Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Ronhovde, P."'
Autor:
Mazaheri, T., Sun, Bo, Scher-Zagier, J., Thind, A. S., Magee, D., Ronhovde, P., Lookman, T., Mishra, R., Nussinov, Z.
Publikováno v:
Phys. Rev. Materials 3, 063802 (2019)
A machine learning approach that we term the `Stochastic Replica Voting Machine' (SRVM) algorithm is presented and applied to a binary and a 3-class classification problems in materials science. Here, we employ SRVM to predict candidate compounds cap
Externí odkaz:
http://arxiv.org/abs/1705.08491
Autor:
Nussinov, Z., Ronhovde, P., Hu, Dandan, Chakrabarty, S., Sahu, M., Sun, Bo, Mauro, N. A., Sahu, K. K.
We survey the application of a relatively new branch of statistical physics--"community detection"-- to data mining. In particular, we focus on the diagnosis of materials and automated image segmentation. Community detection describes the quest of pa
Externí odkaz:
http://arxiv.org/abs/1503.01626
We present a physics inspired heuristic method for solving combinatorial optimization problems. Our approach is specifically motivated by the desire to avoid trapping in metastable local minima- a common occurrence in hard problems with multiple extr
Externí odkaz:
http://arxiv.org/abs/1406.7282
We derive rigorous bounds for well-defined community structure in complex networks for a stochastic block model (SBM) benchmark. In particular, we analyze the effect of inter-community "noise" (inter-community edges) on any "community detection" algo
Externí odkaz:
http://arxiv.org/abs/1306.5794
Community detection in networks refers to the process of seeking strongly internally connected groups of nodes which are weakly externally connected. In this work, we introduce and study a community definition based on internal edge density. Beginnin
Externí odkaz:
http://arxiv.org/abs/1301.3120
Autor:
Ronhovde, Peter, Nussinov, Zohar
Community detection algorithms attempt to find the best clusters of nodes in an arbitrary complex network. Multi-scale ("multiresolution") community detection extends the problem to identify the best network scale(s) for these clusters. The latter ta
Externí odkaz:
http://arxiv.org/abs/1208.5052
Autor:
Hu, Dandan, Sarder, Pinaki, Ronhovde, Peter, Orthaus, Sandra, Achilefu, Samuel, Nussinov, Zohar
Publikováno v:
Journal of Microscopy Volume 253, Issue 1, pages 54 - 64, 2014
We have developed an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells inspired by a multi-resolution community detection (MCD) based network segmentation method. The image processing problem is fra
Externí odkaz:
http://arxiv.org/abs/1208.4662
Publikováno v:
Phys. Rev. E 86, 066106 (2012)
We examine phase transitions between the easy, hard, and the unsolvable phases when attempting to identify structure in large complex networks (community detection) in the presence of disorder induced by network noise (spurious links that obscure str
Externí odkaz:
http://arxiv.org/abs/1204.4167
Publikováno v:
EPL vol 99, 38006 (2012)
We examine a global disorder transition when identifying community structure in an arbitrary complex network. Earlier, we illustrated [Phil. Mag. 92, 406 (2012)] that "community detection" (CD) generally exhibits disordered (or unsolvable) and ordere
Externí odkaz:
http://arxiv.org/abs/1204.3649
Publikováno v:
Phys. Rev. E 85, 016101 (2012)
We apply a replica inference based Potts model method to unsupervised image segmentation on multiple scales. This approach was inspired by the statistical mechanics problem of "community detection" and its phase diagram. Specifically, the problem is
Externí odkaz:
http://arxiv.org/abs/1106.5793