127,097 research outputs found
Self Similar Spherical Collapse Revisited: a Comparison between Gas and Dark Matter Dynamics
We reconsider the collapse of cosmic structures in an Einstein-de Sitter
Universe, using the self similar initial conditions of Fillmore & Goldreich
(1984). We first derive a new approximation to describe the dark matter
dynamics in spherical geometry, that we refer to the "fluid approach". This
method enables us to recover the self-similarity solutions of Fillmore &
Goldreich for dark matter. We derive also new self-similarity solutions for the
gas. We thus compare directly gas and dark matter dynamics, focusing on the
differences due to their different dimensionalities in velocity space. This
work may have interesting consequences for gas and dark matter distributions in
large galaxy clusters, allowing to explain why the total mass profile is always
steeper than the X-ray gas profile. We discuss also the shape of the dark
matter density profile found in N-body simulations in terms of a change of
dimensionality in the dark matter velocity space. The stable clustering
hypothesis has been finally considered in the light of this analytical
approach.Comment: 14 pages, 2 figures, accepted for publication in The Astrophysical
Journa
FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras
In this paper, we develop deep spatio-temporal neural networks to
sequentially count vehicles from low quality videos captured by city cameras
(citycams). Citycam videos have low resolution, low frame rate, high occlusion
and large perspective, making most existing methods lose their efficacy. To
overcome limitations of existing methods and incorporate the temporal
information of traffic video, we design a novel FCN-rLSTM network to jointly
estimate vehicle density and vehicle count by connecting fully convolutional
neural networks (FCN) with long short term memory networks (LSTM) in a residual
learning fashion. Such design leverages the strengths of FCN for pixel-level
prediction and the strengths of LSTM for learning complex temporal dynamics.
The residual learning connection reformulates the vehicle count regression as
learning residual functions with reference to the sum of densities in each
frame, which significantly accelerates the training of networks. To preserve
feature map resolution, we propose a Hyper-Atrous combination to integrate
atrous convolution in FCN and combine feature maps of different convolution
layers. FCN-rLSTM enables refined feature representation and a novel end-to-end
trainable mapping from pixels to vehicle count. We extensively evaluated the
proposed method on different counting tasks with three datasets, with
experimental results demonstrating their effectiveness and robustness. In
particular, FCN-rLSTM reduces the mean absolute error (MAE) from 5.31 to 4.21
on TRANCOS, and reduces the MAE from 2.74 to 1.53 on WebCamT. Training process
is accelerated by 5 times on average.Comment: Accepted by International Conference on Computer Vision (ICCV), 201
Understanding Traffic Density from Large-Scale Web Camera Data
Understanding traffic density from large-scale web camera (webcam) videos is
a challenging problem because such videos have low spatial and temporal
resolution, high occlusion and large perspective. To deeply understand traffic
density, we explore both deep learning based and optimization based methods. To
avoid individual vehicle detection and tracking, both methods map the image
into vehicle density map, one based on rank constrained regression and the
other one based on fully convolution networks (FCN). The regression based
method learns different weights for different blocks in the image to increase
freedom degrees of weights and embed perspective information. The FCN based
method jointly estimates vehicle density map and vehicle count with a residual
learning framework to perform end-to-end dense prediction, allowing arbitrary
image resolution, and adapting to different vehicle scales and perspectives. We
analyze and compare both methods, and get insights from optimization based
method to improve deep model. Since existing datasets do not cover all the
challenges in our work, we collected and labelled a large-scale traffic video
dataset, containing 60 million frames from 212 webcams. Both methods are
extensively evaluated and compared on different counting tasks and datasets.
FCN based method significantly reduces the mean absolute error from 10.99 to
5.31 on the public dataset TRANCOS compared with the state-of-the-art baseline.Comment: Accepted by CVPR 2017. Preprint version was uploaded on
http://welcome.isr.tecnico.ulisboa.pt/publications/understanding-traffic-density-from-large-scale-web-camera-data
Global well-posedness for the critical 2D dissipative quasi-geostrophic equation
We give an elementary proof of the global well-posedness for the critical 2D
dissipative quasi-geostrophic equation. The argument is based on a non-local
maximum principle involving appropriate moduli of continuity.Comment: 7 page
SO(3) Gauge Symmetry and Nearly Tri-bimaximal Neutrino Mixing
In this note I mainly focus on the neutrino physics part in my talk and
report the most recent progress made in \cite{YLW0}. It is seen that the
Majorana features of neutrinos and SO(3) gauge flavor symmetry can
simultaneously explain the smallness of neutrino masses and nearly
tri-bimaximal neutrino mixing when combining together with the mechanism of
approximate global U(1) family symmetry. The mixing angle and
CP-violating phase are in general nonzero and testable experimentally at the
allowed sensitivity. The model also predicts the existence of vector-like
Majorana neutrinos and charged leptons as well as new Higgs bosons, some of
them can be light and explored at the LHC and ILC.Comment: 8 pages, invited talk, contribute to the Proceedings of the 4th
International Conference on Flavor Physics (ICFP2007
New critical frontiers for the Potts and percolation models
We obtain the critical threshold for a host of Potts and percolation models
on lattices having a structure which permits a duality consideration. The
consideration generalizes the recently obtained thresholds of Scullard and Ziff
for bond and site percolation on the martini and related lattices to the Potts
model and to other lattices.Comment: 9 pages, 5 figure
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