OPINION STATEMENT Cerebral small vessel disease (SVD) is characterised by damage to deep grey and white matter structures of the brain and is responsible for a diverse range of clinical problems that include stroke and dementia. In this review, we describe advances in neuroimaging published since January 2015, mainly with magnetic resonance imaging (MRI), that, in general, are improving quantif...

Singular Value Decomposition (SVD) is a popular approach in various network applications, such as link prediction and network parameter characterization. Incremental SVD approaches are proposed to process newly changed nodes and edges in dynamic networks. However, incremental SVD approaches suffer from serious error accumulation inevitably due to approximation on incremental updates. SVD restar...

The main contribution of this paper is to demonstrate that a new randomized SVD algorithm, proposed by Drineas et. al. in [4], is not only of theoretical interest but also a viable and fast alternative to traditional SVD algorithms in applications (e.g. image processing). This algorithm samples a constant number of rows (or columns) of the matrix, scales them appropriately to form a small matri...

In this paper, we propose a novel technique for video shot segmentation and classiication based on the Singular Value Decomposition (SVD). For the input video sequence, we create a feature-frame matrix A, and perform the SVD on it. From this SVD, we are able to not only derive the reened feature space to better segment the video sequence along time axis, but also deene metrics to enable classii...

An adaptive Singular Value Decomposition (SVD) algorithm is an optimal method to obtain spatial multiplexing gain MIMO-OFDM systems. We present an orthogonal reconstruction scheme to obtain more accurate SVD outputs and then the system performance will be greatly enhanced. Moreover this project reduces high cost of implementation and high decomposing latency. Finally adaptive SVD engine is simu...

Objective We explored the association between the total small vessel disease (SVD) score obtained with magnetic resonance imaging and risk factors and outcomes in the Japanese population. Methods The presence of SVD features, including lacunes, cerebral microbleeds, white matter changes, and basal ganglia perivascular spaces on MRI, was summed to obtain a "total SVD score" (range 0-4). Ordinal ...

BACKGROUND Cytokine gene variations are contributory factors in inflammatory pathology. Allele frequencies of interleukin (IL)-1 cluster genes [IL-1A(-889), IL-1B(-511), IL-1B(+3953), IL-1RN Intron 2 VNTR] and tissue necrosis factor (TNF)-alpha gene [TNFA(-308)] were measured in healthy blood donors (healthy control subjects), patients with angiographically normal coronary arteries (patient con...

Singular value decomposition (SVD) is a general-purpose mathematical analysis tool that has been used in a variety of information-retrieval applications. As the size and complexity of retrieval collections increase, it is crucial for our analysis tools to scale accordingly. To this end, we have studied the application of a new theoretically justiied SVD approximation algorithm to the problem of...

In vitro neuronal networks are known to fire in Synchronized Bursting Events (SBEs), with characteristic temporal width of 100 ms. We treat these events as the principal data atoms of the network. Applying SVD (or PCA) to the spatial information, i.e. activity of neurons per burst, we demonstrate characteristic changes that take place over time scales of hours. We consider this as evidence for ...

Subspace-based methods rely on singular value decomposition (SVD) of the sample covariance matrix (SCM) to compute the array signal or noise subspace. For large array, triditional subspace-based algorithms inevitably lead to intensive computational complexity due to both calculating SCM and performing SVD of SCM. To circumvent this problem, a NyströmBased algorithm for array subspace estimation...