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Distinguishers on round-reduced AES have attracted considerable attention in the recent years. Although the number of rounds covered in key-recovery attacks has not been increased since, subspace, yoy...
Filtering is a key step for most applications of airborne LiDAR point clouds. Although lots of filtering algorithms have been put forward in recent years, most of them suffer from parameters setting o...
There are many kinds of attacks that can be mounted on block ciphers: differential attacks, impossible differential attacks, truncated differential attacks, boomerang attacks. We consider generic di...
Enlightened by the theory of Watanabe [Watanabe S (1987) Analysis of Wiener functionals (Malliavin calculus) and its applications to heat kernels. Ann. Probab. 15:1–39] for analyzing generalized rando...
Enlightened by the theory of Watanabe [Watanabe S (1987) Analysis of Wiener functionals (Malliavin calculus) and its applications to heat kernels. Ann. Probab. 15:1–39] for analyzing generalized rando...
Background: Outcome expectation (OE) is known as a psychosocial determinant of leisure time physical activity (LTPA). Despite importance of this construct evaluation, there is no evidence of special q...
Prior research suggests that prediction of semantic and syntactic information prior to the bottom-up input is an important component of language comprehension. Recent work in basic visual and audito...
We examine different ways of numerically computing the distribution function of conditional expectations where the conditioning element takes values in a finite or countably infinite outcome space. Bo...
We consider a multilingual weakly supervised learning scenario where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide the learning in other languages. Pa...
We make an estimate of the likelihood function for the Higgs vacuum expectation value (vev) by imposing anthropic constraints on the existence of atoms while allowing the other parameters of the stand...
Machine learning has facilitated many recent advances in natural language processing and information extraction. Unfortunately, most machine learning methods rely on costly labeled data, which impedes...
We consider the problem of joint modelling of metabolic signals and gene expression in systems biology applications. We propose an approach based on input-output factorial hidden Markov models and pro...
Inferring phylogenetic trees from multiple sequence alignments often relies upon Markov chain Monte Carlo (MCMC) methods to generate tree samples from a posterior distribution. To give a rigorous appr...
We propose a novel approach for nonlinear regression using a two-layer neural network (NN) model structure with sparsity-favoring hierarchical priors on the network weights. We present an expectation ...
In this paper, we investigate risk minimization problem of derivatives based on non-tradable underlyings by means of dynamicg-expectations which are slight different from conditionalg-expectations. In...

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