Robust Stochastic Optimization Made Easy

Chen, Zhi, and Peng Xiong. 2021. RSOME in Python: an open-source package for robust stochastic optimization made easy. Optimization Online.

Chen, Zhi, Melvyn Sim, Peng Xiong. 2020. Robust stochastic optimization made easy with RSOME. Management Science 66(8) 3329–3339.

RSOME in Python (Also solver interfaces)

RSOME (Robust Stochastic Optimization Made Easy) is an open-source Python package for modeling generic optimization problems. Models in RSOME are constructed by variables, constraints, and expressions that are formatted as N-dimensional arrays. These arrays are consistent with the NumPy library in terms of syntax and operations, including broadcasting, indexing, slicing, element-wise operations, and matrix calculation rules, among others. In short, RSOME provides a convenient platform to facilitate developments of optimization models and their applications.

Training One Million Machine Learning Models in Record Time with Ray

Eric Liang and Robert Nishihara, Training One Million Machine Learning Models in Record Time with Ray, Anyscale, December 17, 2022.

Ray and Anyscale are used by companies like Instacart to speed up machine learning training workloads (often demand forecasting) by 10x compared with tools like Celery, AWS Batch, SageMaker, Vertex AI, Dask, and more.

In this blog, we’ll cover:

• Why companies are doing many model training
• How to use Ray to train multiple models
• The properties of Ray that enable efficient many model training 

The Forward-Forward Algorithm

Geoffrey Hinton, The Forward-Forward Algorithm: Some Preliminary Investigations, 2022. (code)

The aim of this paper is to introduce a new learning procedure for neural networks and to demonstrate that it works well enough on a few small problems to be worth serious investigation. The Forward-Forward algorithm replaces the forward and backward passes of backpropagation by two forward passes, one with positive (i.e. real) data and the other with negative data which could be generated by the network itself. Each layer has its own objective function which is simply to have high goodness for positive data and low goodness for negative data. The sum of the squared activities in a layer can be used as the goodness but there are many other possibilities, including minus the sum of the squared activities. If the positive and negative passes can be separated in time, the negative passes can be done offline, which makes the learning much simpler in the positive pass and allows video to be pipelined through the network without ever storing activities or stopping to propagate derivatives.

半導體產業的挑戰

魏哲家，半導體產業的挑戰，玉山科技論壇，2022-12-17 

為什麼聰明人會做蠢事 ?

姚怡平譯，為什麼聰明人會做蠢事? 顛覆高智商等於絕對聰明的常理，助你找出決策的關鍵智慧，商業周刊，2020

David Robson, The Intelligence Trap: Revolutionise Your Thinking and Make Wiser Decisions, Hodder & Stoughton, 2019.

Waki 瓦基，《為什麼聰明人會做蠢事？》讀書心得：掌握變聰明的３種方法，2021-09-01 (也有Podcast)

NeurIPS 2022

 Some information about NeurIPS 2022: 

• Outstanding Paper, Datasets And Benchmarks
• Nice summary by Jim (Linxi) Fan
• Google search "NeurIPS 2022 summary"
• 李宏毅 (Hung-yi Lee)，Chat GPT (可能)是怎麼煉成的 - GPT 社會化的過程

Data Science and Machine Learning

Dirk P. Kroese, Zdravko Botev, Thomas Taimre, and Radislav Vaisman, Data Science and Machine Learning: Mathematical and Statistical Methods, Chapman & Hall, 2019. (Lecture notes and Python files)

The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.

The Impacts of Technological Invention on Economic Growth

A Reamer, The impacts of technological invention on economic growth–a review of the literature, The George Washington Institute of Public Policy, 2014. 

Interpretable Machine Learning

Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, and Chudi Zhong, Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges. Statistics Surveys, 2022. 

Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic. We also identify 10 technical challenge areas in interpretable machine learning and provide history and background on each problem. Some of these problems are classically important, and some are recent problems that have arisen in the last few years. These problems are: (1) Optimizing sparse logical models such as decision trees; (2) Optimization of scoring systems; (3) Placing constraints into generalized additive models to encourage sparsity and better interpretability; (4) Modern case-based reasoning, including neural networks and matching for causal inference; (5) Complete supervised disentanglement of neural networks; (6) Complete or even partial unsupervised disentanglement of neural networks; (7) Dimensionality reduction for data visualization; (8) Machine learning models that can incorporate physics and other generative or causal constraints; (9) Characterization of the “Rashomon set” of good models; and (10) Interpretable reinforcement learning. This survey is suitable as a starting point for statisticians and computer scientists interested in working in interpretable machine learning.

培養思辨能力

• Derek Bok, Our Underachieving Colleges: A Candid Look at How Much Students Learn and Why They Should Be Learning More, Princeton University Press, 2005. (張善楠譯，大學教了沒？：哈佛校長提出的 8 門課，天下文化，2008)
• 以課程而言，書中的部份例子，(143 頁) 具備有量化素養 (quantitative literacy) 以培養思辨能力：算術能力、資料處理、電腦操作、模型化能力、統計概念、機率估算、推理能力

Data-driven research in retail operations

M. Qi, H.Y. Mak, and Z.J.M. Shen, Data‐driven research in retail operations—A review, Naval Research Logistics, 2020, 67 (8), 595-616. (Open access)

We review the operations research/management science literature on data-driven methods in retail operations. This line of work has grown rapidly in recent years, thanks to the availability of high-quality data, improvements in computing hardware, and parallel developments in machine learning methodologies. We survey state-of-the-art studies in three core aspects of retail operations—assortment optimization, order fulfillment, and inventory management. We then conclude the paper by pointing out some interesting future research possibilities for our community.

Reflections on the Evolution of Operations Management

Stephen C. Graves (2021) Reflections on the Evolution of Operations Management. Management Science 67(9):5379-5388. https://doi.org/10.1287/mnsc.2020.3802

In this paper, I provide some observations on how the academic field of operations management has changed over the past 40 years. For this purpose, I have identified and classified the operations management (OM) papers published in Management Science in 1976 and in 2016. From this review, I comment on what’s changed, what’s new, and what we might see in the future. In reflecting on these changes, I also document and discuss how the OM editorial structure and mission have evolved at Management Science over this time.

Robust and Adaptive Optimization

Dimitris Bertsimas and Dick Den Hertog, Robust and Adaptive Optimization, Dynamic Ideas, 2022.

The purpose of this book is to provide a unified, insightful, and original treatment of robust and adaptive optimization.

用大數據抓用戶胃口

林步昇譯，平台假象：拆解超級平台的神話，分析數位生態系的深層特徵，建立在數位環境中不被吞噬的結構性優勢，天下雜誌，2022

Jonathan A. Knee, The Platform Delusion: Who Wins and Who Loses in the Age of Tech Titans, Portfolio, 2021.

非讀 BOOK

Global Optimization via Optimal Decision Trees

Dimitris Bertsimas and Berk Öztürk, Global Optimization via Optimal Decision Trees, arXiv:2202.06017. (Code in Julia)

The global optimization literature places large emphasis on reducing intractable optimization problems into more tractable structured optimization forms. In order to achieve this goal, many existing methods are restricted to optimization over explicit constraints and objectives that use a subset of possible mathematical primitives. These are limiting in real-world contexts where more general explicit and black box constraints appear. Leveraging the dramatic speed improvements in mixed-integer optimization (MIO) and recent research in machine learning, we propose a new method to learn MIO-compatible approximations of global optimization problems using optimal decision trees with hyperplanes (OCT-Hs). This constraint learning approach only requires a bounded variable domain, and can address both explicit and inexplicit constraints. We solve the MIO approximation efficiently to find a near-optimal, near-feasible solution to the global optimization problem. We further improve the solution using a series of projected gradient descent iterations. We test the method on a number of numerical benchmarks from the literature as well as real-world design problems, demonstrating its promise in finding global optima efficiently.

Python for Data Analysis

Wes McKinney, Python for Data Analysis, O'Reilly, third edition, 2022. (Open access)

Wes McKinney is an open source software developer focusing on analytical computing. He created the Python pandas project and is a co-creator of Apache Arrow, his current focus. 

半導體產業人力資源危機與管理

林育中，半導體產業人力資源危機與管理(上)，DIGITIMES，2022-09-07

長期人力資源匱乏問題的解決方式，不外是企業內部作為以及政府政策2種解決方式，政策雖然效果涵蓋面較廣，但是要達到效果的時間頗長，可能緩不濟急；企業內部作為只依賴企業的意志與力量，效果較快而且對企業有針對性。...

密涅瓦的思考習慣訓練

李佳達、劉劭穎、黃禮宏，全球人才搶著學！密涅瓦的思考習慣訓練，究竟出版社，2022

密涅瓦大學決策思考力四大重點

Stroke risk is not linear

Orfanoudaki A, Chesley E, Cadisch C, Stein B, Nouh A, Alberts MJ, et al. (2020) Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk score. PLoS ONE 15(5): e0232414. https://doi.org/10.1371/journal.pone.0232414

Current stroke risk assessment tools presume the impact of risk factors is linear and cumulative. However, both novel risk factors and their interplay influencing stroke incidence are difficult to reveal using traditional additive models. The goal of this study was to improve upon the established Revised Framingham Stroke Risk Score and design an interactive Non-Linear Stroke Risk Score. Leveraging machine learning algorithms, our work aimed at increasing the accuracy of event prediction and uncovering new relationships in an interpretable fashion. A two-phase approach was used to create our stroke risk prediction score. First, clinical examinations of the Framingham offspring cohort were utilized as the training dataset for the predictive model. Optimal Classification Trees were used to develop a tree-based model to predict 10-year risk of stroke. Unlike classical methods, this algorithm adaptively changes the splits on the independent variables, introducing non-linear interactions among them. Second, the model was validated with a multi-ethnicity cohort from the Boston Medical Center. Our stroke risk score suggests a key dichotomy between patients with history of cardiovascular disease and the rest of the population. While it agrees with known findings, it also identified 23 unique stroke risk profiles and highlighted new non-linear relationships; such as the role of T-wave abnormality on electrocardiography and hematocrit levels in a patient’s risk profile. Our results suggested that the non-linear approach significantly improves upon the baseline in the c-statistic (training 87.43% (CI 0.85–0.90) vs. 73.74% (CI 0.70–0.76); validation 75.29% (CI 0.74–0.76) vs 65.93% (CI 0.64–0.67), even in multi-ethnicity populations. The clinical implications of the new risk score include prioritization of risk factor modification and personalized care at the patient level with improved targeting of interventions for stroke prevention.

學習程式設計的心法與方法

心法

Discovering faster matrix multiplication algorithms with reinforcement learning

Fawzi, A., Balog, M., Huang, A. et al. Discovering faster matrix multiplication algorithms with reinforcement learning. Nature 610, 47–53 (2022). https://doi.org/10.1038/s41586-022-05172-4. (data and code)

Improving the efficiency of algorithms for fundamental computations can have a widespread impact, as it can affect the overall speed of a large amount of computations. Matrix multiplication is one such primitive task, occurring in many systems—from neural networks to scientific computing routines. The automatic discovery of algorithms using machine learning offers the prospect of reaching beyond human intuition and outperforming the current best human-designed algorithms. However, automating the algorithm discovery procedure is intricate, as the space of possible algorithms is enormous. Here we report a deep reinforcement learning approach based on AlphaZero for discovering efficient and provably correct algorithms for the multiplication of arbitrary matrices. Our agent, AlphaTensor, is trained to play a single-player game where the objective is finding tensor decompositions within a finite factor space. AlphaTensor discovered algorithms that outperform the state-of-the-art complexity for many matrix sizes. Particularly relevant is the case of 4 × 4 matrices in a finite field, where AlphaTensor’s algorithm improves on Strassen’s two-level algorithm for the first time, to our knowledge, since its discovery 50 years ago. We further showcase the flexibility of AlphaTensor through different use-cases: algorithms with state-of-the-art complexity for structured matrix multiplication and improved practical efficiency by optimizing matrix multiplication for runtime on specific hardware. Our results highlight AlphaTensor’s ability to accelerate the process of algorithmic discovery on a range of problems, and to optimize for different criteria.

Deep reinforcement learning for inventory control

R.N. Boute, J. Gijsbrechts, W. van Jaarsveld, and N. Vanvuchelen, Deep reinforcement learning for inventory control: A roadmap, European Journal of Operational Research, Volume 298, Issue 2, 16 April 2022, Pages 401-412.

Deep reinforcement learning (DRL) has shown great potential for sequential decision-making, including early developments in inventory control. Yet, the abundance of choices that come with designing a DRL algorithm, combined with the intense computational effort to tune and evaluate each choice, may hamper their application in practice. This paper describes the key design choices of DRL algorithms to facilitate their implementation in inventory control. We also shed light on possible future research avenues that may elevate the current state-of-the-art of DRL applications for inventory control and broaden their scope by leveraging and improving on the structural policy insights within inventory research. Our discussion and roadmap may also spur future research in other domains within operations management.

技術陷阱

許恬寧譯，從工業革命到AI時代，技術創新下的資本、勞動力與權力，八旗文化，2020

Carl Benedikt Frey, The Technology Trap: Capital, Labor, and Power in the Age of Automation, Princeton University Press, 2020.

《技術陷阱》縱橫技術發展史，從工業革命談到人工智慧時代，探討技術如何大幅移轉社會成員間的經濟與政治力量分布。本書作者、牛津大學經濟資深研究員弗雷指出，從長期的角度來看，工業革命創造出前所未有的財富與繁榮，然而機械化在當下所帶來的影響，卻使大量人口深受其害。中等收入的工作機會萎縮、薪資停滯不前、勞動所占的收入份額下降，即便利潤大增，卻造成貧富差距飆升。弗雷指出工業革命的潮流，與今日由電腦革命開啟的AI時代，大有相互呼應之處。

機器學習和作業研究的奇妙結合

今晚受邀到系上的 (日夜) 研究生研討會演講，透過10 個面向和實例，說明這兩個領域巧妙的交互作用。機器學習可以當成精準的預測機器，然後根據此預測，使用作業研究對資源做分配或決定好的定價等等；有時候，則是當成好的近似求解器，解決複雜的組合和不確定問題；有時候，作業研究是機器學習的核心步驟和演算法。

剛好，和我的大學部上課衝堂，因為演講內容和課程相關，所以請學生來聽講。

念夜間部的人

教書第一年，教了兩班進修部的同學，上課的時候，不需要老師提醒，都會認真抄筆記；雖然白天上滿了八個小時的班，上課的時候，依然沒有人在趴睡。

隔年換學校，教日間部的同學，上課的情況非常地慘，不小心，兩班就當了一班。

Hold 住

為了加強同學們對作業研究 (OR) 的興趣，花了不少時間整理，加入機器學習、演算法概念。

聽到導生打工到深夜 11 點，今早約談中，給了許多建議 (*)，並且用實例解釋工業系的專業。

Cynthia Rudin wins AAAI Squirrel AI Award

AAAI, Duke Computer Scientist Wins $1 Million Artificial Intelligence Prize, A 'New Nobel', October 12, 2021.

Whether preventing explosions on electrical grids, spotting patterns among past crimes, or optimizing resources in the care of critically ill patients, Duke University computer scientist Cynthia Rudin wants artificial intelligence (AI) to show its work. Especially when it’s making decisions that deeply affect people’s lives.

非暴力溝通

蕭寶森譯，非暴力溝通：愛的語言，光啟文化，2019

Marshall B. Rosenberg, Nonviolent Communication: A Language of Life, 3rd Edition, PuddleDancer Press, 2015.

每個人的價值觀和生活方式或許不同，但卻有共同的感受和需要。非暴力溝通透過「觀察、表達感受、需要、做出具體請求」這四個步驟，使我們能真誠地表達自己，也能深入地傾聽他人，建立心與心之間的連結，無須妥協即可化解衝突，豐富彼此的生命。

出版論文驚魂記

因為決策樹和最佳化的進展，所以想要把它用來解決生產製造的問題。

花了一年多、寫了數千行的程式，萃取出成功的部分結果，投稿到第一個期刊，被退稿。

除錯 (debug)

做迴歸的時候，一般使用平方誤差；可以利用微積分，求得解析解 (*)。

823

這一場炮戰，許多人有不同的解讀。但是，住在那個地方的人，卻是生離死別。

家父可以保送師專，阿嬤擔心炮戰，所以沒有成行，改變了人生的軌跡。

靠數據賣雞蛋

作者／張紹敏 圖片來源：卓杜信，麥當勞、美芝城都是他客戶！七年級面板主管回大武山「賣雞蛋」，靠數據幫爸爸重振家業，Cheers：快樂工作人，2022/08/13

通路

為什麼要自己賣蛋？魏毓恆解釋：「傳統蛋商只要缺蛋，每天都會開車在出貨碼頭等你，不讓你把蛋給別人；可是蛋很多的時候，他們的貨車就會『連續壞一個禮拜』，遲遲不來。」產銷的不平衡，成為大武山陷入困境一大原因，而魏毓恆不打算視而不見。

台灣軟體業的問題與成功策略

 葉肇元，為什麼台灣的軟體業不強？，2020/10/13

 台灣軟體業四個現象

1. 台灣市場對於軟體及服務定價過低，而且人才過度充沛。

2. 軟體的包裝跟行銷、以及授權談判都至為關鍵。

3. 講起台灣軟體創業成功的案例，真的是少得可憐，所以投資人興趣也不是很大。

4. 台灣政府對於推動軟體產業發展的政策好像還不是很夠力。

Just do it!

 一位升研二的學生，因故換成我當指導教授。

這個禮拜討論的時候，他說，想和我一樣寫部落格。

我說，你現在正在讀論文，就把你讀論文的心得，用精簡的文字，分析、並寫下其重點；順便磨練抓重點和文字表達的能力。

同學說，他希望可以找到同好。

我回他說，這個不用等，開始做就好了。

民主不能當飯吃？

我在學生時代，金門人想來台灣，大多只能坐開口笑的平底船。

國中畢業到台灣，深夜時刻，漲潮時間到了，拿著行李，大家拼命往前衝。我們幾個同學只有搶到油桶旁邊的小位子。到了高雄，衣服滿是骯髒物；我永遠記得，餐廳裡那一些人，看我們的眼光 (*)。

電影《叫我第一名》

 Front of the Class 電影名稱《叫我第一名》書名《站在學生前面》

A touching real story would make you cry.  

Celebrate 40 years of the European Journal of Operational Research

Celebrate 40 years of the European Journal of Operational Research, May 2017

Google AdSense 廣告

 本網站的廣告收入將全數捐助公益團體，並將在此公告之。

CNN interview with Mark Liu

Fareed Zakaria, On GPS: Can China afford to attack Taiwan?, CNN.

Fareed Zakaria, On GPS: Taiwan's tech under threat?, CNN.

BCG 問題解決力

徐瑞廷 / 作者，黃菁媺 / 文字整理，BCG問題解決力：一生受用的策略顧問思考法，時報文化，2021/09/07

奠基於十多年的策略顧問經驗，本書將帶你一次學會：

．「問對問題」：找到問題痛點，就能對症下藥，藥到病除。

．「解決問題」：學習BCG顧問提供的實用技巧，以徹底解決問題。

．「規劃與管理工作進度」：學會釐清工作任務、明辨工作優先順序等，有利高效工作。

．「準備商用簡報」：簡報必須包含關鍵訊息，以清楚傳達具說服力的重點。

．「與客戶溝通」：嚴謹的準備與籌劃是溝通的關鍵，成功的溝通可以創造雙贏局面。

．「使用定量分析工具」：定量分析可以協助證明假說，也能確實找到具備高度價值的資料。

．「從訪談中獲得資訊」：掌握訪談步調，不僅從對方那裡獲取資訊，也要適時給予資訊。

 也可以參考麥肯錫解決問題的方法。

Data Scientist

Thomas H. Davenport and D.J. Patil, Is Data Scientist Still the Sexiest Job of the 21st Century?, Harvard Business Review, 4July 15, 2022.

Ten years ago, the authors posited that being a data scientist was the “sexiest job of the 21st century.” A decade later, does the claim stand up? The job has grown in popularity and is generally well-paid, and the field is projected to experience more growth than almost any other by 2029. But the job has changed, in both large and small ways. It’s become better institutionalized, the scope of the job has been redefined, the technology it relies on has made huge strides, and the importance of non-technical expertise, such as ethics and change management, has grown. How it operates in companies — and how executives need to think about managing data science efforts — has changed, too, as businesses now need to create and oversee diverse data science teams rather than searching for data scientist unicorns. Finally, companies need to think about what comes next, and how they can begin to think about democratizing data science.

全台最年輕主任推實驗教育

 陳盈螢，棄醫從教！金門榜首王大鯤：全台最年輕主任推實驗教育，親子天下，2022-07-11

王大鯤對醫學系臨床隨行課程印象深刻。當時跟在小兒科醫師後頭的他其實很揪心：該是活潑玩耍的4、5歲年紀，來醫院求診的小朋友卻因為身體疾病，沒有辦法清楚表達自己的痛感，面對觸診只能嚎啕大哭，爸媽在旁更是無所適從，「醫院好像變成提供輔助，而不是解決問題的場所⋯⋯」王大鯤說。當時他一度有意重考，但一堂課的反思，讓他決定將學測准考證收回抽屜。

那次醫學社會學的課堂討論改變王大鯤的目標，決定投入教育。「那時候我們看的一篇文章，比喻醫生像在河流下游，看見有人溺水就把人救起來⋯⋯可是我會想，為什麼我們沒有人走到河流上游，看看究竟是發生了什麼事？」王大鯤為接近「上游」，開始雙主修社會學系、於此同時獲頒成績最優異的醫卷獎。王大鯤的社會系同學劉晉庭描述，「大鯤大二時打醫學系網球隊，練完球就會直接帶球拍來上社會學，下課開始背寄生蟲⋯⋯我很佩服他！」...

Analysis and prediction of future stock price

Tasha Keeney, ARK’s Expected Value For Tesla In 2026: $4,600 Per Share, ARK, April 14, 2022. (GitHub)

Nicholas Grous and Andrew Kim, ARK’s Expected Value For Roku in 2026: $605 Per Share, ARK, July 07, 2022. (GitHub)

This is one way to predict the stock price, not for purchase recommendation. 

青少年自傷輕生 自殺率連年攀升

公共電視 - 有話好說，青少年自傷輕生 自殺率連年攀升 憂鬱無助墜深淵 青春傷痕與出路

為什麼很多中國人移民到美國之後，就會變得特別愛台灣？

 洛奇漫談，為什麼很多中國人移民到美國之後，就會變得特別愛台灣？

Machine learning and statistical testing at vantagescore

Kimberly Adams, How one credit scoring company is thinking about financial inclusion, Marketplace Tech,  JULY 8, 2022. (more around July 2022)

他高一輟學寫詩、大學6年才畢業，他是今年的菲爾茲獎得主

李忠謙，他高一輟學寫詩、大學6年才畢業，他是今年的菲爾茲獎得主：39歲的普林斯頓數學教授許埈珥，風傳媒，2022-07-06 

相較於他在少年時期厭惡抵抗數學（據稱是小學一次數學考試考差所致）、甚至高中一度輟學寫詩的迷茫過去，許埈珥的人生轉折確實比在麻省理工學院拖地擦黑板的叛逆威爾還要傳奇。...

悼念台灣真摯的友人安倍晉三 (Abe Shinzo) 前首相

作業研究的持續改善

下學年要教我喜歡的作業研究。依照往例，再一次更新之前的版本。

第一學期，考慮確定的環境和有限資源下，使用最佳化 (optimization)，以最大化 (例如利潤) 或最小化目標  (例如成本)。第二學期，準備教不確定性環境下，如何分析、最佳化和做決策 (控制)。

未來10年臺灣競爭力

李昌鴻總經理 (佳世達科技股份有限公司)，未來10年臺灣競爭力，TAIROA 國際論壇，2022年3月9日 

如何提高人均生產力

研究 (Research)

Journal articles:

• C.-H. Hsu and H.-C. Yang, Suboptimal Explainable Scheme for Machining Outcome Estimation, IEEE Robotics and Automation Letters, Vol. 7, No. 3, July 2022, pp. 7834 - 7841.

從歷史看現在和未來

余英時著、顏擇雅編，余英時評政治現實，印刻文學，2022

身處在台灣，面臨不同的政治、文化多元觀點，加上網路時代，充斥各種真假訊息、矛盾的言論，很容易讓人迷失了方向。

透過臉書，可以追蹤不同光譜的人物，增加自己思考的廣度、了解不同的邏輯思維。有一天，看到顏澤雅社長極力推薦「余英時評政治現實」；想到余老師望重士林的聲譽、且不忮不求的個性，二話不說，下單買書。

文言文

 顏擇雅，文言文爭議不該牽涉「去中國化」，親子天下，2022-05-17 

眾所周知，五四知識人想推動的只是現代化，不是「去中國化」。但他們對文言文的貶損，卻是今人望塵莫及。曾有報刊問魯迅應該讀什麼中國書，他的回答是中國書最好少讀，或完全不讀。這一年是一九二五，所以他所謂的中國書就是指文言文。國民黨元老吳稚暉更激烈，主張所有線裝書都應該丟進茅坑。線裝書的語文，當然也是文言文。

Bloated inventories hit Walmart, Target and other retailers’ profits, trucking demand

Mark Solomon, Bloated inventories hit Walmart, Target, and other retailers’ profits, trucking demand, FreightWaves, May 20, 2022. 

However, furniture, home furnishings and appliances, building materials and garden equipment, and a category known as “other general merchandise,” which includes Walmart and Target, among others, reported higher inventory-to-sales ratios, according to government data analyzed by Michigan State.

勇敢打開電的大格局

台大陳良基部長講座，Think Big 勇敢打開電的大格局，2022/05/24 

Risk Exposure Index (REI)

David Simchi-Levi, William Schmidt, and Yehua Wei, From Superstorms to Factory Fires: Managing Unpredictable Supply-Chain Disruptions, Harvard Business Review, 2014.

MIT Forum for Supply Chain Innovation, MIT Professor David Simchi-Levi’s Risk Exposure Index (REI) used by the UN Office for Disaster Risk Reduction: REI methodology beginning to influence thinking beyond the supply chain world, May 2, 2014.

The Big Data Newsvendor

Gah-Yi Ban and Cynthia Rudin, The Big Data Newsvendor: Practical Insights from Machine Learning, Operations Research, Vol. 67, No. 1, Pages: 90–108, 2019. (MSOM Society 2021 Operations Research Best OM Paper Award)

We investigate the data-driven newsvendor problem when one has n observations of p features related to the demand as well as historical demand data. Rather than a two-step process of first estimating a demand distribution then optimizing for the optimal order quantity, we propose solving the “big data” newsvendor problem via single-step machine-learning algorithms. Specifically, we propose algorithms based on the empirical risk minimization (ERM) principle, with and without regularization, and an algorithm based on kernel-weights optimization (KO). The ERM approaches, equivalent to high-dimensional quantile regression, can be solved by convex optimization problems and the KO approach by a sorting algorithm. We analytically justify the use of features by showing that their omission yields inconsistent decisions. We then derive finite-sample performance bounds on the out-of-sample costs of the feature-based algorithms, which quantify the effects of dimensionality and cost parameters. Our bounds, based on algorithmic stability theory, generalize known analyses for the newsvendor problem without feature information. Finally, we apply the feature-based algorithms for nurse staffing in a hospital emergency room using a data set from a large UK teaching hospital and find that (1) the best ERM and KO algorithms beat the best practice benchmark by 23% and 24%, respectively, in the out-of-sample cost, and (2) the best KO algorithm is faster than the best ERM algorithm by three orders of magnitude and the best practice benchmark by two orders of magnitude.

Online Network Revenue Management Using Thompson Sampling

Kris Johnson Ferreira, David Simchi-Levi, and He Wang. (2018). “Online network revenue management using Thompson sampling.” Operations Research, 66(6), 1586-1602. (Supplemental Material, code, MSOM Society 2021 Operations Research Best OM Paper Award)

We consider a price-based network revenue management problem in which a retailer aims to maximize revenue from multiple products with limited inventory over a finite selling season. As is common in practice, we assume the demand function contains unknown parameters that must be learned from sales data. In the presence of these unknown demand parameters, the retailer faces a trade-off commonly referred to as the “exploration-exploitation trade-off.” Toward the beginning of the selling season, the retailer may offer several different prices to try to learn demand at each price (“exploration” objective). Over time, the retailer can use this knowledge to set a price that maximizes revenue throughout the remainder of the selling season (“exploitation” objective). We propose a class of dynamic pricing algorithms that builds on the simple, yet powerful, machine learning technique known as “Thompson sampling” to address the challenge of balancing the exploration-exploitation trade-off under the presence of inventory constraints. Our algorithms have both strong theoretical performance guarantees and promising numerical performance results when compared with other algorithms developed for similar settings. Moreover, we show how our algorithms can be extended for use in general multiarmed bandit problems with resource constraints as well as in applications in other revenue management settings and beyond.

SK 計畫

在系上楊老師的號召下，老師們熱心指導，推動此計劃。今晚是線上的期末報告，所有的學生都用英文報告，覺得很驚艷。

有一位同學說，謝謝這個計劃，讓喜歡接受挑戰的同學，可以拓展自己的視野與能力。有一位同學可能是太開心了，報告完之後一直笑，笑到忍不出遮住臉。

很巧 ，前面報告的幾組同學都是女生，所以我就算了一下，總共35位同學參加期末報告，超過 2/3 是女生。說不定，再過幾年，系館長廊的傑出校友，就會多了幾位女總經理。

我一直以為，新款的 iPhone 手機，語音辨識比不上Google的Android，所以聽不懂我的英文。沒想到，當我打開手機的時候，電腦螢幕裡面的同學說 Thank you very much，iPhone竟然聽懂了，哭哭。

國科會大專學生研究計畫

• 良好的專題和論文，學習「如何學習」的能力、以解決未知問題。畢業專題是很好的訓練，可以培養解決未知問題的能力，增加思辨能力和職場競爭力。
• 提案計畫書參考，歡迎同學們找我當指導老師。也可以找系上老師指導。我之前服務的單位和工業系，有大二生申請通過。
• 國科會大專學生研究計畫，查成果報告。

為什麼工 (資) 管的課程看起來很雜？

(2022) 因為橫跨兩個學院 (工和商)，工業系龐雜的內容，和資管系一樣，所以修改一下標題。其實，我的部落格如何選填大學志願，就是以四個專業和李國鼎的話 (第 17 頁)，說明這兩個系的重要性。

(2014) 常常聽到學生有這樣的疑惑，我試著以營收管理說明之；針對固定 (如旅館房間、網頁上廣告空間) 且易過時 (如機位、時裝) 的容量或庫存供給下，如何有效地分配庫存 (屬於生管) 和 (動態) 定價 (屬於行銷)，以最大化企業之營收；詳細的內容可以參考我的課程。

OR-Gym

Christian D. Hubbs, Hector D. Perez, Owais Sarwar, Nikolaos V. Sahinidis, Ignacio E. Grossmann, John M. Wassick, OR-Gym: A Reinforcement Learning Library for Operations Research Problems, arXiv:2008.06319v2. (Python)

Reinforcement learning (RL) has been widely applied to game-playing and surpassed the best human-level performance in many domains, yet there are few use-cases in industrial or commercial settings. We introduce OR-Gym, an open-source library for developing reinforcement learning algorithms to address operations research problems. In this paper, we apply reinforcement learning to the knapsack, multi-dimensional bin packing, multi-echelon supply chain, and multi-period asset allocation model problems, as well as benchmark the RL solutions against MILP and heuristic models. These problems are used in logistics, finance, engineering, and are common in many business operation settings. We develop environments based on prototypical models in the literature and implement various optimization and heuristic models in order to benchmark the RL results. By re-framing a series of classic optimization problems as RL tasks, we seek to provide a new tool for the operations research community, while also opening those in the RL community to many of the problems and challenges in the OR field.

如果沒有學生佔領了立法院

太陽花學運

如果沒有學生佔領了立法院：
我們不知道馬總統四年前的承諾 (逐字稿)

「兩岸經濟協議是一個綱要性的協議，它基本上先簽一個小而必要的早期收穫條款，然後再一步步地往後簽，不是一步到位。同時呢，它是由海基會跟海協會這兩個單位來簽，本著尊嚴、對等跟互惠的原則。每一次正式協商的前後，都會向立法院報告，並且對外說明協商的進度。協議簽署以後一定會送立法院審議，要通過之後才會生效。」

學習數學的四個層次：(2) 邏輯推理和抽象思考的能力

學習數學的四個層次：(0) 如何學數學，(1) 代表具備基礎的知識與能力，(2) 邏輯推理和抽象思考的能力，(3) 在許多行業的應用，(4) 純粹滿足好奇心或求知慾。

2015/12/1 初稿，持續更新中。

一般性說明

• D. Bok, Our Underachieving Colleges, Princeton University Press, 2007. 張善楠譯，大學教了沒？，天下文化，2008。二十一世紀八個教育目標之一『思辨能力』。(註 1)
• 程式的邏輯 (if 和 for) 數十年沒有變化，學好數學有助於邏輯能力的培養，參考計算機程式補考後的人生。

Competing in the Age of AI

Marco Iansiti and Karim R. Lakhani, Competing in the Age of AI, Harvard Business Review, January-February 2020, pp. 61-67.

Some key points: 

Removing Limits to Scale, Scope, and Learning

Strategies are shifting away from traditional differentiation based on cost, quality, and brand equity and specialized, vertical expertise and toward advantages like business network position, the accumulation of unique data, and the deployment of sophisticated analytics. 

Putting AI at the Firm’s Core: One strategy, A clear architecture, The right capabilities, An agile “product” focus, Multidisciplinary governance.

一開始是最後一名沒關係，只要能後來居上就好了

 2022 台大畢業典禮 Appier 游直翰致詞 (葉正聖老師的逐字稿，新聞)

Garrett van Ryzin talks about optimization

以前教營收管理的時候，讀了不少哥倫比亞商學院 van Ryzin 教授的文章，其中一篇說，他們的研究是在解決10年後的問題。各位注意喔，是商學院！

最近在讀一本書，某教授寫的序，開頭四個字，就是學用落差。

如何選填大學志願

緣起：朋友念南一中理組的兒子要念大學，詢問我的意見，所以才準備了此檔案的大綱 (電子檔)。

聽到我描述 (相關) 電子產品中需要的機構設計和熱流問題，覺得挺有趣的。後來，他選擇了成大機械系。念完 6 年和尚學校的他，聯誼變成一個非常重要的考量因素。

如果喜歡畫畫，以我的投影片而言，20頁 App 軟體就需要創意和美學；22頁的設計，需要美學、藝術、和文學。大學也有相對應的科系，例如數位內容、3D 動畫、多媒體；如果會寫程式，有加值效果。

Integration of Face-to-Face Screening With Real-time Machine Learning to Predict Risk of Suicide Among Adults

Drew Wilimitis, Robert W. Turer, Michael Ripperger, et al., Integration of Face-to-Face Screening With Real-time Machine Learning to Predict Risk of Suicide Among Adults, JAMA Netw Open. 2022; 5(5):e2212095. doi:10.1001/jamanetworkopen.2022.12095.

In this cohort study of 120 398 adult patient encounters, an ensemble learning approach combined suicide risk predictions from the Columbia Suicide Severity Rating Scale and a real-time machine learning model. Combined models outperformed either model alone for risks of suicide attempt and suicidal ideation across a variety of time periods.

Subject to: Dimitris Bertsimas

Subject to: Dimitris Bertsimas

外送的經驗

之前看到一個新聞報導，因為薪水不錯，而且自由，台灣的年輕人喜歡從事外送工作。

二三年前，聽到一個國外新聞，實驗外送機器人，時速30公里的機車，可以在1公尺內完全煞車停止。今天上課的時候，跟同學分享這一個報告，忍不住地提醒同學，如果畢業後，都是從事這個行業，也少了人際間的互動；等到幾年後，公司大規模使用外送機器人而裁員，你大學學的東西全忘了，如何轉職？

2021 Tesla Impact Report

2021 Tesla Impact Report

Autopilot Safety

In 2021, we recorded 0.22 crashes for every million miles driven in which drivers were using Autopilot technology (Autosteer and active safety features). For drivers who were not using Autopilot technology (no Autosteer and active safety features), we recorded 0.77 crashes for every million miles driven. By comparison, NHTSA’s most recent data shows that in the United States there are 1.81 automobile crashes for every million miles driven.

Outracing champion Gran Turismo drivers with deep reinforcement learning

Wurman, P.R., Barrett, S., Kawamoto, K. et al. Outracing champion Gran Turismo drivers with deep reinforcement learning. Nature 602, 223–228 (2022). https://doi.org/10.1038/s41586-021-04357-7.

Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical manoeuvres to pass or block opponents while operating their vehicles at their traction limits. Racing simulations, such as the PlayStation game Gran Turismo, faithfully reproduce the non-linear control challenges of real race cars while also encapsulating the complex multi-agent interactions. Here we describe how we trained agents for Gran Turismo that can compete with the world’s best e-sports drivers. We combine state-of-the-art, model-free, deep reinforcement learning algorithms with mixed-scenario training to learn an integrated control policy that combines exceptional speed with impressive tactics. In addition, we construct a reward function that enables the agent to be competitive while adhering to racing’s important, but under-specified, sportsmanship rules. We demonstrate the capabilities of our agent, Gran Turismo Sophy, by winning a head-to-head competition against four of the world’s best Gran Turismo drivers. By describing how we trained championship-level racers, we demonstrate the possibilities and challenges of using these techniques to control complex dynamical systems in domains where agents must respect imprecisely defined human norms.

愛的教育和歐貝拉

 疫情再起，所以在家下廚。

25 Years of INFORMS

 Anne Robinson, editor, 25 Years of INFORMS, EC2020 Volume 1.

The first section, By the Numbers, leverages Web of Science and Google Scholar for the most-cited articles in our journals over the past 25 years. The next section, Community Choice, represents articles selected by our current editors-in-chief as well as INFORMS Society/Fora leaders, and you’ll see the explanation of why these articles were selected as impactful content. Our third section – The Analytics Movement – highlights some of the critical drivers that led to the INFORMS community embracing analytics and the notion of descriptive, predictive, and prescriptive modeling as a way to describe our work at the proverbial cocktail party. Next, we capture some of the ways that INFORMS and its members have impacted society for the better, including highlights from the Edelman Award and Wagner Prize. Last, but certainly not least, is a collection of thoughts from INFORMS past presidents, some new material and some thoughts recorded during their tenure as INFORMS President.

INFORMS Analytics Collections Vol. 16: Advances in Integrating AI & O.R.

Ramayya Krishnan and Pascal Van Hentenryck, editors, Advances in Integrating AI & O.R., EC2021, Volume 16, April 19, 2021.

The INFORMS strategic initiative in AI resulted in a white paper that summarized the findings and provided a number of recommendations for the INFORMS community. This volume of Editor’s Cut complements the white paper and assembles a collection of papers from the INFORMS community that bridge the AI and O.R. communities. The papers are grouped into five categories:

1. Blending Predictive and Prescriptive Methods
2. AI/ML for Optimization Problems
3. Integrating Predictive and Causal Inference
4. Games, Control, Data-intensive Preference Estimation
5. Unstructured Data Analytics, AI and OR/MS – Innovative Applications

Smart "Predict, then Optimize"

Adam N. Elmachtoub and Paul Grigas, Smart "Predict, then Optimize", Management Science, 2021, 68(1):9-26. (Code in Julia, arXiv. 1st place, INFORMS Junior Faculty Interest Group (JFIG) Paper Competition, 2020)

語言焦慮和人才培育

吃飯時，走過某位老師的教室，聽到的都是英文。如前所述，為了國際生，系上的研究所課程使用英文教學。 其實，每一學期，至少有3門大學部的必修課，使用全英教學。

我有嚴重的語言焦慮 (language anxiety)，碰到英文越好的人，症狀越明顯。就算留學美國，也改不了基因。只好套用朋友的正向思考法，她在外商上班，心想上班免費學英文，不亦樂乎。

對系上的本地生而言，應該蠻辛苦的。但是，長期而言，不論是就業、旅遊、或者拓展人脈，應該有正向的助益。

另外一方面，經過全民數十年的努力，我們已經有知識輸出的能力。台灣科技人才短缺，如果能吸引周遭國家的人才，來台灣就學、就業，不僅可以補充白領階級的不足；在多元思維的環境下，也有助於國際化和推展國際貿易。 

昨天和學校長官開策略會議時，得知系上兩位博士班畢業生，正擔任菲律賓某大學的院長，具體說明， 這一種軟實力的擴散。

The Clinician and Dataset Shift in Artificial Intelligence

Samuel G. Finlayson et al., The Clinician and Dataset Shift in Artificial Intelligence, New England Journal of Medicine, 2021; 385:283-286.

A major driver of AI system malfunction is known as “dataset shift.” Most clinical AI systems today use machine learning, algorithms that leverage statistical methods to learn key patterns from clinical data. Dataset shift occurs when a machine-learning system underperforms because of a mismatch between the data set with which it was developed and the data on which it is deployed. For example, the University of Michigan Hospital implemented the widely used sepsis-alerting model developed by Epic Systems; in April 2020, the model had to be deactivated because of spurious alerting owing to changes in patients’ demographic characteristics associated with the coronavirus disease 2019 pandemic. This was a case in which dataset shift fundamentally altered the relationship between fevers and bacterial sepsis, leading the hospital’s clinical AI governing committee (which one of the authors of this letter chairs) to decommission its use. This is an extreme example; many causes of dataset shift are more subtle. In Table 1, we present common causes of dataset shift, which we group into changes in technology (e.g., software vendors), changes in population and setting (e.g., new demographics), and changes in behavior (e.g., new reimbursement incentives); the list is not meant to be exhaustive.

Deb Raji, There’s more to data than distributions, Mar 31, 2022. 

Jose G. Moreno-Torres et al., A unifying view on dataset shift in classification, Pattern Recognition, Volume 45, Issue 1, January 2012, Pages 521-530.

Introducing and Integrating Machine Learning in an Operations Research Curriculum

Justin J. Boutilier and Timothy C. Y. Chan, Introducing and Integrating Machine Learning in an Operations Research Curriculum: An Application-Driven Course, INFORMS Transactions on Education, 22 Sep 2021.

Artificial intelligence (AI) and operations research (OR) have long been intertwined because of their synergistic relationship. Given the increasing popularity of AI and machine learning in particular, we face growing demand for educational offerings in this area from our students. This paper describes two courses that introduce machine learning concepts to undergraduate, predominantly industrial engineering and operations research students. Instead of taking a methods-first approach, these courses use real-world applications to motivate, introduce, and explore these machine learning techniques and highlight meaningful overlap with operations research. Significant hands-on coding experience is used to build student proficiency with the techniques. Student feedback indicates that these courses have greatly increased student interest in machine learning and appreciation of the real-world impact that analytics can have and helped students develop practical skills that they can apply. We believe that similar application-driven courses that connect machine learning and operations research would be valuable additions to undergraduate OR curricula broadly.

Efficient and targeted COVID-19 border testing via reinforcement learning

Bastani, H., Drakopoulos, K., Gupta, V. et al. Efficient and targeted COVID-19 border testing via reinforcement learning. Nature 599, 108–113 (2021). https://doi.org/10.1038/s41586-021-04014-z (EVA Public Dataset, Off-Policy and Counterfactual Analysis, Open-Source code for Project Eva)

Throughout the coronavirus disease 2019 (COVID-19) pandemic, countries have relied on a variety of ad hoc border control protocols to allow for non-essential travel while safeguarding public health, from quarantining all travellers to restricting entry from select nations on the basis of population-level epidemiological metrics such as cases, deaths or testing positivity rates. Here we report the design and performance of a reinforcement learning system, nicknamed Eva. In the summer of 2020, Eva was deployed across all Greek borders to limit the influx of asymptomatic travellers infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and to inform border policies through real-time estimates of COVID-19 prevalence. In contrast to country-wide protocols, Eva allocated Greece’s limited testing resources on the basis of incoming travellers’ demographic information and testing results from previous travellers. By comparing Eva’s performance against modelled counterfactual scenarios, we show that Eva identified 1.85 times as many asymptomatic, infected travellers as random surveillance testing, with up to 2–4 times as many during peak travel, and 1.25–1.45 times as many asymptomatic, infected travellers as testing policies that utilize only epidemiological metrics. We demonstrate that this latter benefit arises, at least partially, because population-level epidemiological metrics had limited predictive value for the actual prevalence of SARS-CoV-2 among asymptomatic travellers and exhibited strong country-specific idiosyncrasies in the summer of 2020. Our results raise serious concerns on the effectiveness of country-agnostic internationally proposed border control policies3 that are based on population-level epidemiological metrics. Instead, our work represents a successful example of the potential of reinforcement learning and real-time data for safeguarding public health.

我永遠站在「雞蛋」的那方

村上春樹主講，張翔一整理，我永遠站在「雞蛋」的那方，天下雜誌 418期 ，2009/03

今天我以一名小說家的身分來到耶路撒冷。而小說家，正是所謂的職業謊言製造者。

當然，不只小說家會說謊。眾所周知，政治人物也會說謊。外交官、將軍、二手車業務員、屠夫和建築師亦不例外。但是小說家的謊言和其他人不同。沒有人會責怪小說家說謊不道德。相反地，小說家愈努力說謊，把謊言說得愈大愈好，大眾和評論家反而愈讚賞他。為什麼？

今天，我不打算說謊

我的答案是：藉由高超的謊言，也就是創作出幾可亂真的小說情節，小說家才能將真相帶到新的地方，也才能賦予它新的光輝。

紀念鄭南榕

南榕人生

鄭南榕出生於一九四七年，也就是發生二二八事件的那一年，那個驚悚的年代深深影響鄭南榕的一生；在其第一次求職的履歷表上，他這麼寫著：「我出生在二二八事件那一年，那事件帶給我終生的困擾。因為我是個混血兒，父親是在日本時代來台的福州人，母親是基隆人，二二八事件後，我們是在鄰居的保護下，才在台灣人對外省人的報復浪潮裡，免於受害。」後來他之所以強烈主張台灣獨立，並且不惜以身殉道，都和二二八事件有關。他認為：「第一、台灣要走上民主政治的話，一定要先破除國民黨的統治神話；台灣只有獨立，才可能真正民主化，才可能真正回歸人民主權。第二、二二八事件之所以發生，是因為中國與台灣兩地經濟、文化、法治、生活水平相差太遠，強行合併，悲劇自然發生。現在，這種情況再度發生於海峽兩岸，只有台灣獨立，才可以避免另一次二二八事件。」

ACM Turing Award Honors Jack J. Dongarra

ACM, ACM Turing Award Honors Jack J. Dongarra for Pioneering Concepts and Methods Which Have Resulted in World-Changing Computations, March 30, 2022.

ACM, the Association for Computing Machinery, today named Jack J. Dongarra recipient of the 2021 ACM A.M. Turing Award for pioneering contributions to numerical algorithms and libraries that enabled high performance computational software to keep pace with exponential hardware improvements for over four decades. Dongarra is a University Distinguished Professor of Computer Science in the Electrical Engineering and Computer Science Department at the University of Tennessee. He also holds appointments with Oak Ridge National Laboratory and the University of Manchester.

Efficient method for training deep networks with unitary matrices

Bobak Kiani, Randall Balestriero, Yann Lecun, and Seth Lloyd,  projUNN: efficient method for training deep networks with unitary matrices, arXiv:2203.05483v2.

In learning with recurrent or very deep feed-forward networks, employing unitary matrices in each layer can be very effective at maintaining long-range stability. However, restricting network parameters to be unitary typically comes at the cost of expensive parameterizations or increased training runtime. We propose instead an efficient method based on rank-k updates -- or their rank-k approximation -- that maintains performance at a nearly optimal training runtime. We introduce two variants of this method, named Direct (projUNN-D) and Tangent (projUNN-T) projected Unitary Neural Networks, that can parameterize full N-dimensional unitary or orthogonal matrices with a training runtime scaling as O(kN^2). Our method either projects low-rank gradients onto the closest unitary matrix (projUNN-T) or transports unitary matrices in the direction of the low-rank gradient (projUNN-D). Even in the fastest setting (k=1), projUNN is able to train a model's unitary parameters to reach comparable performances against baseline implementations. By integrating our projUNN algorithm into both recurrent and convolutional neural networks, our models can closely match or exceed benchmarked results from state-of-the-art algorithms.

Strong mixed-integer programming formulations for trained neural networks

R. Anderson, J. Huchette, W. Ma, C. Tjandraatmadja, and J.P. Vielma, Strong mixed-integer programming formulations for trained neural networks, Mathematical Programming, 2020, 183(1-2):3-39, ISSN 14364646, URL http://dx.doi.org/10.1007/s10107-020-01474-5.

We present strong mixed-integer programming (MIP) formulations for high-dimensional piecewise linear functions that correspond to trained neural networks. These formulations can be used for a number of important tasks, such as verifying that an image classification network is robust to adversarial inputs, or solving decision problems where the objective function is a machine learning model. We present a generic framework, which may be of independent interest, that provides a way to construct sharp or ideal formulations for the maximum of d affine functions over arbitrary polyhedral input domains. We apply this result to derive MIP formulations for a number of the most popular nonlinear operations (e.g. ReLU and max pooling) that are strictly stronger than other approaches from the literature. We corroborate this computationally, showing that our formulations are able to offer substantial improvements in solve time on verification tasks for image classification networks.

Machine Learning into Metaheuristics: A Survey and Taxonomy

El-Ghazali Talbi, Machine Learning into Metaheuristics: A Survey and Taxonomy, ACM Computing Surveys, Volume 54, Issue 6, July 2022, Article No.: 129, pp 1–32, https://doi.org/10.1145/3459664.

 During the past few years, research in applying machine learning (ML) to design efficient, effective, and robust metaheuristics has become increasingly popular. Many of those machine learning-supported metaheuristics have generated high-quality results and represent state-of-the-art optimization algorithms. Although various appproaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this research topic. In this article, we will investigate different opportunities for using ML into metaheuristics. We define uniformly the various ways synergies that might be achieved. A detailed taxonomy is proposed according to the concerned search component: target optimization problem and low-level and high-level components of metaheuristics. Our goal is also to motivate researchers in optimization to include ideas from ML into metaheuristics. We identify some open research issues in this topic that need further in-depth investigations.

Global and Robust Optimization for Engineering Design

 Berk Öztürk, Global and Robust Optimization for Engineering Design, Ph.D. Thesis, MIT, 2022. (thesis, code, talk)

There is a need to adapt and improve conceptual design methods through better optimization, in order to address the challenge of designing future engineered systems. Aerospace design problems are tightly-coupled optimization problems, and require all-at-once solution methods for design consensus and global optimality. Although the literature on design optimization has been growing, it has generally focused on the use of gradient-based and heuristic methods, which are limited to local and low-dimensional optimization respectively. There are significant benefits to leveraging structured mathematical optimization instead. Mathematical optimization provides guarantees of solution quality, and is fast, scalable, and compatible with using physics-based models in design. More importantly perhaps, there has been a wave of research in optimization and machine learning that provides new opportunities to improve the engineering design process. This thesis capitalizes on two such opportunities.

Tackling Climate Change with Machine Learning

David Rolnic et al., Tackling Climate Change with Machine Learning, ACM Computing Surveys, Volume 55, Issue 2, March 2023, Article No.: 42, pp 1–96, https://doi.org/10.1145/3485128

Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.

from lab bench to public office and back

Chen Chien-jen, Taiwan’s pandemic vice-president — from lab bench to public office and back, Nature 603, 203 (2022)

Two years on from the World Health Organization’s official declaration of the pandemic, I’ve been thinking about lessons I’ve learnt toggling between science and public service. I think all researchers — from bench scientists to physicists to computational social scientists — might find this exercise useful. Government advisers, too.

Principles for Dealing with the Changing World Order by Ray Dalio

 Principles for Dealing with the Changing World Order by Ray Dalio

To make money, study maths or economics at a top university

 Economist, To make money, study maths or economics at a top university, Nov 26th, 2019.

Datasets for machine learning and more

Easy to download:

• Jason Brownlee, Machine learning datasets used in tutorials on MachineLearningMastery.com

• M. Fernández-Delgado, E. Cernadas, S. Barro, & D. Amorim, (2014). Do we need hundreds of classifiers to solve real world classification problems?. The Journal of Machine Learning Research, 15(1), 3133-3181. (121 datasets in a click!)

• https://datasetsearch.research.google.com/

• Prognostics Data Repository

• scikit-learn

讓 AI 幫你最佳化太陽能電池材料的製程參數

採訪撰文 簡克志，美術設計 林洵安，機器學習 x 鈣鈦礦材料：讓 AI 幫你最佳化太陽能電池材料的製程參數！，研之有物，2022-02-21

機器學習輔助材料設計

為了 2050 淨零排放的目標，太陽能發電為不可或缺的再生能源之一，其中「鈣鈦礦太陽能電池」是近年最熱門的研究領域，不僅成本低廉、光電轉換效率也可達到 25%。然而，鈣鈦礦材料在環境中容易降解，影響使用壽命。材料科學家為了做出效能好又穩定的鈣鈦礦「料理」，無不卯足了勁，替這道菜加上各種「食材」，但是越複雜的菜，調出好味道就越困難。人腦畢竟有限，如果交給機器呢？中央研究院「研之有物」專訪院內應用科學研究中心包淳偉研究員，他與團隊訓練了一套機器學習模型，可以又快又準的找出複雜鈣鈦礦材料的最佳化條件！

Interpretable machine learning by Molnar

Molnar, Christoph. “Interpretable machine learning. A Guide for Making Black Box Models Explainable”, 2019. https://christophm.github.io/interpretable-ml-book/.

This book started as a side project when I was working as a statistician in clinical research. I worked four days a week, and on my “day off” I worked on side projects. Eventually, interpretable machine learning became one of my side projects. At first I had no intention of writing a book. Instead, I was simply interested in finding out more about interpretable machine learning and was looking for good resources to learn from. Given the success of machine learning and the importance of interpretability, I expected that there would be tons of books and tutorials on this topic. But I only found the relevant research papers and a few blog posts scattered around the internet, but nothing with a good overview. No books, no tutorials, no overview papers, nothing. This gap inspired me to start writing this book. I ended up writing the book I wished was available when I began my study of interpretable machine learning. My intention with this book was twofold: to learn for myself and to share this new knowledge with others.

分工和效率

備課的時候，除了論文和雜誌以外，我習慣看很多的書，以便整理出適合學生的教材。

系上配置4位助理，所以請助理幫我輸入圖書推薦清單。沒想到，才兩個禮拜，圖書館就已經通知我，可以借其中的十本。更開心的是，學校允許老師較長的借期，算一算，1年後才需回館。

亞當斯密早在十八世紀，就已經強調分工的重要性，以提升所有人的效率。

從排隊理論的觀點來看，如果一個隨機的服務系統，當使用率接近1的時候 ，就會產生事情延誤的狀況。更不要說，需要規劃與思考的工作，太過忙碌，很容易掛一漏萬。

對管理者而言，當然很難決定資源的配置。高強教授當成大校長的時候，使用資料包絡法，分析和比較系所的生產力。某董事長使用類比法，她說：等到你們研究像在坐的某教授一樣好 (剛從中正退休)，再來爭取。

無心插柳

系助理幫我發郵件給研究生，沒想到，有一位菲律賓籍的博一學生，之前在菲律賓當講師，說她努力工作，要來當我的助教。她說不懂中文，很擔心。想想以前，在國外的情景；就安慰她，沒有關係，一切都可以學習。

系上的研究所課程，只要有外籍學生，就 (希望) 用英文上課。沒想到，這位學生已經找了兩位外籍 (和外所) 生要來修機器學習。

我的錄影課程，用中文教學，採翻轉式教學。真的希望，有像台大學生程度的助教，幫我的影片轉成英文。

當然，可遇不可求。只好趕緊準備，每個禮拜的重點提示。

7 real-world applications of reinforcement learning

 Joy Zhang, 7 real-world applications of reinforcement learning, gocoder, February 17, 2022

1. Autonomous driving with Wayve

2. Personalizing your Netflix recommendations

3. Optimizing inventory levels for Walmart

4. Improving search engine results with search.io

5. Improving language models with OpenAI's WebGPT

6. Trading on the financial markets with IBM's DSX platform

7. Robotics with the University of California, Berkeley

台灣有多少人才，正被當蠢才教？

 蔡淇華，台灣有多少人才，正被當蠢才教？，翻轉教育，2021-01-18 

原來學不會，不是「永遠學不會」，是難度、是教法、更是時機的問題。一天學生科翰打電話給我，帶來了這樣的一個消息，更提醒了我這件事，不禁也讓我回想起身邊的幾個例子 ……

Computer Scientists Prove Why Bigger Neural Networks Do Better

Mordechai Rorvig, Computer Scientists Prove Why Bigger Neural Networks Do Better, Quanta Magazine, February 10, 2022.

Interpolation 

An old mathematical result says that to fit n data points with a curve, you need a function with n parameters. (In the previous example, the two points were described by a curve with two parameters.) When neural networks first emerged as a force in the 1980s, it made sense to think the same thing. They should only need n parameters to fit n data points — regardless of the dimension of the data.

“This is no longer what’s happening,” said Alex Dimakis of the University of Texas, Austin. “Right now, we are routinely creating neural networks that have a number of parameters more than the number of training samples. This says that the books have to be rewritten.”

Google Research: Themes from 2021 and Beyond

Jeff Dean, Google Research: Themes from 2021 and Beyond, Google, January 11, 2022.

· Trend 1: More Capable, General-Purpose ML Models

  · Trend 2: Continued Efficiency Improvements for ML

  · Trend 3: ML Is Becoming More Personally and Communally Beneficial

  · Trend 4: Growing Benefits of ML in Science, Health and Sustainability

  · Trend 5: Deeper and Broader Understanding of ML

YouTube video streaming now using A.I. that mastered chess and Go

JEREMY KAHN, YouTube video streaming now using A.I. that mastered chess and Go, Fortune, February 11, 2022.

The artificial intelligence algorithm, called MuZero, was developed by YouTube’s London-based sister company within Alphabet, DeepMind, which is dedicated to advanced A.I. research. When applied to YouTube videos, the system has resulted in a 4% reduction on average in the amount of data the video-sharing service needs to stream to users, with no noticeable loss in video quality.

Unbiggen AI

ELIZA STRICKLAND, Andrew Ng: Unbiggen AI The AI pioneer says it’s time for smart-sized, “data-centric” solutions to big issues, IEEE Spectrum, 09 FEB 2022.

Q: You often talk about companies or institutions that have only a small amount of data to work with. How can data-centric AI help them?

Ng: You hear a lot about vision systems built with millions of images—I once built a face recognition system using 350 million images. Architectures built for hundreds of millions of images don’t work with only 50 images. But it turns out, if you have 50 really good examples, you can build something valuable, like a defect-inspection system. In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn.

珍重再見，台南

台南一住二十多年，有著許多美好的回憶與追憶。

感恩長榮大學和資管系教職員的幫忙。我這一種直來直往的個性，應該得罪了不少人，感謝大家對我的包容。少子化之下，學校依然堅持正派辦學；院裡的老師認真教學與輔導學生，真的很令人感動 (1)。

日缺400萬顆蛋

 吳靜君 、 林良齊 、 陳夢茹，蛋價回不去了？日缺400萬顆蛋 業者：從來沒缺得這麼嚴重，中國時報，2022年1月27日

國內雞蛋主要的供應來源有大成、卜蜂、福壽等公司。蛋供應短缺，卜蜂分析主因黃小玉等國際原物料價格飆漲3成左右，雞農成本壓力大增，若要維持原有供應量，須淘汰一些產量較少的老母雞、改養小雞，小雞長大到能夠下蛋需要一段時間，以致青黃不接。其次，近期天氣不穩定，造成雞隻死亡，也是雞蛋產量減少主因。

小雞餵養、成長到可以下蛋，需要18周的時間，業者預期，今年上半年雞蛋供應、價格都較為混亂，等一段時間雞蛋供應提升，缺口才會弭平。惟業者認為，原物料價格若維持高檔，雞農的成本壓力仍存在，短期內蛋價要回到過去低價，恐怕很困難。

大成分析，前陣子禽流感造成雞隻死亡，又正逢農曆年雞蛋需求旺季，讓雞蛋原本已供不應求的問題，更加雪上加霜。 

The modern mathematics of deep learning

Julius Berner, Philipp Grohs, Gitta Kutyniok, and Philipp Petersen, The modern mathematics of deep learning, arXiv preprint arXiv:2105.04026, 2021.

We describe the new field of mathematical analysis of deep learning. This field emerged around a list of research questions that were not answered within the classical framework of learning theory. These questions concern: the outstanding generalization power of overparametrized neural networks, the role of depth in deep architectures, the apparent absence of the curse of dimensionality, the surprisingly successful optimization performance despite the non-convexity of the problem, understanding what features are learned, why deep architectures perform exceptionally well in physical problems, and which fine aspects of an architecture affect the behavior of a learning task in which way. We present an overview of modern approaches that yield partial answers to these questions. For selected approaches, we describe the main ideas in more detail.