A key characteristic of work on deep learning and neural networks in general is that it relies on representations of the input that support generalization, robust inference, domain adaptation and other desirable functionalities. Much recent progress in the field has focused on efficient and effective methods for computing representations. In this paper, we propose an alternative method that is ...

Cerebral small vessel disease (SVD) is a common cause of vascular cognitive impairment. A number of disease features can be assessed on MRI including lacunar infarcts, T2 lesion volume, brain atrophy, and cerebral microbleeds. In addition, diffusion tensor imaging (DTI) is sensitive to disruption of white matter ultrastructure, and recently it has been suggested that additional information on t...

BACKGROUND Cerebral small vessel disease (SVD) commonly coexists with large artery atherosclerosis (LAA). AIM We evaluate the effect of SVD on stroke recurrence in patients for ischemic stroke with LAA. METHODS We consecutively collected first-ever ischemic stroke patients who were classified as LAA mechanism between Jan 2010 and Dec 2013. Univariate and multivariate Cox analyses were perfo...

In this paper, we present a GPU-accelerated implementation of randomized Singular Value Decomposition (SVD) algorithm on a large matrix to rapidly approximate the top-k dominating singular values and correspondent singular vectors. The fundamental idea of randomized SVD is to condense a large matrix into a small dense matrix by random sampling while keeping the important information. Then perfo...

The Singular Value Decomposition is a key operation in many machine learning methods. Its computational cost, however, makes it unscalable and impractical for applications involving large datasets or real-time responsiveness, which are becoming increasingly common. We present a new method, QUIC-SVD, for fast approximation of the whole-matrix SVD based on a new sampling mechanism called the cosi...

The main goal of this paper is to embed a watermark in the speech signal, using the three techniques such as Discrete Cosine Transform (DCT) along with Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT).In this paper, various combinations were tried for embedding the watermark image into the audio signal such as DWT and SVD, DCT with SVD and DCT, DWT with SVD. Their perform...

Privacy protection is indispensable in data mining, and many privacy-preserving data mining (PPDM) methods have been proposed. One such method is based on singular value decomposition (SVD), which uses SVD to find unimportant information for data mining and removes it to protect privacy. Independent component analysis (ICA) is another data analysis method. If both SVD and ICA are used, unimport...

The Singular Value Decomposition (SVD) is an important tool for linear algebra and can be used to invert or approximate matrices. Although many authors use "SVD" synonymously with "Eigenvector Decomposition" or "Principal Components Transform", it is important to realize that these other methods apply only to symmetric matrices, while the SVD can be applied to arbitrary nonsquare matrices. This...

Singular Value Decomposition (SVD) is one of the most useful techniques for analyzing data in linear algebra. SVD decomposes a rectangular real or complex matrix into two orthogonal matrices and one diagonal matrix. In this work we introduce a new approach to improve the preciseness of the standard Quantum Fourier Transform. The presented Quantum-SVD algorithm is based on the singular value dec...

In this paper, we will demonstrate three-dimensional tomographic reconstruction of space-borne highresolution SAR data using Shanghai as our test site. The high density of high-rise buildings in Shanghai leads to a rather complicated backscattering regime, which is difficult to handle with conventional interferometric processing. For the tomographic signal reconstruction, we use three different...