Dynamics of Generalized Tachyon Field in Teleparallel Gravity

We study dynamics of generalized tachyon scalar field in the framework of teleparallel gravity. This model is an extension of tachyonic teleparallel dark energy model which has been proposed in [26]. In contrast with tachyonic teleparallel dark energy model that has no scaling attractors, here we find some scaling attractors which means that the cosmological coincidence problem can be alleviated. Scaling attractors present for both interacting and non-interacting dark energy, dark matter cases.Comment: 14 pages, 20 figure

Dynamics of Interacting Tachyonic Teleparallel Dark Energy

We consider a tachyon scalar field which is nonminimally coupled to gravity in the framework of teleparallel gravity. We analyze the phase-space of the model, known as tachyonic teleparallel dark energy, in the presence of an interaction between dark energy and background matter. We find that although there exist some late-time accelerated attractor solutions, there is no scaling attractor. So, unfortunately interacting tachyonic teleparallel dark energy cannot alleviate the coincidence problem

Generative Mixture of Networks

A generative model based on training deep architectures is proposed. The model consists of K networks that are trained together to learn the underlying distribution of a given data set. The process starts with dividing the input data into K clusters and feeding each of them into a separate network. After few iterations of training networks separately, we use an EM-like algorithm to train the networks together and update the clusters of the data. We call this model Mixture of Networks. The provided model is a platform that can be used for any deep structure and be trained by any conventional objective function for distribution modeling. As the components of the model are neural networks, it has high capability in characterizing complicated data distributions as well as clustering data. We apply the algorithm on MNIST hand-written digits and Yale face datasets. We also demonstrate the clustering ability of the model using some real-world and toy examples.Comment: 9 page

Abstracts from the 8th International Conference on cGMP Generators, Effectors and Therapeutic Implications

This work was supported by a restricted research grant of Bayer AG