neural network a parameterized, differentiable activation function is used, where the parameter is determined by gradient descent. The goal is to find the optimal regularization value by applying the deep learning model to classification problems from the UCI Machine Learning Repository. © 2022 IEEE.

关键词： Fuzzy inference

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