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Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023

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arXiv 2023年

作者： Jafari, Mahboobeh Sadeghi, Delaram Shoeibi, Afshin Alinejad-Rokny, Hamid Beheshti, Amin García, David López Chen, Zhaolin Acharya, U. Rajendra Gorriz, Juan M. Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Health Data Analytics Program Centre for Applied Artificial Intelligence Macquarie University Sydney2109 Australia Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia Monash University Melbourne Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Department of Psychiatry University of Cambridge United Kingdom

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. © 2023,

关键词： Electroencephalography

State-Feedback Control for Discrete-Time Uncertain Positive Linear Systems Under Denial of Service Attacks

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State-Feedback Control for Discrete-Time Uncertain Positive ...

UKACC International Conference on Control (CONTROL)

作者： Paulo S.P. Pessim Felipe A. Silva Márcia L.C. Peixoto Reinaldo M. Palhares Márcio J. Lacerda Graduate Program in Electrical Engineering Federal University of Minas Gerais Brazil Department of Electrical Engineering Federal University of São João del-Rei - UFSJ Brazil LAMIH laboratory UMR CNRS 8201 Université Polytechnique Hauts-de-France Valenciennes France INSA Hauts-de-France Valenciennes France Department of Electronics Engineering Federal University of Minas Gerais Minas Gerais Brazil Centre for Communications Technology School of Computing and Digital Media London Metropolitan University UK

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

This paper presents conditions in the form of Linear Matrix Inequalities to design state-feedback controllers for discrete-time uncertain positive linear systems in the presence of denial of service (DoS) attacks. The system under attack is modeled after a switched model that allows us to derive the synthesis conditions. Two strategies for control are considered, a hold strategy and a packet-based approach. Numerical experiments illustrate the efficacy of the proposed method of keeping the positiveness and stability of the closed-loop system under the presence of DoS attacks.

关键词： Linear systems Switched systems Design methodology Switches Stability analysis Numerical models Safety

vailá: VERSATILE ANARCHO INTEGRATED LIBERATION ÁNALYSIS IN MULTIMODAL TOOLBOX

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arXiv 2024年

作者： Santiago, Paulo Roberto Pereira Chinaglia, Abel Gonçalves Flanagan, Kira Bedo, Bruno L.S. Mochida, Ligia Yumi Aceros, Juan Bononi, Aline Cesar, Guilherme Manna Biomechanics and Motor Control Laboratory School of Physical Education and Sport USP Brazil Graduate Program in Rehabilitation and Functional Performance Medical School USP Brazil Technology and Sports Performance Analysis Laboratory School of Physical Education USP Brazil Laboratory of Applied Biomechanics and Engineering Brooks College of Health UNF United States Department of Physical Therapy Brooks College of Health UNF United States College of Computing Engineering and Construction UNF United States Municipal Pharmacy Ribeirão Preto Brazil

Human movement analysis is crucial in health and sports biomechanics for understanding physical performance, guiding rehabilitation, and preventing injuries. However, existing tools are often proprietary, expensive, and function as "black boxes", limiting user control and customization. This paper introduces vailá—Versatile Anarcho Integrated Liberation Ánalysis in Multimodal Toolbox—an open-source, Python-based platform designed to enhance human movement analysis by integrating data from multiple biomechanical systems. vailá supports data from diverse sources, including retroreflective motion capture systems, inertial measurement units (IMUs), markerless video capture technology, electromyography (EMG), force plates, and GPS/GNSS systems, enabling comprehensive analysis of movement patterns. Developed entirely in Python 3.11.9, which offers improved efficiency and long-term support, and featuring a straightforward installation process, vailá is accessible to users without extensive programming experience. In this paper, we also present several workflow examples that demonstrate how vailá allows the rapid processing of large batches of data, independent of the type of collection method. This flexibility is especially valuable in research scenarios where unexpected data collection challenges arise, ensuring no valuable data point is lost. We demonstrate the application of vailá in analyzing sit-to-stand movements in pediatric disability, showcasing its capability to provide deeper insights even with unexpected movement patterns. By fostering a collaborative and open environment, vailá encourages users to innovate, customize, and freely explore their analysis needs, potentially contributing to the advancement of rehabilitation strategies and performance optimization. For more details in GitHub repository: https://***/vaila-multimodaltoolbox/vaila and the online documentation: https://*** Codes 92C10, 68U10, 65D18, 65K10 © 2024, CC BY-SA.

关键词： Electromyography

An Analysis Method for Metric-Level Switching in Beat Tracking

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arXiv 2022年

作者： Chiu, Ching-Yu Müller, Meinard Davies, Matthew E.P. Su, Alvin Wen-Yu Yang, Yi-Hsuan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan International Audio Laboratories Erlangen Germany Department of Informatics Engineering Centre for Informatics and Systems The University of Coimbra University of Coimbra Portugal Department of CSIE National Cheng Kung University Taiwan Taiwan AI Labs Taiwan

For expressive music, the tempo may change over time, posing challenges to tracking the beats by an automatic model. The model may first tap to the correct tempo, but then may fail to adapt to a tempo change, or switch between several incorrect but perceptually plausible ones (e.g., half- or double-tempo). Existing evaluation metrics for beat tracking do not reflect such behaviors, as they typically assume a fixed relationship between the reference beats and estimated beats. In this paper, we propose a new performance analysis method, called annotation coverage ratio (ACR), that accounts for a variety of possible metric-level switching behaviors of beat trackers. The idea is to derive sequences of modified reference beats of all metrical levels for every two consecutive reference beats, and compare every sequence of modified reference beats to the subsequences of estimated beats. We show via experiments on three datasets of different genres the usefulness of ACR when being utilized alongside existing metrics, and discuss the new insights that can be gained. © 2022, CC BY.

关键词： Taps

Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

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arXiv 2023年

作者： Chiu, Ching-Yu Müller, Meinard Davies, Matthew E.P. Su, Alvin Wen-Yu Yang, Yi-Hsuan The Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan The International Audio Laboratories Erlangen Germany The Department of Informatics Engineering Centre for Informatics and Systems of the University of Coimbra University of Coimbra Portugal The Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use "post-processing trackers" (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called "predominant local pulses" (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2023, CC BY.

关键词： Dynamic programming

Investigation of factors regarding the effects of COVID-19 pandemic on college students’ depression by quantum annealer

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arXiv 2023年

作者： Choi, Junggu Kim, Kion Park, Soohyun Hur, Juyoen Yang, Hyunjung Kim, Younghoon Lee, Hakbae Han, Sanghoon Yonsei Graduate program in Cognitive Science Yonsei University Seoul03722 Korea Republic of Korea Quantum Computing© Seoul06164 Korea Republic of Department of Psychology Yonsei University Seoul03722 Korea Republic of University College Yonsei University Seoul03722 Korea Republic of Deparment of Applied Statistics Yonsei University Seoul03722 Korea Republic of Department of Statistics and Data science Yonsei University Seoul03722 Korea Republic of

Diverse cases regarding the impact, with its related factors, of the COVID-19 pandemic on mental health have been reported in previous studies. College student groups have been frequently selected as the target population in previous studies because they are easily affected by pandemics. In this study, multivariable datasets were collected from 751 college students based on the complex relationships between various mental health factors. We utilized quantum annealing (QA)-based feature selection algorithms that were executed by commercial D-Wave quantum computers to determine the changes in the relative importance of the associated factors before and after the pandemic. Multivariable linear regression (MLR) and XGBoost models were also applied to validate the QA-based algorithms. Based on the experimental results, we confirm that QA-based algorithms have comparable capabilities in factor analysis research to the MLR models that have been widely used in previous studies. Furthermore, the performance of the QA-based algorithms was validated through the important factor results from the algorithms. Pandemic-related factors (e.g., confidence in the social system) and psychological factors (e.g., decision-making in uncertain situations) were more important in post-pandemic conditions. We believe that our study will serve as a reference for researchers studying similar topics. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiple linear regression

Optimizing the Montgomery Modular Multiplier for a Power- and Area-Efficient Hardware Architecture

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Optimizing the Montgomery Modular Multiplier for a Power- an...

Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Mateus Terribele Leme Guilherme Paim Leandro M. G Rocha Patricia Ücker Vitor G. Lima Rafael Soares Eduardo A. C. da Costa Sergio Bampi Graduate Program in Microelectronics (PGMICRO) Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Graduate Program on Computer Science (PPGC) Federal University of Pelotas (UFPel)

ISBN: (数字)9781728180588

ISBN: (纸本)9781728180595

Cryptography hardware design is a key challenge towards the confidentiality advance in the prominent field of the internet of things (IoT). The rise of IoT embedded devices boosts the demand for power- and area- efficient solutions for cryptography hardware. The higher the robustness of the cryptography algorithm is, the higher are the hardware complexity, the circuit area, and energy consumption. Asymmetric algorithms are a particular class widely employed in ultra-secure cryptosystems. The high time-hardness to break the private-key in asymmetric algorithms is a result of its high mathematical complexity. RSA is an asymmetric algorithm that performs successive modular multiplications to encrypt and de-encrypt the information. Therefore, arithmetic operators are the most significant part regarding circuit area and power dissipation. This work evaluates a design space exploration for power- and area-efficient hardware VLSI design in the modular Montgomery multiplier employed in the RSA algorithm.

关键词： Hardware Computer architecture Adders Power dissipation Public key Very large scale integration

Framework to Creation of Inclusive and Didactic Digital Material for Elderly 15

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Framework to Creation of Inclusive and Didactic Digital Mate...

15th Iberian Conference on Information Systems and Technologies, CISTI 2020

作者： Eliseo, Maria Amelia Oyelere, Solomon Sunday Silva, Camilla Almeida Da Silveira, Ismar Frango Tomczyk, Lukasz Hercovici, Mariana Amato, Cibelle A. De La Higuera Akyar, Ozgur Yasar Martins, Valeria Farinazzo Mackenzie Presbyterian University Computing and Informatics Department São Paulo Brazil University of Eastern Finland School of Computing Joensuu Finland Institute of Educational Studies Pedagogical University of Cracow Cracow Poland Mackenzie Presbyterian University Developmental Disorders Graduate Program São Paulo Brazil Hacettepe University Faculty of Sport Sciences Ankara Turkey

ISBN: (纸本)9789895465903

The elderly population has been increased due better health conditions and quality of life. With a healthier aging, the elderly start to have a more active and participative life in society. They pursue, among other things, to acquire new knowledge and despite being a generation which access to existing technology was restricted, there is an increase in the use of digital technologies, especially in the educational field. However, with advancing age, cognitive and motor skills decrease. Given this scenario, the creation of accessible didactic materials that include the elderly population is necessary. This paper shows a framework to create inclusive digital material for elderly learning. The framework will help teachers to create digital contents that support elderly learners' personalized educational goals. © 2020 AISTI.

关键词： E-learning

programming Variational Quantum Circuits with Quantum-Train Agent

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Programming Variational Quantum Circuits with Quantum-Train ...

Quantum Communications, Networking, and computing (QCNC), International Conference on

作者： Chen-Yu Liu Samuel Yen-Chi Chen Kuan-Cheng Chen Wei-Jia Huang Yen-Jui Chang Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Hon Hai (Foxconn) Research Institute Taipei Taiwan Wells Fargo New York NY USA Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

This study introduces the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework, enabling efficient and scalable programming of variational quantum circuits (VQCs) through quantum-driven parameter updates for the classical slow programmer controlling the fast programmer VQC. By optimizing quantum and classical parameter management, QT-QFWP significantly reduces parameters (by 70–90%) compared to Quantum Long Short-Term Memory (QLSTM) and Quantum Fast Weight programmer (QFWP) while maintaining accuracy. Benchmarking on time-series tasks—including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW)—demonstrates superior efficiency and predictive accuracy. QT-QFWP is particularly advantageous for near-term quantum systems, addressing qubit and gate fidelity constraints, enhancing VQC deployment in time-sensitive applications, and expanding quantum computing’s role in machine learning.

关键词： Quantum system Accuracy Computational modeling Qubit Neural networks programming Predictive models Quantum circuit Physics Long short term memory