Selected work

Representative Papers

Conditional multivariate functional PCA for the reconstruction of temperature and salinity profiles partially sampled by deep-diving marine mammals

Aug 05, 2026

This study addresses the incomplete depth coverage of temperature–salinity profiles collected by deep-diving marine mammals in the Indian Ocean sector of the Southern Ocean, which arises from behavioral differences among individuals. To overcome this limitation, the authors propose a multivariate functional principal component analysis method incorporating geographic covariates. By modeling the mean and covariance structure of complete bivariate profiles, they construct eigenfunction bases and integrate a measurement error model to estimate conditional functional principal scores, enabling high-fidelity reconstruction of truncated profiles across the full depth range. In simulations, the approach improves reconstruction accuracy by 30% for temperature and 33% for salinity in the 20–500 m layer when applied to profiles truncated at 250 m. The method successfully reconstructs approximately 90,000 profiles across a 3-million-square-kilometer region surrounding the French subantarctic islands, marking the first large-scale, accurate recovery of incomplete oceanographic profiles.

0 citationsRead paper

Residue Number System Comparison revisited, a software perspective

May 18, 2026

This work addresses the long-standing challenge of general integer comparison in residue number systems (RNS) by proposing an efficient method based on the introduction of an auxiliary modulus and a single mixed-radix conversion. The approach is applicable to arbitrary RNS moduli sets without imposing restrictions on the input range, thereby overcoming limitations inherent in existing techniques that require specific modulus forms or bounded dynamic ranges. The algorithm achieves a time complexity of O(n²), which can be parallelized to O(log n), significantly outperforming both classical and recent state-of-the-art methods constrained by such assumptions. This advancement provides a novel and practical solution to a critical bottleneck in RNS-based applications, including division, scaling, and cryptographic operations.

0 citationsRead paper

Recent publications

Latest Papers

Conditional multivariate functional PCA for the reconstruction of temperature and salinity profiles partially sampled by deep-diving marine mammals

Aug 05, 2026

This study addresses the incomplete depth coverage of temperature–salinity profiles collected by deep-diving marine mammals in the Indian Ocean sector of the Southern Ocean, which arises from behavioral differences among individuals. To overcome this limitation, the authors propose a multivariate functional principal component analysis method incorporating geographic covariates. By modeling the mean and covariance structure of complete bivariate profiles, they construct eigenfunction bases and integrate a measurement error model to estimate conditional functional principal scores, enabling high-fidelity reconstruction of truncated profiles across the full depth range. In simulations, the approach improves reconstruction accuracy by 30% for temperature and 33% for salinity in the 20–500 m layer when applied to profiles truncated at 250 m. The method successfully reconstructs approximately 90,000 profiles across a 3-million-square-kilometer region surrounding the French subantarctic islands, marking the first large-scale, accurate recovery of incomplete oceanographic profiles.

0 citationsRead paper

Residue Number System Comparison revisited, a software perspective

May 18, 2026

This work addresses the long-standing challenge of general integer comparison in residue number systems (RNS) by proposing an efficient method based on the introduction of an auxiliary modulus and a single mixed-radix conversion. The approach is applicable to arbitrary RNS moduli sets without imposing restrictions on the input range, thereby overcoming limitations inherent in existing techniques that require specific modulus forms or bounded dynamic ranges. The algorithm achieves a time complexity of O(n²), which can be parallelized to O(log n), significantly outperforming both classical and recent state-of-the-art methods constrained by such assumptions. This advancement provides a novel and practical solution to a critical bottleneck in RNS-based applications, including division, scaling, and cryptographic operations.

0 citationsRead paper