Institution profile

University of Parma

Academic institutioneurope · it
Official website
Research library44linked papers
Opportunities0open roles
Selected work

Representative Papers

CalibBEV: LiDAR-Camera Calibration via BEV Alignment

Aug 03, 2026

This work addresses the challenging problem of cross-modal calibration between LiDAR and cameras by proposing the first bird’s-eye-view (BEV)-based alignment framework. The method unifies multimodal data into a shared BEV space and employs a two-stage optimization strategy: it first implicitly regresses coarse calibration parameters and then explicitly aligns cross-modal features, enhanced by a CLIP-inspired contrastive loss to enforce semantic consistency. By integrating domain-specific BEV feature extraction with contrastive learning constraints, the approach significantly outperforms existing methods, achieving state-of-the-art calibration accuracy. On the KITTI and nuScenes benchmarks, it reduces relative rotation error by 51% and 68%, and translation error by 80% and 91%, respectively.

0 citationsRead paper

Approximate Quantum State Preparation Through Proximal Policy Optimization

Jul 23, 2026

This work addresses the challenge of quantum state preparation, where the exponentially growing search space with increasing qubit count renders the discovery of optimal quantum circuits intractable. To overcome this, the authors propose a deep reinforcement learning framework based on Proximal Policy Optimization (PPO), wherein an agent incrementally constructs quantum circuits to approximate a target state while minimizing gate count. This approach introduces deep reinforcement learning into quantum architecture search and achieves, for the first time, joint optimization of high-fidelity state preparation and circuit compactness. Experimental results demonstrate that the method attains approximation fidelities as high as $10^{-14}$ across various predefined and randomly generated target states in systems ranging from 2 to 5 qubits.

0 citationsRead paper

Explaining Weather Bulletins via ILP

Jul 23, 2026

This work addresses the challenge of enhancing the interpretability of weather forecasts by automatically explaining the rationale behind meteorologists’ selection of specific graphical symbols in weather bulletins. Leveraging the FastLAS2 framework, the study extracts Answer Set Programming (ASP) facts from synthetic meteorological data and real bulletins issued by OSMER FVG to construct inductive logic programming (ILP) examples, from which concise and human-readable logical rules are learned. It pioneers the application of non-monotonic ILP to the task of meteorological bulletin interpretation, enabling a symbolic and generalizable model of expert decision-making. The proposed approach not only accurately uncovers the underlying logic governing symbol selection but also demonstrates robust generalizability across different geographical regions and data sources.

0 citationsRead paper
Recent publications

Latest Papers

CalibBEV: LiDAR-Camera Calibration via BEV Alignment

Aug 03, 2026

This work addresses the challenging problem of cross-modal calibration between LiDAR and cameras by proposing the first bird’s-eye-view (BEV)-based alignment framework. The method unifies multimodal data into a shared BEV space and employs a two-stage optimization strategy: it first implicitly regresses coarse calibration parameters and then explicitly aligns cross-modal features, enhanced by a CLIP-inspired contrastive loss to enforce semantic consistency. By integrating domain-specific BEV feature extraction with contrastive learning constraints, the approach significantly outperforms existing methods, achieving state-of-the-art calibration accuracy. On the KITTI and nuScenes benchmarks, it reduces relative rotation error by 51% and 68%, and translation error by 80% and 91%, respectively.

0 citationsRead paper

Approximate Quantum State Preparation Through Proximal Policy Optimization

Jul 23, 2026

This work addresses the challenge of quantum state preparation, where the exponentially growing search space with increasing qubit count renders the discovery of optimal quantum circuits intractable. To overcome this, the authors propose a deep reinforcement learning framework based on Proximal Policy Optimization (PPO), wherein an agent incrementally constructs quantum circuits to approximate a target state while minimizing gate count. This approach introduces deep reinforcement learning into quantum architecture search and achieves, for the first time, joint optimization of high-fidelity state preparation and circuit compactness. Experimental results demonstrate that the method attains approximation fidelities as high as $10^{-14}$ across various predefined and randomly generated target states in systems ranging from 2 to 5 qubits.

0 citationsRead paper

Explaining Weather Bulletins via ILP

Jul 23, 2026

This work addresses the challenge of enhancing the interpretability of weather forecasts by automatically explaining the rationale behind meteorologists’ selection of specific graphical symbols in weather bulletins. Leveraging the FastLAS2 framework, the study extracts Answer Set Programming (ASP) facts from synthetic meteorological data and real bulletins issued by OSMER FVG to construct inductive logic programming (ILP) examples, from which concise and human-readable logical rules are learned. It pioneers the application of non-monotonic ILP to the task of meteorological bulletin interpretation, enabling a symbolic and generalizable model of expert decision-making. The proposed approach not only accurately uncovers the underlying logic governing symbol selection but also demonstrates robust generalizability across different geographical regions and data sources.

0 citationsRead paper