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A regularized transformer with adaptive token fusion for Alzheimer's disease diagnosis in brain magnetic resonance images

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engineering Applications of Artificial Intelligence 2025年 155卷

作者： Lu, Si-Yuan Zhang, Yu-Dong Yao, Yu-Dong School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Jiangsu Nanjing210096 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States

Alzheimer's Disease (AD) poses significant challenges in neuroimaging, where early and accurate diagnosis is essential for timely intervention. Magnetic Resonance Imaging (MRI) serves as a key modality for AD diagnosis, providing detailed anatomical insights without radiation exposure. While traditional Convolutional Neural Networks (CNNs) excel at local feature extraction from MRI data, they fail to capture global contextual information critical for AD detection. To address this limitation, we propose the Lightweight Robust Alzheimer's Disease Vision Transformer (LRAD-ViT), a computationally efficient framework tailored for early AD detection. LRAD-ViT enhances the global learning capabilities of Vision Transformers through a novel adaptive token fusion technique. The proposed method selectively identifies and merges non-essential tokens within brain MRI scans, optimizing computational efficiency without sacrificing diagnostic performance. By dynamically adapting the token structure during model operation, the method prioritizes diagnostically relevant regions in the images. Additionally, randomized learning regularization improves learning dynamics and model robustness. Additionally, randomized learning regularization improves learning dynamics and model robustness. Validation on two brain MRI datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrates that LRAD-ViT achieves a diagnostic accuracy of 93.41 % on AD versus cognitive normal (CN) and 90.95 % on CN versus mild cognitive impairment (MCI), all while reducing computational demands by around 40 %. These metrics substantiate LRAD-ViT's superior diagnostic performance and computational efficiency, making it a promising tool for the early and accurate diagnosis of Alzheimer's Disease, with significant implications for clinical practice. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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Comparative Analysis of AES, RC and Twofish Algorithms for data Encryption and Decryption

Comparative Analysis of AES, RC and Twofish Algorithms for D...

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computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Ahmad Faizal Bin Tajuddin Mohammad Hafiz Mohd Yusof Mohammed Balfaqih Akram A. Almohammedi Sam Darshi Nadhem Qaid College of Computing Informatics and Mathematics Universiti Teknologi MARA UiTM Tapah Malaysia College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Electrical and Electronic Engineering Department Karabük University Karabük Turkey Department of Electrical Engineering Indian Institute of Tehcnology Ropar Punjab India R&D Engineer Lead Marine Powertrain Controls & Electronics Caterpillar Inc Houstan Texas USA

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

This paper presents a comparative analysis of three widely used encryption algorithms, namely AES (Advanced Encryption Standard), RC (Rivest Cipher), and Twofish, with a focus on their performance in data encryption and decryption. The study aims to evaluate the strengths and weaknesses of these algorithms in terms of security, speed, and efficiency. The analysis involves benchmarking the algorithms against various performance metrics, including encryption time, decryption time, key size, and resistance against cryptanalysis attacks. Additionally, the research explores the impact of different data sizes and configurations on the performance of each algorithm. The findings of this study will provide valuable insights for decision-makers and practitioners in selecting the most suitable encryption algorithm for their specific needs, considering the tradeoffs between security and performance.

关键词： Resistance Ciphers Three-dimensional displays data security Benchmark testing Encryption Timing Information technology Standards

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E1 TTS: Simple and Fast Non-Autoregressive TTS

E1 TTS: Simple and Fast Non-Autoregressive TTS

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhijun Liu Shuai Wang Pengcheng Zhu Mengxiao Bi Haizhou Li School of Data Science The Chinese University of Hong Kong Shenzhen P.R. China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen P.R. China Fuxi AI Lab NetEase Inc. Hangzhou China Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper introduces Easy One-Step Text-to-Speech (E1 TTS), an efficient non-autoregressive zero-shot text-to-speech system based on denoising diffusion pretraining and distribution matching distillation. The training of E1 TTS is straightforward; it does not require explicit monotonic alignment between the text and audio pairs. The inference of E1 TTS is efficient, requiring only one neural network evaluation for each utterance. Despite its sampling efficiency, E1 TTS achieves naturalness and speaker similarity comparable to various strong baseline models. Audio samples are available at ***.

关键词： Training Noise reduction Neural networks Signal processing Acoustics Text to speech Speech processing

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High-Fidelity Coherent-One-Way QKD Simulation Framework for 6G Networks: Bridging Theory and Reality

arXiv

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arXiv 2025年

作者： Brazaola-Vicario, Aitor Kouvakis, Vasileios Trevlakis, Stylianos E. Ruiz, Alejandra Boulogeorgos, Alexandros-Apostolos A. Tsiftsis, Theodoros Niyato, Dusit Spain The Department of Research and Development InnoCube P.C. 79 17is Noemvriou Str. Thessaloniki55534 Greece The Department of Electrical and Computer Engineering University of Western Macedonia ZEP Area Kozani50100 Greece The Department of Informatics & Telecommunications University of Thessaly Lamia35100 Greece The College of Computing and Data Science Nanyang Technological University 639798 Singapore

Quantum key distribution (QKD) has been emerged as a promising solution for guaranteeing information-theoretic security. Inspired by this, a great amount of research effort has been recently put on designing and testing QKD systems as well as articulating preliminary application scenarios. However, due to the considerable high-cost of QKD equipment, a lack of QKD communication system design tools, wide deployment of such systems and networks is challenging. Motivated by this, this paper introduces a QKD communication system design tool. First we articulate key operation elements of the QKD, and explain the feasibility and applicability of coherent-one-way (COW) QKD solutions. Next, we focus on documenting the corresponding simulation framework as well as defining the key performance metrics, i.e., quantum bit error rate (QBER), and secrecy key rate. To verify the accuracy of the simulation framework, we design and deploy a real-world QKD setup. We perform extensive experiments for three deployments of diverse transmission distance in the presence or absence of a QKD eavesdropper. The results reveal an acceptable match between simulations and experiments rendering the simulation framework a suitable tool for QKD communication system design. © 2025, CC BY.

关键词： Bit error rate

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Adaptive Solar Energy Storage with Deep Learning for Improved Grid Resilience

Adaptive Solar Energy Storage with Deep Learning for Improve...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： C.Shobana Nageswari Pramod Pandey Sujitha R Sujatha M S. Senthilkumar S. Sujatha Department of ECE R.M.D. Engineering College Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Annapoorana Engineering College (Autonomous) Salem Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Centre for Artificial Intelligence & Data Science (CAI) Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation ( DU) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Implementing renewable energy sources, especially solar power, into the electrical grid has distinct difficulties and potential for improving system resilience. This research investigates an adaptive solar energy storage system using deep learning methodologies, particularly Long Short-Term Memory (LSTM) networks, to enhance energy management and grid stability. The proposed system utilizes past solar generating data and usage patterns to forecast future energy needs and generation capacities. LSTM allows the model to efficiently reflect temporal interdependence and variation in solar energy output, facilitating more precise forecasting. The adaptive storage system modifies its charging and discharging processes in real time, optimizing efficiency and reducing energy waste. LSTM-based forecasting improves the storage system’s response to variations in supply and demand, enhancing load balancing and grid resilience. This method facilitates a change in renewable energy sources while enhancing the resilience of grid infrastructure to sustain delays. Findings indicate that integrating flexible energy storage using advanced deep-learning methodologies may significantly improve the efficacy and dependability of solar energy systems. This paper challenges solar energy intermittency with LSTM-based adaptive storage, enhancing forecasting precision, grid resilience, and energy efficiency enhancing stable energy management and optimized battery use.

关键词： Deep learning Renewable energy sources Computational modeling Solar energy Predictive models Real-time systems Robustness Forecasting Long short term memory Resilience

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MacST: Multi-Accent Speech Synthesis via Text Transliteration for Accent Conversion

MacST: Multi-Accent Speech Synthesis via Text Transliteratio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sho Inoue Shuai Wang Wanxing Wang Pengcheng Zhu Mengxiao Bi Haizhou Li School of Data Science The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) Shenzhen China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) Shenzhen China Fuxi AI Lab NetEase Inc. Hangzhou China Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In accented voice conversion or accent conversion, we seek to convert the accent in speech from one another while preserving speaker identity and semantic content. In this study, we formulate a novel method for creating multi-accented speech samples, thus pairs of accented speech samples by the same speaker, through text transliteration for training accent conversion systems. We begin by generating transliterated text with Large Language Models (LLMs), which is then fed into multilingual TTS models to synthesize accented English speech. As a reference system, we built a sequence-to-sequence model on the synthetic parallel corpus for accent conversion. We validated the proposed method for both native and non-native English speakers. Subjective and objective evaluations further validate our dataset’s effectiveness in accent conversion studies.

关键词： Training Large language models Semantics Signal processing Acoustics Text to speech Multilingual

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Motor imagery enhances performance beyond the imagined action

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Proceedings of the National Academy of sciences of the United States of America 2025年第20期122卷 e2423642122页

作者： Gippert, Magdalena Shih, Pei-Cheng Heed, Tobias Howard, Ian S. Jamshidi Idaji, Mina Villringer, Arno Sehm, Bernhard Nikulin, Vadim V. Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig 04103 Germany Sony Computer Science Laboratories 141-0022 Tokyo Japan Department of Psychology and Centre for Cognitive Neuroscience University of Salzburg 5020 Salzburg Austria School of Engineering Computing and Mathematics Faculty of Science and Engineering University of Plymouth Plymouth PL4 8AA United Kingdom Machine Learning Group Berlin Institute for the Foundations of Learning and Data 10587 Berlin Germany Machine Learning Group Institute of Software Engineering and Theoretical Computer Science Electrical Engineering and Computer Science Faculty Technical University Berlin 10587 Berlin Germany Department of Neurology Martin Luther University of Halle-Wittenberg Halle (Saale) 06120 Germany

Motor imagery is frequently utilized to improve the performance of specific target movements in sports and rehabilitation. In this study, we show that motor imagery can facilitate learning of not only the imagined target movements but also sequentially linked overt movements. Hybrid sequences comprising imagined and physically executed segments allowed participants to learn specific movement characteristics of the executed segments when they were consistently associated with specific imagined segments. Electrophysiological recordings revealed that the degree of event-related synchronization in the alpha and beta bands during a basic motor imagery task was correlated with imagery-evoked motor learning. Thus, both behavioral and neural evidence indicate that motor imagery's benefits extend beyond the imagined movements, improving performance in linked overt movements. This provides decisive evidence for the functional equivalence of imagined and overt movements and suggests applications for imagery in sports and rehabilitation.

关键词： adaptation force field motor imagery motor learning motor sequence

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No-Regret Linear Bandits under Gap-Adjusted Misspecification

arXiv

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arXiv 2025年

作者： Liu, Chong Qiao, Dan Yin, Ming Bogunovic, Ilija Wang, Yu-Xiang Department of Computer Science University at Albany State University of New York AlbanyNY12222 United States Department of Computer Science and Engineering University of California La Jolla San DiegoCA92093 United States Department of Computer Science University of California Santa Barbara Santa BarbaraCA93106 United States Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom Halıcıoğlu Data Science Institute University of California La Jolla San DiegoCA92093 United States

This work studies linear bandits under a new notion of gap-adjusted misspecification and is an extension of Liu et al. (2023). When the underlying reward function is not linear, existing linear bandits work usually relies on a uniform misspecification parameter ϵ that measures the sup-norm error of the best linear approximation. This results in an unavoidable linear regret whenever ϵ > 0. We propose a more natural model of misspecification which only requires the approximation error at each input x to be proportional to the suboptimality gap at x. It captures the intuition that, for optimization problems, near-optimal regions should matter more and we can tolerate larger approximation errors in suboptimal regions. Quite surprisingly, we show that the classical LinUCB algorithm — designed for the realizable case — is automatically robust against such ρ-gap-adjusted misspecification with parameter ρ diminishing at O(1/(d√log T)). It achieves a near-optimal O(√T) regret for problems that the best-known regret is almost linear in time horizon T. We further advance this frontier by presenting a novel phased elimination-based algorithm whose gap-adjusted misspecification parameter ρ = O(1/√d) does not scale with T. This algorithm attains optimal O(√T) regret and is deployment-efficient, requiring only log T batches of exploration. It also enjoys an adaptive O(log T) regret when a constant suboptimality gap exists. Technically, our proof relies on a novel self-bounding argument that bounds the part of the regret due to misspecification by the regret itself, and a new inductive lemma that limits the misspecification error within the suboptimality gap for all valid actions in each batch selected by G-optimal design. Copyright © 2025, The Authors. All rights reserved.

关键词： Errors

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UAV-Aided Covert Federated Learning Networks

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IEEE Network 2025年

作者： Wang, Chao Guo, Shaoyong Xiong, Zehui Xing, Chengwen Zhao, Nan Niyato, Dusit Karagiannidis, George Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore Beijing Institute of Technology School of Information and Electronics Beijing100081 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece

With the increasing emphasis on data privacy, federated learning (FL) networks show great potential through distributed training without directly sharing the raw data. However, the coverage of FL terrestrial servers is usually limited. Therefore, we propose leveraging the unmanned aerial vehicle (UAV) as a mobile flying server to further improve the wireless coverage and training efficiency of FL. Nevertheless, frequent exchanges of model parameters in UAV-assisted FL can result in serious security risks. In order to achieve this, we propose a covert FL scheme assisted by the UAV, which can protect the transmission key features of local models from being detected by wardens. Furthermore, we discuss the FL network and covert communications, as well as the exceptional features of UAV-assisted covert FL. Finally, we present two case studies, one of which focuses on minimising the network latency, while the other concentrates on reducing the energy consumption of covert FL. The simulation results demonstrate the efficacy of the proposed schemes in addressing future challenges. © 1986-2012 IEEE.

关键词： data privacy

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Optimal Green Energy Estimation for Sustainable Electric Vehicle Networks Using Queueing-Based Retrial Process

Optimal Green Energy Estimation for Sustainable Electric Veh...

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Sustainable Energy Technologies and Computational Intelligence (SETCOM), International Conference on

作者： Shreekant Varshney Jainit Raval Rahul Priyadarshi Vidhi Manek Yash Dadheech Manthan Shah Department of Mathematics School of Technology Pandit Deendayal Energy University Gandhinagar Gujarat India Department of Electrical Engineering School of Energy Technology Pandit Deendayal Energy University Gandhinagar Gujarat India Symbiosis Institute of International Business Symbiosis International University Hinjewadi Pune Maharashtra India Department of Center for Data Science Center for Data Science New York University New York NY USA Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gandhinagar Gujarat India Department of Mechanical Engineering School of Technology Pandit Deendayal Energy University Gandhinagar Gujarat India

ISBN: (数字)9798331520540

ISBN: (纸本)9798331520557

This paper presents a stochastic model for addressing a single-server service station offering both EV charging and battery swapping services, considering the realistic retrial-based queueing behavior of the waiting individuals. A computationally efficient method is proposed for determining the steady-state probability distribution of the charging network. The approach involves analyzing the governing differential-difference equations and employing a matrix method to derive the steady-state probability distribution. Further, straightforward formulae are derived for critical system performance indicators, considering both server and system characteristics. Numerical results are visualized in the form of different graphical representations to demonstrate the influence of various parameters on system performance. Finally, the paper concludes with significant findings and future research directions to apprise researchers, decision-makers, and system designers in optimizing combined charging/swapping infrastructure.

关键词： Visualization System performance Computational modeling Stochastic processes Probability distribution Mathematical models Electric vehicle charging Steady-state Batteries Servers

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