Tootfinder

Hybrid Explanation-Guided Learning for Transformer-Based Chest X-Ray Diagnosis
Shelley Zixin Shu, Haozhe Luo, Alexander Poellinger, Mauricio Reyes
https://arxiv.org/abs/2510.12704

Hybrid Explanation-Guided Learning for Transformer-Based Chest X-Ray Diagnosis
Transformer-based deep learning models have demonstrated exceptional performance in medical imaging by leveraging attention mechanisms for feature representation and interpretability. However, these models are prone to learning spurious correlations, leading to biases and limited generalization. While human-AI attention alignment can mitigate these issues, it often depends on costly manual supervision. In this work, we propose a Hybrid Explanation-Guided Learning (H-EGL) framework that combines…

"AI-powered oil spill prediction system can improve emergency response accuracy by up to 25%"
#AI #ArtificialIntelligence #Technology

AI-powered oil spill prediction system can improve emergency response accuracy by up to 25%
Oil spills can be among the most devastating environmental disasters, with the potential to severely damage marine ecosystems, disrupt coastal communities, and impose lasting economic damage. Traditional numerical models, such as MEDSLIK-II, simulate the movement and transformation of oil particles in seawater, but their accuracy has been limited by dependence on expert judgment for tuning critical physical parameters. This manual calibration process, while informed by experience, is not always…

Observer-Based Source Localization in Tree Infection Networks via Laplace Transforms
Kesler O'Connor, Julia M. Jess, Devlin Costello, Manuel E. Lladser
https://arxiv.org/abs/2510.09828

Observer-Based Source Localization in Tree Infection Networks via Laplace Transforms
We address the problem of localizing the source of infection in an undirected, tree-structured network under a susceptible-infected outbreak model. The infection propagates with independent random time increments (i.e., edge-delays) between neighboring nodes, while only the infection times of a subset of nodes can be observed. We show that a reduced set of observers may be sufficient, in the statistical sense, to localize the source and characterize its identifiability via the joint Laplace tra…

Joint Communication Scheduling and Velocity Control for Multi-UAV-Assisted Post-Disaster Monitoring: An Attention-Based In-Context Learning Approach
Yousef Emami, Seyedsina Nabavirazavi, Jingjing Zheng, Hao Zhou, Miguel Gutierrez Gaitan, Kai Li, Luis Almeida
https://arxiv.org/abs/2510.05698

Joint Communication Scheduling and Velocity Control for Multi-UAV-Assisted Post-Disaster Monitoring: An Attention-Based In-Context Learning Approach
Recently, Unmanned Aerial Vehicles (UAVs) are increasingly being investigated to collect sensory data in post-disaster monitoring scenarios, such as tsunamis, where early actions are critical to limit coastal damage. A major challenge is to design the data collection schedules and flight velocities, as unfavorable schedules and velocities can lead to transmission errors and buffer overflows of the ground sensors, ultimately resulting in significant packet loss. Meanwhile, online Deep Reinforcem…

Crosslisted article(s) found for math.PR. https://arxiv.org/list/math.PR/new
[1/1]:
- Observer-Based Source Localization in Tree Infection Networks via Laplace Transforms
Kesler O'Connor, Julia M. Jess, Devlin Costello, Manuel E. Lladser

ZeroFalse: Improving Precision in Static Analysis with LLMs
Mohsen Iranmanesh (Simon Fraser University), Sina Moradi Sabet (Amirkabir University of Technology), Sina Marefat (K. N. Toosi University of Technology), Ali Javidi Ghasr (Ferdowsi University of Mashhad), Allison Wilson (Cyber Risk Solutions), Iman Sharafaldin (Forward Security), Mohammad A. Tayebi (Simon Fraser University)

ZeroFalse: Improving Precision in Static Analysis with LLMs
Static Application Security Testing (SAST) tools are integral to modern software development, yet their adoption is undermined by excessive false positives that weaken developer trust and demand costly manual triage. We present ZeroFalse, a framework that integrates static analysis with large language models (LLMs) to reduce false positives while preserving coverage. ZeroFalse treats static analyzer outputs as structured contracts, enriching them with flow-sensitive traces, contextual evidence,…

Inclusive Easy-to-Read Generation for Individuals with Cognitive Impairments
Fran\c{c}ois Ledoyen, Ga\"el Dias, Alexis Lechervy, Jeremie Pantin, Fabrice Maurel, Youssef Chahir, Elisa Gouzonnat, M\'elanie Berthelot, Stanislas Moravac, Armony Altinier, Amy Khairalla
https://arxiv.org/abs/2510.00691

Inclusive Easy-to-Read Generation for Individuals with Cognitive Impairments
Ensuring accessibility for individuals with cognitive impairments is essential for autonomy, self-determination, and full citizenship. However, manual Easy-to-Read (ETR) text adaptations are slow, costly, and difficult to scale, limiting access to crucial information in healthcare, education, and civic life. AI-driven ETR generation offers a scalable solution but faces key challenges, including dataset scarcity, domain adaptation, and balancing lightweight learning of Large Language Models (LLM…

Discrete Facial Encoding: : A Framework for Data-driven Facial Display Discovery
Minh Tran, Maksim Siniukov, Zhangyu Jin, Mohammad Soleymani
https://arxiv.org/abs/2510.01662 htt…

Discrete Facial Encoding: : A Framework for Data-driven Facial Display Discovery
Facial expression analysis is central to understanding human behavior, yet existing coding systems such as the Facial Action Coding System (FACS) are constrained by limited coverage and costly manual annotation. In this work, we introduce Discrete Facial Encoding (DFE), an unsupervised, data-driven alternative of compact and interpretable dictionary of facial expressions from 3D mesh sequences learned through a Residual Vector Quantized Variational Autoencoder (RVQ-VAE). Our approach first extr…

Automatic Building Code Review: A Case Study
Hanlong Wan, Weili Xu, Michael Rosenberg, Jian Zhang, Aysha Siddika
https://arxiv.org/abs/2510.02634 https://a…

Automatic Building Code Review: A Case Study
Building officials, particularly those in resource-constrained or rural jurisdictions, face labor-intensive, error-prone, and costly manual reviews of design documents as projects increase in size and complexity. The growing adoption of Building Information Modeling (BIM) and Large Language Models (LLMs) presents opportunities for automated code review (ACR) solutions. This study introduces a novel agent-driven framework that integrates BIM-based data extraction with automated verification usin…

Point2RBox-v3: Self-Bootstrapping from Point Annotations via Integrated Pseudo-Label Refinement and Utilization
Teng Zhang, Ziqian Fan, Mingxin Liu, Xin Zhang, Xudong Lu, Wentong Li, Yue Zhou, Yi Yu, Xiang Li, Junchi Yan, Xue Yang
https://arxiv.org/abs/2509.26281

Point2RBox-v3: Self-Bootstrapping from Point Annotations via Integrated Pseudo-Label Refinement and Utilization
Driven by the growing need for Oriented Object Detection (OOD), learning from point annotations under a weakly-supervised framework has emerged as a promising alternative to costly and laborious manual labeling. In this paper, we discuss two deficiencies in existing point-supervised methods: inefficient utilization and poor quality of pseudo labels. Therefore, we present Point2RBox-v3. At the core are two principles: 1) Progressive Label Assignment (PLA). It dynamically estimates instance sizes…