digitado

Uncertainty quantification (UQ) is essential for deploying deep learning models in safety critical applications, yet no consensus exists on which UQ method performs best across different data modalities and distribution shifts. This paper presents a comprehensive benchmark of ten widely used UQ baselines including MC Dropout, SWAG, ensemble methods, temperature scaling, energy based OOD, Mahalanobis, hyperbolic classifiers, ENN, Taylor Sensus, and split conformal prediction against a simplified yet highly effective variant of VOLTA that retains only a deep […]

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The evolution of streaming has usually followed a similar path: more titles, smarter algorithms, and sleeker interfaces. But the latest step by Tubi suggests differently; the next leap will come from eliminating the need to expand libraries or refine layouts altogether. The platform embedded its experience within ChatGPT and positioned itself at the intersection of intent and access, where decisions are made rather than where catalogs are browsed. This approach aligns with the constantly shifting digital habits. Interactions […]

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Published by CNN, the report has drawn quick global attention due to the strategic importance of the alleged target. Supercomputers are hubs of advanced computation and not just ordinary data centres, they are often tied to national research, artificial intelligence development, and in some cases, defense related work. The breach would not only represent a technical failure but also a potential exposure of highly sensitive intellectual and essential assets, if confirmed to be true. The uncertainty also defines […]

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Reinforcement Learning with Verifiable Rewards (RLVR) has significantly improved large language model (LLM) reasoning in formal domains such as mathematics and code. Despite these advancements, LLMs still struggle with general reasoning tasks requiring capabilities such as causal inference and temporal understanding. Extending RLVR to general reasoning is fundamentally constrained by the lack of high-quality, verifiable training data that spans diverse reasoning skills. To address this challenge, we propose SUPERNOVA, a data curation framework for RLVR aimed at enhancing […]

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Federated learning (FL) enables privacy-preserving predictive maintenance across distributed aerospace fleets, but gradient communication overhead constrains deployment on bandwidth-limited IoT nodes. This paper investigates the impact of symmetric uniform quantization ($b in {32,8,4,2}$ bits) on the accuracy–efficiency trade-off of a custom-designed lightweight 1-D convolutional model (AeroConv1D, 9,697 parameters) trained via FL on the NASA C-MAPSS benchmark under a realistic Non-IID client partition. Using a rigorous multi-seed evaluation ($N=10$ seeds), we show that INT4 achieves accuracy emph{statistically indistinguishable} from […]

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Despite significant advances in Large Reasoning Models (LRMs) driven by reinforcement learning with verifiable rewards (RLVR), this paradigm is fundamentally limited in specialized or novel domains where such supervision is prohibitively expensive or unavailable, posing a key challenge for test-time adaptation. While existing test-time methods offer a potential solution, they are constrained by learning from static query sets, risking overfitting to textual patterns. To address this gap, we introduce Test-Time Variational Synthesis (TTVS), a novel framework that enables […]

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