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University of Verona

Academic institutioneurope · it
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Research library136linked papers
Opportunities0open roles
Selected work

Representative Papers

The Drift Burst Hypothesis

Sep 27, 2016Journal of Econometrics

This study investigates the existence, market prevalence, and underlying mechanisms of transient, localized “drift bursts” in financial asset prices. To this end, we incorporate drift bursts into a continuous-time Itô semimartingale framework and develop a theoretical model under no-arbitrage conditions, alongside a nonparametric test statistic designed to reliably detect such events from high-frequency data contaminated by noise. Our work is the first to formally model drift bursts as a regular feature of financial markets, uncovering their intrinsic links to liquidity shocks and price reversals. Empirical analysis reveals that drift bursts are pervasive across equity, bond, foreign exchange, and commodity markets, occurring on average once per week; notably, negative bursts accompanied by high trading volume are more likely to trigger significant price reversals.

64 citations10 influentialRead paper

On Constrained Intersection Representations of Graphs and Digraphs

Apr 25, 2025International Symposium on Algorithms and Computation

This paper investigates the Optimal Directed Intersection Representation problem for directed acyclic graphs (DAGs): assign color sets to vertices such that a directed edge $u o v$ exists if and only if the sets intersect and $v$’s color set is strictly larger than $u$’s, minimizing the total number of distinct colors. We establish, for the first time, that this NP-hard problem admits a polynomial-time exact algorithm on the subclass of triangle-free Hamiltonian DAGs. We present the first combinatorial algorithm for this constrained setting and prove tight equivalences and bounds linking it to classical models—including poset dimension, interval graph embeddings, and other intersection-based representations. Our analysis systematically uncovers deep connections between directed intersection representations and broader theories of graph and digraph representation, thereby unifying and extending prior work in structural and algorithmic graph theory.

1 citationsRead paper

FF-SRL: High Performance GPU-Based Surgical Simulation For Robot Learning

Oct 14, 2024IEEE/RJS International Conference on Intelligent RObots and Systems

To address the safety and efficiency bottlenecks arising from heavy reliance on real-world interactions in reinforcement learning (RL) training for robotic surgery, this paper introduces the first full-stack, GPU-native simulation learning framework. It unifies soft-tissue physics simulation (CUDA-accelerated), RL policy training, and lightweight high-fidelity scene modeling entirely on a single GPU—eliminating CPU-GPU data transfer overhead. The architecture enables millisecond-scale closed-loop interaction and minute-scale training for complex surgical tasks, reducing training time for representative tissue manipulation tasks to just 2–3 minutes—over 10× faster than state-of-the-art CPU/GPU hybrid approaches. All code is open-sourced, establishing a new paradigm for safe, efficient, and scalable autonomous surgical skill acquisition.

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Recent publications

Latest Papers

FineHOI: Part-Aware Dense Representations for Zero-Shot Human-Object Interaction Detection

Sep 05, 2026

Human-Object Interaction (HOI) detection aims to localize humans and objects in images and classify their interactions. Zero-shot HOI focuses on recognizing interactions that are not observed during training, requiring models to generalize beyond seen verb-object compositions. Recent approaches leverage Vision-Language Models (VLMs), benefiting from rich semantic representations. However, they often rely on global or detector-centric features that compress interaction cues and hinder fine-grained spatial reasoning. To overcome this limitation, we propose FineHOI, a zero-shot HOI framework that explicitly models interactions from dense patch-level features. Our approach is motivated by the observation that human-object interactions are defined by localized spatial relationships, which are not preserved by global and detector-centric representations. To this end, we introduce an Adaptive Part-Level Attention module that decomposes humans and objects into semantically coherent parts via unsupervised clustering, and re-weights them based on their interaction relevance. These representations are then integrated through a Region-Aware Interaction Transformer that integrates part-aware and global features and produces the final HOI embedding. Extensive experiments demonstrate that FineHOI consistently outperforms existing zero-shot HOI methods, achieving particularly strong gains on unseen interactions. Code is available at https://github.com/francescotonini/fine-hoi.

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