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Distance metrics are at the core of many processing and machine learning algorithms. In many contexts, it is useful to compute the distance between data using multiple criteria. This naturally leads to consider vector-valued metrics, in which the distance is no longer a real positive number but a vector. In this paper, we propose a principled way to combine several metrics into either a scalar-valued or vector-valued metric. We illustrate our framework by reformulating the popular structural similarity...
We get stationary solutions of a free stochastic partial differential equation. As an application, we prove equality of non-microstate and microstate free entropy dimensions under a Lipschitz like condition on conjugate variables, assuming also the von Neumann algebra embeddable. This includes an -tuple of -Gaussian random variables e.g. for .
We study a generalization of the classical Henstock-Kurzweil integral, known as the strong -integral, introduced by Jarník and Kurzweil. Let be the space of all strongly -integrable functions on a multidimensional compact interval , equipped with the Alexiewicz norm . We show that each element in the dual space of can be represented as a strong -integral. Consequently, we prove that is strongly -integrable on for each strongly -integrable function if and only if is almost everywhere...
We construct two examples of infinite spaces X such that there is no continuous linear surjection from the space of continuous functions onto × ℝcp(X)cp(X). One of these examples is compact. This answers some questions of Arkhangel’skiĭ.
For a holomorphic function ψ defined on a sector we give a condition implying the identity
where A is a sectorial operator on a Banach space X. This yields all common descriptions of the real interpolation spaces for sectorial operators and allows easy proofs of the moment inequalities and reiteration results for fractional powers.
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