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Quantum spin systems: Toroidal classification and geometric duality

Physical Review B 2024年第14期110卷 L140403页

作者： Vahid Azimi-Mousolou Anders Bergman Anna Delin Olle Eriksson Manuel Pereiro Danny Thonig Erik Sjöqvist Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics Department of Physics and Astronomy Department of Applied Physics School of Engineering Sciences (SeRC) Wallenberg Initiative Materials Science for Sustainability (WISE) Wallenberg Initiative Materials Science WISE School of Science and Technology

We demonstrate a toroidal classification for quantum spin systems, revealing an intrinsic geometric duality within this structure. Through our classification and duality, we reveal that various bipartite quantum features in magnon systems can manifest equivalently in both bipartite ferromagnetic and antiferromagnetic materials, based upon the availability of relevant Hamiltonian parameters. Additionally, the results highlight the antiferromagnetic regime as an ultrafast dual counterpart to the ferromagnetic regime, both exhibiting identical capabilities for quantum spintronics and technological applications. Concrete illustrations are provided, demonstrating how splitting and squeezing types of two-mode magnon quantum correlations can be realized across ferro- and antiferromagnetic regimes.

关键词： Magnons Quantum correlations in quantum information Quantum information processing Quantum information theory Spintronics

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Edge detection using the Prewitt operator with fractional order telegraph partial differential equations (PreFOTPDE)

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Multimedia Tools and Applications 2025年第13期84卷 12329-12345页

作者： Tenekeci, Mehmet Emin Abdulazeez, Sadeq Taha Karadağ, Kerim Modanli, Mahmut Department of Computer Engineering Faculty of Engineering Harran University Şanlıurfa Turkey Department of Mathematics College of Basic Education University of Duhok Duhok Iraq Department of Electrical and Electronics Engineering Faculty of Engineering Harran University Şanlıurfa Turkey Depertmants of Mathematics Faculty of Arts and Sciences Harran University Sanliurfa Turkey

Detecting edges in image processing is an important process in image analysis or enhancement. Many methods detected edge information based on the differences in brightness values. Prewitt, the most widely used edge detection method, performs the extraction of edges by partial differential calculation. This study performed fractional order partial differential calculation with the Telegraph equation in the Prewitt method calculation. Filter coefficients are determined by using Grumwald-Letnikov series expansion for fractional partial differential equation calculation. To determine the most appropriate fraction value, five different coefficients were tried. The proposed method is compared with the conventional edge detection methods Sobel, Canny and standard Prewitt. The obtained result showed that the proposed method detects the edges more accurately. In addition, it has been compared with Prewitt fractional order edge detection (FOED) and edge detection method based on ant colony optimization (ACO) methods developed in recent years. Pratt's figure of merit (FOM) metric was used to compare these studies. It was realized that the FOM values calculated with the proposed method were more accurate than the others methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Edge detection

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Energy Management Strategy with a Hybrid Renewable Energy System for a Standalone House 16

Energy Management Strategy with a Hybrid Renewable Energy Sy...

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16th International Conference on applied and Theoretical Electricity, ICATE 2024

作者： Mustata, Laurentiu-Alex Sorea, Gheorghe-Dan Helerea, Elena Transilvania University Electrical Engineering and Computer Science Faculty Electrical Engineering and Applied Physics Department Brasov Romania

ISBN: (纸本)9798350388107

In the case of standalone houses, ensuring a continuous and regulated power supply from renewable sources is crucial. To address their unpredictable nature, an environmentally conscious hybrid renewable energy system combining photovoltaic panels, a battery, and a Diesel generator is considered. A software application simulates the power flow in a standalone house with common AC loads, analysing summer and winter scenarios over 24 hours. The strategy employed provides an optimal energy management by using the Diesel generator only during peak demand when PV is available and to cover loads and maintain battery life when PV is not in operation. The simulation results provide valuable insights for designing such hybrid energy systems and for enhancing system responsiveness to dynamic energy demands. © 2024 IEEE.

关键词： Hybrid power

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An infrastructure software perspective toward computation offloading between executable specifications and foundation models

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Science China(Information Sciences) 2025年第4期68卷 380-382页

作者： Dezhi RAN Mengzhou WU Yuan CAO Assaf MARRON David HAREL Tao XIE Key Laboratory of High Confidence Software Technologies (PKU) Ministry of Education School of Computer SciencePeking University School of Electronics Engineering and Computer Science Peking University Department of Computer Science and Applied Mathematics Weizmann Institute of Science

Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of software engineering theories and methodologies [2]. Instead of replacing existing software modules implemented by symbolic logic, incorporating FMs' capabilities to build software systems requires entirely new modules that leverage the unique capabilities of ***, while FMs excel at handling uncertainty, recognizing patterns, and processing unstructured data, we need new engineering theories that support the paradigm shift from explicitly programming and maintaining user-defined symbolic logic to creating rich, expressive requirements that FMs can accurately perceive and implement.

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Enhancing biometric authentication security through the novel integration of graph theory encryption and chaotic logistic mapping

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Multimedia Tools and Applications 2025年第16期84卷 16909-16943页

作者： El-Shafai, Walid El-Mesady, A. Kamal, F.M. Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Physics and Engineering Mathematics Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University PO 35516 Mansoura Egypt

The escalating reliance on biometric systems for identity verification underscores the imperative for robust data protection mechanisms. Biometric authentication, leveraging unique biological and behavioral characteristics, offers unparalleled precision in individual identification. However, the integrity and confidentiality of biometric data remain paramount concerns, given its susceptibility to compromise. This research delineates the development and implementation of an innovative framework for cancellable biometrics, focusing on facial and fingerprint recognition. This study introduces a novel cancellable biometrics framework that integrates graph theory encryption with three-dimensional chaotic logistic mapping. The methodology encompasses a multifaceted approach: initially employing graph theory for the secure and efficient encryption of biometric data, subsequently enhanced by the complexity and unpredictability of three-dimensional chaotic logistic mapping. This dual-layered strategy ensures the robustness of the encryption, thereby significantly elevating the security of biometric data against unauthorized access and potential compromise. Thus, the resulting cancellable biometrics, characterized by the ability to transform biometric data into an adjustable representation, addresses critical challenges in biometric security. It allows for the revocation and reissuance of biometric credentials, thereby safeguarding the original biometric characteristics of individuals. This feature not only enhances user privacy and data security but also introduces a dynamic aspect to biometric authentication, facilitating adaptability across diverse systems and applications. Preliminary evaluations of the proposed framework demonstrate a marked improvement in the security of face and fingerprint recognition systems. Through the application of graph theory encryption, coupled with three-dimensional chaotic logistic mapping, our framework mitigates the risks associated with t

关键词： Cryptography

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Relation Between electrical Grid Congestion and Bus Characteristics

Relation Between Electrical Grid Congestion and Bus Characte...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Verdaasdonk, Flin Vlasiou, Maria Hoogsteen, Gerwin Hurink, Johann University of Twente Department of Electrical Engineering Mathematics Computer Science Netherlands

ISBN: (纸本)9789531842976

electrical grids are increasingly congested, which causes stability and safety risks. To get more insights in how congestion can be managed, this paper analyses whether bus characteristics exist that have a consistent influence on grid loading. To study this, a metric for quantifying bus influence on grid loading is introduced and applied in two case studies. The first case study investigates if there are characteristics that correlate with congestion consistently, regardless of grid topology. The results from this case study suggest that none of the evaluated characteristics consistently correlate with grid loading. These results imply that topology should be explicitly considered in congestion management. The second case study investigates the variance in the correlation between bus characteristics and influence, within a single topology, over a variety of conditions. The results of the second case study suggest that this correlation is robust to reasonable changes in bus loads. © 2024 IEEE.

关键词： Sensitivity analysis

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Efficient Saliency Map Detection for Low-Light Images Based on Image Gradient

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第2期34卷 852-865页

作者： Lin, Chun-Yi Haq, Muhamad Amirul Chen, Jiun-Han Ruan, Shanq-Jang Naroska, Edwin National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan Hochschule Niederrhein University of Applied Science Faculty of Electrical Engineering and Computer Science Krefeld47805 Germany

Recently, deep learning has been widely employed across various domains. The Convolution Neural Network (CNN), a popular deep learning algorithm, has been successfully utilized in object recognition tasks, such as face recognition, vehicle recognition, and license plate recognition. However, conventional methods for object recognition may not be appropriate for low-light image recognition due to information loss in the dark regions and unexpected noise that can impair object quality. Therefore, the development of specialized techniques for low-light image enhancement has become a major research focus for object detection. This paper proposed a gradient-based saliency map detection method with an improved ResNet architecture that outperforms previous works in detecting multiple or large objects. Additionally, the proposed method enhances images with the object as the center and emphasizes foreground-background differences. Compared with previous works, this paper achieves1.28× improvements in the parameters and 1.32× faster inference speed than the original ResNet architecture. © 1991-2012 IEEE.

关键词： Neural networks

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Arbitrary style transfer system with split-and-transform scheme

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Multimedia Tools and Applications 2024年第23期83卷 62497-62517页

作者： Tu, Ching-Ting Lin, Hwei Jen Tsai, Yihjia Lin, Zi-Jun Department of Applied Mathematics National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan

For the subject of arbitrary image style transfer, there have been some proposed architectures that directly compute the transformation matrix of the whitening and coloring transformation (WCT) to obtain more satisfactory transformation results. However, calculating the transformation matrix of WCT is time-consuming. Li et al. trained a linear transformation module to generate a WCT transformation matrix for any pair of images, i.e., content image and style image, to avoid complex calculations and improves time efficiency. In this work, we introduce a flexible arbitrary image style transfer framework based on the LST, which uses deep neural networks to train a linear transformation matrix as the standard matrix for WCT. For the first part, inverse relationship between the Whitening matrix and the Coloring matrix w.r.t. the same image is enforced during the training of the linear transformation matrix, so that the resulting matrix will be more accurate and closer to the standard matrix of WCT. For the second part, a split-and-transform scheme is proposed. Unlike LST, which transforms the block of feature maps as a whole, the split-and-transform scheme divides the feature block into several smaller blocks and transforms them individually, so that the transformation is more localized, and the more the number of divided blocks, the more localized. In addition, the proposed split-and-transform scheme allows users to determine the number of divided blocks to flexibly control the locality of the transformations. Experimental results demonstrate the effectiveness and flexibility of the proposed framework by the high-quality stylized images and adjustable balance between globality and locality of transformations. The use of the split-and-transform scheme can reduce the computational time while preserving or even improving the stylization results. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Linear transformations

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A Comparative Assessment of eXplainable AI Tools in Predicting Hard Disk Drive Health 32

A Comparative Assessment of eXplainable AI Tools in Predicti...

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32nd Italian Symposium on Advanced Database Systems, SEBD 2024

作者： Amato, Flora Ferraro, Antonino Galli, Antonio La Gatta, Valerio Moscato, Francesco Moscato, Vincenzo Postiglione, Marco Sansone, Carlo Sperlì, Giancarlo Department of Electrical Engineering and Information Technology University of Naples Federico II Italy Department of Information Engineering Electrical Engineering and Applied Mathematics University of Salerno Italy

In addressing the challenge of optimizing maintenance operations in Industry 4.0, recent efforts have focused on predictive maintenance frameworks. However, the effectiveness of these frameworks, largely relying on complex deep learning models, is hindered by their lack of explainability. To address this, we employ eXplainable Artificial Intelligence (XAI) methodologies to make the decision-making process more understandable for humans. Our study, based on a previous work, specifically explores explanations for predictions made by a recurrent neural network-based model designed for a three-dimensional dataset, used to estimate the Remaining Useful Life (RUL) of Hard Disk Drives (HDDs). We compare the explanations provided by different XAI tools, emphasizing the utility of global and local explanations in supporting predictive maintenance tasks. Using the Backblaze Dataset and a Long Short-Term Memory (LSTM) prediction model, our developed explanation framework evaluates Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) tools. Results show that SHAP outperforms LIME across various metrics, establishing itself as a suitable and effective solution for HDD predictive maintenance applications. © 2024 Copyright for this paper by its authors.

关键词： Predictive maintenance

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Integrating cycleGAN and BERT for Chinese text style transfer

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Multimedia Tools and Applications 2024年 1-20页

作者： Chou, Chien-Hsing Chou, Cheng-Hou Hsieh, Yi-Zeng Yang, Tzu-Shien Department of Mathematics and Information Education National Taipei University of Education Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electrical and Computer Engineering Tamkang University New Taipei Taiwan

In this study, we integrate the Bidirectional Encoder Representations from Transformers (BERT) model with the Cycle Generative Adversarial Network (CycleGAN) to create a system for Chinese text style transfer. Natural language processing (NLP) involves converting human languages into data interpretable by computers, enabling applications like text classification, chatbots, and dialogue systems. Recent advancements, such as Google's transformer model and the BERT technique, have significantly improved NLP capabilities through self-attention mechanisms and unsupervised pretraining. Text style transfer modifies the style of texts without altering their semantics. Previous methods like StyIns and models based on disentangled representation learning highlight the challenges of retaining text meaning during style transfer. Our system leverages CycleGAN’s unsupervised learning to convert unpaired data between wuxia and fantasy styles while preserving semantics. Using the pretrained BERT model from the Chinese Knowledge and Information Processing (CKIP) Lab, our experimental results demonstrate successful style conversion, maintaining the original meanings of texts. This integration of BERT and CycleGAN shows promise for further advancements in NLP applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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