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SSRN 2024年

作者： Hopper, John L. Nguyen, Tuong Linh Elliott, Michael S. Al-Qershi, Osamah Schmidt, Daniel F. Makalic, Enes Li, Shuai Fox, Samantha K. Dowty, James G. Peña-Solorzano, Carlos Andres Kwok, Chun Fung Chen, Yuanhong Wang, Chong Lippey, Jocelyn F. Brotchie, Peter Carneiro, Gustavo McCarthy, Davis J. Frazer, Helen M.L. Centre for Epidemiology & Biostatistics Melbourne School of Population and Global Health University of Melbourne Victoria Australia Bioinformatics and Cellular Genomics Unit St Vincent’s Institute of Medical Research Victoria Australia Melbourne Integrative Genomics School of Mathematics and Statistics School of BioSciences Faculty of Science University of Melbourne Victoria Australia Faculty of Information Technology Monash University ClaytonVIC Australia Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care University of Cambridge Cambridge United Kingdom Precision Medicine School of Clinical Sciences at Monash Health Monash University ClaytonVIC Australia Murdoch Children’s Research Institute Royal Children’s Hospital ParkvilleVIC Australia School of Computer Science Australian Institute for Machine Learning University of Adelaide SA Australia Department of Surgery St Vincent’s Hospital Melbourne VIC Australia Faculty of Medicine Dentistry and Health Sciences University of Melbourne Victoria Australia St Vincent’s BreastScreen St Vincent’s Hospital Melbourne VIC Australia Department of Radiology St Vincent’s Hospital Melbourne VIC Australia Centre for Vision Speech and Signal Processing University of Surrey United Kingdom BreastScreen Victoria VIC Australia

Background: Artificial intelligence (AI) algorithms to detect breast cancers from mammographic images are being developed. We evaluated the performance of an algorithm across all stages of the screening process and beyond. Methods: We used an image-specific AI-based breast cancer detection measure learnt from images of women attending BreastScreen Victoria (BRAIx cancer detection measure) to create a woman-specific standard normal mammography-based risk score (BRAIx risk score). Applying logistic regression to a random independent prospective testing cohort of 96,348 women, we estimated risk of invasive breast cancers: (i) detected at cohort entry (525);and (ii) for women given an all-clear, diagnosed during the next 4 years either at future screens (790) or during intervals between screens (308), as a function of the BRAIx risk score and other measures. We estimated the odds ratio per standard deviation (OR) and a greater risk in an upper ***: The BRAIx risk score predicted reader outcomes, recall, cancer detection and future risk. For 4-years risk, OR=2.70 (95% confidence interval (CI) 2.52 to 2.88) overall with evidence for a greater OR in the upper tail (P=10-18). The predicted absolute risk was >10% for 1% of all-clear women. BRAIx risk score was greater for women with a family history and explained 23% (95% CI 21-25%) of familial aspects of risk. Mammographic density was weakly associated with risk before (OR=1.27;95% CI 1.20 to 1.35), but not after, fitting ***: The BRAIx risk score is a strong biomarker for breast cancer that could be used to inform risk-based recall for further assessment, detection and future screening. Its use would automatically identify all-clear women at similar risk to BRCA1 and BRCA2 mutation carriers. BRAIx could also reveal insights into both familial and non-familial causes. Funding: Medical Research Future Fund, the Ramaciotti Foundation, National Breast Cancer Foundation, Cancer *** o

关键词： Mammography

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Harmonized-Multinational qEEG norms (HarMNqEEG)

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NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

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Coding for Write -step-up Memories

Coding for Write -step-up Memories

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2019 IEEE International Symposium on Information Theory, ISIT 2019

作者： Chee, Yeow Meng Kiah, Han Mao Vinck, A. J. Han Vu, Van Khu Yaakobi, Eitan Department of Industrial Systems Engineering and Management National University of Singapore Singapore Singapore School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore Institute of Digital Signal Processing University of Duisburg-Essen Duisburg47057 Germany Computer Science Department Technion - Israel Institute of Technology Haifa Israel

ISBN: (纸本)9781538692912

In this work, we propose and study a new class of non-binary rewriting codes, called write -step-up memories (WM) codes. From an information-theoretic point of view, this coding scheme is a generalization of non-binary write-once memories (WOM) codes. From a practical point of view, this coding scheme can be used not only to increase the lifetime of flash memories but also mitigate their over-shooting problem. We first provide an exact formula for the capacity region and the maximum sum-rate of WM codes. Lastly, we present several explicit constructions of high-rate WM codes with efficient encoding/decoding algorithms. © 2019 IEEE.

关键词： Flash memory

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Parametric multidirectional decomposition of microphone recordings for broadband high-order ambisonic encoding 144

Parametric multidirectional decomposition of microphone reco...

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144th Audio Engineering Society Convention 2018

作者： Politis, Archontis Tervo, Sakari Lokki, Tapio Pulkki, Ville Department of Signal Processing and Acoustics School of Electrical Engineering Aalto University Finland Department of Computer Science School of Science Aalto University Finland

Higher-order Ambisonics (HOA) is a flexible recording and reproduction method, which makes it attractive for several applications in virtual and augmented reality. However, the recording of HOA signals with practical compact microphone arrays is limited to a certain frequency range, which depends on the applied microphone array. In this paper, we present a parametric signal-dependent approach that improves the HOA signals at all frequencies. The presented method assumes that the sound field consists of multiple directional components and a diffuse part. The performance of the method is evaluated in simulations with a rigid microphone array in different direct-to-diffuse and signal-to-noise ratio conditions. The results show that the proposed method has a better performance than the traditional signal-dependent encoding in all the simulated conditions. © 2018 Audio Engineering Society. All Rights Reserved.

关键词： signal encoding

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Using Artificial Intelligence to Predict the Next Deceptive Movement Based on Video Sequence Analysis: A Case Study on a Professional Cricket Player's Movements

Journal of Engineering Research

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Journal of Engineering Research 2025年

作者： A.M. Mutawa Korupalli V Rajesh Kumar K Hemachandran M. Murugappan Computer Engineering Department College of Engineering and Petroleum Kuwait University Safat 5969 13060 Kuwait CoE- Sports Analytics and Management and AI Research Centre School of Business Woxsen University Hyderabad India Intelligent Signal Processing (ISP) Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Block 4 Doha 13133 Kuwait Department of Electronics and Communication Engineering School of Engineering Vels Institute of Sciences Technology and Advanced Studies Chennai Tamilnadu India Center of Excellence for Unmanned Aerial Systems (CoEUAS) Universiti Malaysia Perlis 02600 Perlis Malaysia

This research develops an artificial intelligence-based model to predict the next deceptive movement of athletes by analyzing video sequences of previous movements. Utilizing advanced deep neural network models, we analyze deceptive movements to forecast the next move, with a practical application on the deceptive movements of a professional cricket player. The model employs machine learning techniques such as Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbor (KNN) to enhance prediction accuracy. Achieving up to 70% accuracy, this model rivals human capability, as even highly skilled players can easily fall for deceptive actions. The ability to predict deceptive movements sets humans apart from many intelligent creatures, allowing athletes to avoid predictable actions and gain an edge in various sports. This study applies this concept to cricket, leveraging video data to improve training methods. The results highlight the potential of artificial intelligence in revolutionizing training and performance optimization in sports.

关键词： Artificial Intelligence Deceptive Movements Machine Learning Cricket Performance Optimization

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Gender-Equity Model for Liver Allocation Using Artificial Intelligence (GEMA-AI) for Waiting List Liver Transplant Prioritization

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Clinical Gastroenterology and Hepatology 2025年

作者： Gómez-Orellana, Antonio Manuel Rodríguez-Perálvarez, Manuel Luis Guijo-Rubio, David Gutiérrez, Pedro Antonio Majumdar, Avik McCaughan, Geoffrey W. Taylor, Rhiannon Tsochatzis, Emmanuel A. Hervás-Martínez, César Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Instituto Maimónides de Investigación Biomédica de Córdoba Córdoba Spain Department of Hepatology and Liver Transplantation Hospital Universitario Reina Sofía Instituto Maimónides de Investigación Biomédica de Córdoba Córdoba Spain Department of Medicine University of Córdoba Córdoba Spain Centro de investigación biomédica en red de enfermedades hepáticas y digestivas Madrid Spain Department of Signal Processing and Communications University of Alcalá Alcalá de Henares Spain AW Morrow Gastroenterology and Liver Centre and Australian National Liver Transplant Unit Royal Prince Alfred Hospital Sydney NSW Australia Central Clinical School University of Sydney Sydney NSW Australia Victorian Liver Transplant Unit Austin Health Melbourne VIC Australia Department of Medicine University of Melbourne Melbourne Australia Liver Injury and Cancer Program Centenary Institute Sydney Australia Department of Statistics and Clinical Studies NHS Blood and Transplant Stoke Gifford United Kingdom Sheila Sherlock Liver Unit and UCL Institute for Liver and Digestive Health Royal Free Hospital London United Kingdom

Background & Aims: We aimed to develop and validate an artificial intelligence score (gender-equity model for liver allocation using artificial intelligence [GEMA-AI]) to predict liver transplantation (LT) waiting list outcomes using the same input variables contained in existing models. Methods: This was a cohort study including adult LT candidates enlisted in the United Kingdom (2010–2020) for model training and internal validation and in Australia (1998–2020) for external validation. GEMA-AI combined international normalized ratio, bilirubin, sodium, and the Royal Free Hospital glomerular filtration rate in an explainable artificial neural network. GEMA-AI was compared with gender-equity model for liver allocation corrected by serum sodium (GEMA-Na), Model for End-Stage Liver Disease 3.0, and Model for End-Stage Liver Disease corrected by serum sodium for waiting list prioritization. Results: The study included 9320 patients: 5762 in the training cohort, 1920 in the internal validation cohort, and 1638 in the external validation cohort. The prevalence of 90-day mortality or delisting for sickness ranged from 5.3% to 6% across different cohorts. GEMA-AI showed better discrimination than GEMA-Na, Model for End-Stage Liver Disease corrected by serum sodium, and Model for End-Stage Liver Disease 3.0 in the internal and external validation cohorts, with a more pronounced benefit in women and in patients showing at least 1 extreme analytical value. Accounting for identical input variables, the transition from a linear to a nonlinear score (from GEMA-Na to GEMA-AI) resulted in a differential prioritization of 6.4% of patients within the first 90 days and would potentially save 1 in 59 deaths overall, and 1 in 13 deaths among women. Results did not substantially change when ascites was not included in the models. Conclusions: The use of explainable machine learning models may be preferred over conventional regression-based models for waiting list prioritization in LT. GE

关键词： Artificial Neural Networks Disparities Explainable Artificial Intelligence Gender Liver Allocation Machine Learning

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Joining sound event detection and localization through spatial segregation

arXiv

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arXiv 2019年

作者： Trowitzsch, Ivo Schymura, Christopher Kolossa, Dorothea Obermayer, Klaus Neural Information Processing Group Department of Electrical Engineering and Computer Science Technische Universität Berlin Cognitive Signal Processing Group Department of Electrical Engineering and Information Technology Ruhr-Universität Bochum

—Identification and localization of sounds are both integral parts of computational auditory scene analysis. Although each can be solved separately, the goal of forming coherent auditory objects and achieving a comprehensive spatial scene understanding suggests pursuing a joint solution of the two problems. This work presents an approach that robustly binds localization with the detection of sound events in a binaural robotic system. Both tasks are joined through the use of spatial stream segregation which produces probabilistic time-frequency masks for individual sources attributable to separate locations, enabling segregated sound event detection operating on these streams. We use simulations of a comprehensive suite of test scenes with multiple co-occurring sound sources, and propose performance measures for systematic investigation of the impact of scene complexity on this segregated detection of sound types. Analyzing the effect of spatial scene arrangement, we show how a robot could facilitate high performance through optimal head rotation. Furthermore, we investigate the performance of segregated detection given possible localization error as well as error in the estimation of number of active sources. Our analysis demonstrates that the proposed approach is an effective method to obtain joint sound event location and type information under a wide range of conditions. Copyright © 2019, The Authors. All rights reserved.

关键词： Audition

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Comments on "design of momentum fractional LMS for Hammerstein nonlinear system identification with application to electrically stimulated muscle model"

arXiv

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arXiv 2020年

作者： Wahab, Abdul Khan, Shujaat Khan, Farrukh Zeeshan Department of Mathematics School of Sciences and Humanities Nazarbayev University 53 Kabanbay Batyr Avenue Nur-Sultan010000 Kazakhstan Bio-Imaging Signal Processing and Learning Lab. Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Department of Computer Science University of Engineering and Technology Taxila Taxila47080 Pakistan

The purpose of this article is to discuss some aspects of the convergence analysis performed in the paper [Design of momentum fractional LMS for Hammerstein nonlinear system identification with application to electrically stimulated muscle model, Eur. Phys. J. Plus (2019) 134: 407]. It is highlighted that the way the authors prove convergence suffers a lack of correct and valid mathematical justifications. Copyright © 2020, The Authors. All rights reserved.

关键词： Momentum

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SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification

arXiv

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arXiv 2021年

作者： Phan, Huy Mikkelsen, Kaare Chén, Oliver Y. Koch, Philipp Mertins, Alfred de Vos, Maarten The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The Alan Turing Institute LondonNW1 2DB United Kingdom The Department of Electrical and Computer Engineering Aarhus University Aarhus8200 Denmark The School of Economics Finance and Management University of Bristol BristolBS8 1TU United Kingdom The Institute for Signal Processing University of Lübeck Lübeck23562 Germany Lübeck23562 Germany The Department of Electrical Engineering KU Leuven Leuven3001 Belgium The Department of Development and Regeneration KU Leuven Leuven3001 Belgium

Background: Black-box skepticism is one of the main hindrances impeding deep-learning-based automatic sleep scoring from being used in clinical environments. Methods: Towards interpretability, this work proposes a sequence-to-sequence sleep-staging model, namely SleepTransformer. It is based on the transformer backbone and offers interpretability of the model’s decisions at both the epoch and sequence level. We further propose a simple yet efficient method to quantify uncertainty in the model’s decisions. The method, which is based on entropy, can serve as a metric for deferring low-confidence epochs to a human expert for further inspection. Results: Making sense of the transformer’s self-attention scores for interpretability, at the epoch level, the attention scores are encoded as a heat map to highlight sleep-relevant features captured from the input EEG signal. At the sequence level, the attention scores are visualized as the influence of different neighboring epochs in an input sequence (i.e. the context) to recognition of a target epoch, mimicking the way manual scoring is done by human experts. Conclusion: Additionally, we demonstrate that SleepTransformer performs on par with existing methods on two databases of different sizes. Significance: Equipped with interpretability and the ability of uncertainty quantification, SleepTransformer holds promise for being integrated into clinical settings. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation

arXiv

引用

arXiv 2019年

作者： Zhang, Dong Zhao, Shu Duan, Zhen Chen, Jie Zhang, Yanping Tang, Jie Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

Paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors. It aims to recommend appropriate experts in a discipline to comment on the quality of papers of others in that discipline. How to effectively and accurately recommend reviewers for the submitted papers is a meaningful and still tough task. Generally, the relationship between a paper and a reviewer often depends on the semantic expressions of them. Creating a more expressive representation can make the peer-review process more robust and less arbitrary. So, the representations of a paper and a reviewer are very important for the paper-reviewer recommendation. Actually, a reviewer or a paper often belongs to multiple research fields, which increases difficulty in paper-reviewer recommendation. In this paper, we propose a Multi-Label Classification method using a HIErarchical and transPArent Representation named Hiepar-MLC. Firstly, we introduce a HIErarchical and transPArent Representation (Hiepar) to express the semantic information of the reviewer and the paper. Hiepar is learned from a two-level bidirectional gated recurrent unit based network applying the attention mechanism. It is capable of capturing the two-level hierarchical information (word-sentence-document) and highlighting the elements in reviewers or papers to support the labels. This word-sentence-document information mirrors the hierarchical structure of a reviewer or a paper and captures the exact semantics of them. Then we transform the paper-reviewer recommendation problem into a Multi-Label Classification (MLC) issue, whose multiple research labels exactly guide the learning process. It's flexible that we can select any multi-label classification method to solve the paper-reviewer recommendation problem. Further, we propose a simple multi-label based reviewer assignment (MLBRA) strategy to select the appropriate reviewers. It's interesting that we also explore the paper-reviewer recom

关键词： Digital libraries

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