To address the difficulty of separating background materials from similar associated with use “single-spectral information” for hyperspectral anomaly detection, a fast detection algorithm based on what we term “greedy bilateral smoothing and extended multi-attribute profile” (GBSAED) method is proposed to improve precision operation efficiency. This utilizes smoothing” decompose low-rank part i...

Similar to Grid computing systems, scientific applications in cloud are large scale distributed systems that are deployed on distributed cloud nodes. Scientific applications usually have a lot of communications between the nodes for deployment. Therefore conventional ranking methods are not appropriate for deploying scientific applications. The reason is ranking methods do not consider the rela...

Just as semantic hashing [Salakhutdinov and Hinton2009] can accelerate information retrieval, binary valued embeddings can significantly reduce latency in the retrieval of graphical data. We introduce a simple but effective model for learning such binary vectors for nodes in a graph. By imagining the embeddings as independent coin flips of varying bias, continuous optimization techniques can be...

Introduction: The number, size, shape, and structure of teeth in humans show very wide variation among different populations and sometimes within the same population. The aim of the present study was to determine the prevalence of developmental dental anomalies among patients attending the faculty of dentistry in Rasht, Iran over a period of seven months. Materials and methods: This cross-se...

One of the tasks of optical surveillance is to detect anomalies in large amounts of image data. However, if the size of the anomaly is very small, limited information is available to distinguish it from the surrounding environment. Spectral detection provides a useful source of additional information and may help to detect anomalies with a size of a few pixels or less. Unfortunately, spectral c...

One of the tasks of optical surveillance is to detect anomalies in large amounts of image data. However, if the size of the anomaly is very small, limited information is available to distinguish it from the surrounding environment. Spectral detection provides a useful source of additional information and may help to detect anomalies with a size of a few pixels or less. Unfortunately, spectral c...

Determining the effect of structural perturbations on the eigenvalue spectra of networks is an important problem because the spectra characterize not only their topological structures, but also their dynamical behavior, such as synchronization and cascading processes on networks. Here we develop a theory for estimating the change of the largest eigenvalue of the adjacency matrix or the extreme ...

Spectral clustering consists of two distinct stages: (a) construct an affinity graph from the dataset and (b) cluster the data points through finding an optimal partition of the affinity graph. The focus of the paper is the first step. Existing spectral clustering algorithms adopt Gaussian function to define the affinity graph since it is easy to implement. However, Gaussian function is hard to...