Tootfinder

Credit risk for large portfolios of green and brown loans: extending the ASRF model
Alessandro Ramponi, Sergio Scarlatti
https://arxiv.org/abs/2506.12510 h…

Credit risk for large portfolios of green and brown loans: extending the ASRF model
We propose a credit risk model for portfolios composed of green and brown loans, extending the ASRF framework via a two-factor copula structure. Systematic risk is modeled using potentially skewed distributions, allowing for asymmetric creditworthiness effects, while idiosyncratic risk remains Gaussian. Under a non-uniform exposure setting, we establish convergence in quadratic mean of the portfolio loss to a limit reflecting the distinct characteristics of the two loan segments. Numerical resu…

Sea-Undistort: A Dataset for Through-Water Image Restoration in High Resolution Airborne Bathymetric Mapping
Maximilian Kromer, Panagiotis Agrafiotis, Beg\"um Demir
https://arxiv.org/abs/2508.07760

Sea-Undistort: A Dataset for Through-Water Image Restoration in High Resolution Airborne Bathymetric Mapping
Accurate image-based bathymetric mapping in shallow waters remains challenging due to the complex optical distortions such as wave induced patterns, scattering and sunglint, introduced by the dynamic water surface, the water column properties, and solar illumination. In this work, we introduce Sea-Undistort, a comprehensive synthetic dataset of 1200 paired 512x512 through-water scenes rendered in Blender. Each pair comprises a distortion-free and a distorted view, featuring realistic water effe…

Adaptive Attention Residual U-Net for curvilinear structure segmentation in fluorescence microscopy and biomedical images
Achraf Ait Laydi, Louis Cueff, Mewen Crespo, Yousef El Mourabit, H\'el\`ene Bouvrais
https://arxiv.org/abs/2507.07800

Adaptive Attention Residual U-Net for curvilinear structure segmentation in fluorescence microscopy and biomedical images
Segmenting curvilinear structures in fluorescence microscopy remains a challenging task, particularly under noisy conditions and in dense filament networks commonly seen in vivo. To address this, we created two original datasets consisting of hundreds of synthetic images of fluorescently labelled microtubules within cells. These datasets are precisely annotated and closely mimic real microscopy images, including realistic noise. The second dataset presents an additional challenge, by simulating…

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