Multi-agent Animation Techniques for Traffic Simulation in Urban Environments

We describe an approach of using the multi-agent animation method for traffic simulations. The presented simulation system uses an individual-based agent behaviour model and discrete cell-based roadway configurations. The behaviour model defines the driving characteristics of each vehicle agent in a simulated traffic network, and the cell-based configurations allow real-time dynamic path planning and efficient traffic flow simulations. By incorporating the advantages of discrete cellular automation algorithms and the continuous model of fluid dynamics, our 3D virtual reality traffic simulation system achieves realistic and accurate simulations in virtual environments

AN ANALYSIS OF PROJECTION ANGLE OF THE LONG JUMP

The purpose of this study was to evaluate the optimum projection angle and to determine the characteristics of optimal takeoff techniques. Subjects of this study included 12 male and 12 female elite long jumpers. Their performance was filmed at long jump event of the 8th China National Games. Mathematical methods, including regression analysis were used to determine the relationship of projection angle with projection velocity and projection distance, then to calculate the optimum projection angle. It was concluded that an increase in projection angle, at the expense of a normal loss of projection velocity, would benefit the projection distance according to the capability of Chinese elite long jumpers. It was also concluded that the takeoff leg should extend forward more before active landing in order to avoid extremely small segment angles of the shank

Overexpression of lncRNA UCA1 promotes osteosarcoma progression and correlates with poor prognosis

AbstractLong non-coding RNAs (lncRNAs) have been proved to play important roles in the tumorigenesis and development of several human malignancies. Our study aims to investigate the expression and function of lncRNA-UCA1 in osteosarcoma. lncRNA-UCA1 expression was detected in osteosarcoma tissues and cell lines by using qRT-PCR. Association between lncRNA-UCA1 levels and clinicopathological factors and patient's prognosis was analyzed. The roles of lncRNA-UCA1 in regulating osteosarcoma cell proliferation, apoptosis, migration, and invasion were evaluated in vitro. We found that lncRNA-UCA1 expression was upregulated in osteosarcoma tissues and cell lines. High lncRNA-UCA1 expression was significantly correlated with large tumor size, high tumor grade, positive distant metastasis, and advanced clinical stage. Multivariate regression analysis identified lncRNA-UCA1 overexpression as an independent unfavorable prognostic factor. lncRNA-UCA1 knockdown inhibited osteosarcoma cell proliferation, promoted cell apoptosis, and suppressed cell invasion and migration, whereas lncRNA-UCA1 overexpression showed opposite effects. These findings suggested that lncRNA-UCA1 may contribute to osteosarcoma initiation and progression, and would be not only a novel prognostic marker but also a potential therapeutic target for this disease

Spatial Propagation in Nonlocal Dispersal Fisher-KPP Equations

In this paper we focus on three problems about the spreading speeds of nonlocal dispersal Fisher-KPP equations. First, we study the signs of spreading speeds and find that they are determined by the asymmetry level of the nonlocal dispersal and $f'(0)$, where $f$ is the reaction function. This indicates that asymmetric dispersal can influence the spatial dynamics in three aspects: it can determine the spatial propagation directions of solutions, influence the stability of equilibrium states, and affect the monotone property of solutions. Second, we give an improved proof of the spreading speed result by constructing new lower solutions and using the new "forward-backward spreading" method. Third, we establish the relationship between spreading speed and exponentially decaying initial data. Our result demonstrates that when dispersal is symmetric, spreading speed decreases along with the increase of the exponentially decaying rate. In addition, the results on the signs of spreading speeds are applied to two special cases where we present more details of the influence of asymmetric dispersal

Linear Context Transform Block

Squeeze-and-Excitation (SE) block presents a channel attention mechanism for modeling global context via explicitly capturing dependencies across channels. However, we are still far from understanding how the SE block works. In this work, we first revisit the SE block, and then present a detailed empirical study of the relationship between global context and attention distribution, based on which we propose a simple yet effective module, called Linear Context Transform (LCT) block. We divide all channels into different groups and normalize the globally aggregated context features within each channel group, reducing the disturbance from irrelevant channels. Through linear transform of the normalized context features, we model global context for each channel independently. The LCT block is extremely lightweight and easy to be plugged into different backbone models while with negligible parameters and computational burden increase. Extensive experiments show that the LCT block outperforms the SE block in image classification task on the ImageNet and object detection/segmentation on the COCO dataset with different backbone models. Moreover, LCT yields consistent performance gains over existing state-of-the-art detection architectures, e.g., 1.5$\sim$1.7% AP$^{bbox}$ and 1.0$\sim$1.2% AP$^{mask}$ improvements on the COCO benchmark, irrespective of different baseline models of varied capacities. We hope our simple yet effective approach will shed some light on future research of attention-based models.Comment: AAAI-2020 accepte