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TechRxiv 2021年

作者： Baghdadi, Asma Fourati, Rahma Aribi, Yassine Daoud, Sawsen Dammak, Mariem Mhiri, Chokri Siarry, Patrick Alimi, Adel Chabchoub, Habib Esprit School of Business Computer Science and Applied Mathematics Department Tunis Tunisia University of Sfax BP 1173 Sfax3038 Tunisia Laboratoire Images Signaux et Systèmes Intelligents Université Paris Est Créteil 120 rue Paul Armangot Vitry-sur-Seine94400 France University College of Ranyah Taif University Sciences and Technology department Taif Saudi Arabia Department of neurology Habib Bourguiba University Hospital Sfax Tunisia Neuroscience Laboratory Sfax University SfaxLR-12-SP-19 Tunisia College of Business Al Ain University United Arab Emirates LiSSi Université de Paris 12 Créteil94010 France Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa

Epilepsy affect almost 1% of the worldwide population. An early diagnosis of seizure types is a crucial patient-dependent step for the treatment selection process. The selection of the proper treatment relies on the correct identification of the seizure type. As such, identifying the seizure type has the biggest immediate influence on therapy than the seizure detection, reducing the neurologist’s efforts when reading and detecting seizures in EEG recordings. Most of the existing seizure detection and classification methods are conceptualized following the patient-dependent schema thus fail to perform well with unknown cases. Our work focuses on patient-independent schema for seizure type classification and pays more attention to the explainability of the underlying attention mechanism of our method. Using a channel-wise attention mechanism, a quantification of the EEG channels contribution is enabled. Therefore, results become more interpretable and a visualization of brain lobes contribution by seizure types is allowed. We evaluate our model for seizure detection and type classification on CHB-MIT and the recently released TUH EEG Seizure, respectively. Our model is able to classify 8 seizure types with an accuracy of 98.41%, directly from raw EEG data without any preprocessing. A case study showed a high correlation between the neurological baselines and the interpretable results of our model. © 2021, CC0.

关键词： Patient treatment

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Deep learning for brake squeal: Vibration detection, characterization and prediction

arXiv

引用

arXiv 2020年

作者： Stender, Merten Tiedemann, Merten Spieler, David Schoepflin, Daniel Hoffmann, Norbert Oberst, Sebastian Dynamics Group Hamburg University of Technology Hamburg Germany Brake Systems Development AUDI AG Ingolstadt Germany Department of Computer Science and Mathematics University of Applied Sciences Munich Germany Institute for Aircraft Production Technology Hamburg University of Technology Hamburgo Germany Department of Mechanical Engineering Imperial College London London United Kingdom Centre for Audio Acoustics and Vibration Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Despite significant advances in numerical modeling of brake squeal, the majority of industrial research and design is still conducted experimentally. In this work we report on novel strategies for handling data-intensive vibration testings and gaining better insights into brake system vibrations. To this end, we propose machine learning-based methods to detect and characterize vibrations, understand sensitivities and predict brake squeal. Our aim is to illustrate how interdisciplinary approaches can leverage the potential of data science techniques for classical mechanical engineering challenges. In the first part, a deep learning brake squeal detector is developed to identify several classes of typical sounds in vibration recordings. The detection method is rooted in recent computer vision techniques for object detection. It allows to overcome limitations of classical approaches that rely on spectral properties of the recorded vibrations. Results indicate superior detection and characterization quality when compared to state-of-the-art brake squeal detectors. In the second part, deep recurrent neural networks are employed to learn the parametric patterns that determine the dynamic stability of the brake system during operation. Given a set of multivariate loading conditions, the models learn to predict the vibrational behavior of the structure. The validated models represent virtual twins for the squeal behavior of a specific brake system. It is found that those models can predict the occurrence and onset of brake squeal with high accuracy. Hence, the deep learning models can identify the complicated patterns and temporal dependencies in the loading conditions that drive the dynamical structure into regimes of instability. Large data sets from commercial brake system testing are used to train and validate the deep learning models. Copyright © 2020, The Authors. All rights reserved.

关键词： Object detection

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SELFIES and the future of molecular string representations

arXiv

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arXiv 2022年

作者： Krenn, Mario Ai, Qianxiang Barthel, Senja Carson, Nessa Frei, Angelo Frey, Nathan C. Friederich, Pascal Gaudin, Théophile Gayle, Alberto Alexander Jablonka, Kevin Maik Lameiro, Rafael F. Lemm, Dominik Lo, Alston Moosavi, Seyed Mohamad Nápoles-Duarte, José Manuel Nigam, AkshatKumar Pollice, Robert Rajan, Kohulan Schatzschneider, Ulrich Schwaller, Philippe Skreta, Marta Smit, Berend Strieth-Kalthoff, Felix Sun, Chong Tom, Gary von Rudorff, Guido Falk Wang, Andrew White, Andrew Young, Adamo Yu, Rose Aspuru-Guzik, Alán Erlangen Germany Department of Chemistry Fordham University The Bronx New York United States Department of Mathematics Vrije Universiteit Amsterdam Amsterdam Netherlands Syngenta Jealott’s Hill International Research Centre Bracknell Berkshire United Kingdom Department of Chemistry Imperial College London Molecular Sciences Research Hub White City Campus Wood Lane London United Kingdom Massachusetts Institute of Technology CambridgeMA United States Institute of Theoretical Informatics Karlsruhe Institute of Technology Karlsruhe Germany Institute of Nanotechnology Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Department of Computer Science University of Toronto Canada IBM Research Europe Zürich Switzerland Unaffiliated Sapporo Japan Sion Valais Switzerland Medicinal and Biological Chemistry Group São Carlos Institute of Chemistry University of São Paulo São Paulo Brazil Faculty of Physics University of Vienna Vienna Austria Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany Facultad de Ciencias Químicas Universidad Autónoma de Chihuahua Chihuahua Mexico Department of Computer Science Stanford University CA United States Chemical Physics Theory Group Department of Chemistry University of Toronto Canada Institute for Inorganic and Analytical Chemistry Friedrich-Schiller Universität Jena Jena Germany Institut für Anorganische Chemie Julius-Maximilians-Universität Würzburg Würzburg Germany Lausanne Switzerland Lausanne Switzerland Vector Institute for Artificial Intelligence Toronto Canada Solar Fuels Group Department of Chemistry University of Toronto Canada Department of Chemical Engineering University of Rochester United States Department of Computer Science and Engineering University of California San Diego United States Department of Chemical Engineering and Applied Chemistry University of Toronto Canada Department of Materials Science University of Toronto Canada Toronto

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings – most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science. © 2022, CC BY.

关键词： Molecules

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Uniform stability of some large-scale parallel server networks

arXiv

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arXiv 2019年

作者： Hmedi, Hassan Arapostathis, Ari Pang, Guodong Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Department of Computational and Applied Mathematics George R. Brown College of Engineering Rice University HoustonTX77005 United States

In this paper we study the uniform stability properties of two classes of parallel server networks with multiple classes of jobs and multiple server pools of a tree topology. These include a class of networks with a single non-leaf server pool, such as the 'N' and 'M' models, and networks of any tree topology with class-dependent service rates. We show that with √n safety staffing, and no abandonment, in the Halfin-Whitt regime, the diffusion-scaled controlled queueing processes are exponentially ergodic and their invariant probability distributions are tight, uniformly over all stationary Markov controls. We use a unified approach in which the same Lyapunov function is used in the study of the prelimit and diffusion limit. A parameter called the spare capacity (safety staffing) of the network plays a central role in characterizing the stability results: the parameter being positive is necessary and sufficient that the limiting diffusion is uniformly exponentially ergodic over all stationary Markov controls. We introduce the concept of "system-wide work conserving policies", which are defined as policies that minimize the number of idle servers at all times. This is stronger than the so-called joint work conservation. We show that, provided the spare capacity parameter is positive, the diffusion-scaled processes are geometrically ergodic and the invariant distributions are tight, uniformly over all "system-wide work conserving policies". In addition, when the spare capacity is negative we show that the diffusion-scaled processes are transient under any stationary Markov control, and when it is zero, they cannot be positive *** Codes 90B22 (Primary), 60K25, 49L20, 90B36 (Secondary) Copyright © 2019, The Authors. All rights reserved.

关键词： Diffusion

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Optimal control of thermostatic loads for planning aggregate consumption: Characterization of solution and explicit strategies

arXiv

引用

arXiv 2019年

作者： Fontes, Fernando A.C.C. Halder, Abhishek Becerril, Jorge Kumar, P.R. SYSTEC ISR Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Porto4200-465 Portugal Department of Applied Mathematics University of California Santa CruzCA95064 United States Department of Electrical and Computer Engineering Texas A&M University College StationTX77843 United States

We consider the problem of planning the aggregate energy consumption for a set of thermostatically controlled loads for demand response, accounting price forecast trajectory and thermal comfort constraints. We address this as a continuous-time optimal control problem, and analytically characterize the structure of its solution in the general case. In the special case, when the price forecast is monotone and the loads have equal dynamics, we show that it is possible to determine the solution in an explicit form. Taking this fact into account, we handle the non-monotone price case by considering several subproblems, each corresponding to a time subinterval where the price function is monotone, and then allocating to each subinterval a fraction of the total energy budget. This way, for each time subinterval, the problem reduces to a simple convex optimization problem with a scalar decision variable, for which a descent direction is also known. The price forecasts for the day-ahead energy market typically have no more than four monotone segments, so the resulting optimization problem can be solved efficiently with modest computational resources. Copyright © 2019, The Authors. All rights reserved.

关键词： Continuous time systems

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Enabling Artificial Intelligence of Things (AIoT) Healthcare Architectures and Listing Security Issues

引用

Computational intelligence and neuroscience 2022年第1期2022卷 8421434页

作者： Anil Audumbar Pise Khalid K Almuzaini Tariq Ahamed Ahanger Ahmed Farouk Kumud Pant Piyush Kumar Pareek Stephen Jeswinde Nuagah FinalMile Consultants Private Limited Johannesburg South Africa. School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa. Department of Sustainable Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha Nagar Chennai 602105 Thandalam Tamil Nadu India. National Center for Cybersecurity Technologies Riyadh Saudi Arabia. College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia. Department of Computer Science Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt. Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India. Department of Computer Science & Engineering & Head of IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India. Department of Electrical Engineering Tamale Technical University Tamale Ghana.

A significant study has been undertaken in the areas of health care and administration of cutting-edge artificial intelligence (AI) technologies throughout the previous decade. Healthcare professionals studied smart gadgets and other medical technologies, along with the AI-based Internet of Things (IoT) (AIoT). Connecting the two regions makes sense in terms of improving care for rural and isolated resident individuals. The healthcare industry has made tremendous strides in efficiency, affordability, and usefulness as a result of new research options and major cost reductions. This includes instructions (AIoT-based) medical advancements can be both beneficial and detrimental. While the IoT concept undoubtedly offers a number of benefits, it also poses fundamental security and privacy concerns regarding medical data. However, resource-constrained AIoT devices are vulnerable to a number of assaults, which can significantly impair their performance. Cryptographic algorithms used in the past are inadequate for safeguarding IoT-enabled networks, presenting substantial security risks. The AIoT is made up of three layers: perception, network, and application, all of which are vulnerable to security threats. These threats can be aggressive or passive in nature, and they can originate both within and outside the network. Numerous IoT security issues, including replay, sniffing, and eavesdropping, have the ability to obstruct network communication. The AIoT-H application is likely to be explored in this research article due to its potential to aid with existing and different technologies, as well as bring useful solutions to healthcare security challenges. Additionally, every day, several potential problems and inconsistencies with the AIoT-H technique have been discovered.

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Automated control and optimisation of laser driven ion acceleration

arXiv

引用

arXiv 2023年

作者： Loughran, B. Streeter, M.J.V. Ahmed, H. Astbury, S. Balcazar, M. Borghesi, M. Bourgeois, N. Curry, C.B. Dann, S.J.D. DiIorio, S. Dover, N.P. Dzelzanis, T. Ettlinger, O.C. Gauthier, M. Giuffrida, L. Glenn, G.D. Glenzer, S.H. Green, J.S. Gray, R.J. Hicks, G.S. Hyland, C. Istokskaia, V. King, M. Margarone, D. McCusker, O. McKenna, P. Najmudin, Z. Parisuaña, C. Parsons, P. Spindloe, C. Symes, D.R. Thomas, A.G.R. Treffert, F. Xu, N. Palmer, C.A.J. School of Mathematics and Physics Queen's University Belfast BelfastBT7 1NN United Kingdom Central Laser Facility STFC Rutherford Appleton Laboratory DidcotOX11 0QX United Kingdom Gérard Mourou Center for Ultrafast Optical Science University of Michigan Ann ArborMI48109-2099 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA United States Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G1H9 Canada The John Adams Institute for Accelerator Science Imperial College London LondonSW7 2AZ United Kingdom ELI Beamlines Centre Institute of Physics Czech Academy of Sciences Za Radnicí 835 Dolní Břežany252 41 Czech Republic Department of Applied Physics Stanford University StanfordCA94305 United States Department of Physics SUPA University of Strathclyde GlasgowG4 0NG United Kingdom Faculty of Nuclear Sciences and Physical Engineering Czech Technical University in Prague Prague Czech Republic Department of Mechanical Engineering Stanford University StanfordCA94305 United States Institut für Kernphysik Technische Universität Darmstadt Karolinenplatz 5 Darmstadt64289 Germany

The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimisation of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimisation. Here, an automated, HRR-compatible system produced high fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimisation of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually-optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimisation of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources. © 2023, CC BY.

关键词： Proton beams

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Solution of fractional-order Korteweg-de Vries and Burgers’ equations utilizing local meshless method

引用

Journal of Ocean engineering and Science 2021年

作者： Imtiaz Ahmad Hijaz Ahmad Mustafa Inc Hadi Rezazadeh M. Ali Akbar Mostafa M.A. Khater Lanre Akinyemi Adil Jhangeer Department of Mathematics University of Swabi Pakistan Department of Basic Sciences University of Engineering and Technology Peshawar Pakistan Department of Computer Engineering Biruni University Istanbul Turkey Department of Mathematics Science Faculty Firat University Elazig 23119 Turkey Department of Medical Research China Medical University Taichung Taiwan Faculty of Engineering Technology Amol University of Special Modern Technologies Amol Iran Department of Applied Mathematics University of Rajshahi Bangladesh Department of Mathematics Faculty of Science Jiangsu University Zhenjiang 212013 China Department of Mathematics Obour High Institute For Engineering and Technology Cairo 11828 Egypt Department of Mathematics Lafayette College Easton Pennsylvania USA Department of Mathematics Namal Institute Talagang Road Mianwali 42250 Pakistan

This paper deals with the numerical solution utilizing local version of the meshless collocation method for time-fraction Korteweg de Vries, Burgers’ and Korteweg de Vries-Burgers’ equations. This technique generates sparse and well-conditioned coefficient matrices when applied on a set of uniform or scattered nodes. To check the accuracy of the proposed local meshless technique three test problems are solved numerically.

关键词： Meshless method Caputo derivative Korteweg de Vries equation Burgers’ equation Korteweg de Vries-Burgers’ equation

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Applying data mining technique to predict trends in air pollution in Mumbai

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Journal of Physics: Conference Series 2021年第4期1964卷

作者： Jayakumar Sadhasivam V Muthukumaran J Thimmia Raja V Vinothkumar R Deepa V Nivedita School of Information Technology and Engineering (SITE) Vellore Institute of Technology Vellore India Department of Mathematics School of Applied Science REVA University Bengaluru – 64. School of Computing Kalasalingam Academy of Research and Education Tamilnadu-626128 India Department of Computer Science and Engineering MVJ College of Engineering Bangalore. Chaitanya Bharathi Institute of Technology (A) Hyderabad Telangana India Department of Computer Science and Engineering Dr. MGR Educational and Research Institute Chennai.

Prediction of air quality is a topic of great interest in air quality research due to direct association with health effect. The prediction provides pre-information to the overall population of the area about the status of pollution on which they can take precautionary measures and can protect their health. The problem arises when the level of SO2, NO2 and residual suspended particulate matters in the air increases than that of theirs restricted level. In this paper, the Prophet Algorithm, open source software, is applied to predict the trend of air pollution in the city of Mumbai, Maharashtra. The Prophet is machine learning algorithm to forecast and also to predict time series data. It is based on additive model where non-linear trends are fit with yearly and weekly seasonality. The graphical results are generated after using this algorithm which shows the trending pattern of the pollutants in the air of Mumbai.

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

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