1,444 research outputs found

    Stability analysis for soliton solutions in a gauged CP(1) theory

    Full text link
    We analyze the stability of soliton solutions in a Chern-Simons-CP(1) model. We show a condition for which the soliton solutions are stable. Finally we verified this result numerically.Comment: 13 pages, numerical analysis is added. To be published in Mod. Phys. Lett.

    Near Horizon Superspace

    Get PDF
    The adS_{p+2} x S^{d-p-2} geometry of the near horizon branes is promoted to a supergeometry: the solution of the supergravity constraints for the vielbein, connection and form superfields are found. This supergeometry can be used for the construction of new superconformal theories. We also discuss the Green-Schwarz action for a type IIB string on adS_5 x S_5.Comment: 11 pages, LaTe

    Tevatron Discovery Potential for Fourth Generation Neutrinos: Dirac, Majorana and Everything in Between

    Full text link
    We analyze the power of the Tevatron dataset to exclude or discover fourth generation neutrinos. In a general framework, one can have mixed left- and right-handed neutrinos, with Dirac and Majorana neutrinos as extreme cases. We demonstrate that a single Tevatron experiment can make powerful statements across the entire mixing space, extending LEP's mass limits of 60-80 GeV up to 150-175 GeV, depending on the mixing.Comment: 4 pages, pdflate

    New AdS3AdS_3 Branes and Boundary States

    Get PDF
    We examine D-branes on AdS3AdS_3, and find a three-brane wrapping the entire AdS3AdS_3, in addition to 1-branes and instantonic 2-branes previously discussed in the literature. The three-brane is found using a construction of Maldacena, Moore, and Seiberg. We show that all these branes satisfy Cardy's condition and extract the open string spectrum on them.Comment: 18 pages, late

    Addressing Item-Cold Start Problem in Recommendation Systems using Model Based Approach and Deep Learning

    Full text link
    Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail to generate sensible recommendations for new users and items into the system due to missing information about their past interactions. In this paper, we propose a solution for successfully addressing item-cold start problem which uses model-based approach and recent advances in deep learning. In particular, we use latent factor model for recommendation, and predict the latent factors from item's descriptions using convolutional neural network when they cannot be obtained from usage data. Latent factors obtained by applying matrix factorization to the available usage data are used as ground truth to train the convolutional neural network. To create latent factor representations for the new items, the convolutional neural network uses their textual description. The results from the experiments reveal that the proposed approach significantly outperforms several baseline estimators
    • ‚Ķ
    corecore