12,564 research outputs found
A multiscale constitutive model for intergranular stress corrosion cracking in type 304 austenitic stainless steel
Peer reviewedPublisher PD
A log-free zero-density estimate and small gaps in coefficients of -functions
Let be the Rankin--Selberg -function attached
to automorphic representations and . Let and
denote the contragredient representations associated to
and . Under the assumption of certain upper bounds for
coefficients of the logarithmic derivatives of and
, we prove a log-free zero-density
estimate for which generalises a result due to
Fogels in the context of Dirichlet -functions. We then employ this log-free
estimate in studying the distribution of the Fourier coefficients of an
automorphic representation . As an application we examine the
non-lacunarity of the Fourier coefficients of a modular newform
of weight , level ,
and character . More precisely for and a prime , set
, where We prove that for some
An analog feedback associative memory
A method for the storage of analog vectors, i.e., vectors whose components are real-valued, is developed for the Hopfield continuous-time network. An important requirement is that each memory vector has to be an asymptotically stable (i.e. attractive) equilibrium of the network. Some of the limitations imposed by the continuous Hopfield model on the set of vectors that can be stored are pointed out. These limitations can be relieved by choosing a network containing visible as well as hidden units. An architecture consisting of several hidden layers and a visible layer, connected in a circular fashion, is considered. It is proved that the two-layer case is guaranteed to store any number of given analog vectors provided their number does not exceed 1 + the number of neurons in the hidden layer. A learning algorithm that correctly adjusts the locations of the equilibria and guarantees their asymptotic stability is developed. Simulation results confirm the effectiveness of the approach
Confidence regions and minimax rates in outlier-robust estimation on the probability simplex
We consider the problem of estimating the mean of a distribution supported by
the -dimensional probability simplex in the setting where an
fraction of observations are subject to adversarial corruption. A simple
particular example is the problem of estimating the distribution of a discrete
random variable. Assuming that the discrete variable takes values, the
unknown parameter is a -dimensional vector belonging to
the probability simplex. We first describe various settings of contamination
and discuss the relation between these settings. We then establish minimax
rates when the quality of estimation is measured by the total-variation
distance, the Hellinger distance, or the -distance between two
probability measures. We also provide confidence regions for the unknown mean
that shrink at the minimax rate. Our analysis reveals that the minimax rates
associated to these three distances are all different, but they are all
attained by the sample average. Furthermore, we show that the latter is
adaptive to the possible sparsity of the unknown vector. Some numerical
experiments illustrating our theoretical findings are reported
On the Exceptional Gauged WZW Theories
We consider two different versions of gauged WZW theories with the
exceptional groups and gauged with any of theirs null subgroups. By
constructing suitable automorphism, we establish the equivalence of these two
theories. On the other hand our automorphism, relates the two dual irreducible
Riemannian globally symmetric spaces with different characters based on the
corresponding exceptional Lie groups.Comment: 5 pages, LaTeX fil
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