Singular value decomposition (SVD) is one of the most useful matrix decompositions in linear algebra. Here, a novel application SVD recovering ripped photos was exploited. Recovery done by applying truncated iteratively. Performance evaluated using Frobenius norm. Results from few experimental were decent.

Chlamydia is the most widespread sexually transmitted bacterial disease and a prophylactic vaccine is highly needed. Ideally, this vaccine is required to induce a combined response of Th1 cell-mediated immune (CMI) response in concert with neutralizing antibodies. Using a novel Göttingen minipig animal model, we evaluated the immunogenicity and efficacy of a multi-subunit vaccine formulated in ...

The purpose of this study was to analyze the outcomes and complications between stroke subtypes after intravenous thrombolysis. A total of 471 patients with acute ischemic stroke after intravenous thrombolysis from January 2007 to April 2014 were enrolled and classified according to the Trial of Org 10172 in Acute Stroke Treatment. A multivariate logistic regression model was used to evaluate t...

SUMMARY We have developed two novel methods for Singular Value Decomposition analysis (SVD) of microarray data. The first is a threshold-based method for obtaining gene groups, and the second is a method for obtaining a measure of confidence in SVD analysis. Gene groups are obtained by identifying elements of the left singular vectors, or gene coefficient vectors, that are greater in magnitude ...

BACKGROUND AND PURPOSE Cerebral small vessel disease (SVD) is associated with cognitive impairment. This may be because of decreased microstructural integrity and microvascular perfusion, but data on these relationships are scarce. We determined the relationship between cognition and microvascular perfusion and microstructural integrity in SVD patients, using intravoxel incoherent motion imagin...

Many important applications – from big data analytics to information retrieval, gene expression analysis, and numerical weather prediction – require the solution of large dense singular value decompositions (SVD). In many cases the problems are too large to fit into the computer’s main memory, and thus require specialized out-of-core algorithms that use disk storage. In this paper, we analyze t...

This paper discusses clustering and latent semantic indexing (LSI) aspects of the singular value decomposition (SVD). The purpose of this paper is twofold. The first is to give an explanation on how and why the singular vectors can be used in clustering. And the second is to show that the two seemingly unrelated SVD aspects actually originate from the same source: related vertices tend to be mo...

Finding a way to effectively suppress speckle in SAR images has great significance. K-means singular value decomposition (K-SVD) has shown great potential in SAR image de-noising. However, the traditional K-SVD is sensitive to the position and phase of the characteristics in the image, and the de-noised image by K-SVD has lost some detailed information of the original image. In this paper, we p...

BACKGROUND Cognitive impairment is common in patients with cerebral small vessel disease, but is not well detected using common cognitive screening tests which have been primarily devised for cortical dementias. We developed the Brief Memory and Executive Test (BMET); a rapid screening measure sensitive to the impaired executive function and processing speed characteristic of small vessel disea...

Purpose We investigated cerebral small vessel disease (SVD) in patients with incidental retinal vein occlusion (RVO). Methods This retrospective, case-control, observational trial included 125 patients with RVO who underwent brain magnetic resonance imaging (MRI) and 1105 age-matched controls who underwent comprehensive medical interviews and MRI. Underlying cardiovascular diseases and MRI fi...