Explore real-time threat detection in post-quantum AI inference environments. Learn how to protect against evolving threats and secure model context protocol (mcp) deployments with future-proof ...

Based Detection, Linguistic Biomarkers, Machine Learning, Explainable AI, Cognitive Decline Monitoring Share and Cite: de Filippis, R. and Al Foysal, A. (2025) Early Alzheimer’s Disease Detection from ...

A reader objects to a defense of disparate-impact theory.

Introduction Armed conflict severely impacts health, with indirect deaths often exceeding direct casualties two to four times ...

Analyzing stochastic cell-to-cell variability can potentially reveal causal interactions in gene regulatory networks.

Health researchers need to fully understand the underlying assumptions to uncover cause and effect. Timothy Feeney and Paul Zivich explain Physicians ask, answer, and interpret myriad causal questions ...

Eiko Fried has been appointed professor of Mental Health & Data Science. This combined chair neatly fits the view that understanding complex mental ...

You train the model once, but you run it every day. Making sure your model has business context and guardrails to guarantee reliability is more valuable than fussing over LLMs. We’re years into the ...

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...

Abstract: The debiased estimator is a crucial tool in statistical inference for high-dimensional model parameters. However, constructing such an estimator involves estimating the high-dimensional ...

Stochastic gradient descent (SGD) provides a scalable way to compute parameter estimates in applications involving large-scale data or streaming data. As an alternative version, averaged implicit SGD ...