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.