UROP Proceedings 2021-22

School of Science Department of Physics 65 Neural Information Processing Supervisor: WONG Michael Kwok Yee / PHYS Student: HO Man Fung / PHYS-IRE Course: UROP3100, Fall UROP4100, Spring In last semester’s report, a newmethod of calculating the past history inputs was introduced. However, upon further inspection, it was discovered that this method may cause information leakage, which where unobtainable future data is used for the prediction of current result. Therefore, amendments of the calculation method will be introduced in this report to avoid this problem. In addition, using the feature importance score, the best sets of inputs to be used in the prediction were constructed. Using the newly constructed input set, it was found that accuracy of prediction for multiple machines are better than single machine by about 10% on average. Neural Information Processing Supervisor: WONG Michael Kwok Yee / PHYS Student: LAO Zhiquan / PHYS-IRE Course: UROP1100, Fall UROP2100, Spring The traffic data of Taiwan Highway System collected by the electronic toll sensors are studied. Traffic latency of the highway system is studied through XGBoost. Previous UROP studies have separated the data into multiple clusters through Gaussian Mixture Model (GMM) and predicted the clusters separately. However, the axes of GMM still need to be refined. Two methods, namely the mutual information analysis and single input GMM, are tried to figure out the axes in this study. Although the former generally gives a better result, the latter one makes a large improvement in the Wednesday’s data. Diffusion of Nanoparticles in a Potential Energy Landscape Supervisor: WONG Michael Kwok Yee / PHYS Student: CHEUNG Ho Tin / SSCI Course: UROP1000, Summer The diffusion of nanoparticles in a potential landscape is important in biophysics. However, the diffusion of nanoparticles in a potential energy landscape is different from the normal diffusion as the particles and we do not know much about the properties of the diffusion in a potential energy landscape. With the mathematical model calculated from the Fokker-Plank equation by Wong, we could approximate the diffusion through a slope by dividing the slope into number of steps. We would like to investigate the effects of changing different parameters, including the temperature, the source location, the observation point, the potential difference of the landscape, etc., and shapes of the landscape on the time-varying transmission probability, with aids of the approximation, using Matplotlib in Python.