On the numerical analysis of triplet pair production cross-sections and the mean energy of produced particles for modelling electron-photon cascade in a soft photon field

The double and single differential cross-sections with respect to positron and electron energies as well as the total cross-section of triplet production in the laboratory frame are calculated numerically in order to develop a Monte Carlo code for modelling electron-photon cascades in a soft photon field. To avoid numerical integration irregularities of the integrands, which are inherent to problems of this type, we have used suitable substitutions in combination with a modern powerful program code Mathematica allowing one to achieve reliable higher-precission results. The results obtained for the total cross-section closely agree with others estimated analytically or by a different numerical approach. The results for the double and single differential cross-sections turn out to be somewhat different from some reported recently. The mean energy of the produced particles, as a function of the characteristic collisional parameter (the electron rest frame photon energy), is calculated and approximated by an analytical expression that revises other known approximations over a wide range of values of the argument. The primary-electron energy loss rate due to triplet pair production is shown to prevail over the inverse Compton scattering loss rate at several ($\sim$2) orders of magnitude higher interaction energy than that predicted formerly.Comment: 18 pages, 8 figures, 2 tables, LaTex2e, Iopart.cls, Iopart12.clo, Iopams.st

Experimental analysis of sample-based maps for long-term SLAM

This paper presents a system for long-term SLAM (simultaneous localization and mapping) by mobile service robots and its experimental evaluation in a real dynamic environment. To deal with the stability-plasticity dilemma (the trade-off between adaptation to new patterns and preservation of old patterns), the environment is represented at multiple timescales simultaneously (5 in our experiments). A sample-based representation is proposed, where older memories fade at different rates depending on the timescale, and robust statistics are used to interpret the samples. The dynamics of this representation are analysed in a five week experiment, measuring the relative influence of short- and long-term memories over time, and further demonstrating the robustness of the approach

Inner Space Preserving Generative Pose Machine

Image-based generative methods, such as generative adversarial networks (GANs) have already been able to generate realistic images with much context control, specially when they are conditioned. However, most successful frameworks share a common procedure which performs an image-to-image translation with pose of figures in the image untouched. When the objective is reposing a figure in an image while preserving the rest of the image, the state-of-the-art mainly assumes a single rigid body with simple background and limited pose shift, which can hardly be extended to the images under normal settings. In this paper, we introduce an image "inner space" preserving model that assigns an interpretable low-dimensional pose descriptor (LDPD) to an articulated figure in the image. Figure reposing is then generated by passing the LDPD and the original image through multi-stage augmented hourglass networks in a conditional GAN structure, called inner space preserving generative pose machine (ISP-GPM). We evaluated ISP-GPM on reposing human figures, which are highly articulated with versatile variations. Test of a state-of-the-art pose estimator on our reposed dataset gave an accuracy over 80% on PCK0.5 metric. The results also elucidated that our ISP-GPM is able to preserve the background with high accuracy while reasonably recovering the area blocked by the figure to be reposed.Comment: http://www.northeastern.edu/ostadabbas/2018/07/23/inner-space-preserving-generative-pose-machine

Multiwavelength observations of Mkn 501 during the 1997 high state

During the observation period 1997, the nearby Blazar Mkn 501 showed extremely strong emission and high variability. We examine multiwavelength aspects of this event using radio, optical, soft and hard X-ray and TeV data. We concentrate on the medium-timescale variability of the broadband spectra, averaged over weekly intervals. We confirm the previously found correlation between soft and hard X-ray emission and the emission at TeV energies, while the source shows only minor variability at radio and optical wavelengths. The non-linear correlation between hard X-ray and TeV fluxes is consistent with a simple analytic estimate based on an SSC model in which Klein-Nishina effects are important for the highest-energy electrons in the jet, and flux variations are caused by variations of the electron density and/or the spectral index of the electron injection spectrum. The time-averaged spectra are fitted with a Synchrotron Self-Compton (SSC) dominated leptonic jet model, using the full Klein-Nishina cross section and following the self-consistent evolution of relativistic particles along the jet, accounting for gamma-gamma absorption and pair production within the source as well as due to the intergalactic infrared background radiation. The contribution from external inverse-Compton scattering is tightly constrained by the low maximum EGRET flux and found to be negligible at TeV energies. We find that high levels of the X-ray and TeV fluxes can be explained by a hardening of the energy spectra of electrons injected at the base of the jet, in remarkable contrast to the trend found for gamma-ray flares of the flat-spectrum radio quasar PKS 0528+134.Comment: accepted for publication in ApJ, 31 pages, 11 figure

Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image

We describe the first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and show that 2D joints alone carry a surprising amount of information about body shape. The problem is challenging because of the complexity of the human body, articulation, occlusion, clothing, lighting, and the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D body joint locations. We then fit (top-down) a recently published statistical body shape model, called SMPL, to the 2D joints. We do so by minimizing an objective function that penalizes the error between the projected 3D model joints and detected 2D joints. Because SMPL captures correlations in human shape across the population, we are able to robustly fit it to very little data. We further leverage the 3D model to prevent solutions that cause interpenetration. We evaluate our method, SMPLify, on the Leeds Sports, HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect to the state of the art.Comment: To appear in ECCV 201

Clothes size prediction from dressed-human silhouettes

© 2017, Springer International Publishing AG. We propose an effective and efficient way to automatically predict clothes size for users to buy clothes online. We take human height and dressed-human silhouettes in front and side views as input, and estimate 3D body sizes with a data-driven method. We adopt 20 body sizes which are closely related to clothes size, and use such 3D body sizes to get clothes size by searching corresponding size chart. Previous image-based methods need to calibrate camera to estimate 3D information from 2D images, because the same person has different appearances of silhouettes (e.g. size and shape) when the camera configuration (intrinsic and extrinsic parameters) is different. Our method avoids camera calibration, which is much more convenient. We set up our virtual camera and train the relationship between human height and silhouette size under this camera configuration. After estimating silhouette size, we regress the positions of 2D body landmarks. We define 2D body sizes as the distances between corresponding 2D body landmarks. Finally, we learn the relationship between 2D body sizes and 3D body sizes. The training samples for each regression process come from a database of 3D naked and dressed bodies created by previous work. We evaluate the whole procedure and each process of our framework. We also compare the performance with several regression models. The total time-consumption for clothes size prediction is less than 0.1, s and the average estimation error of body sizes is 0.824, cm, which can satisfy the tolerance for customers to shop clothes online

Neutrinos produced by ultrahigh-energy photons at high red shift

Some of the proposed explanations for the origin of ultrahigh-energy cosmic rays invoke new sources of energetic photons (e.g., topological defects, relic particles, etc.). At high red shift, when the cosmic microwave background has a higher temperature but the radio background is low, the ultrahigh-energy photons can generate neutrinos through pair-production of muons and pions. Neutrinos produced at high red shift by slowly evolving sources can be detected. Rapidly evolving sources of photons can be ruled out based on the existing upper limit on the neutrino flux.Comment: 4 pages, revtex; to appear in Phys. Rev. Let

Inclusive J/ψ production at mid-rapidity in pp collisions at √s = 5.02 TeV

Inclusive J/ψ production is studied in minimum-bias proton-proton collisions at a centre-of-mass energy of s = 5.02 TeV by ALICE at the CERN LHC. The measurement is performed at mid-rapidity (|y| < 0.9) in the dielectron decay channel down to zero transverse momentum pT, using a data sample corresponding to an integrated luminosity of Lint = 19.4 ± 0.4 nb−1. The measured pT-integrated inclusive J/ψ production cross sec- tion is dσ/dy = 5.64 ± 0.22(stat.) ± 0.33(syst.) ± 0.12(lumi.) μb. The pT-differential cross section d2σ/dpTdy is measured in the pT range 0–10 GeV/c and compared with state-of- the-art QCD calculations. The J/ψ 〈pT〉 and 〈pT2〉 are extracted and compared with results obtained at other collision energies. [Figure not available: see fulltext.]