Publications

I metodi di Intelligenza Artificiale hanno raggiunto risultati notevoli in diversi ambiti, ma la loro applicazione efficace a dati dinamici e strutturati rimane una sfida significativa. Questa tesi indaga tecniche avanzate di IA per l’apprendimento continuo e robusto in scenari in cui i dati evolvono nel tempo e presentano complesse dipendenze. La ricerca esplora diverse direzioni complementari per affrontare le limitazioni dei modelli attuali in termini di adattabilità e resilienza. In primo luogo, vengono studiati metodi di apprendimento continuo per consentire alle reti neurali di apprendere da flussi sequenziali di dati senza dimenticare le conoscenze acquisite in precedenza. Viene proposto un approccio basato sulla distillazione che sfrutta i Vision Transformer, in cui le rappresentazioni di attenzione vengono trasferite tra modelli teacher e student, migliorando la stabilità. Inoltre, viene sviluppata una strategia di prompt learning basata sugli embedding del modello CLIP, che seleziona dinamicamente prompt specifici per ciascun task, migliorando le prestazioni. La seconda linea di ricerca della tesi riguarda il federated learning, un contesto distribuito in cui le informazioni strutturate emergono naturalmente dalla collaborazione tra i client. Viene introdotto un nuovo meccanismo di difesa contro gli attacchi backdoor, che sfrutta le proprietà spettrali delle rappresentazioni locali dei dati per identificare e mitigare i partecipanti malevoli attraverso tecniche di sintesi e allineamento dei dati. Infine, la tesi analizza attacchi backdoor adattivi e le relative difese, sottolineando come tali vulnerabilità rappresentino una minaccia critica per i processi e le infrastrutture industriali. Nel complesso, il lavoro contribuisce alla progettazione di modelli di IA capaci di adattamento continuo, collaborazione sicura e sfruttamento efficace delle informazioni strutturali per applicazioni reali e industriali.

This report is the transcript of what was discussed in a convention at the Endocrinology Unit in Modena, Italy, in the form of the aporetic dialogs of ancient Greece. It is the third episode of a series of four discussions on the differences between males and females, with a multidisciplinary approach. In this work, the role of testosterone in gender differences in the aptitude for mathematics is explored. First, the definitions of mathematical abilities were provided together with any gender difference in the distribution of females and males in science, technology, engineering, and mathematics subjects. A clear predominance of males is evident at most science, technology, engineering, and mathematics education levels, especially in advanced academic careers. Then, the discussants were divided into two groups: group 1, which illustrated the thesis that testosterone promotes the development of logical‒mathematical skills, and group 2, which, in contrast, asserted the inconsistency of a direct role of testosterone in improving cognitive abilities and that socio-cultural factors should be considered on the basis of this gender gap. In the end, an expert referee (a female engineer) tried to resolve the aporia: are the two theories equivalent or is one superior?.

This study presents an interdisciplinary methodology for detecting biblical references in Latin patristic literature through an innovative combination of rigorous philological approach and Natural Language Processing (NLP) techniques. Focusing on one of the most influential ancient Christian commentaries on the Bible, Augustine of Hippo’s De Genesi ad litteram, and its relationship with Latin biblical texts (specifically, Jerome’s Vulgate and pre-Vulgate versions), this research introduces a token-based classification system for intertextual references, enriched with semantic annotations and supported by the INCEpTION platform. The first section shows how this numerical classification system accounts for exact matches, lemmatized forms, roots, synonyms, and other forms of semantic parallels (here referred to as “structures”), capturing a wide spectrum of textual similarity. To enhance automatic retrieval of these intertextual connections, we fine-tune BERT-based language models for Latin, incorporating contrastive learning and hard negative mining. In the second section, experimental results show that finetuned models significantly outperform baseline models at various levels of textual similarity. This work highlights the utility of computational models in overcoming the traditional dichotomy between explicit quotations and implicit allusions, embracing multiple intermediate nuances of similarity and offering a scalable approach to the study of intertextuality in ancient writings.

Inference-time scaling has emerged as an effective way to improve generative models at test time by using a verifier to score and select candidate outputs. A common choice is to employ Multimodal Large Language Models (MLLMs) as verifiers, which can improve performance but introduce substantial inference-time cost. Indeed, diffusion pipelines operate in an autoencoder latent space to reduce computation, yet MLLM verifiers still require decoding candidates to pixel space and re-encoding them into the visual embedding space, leading to redundant and costly operations. In this work, we propose Verifier on Hidden States (VHS), a verifier that operates directly on intermediate hidden representations of Diffusion Transformer (DiT) single-step generators. VHS analyzes generator features without decoding to pixel space, thereby reducing the per-candidate verification cost while improving or matching the performance of MLLM-based competitors. We show that, under tiny inference budgets with only a small number of candidates per prompt, VHS enables more efficient inference-time scaling reducing joint generation-and-verification time by 63.3%, compute FLOPs by 51% and VRAM usage by 14.5% with respect to a standard MLLM verifier, achieving a +2.7% improvement on GenEval at the same inference-time budget.

Technologies to enable safe and effective collaboration and coexistence between humans and robots have gained significant importance in the last few years. A critical component useful for realizing this collaborative paradigm is the understanding of human and robot 3D poses using non-invasive systems. Therefore, in this paper, we propose a novel vision-based system leveraging depth data to accurately establish the 3D locations of skeleton joints. Specifically, we introduce the concept of Pose Nowcasting, denoting the capability of the proposed system to enhance its current pose estimation accuracy by jointly learning to forecast future poses. The experimental evaluation is conducted on two different datasets, providing accurate and real-time performance and confirming the validity of the proposed method on both the robotic and human scenarios.

A gene fusion is a chromosomal aberration from juxtaposing separate genes. Since some gene fusions are involved in tumorigenesis, proper gene fusion investigation and analysis are crucial in the literature. After DNA/RNA sample extraction, detecting gene fusions requires first gene fusion detection tools, which usually provide many false positives. Given the high experimental costs in wet lab validation of a single fusion, gene fusion prioritization tools were made available over the years to significantly narrow down candidate gene fusions for validation (e.g., Oncofuse, Pegasus, DEEPrior, ChimerDriver). Although a few reviews about gene fusion detection tools are available, a benchmark on prioritization tools is not available yet in the literature. The aim of this paper is twofold: 1. to provide a curated dataset for a fair gene fusion prioritization tool evaluation. 2. to develop a proper comparison based on time, resources, and tool confidence on selected gene fusions. Based on this benchmark, it can be stated that ChimerDriver is the most reliable tool for prioritizing oncogenic fusions.

Barcodes are a critical technology in industrial automation, logistics, and retail, enabling fast and reliable data capture. While deep learning has significantly improved barcode localization accuracy, most modern architectures remain too computationally demanding for real-time deployment on embedded systems without dedicated hardware acceleration. In this work, we present BaFaLo (Barcode Fast Localizer), an ultra-lightweight segmentation-based neural network for barcode localization. Our model is specifically optimized for real-time performance on low-power CPUs while maintaining high localization accuracy for both 1D and 2D barcodes. It features a two-branch architecture—comprising a local feature extractor and a global context module—and is tailored for low-resolution inputs to improve inference speed further. We benchmark BaFaLo against several lightweight architectures for object detection or segmentation, including YOLO Nano, Fast-SCNN, BiSeNet V2, and ContextNet, using the BarBeR dataset. BaFaLo achieves the fastest inference time among all deep-learning models tested, operating at 57.62ms per frame on a single CPU core of a Raspberry Pi 3B+. Despite its compact design, it achieves a decoding rate nearly equivalent to YOLO Nano for 1D barcodes and only 3.5 percentage points lower for 2D barcodes while being approximately nine times faster.

CNNs and Transformer-based architectures are recently dominating the field of 3D medical segmentation. While CNNs face limitations in the local receptive field, Transformers require significant memory and data, making them less suitable for analyzing large 3D medical volumes. Consequently, fully convolutional network models like U-Net are still leading the 3D segmentation scenario. Although efforts have been made to reduce the Transformers computational complexity, such optimized models still struggle with content-based reasoning. This paper examines Mamba, a Recurrent Neural Network (RNN) based on State Space Models (SSMs), which achieves linear complexity and has outperformed Transformers in long-sequence tasks. Specifically, we assess Mamba’s performance in 3D medical segmentation using three widely recognized and commonly employed datasets and propose architectural enhancements to improve its segmentation effectiveness by mitigating the primary shortcomings of existing Mamba-based solutions.