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    Invariant measures under geodesic flow

    , Article Houston Journal of Mathematics ; Volume 33, Issue 1 , 2007 , Pages 163-167 ; 03621588 (ISSN) Fanai, H. R ; Sharif University of Technology
    2007
    Abstract
    For a compact Riemannian manifold with negative curvature, the Liouville measure, the Bowen-Margulis measure and the Harmonic measure are three natural invariant measures under the geodesic flow. We show that if any two of the above three measure classes coincide then the space is locally symmetric, provided the function with respect to which the equilibrium state is the Harmonic measure, depends only on the foot points. © 2007 University of Houston  

    On the Topological Entropy of Geodesic Flows

    , M.Sc. Thesis Sharif University of Technology Reshadat, Zahra (Author) ; Razvan, Mohammad Reza (Supervisor) ; Nassiri, Meysam (Supervisor)
    Abstract
    Let M be a connected, compact, Riemannian manifold. Geodesic flow is a flow on the unit tangent bundle of M . This flow can be studied in dynamics prespective. for example entropy or complexity of the geodesic flow. in this thesis we will follow methods of entropy estimation or computing for geodesic flow. we will follow the method of anthony manning and Ricardo Mañe for proving such result. Maning present two results linking the topological entropy of the geodesic flow on M. we expalin how he find exponential growth rate volume of balls in universal cover as a lower bound for topologycal entropy. another theorem , Mañe represent the equlity between exponential growth rate of avrage of... 

    Unsupervised domain adaptation via representation learning and adaptive classifier learning

    , Article Neurocomputing ; Volume 165 , 2015 , Pages 300-311 ; 09252312 (ISSN) Gheisari, M ; Baghshah Soleimani, M ; Sharif University of Technology
    Abstract
    The existing learning methods usually assume that training data and test data follow the same distribution, while this is not always true. Thus, in many cases the performance of these methods on the test data will be severely degraded. In this paper, we study the problem of unsupervised domain adaptation, where no labeled data in the target domain is available. The proposed method first finds a new representation for both the source and the target domain and then learns a prediction function for the classifier by optimizing an objective function which simultaneously tries to minimize the loss function on the source domain while also maximizes the consistency of manifold (which is based on...