Random convex programs (RCPs) are convex optimization problems subject to a finite number N of random constraints. The optimal objective value J∗ of an RCP is thus a random variable. We study the probability with which J∗ is no longer optimal if a further random constraint is added to the problem (violation probability, V ∗). It turns out that this probability rapidly concentrates near zero as ...

introduction: the aim of this study was to assess of both implicit and explicit motor sequence learning in children with attention deficit hyper activity disorder (adhd) and typical peers , by serial reaction time task. materials and methods: the sample size for each group was 12. the study sample was included 24 children with adhd, in explicit learning group and implicit learning group.  and 2...

We study statistical properties of zeros of random polynomials and random analytic functions associated with the pseudoeuclidean group of symmetries SU(1, 1), by utilizing both analytical and numerical techniques. We first show that zeros of the SU(1, 1) random polynomial of degree N are concentrated in a narrow annulus of the order of N around the unit circle on the complex plane, and we find ...

Stein's method is used to prove approximations in total variation to the distributions of integer valued random variables by (possibly signed) compound Poisson measures. For sums of independent random variables, the results obtained are very explicit, and improve upon earlier work of Kruopis (1983) and Cekanavicius (1997); coupling methods are used to derive concrete expressions for the error b...

We consider a variation of O(N)-symmetric vector models in which the vector components are Grassmann numbers. We show that these theories generate the same sort of random polymer models as the O(N) vector models and that they lie in the same universality class in the large-N limit. We explicitly construct the double-scaling limit of the theory and show that the genus expansion is an alternating...

In this paper, we develop an approach for optimizing the explicit binomial confidence interval recently derived by Chen et al. The optimization reduces conservativeness while guaranteeing prescribed coverage probability. 1 Explicit Formula of Chen et al. Let X be a Bernoulli random variable defined in probability space (Ω,F ,Pr) with distribution Pr{X = 1} = 1 − Pr{X = 0} = p ∈ (0, 1). It is a ...

In this experiment, we examined the extent to which error-driven learning may operate under implicit learning conditions. We compared error monitoring in a sequence learning task in which stimuli consisted of regular, irregular, or random sequences. Subjects were either informed (explicit condition) or not informed (implicit condition) about the existence of the sequence. For both conditions, r...

Extension of components should be consistent in the sense that the extending component does not introduce any unexpected behaviour. We formulate requirements which allow consistent extension of components in presence of explicit invariants. We concentrate on the issue of extension consistency with forwarding as the reuse mechanism, and discuss the additional problems which require solution when...

We present an explicit method to produce upper bounds for the dimension of the moduli spaces of complete integral Gorenstein curves with prescribed symmetric Weierstrass semigroups.