4 Contributo in Atti di Convegno (Proceeding)

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Transformer Neural Network-Based Design and Analysis of Nano-Scale Ferroelectric NCFETs With Low Subthreshold Swing

In this work, we employ Transformer Neural Network (TNN) to predict optimal materials and design parameters of nano-scale Ferroelectric Negative Capacitance Field-Effect Transistors (NCFETs) targeting low subthreshold swing (SS). The TNN model is trained on a dataset, encompassing various ferroelectric materials, gate oxide thicknesses, channel lengths, and other critical parameters. The predicted parameters are subsequently validated and refined using Finite Element Method (FEM) tools, which...

Verification of Configurable SRA Systems

Many digital systems are designed as collections of asynchronous processes orchestrated by a domain-specific scheduler. The verification of such scheduler-restricted asynchronous systems (SRA) is challenging due to process-process and process-scheduler interactions. In this paper, we tackle the problem of verifying configurable SRA. A configurable SRA describes an unbounded family of possible SRA, each resulting from an instantiation satisfying given configuration constraints; our goal is pro...

Thesis Proposal: LLMs post-training for multilingual medical tasks. Instruction-Tuning, Continual-Pretraining or Reasoning?

Adapting Large Language Models to the medical domain remains an active area of research, with multiple strategies proposed to leverage annotated and unannotated data effectively. In this work, we propose a thesis outline to compare three common adaptation approaches{---}Instruction Tuning, Continual Pretraining, and Reasoning-oriented Training. We identify 5 dimensions to analyse: i) the interaction between the adaptation technique and the tasks; ii) the impact of the data size on the downstr...

Overview of the CRF 2026 Shared Task on Clinical Case Report Forms Filling

Case Report Forms (CRFs) are structured instruments widely used in clinical research to systematically collect patient information according to predefined protocols. In practice, CRFs are often manually completed by clinicians based on patients' clinical reports, a process that is time-consuming and prone to inconsistencies. Despite their central role in medical studies, automatic population of CRFs from clinical narratives remains largely underexplored in the Natural Language Processing comm...

Is Agentic RAG worth it? An experimental comparison of {RAG} approaches

Retrieval-Augmented Generation (RAG) systems are usually defined by the combination of a generator and a retrieval component that extracts textual context from a knowledge base to answer user queries. However, such basic implementations exhibit several limitations, including noisy or suboptimal retrieval, misuse of retrieval for out-of-scope queries, weak query{--}document matching, and variability or cost associated with the generator. These shortcomings have motivated the development of ``E...

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