Three-dimensional (3D) integration is promising to provide dramatic performance and energy efficiency improvement to 3D logic-DRAM integrated computing system, but also poses significant challenge to the yield. To address this challenge, this paper explores a way to leverage logic-DRAM co-design to reactivate unused spares and thereby enable the cost-efficient technique to repair 3D integration...

This paper describes a quaternion implementation of an Unscented Kalman Filter for attitude estimation on CubeSats using measurements of a sun vector, a magnetic field vector and angular velocity. For faster convergence of the attitude estimate, a SVD-method solving Wahba’s problem has been proposed, which provides an initial attitude estimate. Using unit quaternions provides a singularity free...

Matrix factorization is widely used in personalized recommender systems, text mining, and computer vision. A general assumption to construct matrix approximation is that the original matrix is of global low rank, while Joonseok Lee et al. proposed that many real matrices may be not globally low rank, and thus a locally low-rank matrix approximation method has been proposed [11]. However, this k...

A well-recognized limitation of kernel learning is the requirement to handle a kernelmatrix, whose size is quadratic in the number of training examples. Many methodshave been proposed to reduce this computational cost, mostly by using a subset of thekernel matrix entries, or some form of low-rank matrix approximation, or a randomprojection method. In this paper, we study lower b...

The talk will present a reliable Krylov-subspace method for estimating the spectral condition number of a matrix A. The main difficulty in estimating the condition number is the estimation of the smallest singular value  min of A. Our method estimates this value by solving a consistent least-squares minimization problem with a known minimizer using a specific Krylov-subspace method called LSQR...

This paper proposes a novel voice activity detector (VAD) based on singular value decomposition (SVD). The spectro-temporal characteristics of background noise region can be easily analyzed by SVD. The proposed method naturally drops hangover algorithm from VAD. Moreover, it adaptively changes the decision threshold by employing the most dominant singular value of the observation matrix in the ...

The technique of hierarchical matrices is used to construct a solution operator for a discrete elliptic boundary value problem. The solution operator can be determined once for all from a recursive domain decomposition structure. Then, given boundary values and a source term, the solution can be evaluated by applying the solution operator. The complete procedure yields all components of the sol...

In this paper, we extend the well known QR-updating scheme to a similar but more versatile and generally applicable scheme for updating the singular value decomposition (SVD). This is done by supplementing the QR-updating with a Jacobi-type SVD procedure, where apparently only a few SVD steps after each QR-update su ce in order to restore an acceptable approximation for the SVD. This then resul...

Locating recombination hotspots in genomic data is an important but difficult task. Current methods frequently rely on estimating complicated models at high computational cost. In this paper we develop an extremely fast, scalable method for inferring recombination hot spots in a population of genomic sequences that is based on the singular value decomposition. Our method performs well in severa...

This paper presents a comparison of two architectures for belief propagation in evidential networks, namely the binary join tree using joint belief functions [9] and the modified binary join tree using conditional belief functions [2]. This comparison is done from the perspective of graphical structure, messagepassing scheme, computational efficiency, storage efficiency, and complexity analysis...