In recent years, Large Language Models (LLMs) have achieved almost human-like performance on various tasks. While some LLMs have been trained on multilingual data, most of the training data is in English. Hence, their...
Speculative attacks are still an active threat today that, even if initially focused on the x86 platform, reach across all modern hardware architectures. RISC-V is a newly proposed open instruction set architecture th...
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Artificial intelligence (AI) models are prevalent today and provide a valuable tool for artists. However, a lesser-known artifact that comes with AI models that is not always discussed is the glitch. Glitches occur fo...
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In this paper we use proof mining methods to compute rates of (T-)asymptotic regularity of the generalized Krasnoselskii-Mann-type iteration associated to a nonexpansive mapping T: X → X in a uniformly convex normed ...
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In 1983, Zalinescu showed that the squared norm of a uniformly convex normed space is uniformly convex on bounded subsets. We extend this result to the metric setting of uniformly convex hyperbolic spaces. We derive a...
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Medical data is sensitive personal data which, according to GDPR and HIPAA, necessitates regulations concerning their use. Anonymizing this data prior to research would allow for broader access, due to a lower sensiti...
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ISBN:
(数字)9798350362480
ISBN:
(纸本)9798350362497
Medical data is sensitive personal data which, according to GDPR and HIPAA, necessitates regulations concerning their use. Anonymizing this data prior to research would allow for broader access, due to a lower sensitivity. Privacy-aware data synthesis has been proposed as a solution. However, current algorithms face difficulties in synthesizing medical data while maintaining privacy and utility. This is due to the structure of medical data which consists of multiple interlinked tables with high dimensional columns containing sequential aspects of the patient trajectory. The resulting number of correlations is intractable to model naively and, if relational correlations are not accounted for, the resulting data has poor utility (e.g., leads to invalid patient trajectories). In this paper, we present MARE, a relational synthesis algorithm which focuses on a set of core correlations found in relational data while pruning others. The resulting lower computational complexity allows MARE to produce accurate relational data. We showcase that MARE can synthesize multiple medical datasets, which contain sequential aspects, while maintaining utility in form of inter-table and inter-row correlations and privacy guarantees.
In this paper we obtain, by using proof mining methods, quantitative results on the asymptotic regularity of the viscosity approximation method (VAM) with error terms for m-accretive operators in Banach spaces. For co...
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The class of uniformly smooth hyperbolic spaces was recently introduced by Pinto as a common generalization of both CAT(0) spaces and uniformly smooth Banach spaces, in a way that Reich’s theorem on resolvent converg...
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Prunescu and Sauras-Altuzarra showed that all C-recursive sequences of natural numbers have an arithmetic div-mod representation that can be derived from their generating function. This representation consists of comp...
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