Tootfinder

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
Shae McFadden, Myles Foley, Mario D'Onghia, Chris Hicks, Vasilios Mavroudis, Nicola Paoletti, Fabio Pierazzi
https://arxiv.org/abs/2508.18839

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
Malware detection in real-world settings must deal with evolving threats, limited labeling budgets, and uncertain predictions. Traditional classifiers, without additional mechanisms, struggle to maintain performance under concept drift in malware domains, as their supervised learning formulation cannot optimize when to defer decisions to manual labeling and adaptation. Modern malware detection pipelines combine classifiers with monthly active learning (AL) and rejection mechanisms to mitigate t…

"If denial of collective self-determination of a people is a sin of the modern age, as Israeli defenders often repeat, what of the self-determination of Palestinians? "
The Most Important Book of 2025 | Portside
https://portside.org/2025-08-07/most-important-book-2025

A NICER view of the 1.4 solar-mass edge-on pulsar PSR J0614--3329
Lucien Mauviard, Sebastien Guillot, Tuomo Salmi, Devarshi Choudhury, Bas Dorsman, Denis Gonz\'alez-Caniulef, Mariska Hoogkamer, Daniela Huppenkothen, Christine Kazantsev, Yves Kini, Jean-Francois Olive, Pierre Stammler, Anna L. Watts, Melissa Mendes, Nathan Rutherford, Achim Schwenk, Isak Svensson, Slavko Bogdanov, Matthew Kerr, Paul S. Ray, Lucas Guillemot, Isma\"el Cognard, Gilles Theureau

A NICER view of the 1.4 solar-mass edge-on pulsar PSR J0614--3329
Four neutron star radius measurements have already been obtained by modeling the X-ray pulses of rotation-powered millisecond pulsars observed by the Neutron Star Interior Composition ExploreR (NICER). We report here the radius measurement of PSR J0614$-$3329 employing the same method with NICER and XMM-Newton data using Bayesian Inference. For all different models tested, including one with unrestricted inclination prior, we retrieve very similar non-antipodal hot regions geometries and radii.…

Enforcing tail calibration when training probabilistic forecast models
Jakob Benjamin Wessel, Maybritt Schillinger, Frank Kwasniok, Sam Allen
https://arxiv.org/abs/2506.13687

Enforcing tail calibration when training probabilistic forecast models
Probabilistic forecasts are typically obtained using state-of-the-art statistical and machine learning models, with model parameters estimated by optimizing a proper scoring rule over a set of training data. If the model class is not correctly specified, then the learned model will not necessarily issue forecasts that are calibrated. Calibrated forecasts allow users to appropriately balance risks in decision making, and it is particularly important that forecast models issue calibrated predicti…