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University of Lausanne

Academic institutioneurope · ch
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Research library131linked papers
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Selected work

Representative Papers

Defining Atomicity (and Integrity) for Snapshots of Storage in Forensic Computing

May 21, 2025

In digital forensics, the atomicity and integrity of storage snapshots lack rigorous definitions that jointly guarantee both instantaneousness and causal ordering—undermining evidentiary admissibility in legal proceedings. To address this, we propose a novel atomicity definition grounded in causal consistency, overcoming the limitation of conventional time-based atomicity models. We further rectify conceptual flaws in existing integrity definitions and introduce a revised, theoretically sound yet engineering-practical integrity criterion—explicitly supporting copy-on-write (CoW) implementations. Our approach integrates causal modeling, formal snapshot semantics, CoW mechanism analysis, and formalization of forensic quality criteria, yielding a verifiable snapshot semantic framework. This work establishes the first theoretical foundation for forensic tool design that unifies causal ordering with instantaneous state capture, thereby significantly enhancing the forensic validity and judicial admissibility of live data acquisition.

7 citations4 influentialRead paper

Maximizing domain generalization in fetal brain tissue segmentation: the role of synthetic data generation, intensity clustering and real image fine-tuning

Nov 11, 2024arXiv.org

Fetal brain MRI segmentation suffers from poor cross-device/center generalization and severe scarcity of annotated data. To address these challenges, we propose a three-stage synergistic paradigm: synthetic data generation, intensity-based clustering modeling, and lightweight weight-averaged fine-tuning. First, we empirically demonstrate that SynthSeg—driven by Gaussian Mixture Models (GMM)—exhibits superior robustness over physics-based simulators. Second, we reveal the critical role of intensity clustering in out-of-distribution (OOD) generalization under low-category settings. Third, we pioneer the integration of SynthSeg with weight-averaged few-shot fine-tuning, enabling single-domain adaptation that improves performance across multiple unseen domains. Evaluated on fetal MRI datasets acquired from diverse unknown scanners and protocols, our method achieves an average Dice improvement of 4.2%. Furthermore, we distill five transferable practical guidelines, establishing a novel paradigm for cross-organ and cross-modality generalization.

1 citationsRead paper
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