arXiv:2507.12878v2 Announce Type: replace Abstract: The identification of Linear Time-Varying (LTV) systems from input-output data is a fundamental yet challenging ill-posed inverse problem. This work introduces a unified Bayesian framework that models the system’s impulse response, $h(t, tau)$, as a stochastic process. We decompose the response into a posterior mean and a random fluctuation term, a formulation that provides a principled approach for quantifying uncertainty, unifies intrinsic channel variability and epistemic uncertainty through a common posterior representation, and […]

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arXiv:2604.01251v1 Announce Type: new Abstract: Camouflaged scene understanding (CSU) has attracted significant attention due to its broad practical implications. However, in this field, robust image-text cross-modal alignment remains under-explored, hindering deeper understanding of camouflaged scenarios and their related applications. To this end, we focus on the typical image-text retrieval task, and formulate a new task dubbed “camouflage-aware image-text retrieval” (CA-ITR). We first construct a dedicated camouflage image-text retrieval dataset (CamoIT), comprising $sim$10.5K samples with multi-granularity textual annotations. Benchmark […]

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Author(s): Abhishek Yadav Originally published on Towards AI. A hands-on guide to building synthetic data that looks, feels, and behaves like the real world without privacy risk Photo by Luke Chesser on Unsplash Healthcare organizations sit on treasure chests of data be it the appointments, lab results, care journeys, billing patterns, social determinants. Yet the very rules designed to protect patient privacy often make it nearly impossible for analysts and data scientists to experiment freely. And that’s a […]

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We present a systematic method for exactly enforcing Dirichlet, Neumann, and Robin type conditions on general quadrilateral domains with arbitrary curved boundaries. Our method is built upon exact mappings between general quadrilateral domains and the standard domain, and employs a combination of TFC (theory of functional connections) constrained expressions and transfinite interpolations. When Neumann or Robin boundaries are present, especially when two Neumann (or Robin) boundaries meet at a vertex, it is critical to enforce exactly the induced […]

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Most stablecoins are backed by either fiat reserves or crypto assets, a model that’s proven functional but fundamentally limited. Tharwa is building something different: a stablecoin collateralized by a diversified portfolio of real-world assets, UAE real estate, gold, oil, treasury bills, managed by AI-driven rebalancing algorithms. It’s an ambitious technical stack that attempts to solve the yield problem plaguing dollar-pegged stablecoins while maintaining stability through diversification rather than overcollateralization. Saeed Al Fahim, founder of Tharwa and member of […]

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arXiv:2603.03464v1 Announce Type: new Abstract: We introduce Graph Hopfield Networks, whose energy function couples associative memory retrieval with graph Laplacian smoothing for node classification. Gradient descent on this joint energy yields an iterative update interleaving Hopfield retrieval with Laplacian propagation. Memory retrieval provides regime-dependent benefits: up to 2.0~pp on sparse citation networks and up to 5 pp additional robustness under feature masking; the iterative energy-descent architecture itself is a strong inductive bias, with all variants (including the memory-disabled […]

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I really think “give AI total control of my computer and therefore my entire life” is going to look so foolish in retrospect that everyone who went for this is going to look as dumb as Jimmy Fallon holding up a picture of his Bored Ape — Christopher Mims, Technology columnist at The Wall Street Journal Tags: ai, security

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arXiv:2512.17131v3 Announce Type: replace-cross Abstract: We propose Generalized Primal Averaging (GPA), an extension of Nesterov’s method that unifies and generalizes recent averaging-based optimizers like single-worker DiLoCo and Schedule-Free, within a non-distributed setting. While DiLoCo relies on a memory-intensive two-loop structure to periodically aggregate pseudo-gradients using Nesterov momentum, GPA eliminates this complexity by decoupling Nesterov’s interpolation constants to enable smooth iterate averaging at every step. Structurally, GPA resembles Schedule-Free but replaces uniform averaging with exponential moving averaging. Empirically, GPA […]

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