半導體產業一甲子的演化與展望

 黃欽勇，半導體產業一甲子的演化與展望，電子時報，2021-12-28

 

螺絲廠產業智慧化轉型

蘇文彬，【5G專網工廠現場直擊】螺絲廠結合5G、大數據分析提高生產力、降低成本，加速產業智慧化轉型，iThome，2021-12-10

「一顆相同的螺絲，依照客戶訂單需求，不同的呎吋、處理方式，產生上萬種品項」，位於高雄的螺絲業者久陽精密總經理吳居諺一語道出螺絲產業現況。

在高雄梓官、橋頭、崗山一帶，為國內螺絲產業群聚地，臺灣將近有1,800多家螺絲業者，所生產的螺絲不只用於國內，也外銷至國外，從航太、醫療、電動車到3C電子，都有採用螺絲，臺灣更有「螺絲王國」之名，更是僅次於中國、德國，為全球第三大扣件出口國。

然而，螺絲產業卻面臨一些問題，由於客戶訂單需求的多樣化，導致螺絲生產少量多樣的現象，以久陽精密為例，該公司一年生產的螺絲品項就多達1萬3千種以上，每年產出約17億隻螺絲，在製造過程中，消耗約5萬件模具，使用7萬5千個船形物流桶。這樣複雜的生產過程中，過去大多是仰賴老師傅，以人工方式回報生產數據，再由專人登打輸入，但容易因為手寫錯誤，或是手動登打輸入數據也可能出錯。此外，傳產業的製造現場勞動人力還面臨逐漸老化，經驗不易傳承，人才斷層的危機。...

寄語阿成

陳素貞「寄語阿成」全文

台灣的陳文成基金會要舉辦四十周年紀念活動，也要出版紀念集，我覺得應該寫幾個字。距上次提筆，那是你十周年紀念時。在歲月的流轉中，怎麼三十年就過去了？

在你猝然離去後，每次想到你，尤其是想起你死得那麼慘，那麼多的內傷，就心痛得不能自已。為了生活，只得把對你的思念埋在記憶深處。

遠山呼喚

 遠山呼喚

創辦遠山呼喚，就是因為我們想要創造永續的教育。我們要透過建立平等發展的組織架構，開啟跨國界的教育系統，經營長遠的人才計畫，去追求長期教育的夢想。

我們相信有一天，「源自台灣的教育」也能成為國際品牌，這個品牌代表長期、代表平等、代表專業、也代表我們充滿溫暖的家鄉。

What are the most important statistical ideas of the past 50 years?

Andrew Gelman and Aki Vehtari, What are the most important statistical ideas of the past 50 years?, arXiv:2012.00174v5.

We review the most important statistical ideas of the past half century, which we categorize as: counterfactual causal inference, bootstrapping and simulation-based inference, overparameterized models and regularization, Bayesian multilevel models, generic computation algorithms, adaptive decision analysis, robust inference, and exploratory data analysis. We discuss key contributions in these subfields, how they relate to modern computing and big data, and how they might be developed and extended in future decades. The goal of this article is to provoke thought and discussion regarding the larger themes of research in statistics and data science.

在台一度「學到怕」的程式語言，在史丹佛只求「會開機」的課堂上學會了！

 賴冠穎，在台一度「學到怕」的程式語言，在史丹佛只求「會開機」的課堂上學會了！現在他把方法帶回台灣，換日線，2021/11/12

在畢業門檻的 15 門課裡，扣除 10 門材料系上的課程之外，學生還必須要再跨系選修 5 門課。在學長的強烈建議下，他修了「CS106A 程式設計方法論」（Programming methodology），當時這門課的修課條件僅標註著「會開機就行」，讓原本對程式語言信心全失的 Jerry 決定再給自己一次機會。想不到這門課，也讓他對自己的職涯靈感「正式開機」！

書 Matching Supply with Demand

G. Cachon and C. Terwiesch, Matching Supply with Demand: An Introduction to Operations Management, 1st Edition, McGraw-Hill, 2006. (有第四版)

本書是作者針對 Wharton 企管碩士 (MBA) 核心課程 Operations Management (作業管理) 所發展出來的課本。

根據實際的公司和數據，透過清楚和基礎的數學分析，並融入最新的研究結果，呈現出一本讓人眼睛一亮的好書。

兩位作者的論文被高度引用，說明名校的教授能夠創造知識，也努力地將之轉成教材，教學與研究並進。

金門觀光主題曲【牆外】

 關於那一道墻的故事... 黃明志金門觀光主題曲【牆外】Ft. 小花 @鬼才做音樂 2021 Ghosician

How frustration can make us more creative

Tim Harford, How frustration can make us more creative, TED, 2016/2/3. (8:20 處的策略，類似強化學習的探索 (exploration))

John N. Tsitsiklis on writing

 John N. Tsitsiklis, On writing, MIT. 

A unified framework for stochastic optimization

W.B. Powell, A unified framework for stochastic optimization, European Journal of Operational Research, 2019, Volume 275, Issue 3, 16 June 2019, Pages 795-821. (pdf)

Stochastic optimization is an umbrella term that includes over a dozen fragmented communities, using a patchwork of sometimes overlapping notational systems with algorithmic strategies that are suited to specific classes of problems. This paper reviews the canonical models of these communities, and proposes a universal modeling framework that encompasses all of these competing approaches. At the heart is an objective function that optimizes over policies that is standard in some approaches, but foreign to others. We then identify four meta-classes of policies that encompasses all of the approaches that we have identified in the research literature or industry practice. In the process, we observe that any adaptive learning algorithm, whether it is derivative-based or derivative-free, is a form of policy that can be tuned to optimize either the cumulative reward (similar to multi-armed bandit problems) or final reward (as is used in ranking and selection or stochastic search). We argue that the principles of bandit problems, long a niche community, should become a core dimension of mainstream stochastic optimization.

洗髮精董座投身環保面膜

楊絲貽，「生命有限可留下什麼？」洗髮精董座投身環保面膜，商業周刊第1765期，2021.09.08

「全球品牌都會變綠，遲早而已，」歐萊德董事長葛望平說。

歐萊德是台灣企業中，很早就喊出在一定時間內，承諾達成百分之百再生能源的企業；過去幾年，他們不僅研發出百分之百再生塑膠材料（PCR，Post-Consumer Recycled）製成的洗髮精瓶身、全球第一支再生壓頭，2020年全品項也達成碳中和成果。...

47-779 Quantum Integer Programming

Prof. Sridhar Tayur, Dr. Davide Venturelli, and David Bernal, 47-779 Quantum Integer Programming (QuIP), Fall 2020. (lecture note)

This course is primarily designed for graduate students (and advanced undergraduates) across CMU campuses interested in integer programming (with non-linear objective functions) and the potential of near-term quantum computing for solving combinatorial optimization problems. By the end of the semester, someone enrolled in this course should be able to:

• Identify the current status of quantum computing and its potential uses for integer programming
• Access and use quantum computing resources (such as DWave Quantum Annealers)
• Set up a given integer program to be solved with quantum computing
• Work in groups collaboratively on a state-of-the-art project regarding applications of quantum computing and integer programming

Moonshot: Quantum Computing

Sridhar Tayur, Moonshot: Quantum Computing, Tepper CMU, 2018/7/7

用AI改善機關作業效率，法務部靠IT再造為轉型打底

蘇文彬，用AI改善機關作業效率，法務部靠IT再造為轉型打底，iThome，2021-10-28 

今年2月，法務部旗下各檢察機關有了一套新的語音辨識系統，透過中文語音辨識技術，不只可以快速產生一般會議的文字紀錄，甚至成為檢察機關偵辦案件的幫手，將蒐集各式影音證據，利用語音辨識快速產生逐字稿，讓檢察官能夠快速勘驗案件相關證據，不需再聽完完整的語音資料，快速找到可能的線索。

法務部這套語音辨識系統背後使用AI Labs的雅婷逐字稿語音辨識技術，只用了大量了會議錄音檔來訓練，而為了讓系統能夠辨識專業的司法用語，還搭配司法用語詞彙，供業者不斷調校AI模型，目前可以進行純國語的辨識，辨識率也達到法務部設定目標。

近幾年大力推動AI應用的法務部資訊處長鄭輝彬表示，內部測試結果，辨識準確率能達到9成以上，除了法務部與檢察機關，明年計畫也將讓行政執行、矯正機關試用。...

台積電的訂價策略

陳良榕，台積電怎麼贏？， 天下雜誌 609 期，2016-10-25

經營人的學習與成長

張忠謀，經營人的學習與成長，台灣玉山科技協會20周年慶祝大會暨論壇-晚宴，10月26日

探求歷史真相與責任的開始

促進轉型正義委員會，「探求歷史真相與責任的開始：壓迫體制及其圖像」發表會會議手冊，2021/05/07 

本會於110年5月4日舉辦「探求歷史真相與責任的開始：壓迫體制及其圖像」發表會，提出釐清壓迫體制的四個層次：一、釐清加害體制圖像；二、釐清加害行為圖像；三、辨識加害者與參與者；四、加害者究責。

此外，本會並以組成壓迫體制的重要支柱—軍事審判制度及情治機關運作為主軸，發表本會兩年多來的調查研究成果。

校園安定系統之佈建運用情形

會友集

余英時著、彭國翔編，會友集(上)  (下)：余英時序文集，三民，2010

基於對國家、社會的文化的關懷，發乎真誠的良知和深厚的學養，引導讀者在諸多知識和價值的世界中穿梭游戈而不致迷失方向。

本書彙集了余英時先生為海內外學者所作序文五十一篇，所論涵蓋從先秦至當代三千年中國歷史，貫通歷史、文學、哲學、宗教、政治、社會、經濟、法律等諸多學科，精闢分析學術和時代的課題，篇篇由其深厚的學養和真誠的良知所發。「內聖外王」連續一貫，學術研究與價值操守、文化關懷相互支援、有機結合，將所序著作的論題置入一個更為深廣的脈絡之中，引領讀者進入一個更為「廣大精微」意義世界。

Taiwan: Last Week Tonight with John Oliver (HBO)

Taiwan: Last Week Tonight with John Oliver (HBO), 2021/10/25

自駕車挑戰賽

非凡財經新聞，比職業賽車手厲害?"演算法"當駕駛員 自駕車時速飆218km 德國團隊奪冠抱走2800萬獎金，20211025 (Indy Autonomous Challenge)

島嶼天光

滅火器樂團，島嶼天光，2014年3月30日

Which Programming Languages Use the Least Electricity?

David Cassel, Which Programming Languages Use the Least Electricity?, the New Stack, 20 May 2018.

Last year a team of six researchers in Portugal from three different universities decided to investigate this question, ultimately releasing a paper titled “Energy Efficiency Across Programming Languages.” They ran the solutions to 10 programming problems written in 27 different languages, while carefully monitoring how much electricity each one used — as well as its speed and memory usage.

Minimum-Distortion Embedding

Akshay Agrawal, Alnur Ali and Stephen Boyd (2021), "Minimum-Distortion Embedding", Foundations and Trends® in Machine Learning: Vol. 14: No. 3, pp 211-378. http://dx.doi.org/10.1561/2200000090. 

We consider the vector embedding problem. We are given a finite set of items, with the goal of assigning a representative vector to each one, possibly under some constraints (such as the collection of vectors being standardized, i.e., have zero mean and unit covariance). We are given data indicating that some pairs of items are similar, and optionally, some other pairs are dissimilar. For pairs of similar items, we want the corresponding vectors to be near each other, and for dissimilar pairs, we want the corresponding vectors to not be near each other, measured in Euclidean distance. We formalize this by introducing distortion functions, defined for some pairs of the items. Our goal is to choose an embedding that minimizes the total distortion, subject to the constraints. We call this the minimum-distortion embedding (MDE) problem.

This monograph is accompanied by an open-source Python package, PyMDE, for approximately solving MDE problems. Users can select from a library of distortion functions and constraints or specify custom ones, making it easy to rapidly experiment with different embeddings. Because our algorithm is scalable, and because PyMDE can exploit GPUs, our software scales to data sets with millions of items and tens of millions of distortion functions. Additionally, PyMDE is competitive in runtime with specialized implementations of specific embedding methods. To demonstrate our method, we compute embeddings for several real-world data sets, including images, an academic co-author network, US county demographic data, and single-cell mRNA transcriptomes.

DeepMind’s AI predicts almost exactly when and where it’s going to rain

Will Douglas Heaven, DeepMind’s AI predicts almost exactly when and where it’s going to rain, MIT Technology Review, September 29, 2021.

In a blind comparison with existing tools, several dozen experts judged DGMR’s forecasts to be the best across a range of factors—including its predictions of the location, extent, movement, and intensity of the rain—89% of the time. The results were published in a Nature paper today.

解決問題理論與實務

張毓思，1700人搶48，錄取率不到10％　台大現象級通識課，是在教什麼？，天下Web only，2021-02-26

因為要修這門由台大領導學程開設的課程可不容易，學生修課名額48人，今年申請修課與旁聽的總和已達到1737件，學生不但要通過第二階段的面試，還要參加週末整日的工作坊才能搶到修課名額。以申請修課（非旁聽）的學生來算，48個名額有501人申請，錄取率不到10％。

經營人的學習與成長

張忠謀，經營人的學習與成長，台灣玉山科技協會20周年慶祝大會暨論壇-晚宴，2021年10月26日

Digital Transformation: Survive and Thrive in an Era of Mass Extinction

Thomas M. Siebel,  Digital Transformation: Survive and Thrive in an Era of Mass Extinction, RosettaBooks, 2019.

From visionary Silicon Valley entrepreneur Tom Siebel comes a penetrating examination of the new technologies that are disrupting business and government--and how organizations can harness them to transform into digital enterprises.

Industry 4.0: Opportunities and Challenges for Operations Management

Tava Lennon Olsen, Brian Tomlin (2020) Industry 4.0: Opportunities and Challenges for Operations Management. Manufacturing & Service Operations Management, 22(1):113-122. (pdf)

Industry 4.0 connotes a new industrial revolution centered around cyber-physical systems. It posits that the real-time connection of physical and digital systems, along with new enabling technologies, will change the way that work is done and therefore, how work should be managed. It has the potential to break, or at least change, the traditional operations trade-offs among the competitive priorities of cost, flexibility, speed, and quality. This article describes the technologies inherent in Industry 4.0 and the opportunities and challenges for research in this area. The focus is on goods-producing industries, which include both the manufacturing and agricultural sectors. Specific technologies discussed include additive manufacturing, the internet of things, blockchain, advanced robotics, and artificial intelligence.

科技部半導體射月計畫 109 年度產學技術交流

 科技部半導體射月計畫 109 年度產學技術交流

• Boris Murmann, tinyML: The Perfect Storm for Innovation in Ultra-Low-Power System Design 
• 梁伯嵩，IC 運算平台趨勢: 數位運算、人工智慧與量子運算
• Tetsu Ohtou, Semiconductor Process and Equipment Technology for Advanced Logic Devices 
• 陳俊雄，汽車產業及感測元件發展趨勢

疫情中從海外看台灣

孟買春秋，疫情中從海外看台灣，思想坦克，2021 年 9 月 24 日

經過兩年，我和丈夫終於回到普羅旺斯的家，離開台北時對台灣之外的疫情世界忐忑不安，畢竟過去一年多台灣彷彿世外桃源，不知疫情為何物。然而抵達南法十多天之後，緊張的心情似乎已經消失了，取而代之的是令我無比驕傲的台灣人身分。

Quantum Computers, Explained With Quantum Physics

 Quanta Magazine, Quantum Computers, Explained With Quantum Physics, 2021/6/8

一流的人讀書，都在哪裡畫線？

Eiji Doi 著，歐凱寧譯，一流的人讀書，都在哪裡畫線？：菁英閱讀的深思考技術，天下雜誌，2021

進入社會後，讀書，有個重要的任務，就是投資自己的生涯，從龐雜、陌生的領域中建立起讓自己成長的知識基礎。

Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies

Paul Vicol, Luke Metz, and Jascha Sohl-Dickstein, Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies, ICML 2021. (paper, Outstanding Paper Awards)

Unrolled computation graphs arise in many scenarios, including training RNNs, tuning hyperparameters through unrolled optimization, and training learned optimizers. Current approaches to optimizing parameters in such computation graphs suffer from high variance gradients, bias, slow updates, or large memory usage. We introduce a method called Persistent Evolution Strategies (PES), which divides the computation graph into a series of truncated unrolls, and performs an evolution strategies-based update step after each unroll. PES eliminates bias from these truncations by accumulating correction terms over the entire sequence of unrolls. PES allows for rapid parameter updates, has low memory usage, is unbiased, and has reasonable variance characteristics. We experimentally demonstrate the advantages of PES compared to several other methods for gradient estimation on synthetic tasks, and show its applicability to training learned optimizers and tuning hyperparameters.

Guessing Random Additive Noise Decoding

 Prof. Muriel Médard, Guessing Random Additive Noise Decoding (GRAND), Rice U ECE, 2020年9月7日

裕利讓疫苗物流履歷可全程追溯

余至浩，善用IT克服冷鏈運輸大挑戰，裕利讓疫苗物流履歷可全程追溯，iThome，2021-07-16

溫度控制

COVID-19疫苗配送的過程中，費而隱指出，運輸端是最大挑戰。他解釋，疫苗還在倉儲冷藏庫或冷凍庫內時，對於溫度監控相較容易許多，但只要出了物流中心，疫苗的溫度就會一直變化，很難保持恆溫，例如司機開關車門或打開保冷箱，它的溫度就會產生波動，「所以車上溫度控制要非常小心。」費而隱強調。

Strait of Emergency?

Rachel Esplin Odell and Eric Heginbotham; Bonny Lin and David Sacks; Kharis Templeman; Oriana Skylar Mastro, Strait of Emergency? Debating Beijing’s Threat to Taiwan, Foreign Affairs, September/October 2021. (輸入電郵，可以收到全文連結)

Global Taiwan Watch (全球台灣觀察)，美國智庫激辯中國侵台論，FB

A high-bias, low-variance introduction to Machine Learning for physicists

Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G.R. Day, Clint Richardson, Charles K. Fisher, David J. Schwab, A high-bias, low-variance introduction to Machine Learning for physicists, Phyics Reports, 810 (2019) 1-124. (Python, Github)

Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, generalization, and gradient descent before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python Jupyter notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists may be able to contribute.

紀念余英時先生

蘇曉康，「我在哪裡，哪裡就是中國」──蘇曉康談余英時的氣節，報導者，2021/8/7

Chris Buckley, Ying-shih Yu, Renowned Scholar of Chinese Thought, Dies at 91, NYT, Aug. 10, 2021

李弘祺，紀念余英時先生，余先生的知識人情懷，思想坦克，2021/8/11

momo 訂單不延遲心法是什麼

陳宜伶，momo 訂單不延遲心法是什麼？谷元宏獨家解析疫情下電商必勝策略，buzzorange，2021-09-01

供應鏈和選址

谷元宏表示，沒有人能料到疫情，是因為 momo 已先洞察到電商未來兩到三年的大趨勢，所以有預先佈局衛星倉，把最常搶購一空的貨事先備齊。「你會希望消費者上來都是找得到貨的，更要思考有這麼多貨，該怎麼把貨送給客人？」

谷元宏舉例說，在網際網路建立初期，最難處理的技術問題不是演算法，而是「量」，「甚至現在到了逢年過節，火車票難搶也是『量』的問題。」

所以 momo 率先想到的是，「該怎麼解決同一時間蜂擁而來的量，這些量一定要分散，所以我們整個物流的布局就以分散式布局為主。」

洞悉市場的人

林錦慧譯，洞悉市場的人：量化交易之父吉姆‧西蒙斯與文藝復興公司的故事，天下文化，2020

Gregory Zuckerman, The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution, Penguin Group, 2019

在投資界，沒有哪個傳奇大師比吉姆‧西蒙斯更加神祕，他的前半生是頂尖數學家，不但曾幫助美國政府破解蘇聯密碼，還成功打造世界級的數學系。

但四十歲後，他轉戰投資界，開啟量化投資的風潮。他旗下的大獎章基金1988年至2018年的平均年化報酬率高達66.1％，就算扣除基金收取的各項費用，平均年化報酬率也高達39.1％ (*)，遠勝過巴菲特、彼得‧林區、雷‧達利歐、喬治‧索羅斯等股票大師。

他聘請頂尖數學家、物理學家與電腦工程師，藉由蒐集市場各項數據發展出一套演算法，找到買進與賣出的交易訊號，讓機器自動交易，他在華爾街掀起量化交易革命。不但成功打敗市場，還躲過歷年來的重大股災，持續擁有優異績效。

西蒙斯和他的同事擁有的財富已經富可敵國，現在開始轉往科學研究、教育與政治界發展。其中公司的前執行長羅伯特‧莫瑟還成為川普的大金主，莫瑟的女兒更是劍橋分析公司的主要投資人，在美國政壇發揮影響力。

本書作者古格里‧佐克曼採訪西蒙斯多位現任與離職員工後，寫下這個金融史上的傳奇故事，幫助我們一窺這位頂尖數學家如何掀起量化交易革命，顛覆華爾街的傳統模式。

玉山2大AI關鍵平臺大解密

 王若樸，【先用AI研發雲創新，再靠MLaaS將AI嵌入業務系統】玉山2大AI關鍵平臺大解密，iThome，2021-08-17

Machine Learning Summer School 2021

NTU, Machine Learning Summer School 2021, Taipei, August 2-20, 2021.

美國政府供應鏈報告

White House, Biden-⁠Harris Administration Announces Supply Chain Disruptions Task Force to Address Short-Term Supply Chain Discontinuities, JUNE 08, 2021 (full report)

邱韞蓁 編譯，衝擊台灣「護國神山群」的美國政府供應鏈報告，23項具體措施完整公開，商周頭條，2021.06.18

吳中傑、黃靖萱、蔡靚萱，計畫新美國》二戰後最大投資來了！一份提台灣84次的報告，開啟大補貼時代，《商業周刊》第 1756 期，2021.07.07 

Control Systems and Reinforcement Learning

S. Meyn, Control Systems and Reinforcement Learning, Cambridge University Press, 2021. (pdf)

推廣教育的教學評量

這一個禮拜，教深度學習推廣教育。因為疫情，遠距教學。學員是18位大學生和11位高中生，做了一個 (匿名的) 問卷調查。已經好久，沒有看到這麼多正向的肯定。學習動機真的很重要。

運動家精神和感人的故事

Caspi Wang，【動誌專訪】大賽前做足意象訓練！黃筱雯常幻想：「打到第幾場可以拿到獎牌？」，動誌，2021年05月05日

How Software Is Eating the Car

Robert N. Charette, How Software Is Eating the Car, IEEE Spectrum, 07 Jun 2021.

Ten years ago, only premium cars contained 100 microprocessor-based electronic control units (ECUs) networked throughout the body of a car, executing 100 million lines of code or more. Today, high-end cars like the BMW 7-series with advanced technology like advanced driver-assist systems (ADAS) may contain 150 ECUs or more, while pick-up trucks like Ford’s F-150 top 150 million lines of code. Even low-end vehicles are quickly approaching 100 ECUs and 100 million of lines of code as more features that were once considered luxury options, such as adaptive cruise control and automatic emergency braking, are becoming standard....

Probability in Electrical Engineering and Computer Science

Jean Walrand, Probability in Electrical Engineering and Computer Science: An Application-Drive Course, Springer, 2021. (pdf, Python)

Showcases techniques of applied probability with applications in EE and CS

Presents all topics with concrete applications so students see the relevance of the theory

Illustrates methods with Jupyter notebooks that use widgets to enable the users to modify parameters

This book is open access, which means that you have free and unlimited access.

Linear Algebra, Signal Processing, and Wavelets - A Unified Approach

Øyvind Ryan, Linear Algebra, Signal Processing, and Wavelets - A Unified Approach, Python Version, Springer, 2019.  (code, Python)

1 Sound and Fourier Series

2 Digital Sound and Discrete Fourier Analysis

3 Discrete Time Filters

4 Motivation for Wavelets and Some Simple Examples

5 The Filter Representation of Wavelets

6 Constructing Interesting Wavelets

7 The Polyphase Representation of Filter Bank Transforms

8 Digital Images

9 Using Tensor Products to Apply Wavelets to Images

Appendix Basic Linear Algebra 

張忠謀在 APEC 非正式領袖會議的致詞

李玟儀

This Informal Retreat has been called to discuss how Asia-Pacific can collaborate to move through the COVID health crisis, and to accelerate the post-COVID economic recovery. Chinese Taipei will address these two topics specifically.

從AI到智慧醫療

蔣榮先，從AI到智慧醫療，商周，2020

本書作者蔣榮先教授為台灣資訊傑出人才，任教於成大醫學院、擔任成大醫院資訊長，從事醫療科技的尖端研究，並熟悉產業最新發展，深諳跨領域合作的必要性。

他以深入淺出的解說，配合大量生活應用實例、資訊圖表，描繪即將到來的AI智慧醫療新世界。值得醫界、產業界、藥界、醫材界的學者、專家參考，同時適合對健康醫療科技新知有興趣的大眾閱讀。

Politics, health collided in Taiwan's tortured BioNTech vaccine talks

Yimou Lee and Ben Blanchard, Politics, health collided in Taiwan's tortured BioNTech vaccine talks, Reuters, July 12, 2021.

Numerical Python

Robert Johansson, Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib, Apress, 2019. (code)

 Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. 

Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics

Justin Solomon, Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics,  1st Edition, A K Peters/CRC Press, 2015. (pdf)

Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic design from a practical standpoint and provides insight into the theoretical tools needed to support these skills.

The book covers a wide range of topics—from numerical linear algebra to optimization and differential equations—focusing on real-world motivation and unifying themes. It incorporates cases from computer science research and practice, accompanied by highlights from in-depth literature on each subtopic. Comprehensive end-of-chapter exercises encourage critical thinking and build students’ intuition while introducing extensions of the basic material.

The text is designed for advanced undergraduate and beginning graduate students in computer science and related fields with experience in calculus and linear algebra. For students with a background in discrete mathematics, the book includes some reminders of relevant continuous mathematical background.

科技魅癮 (數位季刊)

 科技魅癮 

《科技魅癮》每期都精選1個國際關注的科技議題，邀請1位國內資深學者擔任客座編輯，並訪談多位來自相關領域的科研菁英，以「領路人」、「談觀點」、「探研究」、「躍思考」、「看世界」、「飆研值」等6個專欄，自多元視角剖析、探討該領域在臺灣及全球的研發現況、未來發展、相關衝擊及影響，希望藉由這個平臺，促進科際交流及跨域合作、激發科研人員創意思維、拓展產學鏈結網絡，進而增進國內研發能量。

沈孟儒院長：「精準健康，用科技讓醫療深植人心」，2021-03-31

簡禎富教授：「產業維新的初衷，是為了讓臺灣社會邁向以人為本的智慧生活。」，2021-06-09

The CEO of Pfizer on Developing a Vaccine in Record Time

Albert Bourla, The CEO of Pfizer on Developing a Vaccine in Record Time, Harvard Business Review, May–June 2021.

On March 19, 2020, as Covid-19 swept across the world, Bourla challenged everyone at Pfizer and its partner BioNTech—a German company focused on cancer immunotherapies—to “make the impossible possible”: develop a vaccine more quickly than anyone ever had before, ideally within six months and certainly before the end of the year.

Less than eight months later, on Sunday, November 8, they discovered that they had succeeded: Their combined phase two and three trials showed a 95% efficacy rate. In the spring, thanks to their work and that of the other companies whose vaccines have been authorized, 300 million doses should be available around the world.

It took a moon-shot challenge, out-of-the-box thinking, intercompany cooperation, liberation from bureaucracy, and most of all, hard work from everyone at Pfizer and BioNTech to accomplish what they did in 2020. Organizations of any size or in any industry can learn from these strategies to solve their own problems and to produce important work that benefits a broad swath of society.

SOMATIC - commercial bathroom cleaning robotics

SOMATIC - commercial bathroom cleaning robotics, 2021/5/6

敗部區疫苗的逆襲

楊惠君、陳潔敗文字，楊子磊攝影，敗部區疫苗的逆襲──從賽諾菲到國光，傳統疫苗大廠為何栽跟斗？有復活機會嗎？，報導者，2021/6/30

「國光生技」是台灣最老牌的人用疫苗廠，但與他們合作生產四價流感疫苗的國際伙伴──法國大藥廠賽諾菲集團（Sanofi）一樣，在COVID-19疫苗研發的第一階段競逐，落入敗部區。兩家藥廠的重組蛋白疫苗跌倒路徑也相似：「臨床試驗劑量計算失誤，沒有激發足夠的免疫反應」，只能退回起跑點再來過。...

Reinforcement Learning for Industrial AI with Pieter Abbeel

The TWIML AI Podcast, Reinforcement Learning for Industrial AI with Pieter Abbeel - #476, 2021/4/20.

Today we’re joined by Pieter Abbeel, a Professor at UC Berkeley, co-Director of the Berkeley AI Research Lab (BAIR), as well as Co-founder and Chief Scientist at Covariant.

In our conversation with Pieter, we cover a ton of ground, starting with the specific goals and tasks of his work at Covariant, the shift in needs for industrial AI application and robots, if his experience solving real-world problems has changed his opinion on end to end deep learning, and the scope for the three problem domains of the models he’s building.

We also explore his recent work at the intersection of unsupervised and reinforcement learning, goal-directed RL, his recent paper “Pretrained Transformers as Universal Computation Engines” and where that research thread is headed, and of course, his new podcast Robot Brains, which you can find on all streaming platforms today!

The complete show notes for this episode can be found at twimlai.com/go/476.

Optimization modelling: A practical approach

Ruhul Amin Sarker and Charles S. Newton, Optimization modelling: A practical approach, CRC Press, 2007. 

Although a useful and important tool, the potential of mathematical modelling for decision making is often neglected. Considered an art by many and weird science by some, modelling is not as widely appreciated in problem solving and decision making as perhaps it should be. And although many operations research, management science, and optimization books touch on modelling techniques, the short shrift they usually get in coverage is reflected in their minimal application to problems in the real world. Illustrating the important influence of modelling on the decision making process, Optimization Modelling: A Practical Approach helps you come to grips with a wide range of modelling techniques.

智能工廠來了

陳泳翰，智能工廠來了！：一場水五金與手工具的創新實驗紀錄，天下雜誌，2021

歷時兩年半的「水五金與手工具產業智動化計畫」，簡稱「水手計畫」，同時也象徵著水手們乘風破浪的勇敢無懼精神。來自上銀科技的精密機械工程師，攜手四間傳統工廠：隴鈦銅器、勝泰衛材、銳泰精密、伯鑫⼯具，讓機器人實現產線智慧化、自動化的願景，不僅減輕第一線的人力負擔，也改變了黑手工廠形象，吸引更多年輕世代投身其中。

Algorithms for Decision Making

Mykel Kochenderfer, Tim Wheeler, and Kyle Wray, Algorithms for Decision Making, MIT Press, 2022. (pdf available online)

A broad introduction to algorithms for optimal decision making under uncertainty. We cover a wide variety of topics related to decision making, introducing the underlying mathematical problem formulations and the algorithms for solving them. Figures, examples, and exercises are provided to convey the intuition behind the various approaches. This text is intended for advanced undergraduates and graduate students as well as professionals. The book requires some mathematical maturity and assumes prior exposure to multivariable calculus, linear algebra, and probability concepts. Some review material is provided in the appendix. Disciplines where the book would be especially useful include mathematics, statistics, computer science, aerospace, electrical engineering, and operations research. Fundamental to this textbook are the algorithms, which are all implemented in the Julia programming language.

Prof. Kochenderfer also teaches 2 related courses at Stanford. 

(年輕人) 如何脫離貧窮

一般人都可以經由努力而達到的方法 (註 12) 

找工作可分成學校教育、個人努力、企業、和社會大環境等四大面向。不論是 22 K 政策、學費、企業的在職訓練等等，都不是我們可以掌握的。當大環境不容易改變，個人只有自求多福。所以，本文希望能夠提供一些可行的辦法，以幫助年輕人。方法是在大學四年 (和終身學習) (註 1)，培養三大類的能力，一是一般的能力，語言、電腦使用，二是專業，三是強化個人特質和團隊合作精神。有了這些能力，也才有機會選擇自己喜歡的工作。 

Interpretable predictive maintenance for hard drives

Maxime Amram, Jack Dunn, Jeremy J.Toledano, and Ying Daisy Zhuo, Interpretable predictive maintenance for hard drives, Machine Learning with Applications, Volume 5, 15 September 2021, 100042.

Existing machine learning approaches for data-driven predictive maintenance are usually black boxes that claim high predictive power yet cannot be understood by humans. This limits the ability of humans to use these models to derive insights and understanding of the underlying failure mechanisms, and also limits the degree of confidence that can be placed in such a system to perform well on future data. We consider the task of predicting hard drive failure in a data center using recent algorithms for interpretable machine learning. We demonstrate that these methods provide meaningful insights about short- and long-term drive health, while also maintaining high predictive performance. We also show that these analyses still deliver useful insights even when limited historical data is available, enabling their use in situations where data collection has only recently begun.

Deep Learning for AI

Yoshua Bengio, Yann Lecun, and Geoffrey Hinton, Deep Learning for AI, Communications of the ACM, July 2021, Vol. 64 No. 7, Pages 58-65.

Research on artificial neural networks was motivated by the observation that human intelligence emerges from highly parallel networks of relatively simple, non-linear neurons that learn by adjusting the strengths of their connections. This observation leads to a central computational question: How is it possible for networks of this general kind to learn the complicated internal representations that are required for difficult tasks such as recognizing objects or understanding language? Deep learning seeks to answer this question by using many layers of activity vectors as representations and learning the connection strengths that give rise to these vectors by following the stochastic gradient of an objective function that measures how well the network is performing. It is very surprising that such a conceptually simple approach has proved to be so effective when applied to large training sets using huge amounts of computation and it appears that a key ingredient is depth: shallow networks simply do not work as well.

Boeing's 737 MAX: A Failure of Management, Not Just Technology

Michael A. Cusumano, Boeing's 737 MAX: A Failure of Management, Not Just Technology, Communications of the ACM, January 2021, Vol. 64, No. 1, Pages 22-25.

The congressional report had extensive access to company email and documents as well as detailed media coverage. These sources all describe the same decisions along with gradual but fundamental changes in Boeing's strategy and culture.

The Future of Supply Chains

Paul Marks, The Future of Supply Chains, Communications of the ACM, July 2021, Vol. 64, No. 7, Pages 19-21.

Today's supply chains are labor-intensive and expensive to run. A number of autonomous systems that reduce the human factor are about to change all that.

What do the sidewalks around us, the airspace above us, interstate freeways, and deep ocean shipping lanes have in common? The answer is that they are all places where developers of autonomous technology are trying to revolutionize the economics of supply chains. The plan is to use robotic technology to deliver anything from packages to take-out food, groceries, or bulk freight in ways that can reduce the logistics industry's dependence on that most expensive of supply chain costs: human labor. if the use of electric drivetrains can cut carbon emissions too, so much the better.

Revenue Management and Pricing Analytics

Guillermo Gallego and Huseyin Topaloglu, Revenue Management and Pricing Analytics, Springer, 2019.

The book is divided into three parts: traditional revenue management, revenue management under customer choice, and pricing analytics. Each part is approximately of the same length and written in a self-contained way, so readers can read them independently, although reading the first part may make the second part easier to understand. Each chapter ends with bibliographical notes where the reader can find the sources of the material covered as well as many useful references. Proofs of some important technical results can be found in the appendix of each chapter. Solving the end-of-chapter problems helps reinforce the material in the book, with some of the questions expanding on the subject....

There is enough material in the book for a full-semester course for advanced undergraduate or master’s students. Parts I and II can be covered in about 9 weeks and Part III in about 4weeks excluding the last two chapters on online learning and competition, which can be assigned as independent readings. 

Thinking, Fast and Slow (快思慢想)

Daniel Kahneman, Thinking, Fast and Slow, Penguin Group UK, 2012

作者融合數十年 (心理) 學界的研究，說明人是如何思考和選擇，遣詞用字清楚易懂，值得花時間細讀 (註 1)。

作者和 Amos Tversky 提出展望理論 (Prospect theory)，比經濟學的預期效用理論 (expected utility theory) 更精準地描述人類的決策方法，對行為經濟學有深遠的影響，因而得到 2002 年的諾貝爾經濟學獎 (註 2)。

至於在企業的應用，可以參考作者的另外一篇文章。

(21/6/17) 有朋友在群組，轉貼台南市發布的「零接觸採檢站」新聞稿影片。看到整個流程的設計，覺得很有教育意義，所以就轉發到其他的群組。有趣的是，有人覺得這個是「台南市政府的大內宣」。我就上網查了一下資料，實際的狀況是，台積電要捐八部車子，這只是第一部，而且有許多媒體的報導。快思慢想第七章 (A machine for jumping to conclusions) 就是在講這種人類思維的現象。 

(註 1)  英文版的內文沒有說明或任何記號，所以不容易知道文章何處有註釋。中譯本的內文有譯注；但是，刪除 51 頁的註釋和索引；例如出現多次的 WYSIATI (what you see is all there is) 目錄也沒有，除非第一次就讀到且記住，否則很難瞭解。

(註 2) 原文 Daniel Kahneman and Amos Tversky, Prospect Theory: An Analysis of Decision Under Risk, Econometrica, Vol. 47, No. 2, 1979, pp. 263-292。Amos Tversky 於 1996 過世，所以沒有得獎。書中 279 頁說明其成功的原因

Knowledge of perception and ignorance about decision theory both contributed to a large step forward in our research.

Analytics and Bikes: Riding Tandem with Motivate to Improve Mobility

Daniel Freund, S. G. Henderson, E. O’Mahony, and D. B. Shmoys, Analytics and Bikes: Riding Tandem with Motivate to Improve Mobility, INFORMS Journal on Applied Analytics, Vol. 49, No. 5, 2019, Pages 307-396. (First Prize: Wagner Prize for Excellence in Operations Research Practice)

Bike-sharing systems are now ubiquitous across the United States. We have worked with Motivate, the operator of the systems in, for example, New York, Chicago, and San Francisco, to both innovate a data-driven approach to managing their day-to-day operations and provide insight on several central issues in the design of its systems. This work required the development of a number of new optimization models, characterization of their mathematical structure, and use of this insight in designing algorithms to solve them. Here, we focus on two particularly high-impact projects: an initiative to improve the allocation of docks to stations and the creation of an incentive scheme to crowdsource rebalancing. Both of these projects have been fully implemented to improve the performance of Motivate’s systems across the country; for example, the Bike Angels program in New York City yields a system-wide improvement comparable with that obtained through Motivate’s traditional rebalancing efforts at far less financial and environmental cost.

Reinforcement learning is enough to reach general AI

David Silver, Satinder Singh, Doina Precup, and Richard S. Sutton, Reward is enough, Artificial Intelligence, Volume 299, October 2021, 103535.

In this article we hypothesise that intelligence, and its associated abilities, can be understood as subserving the maximisation of reward. Accordingly, reward is enough to drive behaviour that exhibits abilities studied in natural and artificial intelligence, including knowledge, learning, perception, social intelligence, language, generalisation and imitation. This is in contrast to the view that specialised problem formulations are needed for each ability, based on other signals or objectives. Furthermore, we suggest that agents that learn through trial and error experience to maximise reward could learn behaviour that exhibits most if not all of these abilities, and therefore that powerful reinforcement learning agents could constitute a solution to artificial general intelligence.

Supply chain of COVID vaccines

Asher Mullard, How COVID vaccines are being divvied up around the world: Canada leads the pack in terms of doses secured per capita, Nature,  30 November 2020.

But the makers of the three vaccines that seem closest to widespread distribution — AstraZeneca, Pfizer and Moderna — estimate a total production capacity of 5.3 billion doses for 2021, which could cover between 2.6 billion and 3.1 billion people, depending on whether AstraZeneca’s vaccine is administered in two doses or one and a half (see ‘Vaccine pre-orders’).

It's Okay.

Golden Buzzer: Nightbirde's Original Song Makes Simon Cowell Emotional - America's Got Talent 2021.

Nightbirde, “You can’t wait until life isn’t hard anymore before you decide to be happy.” 

台灣的疫苗戰略新思維

黃韻如、賴育宏、鄭如韻，台灣作為防疫優等生　為何在「疫苗戰」失去主控權？（上），ETtoday新聞雲，2021年02月09日

近日，各界輿論聚焦在疫苗採購中僵化的《政府採購法》，關切中央流行疫情指揮中心在國際疫苗的採購談判及取得進度、國產疫苗供應鏈的開發瓶頸等問題時，本評論希望拉高討論的視角，以國家資本與國際人脈的戰略角度，參考新加坡與以色列經驗，審視台灣在這場世紀抗疫之戰，為什麼在疫苗賽事之中落後的根本原因，也藉此窺探台灣在生醫新創的國際產業生態圈所面臨到的困境。

黃韻如、鄭如韻、高子翔、蘇育平， 以色列疫苗採購奪冠，為何台灣不行？台灣生技人才出走的警訊，ETtoday新聞雲，2021年02月27日 

本文分析重點有二：

一、以人口不到千萬的年輕國家以色列為例，如何從不可抗拒的大離散（diaspora）動盪飄搖命運中遍地開花，在緊繃的疫情中，透過總理納坦雅胡與 Pfizer 猶太裔執行長Albert Bourla的 17 通電話，讓最初疫情失控的以色列，優先取得疫苗及施打保障 [5]，扭轉局面。

二、帶領讀者認識台灣生技人才同樣經歷出走全球的離散、遍地開花，進而組織自發性的台灣生技社群，期許台灣政府正視並鏈接散居在海外的台灣生技製藥菁英。

台灣在疫苗採購上面臨許多不可抗力的因素，文末，本團隊建議台灣，如何藉由防疫黃金三角的架構，善用台灣人才、鏈結國際人脈網絡、增強國內產業體質，優化深化台灣「平時備戰，戰時作戰」的韌性與永續。

A Lyapunov Analysis of Accelerated Methods in Optimization

Ashia C. Wilson, Ben Recht, and Michael I. Jordan, A Lyapunov Analysis of Accelerated Methods in Optimization, Journal of Machine Learning Research, 2021, Vol. 22, No. 113, pp. 1−34.

Accelerated optimization methods, such as Nesterov’s accelerated gradient method, play a significant role in optimization. Several accelerated methods are provably optimal under standard oracle models. Such optimality results are obtained using a technique known as estimate sequences which yields upper bounds on convergence properties. The technique of estimate sequences has long been considered difficult to understand and deploy, leading many researchers to generate alternative, more intuitive methods and analyses. We show there is an equivalence between the technique of estimate sequences and a family of Lyapunov functions in both continuous and discrete time. This connection allows us to develop a unified analysis of many existing accelerated algorithms, introduce new algorithms, and strengthen the connection between accelerated algorithms and continuous-time dynamical systems.

Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm

Defeng Sun, Kim-Chuan Toh, and Yancheng Yuan, Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm, Journal of Machine Learning Research, 2021, Vol. 22, No. 9, pp. 1−32.

Clustering is a fundamental problem in unsupervised learning. Popular methods like Kmeans, may suffer from poor performance as they are prone to get stuck in its local minima. Recently, the sum-of-norms (SON) model (also known as the convex clustering model) has been proposed by Pelckmans et al. (2005), Lindsten et al. (2011) and Hocking et al. (2011). The perfect recovery properties of the convex clustering model with uniformly weighted all-pairwise-differences regularization have been proved by Zhu et al. (2014) and Panahi et al. (2017). However, no theoretical guarantee has been established for the general weighted convex clustering model, where better empirical results have been observed. In the numerical optimization aspect, although algorithms like the alternating direction method of multipliers (ADMM) and the alternating minimization algorithm (AMA) have been proposed to solve the convex clustering model (Chi and Lange, 2015), it still remains very challenging to solve large-scale problems. In this paper, we establish sufficient conditions for the perfect recovery guarantee of the general weighted convex clustering model, which include and improve existing theoretical results in (Zhu et al., 2014; Panahi et al., 2017) as special cases. In addition, we develop a semismooth Newton based augmented Lagrangian method for solving large-scale convex clustering problems. Extensive numerical experiments on both simulated and real data demonstrate that our algorithm is highly efficient and robust for solving large-scale problems. Moreover, the numerical results also show the superior performance and scalability of our algorithm comparing to the existing first-order methods. In particular, our algorithm is able to solve a convex clustering problem with 200,000 points in R^3 in about 6 minutes.

The Nutritious Supply Chain: Optimizing Humanitarian Food Assistance

Koen Peters, Sérgio Silva, Rui Gonçalves, Mirjana Kavelj, Hein Fleuren, Dick den Hertog, Ozlem Ergun, and Mallory Freeman, The Nutritious Supply Chain: Optimizing Humanitarian Food Assistance, INFORMS Journal on Optimization, Volume 3, Issue 2, Spring 2021, Pages:200–226.

The World Food Programme (WFP) is the largest humanitarian agency fighting hunger worldwide, reaching approximately 90 million people with food assistance across 80 countries each year. To deal with the operational complexities inherent in its mandate, WFP has been developing tools to assist its decision makers with integrating supply chain decisions across departments and functional areas. This paper describes a mixed integer linear programming model that simultaneously optimizes the food basket to be delivered, the sourcing plan, the delivery plan, and the transfer modality of a long-term recovery operation for each month in a predefined time horizon. By connecting traditional supply chain elements to nutritional objectives, we are able to make significant breakthroughs in the operational excellence of WFP’s most complex operations. We show three examples of how the optimization model is used to support operations: (1) to reduce the operational costs in Iraq by 12% without compromising the nutritional value supplied, (2) to manage the scaling-up of the Yemen operation from three to six million beneficiaries, and (3) to identify sourcing strategies during the El Niño drought of 2016.

台灣的疫情和疫苗

 臉友轉貼。我竟然耐心地看完了，順便追蹤原作者。文章裡面介紹的兩本書，列為待讀清單。

邱斯華，6月4日上午6:44 

<我對疫苗的想法>

最近在 Line 群組裡老是有群友在黨同伐異、互添仇恨值，搞的我只剩下退群求個清靜，或是寫下我自己的分析看法，讓雙方冷靜停戰這兩個選擇。不敢說我看的一定正確，也祈請各方指正我的偏誤。

各位熟知我文章風格的朋友都知道我是個中立派，因為在我眼中，在歷史上也是，沒有永遠真善美的黨派。在瞬變的世局中，我們只能盡量摘掉有色眼鏡來看清這個真實世界，儘量逼近真實。

Probabilistic Machine Learning

 "Probabilistic Machine Learning" - a book series by Kevin Murphy

Kevin Patrick Murphy, Probabilistic Machine Learning: An Introduction, MIT Press, 2021. (Python codes)

新的版本，作者花了很多時間整理。以第一部分的數學基礎為例，使用機器學習說明相關的概念。也納入時事，例如 2.3.1 節的 Testing for COVID-19。 

台積電捐贈「零接觸採檢站」予奇美醫院 工研院「正壓採檢亭」詢問度高

公視晚間新聞，台積電捐贈「零接觸採檢站」予奇美醫院 工研院「正壓採檢亭」詢問度高，2021年5月28日

Reducing Conservatism in Robust Optimization

E. Roos and D. den Hertog, Reducing conservatism in robust optimization, INFORMS Journal on Computing, Fall 2020, Volume 32, Issue 4, pp. 1109–1127.

Although robust optimization is a powerful technique in dealing with uncertainty in optimization, its solutions can be too conservative. More specifically, it can lead to an objective value much worse than the nominal solution or even to infeasibility of the robust problem. In practice, this can lead to robust solutions being disregarded in favor of the nominal solution. This conservatism is caused by both the constraint-wise approach of robust optimization and its core assumption that all constraints are hard for all scenarios in the uncertainty set. This paper seeks to alleviate this conservatism by proposing an alternative robust formulation that condenses all uncertainty into a single constraint, binding the worst-case expected violation in the original constraints from above. Using recent results in distributionally robust optimization, the proposed formulation is shown to be tractable for both right- and left-hand side uncertainty. A computational study is performed with problems from the NETLIB library. For some problems, the percentage of uncertainty is magnified fourfold in terms of increase in objective value of the standard robust solution compared with the nominal solution, whereas we find solutions that safeguard against over half the violation at only a 10th of the cost in objective value. For problems with an infeasible standard robust counterpart, the suggested approach is still applicable and finds both solutions that safeguard against most of the uncertainty at a low price in terms of objective value.

Industrial AI

Jay Lee, Industrial AI: Applications with Sustainable Performance, Springer, 2020.

This book introduces Industrial AI in multiple dimensions. Industrial AI is a systematic discipline which focuses on developing, validating and deploying various machine learning algorithms for industrial applications with sustainable performance. Combined with the state-of-the-art sensing, communication and big data analytics platforms, a systematic Industrial AI methodology will allow integration of physical systems with computational models. The concept of Industrial AI is in infancy stage and may encompass the collective use of technologies such as Internet of Things, Cyber-Physical Systems and Big Data Analytics under the Industry 4.0 initiative where embedded computing devices, smart objects and the physical environment interact with each other to reach intended goals. A broad range of Industries including automotive, aerospace, healthcare, semiconductors, energy, transportation, mining, construction, and industrial automation could harness the power of Industrial AI to gain insights into the invisible relationship of the operation conditions and further use that insight to optimize their uptime, productivity and efficiency of their operations. In terms of predictive maintenance, Industrial AI can detect incipient changes in the system and predict the remains useful life and further to optimize maintenance tasks to avoid disruption to operations.

In Defense of a Liberal Education

Fareed Zakaria, In Defense of a Liberal Education, W. W. Norton & Company; 1 edition, March 28, 2016.

The liberal arts are under attack. The governors of Florida, Texas, and North Carolina have all pledged that they will not spend taxpayer money subsidizing the liberal arts, and they seem to have an unlikely ally in President Obama. While at a General Electric plant in early 2014, Obama remarked, "I promise you, folks can make a lot more, potentially, with skilled manufacturing or the trades than they might with an art history degree." These messages are hitting home: majors like English and history, once very popular and highly respected, are in steep decline. 

Markov-chain Monte Carlo for disease dynamics modelling

If you search "pandemic markov chain" under Google scholar, you will find many articles on this topic. For example, Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers by A Endo, E van Leeuwen, M Baguelin - Epidemics, 2019 - Elsevier.

馬友友演奏巴哈大提琴組曲

Yo Yo Ma - Bach Six Cello Suites - BBC Proms 2015

「巴哈的大提琴組曲是我一直以來的音樂夥伴。在將近60年裡，給了我寄託、安慰和快樂，無論是在面對壓力、慶祝，抑或是失去的種種生命時刻。即使經過了三百年的今天，這種音樂仍具有這樣強大的力量、仍可以繼續幫助我們度過艱難的時期。與所有文化一樣，音樂可以幫助我們了解我們的環境、彼此以及我們自己。文化可以幫助我們想像美好的未來，將『他們』變成『我們』，使我們團結在一起。而這些事情在現今社會如此重要。」——馬友友

EE104/CME107 Introduction to Machine Learning

Sanjay Lall, EE104/CME107: Introduction to Machine Learning, Stanford University, Spring Quarter, 2021. (quarter: 10 weeks, Julia, YouTube)

Introduction to machine learning. Formulation of supervised and unsupervised learning problems. Regression and classification. Data standardization and feature engineering. Loss function selection and its effect on learning. Regularization and its role in controlling complexity. Validation and overfitting. Robustness to outliers. Simple numerical implementation. Experiments on data from a wide variety of engineering and other disciplines.

共同作者為 S. Boyd，兩位教授的寫作非常清楚，推薦。 

OSQP: an operator splitting solver for quadratic programs

Bartolomeo Stellato, Goran Banjac, Paul Goulart, Alberto Bemporad, and Stephen Boyd, OSQP: an operator splitting solver for quadratic programs, Mathematical Programming Computation, 2020, vol 12, no. 4, pp. 637–672. (Best Paper of the Year)

We present a general-purpose solver for convex quadratic programs based on the alternating direction method of multipliers, employing a novel operator splitting technique that requires the solution of a quasi-definite linear system with the same coefficient matrix at almost every iteration. Our algorithm is very robust, placing no requirements on the problem data such as positive definiteness of the objective function or linear independence of the constraint functions. It can be configured to be division-free once an initial matrix factorization is carried out, making it suitable for real-time applications in embedded systems. In addition, our technique is the first operator splitting method for quadratic programs able to reliably detect primal and dual infeasible problems from the algorithm iterates. The method also supports factorization caching and warm starting, making it particularly efficient when solving parametrized problems arising in finance, control, and machine learning. Our open-source C implementation OSQP has a small footprint, is library-free, and has been extensively tested on many problem instances from a wide variety of application areas. It is typically ten times faster than competing interior-point methods, and sometimes much more when factorization caching or warm start is used. OSQP has already shown a large impact with tens of thousands of users both in academia and in large corporations.

Software

Users can call OSQP from C, C++, Fortran, Python, Matlab, R, Julia, Ruby and Rust, and via parsers such as CVXPY [1,26], JuMP [33], and YALMIP [65]. 

使用 Python 呼叫 Gurobi 解背包問題 (Knapsack problem)

背包問題 (Knapsack problem) 是一種組合最佳化的 NP 完全問題。問題可以描述為：給定一組物品，每種物品都有自己的重量和價格，在限定的總重量內，我們如何選擇，才能使得物品的總價格最高。

Vattenfall Optimizes Offshore Wind Farm Design

Martina Fischetti, Jesper Runge Kristoffersen, Thomas Hjort, Michele Monaci, and David Pisinger,  Vattenfall Optimizes Offshore Wind Farm Design, INFORMS Journal on Applied Analytics, 2020, Vol. 50, No. 1, pp. 80–94.

In this paper, we describe the use of operations research for offshore wind farm design in Vattenfall, one of the world’s leading companies in the generation of offshore wind energy. We focus on two key aspects that Vattenfall must address in its wind farm design process. The first is determining where to locate the turbines. This aspect is important because the placement of each turbine creates interference on the neighboring turbines, causing a power loss at the overall farm level. The optimizers must minimize this interference based on the wind conditions; however, they must also consider the other costs involved, which depend on factors such as the water depth or soil conditions at each position. The second aspect involves determining how to interconnect the turbines with cables (i.e., cable optimization). This requires Vattenfall to consider both the immediate costs and long-term costs connected with the electrical infrastructure. We developed mixed-integer programming models and matheuristic techniques to solve the two problems as they arise in practical applications. The resulting tools have given Vattenfall a competitive advantage at multiple levels. They facilitate increased revenues and reduced costs of approximately 10 million euros of net present value (NPV) per farm, while ensuring a much faster, more streamlined, and efficient design process. Considering only the sites that Vattenfall has already acquired using our optimization tools, the company experienced NPV gains of more than 150 million euros. This has contributed substantially to its competitiveness in offshore tenders and made green energy cheaper for its end customers. The tools have also been used to design the first wind farms that will be constructed subsidy-free.

Martina Fischetti and David Pisinger, Mathematical Optimization and Algorithms for Offshore Wind Farm Design: An Overview, Business & Information Systems Engineering, 2019, Vol.61, No. 4, pp. 469-485. (Further details)

M. Fischetti, Mixed-integer models and algorithms for wind farm layout optimization. Master’s thesis, University of Padova, 2014. (Stochastic programming for wake effect)

Fischetti M, Fischetti M (2016) Matheuristics. Mart´ı P, Panos P, Resende MG, eds. Handbook of Heuristics (Springer International Publishing, Cham, Switzerland), 1–33.

Grokking Bitcoin

Kalle Rosenbaum, Grokking Bitcoin, Manning, April 2019.

If you think Bitcoin is just an alternative currency for geeks, it's time to think again. Grokking Bitcoin opens up this powerful distributed ledger system, exploring the technology that enables applications both for Bitcoin-based financial transactions and using the blockchain for registering physical property ownership. With this fully illustrated, easy-to-read guide, you'll finally understand how Bitcoin works, how you can use it, and why you can trust the blockchain.

Pitfalls and protocols of data science in manufacturing practice

Chia-Yen Lee & Chen-Fu Chien, Pitfalls and protocols of data science in manufacturing practice, Journal of Intelligent Manufacturing, 2020.

Driven by ongoing migration for Industry 4.0, the increasing adoption of artificial intelligence, big data analytics, cloud computing, Internet of Things, and robotics have empowered smart manufacturing and digital transformation. However, increasing applications of machine learning and data science (DS) techniques present a range of procedural issues including those that involved in data, assumptions, methodologies, and applicable conditions. Each of these issues may increase difficulties for implementation in practice, especially associated with the manufacturing characteristics and domain knowledge. However, little research has been done to examine and resolve related issues systematically. Gaps of existing studies can be traced to the lack of a framework within which the pitfalls involved in implementation procedures can be identified and thus appropriate procedures for employing effective methodologies can be suggested. This study aims to develop a five-phase analytics framework that can facilitate the investigation of pitfalls for intelligent manufacturing and suggest protocols to empower practical applications of the DS methodologies from descriptive and predictive analytics to prescriptive and automating analytics in various contexts.

Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges

Duncan Simester, Artem Timoshenko, and Spyros I. Zoumpoulis, Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges, Management Science, 2020, Vol. 66, No. 6, Pages: 2495–2522.

We investigate how firms can use the results of field experiments to optimize the targeting of promotions when prospecting for new customers. We evaluate seven widely used machine-learning methods using a series of two large-scale field experiments. The first field experiment generates a common pool of training data for each of the seven methods. We then validate the seven optimized policies provided by each method together with uniform benchmark policies in a second field experiment. The findings not only compare the performance of the targeting methods, but also demonstrate how well the methods address common data challenges. Our results reveal that when the training data are ideal, model-driven methods perform better than distance-driven methods and classification methods. However, the performance advantage vanishes in the presence of challenges that affect the quality of the training data, including the extent to which the training data captures details of the implementation setting. The challenges we study are covariate shift, concept shift, information loss through aggregation, and imbalanced data. Intuitively, the model-driven methods make better use of the information available in the training data, but the performance of these methods is more sensitive to deterioration in the quality of this information. The classification methods we tested performed relatively poorly. We explain the poor performance of the classification methods in our setting and describe how the performance of these methods could be improved.

Machine Learning Under a Modern Optimization Lens

Dimitris Bertsimas and Jack Dunn, Machine Learning Under a Modern Optimization Lens, Dynamic Ideas LLC, 2019.

The book provides an original treatment of machine learning (ML) using convex, robust and mixed integer optimization that leads to solutions to central ML problems at large scale that can be found in seconds/minutes, can be certified to be optimal in minutes/hours, and outperform classical heuristic approaches in out-of-sample experiments.

Efficient Large-Scale Internet Media Selection Optimization for Online Display Advertising

Courtney Paulson, Lan Luo, and Gareth M. James, “Efficient Large-ScaleInternet Media Selection Optimization for Online Display Advertising,” Journal of Marketing Research, vol. 55, 2018, pp. 489-506. 

In today's digital market, the number of websites available for advertising has ballooned into the millions. Consequently, firms often turn to ad agencies and demand-side platforms (DSPs) to decide how to allocate their Internet display advertising budgets. Nevertheless, most extant DSP algorithms are rule-based and strictly proprietary. This article is among the first efforts in marketing to develop a nonproprietary algorithm for optimal budget allocation of Internet display ads within the context of programmatic advertising. Unlike many DSP algorithms that treat each ad impression independently, this method explicitly accounts for viewership correlations across websites. Consequently, campaign managers can make optimal bidding decisions over the entire set of advertising opportunities. More importantly, they can avoid overbidding for impressions from high-cost publishers, unless such sites reach an otherwise unreachable audience. The proposed method can also be used as a budget-setting tool, because it readily provides optimal bidding guidelines for a range of campaign budgets. Finally, this method can accommodate several practical considerations including consumer targeting, target frequency of ad exposure, and mandatory media coverage to matched content websites.

Algorithm: Coordinate descent algorithm for budget optimization problem (7).

從iPhone、汽車到香蕉的貿易之旅

 曹嬿恆、簡萓靚譯，從iPhone、汽車到香蕉的貿易之旅：一本破解關於貿易逆差、經貿協定與全球化迷思，商周出版，2020

Fred P. Hochberg, Trade Is Not a Four-Letter Word: How Six Everyday Products Explain Global Trade—And Destroy the America First Myth, Avid Reader Press, 2020.

iPhone如何推翻美中「貿易逆差」的說法？

本田休旅車居然比福特和通用汽車更「美國」？(*)

失去國外市場，《冰與火之歌》甚至整個好萊塢都會消失殆盡？

塔可沙拉、本田汽車、香蕉、iPhone、大學文憑、《冰與火之歌》，

用六項商品的故事，逐步解開貿易的謎團！

貿易決定了我們餐桌上的選項，決定了購物的所有價格，

決定了哪家工廠將會慘澹關門，決定了哪個集團將統御世界；

但對於這個市場依舊存在著太多誤解，包括：

‧中國是糟糕的貿易夥伴？

‧貿易赤字代表國家嚴重損失？

‧關稅是外國人要付的？

‧進口品買得愈少，我們的日子過得愈好？

其實，貿易赤字根本無法反映雙邊的經濟狀況，

甚至會因通貨升值與貶值而波動，

而且商品價值只計入最終完成組裝的供應商所在國家，

使得號稱美國設計發明、風靡世界的iPhone，

身上所有瑞士陀螺儀、日本視網膜螢幕、以及美國玻璃的價值，

完全計入中國經濟！

 (*) pdf.

產品化物件偵測技術

徐宏民，產品化物件偵測技術 (一)，電子時報，2021-04-07

幾十年來電腦視覺研究試著在這關鍵的物件偵測技術上帶來突破。可以想像一下，電腦如何在由一堆影像畫素值中標定可能的物件？框列出可能位置，再逐一判斷是否有物件存在，是工程上「較容易」實現的方式。一般而言有三個主要步驟：候選區域(region proposal)計算、物件分類、以及後處理。 

徐宏民，產品化物件偵測技術(二)，電子時報，2021-04-13

最關鍵的問題是正確率。正確率的描述非常籠統，一般我們會更細分為precision(P)以及recall(R)，前者代表所回報的物件中有多少比例是正確的，比如說畫面中框列了10輛車子，有幾輛是對的；後者代表實際的物件標的中找到多少比例，例如畫面中有10輛車子，實際框列了幾台。...

我們很難設計單一演算法P跟R都是完美無缺。一般在檢測環境(AOI、自駕時)中比較在乎recall，所以會刻意將所有可能物件挑出，但是會造成P下降(多了假警報)，解決方法是接續使用其他演算法再進行過濾，剔除誤判，或是利用其他訊號源再確認，比如說使用雷達訊號標定可能物件之後，再使用攝影機辨認是否為車輛。

在某些應用中比較在乎precision，可以犧牲recall。例如搜尋系統中。尋找大量照片時，因為使用者不清楚有多少真實標的(例如：狗)存在，我們只需將有把握的標的呈現出來，並按照信心度排序，就能滿足使用者的需要。一般推薦系統也是採用這樣的策略，確保使用者的滿意度。...

解決anchor在實際場域上的限制，可以試著修改或是增減需要的anchor種類。不過另一種常見的作法是直接使用anchor-free的策略(如FCOS)，不使用預設模板，在偵測時，以某個基準點，往外推估可能物件的長寬，在實際使用上有不錯的效能。

Machine Learning Faces a Reckoning in Health Research

Megan Scudellari, Machine Learning Faces a Reckoning in Health Research, IEEE Spectrum, 29 Mar 2021.

In a paper describing her team’s analysis of 511 other papers, Ghassemi’s team reported that machine learning papers in healthcare were reproducible far less often than in other machine learning subfields. The group’s findings were published this week in the journal Science Translational Medicine. And in a systematic review published in Nature Machine Intelligence, 85 percent of studies using machine learning to detect COVID-19 in chest scans failed a reproducibility and quality check, and none of the models was near ready for use in clinics, the authors say.

“We were surprised at how far the models are from being ready for deployment,” says Derek Driggs, co-author of the paper from the lab of Carola-Bibiane Schönlieb at the University of Cambridge. “There were many flaws that should not have existed.”