arXiv subjects

Kateryna Mishchenko

Publications and source records attributed to Kateryna Mishchenko.

4 recordsLinked to original sources

arXiv2025

Assessing High-Risk AI Systems under the EU AI Act: From Legal Requirements to Technical Verification

The implementation of the AI Act requires practical mechanisms to verify compliance with legal obligations, yet concrete and operational mappings from high-level requirements to verifiable assessment activities remain limited, contributing to uneven readiness across Member States. This paper presents a structured mapping that translates high-level AI Act requirements into concrete, implementable verification activities applicable across the AI lifecycle. The mapping is derived through a systematic process in which legal requirements are decomposed into operational sub-requirements and grounded in authoritative standards and recognised practices. From this basis, verification activities are identified and characterised along two dimensions: the type of verification performed and the lifecycle target to which it applies. By making explicit the link between regulatory intent and technical and organisational assurance practices, the proposed mapping reduces interpretive uncertainty and provides a reusable reference for consistent, technology-agnostic compliance verification under the AI Act.

cs.CY↗

arXiv2008

Efficient Implementation of the AI-REML Iteration for Variance Component QTL Analysis

Regions in the genome that affect complex traits, quantitative trait loci (QTL), can be identified using statistical analysis of genetic and phenotypic data. When restricted maximum-likelihood (REML) models are used, the mapping procedure is normally computationally demanding. We develop a new efficient computational scheme for QTL mapping using variance component analysis and the AI-REML algorithm. The algorithm uses an exact or approximative low-rank representation of the identity-by-descent matrix, which combined with the Woodbury formula for matrix inversion results in that the computations in the AI-REML iteration body can be performed more efficiently. For cases where an exact low-rank representation of the IBD matrix is available a-priori, the improved AI-REML algorithm normally runs almost twice as fast compared to the standard version. When an exact low-rank representation is not available, a truncated spectral decomposition is used to determine a low-rank approximation. We show that also in this case, the computational efficiency of the AI-REML scheme can often be significantly improved.

q-bio.QM↗

arXiv2007

Newton-type Methods for REML Estimation in Genetic Analysis of Quantitative Traits

Robust and efficient optimization methods for variance component estimation using Restricted Maximum Likelihood (REML) models for genetic mapping of quantitative traits are considered. We show that the standard Newton-AI scheme may fail when the optimum is located at one of the constraint boundaries, and we introduce different approaches to remedy this by taking the constraints into account. We approximate the Hessian of the objective function using the average information matrix and also by using an inverse BFGS formula. The robustness and efficiency is evaluated for problems derived from two experimental data from the same animal populations.

q-bio.OT↗

arXiv2007

Adapted Downhill Simplex Method for Pricing Convertible Bonds

The paper is devoted to modeling optimal exercise strategies of the behavior of investors and issuers working with convertible bonds. This implies solution of the problems of stock price modeling, payoff computation and min-max optimization. Stock prices (underlying asset) were modeled under the assumption of the geometric Brownian motion of their values. The Monte Carlo method was used for calculating the real payoff which is the objective function. The min-max optimization problem was solved using the derivative-free Downhill Simplex method. The performed numerical experiments allowed to formulate recommendations for the choice of appropriate size of the initial simplex in the Downhill Simplex Method, the number of generated trajectories of underlying asset, the size of the problem and initial trajectories of the behavior of investors and issuers.

q-fin.PR↗