OBJECTIVE To determine whether cerebral small-vessel disease (SVD) is a specific risk factor for depression, whether any association is mediated via white matter damage, and to study the role of depressive symptoms and disability on quality of life (QoL) in this patient group. METHODS Using path analyses in cross-sectional data, we modeled the relationships among depression, disability, and Q...

Background—Prosthesis-patient mismatch (P-PtM) after aortic valve replacement results in disturbed valve performance associated with increased pressure gradients. However, it is unknown whether this can be related to future structural valve deterioration (SVD) of the bioprosthesis. Methods and Results—In 564 patients (mean age, 74 5 years) receiving an aortic valve bioprosthesis, clinical follo...

Traditional dictionary learning method work by vectorizing long signals, and training on the frames of the data, thereby restricting the learning to time-localized atoms. We study a shift-tolerant approach to learning dictionaries, whereby the features are learned by training on shifted versions of the signal of interest. We propose an optimized Subspace Clustering learning method to accommodat...

In recent years there has been growing interest in designing dictionaries for image classification. These methods, however, neglect the fact that data of interest often has non-linear structure. Motivated by the fact that this non-linearity can be handled by the kernel trick, we propose learning of dictionaries in the high-dimensional feature space which are simultaneously reconstructive and di...

In this paper, we propose a robust method for monocular face shape reconstruction (MFSR) using a sparse set of facial landmarks that are detected by most of the off-theshelf landmark detectors. Different from the classical shape-from-shading framework, we formulate the MFSR problem as a Two-Fold Coupled Structure Learning (2FCSL) process, which consists of learning a regression between two subs...

We present a novel approach to low-level vision problems that combines sparse coding and deep networks pre-trained with denoising auto-encoder (DA). We propose an alternative training scheme that successfully adapts DA, originally designed for unsupervised feature learning, to the tasks of image denoising and blind inpainting. Our method’s performance in the image denoising task is comparable t...

We propose a new approach to reconstructing ECG signal from undersampled data based on constructing a combined overcomplete dictionary. The dictionary is obtained by combining the trained dictionary by K-SVD dictionary learning algorithm with universal types of dictionary such as DCT or wavelet basis. Using the trained overcomplete dictionary, the proposed method can find sparse approximation b...

We propose a new algorithm for the design of overcomplete dictionaries for sparse coding that generalizes the Sparse Coding Neural Gas (SCNG) algorithm such that it is not bound to a particular approximation method for the coefficients of the dictionary elements. In an application to image reconstruction, a dictionary that has been learned using this algorithm outperforms a dictionary that has ...

Magnetic configuration scans in the range 1.1 < ι0 < 1.5 in the H-1 flexible heliac have shown a detailed rotational transform dependence of plasma density and fluctuation spectra. Poloidal Mirnov arrays reveal magnetic fluctuations in the range 1-200kHz in plasma produced by RF heating in H, D and He mixtures ranging from highly coherent, often multi-frequency, to broad band. Both positive (st...