It was recently shown that SVD and matrix inversion can be approximated in quantum log-space [1] for well formed matrices. This can be interpreted as a fully logarithmic quantum approximation scheme for both problems. We show that if prBQL = prBPL then every fully logarithmic quantum approximation scheme can be replaced by a probabilistic one. Hence, if classical algorithms cannot approximate t...

We introduce a new parallel algorithm for approximate breadthfirst ordering of an unweighted graph by using bounded asynchrony to parametrically control both the performance and error of the algorithm. This work is based on the k-level asynchronous (KLA) paradigm that trades expensive global synchronizations in the level-synchronous model for local synchronizations in the asynchronous model, wh...

We introduce a new dimension to the widely studied on-line approximate string matching problem, by introducing an error threshold parameter so that the algorithm is allowed to miss occurrences with probability . This is particularly appropriate for this problem, as approximate searching is used to model many cases where exact answers are not mandatory. We show that the relaxed version of the pr...

M computing systems are designed to prevent errors in computation, memory, and communication. Guarding against errors however requires energy, temporal redundancy, or spatial redundancy and therefore consumes resources. But not all systems need to be free of errors: In some systems, either by explicit design or by the nature of the problems they solve, system output quality degrades gracefully ...

We exploit sketch techniques, especially the Count-Min sketch, a memory, and time efficient framework which approximates the frequency of a word pair in the corpus without explicitly storing the word pair itself. These methods use hashing to deal with massive amounts of streaming text. We apply CountMin sketch to approximate word pair counts and exhibit their effectiveness on three important NL...

Using neural network we try to model the unknown function f for given input-output data pairs. The connection strength of each neuron is updated through learning. Repeated simulations of crisp neural network produce different values of weight factors that are directly affected by the change of different parameters. We propose the idea that for each neuron in the network, we can obtain quasi-fuz...

Skyline queries retrieve promising data objects that are not dominated in all the attributes of interest. However, in many cases, a user may not be interested in a skyline set computed over the entire dataset, but rather over a specified range of values for each attribute. For example, a user may look for hotels only within a specified budget and/or in a particular area in the city. This leads ...

Missing-feature imputation or reconstruction is used in noiserobust automatic speech recognition to recover the unobserved clean speech information. Reconstruction methods often use the noise-corrupted observations and a clean speech prior to calculate a point estimate for the unobserved clean speech features, whereas the approach proposed in this work associates the unobserved clean speech fea...

Min PB is the class of minimization problems whose objective functions are bounded by a polynomial in the size of the input. We show that there exist several problems that are Min PB-complete with respect to an approximation preserving reduction. These problems are very hard to approximate; in polynomial time they cannot be approximated within nε for some ε > 0, where n is the size of the input...