Misdeclared hazardous cargo inside sealed maritime containers continues to drive ship fires, port disruptions, and avoidable losses for crews, carriers, and coastal communities. Ports and carriers already use non-intrusive inspection (NII) imaging and document checks, but these systems are often treated as separate queues rather than a single integrated decision system. This paper proposes a practical multimodal framework that fuses radiographic sensing with shipping document intelligence to flag hazardous misdeclaration risks without opening a container. The approach combines […]

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arXiv:2603.03700v1 Announce Type: new Abstract: Despite the remarkable empirical success of score-based diffusion models, their statistical guarantees remain underdeveloped. Existing analyses often provide pessimistic convergence rates that do not reflect the intrinsic low-dimensional structure common in real data, such as that arising in natural images. In this work, we study the statistical convergence of score-based diffusion models for learning an unknown distribution $mu$ from finitely many samples. Under mild regularity conditions on the forward diffusion process and the […]

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arXiv:2206.03034v4 Announce Type: replace-cross Abstract: This work presents a new procedure for obtaining predictive distributions in the context of Gaussian process (GP) modeling, with a relaxation of the interpolation constraints outside ranges of interest: the mean of the predictive distributions no longer necessarily interpolates the observed values when they are outside ranges of interest, but are simply constrained to remain outside. This method called relaxed Gaussian process (reGP) interpolation provides better predictive distributions in ranges of interest, especially […]

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arXiv:2601.20032v1 Announce Type: new Abstract: Health influencers play a growing role in shaping public beliefs, yet their content is often conveyed through conversational narratives and rhetorical strategies rather than explicit factual claims. As a result, claim-centric verification methods struggle to capture the pragmatic meaning of influencer discourse. In this paper, we propose TAIGR (Takeaway Argumentation Inference with Grounded References), a structured framework designed to analyze influencer discourse, which operates in three stages: (1) identifying the core influencer recommendation–takeaway; […]

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arXiv:2601.18838v1 Announce Type: new Abstract: Particle Mesh Ewald (PME) methods accelerated through Fast Fourier Transforms (FFTs) for their reciprocal part are widely used to solve N -body problems over periodic structures with Laplace-like kernels. The FFT dependence of classical PME may mitigate its performance on parallel distributed-memory architectures. We here introduce a new variant of the reciprocal part based on Sum of Kronecker Products (SKP) instead of FFT. Moreover, our implementation of this new method is not linearithmic […]

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arXiv:2603.15821v1 Announce Type: new Abstract: The assumption that prediction-equivalent models produce equivalent explanations underlies many practices in explainable AI, including model selection, auditing, and regulatory evaluation. In this work, we show that this assumption does not hold. Through a large-scale empirical study across 24 datasets and multiple model classes, we find that models with identical predictive behavior can produce substantially different feature attributions. This disagreement is highly structured: models within the same hypothesis class exhibit strong agreement, while […]

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arXiv:2603.06703v1 Announce Type: new Abstract: Graph databases are widely used in systems that manage rich metadata, yet current modelling practices often embed descriptive attributes directly in nodes, leading to redundancy and inconsistent semantics. This paper introduces the Fifth Graph Normal Form (5GNF), a trait-based normalization framework for property graphs that represents recurring metadata as canonical Trait Nodes connected through HAS_TRAIT relationships. We formalize trait functional dependencies (tFDs) and present the TraitExtraction5GNF algorithm for identifying and extracting reusable traits. […]

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arXiv:2602.16831v1 Announce Type: new Abstract: This paper presents a low-thrust trajectory optimization strategy to achieve a near-circular lunar orbit for a CubeSat injected into a lunar flyby trajectory. The 12U CubeSat HORYU-VI is equipped with four Hall-effect thrusters and designed as a secondary payload on NASA’s Space Launch System under the Artemis program. Upon release, the spacecraft gains sufficient energy to escape the Earth-Moon system after a lunar flyby. The proposed trajectory is decomposed into three phases: (1) […]

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I found this book “AI Systems Performance Engineering” by Chris Fregly [1]. There is another book “Machine Learning Systems” by harvard [2]. Which book is the best of option to learn about optimizing/high performance ML / Deep Learning? [1] – https://www.oreilly.com/library/view/ai-systems-performance/9798341627772/ [2] – https://mlsysbook.ai/book/contents/core/efficient_ai/efficient_ai.html submitted by /u/rlopes404 [link] [comments]

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