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1st IEEE World Symposium on Digital Intelligence for Systems and Machines, DISA 2018

作者： Kiss, Peter Fonyo, David Horvath, Tomas Department of Data Science and Engineering Faculty of Informatics ELTE - Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9781538651025

BlaBoO is an open source software initiative, that aims to help find optimal solution for the widest possible range of optimization problems such as, amongst others, hyperparameter tuning of machine learning algorithms. The software is able to optimize the parameters of any black-box function runnable in command line. It needs no cumbersome installation, launching the application requires running a Java executable file because of what the application runs under various platforms. BlaBoO provides a GUI as well as a batch mode processing abilities. It is easy to extend it by new optimization algorithms. BlaBoO is intended not only for managing black-box optimization experiments but also for education purposes. Its features make it a valuable tool for (not only beginner) machine learning practitioners, students and teachers as well. © 2018 IEEE.

关键词： Open source software

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Disentangling high-order mechanisms and high-order behaviours in complex systems

arXiv

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arXiv 2022年

作者： Rosas, Fernando E. Mediano, Pedro A.M. Luppi, Andrea I. Varley, Thomas F. Lizier, Joseph T. Stramaglia, Sebastiano Jensen, Henrik J. Marinazzo, Daniele Data Science Institute Imperial College London United Kingdom Centre for Psychedelic Research Imperial College London United Kingdom Centre for Complexity Science Imperial College London United Kingdom Department of Psychology University of Cambridge United Kingdom Department of Psychology Queen Mary University of London United Kingdom University Division of Anaesthesia University of Cambridge United Kingdom Department of Clinical Neurosciences University of Cambridge United Kingdom Leverhulme Centre for the Future of Intelligence University of Cambridge United Kingdom Department of Psychological & Brain Sciences Indiana University BloomingtonIN United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Centre for Complex Systems Faculty of Engineering The University of Sydney Australia Dipartimento Interateneo di Fisica Università Degli Studi di Bari Aldo Moro Italy INFN Sezione di Bari Italy Department of Mathematics Imperial College London United Kingdom Institute of Innovative Research Tokyo Institute of Technology Japan Department of Data Analysis Ghent University Belgium

Battiston et al. (arXiv:2110.06023) provide a comprehensive overview of how investigations of complex systems should take into account interactions between more than two elements, which can be modelled by hypergraphs and studied via topological data analysis. Following a separate line of enquiry, a broad literature has developed information-theoretic tools to characterize high-order interdependencies from observed data. While these could seem to be competing approaches aiming to address the same question, in this correspondence we clarify that this is not the case, and that a complete account of higher-order phenomena needs to embrace both. Copyright © 2022, The Authors. All rights reserved.

关键词： Large scale systems

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Hyperspectral image classification using Uniform Manifold Approximation and Projection with fusion deep learning network

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Applied Soft Computing 2025年 180卷

作者： Chen Lou Mohammed A.A. Al-qaness Sike Ni Dalal Al-Alimi Robertas Damaševičius Saeed Hamood Alsamhi College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua 321004 China Zhejiang Institute of Photoelectronics Jinhua 321004 China Faculty of Engineering Sana’a University Sana’a 12544 Yemen Centre of Real Time Computer Systems Kaunas University of Technology Kaunas 51368 Lithuania Insight Centre for Data Analytics University of Galway Ireland School of ICT Bahrain Polytechnic Isa Town Bahrain Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 Republic of Korea

Hyperspectral images (HSI) are crucial for remote sensing applications as they provide detailed spectral information that accurately identifies and analyzes various materials and land features. HSI classification faces challenges due to large data volumes and high dimensionality. Dimensionality reduction techniques help address these problems by simplifying model computation, reducing redundancy, and improving feature selection. However, traditional methods struggle to capture nonlinear structures and local–global relationships in hyperspectral data. We propose a new multi-feature fusion classification model called the Uniform Manifold Approximation and Projection (UMAP) and Simple Attention Module (SimAM) mechanism fusion network (UMAPSAMFN). The main workflow of the model consists of several steps. The network uses UMAP to map high-dimensional HSI data into a low-dimensional space while maintaining a local and global structure. The feature data are sent to the convolutional neural network (CNN) and graph convolutional network (GCN) modules to capture pixel-level features and contextual information. These data are separately processed through multi-head attention modules to enhance the ability to represent feature information. Finally, the processed data are jointly fed into a fusion module with an attention mechanism to boost feature information and achieve deep modeling results. The experimental results on three benchmark HSI datasets show that UMAPSAMFN consistently exhibits the highest classification accuracy, with overall classification accuracies of 93.28%, 96.13%, and 97.73% on the Indian Pines, Pavia University, and Salinas datasets, respectively.

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Supply-demand optimization in the product distribution process through the branching method

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IOP Conference Series: Materials science and Engineering 2021年第1期1072卷

作者： V Lukitosari A P Subriadi Mathematics Department Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology (ITS) Surabaya 60111 Indonesia Department of Information System Faculty of Intelligent Electrical and Informatics Technology Sepuluh Nopember Institute of Technology (ITS) Surabaya 60111 Indonesia

Interaction and relationship between seller-buyer for the availability of certain products always involve the distribution process from suppliers to a number of customers so that demand at each destination is fulfilled. If the product cannot be fulfilled, the customer will find another supplier to meet the product's shortcomings. Thus the supplier is trying to prevent the supply of other competitors of the same type. Effective logistical arrangements in the distribution of goods need to pay attention to the behavior characteristics of shipping costs. The purpose of this study is to minimize shipping costs with a distribution scheme that can optimize product loads. By using the Balinski linear approach, the problem of sending fixed costs can be solved by the branching method. The branching method gradually produces new problem-solving branches with different objective functions and constraints. Each value branch could be compared so that the optimal solution for the cost is found.

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Analysis of case files of children reporting non-fatal strangulation as part of a sexual assault

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Child abuse & neglect 2025年 167卷 107504页

作者： Cath White Glen P Martin Rabiya Majeed-Ariss Saint Mary's Sexual Assault Referral Centre Manchester University Hospitals NHS Foundation Trust Manchester United Kingdom Institute for Addressing Strangulation United Kingdom School of Social Sciences Division of Criminology University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom. Electronic address: cathw@ifas.org.uk. Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom. Saint Mary's Sexual Assault Referral Centre Manchester University Hospitals NHS Foundation Trust Manchester United Kingdom.

BACKGROUND:There is increasing awareness of non-fatal strangulation (NFS) in interpersonal violence in adults but less in the literature regarding children and young people (CYP). OBJECTIVE:The aims of this study are: (1) identify NFS prevalence amongst CYP undergoing an acute forensic medical examination (FME) reporting rape or sexual assault; (2) explore the characteristics of CYP reporting NFS against those who did not, including suspect and assault characteristics; (3) explore the prevalence of the symptoms and signs associated with NFS and (4) make comparison with the findings of adults. METHODS:data from case files of CYP undergoing an acute (FME) during the seven-year period 2017-2023, following an allegation or suspicion of rape or sexual assault, were analysed. Cases with a history of NFS as part of the acute assault were compared to a control group. RESULTS:1802 CYP attended for an acute FME in the 7- year- study period. NFS prevalence was 5.05 % with an upward trend. NFS CYP tended to be older White girls with higher rates of mental health problems than controls. 83.52 % reported at least one symptom or sign at the time of NFS, with 31 % having an objective injury at FME. 16.5 % said the alleged perpetrator had also strangled them previously. All alleged perpetrators were male with over a third being a CYP. CONCLUSIONS:The prevalence of NFS in CYP doubled during the study period, was found mainly in the older girls, many with preexisting mental health problems. More research is required to explore this phenomenon.

关键词： CYP Forensic medical examination Non-fatal strangulation Rape and sexual assault

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P18.04.B SINGLE CELL TRANSCRIPTOMIC ANALYSIS IDENTIFIES DISTINCT INTRATUMORAL SUBCLONES IN MENINGIOMA

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Neuro-Oncology 2023年第SUPPLEMENT_2期25卷 ii122–ii122页

作者： Blume, C Tang, Y Dogan, H Ritter, M Dentro, S von Deimling, A Gerstung, M Schlesner, M Sahm, F Maas, S L N University Hospital Heidelberg Dept. of Neuropathology Heidelberg Germany German Cancer Research Center (DKFZ) German Consortium for Translational Cancer Research (DKTK) Clinical Cooperation Unit Neuropathology Heidelberg Germany Division of AI in Oncology German Cancer Research Center (DKFZ) Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Hinxton United Kingdom Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany Department of Pathology Leiden University Medical Center Leiden Netherlands Department of Pathology Erasmus Medical Center Rotterdam Netherlands

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Beware of "Explanations" of AI

arXiv

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arXiv 2025年

作者： Martens, David Shmueli, Galit Evgeniou, Theodoros Bauer, Kevin Janiesch, Christian Feuerriegel, Stefan Gabel, Sebastian Goethals, Sofie Greene, Travis Klein, Nadja Kraus, Mathias Kühl, Niklas Perlich, Claudia Verbeke, Wouter Zharova, Alona Zschech, Patrick Provost, Foster University of Antwerp Department of Engineering Management Antwerp2000 Belgium National Tsing Hua University Institute of Service Science Hsinchu30013 Taiwan INSEAD Technology and Business Fontainebleau77300 France Goethe University Frankfurt Department of Information Systems Frankfurt60629 Germany TU Dortmund University Department of Computer Science Dortmund44227 Germany LMU Munich Munich Center for Machine Learning Munich80539 Germany Erasmus University Rotterdam School of Management Rotterdam3062 Netherlands Copenhagen Business School Department of Digitalization Copenhagen2000 Denmark Karlsruhe Institute of Technology Scientific Computing Center Karlsruhe76131 Germany University of Regensburg Faculty of Informatics and Data Science Regensburg93053 Germany University of Bayreuth Faculty of Law Business and Economics Bayreuth95440 Germany New York University Department of Technology Operations and Statistics New YorkNY10012 United States KU Leuven Faculty of Economics and Business Leuven3000 Belgium Humboldt-Universität zu Berlin School of Business and Economics Berlin10099 Germany Leipzig University Faculty of Economics and Management Science Leipzig04109 Germany

Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artificial intelligence (XAI), highlighting the potential of explanations to enhance trust, support adoption, and meet regulatory standards. However, the question of what constitutes a "good" explanation is dependent on the goals, stakeholders, and context. At a high level, psychological insights such as the concept of mental model alignment can offer guidance, but success in practice is challenging due to social and technical factors. As a result of this ill-defined nature of the problem, explanations can be of poor quality (e.g. unfaithful, irrelevant, or incoherent), potentially leading to substantial risks. Instead of fostering trust and safety, poorly designed explanations can actually cause harm, including wrong decisions, privacy violations, manipulation, and even reduced AI adoption. Therefore, we caution stakeholders to beware of explanations of AI: while they can be vital, they are not automatically a remedy for transparency or responsible AI adoption, and their misuse or limitations can exacerbate harm. Attention to these caveats can help guide future research to improve the quality and impact of AI explanations. © 2025, CC BY.

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Lessons Learned from the Immrep23 Tcr-Epitope Prediction Challenge

SSRN

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SSRN 2024年

作者： Nielsen, Morten Eugster, Anne Jensen, Mathias Fynbo Goel, Manisha Tiffeau-Mayer, Andreas Pelissier, Aurelien Valkiers, Sebastiaan Martínez, María Rodríguez Meynard-Piganeeau, Barthélémy Greiff, Victor Mora, Thierry Walczak, Aleksandra M. Croce, Giancarlo Moreno, Dana L. Gfeller, David Meysman, Pieter Barton, Justin Department of Health Technology Technical University of Denmark LyngbyDK-2800 Denmark Center for Regenerative Therapies Dresden Faculty of Medicine TU Dresden Dresden Germany Division of Infection and Immunity University College London United Kingdom Institute of Computational Life Sciences ZHAW Winterthur8400 Switzerland Biomedical Informatics and Data Science Yale School of Medicine New Haven06510 United States Computational and Quantitative Biology LCQB UMR 7238 Institut de Biologie Paris Seine CNRS Sorbonne Universite Paris75005 France Department of Immunology and Transfusion Medicine University of Oslo Oslo University Hospital Oslo Norway Laboratoire de Physique de l’Ecole Normale Supérieure ENS Université PSL CNRS Sorbonne Université Université de Paris Cité ParisF-75005 France Department of Oncology UNIL CHUV Ludwig Institute for Cancer Research University of Lausanne Lausanne Switzerland University of Antwerp Antwerp Belgium Institute of Structural and Molecular Biology University of London United Kingdom

Here, we present the findings from IMMREP23, the second benchmark competition focused on predicting the specificity of TCR-pMHC *** interaction of T cell receptors (TCR) towards their pMHC target is the cornerstone of the cellular immune system. Over the last decade, substantial progress has been made within the field of TCR specificity prediction, providing proof of concept for predicting TCR-pMHC interactions in a narrow antigen space where substantial training data is available. However, a significant challenge persists in extending the predictive capability to novel "unseen" pMHC targets. Furthermore, typical of advancements in prediction methodologies, the performance of proposed methods is often challenged when evaluated outside the initial publication and data *** address these issues, IMMREP23 challenge invited participants to predict, for a given test set of TCR-pMHC pairs, the likelihood that a pair would bind. A total of 53 teams participated, providing a total of 398 *** benchmark confirms that current methods achieve reasonable performance in the "seen" pHLA setting. However, most participating methods had close to random performance on the subset of "unseen" peptides, underlining that this prediction challenge remains essentially ***, another key lesson from the benchmark is the critical issue of data leakage. Specifically, the procedure employed in IMMREP23 led to biases in the negative test data set. These biases were identified by several participating teams, and complicated the interpretation of the benchmark results. Based on these results, we put forward suggestions on how future competitions could avoid such data leakages and biases. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Informal caregivers' attitudes and compliance towards a connected health platform for home care support: Insights from a long-term exposure

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Gerontechnology 2019年第4期18卷 231-242页

作者： Guisado-Fernández, Estefanía Giunti, Guido Mackey, Laura Silva, Paula Alexandra Blake, Catherine Caulfield, Brian Insight Centre for Data Analytics University College Dublin 3rd floor O'Brien Science Building East Dublin Ireland M3S Research Unit Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland School of Public Health Physiotherapy and Sports Science University College Dublin Dublin Ireland Department of Computer Science Center for Informatics and Systems of the University of Coimbra (CISUC) University of Coimbra Coimbra Portugal

Background When designing Connected Health (CH) solutions for home care, it is vital to focus on usability and user experience to ensure that technologies are easy to use and meet users' expectations and needs. Generally, the usability and user experience tests are conducted during short-term exposure, which does not allow a true understanding of how the technology can help with the home caring tasks. Research aim We aim to investigate informal caregivers' feedback on the utility and usability of a CH monitoring platform for People with Dementia (PwD) during a period of extended use in the natural living context, and to understand how this was related to compliance patterns. Methods Informal caregiver's feedback about the CH platform, usability, and the impact of short-term versus long-term exposure were investigated through semi-structured individual interviews at the beginning and end of a 6-month deployment in the home care setting. Informal caregivers' compliance with the CH platform was analysed from their daily platform utilization during the deployment time. Results 11 informal caregivers agreed to participate. There was a change in the participants' opinions about the CH platform between the short-term and the long-term exposure feedback. Their initial impressions about what the CH platform could offer them to improve their delivery of home care for the PwD did not correspond with what they found that the CH platform could provide them following the long-term exposure. If at the beginning they saw the CH platform as a helpful tool to facilitate home care delivery and to improve their self-efficacy, after the deployment they expressed that because of the way the platform was designed it was mainly conceived for dementia research benefit and not to fulfill their caring needs. Compliance with the CH platform was quite low and similar between all participants. Conclusions Most contemporary CH studies are not conducted in real-life settings and without enough dur

关键词： Connected health Dementia Informal caregiver Usability User experience

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Identification of cell type-specific cell-penetrating peptides through in vivo phage display leveraged by next generation sequencing

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Biomedicine and Pharmacotherapy 2025年 182卷 117740-117740页

作者： Kohl, Franziska Laufkötter, Oliver Firth, Mike Krimpenfort, Luc Mangla, Priyanka Ansarizadeh, Mohammadhassan Geylan, Gökçe Eklund, Lauri De Maria, Leonardo Jakobsson, Lars Wiseman, John Centre for Genomics Research Discovery Sciences BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden Department of Life Science Informatics B-IT Rheinische Friedrich-Wilhelms-Universität Bonn Germany Data Sciences and Quantitative Biology Discovery Sciences BioPharmaceuticals R&D AstraZeneca Cambridge United Kingdom Oligonucleotides and Targeted Delivery Discovery Sciences BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden Oulu Center for Cell-Matrix Research University of Oulu Oulu Finland Faculty of Biochemistry and Molecular Medicine University of Oulu Oulu Finland Biocenter Oulu University of Oulu Oulu Finland Molecular AI Discovery Sciences BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden Division of Systems and Synthetic Biology Department of Life Sciences Chalmers University of Technology Gothenburg Sweden Research and Early Development Respiratory & Immunology BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden

Vascular anomalies (VA) refer to abnormal blood or lymphatic vessel architecture, most often as a result of dysregulated growth. Venous malformations (VM), a subgroup of VAs, are triggered by activating mutations in the Angiopoietin/TIE2-PI3K/AKT/mTOR signaling pathway with TIE2 L914F (gene name TEK) being one of the most frequent mutations in patients with VMs. Although systemic targeting of the overactivated pathway is possible, it would be a therapeutic advantage to restrict treatment to only the affected lesions. To identify peptides with potential selective binding to TIE2 L914F lesions we applied in vivo phage display to TIE2 L914F-overexpressing endothelial cells (ECs) in a subcutaneous matrigel xenograft mouse model of VMs. By panning for lesion-targeting phages in combination with subcellular fractionation, a screen for cell-penetrating candidate phages was established. Employing Next Generation Sequencing (NGS) and a refined bioinformatic analysis we were able to identify many novel cell-penetrating peptides (CPPs). To pinpoint the most selective and viable CCP candidates a hierarchical clustering algorithm was utilized. This method aggregated CPPs with highly similar sequences into a small number of clusters from which consensus sequences could be derived. Selected candidate CPPs exhibited uptake in TIE2 L914F-expressing human umbilical vein endothelial cells (HUVEC) in culture and were able to deliver siRNA into these cells. In conclusion, our NGS bioinformatic-supported approach led to the identification of novel and selective CPPs capable of transporting a siRNA cargo into targeted cells. © 2024

关键词： Bioinformatic analysis Cell-penetrating peptides In vivo phage display Next generation sequencing Vascular anomalies

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