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We prove a general time-space lower bound that applies for a large class of learning problems and shows that for every problem in that class, any learning algorithm requires either a memory of quadratic size or an exponential number of samples. As a special case, this gives a new proof for the time-space lower bound for parity learning [R16]. Our result is stated in terms of the norm of the matrix...
Impossible differential cryptanalysis is an analysis method by constructing impossible differential path, eliminating the keys satisfying this path, and finally recovering the secret keys. This paper has utilized a new property of Mix Columns Transformation, constructed a new 4-round impossible differential path, added 1-round and 3-round possible differential path before and behind this path respectively,...
NP Complete problems are one of the most complex problems in computer science but their vast applications in real world always pushes the scientists to explore new ways to solve them. We extended the original problem definition of Boolean Satisfiability Problem to finding all satisfiable solutions of a given problem instance and used massively parallel architecture of CUDA (Compute Unified Device...
Pervasive use of pointers in large-scale real-world applications continues to make points-to analysis an important optimization-enabler. Rapid growth of software systems demands a scalable pointer analysis algorithm. A typical inclusion-based points-to analysis iteratively evaluates constraints and computes a points-to solution until a fixpoint. In each iteration, (i) points-to information is propagated...
Our work has two main contributions. The first is to give a cost performance search order which checks fractional SPs by not only maximizing the expectation for the R-D gains but also minimizing the computational cost at the same time. We assign 48 SPs to one of six categories with different cost performance priorities. Each category has a different probability of being the optimal fractional MV (performance),...
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