Institution profile

Fachhochschule Nordwestschweiz

Academic institutioneurope · ch
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Research library13linked papers
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Selected work

Representative Papers

Electric Potential Patterns Forecasting in the Southern Hemisphere with Deep Learning Techniques

Sep 25, 2026

This study addresses the challenge of real-time, accurate prediction of the spatial structure of the high-latitude ionosphere in the Southern Hemisphere by introducing conditional probabilistic diffusion models into full-spatial-structure forecasting for the first time. Methodologically, a U-Net-based architecture is employed to integrate multi-source data from SuperDARN radars and L1 solar wind observations, with systematic comparisons conducted between deterministic baselines and diffusion models regarding long-term forecasting performance. The results demonstrate that this probabilistic generative framework significantly outperforms conventional deterministic approaches in extended-horizon predictions and highly dynamic scenarios, such as severe geomagnetic storms. Consequently, this work establishes a novel paradigm for operational space weather forecasting.

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Agentic AI-Powered Re-Identification: An Emerging, Scalable Threat to Mobility Microdata Privacy

Jun 26, 2026

This work addresses the severe re-identification privacy risks posed by fine-grained location data collected by commercial data brokers, which traditional attacks struggle to scale due to reliance on manual analysis. The paper proposes the first end-to-end automated re-identification framework, leveraging large language model agents to autonomously harvest publicly available online information and integrate public records, social media profiles, and spatiotemporal trajectory matching algorithms—enabling large-scale identity inference without human intervention. Evaluated on a simulated dataset containing home and workplace address anchors, the method successfully re-identifies 18 out of 43 individuals (41.9%), achieving 72% accuracy among identifiable subjects. This study demonstrates, for the first time in a realistic setting, the feasibility of fully automated, low-cost, and highly efficient re-identification from mobile microdata, fundamentally challenging the conventional paradigm that depends on expert involvement.

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Log-Ratio Propagation on the Simplex: A Theory of Cellwise Contamination for Compositional Data

May 29, 2026

This study addresses the ill-posedness of conventional Euclidean robust methods on the simplex when a single compositional component is contaminated, which induces a global shift in log-ratio coordinates. Building upon a scale-invariant multiplicative perturbation model, the work establishes the first theoretical framework for cellwise contamination in compositional data, proving that contamination in one component manifests as a rank-one shift in log-ratio space. The authors introduce a contamination propagation theorem and an influence function–based diagnostic fingerprint. By leveraging centered log-ratio transformation, isometric log-ratio coordinates, and contrast matrix analysis, they quantify the cellwise breakdown points of MCD, S-, τ-, and coordinatewise M-estimators, revealing a reduction by a factor of $(D-1)/D$ compared to their Euclidean counterparts. Furthermore, they demonstrate that the variability matrix’s influence function precisely identifies contaminated components, thereby laying the theoretical foundation for cellwise robust analysis of compositional data.

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Low-Magnification SEM May Suffice: Interpretable Deep Learning for Multi-Scale Fracture-Cause Classification in Zirconia-Toughened Alumina

May 28, 2026

This study addresses the limitations of conventional fracture analysis for ceramic implants, which relies on high-magnification scanning electron microscopy (SEM)—a time-consuming and subjective process. The authors propose an interpretable deep learning approach based on Vision Transformers to automatically classify fracture origins in zirconia-toughened alumina ceramics—specifically green-state, hard-machining, and material defects—using multi-scale SEM images. They demonstrate for the first time that low-magnification SEM (50×) contains sufficient diagnostic information, achieving classification performance comparable to that of high-magnification images. By integrating Grad-CAM, the model provides spatially interpretable predictions aligned with established fractographic standards. Despite severe class imbalance, the method attains 0.907 accuracy and 0.888 macro F1-score, enabling effective low-magnification prescreening and substantially reducing reliance on high-magnification SEM.

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Recent publications

Latest Papers

Electric Potential Patterns Forecasting in the Southern Hemisphere with Deep Learning Techniques

Sep 25, 2026

This study addresses the challenge of real-time, accurate prediction of the spatial structure of the high-latitude ionosphere in the Southern Hemisphere by introducing conditional probabilistic diffusion models into full-spatial-structure forecasting for the first time. Methodologically, a U-Net-based architecture is employed to integrate multi-source data from SuperDARN radars and L1 solar wind observations, with systematic comparisons conducted between deterministic baselines and diffusion models regarding long-term forecasting performance. The results demonstrate that this probabilistic generative framework significantly outperforms conventional deterministic approaches in extended-horizon predictions and highly dynamic scenarios, such as severe geomagnetic storms. Consequently, this work establishes a novel paradigm for operational space weather forecasting.

0 citationsRead paper

Agentic AI-Powered Re-Identification: An Emerging, Scalable Threat to Mobility Microdata Privacy

Jun 26, 2026

This work addresses the severe re-identification privacy risks posed by fine-grained location data collected by commercial data brokers, which traditional attacks struggle to scale due to reliance on manual analysis. The paper proposes the first end-to-end automated re-identification framework, leveraging large language model agents to autonomously harvest publicly available online information and integrate public records, social media profiles, and spatiotemporal trajectory matching algorithms—enabling large-scale identity inference without human intervention. Evaluated on a simulated dataset containing home and workplace address anchors, the method successfully re-identifies 18 out of 43 individuals (41.9%), achieving 72% accuracy among identifiable subjects. This study demonstrates, for the first time in a realistic setting, the feasibility of fully automated, low-cost, and highly efficient re-identification from mobile microdata, fundamentally challenging the conventional paradigm that depends on expert involvement.

0 citationsRead paper

Log-Ratio Propagation on the Simplex: A Theory of Cellwise Contamination for Compositional Data

May 29, 2026

This study addresses the ill-posedness of conventional Euclidean robust methods on the simplex when a single compositional component is contaminated, which induces a global shift in log-ratio coordinates. Building upon a scale-invariant multiplicative perturbation model, the work establishes the first theoretical framework for cellwise contamination in compositional data, proving that contamination in one component manifests as a rank-one shift in log-ratio space. The authors introduce a contamination propagation theorem and an influence function–based diagnostic fingerprint. By leveraging centered log-ratio transformation, isometric log-ratio coordinates, and contrast matrix analysis, they quantify the cellwise breakdown points of MCD, S-, τ-, and coordinatewise M-estimators, revealing a reduction by a factor of $(D-1)/D$ compared to their Euclidean counterparts. Furthermore, they demonstrate that the variability matrix’s influence function precisely identifies contaminated components, thereby laying the theoretical foundation for cellwise robust analysis of compositional data.

0 citationsRead paper

Low-Magnification SEM May Suffice: Interpretable Deep Learning for Multi-Scale Fracture-Cause Classification in Zirconia-Toughened Alumina

May 28, 2026

This study addresses the limitations of conventional fracture analysis for ceramic implants, which relies on high-magnification scanning electron microscopy (SEM)—a time-consuming and subjective process. The authors propose an interpretable deep learning approach based on Vision Transformers to automatically classify fracture origins in zirconia-toughened alumina ceramics—specifically green-state, hard-machining, and material defects—using multi-scale SEM images. They demonstrate for the first time that low-magnification SEM (50×) contains sufficient diagnostic information, achieving classification performance comparable to that of high-magnification images. By integrating Grad-CAM, the model provides spatially interpretable predictions aligned with established fractographic standards. Despite severe class imbalance, the method attains 0.907 accuracy and 0.888 macro F1-score, enabling effective low-magnification prescreening and substantially reducing reliance on high-magnification SEM.

0 citationsRead paper