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arXiv 2025年

作者： Hu, Youbing Cheng, Yun Saukh, Olga Ozdemir, Firat Lu, Anqi Cao, Zhiqiang Li, Zhijun Faculty of Computing Harbin Institute of Technology China Swiss Data Science Center Zurich Switzerland Graz University of Technology Austria Complexity Science Hub Vienna Austria

dataset distillation has emerged as a strategy to compress real-world datasets for efficient training. However, it struggles with large-scale and high-resolution datasets, limiting its practicality. This paper introduces a novel resolution-independent dataset distillation method Focused dataset Distillation (FocusDD), which achieves diversity and realism in distilled data by identifying key information patches, thereby ensuring the generalization capability of the distilled dataset across different network architectures. Specifically, FocusDD leverages a pre-trained Vision Transformer (ViT) to extract key image patches, which are then synthesized into a single distilled image. These distilled images, which capture multiple targets, are suitable not only for classification tasks but also for dense tasks such as object detection. To further improve the generalization of the distilled dataset, each synthesized image is augmented with a downsampled view of the original image. Experimental results on the ImageNet-1K dataset demonstrate that, with 100 images per class (IPC), ResNet50 and MobileNet-v2 achieve validation accuracies of 71.0% and 62.6%, respectively, outperforming state-of-the-art methods by 2.8% and 4.7%. Notably, FocusDD is the first method to use distilled datasets for object detection tasks. On the COCO2017 dataset, with an IPC of 50, YOLOv11n and YOLOv11s achieve 24.4% and 32.1% mAP, respectively, further validating the effectiveness of our approach. Copyright © 2025, The Authors. All rights reserved.

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CollEX – A Multimodal Agentic RAG System Enabling Interactive Exploration of Scientific Collections

arXiv

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arXiv 2025年

作者： Schneider, Florian Ahmadi, Narges Baba Ahmadi, Niloufar Baba Vogel, Iris Semmann, Martin Biemann, Chris Hub of Computing and Data Science Center for Sustainable Research Data Management University of Hamburg Germany

In this paper, we introduce CollEX, an innovative multimodal agentic Retrieval-Augmented Generation (RAG) system designed to enhance interactive exploration of extensive scientific collections. Given the overwhelming volume and inherent complexity of scientific collections, conventional search systems often lack necessary intuitiveness and interactivity, presenting substantial barriers for learners, educators, and researchers. CollEX addresses these limitations by employing state-of-the-art Large Vision-Language Models (LVLMs) as multimodal agents accessible through an intuitive chat interface. By abstracting complex interactions via specialized agents equipped with advanced tools, CollEX facilitates curiosity-driven exploration, significantly simplifying access to diverse scientific collections and records therein. Our system integrates textual and visual modalities, supporting educational scenarios that are helpful for teachers, pupils, students, and researchers by fostering independent exploration as well as scientific excitement and curiosity. Furthermore, CollEX serves the research community by discovering interdisciplinary connections and complementing visual data. We illustrate the effectiveness of our system through a proof-of-concept application containing over 64,000 unique records across 32 collections from a local scientific collection from a public university. © 2025, CC BY-SA.

关键词： Search engines

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Multi-Dimensional Research Software Categorization

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computing in science and Engineering 2025年

作者： Hasselbring, Wilhelm Druskat, Stephan Bernoth, Jan Betker, Philine Felderer, Michael Ferenz, Stephan Hermann, Ben Lamprecht, Anna-Lena Linxweiler, Jan Prat, Arnau Rumpe, Bernhard Schoening-Stierand, Katrin Yang, Shinhyung Kiel University Kiel24098 Germany Berlin12489 Germany University of Potsdam Potsdam14476 Germany Helmholtz Centre for Infection Research Department for Epidemiology Brunswick Germany University of Cologne Cologne51147 Germany Carl von Ossietzky Universität Oldenburg Department of Computer Science Oldenburg26129 Germany Secure Software Engineering Group TU Dortmund Dortmund44227 Germany TU Braunschweig Braunschweig38106 Germany Braunschweig38108 Germany RWTH Aachen University Germany University of Hamburg Hub of Computing and Data Science Hamburg22761 Germany

Research software has been categorized in different contexts to serve different goals. We start with a look at what research software is, before we discuss the purpose of research software categories. We propose a multi-dimensional categorization of research software. We present a template for characterizing such categories. As selected dimensions, we present our proposed role-based, readiness-based, developer-based, and dissemination-based categories. Since our work has been inspired by various previous efforts to categorize research software, we discuss them as related works. We characterize all these categories via the previously introduced template, to enable a systematic comparison. We report on the multi-dimensional categorization of selected research software examples. © 1999-2011 IEEE.

关键词： Aerospace engineering Analytical models data collection data models data visualization Monitoring Numerical models Software Software development management Software engineering

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Fine-grained quotation detection and attribution in German news articles 20

Fine-grained quotation detection and attribution in German n...

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20th Conference on Natural Language Processing, KONVENS 2024

作者： Petersen-Frey, Fynn Biemann, Chris Hub of Computing and Data Science Language Technology Group Universität Hamburg Germany

The task of quotation detection and attribution deals with identifying quotation spans together with their associated role spans such as the speaker. We describe an approach to solve the task of fine-grained quotation detection and attribution using a sequence-to-sequence transformer model with constrained decoding. Our model improves vastly upon the existing baselines on the German news articles quotation dataset, thereby making it feasible for a first time to automatically extract attributed quotations from German news articles. We provide an extensive description of our method, discuss alternative approaches, performed experiments using multiple foundation language models and method variants, and analyzed our model’s prediction errors. Our source code and trained models are available1,. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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UHH at AVeriTeC: RAG for Fact-Checking with Real-World Claims 7

UHH at AVeriTeC: RAG for Fact-Checking with Real-World Claim...

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7th Fact Extraction and VERification Workshop, FEVER 2024

作者： Sevgili, Özge Nikishina, Irina Yimam, Seid Muhie Semmann, Martin Biemann, Chris Language Technology Group Dept. of Informatics & Hub of Computing and Data Science Universität Hamburg Germany

ISBN: (纸本)9798891761728

This paper presents UHH’s approach developed for the AVeriTeC shared task. The goal of the challenge is to verify given real-world claims with evidences from the Web. In this shared task, we investigate a Retrieval-Augmented Generation (RAG) model, which mainly contains retrieval, generation, and augmentation components. We start with the selection of the top 10k evidences via BM25 scores, and continue with two approaches to retrieve the most similar evidences: (1) to retrieve top 10 evidences through vector similarity, generate questions for them, and rerank them or (2) to generate questions for the claim and retrieve the most similar evidence, again, through vector similarity. After retrieving the top evidences, a Large Language Model (LLM) is prompted using the claim along with either all evidences or individual evidence to predict the label. Our system submission, UHH, using the first approach and individual evidence prompts, ranks 6th out of 23 systems. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models

arXiv

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arXiv 2025年

作者： Barman, Kristian G. Caron, Sascha Sullivan, Emily de Regt, Henk W. de Austri, Roberto Ruiz Boon, Mieke Färber, Michael Fröse, Stefan Hasibi, Faegheh Ipp, Andreas Kapoor, Rukshak Kasieczka, Gregor Kostić, Daniel Krämer, Michael Golling, Tobias Lopez, Luis G. Marco, Jesus Otten, Sydney Pawlowski, Pawel Vischia, Pietro Weber, Erik Weniger, Christoph CLPS - Centre for Logic and Philosophy of Science UGent Belgium IMAPP Radboud University and Nikhef NL Netherlands Utrecht University Netherlands Institute for Science in Society Radboud University Netherlands CSIC-UV Spain University of Twente Netherlands TU Dresden & ScaDS.AI Germany ErUM-Data-Hub & TU Dortmund University Germany Computing and Information Science Radboud University Netherlands TU Wien Austria Patiala India Universität Hamburg Germany Institute of Philosophy University of Leiden Netherlands RWTH Aachen University Germany University of Geneva Switzerland Munich Center for Mathematical Philosophy LMU Munich Germany CSIC-UC Spain Radboud University Netherlands Ippen Digital Germany Universidad de Oviedo ICTEA Spain GRAPPA Institute of Physics University of Amsterdam Netherlands

This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we refer to as Large Physics Models (LPMs). These models, based on foundation models such as Large Language Models (LLMs) - trained on broad data - are tailored to address the unique demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing insights from physical theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability through testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining Large Physics Models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models. © 2025, CC BY.

关键词： Benchmarking

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RSVP for VPSA: A Meta Design Study on Rapid Suggestive Visualization Prototyping for Visual Parameter Space Analysis

arXiv

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arXiv 2024年

作者： Klaffenboeck, Manfred Gleicher, Michael Sorger, Johannes Wimmer, Michael Möller, Torsten The Institute of Visual Computing and Human Centered Technology TU Wien Austria The Complexity Science Hub Vienna Austria The Department of Computer Sciences The University of Wisconsin - Madison United States The Faculty of Computer Science The University of Vienna The Research Network Data Science Uni Wien Austria

Visual Parameter Space Analysis (VPSA) enables domain scientists to explore input-output relationships of computational models. Existing VPSA applications often feature multi-view visualizations designed by visualization experts for a specific scenario, making it hard for domain scientists to adapt them to their problems without professional help. We present RSVP, the Rapid Suggestive Visualization Prototyping system encoding VPSA knowledge to enable domain scientists to prototype custom visualization dashboards tailored to their specific needs. The system implements a task-oriented, multi-view visualization recommendation strategy over a visualization design space optimized for VPSA to guide users in meeting their analytical demands. We derived the VPSA knowledge implemented in the system by conducting an extensive meta design study over the body of work on VPSA. We show how this process can be used to perform a data and task abstraction, extract a common visualization design space, and derive a task-oriented VisRec strategy. User studies indicate that the system is user-friendly and can uncover novel insights. © 2024, CC BY.

关键词： Visualization

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The Vertebrate Breed Ontology: Toward Effective Breed data Standardization

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Journal of Veterinary Internal Medicine 2025年第4期39卷 e70133页

作者： Kathleen R. Mullen Imke Tammen Nicolas A. Matentzoglu Marius Mather James P. Balhoff Elizabeth Esdaile Gregoire Leroy Carissa A. Park Halie M. Rando Nadia T. Saklou Tracy L. Webb Nicole A. Vasilevsky Christopher J. Mungall Melissa A. Haendel Frank W. Nicholas Sabrina Toro Department of Genetics University of North Carolina at Chapel Hill Chapel Hill North Carolina USA Sydney School of Veterinary Science The University of Sydney Sydney New South Wales Australia Semanticly Ltd Athens Greece Sydney Informatics Hub The University of Sydney Sydney New South Wales Australia Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill North Carolina USA Veterinary Genetics Laboratory School of Veterinary Medicine University of California Davis California USA Animal Production and Health Division Food and Agriculture Organization of the United Rome Italy Department of Animal Science Iowa State University Ames Iowa USA Department of Computer Science Smith College Northampton Massachusetts USA Department of Clinical Sciences Colorado State University Fort Collins Colorado USA Data Collaboration Center Critical Path Institute Tucson Arizona USA Lawrence Berkeley National Laboratory Berkeley California USA

Background Limited universally-adopted data standards in veterinary medicine hinder data interoperability and therefore integration and comparison; this ultimately impedes the application of existing information-based tools to support advancement in diagnostics, treatments, and precision medicine. Hypothesis/Objectives A single, coherent, logic-based standard for documenting breed names in health, production, and research-related records will improve data use capabilities in veterinary and comparative medicine. Animals No live animals were used. Methods The Vertebrate Breed Ontology (VBO) was created from breed names and related information compiled from the Food and Agriculture Organization of the United Nations, breed registries, communities, and experts, using manual and computational approaches. Each breed is represented by a VBO term that includes breed information and provenance as metadata. VBO terms are classified using description logic to allow computational applications and Artificial Intelligence–readiness. Results VBO is an open, community-driven ontology representing over 19 500 livestock and companion animal breed concepts covering 49 species. Breeds are classified based on community and expert conventions (e.g., cattle breed) and supported by relations to the breed's genus and species indicated by National Center for Biotechnology Information (NCBI) Taxonomy terms. Relationships between VBO terms (e.g., relating breeds to their foundation stock) provide additional context to support advanced data analytics. VBO term metadata includes synonyms, breed identifiers/codes, and attributed cross-references to other databases. Conclusion and Clinical Importance The adoption of VBO as a standard for breed names in databases and veterinary electronic health records enhances veterinary data interoperability and computability, supporting precision medicine.

关键词： animal population groups biological ontologies breeding genetics precision medicine theriogenology

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Workflowhub: a registry for computational workflows

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Scientific data 2025年第1期12卷 1-19页

作者： Ove Johan Ragnar Gustafsson Sean R. Wilkinson Finn Bacall Stian Soiland-Reyes Stuart Owen Nick Juty Carole Goble Simone Leo Luca Pireddu José M. Fernández Salvador Capella-Gutierrez Tom Brown Hervé Ménager Björn Grüning Frederik Coppens Australian BioCommons University of Melbourne Melbourne Victoria Australia Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory Oak Ridge Tennessee USA Department of Computer Science The University of Manchester Manchester UK Informatics Institute University of Amsterdam Amsterdam The Netherlands Center for Advanced Studies Research and Development in Sardinia (CRS4) Pula Cagliari Italy Barcelona Supercomputing Center (BSC) Barcelona Spain Spanish National Bioinformatics Institute (INB) Barcelona Spain Leibniz Institute for Zoo- and Wildlife Research Berlin Germany Institut Pasteur Université Paris Cité Bioinformatics of Biostatistics Hub Paris 75015 France CNRS UMS 3601 Institut Français de Bioinformatique Evry France Albert-Ludwigs-Universität Freiburg Freiburg Germany VIB Data Core VIB Technologies Ghent Belgium

The rising popularity of computational workflows is driven by the need for repetitive and scalable data processing, sharing of processing know-how, and transparent methods. As both combined records of analysis and descriptions of processing steps, workflows should be reproducible, reusable, adaptable, and available. Workflow sharing presents opportunities to reduce unnecessary reinvention, promote reuse, increase access to best practice analyses for non-experts, and increase productivity. In reality, workflows are scattered and difficult to find, in part due to the diversity of available workflow engines and ecosystems, and because workflow sharing is not yet part of research practice. Workflowhub provides a unified registry for all computational workflows that links to community repositories, and supports both the workflow lifecycle and making workflows findable, accessible, interoperable, and reusable (FAIR). By interoperating with diverse platforms, services, and external registries, Workflowhub adds value by supporting workflow sharing, explicitly assigning credit, enhancing FAIRness, and promoting workflows as scholarly artefacts. The registry has a global reach, with hundreds of research organisations involved, and more than 800 workflows registered.

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High Performance computing Framework for Variable Selection on Genome-wide Association Studies

High Performance Computing Framework for Variable Selection ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Xiang Liu, Xueling Diao, Jing Zheng, Mengyao Li, Jihe Wei, Dehui Xie, Qipeng Li, Xia Jiang, Linshan National University Health System Group Technology Office Singapore National University of Singapore School of Computing Singapore Peking University School Hospital of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases Department of Preventive Dentistry China Harvard University Harvard T.H. Chan School of Public Health United States Peking University School of Software and Microelectronics China Hong Kong University of Science and Technology IoT Thrust Information Hub Guangzhou China Eth Zurich Department of Computer Science Switzerland National University of Singapore Institute of Data Science Singapore

ISBN: (纸本)9798350386226

Variable selection for genome-wide association studies (GWAS) has been a major research focus for decades. With the exponential growth of biological and biomedical data in the era of big data, scientists are confronted with the challenge of extracting meaningful information from vast datasets while managing the inherent heterogeneity in bioinformatics. To date, there are no highly effective tools that support high-dimensional datasets and achieve robust variable selection performance, all while accounting for the non-i.i.d. features and structured relatedness among explanatory and response *** address these challenges, we introduce the first high-performance computing framework for variable selection in GWAS. Our framework integrates various state-of-the-art methods, allowing researchers to easily combine different techniques and fully explore their potential. Additionally, our approach employs novel optimization strategies to solve the problem efficiently, even for high-dimensional data with sparse characteristics. By processing the data holistically, the framework delivers comprehensive analysis and accurate linkage mapping associations. Designed for ease of use, the framework is implemented in Python and offers seamless deployment, making it accessible to a wide range of researchers. © 2024 IEEE.

关键词： Confounding factors genome-wide association study structured relatedness variable selection

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