arXiv:2507.07469v3 Announce Type: replace Abstract: We introduce Galerkin-ARIMA and Galerkin-SARIMA, a projection-based extension of classical ARIMA/SARIMA that replaces rigid linear lag operators with low-dimensional Galerkin basis expansions while preserving the familiar AR-MA decomposition. Experiments on synthetic series and on quarterly GDP and daily S&P 500 returns show that Galerkin-SARIMA matches or improves forecast accuracy relative to classical ARIMA/SARIMA. Estimation is closed-form via a two-stage least-squares procedure, and the closed-form two-stage estimator enables efficient rolling-window re-estimation while preserving the […]

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arXiv:2605.02901v1 Announce Type: new Abstract: This work aims to establish an end-to-end system for tracking of physical 3D objects for virtual reality (VR) applications. We focus on training applications requiring real-time tracking of the position of small physical objects and their reflection in VR space. Out goal is to perform object tracking in a “plug and play” manner, without using complex systems with quite large tracking devices or manually implementing object tracking. We therefore propose a system for […]

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arXiv:2604.02382v1 Announce Type: new Abstract: The scale and complexity of modern cloud infrastructure have made Infrastructure-as-Code (IaC) essential for managing deployments. While large Language models (LLMs) are increasingly being used to generate IaC configurations from natural language, user requests are often underspecified. Unlike traditional code generation, IaC configurations cannot be executed cheaply or iteratively repaired, forcing the LLMs into an almost one-shot regime. We observe that ambiguity in IaC exhibits a tractable compositional structure: configurations decompose into three […]

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How zero-cost, authoritative-sounding text is breaking institutional accountability On January 16, the chief constable of West Midlands Police in the United Kingdom stepped down after admitting that an AI tool had been used to help draft an official safety assessment that cited a football match that never took place. The report was used to justify banning supporters of Maccabi Tel Aviv from attending a Europa League match against Aston Villa in November 2025. Embedded in the assessment was […]

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arXiv:2603.16961v1 Announce Type: new Abstract: The rapid growth of electric vehicles (EVs) requires more effective charging infrastructure planning. Infrastructure layout not only determines deployment cost, but also reshapes charging behavior and influences overall system performance. In addition, destination charging and en-route charging represent distinct charging regimes associated with different power requirements, which may lead to substantially different infrastructure deployment outcomes. This study applies an agent-based modeling framework to generate trajectory-level latent public charging demand under three charging regimes […]

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Monitoring behavioural drift, the sustained shift in an individual’s daily activity, sleep, or social patterns offers a significant lens for early mental health intervention. However, detecting these drifts in free-living settings remains challenging due to the absence of ground-truth labels, the temporal complexity of human behaviour, and fragmentation across heterogeneous sensing modalities. This paper proposes a multimodal approach to quantify and detect behavioural drift using longitudinal data from over 500 university students in the NetHealth cohort. We extract […]

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This study explores the extent to which deep learning models can predict groove and its related perceptual dimensions directly from audio signals. We critically examine the effectiveness of seven state-of-the-art deep learning models in predicting groove ratings and responses to groove-related queries through the extraction of audio embeddings. Additionally, we compare these predictions with traditional handcrafted audio features. To better understand the underlying mechanics, we extend this methodology to analyze predictions based on source-separated instruments, thereby isolating the […]

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We give new differentially private algorithms for the classic problems of learning decision lists and large-margin halfspaces in the PAC and online models. In the PAC model, we give a computationally efficient algorithm for learning decision lists with minimal sample overhead over the best non-private algorithms. In the online model, we give a private analog of the influential Winnow algorithm for learning halfspaces with mistake bound polylogarithmic in the dimension and inverse polynomial in the margin. As an […]

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