‎for a given measure space $(x,{mathscr b},mu)$ we construct all measure spaces $(y,{mathscr c},lambda)$ in which $(x,{mathscr b},mu)$ is embeddable‎. ‎the construction is modeled on the ultrafilter construction of the stone--v{c}ech compactification of a completely regular topological space‎. ‎under certain conditions the construction simplifies‎. ‎examples are given when this simplification o...

In this paper, we propose a high capacity dataembedding system in DCT domain and review the existing high capacity data embedding systems both in spatial domain as well as transform domain. The embedding algorithm is based on the quantized projection embedding method with some enhancement to achieve high embedding rates. The embedding is done on the quantized DCT coefficients using the concept ...

The method of lag-embedding, common in the analysis of signals in the context of nonlinear dynamics, requires the selection of an embedding dimension. This embedding dimension is analogous to the model order in a linear prediction model, but the order of a linear prediction model is of little use in characterizing chaotic signals or in indicating an appropriate embedding dimension for nonlinear...

To reduce the rapidly growing computational cost of the dual-fermion lattice calculation with increasing system size, we introduce two embedding schemes. One is the real fermion embedding, and the other is the dual-fermion embedding. Our numerical tests show that the real fermion and dual-fermion embedding approaches converge to essentially the same result. The application on the Anderson disor...

Simbed, standing for similarity-based embedding, is a new method of embedding high-dimensional data. It relies on the preservation of pairwise similarities rather than distances. In this respect, Simbed can be related to other techniques such as stochastic neighbor embedding and its variants. A connection with curvilinear component analysis is also pointed out. Simbed differs from these methods...

Dimension reduction is defined as the process of mapping high-dimensional data to a lowerdimensional vector space. Most machine learning and data mining techniques may not be effective for high-dimensional data. In order to handle this data adequately, its dimensionality needs to be reduced. Dimensionality reduction is also needed for visualization, graph embedding, image retrieval and a variet...

background and objective: formalin-fixed paraffin-embedded tissues are a valuable source of dna for molecular studies. we designed and optimized an efficient procedure for dna extraction from formalin-fixed paraffin embedded tissues. materials and methods: seventy three blocks of cervical paraffin-embedded tissues were investigated. dna was extracted using 45 minutes boiling in alkaline solutio...

There are a lot of methods for embedding hidden information in JPEG format but no method have been presented so far with a high capacity and same accuracy. The loss of JPEG compression and the effects of such compression on the images have prevented the location area from being easily used for embedding and most of the methods apply the DCT coefficients of JPEG algorithm for this aspect. This p...