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Freezing of Gait (FOG) is a debilitating motor symptom commonly experienced by individuals with Parkinson’s Disease (PD) which often leads to falls and reduced mobility. Timely and accurate prediction of FOG episodes is essential for enabling proactive interventions through assistive technologies. This study presents a reinforcement learning-based framework designed to identify optimal pre-FOG onset points, thereby extending the prediction horizon for anticipatory cueing systems. The model implements a Double Deep Q-Network (DDQN) architecture enhanced with Prioritized Experience Replay […]

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Physical dynamical systems can be viewed as natural information processors: their systems preserve, transform, and disperse input information. This perspective motivates learning not only from data generated by such systems, but also how to measure them in a way that extracts the most useful information for a given task. We propose a general computing framework for adaptive information extraction from dynamical systems, in which a trainable attention module learns both where to probe the system state and how […]

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Hate speech on social media reproduces norms of inequality and gender stereotypes, disproportionately affecting women. This study proposes a hybrid approach that integrates emotional tone classification with explicit hostility detection to strengthen preventive moderation. We constructed a corpus from three open datasets (1,236,371 records; 1,003,991 after ETL) and represented the text using TF-IDF and contextual RoBERTa embeddings. We trained individual models (RoBERTa fine-tuned, Random Forest, and XGBoost) and a stacking metamodel (Gradient Boosting) that combines their probabilities. On […]

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This paper introduces a novel Transformer-Driven Pipeline that seamlessly integrates acoustic hearing, automated speech transcription, and writing synthesis into a unified end-to-end framework powered by advanced transformer architectures. Beginning with raw acoustic inputs captured via microphones, the pipeline preprocesses audio signals into spectrogram representations, leveraging stacked transformer encoders with multi-head self-attention to extract contextualized phonetic and prosodic features. These features feed into a sequence-to-sequence transcription module, where cross-attention mechanisms align auditory patterns with linguistic tokens, achieving robust speech-to-text […]

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Built a dataset scoring every testable claim from Marcus’s 474 Substack posts. Two pipelines (Claude Opus 4.6 and ChatGPT Codex) analyzed the corpus, then a reconciliation layer compared outputs. 52% supported, 34% mixed, 6.4% contradicted among assessable claims. Distribution is more interesting than the topline: specific technical observations (LLM security vulnerabilities, Sora quality, agent readiness) score 88-100% supported with zero contradictions. His bubble/scam predictions are the single worst cluster out of 54. Falsifiability drives the split. Nearly a […]

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The application of quantum machine learning (QML) to security-relevant problems has attracted growing attention, yet its practical behavior in realistic workloads remains insufficiently characterized. This paper investigates the feasibility and limitations of variational quantum classifiers (VQCs) in a representative side-channel analysis (SCA) setting focused on distinguishing real from dummy power traces. A controlled benchmark framework is developed to examine training stability, sensitivity to key design parameters, and resource–performance trade-offs under realistic constraints. Hardware-relevant factors, including finite measurement budgets […]

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Some AI founders are using a novel valuation mechanism to manufacture unicorn status.

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Coordinating multiple autonomous agents to explore and serve spatially heterogeneous demand requires jointly learning unknown spatial patterns and planning trajectories that maximize task performance. Pure model-based approaches provide structured uncertainty estimates but lack adaptive policy learning, while deep reinforcement learning often suffers from poor sample efficiency when spatial priors are absent. This paper presents a hybrid belief-reinforcement learning (HBRL) framework to address this gap. In the first phase, agents construct spatial beliefs using a Log-Gaussian Cox Process (LGCP) […]

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Axios COO Allison Murphy explains how the company uses AI to support local reporters, streamline newsroom workflows, and deliver high-impact local journalism at scale.

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OpenAI introduces the Learning Outcomes Measurement Suite to assess AI’s impact on student learning across diverse educational environments over time.

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