arXiv:2602.12489v1 Announce Type: new Abstract: We propose a novel method for establishing correspondence between two sequences of 2D images. One particular application of this technique is slice-level content navigation, where the goal is to localize specific 2D slices within a 3D volume or determine the anatomical coverage of a 3D scan based on its 2D slices. This serves as an important preprocessing step for various diagnostic tasks, as well as for automatic registration and segmentation pipelines. Our approach […]

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Deep learning training is non-deterministic: identical code with different random seeds produces models that agree on aggregate metrics but disagree on individual predictions, with per-class AUC swings exceeding 20 percentage points on rare clinical classes. We present a framework for verified bit-identical training that eliminates three sources of randomness: weight initialization (via structured orthogonal basis functions), batch ordering (via golden ratio scheduling), and non-deterministic GPU operations (via architecture selection and custom autograd). The pipeline produces MD5-verified identical trained […]

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arXiv:2604.02361v1 Announce Type: new Abstract: Detecting Internet routing instability is a critical yet challenging task, particularly when relying solely on endpoint active measurements. This study introduces TRACE, a MachineLearning (ML)pipeline designed to identify route changes using only traceroute latency data, thereby ensuring independence from control plane information. We propose a robust feature engineering strategy that captures temporal dynamics using rolling statistics and aggregated context patterns. The architecture leverages a stacked ensemble of Gradient Boosted Decision Trees refined by […]

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Pathological complete response (pCR) after neoadjuvant therapy is an important indicator of treatment response and prognosis in breast cancer. Multi-modal breast MRI provides complementary information for pCR prediction, but existing methods often assume complete modality availability and do not fully exploit the complementary value of radiomics and deep features. To address these limitations, we propose a radiomics-guided multi-modal learning framework for pCR prediction from breast MRI under incomplete modality settings. The model employs a multi-branch 2.5D encoder to […]

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Efficient brain tumor diagnosis is crucial for early treatment; however, it is challenging because of lesion variability and image complexity. We evaluated convolutional neural networks (CNNs) in a federated learning (FL) setting, comparing models trained on original versus preprocessed MRI images (resizing, grayscale conversion, normalization, filtering, and histogram equalization). Preprocessing alone yielded negligible gains; combined with test-time augmentation (TTA), it delivered consistent, statistically significant improvements in federated MRI classification (p<0.001). In practice, TTA should be the default inference […]

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arXiv:2601.14290v1 Announce Type: new Abstract: Small Language Models (SLMs, under 10B parameters) are attractive for private, on-device deployment, yet they frequently fail on strict constraint-satisfaction problems due to linear, overconfident reasoning traces that do not recover from early mistakes. We introduce Verifier-Guided Distillation, a training protocol that transfers the process of error repair – explicit conflict detection and backtracking – rather than only correct final answers. By training a 7B model on verified reasoning traces that include mistakes […]

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Objective: Algorithmic fairness is essential for equitable and trustworthy machine learning in healthcare. Most fairness tools emphasize single-axis demographic comparisons and may miss compounded disparities affecting intersectional populations. This study introduces Fairlogue, a toolkit designed to operationalize intersectional fairness assessment in observational and counterfactual contexts within clinical settings. Methods: Fairlogue is a Python-based toolkit composed of three components: 1) an observational framework extending demographic parity, equalized odds, and equal opportunity difference to intersectional populations; 2) a counterfactual framework […]

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arXiv:2512.12988v2 Announce Type: replace-cross Abstract: Mixture models are widely used in modeling heterogeneous data populations. A standard approach of mixture modeling assumes that the mixture component takes a parametric kernel form. In many applications, making parametric assumptions on the latent subpopulation distributions may be unrealistic, which motivates the need for nonparametric modeling of the mixture components themselves. In this paper, we study finite mixtures with nonparametric mixture components, using a Bayesian nonparametric modeling approach. In particular, it is […]

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arXiv:2602.03854v1 Announce Type: new Abstract: Autonomous vehicles (AVs) are emerging as a transformative innovation in transportation, offering potential benefits in safety, sustainability, and efficiency. Saudi Arabian adoption of AVs aligns with Vision 2030, emphasizing smart mobility through initiatives such as the Riyadh Autonomous Metro and self-driving cars. This study explores Saudi citizens perceptions of AVs before and after exposure to these technologies and examines whether demographic factors age, gender, education level, and driving habits affect acceptance. Using quantitative […]

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