We present CHARAGRAM embeddings, a simple approach for learning character-based compositional models to embed textual sequences. A word or sentence is represented using a character n-gram count vector, followed by a single nonlinear transformation to yield a low-dimensional embedding. We use three tasks for evaluation: word similarity, sentence similarity, and part-of-speech tagging. We demonst...

Image Registration is an important part of computer vision. We propose a method of image registration by obtaining best similarity of local geometric figure that utilizes opposite core difference (OCD) of corresponding local figure. This method gets initial matching after describing precisely SIFT points by constructing feature subspaces based on the detection of SIFT feature points. Then we de...

Modified similarity renormalization of Hamiltonians is proposed, that performes by means of flow equations the similarity transformation of Hamiltonian in the particle number space. This enables to renormalize in the energy space the field theoretical Hamiltonian and makes possible to work in a severe trancated Fock space for the renormalized Hamiltonian. ∗Posters presented on the workshop ”Con...

This paper presents a novel, knowledge-based method for measuring semantic similarity in support of applications aimed at organizing and etrieving relevant textual information. We show how a quantitative context may be established for what is essentially qualitative in nature by ffecting a topological transformation of the lexicon into a metric space where distance is well-defined. We illustrat...

In this article a new unitary similarity transformation of a normal matrix to complex symmetric form will be discussed. A constructive proof as well as some properties and examples will be given.

We report on recent progress of the implementation of the similarity renormalization group (SRG) for three-body interactions in a one-dimensional, bosonic model system using the planewave basis. We discuss our implementation of the flow equations and show results that confirm that results in the three-body sector remain unchanged by the transformation of the Hamiltonian. We also show how the SR...

Fractals are self-similar images. There are many techniques for generating fractals. IFS are one of them. IFS use a set of linear transformations for generation of fractals. We propose a method of modifying IFS such that non-linear transformations can be used for generating pleasant looking fractals. We also show a technique which ensures convergence of the fractal Images. Non-linear fractals h...

Much researchs are currently carried out in the eld of fractal coding of images. Fractal models try to identify and represent self-similarity relationships within images through the use of spatial transformations of the signal associating shrinking, translation, isometries and scaling. These transformations can be interpreted frequentialy and are particularly suited to an interpretation in the ...

Under image scaling transform, two interaction models of image intensity gradient fields display unusual power scaling laws. These scalings are very universal in texture images and images of natural scenes, and robust against noise and image local deformation. An image similarity measure is developed in terms of these scaling laws. This similarity measure has graceful properties such as invaria...

Many forms of word relatedness have been developed, providing different perspectives on word similarity. We introduce a Bayesian probabilistic tensor factorization model for synthesizing a single word vector representation and per-perspective linear transformations from any number of word similarity matrices. The resulting word vectors, when combined with the per-perspective linear transformati...