Background: Pediatric dental disease remains one of the most prevalent and inequitable chronic health conditions worldwide. Although strong epidemiological evidence links oral health outcomes to socio-economic and demographic determinants, most artificial intelligence (AI) applications in dentistry rely on image-based diagnosis and black-box prediction models, limiting transparency and ethical applicability in pediatric populations. Objective: This study aimed to develop and evaluate an explainable machine learning framework for pediatric dental risk stratification that prioritizes interpretability, calibration, and ethical deployment over […]

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Reinforcement learning has recently gained traction as a means to improve combinatorial optimization methods, yet its effectiveness within local search metaheuristics specifically remains comparatively underexamined. In this study, we evaluate a range of reinforcement learning-based neighborhood selection strategies — multi-armed bandits (upper confidence bound, $ε$-greedy) and deep reinforcement learning methods (proximal policy optimization, double deep $Q$-network) — and compare them against multiple baselines across three different problems: the traveling salesman problem, the pickup and delivery problem with time […]

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arXiv:2601.07958v1 Announce Type: new Abstract: Speaker-specific anti-spoofing and synthesis-source tracing are central challenges in audio anti-spoofing. Progress has been hampered by the lack of datasets that systematically vary model architectures, synthesis pipelines, and generative parameters. To address this gap, we introduce LJ-Spoof, a speaker-specific, generatively diverse corpus that systematically varies prosody, vocoders, generative hyperparameters, bona fide prompt sources, training regimes, and neural post-processing. The corpus spans one speakers-including studio-quality recordings-30 TTS families, 500 generatively variant subsets, 10 bona […]

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Colleen Hroncich When millions of children struggle to sit still, focus, and conform to rigid classroom expectations, it’s become an epidemic of ADHD and other disorders. The New York Times is beginning to consider what should have been obvious all along: Maybe the problem isn’t the children. Others, including my colleague Kerry McDonald, have been raising these concerns for years. As Kerry notes, Boston College psychology Professor Peter Gray has described ADHD as a “failure to adapt to […]

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arXiv:2601.06429v1 Announce Type: cross Abstract: Foundation models pre-trained on large-scale source datasets are reshaping the traditional training paradigm for time series classification. However, existing time series foundation models primarily focus on forecasting tasks and often overlook classification-specific challenges, such as modeling interpretable shapelets that capture class-discriminative temporal features. To bridge this gap, we propose UniShape, a unified shape-aware foundation model designed for time series classification. UniShape incorporates a shape-aware adapter that adaptively aggregates multiscale discriminative subsequences (shapes) into […]

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arXiv:2601.02373v1 Announce Type: new Abstract: In 5G and next-generation mobile ad-hoc networks, reliable handover is a key requirement, which guarantees continuity in connectivity, especially for mobile users and in high-density scenarios. However, conventional handover triggers based on instantaneous channel measurements are prone to failures and the ping-pong effect due to outdated or inaccurate channel state information. To address this, we introduce Deep-SIC, a knowledge-based channel prediction model that employs a Transformer-based approach to predict channel quality and optimise […]

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arXiv:2601.03429v1 Announce Type: new Abstract: Machine learning (ML) explainability is central to algorithmic transparency in high-stakes settings such as predictive diagnostics and loan approval. However, these same domains require rigorous privacy guaranties, creating tension between interpretability and privacy. Although prior work has shown that explanation methods can leak membership information, practitioners still lack systematic guidance on selecting or deploying explanation techniques that balance transparency with privacy. We present DeepLeak, a system to audit and mitigate privacy risks in […]

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arXiv:2601.13698v1 Announce Type: cross Abstract: Fairness and privacy are two vital pillars of trustworthy machine learning. Despite extensive research on these individual topics, the relationship between fairness and privacy has received significantly less attention. In this paper, we utilize the information-theoretic measure Chernoff Information to highlight the data-dependent nature of the relationship among the triad of fairness, privacy, and accuracy. We first define Noisy Chernoff Difference, a tool that allows us to analyze the relationship among the triad […]

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