The compliance industrial complex is selling a new product. Having peddled GDPR checklists and SOC2 reports for years, the same vendors are offering AI governance frameworks with the promises of making your neural networks ethical, safe, and compliant. While the policies sound reasonable, with impressive documentation and an expensive consultant, none of it is stopping breaches. As a cybersecurity ghostwriter who has collaborated with several Silicon Valley Startups and tech founders in the APAC region, I’ve watched hundreds […]

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arXiv:2603.20306v1 Announce Type: new Abstract: We present a review and analysis of scientific paper embellishments — simple visual elements that are deeply integrated into the text of scientific publications. These embellishments are increasingly used in research papers, which have the potential to enhance textual descriptions, strengthen connections between figures and content, and improve internal textual coherence, while also carrying the risk of disrupting the reading experience. As their exact impact is not yet well understood, we conducted a […]

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Every AI-powered iOS app starts with the same assumption: “We’ll just call the AI API and show the result.” That assumption works — right up until real users, real devices, and real constraints enter the system. AI demos look simple. Production AI on iOS is not. This article is about what breaks when AI is treated as a regular API call — and what iOS engineers inevitably learn when shipping AI-powered features to real users. The Mental Model […]

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arXiv:2510.21974v2 Announce Type: replace-cross Abstract: We introduce Deep Jump Gaussian Processes (DJGP), a novel method for surrogate modeling of a piecewise continuous function on a high-dimensional domain. DJGP addresses the limitations of conventional Jump Gaussian Processes (JGP) in high-dimensional input spaces by integrating region-specific, locally linear projections with JGP modeling. These projections employ region-dependent matrices to capture local low-dimensional subspace structures, making them well suited to the inherently localized modeling behavior of JGPs, a variant of local Gaussian […]

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arXiv:2602.09055v1 Announce Type: new Abstract: This paper considers the scattering of a time-harmonic acoustic plane wave by an elastic body with an unbounded periodic surface. The original problem can be confined to the analysis of the fields in one periodic cell. With the help of the perfectly matched layer (PML) technique, we can truncate the unbounded physical domain into a bounded computational domain. By respectively constructing the equivalent transparent boundary conditions of acoustic and elastic waves simultaneously, the […]

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Modal methods are a long-standing approach to physical modelling synthesis. Extensions to nonlinear problems are possible, including the case of a high-amplitude vibration of a string. A modal decomposition leads to a densely coupled nonlinear system of ordinary differential equations. Recent work in scalar auxiliary variable techniques has enabled construction of explicit and stable numerical solvers for such classes of nonlinear systems. On the other hand, machine learning approaches (in particular neural ordinary differential equations) have been successful […]

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arXiv:2402.05187v3 Announce Type: replace Abstract: Policy Mirror Descent (PMD) is a popular framework in reinforcement learning, serving as a unifying perspective that encompasses numerous algorithms. These algorithms are derived through the selection of a mirror map and enjoy finite-time convergence guarantees. Despite its popularity, the exploration of PMD’s full potential is limited, with the majority of research focusing on a particular mirror map — namely, the negative entropy — which gives rise to the renowned Natural Policy Gradient […]

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arXiv:2601.18846v1 Announce Type: new Abstract: Benchmarking in continuous black-box optimisation is hindered by the limited structural diversity of existing test suites such as BBOB. We explore whether large language models embedded in an evolutionary loop can be used to design optimisation problems with clearly defined high-level landscape characteristics. Using the LLaMEA framework, we guide an LLM to generate problem code from natural-language descriptions of target properties, including multimodality, separability, basin-size homogeneity, search-space homogeneity and globallocal optima contrast. Inside […]

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arXiv:2602.22954v1 Announce Type: cross Abstract: In this work, we analyze alternative effective sample size (ESS) metrics for importance sampling algorithms, and discuss a possible extended range of applications. We show the relationship between the ESS expressions used in the literature and two entropy families, the R’enyi and Tsallis entropy. The R’enyi entropy is connected to the Huggins-Roy’s ESS family introduced in cite{Huggins15}. We prove that that all the ESS functions included in the Huggins-Roy’s family fulfill all the […]

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