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.

深度強化學習 (deep reinforcement learning) 教學

考慮理論和程式間取得平衡，以下是不錯的選擇

1. Maxim Lapan, Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more, Packt Publishing, 2018. 使用 PyTorch.
2. Microsoft, Reinforcement Learning Explained, edx. 使用 Microsoft CNTK 和 Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, A Bradford Book, 2018. 
3. Matthias Plappert, Deep Reinforcement Learning for Keras
4. Sudharsan Ravichandiran , Sean Saito , Rajalingappaa Shanmugamani, and Yang Wenzhuo, Python Reinforcement Learning, Packt, 2019.
5. Thomas Simonini, Deep reinforcement learning Course. 使用 Tensorflow

這一生，你想留下什麼？：史丹佛的10堂領導課 (Leading Matters: Lessons from My Journey)

廖月娟譯，這一生，你想留下什麼？：史丹佛的10堂領導課，天下文化 ，2018
John L. Hennessy, Leading Matters: Lessons from My Journey, Stanford Business Books, 2018.

第一章　謙卑：高績效領導的基礎
第二章　真誠與信賴：高績效領導的關鍵
第三章　領導就是服務：了解誰為誰工作
第四章　同理心：塑造領導人和機構的要素
第五章　勇氣：為了機構和社群挺身而出
第六章　協力與團隊合作：你無法單打獨鬥
第七章　創新：產業與學術界成功之鑰
第八章　求知欲：終身學習的重要性
第九章　說故事：溝通願景
第十章　遺澤：這一生，你想留下什麼？
結　語　打造未來，讓世界變得更好
後　記　以書為師：漢尼斯的圖書館

余英時回憶錄

余英時，余英時回憶錄，允晨文化，2018

從一九三七年抗日開始到今天，是中國現代史上變亂最劇烈的一段時期。我深切感到：如何將這一特殊歷史階段的重大變動在訪談稿中呈現出來，其意義遠大於追溯我個人生命史的發展。回憶錄因個人的處境互異而各有不同，這是不可避免的。我一生都在研究和教學中渡過，因此回憶也只能騁馳在學術、思想和文化的領域之內。不用說，我所經歷的世變也是通過這一特殊領域得來的。我希望我的回憶對於這一段歷史流變的認識稍有所助。同時我也相信，一定會有和我同代的其他學人，以不同方式留下他們的回憶。這樣的回憶越多越好，可以互證所同、互校所異。出版這部「回憶錄」的另一動機：拋磚引玉，激起更多學人追憶往事的興趣。如果允許我再有一個奢望，我想說：我在《回憶錄》中所記述的個人學思歷程，無論得失如何，也許可以獻給新一代求學的朋友們，作為一種參考。

Managerial Economics by Allen, et al.

W. Bruce Allen, Neil A. Doherty, Keith Weigelt, and Edwin Mansfield, Managerial Economics: Theory, Applications, and Cases, W. W. Norton & Company, Sixth Edition, 2005.

The Fifth Edition of Managerial Economics heralds a new era for this classic text. Carrying on the tradition established by Edwin Mansfield in the book's earlier editions, Bruce Allen, Neil Doherty, and Keith Weigelt—all of the University of Pennsylvania's Wharton School—have prepared an exciting revision that capitalizes on proven strengths while embracing new developments in the field. Retaining a hallmark of Managerial Economics, the authors include a wealth of cases and applications that consistently anchor the exposition in the real world of business decision making. New to the Fifth Edition is a greater focus on applied microeconomics, with two new chapters, one on auctions and another on the principal-agent problems of firms. These new chapters, numerous new cases and applications throughout the book, and an exciting new package of electronic ancillaries promise to make the Fifth Edition of Managerial Economics the best yet.

幾年前為了教營收管理讀的書，推薦。

世界是在變好還是變糟？讓數字說話吧

Steven Pinker, Is the world getting better or worse? A look at the numbers, 2018/5/21

聽起來很熟悉？台灣目前的情況不理想，很多人開始懷念威權時代的「好」。我們真的應該做一個表，比較社會的各個面向，以了解真相。

Machine learning can boost the value of wind energy

Sims Witherspoon and Will Fadrhonc, Machine learning can boost the value of wind energy, Google, Feb 26, 2019

拚經濟：一本國民指南 (Economics: The User’s Guide)

潘勛、楊明暐譯，拚經濟：一本國民指南，雅言文化，2018

Ha-Joon Chang, Economics: The User’s Guide, Bloomsbury Pub Plc USA, 2015

經濟學95%是常識。它不只不是高深科學，它甚至連科學都不是。所有理論都是特定時空背景的產物，沒有最正確，沒有最客觀，只有最符合產業環境需求，或最符合國民道德價值。因此，經濟學應該是一門政治思辨之學。

2008金融海嘯讓經濟學界相當難堪。全世界都質疑為何連諾貝爾獎得主也無法預見危機。媒體批評專家研究都是自己爽。

實務界則抱怨經濟系畢業生只會設計數學模型，卻無法把演算結果化為企業策略或政策建議。還沒畢業的經濟系學生則呼籲教科書應該整個重寫，學程也要重新設計。

本書雖也對學校教的經濟學提出批評，但提出解方更進一步。作者認為，經濟學既然攸關民生，經濟學就應該是國民素養。國民沒變成經濟公民，經濟議題沒進入民主對話，拚經濟就會變成拚少數人的經濟。 

本書把國民進行經濟思考應該具備的素養一網打盡：如何解讀數據、如何追求成長、如何規範市場等等。重要概念都有清楚交代，全沒使用數學公式。