諾基亞董事長學習和撰寫機器學習程式

林麗冠，確保所有員工都了解人工智慧》諾基亞董事長為機器學習重返校園，哈佛商業評論，2018/12/18

Risto Siilasmaa, The Chairman of Nokia on Ensuring Every Employee Has a Basic Understanding of Machine Learning - Including Him.

Learning Basketball Dribbling Skills Using Trajectory Optimization and Deep Reinforcement Learning

Libin Liu, Jessica Hodgins (August 2018). Learning Basketball Dribbling Skills Using Trajectory Optimization and Deep Reinforcement Learning. ACM Transactions on Graphics, 37(4).

Basketball is one of the world's most popular sports because of the agility and speed demonstrated by the players. This agility and speed makes designing controllers to realize robust control of basketball skills a challenge for physics-based character animation. The highly dynamic behaviors and precise manipulation of the ball that occur in the game are difficult to reproduce for simulated players. In this paper, we present an approach for learning robust basketball dribbling controllers from motion capture data. Our system decouples a basketball controller into locomotion control and arm control components and learns each component separately. To achieve robust control of the ball, we develop an efficient pipeline based on trajectory optimization and deep reinforcement learning and learn non-linear arm control policies. We also present a technique for learning skills and the transition between skills simultaneously. Our system is capable of learning robust controllers for various basketball dribbling skills, such as dribbling between the legs and crossover moves. The resulting control graphs enable a simulated player to perform transitions between these skills and respond to user interaction.

A computer vision system for deep learning-based detection of patient mobilization activities in the ICU

Serena Yeung, Francesca Rinaldo, Jeffrey Jopling, Bingbin Liu, Rishab Mehra, N. Lance Downing, Michelle Guo, Gabriel M. Bianconi, Alexandre Alahi, Julia Lee, Brandi Campbell, Kayla Deru, William Beninati, Li Fei-Fei & Arnold Milstein, A computer vision system for deep learning-based detection of patient mobilization activities in the ICU, npj Digital Medicine, volume 2, Article number: 11 (2019)

Early and frequent patient mobilization substantially mitigates risk for post-intensive care syndrome and long-term functional impairment. We developed and tested computer vision algorithms to detect patient mobilization activities occurring in an adult ICU. Mobility activities were defined as moving the patient into and out of bed, and moving the patient into and out of a chair. A data set of privacy-safe-depth-video images was collected in the Intermountain LDS Hospital ICU, comprising 563 instances of mobility activities and 98,801 total frames of video data from seven wall-mounted depth sensors. In all, 67% of the mobility activity instances were used to train algorithms to detect mobility activity occurrence and duration, and the number of healthcare personnel involved in each activity. The remaining 33% of the mobility instances were used for algorithm evaluation. The algorithm for detecting mobility activities attained a mean specificity of 89.2% and sensitivity of 87.2% over the four activities (*); the algorithm for quantifying the number of personnel involved attained a mean accuracy of 68.8% (**).

Optimizing schools’ start time and bus routes

D. Bertsimas, A. Delarue, and S. Martin, Optimizing schools’ start time and bus routes, PNAS, 2019. (Technical Appendix)

Maintaining a fleet of buses to transport students to school is a major expense for school districts. To reduce costs by reusing buses between schools, many districts spread start times across the morning. However, assigning each school a time involves estimating the impact on transportation costs and reconciling additional competing objectives. Facing this intricate optimization problem, school districts must resort to ad hoc approaches, which can be expensive, inequitable, and even detrimental to student health. For example, there is medical evidence that early high school starts are impacting the development of an entire generation of students and constitute a major public health crisis. We present an optimization model for the school time selection problem (STSP), which relies on a school bus routing algorithm that we call biobjective routing decomposition (BiRD). BiRD leverages a natural decomposition of the routing problem, computing and combining subproblem solutions via mixed integer optimization. It significantly outperforms state-of-the-art routing methods, and its implementation in Boston has led to $5 million in yearly savings, maintaining service quality for students despite a 50-bus fleet reduction. Using BiRD, we construct a tractable proxy to transportation costs, allowing the formulation of the STSP as a multiobjective generalized quadratic assignment problem. Local search methods provide high-quality solutions, allowing school districts to explore tradeoffs between competing priorities and choose times that best fulfill community needs. In December 2017, the development of this method led the Boston School Committee to unanimously approve the first school start time reform in 30 years.

美國高中以下的學生，可以選擇坐校車上下學。

The Power of Optimization Over Randomization in Designing Experiments Involving Small Samples

D. Bertsimas, M. Johnson, and N. Kallus, The Power of Optimization Over Randomization in Designing Experiments Involving Small Samples, Operations Research, Vol. 63, No. 4, July–August 2015, pp. 868–876.

Random assignment, typically seen as the standard in controlled trials, aims to make experimental groups statistically equivalent before treatment. However, with a small sample, which is a practical reality in many disciplines, randomized groups are often too dissimilar to be useful. We propose an approach based on discrete linear optimization (*) to create groups whose discrepancy in their means and variances is several orders of magnitude smaller than with randomization. We provide theoretical and computational evidence that groups created by optimization have exponentially lower discrepancy than those created by randomization and that this allows for more powerful statistical inference.

(*) Equation (1) in the paper.