The Elements of Style

William Strunk Jr. and E.B. White, The Elements of Style, Pearson, 1999.

英文寫作聖經，值得一讀再讀。

5 of the biggest threats to our oceans

United Nations Development Programme (UNDP), 2017/6/30 (5 videos)

Douglas McCauley, Here are 5 of the biggest threats to our oceans, and how we can solve them, 08 Jun 2018.

Artificial Intelligence: A Modern Approach

Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3/e, Prentice Hall, 2009.

完整且經典的書。Python 碼。

On a Formal Model of Safe and Scalable Self-driving Cars (自駕車)

Shai Shalev-Shwartz, Shaked Shammah, and Amnon Shashua, On a Formal Model of Safe and Scalable Self-driving Cars, arXiv:1708.06374, Mobileye, 2017.

In order to gain perspective over the typical values for such probabilities, consider public accident statistics in the United States. The probability of a fatal accident for a human driver in 1 hour of driving is 10^−6. From the Lemma above, if we want to claim that an AV meets the same probability of a fatal accident, one would need more than 10^6 hours of driving. Assuming that the average speed in 1 hour of driving is 30 miles per hour, the AV would need to drive 30 million miles to have enough statistical evidence that the AV under test meets the same probability of a fatal accident in 1 hour of driving as a human driver.... (*) 

Risk-based policies for airport security checkpoint screening (機場安檢檢查站檢查)

L.A. McLay, A.J. Lee, and S.H. Jacobson, Risk-based policies for airport security checkpoint screening, Transportation Science, Volume 44, Issue 3, August 2010, pp. 333-349. (Informs 2018 Impact Prize) 

Passenger screening is an important component of aviation security that incorporates real-time passenger screening strategies designed to maximize effectiveness in identifying potential terrorist attacks. This paper identifies a methodology that can be used to sequentially and optimally assign passengers to aviation security resources. An automated prescreening system determines passengers' perceived risk levels, which become known as passengers check in. The levels are available for determining security class assignments sequentially as passengers enter security screening. A passenger is then assigned to one of several available security classes, each of which corresponds to a particular set of screening devices. The objective is to use the passengers' perceived risk levels to determine the optimal policy for passenger screening assignments that maximize the expected total security, subject to capacity and assignment constraints. The sequential passenger assignment problem is formulated as a Markov decision process, and an optimal policy is found using dynamic programming. The general result from the sequential stochastic assignment problem is adapted to provide a heuristic for assigning passengers to security classes in real time. A condition is provided under which this heuristic yields the optimal policy. The model is illustrated with an example that incorporates data extracted from the Official Airline Guide.