Po-production in lead: A benchmark between Geant4, FLUKA and MCNPX

On the last SATIF a comparison between the measured activities of the polonium isotopes Po-208, Po-209 and Po-210 and the simulated results using MCNPX2.7.0 was presented. The lead samples were cut from the SINQ spallation target at the Paul Scherrer Institut (PSI) and irradiated in 2000/2001 by 575 MeV protons. The Po-isotopes were separated using radiochemical methods by the group of D. Schumann at PSI and measured. Choosing the default model in MCNPX, Bertini-Dresner, the prediction underestimated the measured activities by up to several orders of magnitude. Therefore the Li\`ege intranuclear-cascade model (INCL4.6) coupled to the de-excitation model ABLA07 were implemented into MCNPX2.7.0 and very good agreement was found to the measurement. The reason for the disagreement was traced back to a suppression of alpha reactions on the lead isotopes leading to Po and neglecting the triton capture on Pb-208, which leads to Pb-210 and decays into Po-210 with a much longer life time (22.3 years) than the decay of Po-210 itself (138 days). The prediction of the Po-isotope activities turns out to be a sensitive test for models and codes as it requires the accurate treatment of reaction channels not only with neutrons, protons and pions but also with alphas and tritons, which are not considered in intra-nuclear cascade models of the first generation. Therefore it was decided to perform a benchmark by comparing the results obtained with MCNPX2.7.0 using INCL4.6/ABLA07 to the predictions of FLUKA and Geant4. Since the model of the SINQ spallation source requires an elaborate geometry a toy model was setup. The toy model has a simplified geometry preserving the main features of the original geometry. The results for the activities of the three Po-isotopes and Pb-210 as well as the energy spectra for alphas and tritons obtained with the three particle transport Monte Carlo codes are presented.Comment: 15 pages, 11 figures, Presented paper at the 13th Meeting of the task-force on Shielding aspects of Accelerators, Targets and Irradiation Facilities (SATIF-13), HZDR, October 10-12, 2016, Dresden, German

Simulation of time-dependent compressible viscous flows using central and upwind-biased finite-difference techniques

Four time-dependent numerical algorithms for the prediction of unsteady, viscous compressible flows are compared. The analyses are based on the time-dependent Navier-Stokes equations expressed in a generalized curvilinear coordinate system. The methods tested include three traditional central-difference algorithms, and a new upwind-biased algorithm utilizing an implicit, time-marching relaxation procedure based on Newton iteration. Aerodynamic predictions are compared for internal duct-type flows and cascaded turbomachinery flows with spatial periodicity. Two-dimensional internal duct-type flow predictions are performed using an H-type grid system. Planar cascade flows are analyzed using a numerically generated, capped, body-centered, O-type grid system. Initial results are presented for critical and supercritical steady inviscid flow about an isolated cylinder. These predictions are verified by comparisons with published computational results from a similar calculation. Results from each method are then further verified by comparison with experimental data for the more demanding case of flow through a two-dimensional turbine cascade. Inviscid predictions are presented for two different transonic turbine cascade flows. All of the codes demonstrate good agreement for steady viscous flow about a high-turning turbine vane with a leading edge separation. The viscous flow results show a marked improvement over the inviscid results in the region near the separation bubble. Viscous flow results are then further verified in finer detail through comparison with the similarity solution for a flat plate boundary-layer flow. The usefulness of the schemes for the prediction of unsteady flows is demonstrated by examining the unsteady viscous flow resulting from a sinusoidally oscillating flat plate in the vicinity of a stagnant fluid. Predicted results are compared with the analytical solution for this flow. Finally, numerical results are compared with flow visualization and experimental data for the unsteady flow resulting from an impulsively started cylinder. Each algorithm demonstrates unique qualities which may be interpreted as either advantageous or disadvantageous, making it difficult to select an optimum scheme. The preferred method is perhaps best chosen based on the experience of the user and the particular application

Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion

Facial landmark detection, head pose estimation, and facial deformation analysis are typical facial behavior analysis tasks in computer vision. The existing methods usually perform each task independently and sequentially, ignoring their interactions. To tackle this problem, we propose a unified framework for simultaneous facial landmark detection, head pose estimation, and facial deformation analysis, and the proposed model is robust to facial occlusion. Following a cascade procedure augmented with model-based head pose estimation, we iteratively update the facial landmark locations, facial occlusion, head pose and facial de- formation until convergence. The experimental results on benchmark databases demonstrate the effectiveness of the proposed method for simultaneous facial landmark detection, head pose and facial deformation estimation, even if the images are under facial occlusion.Comment: International Conference on Computer Vision and Pattern Recognition, 201

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

We focus on the challenging task of real-time semantic segmentation in this paper. It finds many practical applications and yet is with fundamental difficulty of reducing a large portion of computation for pixel-wise label inference. We propose an image cascade network (ICNet) that incorporates multi-resolution branches under proper label guidance to address this challenge. We provide in-depth analysis of our framework and introduce the cascade feature fusion unit to quickly achieve high-quality segmentation. Our system yields real-time inference on a single GPU card with decent quality results evaluated on challenging datasets like Cityscapes, CamVid and COCO-Stuff.Comment: ECCV 201

TiDeH: Time-Dependent Hawkes Process for Predicting Retweet Dynamics

Online social networking services allow their users to post content in the form of text, images or videos. The main mechanism driving content diffusion is the possibility for users to re-share the content posted by their social connections, which may then cascade across the system. A fundamental problem when studying information cascades is the possibility to develop sound mathematical models, whose parameters can be calibrated on empirical data, in order to predict the future course of a cascade after a window of observation. In this paper, we focus on Twitter and, in particular, on the temporal patterns of retweet activity for an original tweet. We model the system by Time-Dependent Hawkes process (TiDeH), which properly takes into account the circadian nature of the users and the aging of information. The input of the prediction model are observed retweet times and structural information about the underlying social network. We develop a procedure for parameter optimization and for predicting the future profiles of retweet activity at different time resolutions. We validate our methodology on a large corpus of Twitter data and demonstrate its systematic improvement over existing approaches in all the time regimes.Comment: The manuscript has been accepted in the 10th International AAAI Conference on Web and Social Media (ICWSM 2016

SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity

Social networking websites allow users to create and share content. Big information cascades of post resharing can form as users of these sites reshare others' posts with their friends and followers. One of the central challenges in understanding such cascading behaviors is in forecasting information outbreaks, where a single post becomes widely popular by being reshared by many users. In this paper, we focus on predicting the final number of reshares of a given post. We build on the theory of self-exciting point processes to develop a statistical model that allows us to make accurate predictions. Our model requires no training or expensive feature engineering. It results in a simple and efficiently computable formula that allows us to answer questions, in real-time, such as: Given a post's resharing history so far, what is our current estimate of its final number of reshares? Is the post resharing cascade past the initial stage of explosive growth? And, which posts will be the most reshared in the future? We validate our model using one month of complete Twitter data and demonstrate a strong improvement in predictive accuracy over existing approaches. Our model gives only 15% relative error in predicting final size of an average information cascade after observing it for just one hour.Comment: 10 pages, published in KDD 201