Publications

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.

Reproducibility policies promise "checkable" medical-imaging science, yet many submissions still ship unverifiable artifacts. Our analysis of 3722 MICCAI papers shows code-linking rising from 51.8% (2021) to 72.5% (2025), but ~13% of linked repositories are inaccessible or empty. We present paper-snitch, a reviewer-facing decision-support tool that turns these signals into an evidence-grounded report. Paper-snitch parses PDFs, resolves and sanity-checks repositories, and applies policy-aware checklists aligned with MICCAI expectations, producing a review-time verifiability score decomposed into interpretable sub-scores plus criterion-linked excerpts and artifacts reviewers can inspect. It never executes untrusted code or attempts GPU-heavy reproduction, focusing instead on bounded, verifiable checks. We compare paper-snitch on 100 randomly sampled MICCAI 2025 papers with human annotators using shared evaluation criteria, indicating that automated, bounded checks can scale reproducibility screening while keeping final decisions with reviewers.

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.

Accurate delineation of maxillofacial anatomy in Cone-Beam Computed Tomography (CBCT) is essential for dental planning, but robust automated segmentation remains challenging, due to limited public multi-structure datasets and the high computational burden of 3D deep learning models. We present and release ToothFairy3, a large-scale CBCT benchmark that extends ToothFairy2 with 102 additional fully annotated scans and an expanded taxonomy covering 77 classes, including 32 tooth-specific pulp cavities and small neurovascular structures. ToothFairy3 comprises 582 volumes (over 40000 annotated objects), with 532 released with voxel-level labels and 50 held out for leakage-free, server-side evaluation. We also introduce U-Mamba2, an efficient U-Net-style architecture that inserts a Mamba2 state-space block at the bottleneck to capture global context with favorable computational scaling. Our proposed domain-informed training further improves the learning of maxillofacial anatomies. Across CNN, Transformer, and Mamba baselines, U-Mamba2 achieves competitive Dice/HD95 scores with lower latency and, compared with training on state-of-the-art public CBCT datasets, ToothFairy3-trained models generalize best to the hidden test set, particularly for maxillary structures.

Ensuring that facial images conform to widely adopted quality guidelines is a crucial step in optimizing the document enrollment workflow, which includes the face verification task. In this paper, we focus on the ISO/ICAO standard, which defines the requirements for facial photographs used in official documents, such as passports, ensuring consistency in face quality and thereby improving reliable recognition by both humans and biometric systems. Generally, ISO/ICAO compliance verification is manually performed through a slow, subjective, and non-scalable process, then to address these challenges, we introduce a fully automated system that assesses face compliance directly from the official standard requirements, eliminating dependence on predefined, hand-crafted features and empirically set thresholds. The method integrates a language model with an innovative prompt learning strategy and a contrastive learning paradigm to assess whether a given facial image satisfies specific quality criteria. Experimental evaluations demonstrate that our method achieves competitive accuracy compared to both academic and commercial baselines. By facilitating the integration and maintenance of compliance regulations, the proposed framework offers a practical, scalable, and regulation-centric solution for automated image quality verification. All code and models are publicly available1.

Adapting large pre-trained models to downstream tasks often produces task-specific parameter updates that are expensive to relearn for every model variant. While recent work has shown that such updates can be transferred between models with identical architectures, transferring them across models of different widths remains unexplored. In this work, we introduce Theseus, a training-free method for transporting task updates across heterogeneous-width models. Rather than matching parameters, we characterize a task update by the functional effect it induces on intermediate representations. We formalize task-vector transport as a functional matching problem on observed activations and show that, after aligning representation spaces via orthogonal Procrustes analysis, it admits a stable closed-form solution that preserves the geometry of the update. We evaluate Theseus on vision and language models across different widths, showing consistent improvements over baselines without additional training or backpropagation. Our results show that task updates can be meaningfully transferred across architectures when task identity is defined functionally rather than parametrically. Code is available at https://github.com/apanariello4/merge-and-rebase.

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.