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 $\theta_{13}$ 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