Tootfinder

Smooth Flow Matching
Jianbin Tan, Anru R. Zhang
https://arxiv.org/abs/2508.13831 https://arxiv.org/pdf/2508.13831…

Smooth Flow Matching
Functional data, i.e., smooth random functions observed over a continuous domain, are increasingly available in areas such as biomedical research, health informatics, and epidemiology. However, effective statistical analysis for functional data is often hindered by challenges such as privacy constraints, sparse and irregular sampling, infinite dimensionality, and non-Gaussian structures. To address these challenges, we introduce a novel framework named Smooth Flow Matching (SFM), tailored for g…

Today in Edinburgh, talk at LFCS, then performance at Edinburgh Fringe.
https://informatics.ed.ac.uk/lfcs/lfcs-seminar-tuesday-5th-august-michael-sperber

“And note the rare special case illustrated by Harry S Truman: the S in this name never takes a full stop, because it's not an abbreviation for anything; President #Truman's parents actually gave him the middle name S.”
Wow, just wow. Didn't know. My world view is crumbling.

Tao-Technology for Teen Mobile Use: Harmonizing Adaptation, Autonomy, and Reflection
Pengyu Zhu, Janghee Cho
https://arxiv.org/abs/2507.12204 https://

Tao-Technology for Teen Mobile Use: Harmonizing Adaptation, Autonomy, and Reflection
Adolescents' mobile technology use is often regulated through rigid control mechanisms that fail to account for their autonomy and natural usage patterns. Drawing on Taoist philosophy, particularly Wu Wei, Yin-Yang, and Zi Ran, this position paper proposes Tao-Technology, a self-organizing, adaptive regulatory framework. Integrating insights from Reflective Informatics and Information Ecologies, we explore how mobile technology can dynamically adjust to context while fostering self-reflection a…

Humanity's Last Code Exam: Can Advanced LLMs Conquer Human's Hardest Code Competition?
Xiangyang Li, Xiaopeng Li, Kuicai Dong, Quanhu Zhang, Rongju Ruan, Xinyi Dai, Xiaoshuang Liu, Shengchun Xu, Yasheng Wang, Ruiming Tang
https://arxiv.org/abs/2506.12713

Humanity's Last Code Exam: Can Advanced LLMs Conquer Human's Hardest Code Competition?
Code generation is a core capability of large language models (LLMs), yet mainstream benchmarks (e.g., APPs and LiveCodeBench) contain questions with medium-level difficulty and pose no challenge to advanced LLMs. To better reflected the advanced reasoning and code generation ability, We introduce Humanity's Last Code Exam (HLCE), comprising 235 most challenging problems from the International Collegiate Programming Contest (ICPC World Finals) and the International Olympiad in Informatics (IOI)…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/3]:
- Compression Hacking: A Supplementary Perspective on Informatics Properties of Language Models fro...
Zang, Ning, Wei, Dou, Zhang, Mo, Li, Gui, Zhang, Huang

VaxPulse: Active Global Vaccine Infodemic Risk Assessment
Gerardo Luis Dimaguila (Epidemiology Informatics, Centre for Health Analytics, Melbourne Children's Campus, Australia, Department of Paediatrics, The University of Melbourne, Australia), Muhammad Javed (Epidemiology Informatics, Centre for Health Analytics, Melbourne Children's Campus, Australia, Global Vaccine Data Network, University of Auckland, New Zealand), Jeremiah Munakabayo (Epidemiology Informatics, Centre for H…

VaxPulse: Active Global Vaccine Infodemic Risk Assessment
Vaccine infodemics, driven by misinformation, disinformation, and inauthentic online behaviours, pose significant threats to global public health. This paper presents our response to this challenge, demonstrating how we developed VaxPulse Vaccine Infodemic Risk Assessment Lifecycle (VIRAL), an AI-powered social listening platform designed to monitor and assess vaccine-related infodemic risks. Leveraging interdisciplinary expertise and international collaborations, VaxPulse VIRAL integrates mach…

I'll be in Edinburgh next week, mainly for the Fringe, but I'll also give a talk at LFS/U Edinburgh on Tuesday:
https://informatics.ed.ac.uk/lfcs/lfcs-seminar-tuesday-5th-august-michael-sperber

Optimizing Mesh to Improve the Triangular Expansion Algorithm for Computing Visibility Regions
Jan Mikula (Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague), Miroslav Kulich (Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague)

Optimizing Mesh to Improve the Triangular Expansion Algorithm for Computing Visibility Regions
This paper addresses the problem of improving the query performance of the triangular expansion algorithm (TEA) for computing visibility regions by finding the most advantageous instance of the triangular mesh, the preprocessing structure. The TEA recursively traverses the mesh while keeping track of the visible region, the set of all points visible from a query point in a polygonal world. We show that the measured query time is approximately proportional to the number of triangle edge expansio…

Detection of Adverse Drug Events in Dutch clinical free text documents using Transformer Models: benchmark study
Rachel M. Murphy (Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam, The Netherlands), Nishant Mishra (Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam, The Netherlands), Nicolette F. de Keizer (Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam, T…

Detection of Adverse Drug Events in Dutch clinical free text documents using Transformer Models: benchmark study
In this study, we set a benchmark for adverse drug event (ADE) detection in Dutch clinical free text documents using several transformer models, clinical scenarios and fit-for-purpose performance measures. We trained a Bidirectional Long Short-Term Memory (Bi-LSTM) model and four transformer-based Dutch and/or multilingual encoder models (BERTje, RobBERT, MedRoBERTa.nl, and NuNER) for the tasks of named entity recognition (NER) and relation classification (RC) using 102 richly annotated Dutch I…

GloBIAS: strengthening the foundations of BioImage Analysis
A. A. Corbat (BioImage Informatics Unit, Science for Life Laboratory and Department of Information Technology, Uppsala University, Sweden), C. G. Walther (German BioImaging, Gesellschaft f\"ur Mikroskopie und Bildanalyse e.V., Konstanz, Germany, University of Vienna, Vienna, Austria), L. R. de la Ballina (Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebe…

GloBIAS: strengthening the foundations of BioImage Analysis
There is a global need for BioImage Analysis (BIA) as advances in life sciences increasingly rely on cutting-edge imaging systems that have dramatically expanded the complexity and dimensionality of biological images. Turning these data into scientific discoveries requires people with effective data management skills and knowledge of state-of-the-art image processing and data analysis, in other words, BioImage Analysts. The Global BioImage Analysts' Society (GloBIAS) aims to enhance the profile…

Benchmarking Large Language Models for Polymer Property Predictions
Sonakshi Gupta, Akhlak Mahmood, Shivank Shukla, Rampi Ramprasad
https://arxiv.org/abs/2506.02129

Benchmarking Large Language Models for Polymer Property Predictions
Machine learning has revolutionized polymer science by enabling rapid property prediction and generative design. Large language models (LLMs) offer further opportunities in polymer informatics by simplifying workflows that traditionally rely on large labeled datasets, handcrafted representations, and complex feature engineering. LLMs leverage natural language inputs through transfer learning, eliminating the need for explicit fingerprinting and streamlining training. In this study, we finetune …

Disaster Informatics after the COVID-19 Pandemic: Bibliometric and Topic Analysis based on Large-scale Academic Literature
Ngan Tran, Haihua Chen, Ana Cleveland, Yuhan Zhou
https://arxiv.org/abs/2507.16820

Disaster Informatics after the COVID-19 Pandemic: Bibliometric and Topic Analysis based on Large-scale Academic Literature
This study presents a comprehensive bibliometric and topic analysis of the disaster informatics literature published between January 2020 to September 2022. Leveraging a large-scale corpus and advanced techniques such as pre-trained language models and generative AI, we identify the most active countries, institutions, authors, collaboration networks, emergent topics, patterns among the most significant topics, and shifts in research priorities spurred by the COVID-19 pandemic. Our findings hig…

Lightweight Language Models are Prone to Reasoning Errors for Complex Computational Phenotyping Tasks
Sarah Pungitore, Shashank Yadav, David Maughan, Vignesh Subbian
https://arxiv.org/abs/2507.23146

Lightweight Language Models are Prone to Reasoning Errors for Complex Computational Phenotyping Tasks
Objective: Although computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications, it is time-intensive because of manual data review. We previously assessed the ability of LLMs to perform computational phenotyping tasks using computable phenotypes for ARF respiratory support therapies. They successfully performed concept classification and classification of single-therapy phenotypes, but underperformed on multiple-therapy phenotype…

Gender and Discipline Shape Length, Content and Tone of Grant Peer Review Reports
Stefan M\"uller, Gabriel Okasa, Michaela Strinzel, Anne Jorstad, Katrin Milzow, Matthias Egger
https://arxiv.org/abs/2507.00103

Gender and Discipline Shape Length, Content and Tone of Grant Peer Review Reports
Peer review by experts is central to the evaluation of grant proposals, but little is known about how gender and disciplinary differences shape the content and tone of grant peer review reports. We analyzed 39,280 review reports submitted to the Swiss National Science Foundation between 2016 and 2023, covering 11,385 proposals for project funding across 21 disciplines from the Social Sciences and Humanities (SSH), Life Sciences (LS), and Mathematics, Informatics, Natural Sciences, and Technolog…

Impact of Geant4's Electromagnetic Physics Constructors on Accuracy and Performance of Simulations for Rare Event Searches
H. Kluck (Institut f\"ur Hochenergiephysik der \"Osterreichischen Akademie der Wissenschaften, Wien, Austria, Atominstitut, Technische Universit\"at Wien, Wien, Austria), R. Breier (Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia), A. Fu{\ss} (Institut f\"ur Hochenergiephysik der \"Osterreichis…

Impact of Geant4's Electromagnetic Physics Constructors on Accuracy and Performance of Simulations for Rare Event Searches
A primary objective in contemporary low background physics is the search for rare and novel phenomena beyond the Standard Model of particle physics, e.g. the scattering off of a potential Dark Matter particle or the neutrinoless double beta decay. The success of such searches depends on a reliable background prediction via Monte Carlo simulations. A widely used toolkit to construct these simulations is Geant4, which offers the user a wide choice of how to model the physics of particle interacti…

Exciton photoemission from a ground state of a solid Ta2Pd3Te5
Siwon Lee, Kyung-Hwan Jin, SeongJin Kwon, Hyunjin Jung, Choongjae Won, Sang-Wook Cheong, Gil Young Cho, Jaeyoung Kim, Han Woong Yeom
https://arxiv.org/abs/2507.16453

Exciton photoemission from a ground state of a solid Ta2Pd3Te5
Excitons are bosonic quasiparticles with a variety of applications in optoelectronics, photosyn thesis, and dissipationless informatics, and their lifetime can become sufficiently long to form a quantum condensate. While exciton condensation has been predicted to occur as a ground state of a solid, so called an excitonic insulator, whose material realization has been elusive. Here we report the observation of direct photoemission signals from excitons in a ground state of a very recent excitoni…

SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping
Sarah Pungitore, Shashank Yadav, Molly Douglas, Jarrod Mosier, Vignesh Subbian
https://arxiv.org/abs/2506.16359

SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping
Objective: Computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications. However, it is time-intensive because of manual data review, limited automation, and difficulties in adapting algorithms across sources. Since LLMs have demonstrated promising capabilities for text classification, comprehension, and generation, we posit they will perform well at repetitive manual review tasks traditionally performed by human experts. To suppor…