Multispectral Deep Neural Networks for Pedestrian Detection

Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving. We deeply analyze Faster R-CNN for multispectral pedestrian detection task and then model it into a convolutional network (ConvNet) fusion problem. Further, we discover that ConvNet-based pedestrian detectors trained by color or thermal images separately provide complementary information in discriminating human instances. Thus there is a large potential to improve pedestrian detection by using color and thermal images in DNNs simultaneously. We carefully design four ConvNet fusion architectures that integrate two-branch ConvNets on different DNNs stages, all of which yield better performance compared with the baseline detector. Our experimental results on KAIST pedestrian benchmark show that the Halfway Fusion model that performs fusion on the middle-level convolutional features outperforms the baseline method by 11% and yields a missing rate 3.5% lower than the other proposed architectures.Comment: 13 pages, 8 figures, BMVC 2016 ora

Tachyon mass, c-function and Counting localized degrees of freedom

We discuss the localized tachyon condensation in the non-supersymmetric orbifold theories by taking the cosmological constant as the measure of degrees of freedom (d.o.f). We first show asymptotic density of state is not a proper quantity to count the 'localized' d.o.f. We then show that localized d.o.f lead us a c-function given by the lightest tachyon mass, which turns out to be the same as the tachyon potential recently suggested by Dabholkar and Vafa. We also argue that delocalized d.o.f also encode information on the process of localized tachyon condensation in the g-function, based on the fact that the global geometry of the orbifolds is completely determined by the local geometry around the fixed points. For type II, both c- and g-function respect the stability of the supersymmetric models and both allow all the process suggested by Adams, Polchinski and Silverstein.Comment: 15 pages, 2 figures, v2: typo corrected, reference added. v3: abstract stretche

Numerical action reconstruction of the dynamical history of dark matter haloes in N-body simulations

We test the ability of the numerical action method (NAM) to recover the individual orbit histories of mass tracers in an expanding universe in a region of radius 26Mpc/h, given the masses and redshift-space coordinates at the present epoch. The mass tracers are represented by dark matter haloes identified in a high resolution N-body simulation of the standard LCDM cosmology. Since previous tests of NAM at this scale have traced the underlying distribution of dark matter particles rather than extended haloes, our study offers an assessment of the accuracy of NAM in a scenario which more closely approximates the complex dynamics of actual galaxy haloes. We show that NAM can recover present-day halo distances with typical errors of less than 3 per cent, compared to 5 per cent errors assuming Hubble flow distances. The total halo mass and the linear bias were both found to be constained at the 50 per cent level. The accuracy of individual orbit reconstructions was limited by the inability of NAM, in some instances, to correctly model the positions of haloes at early times solely on the basis of the redshifts, angular positions, and masses of the haloes at the present epoch. Improvements in the quality of NAM reconstructions may be possible using the present-day three-dimensional halo velocities and distances to further constrain the dynamics. This velocity data is expected to become available for nearby galaxies in the coming generations of observations by SIM and GAIA.Comment: 12 pages, 9 figures. submitted to MNRA

Solving a Generalized Heron Problem by means of Convex Analysis

The classical Heron problem states: \emph{on a given straight line in the plane, find a point $C$ such that the sum of the distances from $C$ to the given points $A$ and $B$ is minimal}. This problem can be solved using standard geometry or differential calculus. In the light of modern convex analysis, we are able to investigate more general versions of this problem. In this paper we propose and solve the following problem: on a given nonempty closed convex subset of $\R^s$, find a point such that the sum of the distances from that point to $n$ given nonempty closed convex subsets of $\R^s$ is minimal

Forecasting Natural Hazards and Disasters in Selected Southeast Asian Countries: The Need for Cooperative Action

With Southeast Asian countries like Cambodia, Indonesia, Lao PDR, the Philippines, and Viet Nam experiencing the increasing occurrence of weather and climate-related hazards and disasters in recent years, some of which they commonly share due to their close proximity to each other, it thus becomes important for them to cooperate and coordinate with one another in addressing said hazards and disasters.Southeast Asia, Philippines, weather, climate-related disasters, natural disasters

Superpixel-based Semantic Segmentation Trained by Statistical Process Control

Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks. However, considering that neighboring pixels are heavily dependent on each other, both learning and testing of these methods have a lot of redundant operations. To resolve this problem, the proposed network is trained and tested with only 0.37% of total pixels by superpixel-based sampling and largely reduced the complexity of upsampling calculation. The hypercolumn feature maps are constructed by pyramid module in combination with the convolution layers of the base network. Since the proposed method uses a very small number of sampled pixels, the end-to-end learning of the entire network is difficult with a common learning rate for all the layers. In order to resolve this problem, the learning rate after sampling is controlled by statistical process control (SPC) of gradients in each layer. The proposed method performs better than or equal to the conventional methods that use much more samples on Pascal Context, SUN-RGBD dataset.Comment: Accepted in British Machine Vision Conference (BMVC), 201

A second-order continuity domain-decomposition technique based on integrated Chebyshev polynomials for two-dimensional elliptic problems

This paper presents a second-order continuity non-overlapping domain decomposition (DD) technique for numerically solving second-order elliptic problems in two-dimensional space. The proposed DD technique uses integrated Chebyshev polynomials to represent the solution in subdomains. The constants of integration are utilized to impose continuity of the second-order normal derivative of the solution at the interior points of subdomain interfaces. To also achieve a C2 (C squared) function at the intersection of interfaces, two additional unknowns are introduced at each intersection point. Numerical results show that the present DD method yields a higher level of accuracy than conventional DD techniques based on differentiated Chebyshev polynomials

The Impact of Increased Import Competition from the People’s Republic of China on Income Inequality and Household Welfare in Viet Nam

This paper examines the surge of imports from the PRC to Viet Nam from 2000 to 2014 in order to evaluate the effects of increased exposure to trade with the PRC on income inequality and household welfare in Viet Nam. Using household level data from the Viet Nam Household Living Standard Survey and combining it with measures of trade exposure, we find that increased imports led to a fall in inequality at the provincial and district level. We distinguish between intermediate and final goods and find similar results. In order to better understand the relative gains and losses across income groups, we apply a quantile regression approach. Our results indicate that increased imports were more often positively correlated with household income for households located in the lower quantiles. In contrast, for households in the upper quantiles the correlation is either negative or less pronounced