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Alexander Beznosikov

Publications and source records attributed to Alexander Beznosikov.

3 recordsLinked to original sources

Noninvasive H3 K27M screening in pediatric diffuse midline glioma using radiomics on heterogeneous T2-weighted MRI

Histone H3K27M mutation status defines a clinically aggressive subgroup of pediatric diffuse midline glioma and informs prognosis and trial eligibility, but confirmation usually requires tissue sampling from eloquent midline structures. We evaluated whether radiomics from routinely available T2-weighted MRI can provide an adjunctive screening signal in a heterogeneous referral-style cohort, where scans are often acquired externally and T2-weighted imaging is the only consistently available sequence. Ninety-eight pediatric patients with tissue-confirmed status were analyzed (73 mutation-positive, 25 wild-type). Expert tumor segmentations defined the regions of interest for PyRadiomics feature extraction after isotropic resampling, dual skull stripping, and multi-scale filtering. We systematically ablated preprocessing, correlation pruning with repeated recursive feature elimination, tumor volume, and TabDDPM synthetic minority augmentation across 100 stratified train/test splits with real-only test sets. Pure radiomics achieved accuracy 0.664 and F1-score 0.784. The best pipeline used preprocessing, feature selection, and volume with CatBoost, achieving accuracy 0.730$\pm$0.068 and F1-score 0.826$\pm$0.044. TabDDPM improved TabPFN to F1-score 0.81$\pm$0.05 at 200 augmented rows. These results support T2-weighted radiomics as a moderate screening and triage aid, not a replacement for tissue-based diagnosis.

q-bio.QM

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness

In recent years, non-convex optimization problems are more often described by generalized $(L_0, L_1)$-smoothness assumption rather than standard one. Meanwhile, severely corrupted data used in these problems has increased the demand for methods capable of handling heavy-tailed noises, i.e., noises with bounded $κ$-th moment. Motivated by these real-world trends and challenges, we explore sign-based methods in this setup and demonstrate their effectiveness in comparison with other popular solutions like clipping or normalization. In theory, we prove the first-known high probability convergence bounds under $(L_0, L_1)$-smoothness and heavy-tailed noises with mild parameter dependencies. In the case of standard smoothness, these bounds are novel for sign-based methods as well. In particular, SignSGD with batching achieves sample complexity $\tilde{O}\left(\left(\frac{ΔL_0d}{\varepsilon^2} + \frac{ΔL_1d^\frac{3}{2}}{\varepsilon}\right)\left[1 + \left(\fracσ{\varepsilon}\right)^\fracκ{κ-1}\right]\right), κ\in (1,2]$. Under the assumption of symmetric noises, SignSGD with Majority Voting can robustly work on the whole range of $κ\in (0,2]$ with complexity $\tilde{O}\left(\left(\frac{ΔL_0d}{\varepsilon^2} + \frac{ΔL_1d^\frac{3}{2}}{\varepsilon}\right)\left[\frac{1}{κ^2} + \frac{σ^2}{\varepsilon^2}\right]\right)$. We also obtain results for parameter-agnostic setups, Polyak-Lojasiewicz functions and momentum-based methods (in expectation). Our theoretical findings are supported by the superior performance of sign-based methods in training Large Language Models compared to clipping and normalization.

math.OC