This paper compares Google's BBR-v3 Congestion Control Algorithm to eight others over SpaceX's Starlink network, demonstrating its fairness and throughput maximization in high-latency, variable satell…

The paper analyzes the structured CVP distance on the log-unit lattice of cyclotomic fields, significantly reducing the conjectured CDPR factor for the ML-KEM cryptosystem from exponential to sub-poly…

The paper develops an explicit multi-linear polynomial form for binary polynomial optimization problems after eliminating a subset of variables, allowing for characterization of new special classes wi…

The paper proposes two novel multi-column RBFN architectures, MC-PSO and MC-APSO, that combine parallel RBFN structures with swarm optimization to significantly outperform existing methods in accuracy…

This paper constructs strictly convex quadratic problems and initial points for which the long Barzilai--Borwein method does not converge root-superlinearly.

This paper introduces Swiss-Bench 003, an expanded evaluation framework assessing LLM reliability and adversarial security across eight dimensions using 808 Swiss-specific items, revealing that self-g…

This paper proposes Wat3R, a cross-domain semi-supervised learning framework for adapting 3D reconstruction models from air to underwater scenes using unlabeled real underwater video footage and a tea…

This paper describes the use of a trie data structure to enhance experiment efficiency in comparative pipeline experiments for cascading retrieval pipelines using PyTerrier, observing a 26% reduction…

A new method for selecting knots in Generalized Additive Models using an extension of adaptive splines and a customized Fellner-Schall scheme.

OrbitBFT introduces a novel two-stage hierarchical BFT consensus protocol that enables scalable and robust Byzantine Fault-Tolerant coordination for large-scale Low Earth Orbit satellite constellation…

This paper extends gradient boosting to functions of vector inputs using a simple algorithm with histogram-based decision trees.

The paper proposes GloResNet, a lightweight 3D CNN that effectively predicts brain injury in preterm infants using T2-weighted MRI, achieving an average accuracy of 75.18%.

This paper introduces BBOmix, an open-source benchmark for unsupervised representation learning on real-world biological data.