On the impact of connected automated vehicles in freeway work zones: A cooperative cellular automata model based approach

PurposeFreeway work zones have been traffic bottlenecks that lead to a series of problems, including long travel time, high-speed variation, driver’s dissatisfaction and traffic congestion. This research aims to develop a collaborative component of connected and automated vehicles (CAVs) to alleviate negative effects caused by work zones. Design/methodology/approach\ua0The proposed cooperative component is incorporated in a cellular automata model to examine how and to what scale CAVs can help in improving traffic operations. Findings\ua0Simulation results show that, with the proposed component and penetration of CAVs, the average performances (travel time, safety and emission) can all be improved and the stochasticity of performances will be minimized too. Originality/valueTo the best of the authors’ knowledge, this is the first research that develops a cooperative mechanism of CAVs to improve work zone performance

Subdomain Adaptation with Manifolds Discrepancy Alignment

Reducing domain divergence is a key step in transfer learning problems. Existing works focus on the minimization of global domain divergence. However, two domains may consist of several shared subdomains, and differ from each other in each subdomain. In this paper, we take the local divergence of subdomains into account in transfer. Specifically, we propose to use low-dimensional manifold to represent subdomain, and align the local data distribution discrepancy in each manifold across domains. A Manifold Maximum Mean Discrepancy (M3D) is developed to measure the local distribution discrepancy in each manifold. We then propose a general framework, called Transfer with Manifolds Discrepancy Alignment (TMDA), to couple the discovery of data manifolds with the minimization of M3D. We instantiate TMDA in the subspace learning case considering both the linear and nonlinear mappings. We also instantiate TMDA in the deep learning framework. Extensive experimental studies demonstrate that TMDA is a promising method for various transfer learning tasks

Effects of the Aidi Dripping Pills on Immune Functions of the Tumor-bearing Mouse

ObjectiveTo study the effects of Aidi Dripping Pills on immune functions of the tumor-bearing mouse on the basis of the previous experimental studies on its tumor-inhibiting and life-prolonging effects.MethodsBy using the transplantation tumor mouse models, the effects of Aidi Dripping Pills on the lymphocyte transformation rate and the hemolysin formation in the S180 tumor-bearing mice, and on the phagocytic function of macrophages in the abdominal cavity of H22 tumor-bearing mice were investigated.ResultsIn the 2.25 g/kg and 1.125 g/kg Aidi Dripping Pills groups, the lymphocyte transformation rates in the S180 tumor-bearing mice were significantly higher than that of the control group (P<0.01). In all the Aidi Dripping Pills groups, HC50 significantly increased (P<0.01 or P<0.05), carbon granular clearance significantly raised, and both the phagocytic index and phagocytic coefficient were significantly higher than those in the control group (P<0.01 or P<0.05).ConclusionThe Aidi Dripping Pills can significantly increase the cellular immune function, the humoral immune function and the phagocytic function of the mononuclear-macrophages, so it may show anti-tumor effects by enhancing the function of the reticuloendothelial system

Towards automated infographic design: Deep learning-based auto-extraction of extensible timeline

Designers need to consider not only perceptual effectiveness but also visual styles when creating an infographic. This process can be difficult and time consuming for professional designers, not to mention non-expert users, leading to the demand for automated infographics design. As a first step, we focus on timeline infographics, which have been widely used for centuries. We contribute an end-to-end approach that automatically extracts an extensible timeline template from a bitmap image. Our approach adopts a deconstruction and reconstruction paradigm. At the deconstruction stage, we propose a multi-task deep neural network that simultaneously parses two kinds of information from a bitmap timeline: 1) the global information, i.e., the representation, scale, layout, and orientation of the timeline, and 2) the local information, i.e., the location, category, and pixels of each visual element on the timeline. At the reconstruction stage, we propose a pipeline with three techniques, i.e., Non-Maximum Merging, Redundancy Recover, and DL GrabCut, to extract an extensible template from the infographic, by utilizing the deconstruction results. To evaluate the effectiveness of our approach, we synthesize a timeline dataset (4296 images) and collect a real-world timeline dataset (393 images) from the Internet. We first report quantitative evaluation results of our approach over the two datasets. Then, we present examples of automatically extracted templates and timelines automatically generated based on these templates to qualitatively demonstrate the performance. The results confirm that our approach can effectively extract extensible templates from real-world timeline infographics.Comment: 10 pages, Automated Infographic Design, Deep Learning-based Approach, Timeline Infographics, Multi-task Mode

(4aR,8aR)-2,3-Diphenyl-4a,5,6,7,8,8a-hexa­hydro­quinoxaline

The title mol­ecule, C20H20N2, is chiral; the absolute configuration follows from the known chirality of the input reagents. In addition to van der Waals forces, C—H⋯π ring inter­actions are also present. The angle between the planes of the phenyl rings is 65.6 (1)°. The heterocyclic ring of the quinoxaline system has a twist-boat configuration, while the cyclohexane ring has a chair configuration