Quantum correction to classical gravitational interaction between two polarizable objects

When gravity is quantized, there inevitably exist quantum gravitational vacuum fluctuations which induce quadrupole moments in gravitationally polarizable objects and produce a quantum correction to the classical Newtonian interaction between them. Here, based upon linearized quantum gravity and the leading-order perturbation theory, we study, from a quantum field-theoretic prospect, this quantum correction between a pair of gravitationally polarizable objects treated as two-level harmonic oscillators. We find that the interaction potential behaves like $r^{-11}$ in the retarded regime and $r^{-10}$ in the near regime. Our result agrees with what was recently obtained in different approaches. Our study seems to indicate that linearized quantum gravity is robust in dealing with quantum gravitational effects at low energies.Comment: 10 pages. Accepted for publication in PL

Interaction between two gravitationally polarizable objects induced by thermal bath of gravitons

The quadrupole-quadrupole interaction between a pair of gravitationally polarizable objects induced by vacuum fluctuations of the quantum linearized gravitational field is first obtained with a relatively simple method, which is then used to investigate the contribution of thermal fluctuations of a bath of gravitons to the interaction at temperature $T$. Our result shows that, in the high temperature limit, the contribution of thermal fluctuations dominates over that of vacuum fluctuations and the interaction potential behaves like $T/ r^{10}$, where $r$ is the separation between the objects, and in the low temperature limit, the contribution of thermal fluctuations is proportional to $T^{10}/r$, which only provides a small correction to the interaction induced by zero-point fluctuations.Comment: 11 pages. Accepted by PR

Analysis of Pedestrian Safety Using Micro-simulation and Driving Simulator

In recent years, traffic agencies have begun to place emphasis on the importance of pedestrian safety. In the United States, nearly 70,000 pedestrians were reported injured in 2015. Although the number only account for 3% of all the people injured in traffic crashes, the number of pedestrian fatalities is still around 15% of total traffic fatalities. Furthermore, the state of Florida has consistently ranked as one of the worst states in terms of pedestrian crashes, injuries and fatalities. Therefore, it is befitting to focus on the pedestrian safety. This dissertation mainly focused on pedestrian safety at both midblock crossings and intersections by using micro-simulation and driving simulator. First, this study examined if the micro-simulation models (VISSIM and SSAM) could estimate pedestrian-vehicle conflicts at signalized intersections. A total of 42 video-hours were recorded at seven signalized intersections for field data collection. The observed conflicts from the field were used to calibrate VISSIM and replicate the conflicts. The calibrated and validated VISSIM model generated the pedestrian-vehicle conflicts from SSAM software using the vehicle trajectory data in VISSIM. The mean absolute percent error (MAPE) was used to determine the optimum TTC and PET thresholds for pedestrian-vehicle conflicts and linear regression analysis was used to study the correlation between the observed and simulated conflicts at the established thresholds. The results indicated the highest correlation between the simulated and observed conflicts when the TTC parameter was set at 2.7 and the PET was set at 8. Second, the driving simulator experiment was designed to assess pedestrian safety under different potential risk factors at both midblock crossings and intersections. Four potential risk factors were selected and 67 subjects participated in this experiment. In order to analyze pedestrian safety, the surrogate safety measures were examined to evaluate these pedestrian-vehicle conflicts. Third, by using the driving simulator data from the midblock crossing scenario, typical examples of drivers\u27 deceleration rate and the distance to crosswalk were summarized, which exhibited a clear drivers\u27 avoidance pattern during the vehicle pedestrian conflicts. This pattern was summarized into four stages, including the brake response stage, the deceleration adjustment stage, the maximum deceleration stage, and the brake release stage. In addition, the pedestrian-vehicle conflict prediction model was built to predict the minimum distance between vehicle and pedestrian. Finally, this study summarized the three different kinds of data that were to evaluate the pedestrian safety, including field data, simulation data, and driving simulator data. The process of combining of field data, simulation data, and simulator data was proposed. The process would show how the researches could evaluate the pedestrian safety by using the field observations, micro-simulation, and driving simulator

Analysis of taxi drivers\u27 driving behavior based on a driving simulator experiment

Due to comfort, convenience, and flexibility, taxis become more and more prevalent in China, especially in large cities. According to a survey reported by Beijing Traffic Development Research Center, there were 696 million taxi person-rides in Beijing in 2011. However, many violations and road crashes that were related to taxi drivers occurred more frequently. The survey showed that there were a total of 17,242 taxi violations happened in Beijing in only one month in 2003, which accounted for 56% of all drivers\u27 violations. Besides, taxi drivers also had a larger accident rate than other drivers, which showed that nearly 20% of taxi drivers had accidents each year. This study mainly focuses on investigating differences in driving behavior between taxi drivers and non-professional drivers. To examine the overall characteristics of taxi drivers and non-professional drivers, this study applied a hierarchical driving behavior assessment method to evaluate driving behaviors. This method is divided into three levels, including low-risk level, medium-risk level, and high-risk level. Low-risk level means the basic vehicle control. Medium-risk level refers to the vehicle dynamic decision. High-risk level represents the driver avoidance behavior when facing a potential crash. The Beijing Jiatong University (BJTU) driving simulator was applied to test different risk level scenarios which purpose is to find out the differences between taxi drivers and non-professional drivers on driving behaviors. Nearly 60 subjects, which include taxi drivers and non-professional drivers, were recruited in this experiment. Some statistical methods were applied to analyze the data and a logistic regression model was used to perform the high-risk level. The results showed that taxi drivers have more driving experience and their driving style is more conservative in the basic vehicle control level. For the car following behavior, taxi drivers have smaller following speed and larger gap compared to other drivers. For the yellow indication judgment behavior, although taxi drivers are slower than non-professional drivers when getting into the intersection, taxi drivers are more likely to run red light. For the lane changing behavior, taxi drivers\u27 lane changing time is longer than others and lane changing average speed of taxi drivers is lower than other drivers. Another different behavior in high-risk level is that taxi drivers are more inclined to turn the steering wheel when facing a potential crash compared to non-professional drivers. However, non-professional drivers have more abrupt deceleration behaviors if they have the same situation. According to the experiment results, taxi drivers have a smaller crash rate compared to non-professional drivers. Taxi drivers spend a large amount of time on the road so that their driving experience must exceed that of non-professional drivers, which may bring them more skills. It is also speculated that because taxi drivers spend long hours on the job they probably have developed a more relaxed attitude about congestion and they are less likely to be candidates for road rage and over aggressive driving habits

Multi-turn Dialogue Model Based on the Improved Hierarchical Recurrent Attention Network

When considering the multi-turn dialogue systems, the model needs to generate a natural and contextual response. At present, HRAN, one of the most advanced models for multi-turn dialogue problems, uses a hierarchical recurrent encoder-decoder combined with a hierarchical attention mechanism. However, for complex conversations, the traditional attention-based RNN does not fully understand the context, which results in attention to the wrong context that generates irrelevant responses. To solve this problem, we proposed an improved hierarchical recurrent attention network, a self-attention network (HSAN), instead of RNN, to learn word representations and utterances representations. Empirical studies on both Chinese and English datasets show that the proposed model has achieved significant improvement

Changing distribution of migrant population and influencing factors in urban China: economic transition, public policy, and amenities

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