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From the 1 of 5 papers with an AI index.

most citedQuantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods

1 citations

5 papers

astro-ph.HE2026

Braking indices as probes of r-mode spin-down in young pulsars

Shahram Abbassi, Armin Memarian, Evelyn Sophia Guest +1

The paper develops a timing-based framework using a four‑torque spin‑down model to interpret braking‑index measurements of young pulsars and to constrain possible r‑mode (current‑q…

stat.ME2026

Restricted nonlinear shrinkage of high-dimensional residual covariance matrices in multivariate regressions

Hamid Karamikabir, Mohammad Arashi

We study estimation of the p*p residual scatter (shape) matrix in a high-dimensional multivariate linear regression, where p and n grow proportionally. When the coefficient matrix…

stat.ME2026

Mens: Nonlinear shrinkage estimation in nonparanormal models for financial applications

Hamid Karamikabir, Mohammad Arashi

We develop a theory of nonlinear shrinkage covariance estimation for nonparanormal (Gaussian-copula) models, in which each observed coordinate is an unknown strictly increasing tra…

cs.LG20261 cited

Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods

Roozbeh Razavi-Far, Mohammad Meymani, Erfan Mahmoudinia +6

Machine learning has revolutionized numerous industrial domains. Despite recent advances, machine learning models remain vulnerable to adversarial threats. Adversarial machine lear…

hep-ph2026

Impact of momentum-dependent drag coefficient on energy loss of charm and bottom quarks in QGP

Marjan Rahimi Nezhad, Fatemeh Taghavi-Shahri, Kurosh Javidan

This paper investigates the influence of heavy-quark momentum on their interaction rate and the resulting drag coefficient in a quark-gluon plasma. To go beyond simplified treatmen…