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.
Qiskit-symb is a Python package designed to enable the symbolic evaluation of quantum states and quantum operators in Parameterized Quantum Circuits (PQCs) defined using Qiskit. This open-source project has been integrated into the official Qiskit Ecosystem platform, making it more accessible to the rapidly growing community of Qiskit users.
Given a PQC with free parameters, qiskit-symb can...
This study aims to improve the performance of event classification in collider physics by introducing a foundation model in deep learning. Event classification is a typical problem in collider physics, where the goal is to distinguish the signal events from the background events as much as possible to search for new phenomena in nature. Although deep learning can provide significant...
The AI4EOSC (Artificial Intelligence for the European Open Science Cloud) project aims at contributing to the landscape of Artificial Intelligence (AI) research with a comprehensive and user-friendly suite of tools and services within the framework of the European Open Science Cloud (EOSC). This innovative platform is specifically designed to empower researchers by enabling the...
The educational needs in the future classroom need to focus on a combination of student engagement in learning, inquiry-based approaches, curiosity, imagination, and design thinking. Smart classrooms leverage the advancements in Internet of Things to create intelligent, interconnected learning environments that enhance the quality of life and educational outcomes of students. With advancements...
