Description
During the last decade, Artificial Intelligence (AI) and statistical learning techniques have started to become pervasive in scientific applications, exploring the adoption of novel algorithms, modifying the design principles of application workflows, and impacting the way in which grid and cloud computing services are used by a diverse set of scientific communities. This track aims at discussing problems, solutions and application examples related to this area of research, ranging from R&D activities to production-ready solutions. Topics of interests in this track include: AI-enabled scientific workflows; novel approaches in scientific applications adopting machine learning (ML) and deep learning (DL) techniques; cloud-integrated statistical learning as-a-service solutions; anomaly detection techniques; predictive and prescriptive maintenance; experience with MLOps practices; AI-enabled adaptive simulations; experience on ML/DL models training and inference on different hardware resources for scientific applications.
Ensuring the reproducibility of physics results is a critical challenge in high-energy physics (HEP). In this study, we aim to develop a system that automatically extracts analysis procedures from HEP publications and generates executable analysis code capable of reproducing published results, leveraging recent advances in large language models (LLMs).
Our approach employs open-source LLMs...
This study investigates how LLMs can act as generative reasoning engines to interpret and restructure complex archival document collections. Building upon earlier work with the President’s Personal File (PPF 9: Gifts) from the Franklin D. Roosevelt Presidential Library, the research explores how an LLM can infer relationships, sequences, and contextual features from textual and descriptive...
The rapid spread of large language models (LLM) in higher education has intensified discussions about their promise as instructional support tools and their risks as enablers of academic misconduct. Depending on how they are used, LLMs can assist instructors in developing more efficient learning and evaluation materials, as well as students to prepare for a test, or they can undermine...
Multivariate time series forecasting often suffers from noise interference, inconsistent dynamics across variables, and limited capacity to capture both short-term fluctuations and long-term trends. This paper proposes a novel framework that addresses these challenges through three coordinated modules. First, a channel-wise modulation mechanism selectively filters anomalous patterns by...
