The PMNLV model extends single-neuron overdispersion to populations via matrix-normal gain priors, showing shared co-variability highest in V1 and declining along the mouse visual hierarchy.
Cross-Validatory Choice and Assessment of Statistical Pre- dictions
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Developers using AI assistants exhibit more stable emotions and greater focus on code creation, evaluation, and verification, captured in a new four-dimensional S-IASE model from retrospective labeling of screen recordings, surveys, and interviews.
Maximum-likelihood-based posterior predictive checks detect model misspecification better than event-level versions for uncertain spin tilts, but current detector sensitivity limits their power; the Gaussian Component Spins model underpredicts high spin magnitudes and overpredicts anti-aligned tilts
Opal enables private long-term memory for personal AI by decoupling reasoning to a trusted enclave with a lightweight knowledge graph and piggybacking reindexing on ORAM accesses.
Case study of 18,020 Kubernetes PRs shows label-diff congruence is prevalent and stable, with higher congruence linked to fewer review participants among core developers and more among one-time contributors.
PG-TMT couples a physics-aligned tri-branch encoder with EVT-calibrated decision rules to achieve higher PR-AUC and shorter detection times at controlled false-alarm rates across multiple bearing datasets.
A consistent two-stage GPH-filtered Hannan-Rissanen generalized information criterion for selecting finite AR and MA orders in ARFIMA models with growing candidate sets.
FunnelNet is a ~5.4k-parameter CNN that detects heart murmurs from PCG signals at 85% accuracy and deploys in real time on edge hardware via TinyML.
This paper reviews methods for assessing credibility of simulation architectures made from multiple models, comparing sensitivity analysis, expert judgment, AI explainability, and network techniques on rigor, generalization, and resource requirements.
A curated dataset of counterintuitive discrete probability problems with human solutions, built to benchmark LLM reasoning on bias-prone tasks.
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Counterintuitive problems in discrete probability
A curated dataset of counterintuitive discrete probability problems with human solutions, built to benchmark LLM reasoning on bias-prone tasks.