However, we then revise this hardness assumption by dropping the permutation requirement and considering arbitrary sparse high degree polynomials. We argue that this type of assumption is much better suited for space-hardness rather than timed cryptography. We then proceed to construct both space-lock...
// possible permutation (string is sorted in reverse order) if(--i==0){ return; } } // find the highest index `j` to the right of index `i` such that // `s[j] > s[i-1]` (`s[i…n-1]` is sorted in reverse order) intj=n-1; while(j>i&&s[j]<=s[i-1]){ j--;...
A Randomized Scheduler with Probabilistic Guarantees of Finding Bugs Sebastian Burckhardt Microsoft Research sburckha@microsoft.com Pravesh Kothari Indian Institute of Technology, Kanpur praveshk@iitk.ac.in Santosh Nagarakatte University of Pennsylvania santoshn@cis.upenn.edu Madanlal Musuvathi Microsoft ...
N. Sharygina and H. Veith (Eds.): CAV 2013, LNCS 8044, pp. 640–655, 2013. c Springer-Verlag Berlin Heidelberg 2013 Finite Model Finding in SMT 641 models. So we focus on finite models, which can be enumerated and represented sym- bolically. More precisely, since SMT solvers work ...
aThis paper deals with a strategic issue in the stable marriage model with complete preference lists (i.e., a preference list of an agent is a permutation of all the members of the opposite sex). Given complete preference lists of n men over n women, and a marriage μ, we consider the...
Let’s called array1 as A and array2 as B, each with size m and n. The obvious brute-force solution is to scan through each element in A, and for each element in A, scan if that element exist in B. The running time complexity is O(m*n). Not good! Can we do better? Absolut...
where Q isorthogonal and L is diagonal. Since the eigen-decomposition is unique(up to a permutation of the columns of Q and L), we know that V musttherefore contain the eigenvectors of D'D in its columns, and U mustcontain the eigenvectors of DD' in its columns. This is the origin ...
permutationTest <- function(x,y,reps){ estimates <- c() observed <- distanceCorrelation(x,y) N <- length(x) for(i in 1:reps){ y_i <- sample(y, length(y), replace = T) estimates <- append(estimates, distanceCorrelation(x, y_i)) ...
step(n); b = 0; Figure 5: A program with two bugs of depth 1 that are hard to find with naive randomized schedulers that flip a coin in each step. PCT finds both these bugs with a probability 1/2. Consider a naive randomized scheduler that flips a coin in each step to decide ...
The algorithm utilizes that a valid aggregation with n states correspond to a set of n eigenvectors of the dynamics matrix such that these respect the same permutation symmetry with n orbits. We exemplify the applicability of the algorithm by employing it to identify coarse grained representations ...