Synopsis Parallel imaging relies on fully-sampled calibration data to estimate k-space kernels or sensitivities used to reconstruct subsampled acquisitions. Emerging techniques use low-rank modeling, or joint estimation of sensitivities and image content via nonlinear optimization, to reduce the dependency on calibration data. In a typical study, images at multiple echoes/contrasts are acquired...

 ‎Let $V$ be an $n$-dimensional complex inner product space‎. ‎Suppose‎ ‎$H$ is a subgroup of the symmetric group of degree $m$‎, ‎and‎ ‎$chi‎ :‎Hrightarrow mathbb{C} $ is an irreducible character (not‎ ‎necessarily linear)‎. ‎Denote by $V_{chi}(H)$ the symmetry class‎ ‎of tensors associated with $H$ and $chi$‎. ‎Let $K(T)in‎ (V_{chi}(H))$ be the operator induced by $Tin‎ ‎text{End}(V)$‎. ‎Th...

This paper presents a new video restoration scheme based on the joint sparse and lowrank matrix approximation. By grouping similar patches in the spatiotemporal domain, we formulate the video restoration problem as a joint sparse and low-rank matrix approximation problem. The resulted nuclear norm and `1 norm related minimization problem can also be efficiently solved by many recently developed...

We consider the problem of computing low-rank approximations of matrices. The novel aspects of our approach are that we require the low-rank approximations be written in a factorized form with sparse factors and the degree of sparsity of the factors can be traded oo for reduced reconstruction error by certain user determined parameters. We give a detailed error analysis of our proposed algorith...

Associated with every commuting m-tuples of operators on a complex Hilbert space X is its Aluthge transform. In this paper we show that and transform have the same joint essential spectrum. Further, it shown spectrum contained in numerical range

In many applications such as data compression, imaging or genomic data analysis, it is important to approximate a given m × n matrix A by a matrix B of rank at most k which is much smaller than m and n. The best rank k approximation can be determined via the singular value decomposition which, however, has prohibitively high computational complexity and storage requirements for very large m and...

Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. This dissertation extends the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well known techniques in data analysis, such as nonnegative matrix factorization, matrix compl...

background:  the glenohumeral joint becomes dislocated more than any other major joint because it maintains a wide range of motion and its stability is inherently weak. the most common complication following acute initial shoulder dislocation is recurrent dislocation or chronic instability. imbalance of strength and range of motion in individuals with anterior dislocation can be a contributing ...

We consider higher-rank versions of the standard numerical range for matrices. A central motivation for this investigation comes from quantum error correction. We develop the basic structure theory for the higher-rank numerical ranges, and give a complete description in the Hermitian case. We also consider associated projection compression problems.

In this paper, the stress intensity factor (SIF) expression for defected butt welds containing undercut and inclined lack of penetration (LOP) subject to far-field tensile stress is derived. Some of the standards such as ISO 5817 and BS EN 25817 have specified allowable limits for the length of the undercut and LOP defects and for the height of the weld. In addition, EN 29692 standard has deter...