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Existing support vector machines(SVM) models are sensitive to noise and lack sparsity, which limits their performance. To address these issues, we combine the elastic net loss with a robust loss framework to construct a sparse $varepsilon$-insensitive bounded asymmetric elastic net loss, and integrate it with SVM to build $varepsilon$ Insensitive Zone Bounded Asymmetric Elastic Net Loss-based SVM($varepsilon$-BAEN-SVM). $varepsilon$-BAEN-SVM is both sparse and robust. Sparsity is proven by showing that samples inside the $varepsilon$-insensitive band are not support vectors. […]

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In computational cytology, detecting malignancy on whole-slide images is difficult because malignant cells are morphologically diverse yet vanishingly rare amid a vast background of normal cells. Accurate detection of these extremely rare malignant cells remains challenging due to large class imbalance and limited annotations. Conventional weakly supervised approaches, such as multiple instance learning (MIL), often fail to generalize at the instance level, especially when the fraction of malignant cells (witness rate) is exceedingly low. In this study, we […]

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In computational cytology, detecting malignancy on whole-slide images is difficult because malignant cells are morphologically diverse yet vanishingly rare amid a vast background of normal cells. Accurate detection of these extremely rare malignant cells remains challenging due to large class imbalance and limited annotations. Conventional weakly supervised approaches, such as multiple instance learning (MIL), often fail to generalize at the instance level, especially when the fraction of malignant cells (witness rate) is exceedingly low. In this study, we […]

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Insider threats pose a persistent and evolving challenge to contemporary software ecosystems, where privileged users can exploit access for malicious purposes, often evading traditional perimeter-based defences. This paper introduces a novel hybrid framework that synergistically integrates zero-knowledge proofs (ZKPs) and behavioural analytics to detect and mitigate such threats with enhanced privacy and precision. ZKPs enable secure authentication and data verification without revealing sensitive information, ensuring compliance with privacy regulations like GDPR while thwarting unauthorized access. Complementarily, our behavioural […]

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This paper presents a mathematical formalization of human–computer interaction under a zero-distance constraint, introducing a degenerate formulation of Fitts’s Law. In classical models, movement time depends logarithmically on spatial distance and target size. By enforcing D→0, the Index of Difficulty converges to zero, and movement time reduces to a constant equal to the physiological intercept, yielding a constant-time interaction model. A rigorous ε–δ limit analysis proves convergence, while an optimization formulation shows that zero-distance interaction achieves the global […]

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We present textsc{Aegypti}, a hybrid algorithm for detecting a single triangle in an undirected graph ( G = (V, E) ) with ( n = |V| ) vertices and ( m = |E| ) edges. The algorithm operates in two phases. In the emph{fast path}, a clique-constrained Union-Find structure (textsc{FastCliqueUF}) streams over the edges and merges components only when the union remains a clique; the moment any component reaches size~( geq 3 ), a triangle witness is returned. […]

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Equality saturation explores equivalent program expressions via e-graphs, and its final step—extraction—selects one representative per equivalence class to form an output tree. Standard extraction minimizes a decomposable, single-node cost function that cannot capture multi-node structural patterns exploited by downstream systems such as SMT preprocessors and compiler backends. We formalize pattern-aware extraction as a weighted pattern cover problem on AND-OR directed acyclic graphs and establish its correspondence to tree covering in compiler instruction selection. Three challenges arise: annotation ambiguity […]

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Deformable medical image registration aims to spatially align anatomical structures across volumetric scans. Recent transformer-based methods achieve high overlap accuracy but often produce deformation fields with topological violations. We propose CTCF, a Cascade Transformer for Coarse-to-Fine registration that wraps a lightweight coarse-and-refine envelope around a core registration module. Level 1 provides a coarse displacement estimate at quarter resolution, Level 2 performs the main registration via a Swin Transformer encoder with deformable cross-attention and a learned super-resolution decoder, and […]

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As blockchain technology evolves from specialized financial tools to foundational infrastructure for Web3, the necessity for rigorous performance validation becomes paramount. Stress testing—defined as the evaluation of system stability under extreme workloads—is critical for identifying bottlenecks in consensus mechanisms and peer-to-peer communication. This survey provides an exhaustive analysis of web-based stress testing frameworks. Unlike traditional CLI-based tools, web-based frameworks provide real-time telemetry and distributed orchestration capabilities essential for modern decentralized applications. We categorize existing literature into three generations […]

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