We present an efficient algorithm for calculating q-gram frequencies on strings represented in compressed form, namely, as a straight line program (SLP). Given an SLP T of size n that represents string T , the algorithm computes the occurrence frequencies of all q-grams in T , by reducing the problem to the weighted q-gram frequencies problem on a trie-like structure of size m = |T | − dup(q, T...

Suppose independent observations Xi , i = 1, . . . , n are observed from a mixture model f (x;Q) ≡ ∫ f (x; ) dQ( ), where is a scalar and Q( ) is a nondegenerate distribution with an unspecified form. We consider to estimate Q( ) by nonparametric maximum likelihood (NPML) method under two scenarios: (1) the likelihood is penalized by a functional g(Q); and (2) Q is under a constraint g(Q) = g0....

Learning can be an effective way for robot systems to deal with dynamic environments and changing task conditions. However, popular singlerobot learning algorithms based on discounted rewards, such as Q learning, do not achieve cooperation (i.e., purposeful division of labor) when applied to task-level multirobot systems. A tasklevel system is defined as one performing a mission that is decompo...

The iLQG algorithm optimizes trajectories by iteratively constructing locally optimal linear feedback controllers under a local linearization of the dynamics p(xt+1|xt,ut) = N (fxtxt + futut,Ft) and a quadratic expansion of the rewards r(xt,ut) (Tassa et al., 2012). Under linear dynamics and quadratic rewards, the action-value function Q(xt,ut) and value function V (xt) are locally quadratic an...

A subfamily F ′ of a set family F is said to q-represent F if for every A ∈ F and B of size q such that A ∩ B = ∅ there exists a set A ∈ F ′ such that A ∩ B = ∅. In a recent paper [SODA 2014] three of the authors gave an algorithm that given as input a family F of sets of size p together with an integer q, efficiently computes a q-representative family F ′ of F of size approximately ( p+q p ) ,...