6,104 research outputs found
Connectivity Threshold for random subgraphs of the Hamming graph
We study the connectivity of random subgraphs of the -dimensional Hamming
graph , which is the Cartesian product of complete graphs on
vertices. We sample the random subgraph with an i.i.d.\ Bernoulli bond
percolation on with parameter . We identify the window of the
transition: when the probability that the graph is
connected goes to , while when it converges to
.
We also investigate the connectivity probability inside the critical window,
namely when .
We find that the threshold does not depend on , unlike the phase
transition of the giant connected component the Hamming graph (see [Bor et al,
2005]). Within the critical window, the connectivity probability does depend on
d. We determine how.Comment: 10 pages, no figure
Generalized off-equilibrium fluctuation-dissipation relations in random Ising systems
We show that the numerical method based on the off-equilibrium
fluctuation-dissipation relation does work and is very useful and powerful in
the study of disordered systems which show a very slow dynamics. We have
verified that it gives the right information in the known cases (diluted
ferromagnets and random field Ising model far from the critical point) and we
used it to obtain more convincing results on the frozen phase of
finite-dimensional spin glasses. Moreover we used it to study the Griffiths
phase of the diluted and the random field Ising models.Comment: 20 pages, 10 figures, uses epsfig.sty. Partially presented at
StatPhys XX in a talk by one of the authors (FRT). Added 1 reference in the
new versio
Am I Done? Predicting Action Progress in Videos
In this paper we deal with the problem of predicting action progress in
videos. We argue that this is an extremely important task since it can be
valuable for a wide range of interaction applications. To this end we introduce
a novel approach, named ProgressNet, capable of predicting when an action takes
place in a video, where it is located within the frames, and how far it has
progressed during its execution. To provide a general definition of action
progress, we ground our work in the linguistics literature, borrowing terms and
concepts to understand which actions can be the subject of progress estimation.
As a result, we define a categorization of actions and their phases. Motivated
by the recent success obtained from the interaction of Convolutional and
Recurrent Neural Networks, our model is based on a combination of the Faster
R-CNN framework, to make frame-wise predictions, and LSTM networks, to estimate
action progress through time. After introducing two evaluation protocols for
the task at hand, we demonstrate the capability of our model to effectively
predict action progress on the UCF-101 and J-HMDB datasets
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