arXiv:2604.05064v1 Announce Type: new Abstract: Synthetic data is essential for training foundation models for time series (FMTS), but most generators assume static correlations, and are typically missing realistic inter-channel dependencies. We introduce DynLMC, a Dynamic Linear Model of Coregionalization, that incorporates time-varying, regime-switching correlations and cross-channel lag structures. Our approach produces synthetic multivariate time series with correlation dynamics that closely resemble real data. Fine-tuning three foundational models on DynLMC-generated data yields consistent zero-shot forecasting improvements across nine benchmarks. […]

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arXiv:2602.21948v1 Announce Type: cross Abstract: Generative Adversarial Networks (GAN) have been used in many studies to synthesise mixed tabular data. Conditional tabular GAN (CTGAN) have been the most popular variant but struggle to effectively navigate the risk-utility trade-off. Bayesian GAN have received less attention for tabular data, but have been explored with unstructured data such as images and text. The most used technique employed in Bayesian GAN is Markov Chain Monte Carlo (MCMC), but it is computationally intensive, […]

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Vehicular Ad-hoc Networks (VANETs) face critical challenges regarding intermittent connectivity and latency due to high node mobility, often resulting in a performance trade-off between reactive and proactive routing paradigms. This study aims to resolve these inherent limitations and ensure reliable communication in volatile environments. We propose a novel context-aware framework, the Dynamic Hybrid Routing Protocol (DHRP), which integrates Ad hoc On-Demand Distance Vector (AODV) and Optimized Link State Routing (OLSR). Distinguished by a predictive multi-criteria switching logic and […]

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Advances in multi-modal large language models (MLLMs) have inspired time series understanding and reasoning tasks, that enable natural language querying over time series, producing textual analyses of complex temporal dynamics. Recent attempts hybridize numerical time series with their visualized plots, facilitating precise value reasoning and visual structure comprehension for comprehensive time series understanding of MLLMs. However, effective cross-modal integration remains challenging due to fine-grained temporal misalignment across modalities and severe entanglement between shared and modality-specific semantics, which hinder […]

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CloudFlare announced its first large-scale layoff. CEO Matthew Prince says because of AI efficiency gains, the company doesn’t need as many support roles.

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In the increasingly complex landscape of enterprise service management, the remarkable transformation led by Aravind Barla at a global Fortune 100 company stands as a compelling testament to visionary leadership and strategic technical execution. This next-generation Employee Experience Platform (EXP) implementation, serving over 100,000 employees across multiple continents, has redefined industry standards for service delivery integration and AI-driven automation while establishing new benchmarks for what’s possible in large-scale digital transformation initiatives. Inheriting a Fragmented Ecosystem When Aravind Barla […]

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To overcome the low accuracy of conventional methods for estimating liquid volume and food nutrient content in bowl-type tableware, as well as the tool dependence and time-consuming nature of manual measurements, this study proposes an integrated approach that combines geometric reconstruction with deep learning–based segmentation. After a one-time camera cali-bration, only a frontal and a top-down image of a bowl are required. The pipeline automatically extracts key geometric information, including rim diameter, base diameter, bowl height, and the […]

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