February 2026

Support matrix machine (SMM) is an emerging classification framework that directly handles matrix-structured observations, thereby avoiding the spatial correlations destroyed by vectorization. However, most existing SMM variants rely on convex or nonconvex surrogate loss functions, which may lead to high sensitivity to noise. To address this issue, we propose a novel Heaviside low-rank SMM model called HL-SMM, which leverages the Heaviside loss instead of the common hinge or ramp losses for robustness. Moreover, the low-rank constraint is adopted […]

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Developments in the machine learning voting domain have shown both promising results and risks. Trained models perform well on ballot classification tasks (> 99% accuracy) but are at risk from adversarial example attacks that cause misclassifications. In this paper, we analyze an attacker who seeks to deploy adversarial examples against machine learning ballot classifiers to compromise a U.S. election. We first derive a probabilistic framework for determining the number of adversarial example ballots that must be printed to […]

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Hi everyone, I’m a research student working independently on a project, and I recently finished a paper with results that I believe are solid and meaningful. I’m still new to the academic publishing process, though, and I’d really appreciate some guidance. I learned that for posting on arXiv you sometimes need an endorsement, but since I did this work solo, I’m not sure how to move forward or who to approach. What are the usual steps for someone […]

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The growing deployment of Internet of Things (IoT) devices in smart cities and industrial environments increases vulnerability to stealthy, multi-stage advanced persistent threats (APTs) that exploit wireless communication. Detection is challenging due to severe class imbalance in network traffic, which limits the effectiveness of traditional deep learning approaches and their lack of explainability in classification decisions. To address these challenges, this paper proposes a neurosymbolic architecture that integrates an optimized BERT model with logic tensor networks (LTN) for […]

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Bayesian Inference Explained Like You’re Guessing Who Ate Your Leftovers Let me tell you about the worst Wednesday of my life. I’m sitting at my desk. My left sock is wet for reasons I still don’t understand. My dog is losing his mind at a squirrel outside. And I’m staring at a Wikipedia page about “Bayesian inference” that reads like it was written by someone who actively hates the concept of fun. You’ve been doing Bayesian inference your whole life. […]

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Batman made his first appearance in 1939. When you have a character who has been around for 80 years, they’re bound to change their appearance from time to time. So, it only makes sense for video games like Batman: Arkham Knight to take advantage of this and add different alternative costumes that players can pick and choose from. Some of these were hits, some of these were misses, and some were just okay. Let’s take a look at […]

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In this study, a novel idea, Uncertainty Structure Estimation (USE), a lightweight, algorithm-agnostic procedure that emphasizes the often-overlooked role of unlabeled data quality is introduced for Semi-supervised learning (SSL). SSL has achieved impressive progress, but its reliability in deployment is limited by the quality of the unlabeled pool. In practice, unlabeled data are almost always contaminated by out-of-distribution (OOD) samples, where both near-OOD and far-OOD can negatively affect performance in different ways. We argue that the bottleneck does […]

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