The Influence of Culture on Attitudes Towards Humorous Advertising

open access articl

Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation

We rethink a well-know bottom-up approach for multi-person pose estimation and propose an improved one. The improved approach surpasses the baseline significantly thanks to (1) an intuitional yet more sensible representation, which we refer to as body parts to encode the connection information between keypoints, (2) an improved stacked hourglass network with attention mechanisms, (3) a novel focal L2 loss which is dedicated to hard keypoint and keypoint association (body part) mining, and (4) a robust greedy keypoint assignment algorithm for grouping the detected keypoints into individual poses. Our approach not only works straightforwardly but also outperforms the baseline by about 15% in average precision and is comparable to the state of the art on the MS-COCO test-dev dataset. The code and pre-trained models are publicly available online.Comment: Accepted by AAAI 2020 (the Thirty-Fourth AAAI Conference on Artificial Intelligence

An Improved Scheduling with Advantage Actor-Critic for Storm Workloads

Various resources as the essential elements of data centers, and the completion time is vital to users. In terms of the persistence, the periodicity and the spatial-temporal dependence of stream workload, a new Storm scheduler with Advantage Actor-Critic is proposed to improve resource utilization for minimizing the completion time. A new weighted embedding with a Graph Neural Network is designed to depend on the features of a job comprehensively, which includes the dependence, the types and the positions of tasks in a job. An improved Advantage Actor-Critic integrating task chosen and executor assignment is proposed to schedule tasks to executors in order to better resource utilization. Then the status of tasks and executors are updated for the next scheduling. Compared to existing methods, experimental results show that the proposed Storm scheduler improves resource utilization. The completion time is reduced by almost 17\% on the TPC-H data set and reduced by almost 25\% on the Alibaba data set

Estimation of soil and vegetation temperatures with multiangular thermal infrared observations: IMGRASS, HEIFE, and SGP 1997 experiments

The potential of directional observations in the thermal infrared region for land surface studies is a largely uncharted area of research. The availability of the dual-view Along Track Scanning Radiometer (ATSR) observations led to explore new opportunities in this direction. In the context of studies on heat transfer at heterogeneous land surfaces, multiangular thermal infrared (TIR) observations offer the opportunity of overcoming fundamental difficulties in modeling sparse canopies. Three case studies were performed on the estimation of the component temperatures of foliage and soil. The first one included the use of multi-temporal field measurements at view angles of 0°, 23° and 52°. The second and third one were done with directional ATSR observations at view angles of 0° and 53° only. The first one was a contribution to the Inner-Mongolia Grassland Atmosphere Surface Study (IMGRASS) experiment in China, the second to the Hei He International Field Experiment (HEIFE) in China and the third one to the Southern Great Plains 1997 (SGP 1997) experiment in Oklahoma, United States. The IMGRASS experiment provided useful insights on the applicability of a simple linear mixture model to the analysis of observed radiance. The HEIFE case study was focused on the large oasis of Zhang-Ye and led to useful estimates of soil and vegetation temperatures. The SGP 1997 contributed a better understanding of the impact of spatial heterogeneity on the accuracy of retrieved foliage and soil temperatures. Limitations in the approach due to varying radiative and boundary layer forcing and to the difference in spatial resolution between the forward and the nadir view are evaluated through a combination of modeling studies and analysis of field data

Hybrid Renormalization for Quasi Distribution Amplitudes of A Light Baryon

We develop a hybrid scheme to renormalize quasi distribution amplitudes of a light baryon on the lattice, which combines the self-renormalization and ratio scheme. By employing self-renormalization, the UV divergences and linear divergence at large spatial separations in quasi distribution amplitudes are removed without introducing extra nonperturbative effects, while making a ratio with respect to the zero-momentum matrix element can properly remove the UV divergences in small spatial separations. As a specific application, distribution amplitudes of the $\Lambda$ baryon made of $uds$ are investigated, and the requisite equal-time correlators, which define quasi distribution amplitudes in coordinate space, are perturbatively calculated up to the next-to-leading order in strong coupling constant $\alpha_s$. These perturbative equal-time correlators are used to convert lattice QCD matrix elements to the continuum space during the renormalization process. Subsequently, quasi distribution amplitudes are matched onto lightcone distribution amplitudes by integrating out hard modes and the corresponding hard kernels are derived up to next-to-leading order in $\alpha_s$ including the hybrid counterterms. These results are valuable in the lattice-based investigation of the lightcone distribution amplitudes of a light baryon from the first principles of QCD.Comment: 25 pages, 4 figure