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Adversarial Surrogate Risk Bounds for Binary Classification
Natalie S. Frank
https://arxiv.org/abs/2506.09348 https://arxiv.org/pdf/2…

Adversarial Surrogate Risk Bounds for Binary Classification
A central concern in classification is the vulnerability of machine learning models to adversarial attacks. Adversarial training is one of the most popular techniques for training robust classifiers, which involves minimizing an adversarial surrogate risk. Recent work characterized when a minimizing sequence of an adversarial surrogate risk is also a minimizing sequence of the adversarial classification risk for binary classification -- a property known as adversarial consistency. However, thes…

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Bug Classification in Quantum Software: A Rule-Based Framework and Its Evaluation
Mir Mohammad Yousuf, Shabir Ahmad Sofi
https://arxiv.org/abs/2506.10397 h…

Bug Classification in Quantum Software: A Rule-Based Framework and Its Evaluation
Accurate classification of software bugs is essential for improving software quality. This paper presents a rule-based automated framework for classifying issues in quantum software repositories by bug type, category, severity, and impacted quality attributes, with additional focus on quantum-specific bug types. The framework applies keyword and heuristic-based techniques tailored to quantum computing. To assess its reliability, we manually classified a stratified sample of 4,984 issues from a …

🏃‍♂️ High-volume exercise tied to increased coronary artery calcification score
https://medicalxpress.com/news/2025-05-high-volume-coronary-artery-calcification.html

High-volume exercise tied to increased coronary artery calcification score
Male athletes with high-volume exercise training have a higher burden of calcified plaque than male nonathletes, according to a review published in the June issue of JACC: Advances.

The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset
Tyler J. Richards, Adam E. Flanders, Errol Colak, Luciano M. Prevedello, Robyn L. Ball, Felipe Kitamura, John Mongan, Maryam Vazirabad, Hui-Ming Lin, Anne Kendell, Thanat Kanthawang, Salita Angkurawaranon, Emre Altinmakas, Hakan Dogan, Paulo Eduardo de Aguiar Kuriki, Arjuna Somasundaram, Christopher Ruston, Deniz Bulja, Naida Spahovic, Jennifer Sommer, Sirui Jiang, Eduardo Moreno Judice de Mattos Farina, Edu…

The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset
The Radiological Society of North America (RSNA) Lumbar Degenerative Imaging Spine Classification (LumbarDISC) dataset is the largest publicly available dataset of adult MRI lumbar spine examinations annotated for degenerative changes. The dataset includes 2,697 patients with a total of 8,593 image series from 8 institutions across 6 countries and 5 continents. The dataset is available for free for non-commercial use via Kaggle and RSNA Medical Imaging Resource of AI (MIRA). The dataset was cre…

Optimal Transport for $e/\pi^0$ Particle Classification in LArTPC Neutrino Experiments
David Caratelli, Nathaniel Craig, Chuyue Fang, Jessica N. Howard
https://arxiv.org/abs/2506.09238

Optimal Transport for $e/π^0$ Particle Classification in LArTPC Neutrino Experiments
The efficient classification of electromagnetic activity from $π^0$ and electrons remains an open problem in the reconstruction of neutrino interactions in Liquid Argon Time Projection Chamber (LArTPC) detectors. We address this problem using the mathematical framework of Optimal Transport (OT), which has been successfully employed for event classification in other HEP contexts and is ideally suited to the high-resolution calorimetry of LArTPCs. Using a publicly available simulated dataset fro…

Early and Accurate Recession Detection Using Classifiers on the Anticipation-Precision Frontier
Pascal Michaillat
https://arxiv.org/abs/2506.09664 https://…

Early and Accurate Recession Detection Using Classifiers on the Anticipation-Precision Frontier
This paper develops a new method for detecting US recessions in real time. The method constructs millions of recession classifiers by combining unemployment and vacancy data to reduce detection noise. Classifiers are then selected to avoid both false negatives (missed recessions) and false positives (nonexistent recessions). The selected classifiers are therefore perfect, in that they identify all 15 historical recessions in the training period without any false positives. By further selecting …

Asymptotic Normality of Infinite Centered Random Forests -Application to Imbalanced Classification
Moria Mayala (LPSM), Erwan Scornet (LPSM), Charles Tillier (LMV), Olivier Wintenberger (LPSM)
https://arxiv.org/abs/2506.08548

Asymptotic Normality of Infinite Centered Random Forests -Application to Imbalanced Classification
Many classification tasks involve imbalanced data, in which a class is largely underrepresented. Several techniques consists in creating a rebalanced dataset on which a classifier is trained. In this paper, we study theoretically such a procedure, when the classifier is a Centered Random Forests (CRF). We establish a Central Limit Theorem (CLT) on the infinite CRF with explicit rates and exact constant. We then prove that the CRF trained on the rebalanced dataset exhibits a bias, which can be r…

Classification of multiple segmented profiles, application to the study of the neuronal protein Tau
Vincent Brault, Emilie Lebarbier, Am\'elie Rosier, Virginie Stoppin-Mellet
https://arxiv.org/abs/2506.10126

Classification of multiple segmented profiles, application to the study of the neuronal protein Tau
This work is motivated by an application in neuroscience, in particular by the study of the (dys)functioning of a protein called Tau. The objective is to establish a classification of intensity profiles, according to the presence or absence of the protein and its monomer or dimer proportion. For this, we propose a Gaussian mixture model with a fixed number of clusters whose mean parameters are constrained and shared by the clusters. The inference of this model is done via the classical EM algor…

Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification
Fabian C. Pe\~na, Steffen Herbold
https://arxiv.org/abs/2506.08581

Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification
This work evaluates Sentence-BERT for a multi-label code comment classification task seeking to maximize the classification performance while controlling efficiency constraints during inference. Using a dataset of 13,216 labeled comment sentences, Sentence-BERT models are fine-tuned and combined with different classification heads to recognize comment types. While larger models outperform smaller ones in terms of F1, the latter offer outstanding efficiency, both in runtime and GFLOPS. As result…