Speech dereverberation remains an open problem after more than three decades of research. The most challenging step in speech dereverberation is blind channel identification (BCI). Although many BCI approaches have been developed, their performance is still far from satisfactory for practical applications. The main difficulty in BCI lies in finding an appropriate acoustic model, which not only ...

We present deterministic polynomially space bounded algorithms for the closest vector problem for all lp-norms, 1 < p < ∞, and all polyhedral norms, in particular for the l1norm and the l∞-norm. For all lp-norms with 1 < p < ∞ the running time of the algorithm is p · log2(r)n, where r is an upper bound on the size of the coefficients of the target vector and the lattice basis and n is the dimen...

Existing studies on time series are based on two categories of distance functions. The first category consists of the Lp-norms. They are metric distance functions but cannot support local time shifting. The second category consists of distance functions which are capable of handling local time shifting but are nonmetric. The first contribution of this paper is the proposal of a new distance fun...

NIE, TIANTIAN. Quadratic Programming with Discrete Variables. (Under the direction of Dr. Shu-Cherng Fang.) In this dissertation, we study the quadratic programming problem with discrete variables (DQP). DQP is important in theory and practice, but the combination of the quadratic feature of the objective function and the discrete nature of the feasible domain makes it hard to solve. In this th...

So far, we have seen streaming algorithms for two important variants of Lp-norm estimation problem: L0-norm estimation (the distinct elements problem) and L2-norm estimation. We also noted that the L1norm estimation problem (at least, when we do not allow element deletions) corresponds to just computing the length of the stream and thus can be trivially solved in O(log n) space. Therefore, the ...

Graph-based clustering methods perform clustering on a fixed input data graph. If this initial construction is of low quality then the resulting clustering may also be of low quality. Moreover, existing graph-based clustering methods require post-processing on the data graph to extract the clustering indicators. We address both of these drawbacks by allowing the data graph itself to be adjusted...

A strategy for multiple removal consists of estimating a model of the multiples and then adaptively subtracting this model from the data by estimating shaping filters. A possible and efficient way of computing these filters is by minimizing the difference or misfit between the input data and the filtered multiples in a least-squares sense. Therefore, the signal is assumed to have minimum energy...

Whereas large-scale statistical analyses can robustly identify disease-gene relationships, they do not accurately capture genotype-phenotype correlations or disease mechanisms. We use multiple lines of independent evidence to show that different variant types in a single gene, SATB1, cause clinically overlapping but distinct neurodevelopmental disorders. Clinical evaluation 42 individuals carry...