Although the human genome has been sequenced, progress in understanding gene regulation in humans has been particularly slow. Many computational approaches developed for lower eukaryotes to identify cis-regulatory elements and their associated target genes often do not generalize to mammals, largely due to the degenerate and interactive nature of such elements. Motivated by the switch-like beha...

A constrained pseudo random function (PRF) behaves like a standard PRF, but with the added feature that the (master) secret key holder, having secret key K, can produce a constrained key, Kf , that allows for the evaluation of the PRF on a subset of the domain as determined by a predicate function f within some family F . While previous constructions gave constrained PRFs for poly-sized circuit...

A parallel version of a nite diierence discretization of PDEs on sparse grids is proposed. Sparse grids or hyperbolic crosspoints can be used for the eecient representation of solutions of a boundary value problem, especially in high dimensions , because the number of grid points depends only weakly on the dimension. So far only thècombination' technique for regular sparse grids was available o...

Abstract The sketch-and-project, as a general archetypal algorithm for solving linear systems, unifies variety of randomized iterative methods such the Kaczmarz and coordinate descent. However, since it aims to find least-norm solution from system, sparse can not be included. This motivates us propose more framework, called sketched Bregman projection (SBP) method, in which we are able solution...

We introduce an algorithm that, given n objects, learns a similarity matrix over all n pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen triplet-based relative-similarity queries. Each query has the form “is object a more similar to b or to c?” and is chosen to be maximally informative given the preceding responses. The output is an embedding of the objec...

We provide a new insight of the difficulty of nonparametric estimation of a whole function. A new method is invented for finding a minimax lower bound of globally estimating a function. The idea is to adjust automatically the direction to the nearly hardest I-dimensional subproblem at each location, and to use locally the difficulty of I-dimensional subproblem. In a variety of contexts, our met...

Adversarial training is a method for enhancing neural networks to improve the robustness against adversarial examples. Besides security concerns of potential examples, can also generalization ability networks, train robust and provide interpretability networks. In this work, we introduce in time series analysis enhance better by taking finance field as an example. Rethinking existing research o...