A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy

Emma Beede, Elizabeth Baylor, Fred Hersch, Anna Iurchenko, Lauren Wilcox, Paisan Ruamviboonsuk, Laura M. Vardoulakis, A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy, CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, April 2020, Pages 1–12, https://doi.org/10.1145/3313831.3376718.

Referral Determinations

All images were initially assessed by a nurse then sent to an ophthalmologist for review. The ability to assess fundus photos for DR varied from nurse to nurse. While most nurses told us they felt comfortable assessing for the presence of DR, they didn’t know how to determine the severity if present. P4 told us, “I know if it’s not normal, but I don’t know what to call it.” To make the ultimate decision of whether a patient needs to be referred to an ophthalmologist for an exam and potentially for treatment, the nurse turned to the ophthalmologist or retinal specialist, who are most often remote.

NBDT: Neural-Backed Decision Trees

Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez, NBDT: Neural-Backed Decision Trees,  arXiv:2004.00221, 2020

We forgo this dilemma by creating Neural-Backed Decision Trees (NBDTs) that (1) achieve neural network accuracy and (2) require no architectural changes to a neural network. NBDTs achieve accuracy within 1% of the base neural network on CIFAR10, CIFAR100, TinyImageNet, using recently state-of-the-art WideResNet; and within 2% of EfficientNet on ImageNet. This yields state-of-the-art explainable models on ImageNet, with NBDTs improving the baseline by ~14% to 75.30% top-1 accuracy. Furthermore, we show interpretability of our model's decisions both qualitatively and quantitatively via a semi-automatic process. Code and pretrained NBDTs can be found at this https URL.

Digital Social Innovation (數位社會創新)

Audrey Tang (唐鳳), Digital Social Innovation (數位社會創新), 2020/4/21

Let Taiwan into the World Health Organisation

Economist, Let Taiwan into the World Health Organisation, 2020/03/26.

Spare a moment and admire Taiwan. Its handling of the new coronavirus pandemic has so far saved many, many lives. The figures tell the story. A country of 24m, it has far fewer infections than its neighbours: just 235 as of March 25th, with only two deaths...

原則：生活和工作 (Principles: Life and Work)

陳世杰、諶悠文、戴至中譯，原則：生活和工作，商業周刊，2018
Ray Dalio, Principles: Life and Work, Simon & Schuster, 2017 (excerpt)

 瑞．達利歐出身美國普通中產家庭，26歲時被投資公司炒魷魚，在自己的兩房公寓室白手起家創辦了橋水，並在接下來超過42年裡，把橋水打造成了獲《財星》（Fortune）雜誌評選為美國第五重要的私人公司。現在橋水管理資金超過1,500億美元，截至2015年年底，盈利超過450億美元。達利歐曾成功預測2008年金融危機，成為華爾街教父級大神。 

一路以來，達利歐曾入選世界百大最具影響力人物［《時代》（Time）］與百大富豪［《富比世》（Forbes）］，而且由於他獨特的投資原則改變了業界，《CIO》更稱他是「投資界的史蒂夫．賈伯斯」。 

他是怎麼辦到的？靠的是「原則」！他從1982年看錯墨西哥債務危機、狠狠跌交的經驗中吸取教訓，提煉決策標準，日積月累，總結成一組「原則」，包含21條高層原則、139條中原則和365條分原則，涵蓋為人處事、公司管理兩大方面，是橋水的員工手冊，橋水依循進行日常管理，也是橋水成為全球最強避險基金的祕密。

光學檢測獲利王牧德科技

吳靜芳，光學檢測獲利王！高CP值媲美世界龍頭，百大企業九成是牧德客戶，天下雜誌 672期，2019-05-06

「現在 PCB 愈做愈小，朝30微米以下發展，人工目檢漸漸不可行，」工業技術研究院產科國際所分析師黃仲宏觀察，在自動化趨勢下，AOI 早已是PCB廠的剛性需求。...

財務自由的人生

楊應超，財務自由的人生：跟著首席分析師楊應超學華爾街的投資技巧和工作效率，40 歲就過 FIRE，天下文化，2019

機構投資者》、《金融時報》、《格林威治》、《亞元雜誌》評選為第一名首席外資分析師楊應超不藏私，首度公開他征戰全球頂尖投行的投資和工作心法，幫助你40歲前達到FIRE，過不再為錢煩惱的優質人生！

Response to the accusations of Dr. Tedros, the Director General of the WHO, against Taiwan

CNBC, WHO chief addresses death threats and racist insults: 'I don't give a damn', 2020/4/9

Vivi Lin, An Open Letter to Dr Tedros 一封致予譚德塞博士的公開信

『雅琴看世界 The View with Catherine Chang』2020.04.09

Economists (and Economics) in Tech Companies

Susan Athey  and Michael Luca, Economists (and Economics) in Tech Companies, Journal of Economic Perspectives, 2019, 33 (1): 209-30.

Amazon’s hiring has been especially notable, as they have hired more than one hundred economists in the past five years, making them the largest employer of tech economists. In fact, Amazon now has more PhD economists than the largest academic economics department....

Algorithms for Optimization by Kochenderfer and Wheeler

Mykel J. Kochenderfer and Tim A. Wheeler, Algorithms for Optimization, The MIT Press, 2019.

This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. 

Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.

Course website and slides.

Kissinger on Kissinger: Reflections on Diplomacy, Grand Strategy, and Leadership

Winston Lord, Kissinger on Kissinger: Reflections on Diplomacy, Grand Strategy, and Leadership, All Points Books, 2019.

Critical care capacity

Shubham Singhal, Patrick Finn, Pooja Kumar, Matt Craven, and Sven Smit, Critical care capacity: The number to watch during the battle of COVID-19, McKinsey, March 2020.

How much should we increase capacity? It depends on the starting point of each country, but in most instances is four to five times. This increase is possible; and is part of the focus of the health response across the world. But we strongly suggest to healthcare leaders to put this sentence on top of their and their colleagues’ proverbial inbox: Start watching critical care capacity....

Even in advanced economies like the United States, 25 percent of households live from paycheck to paycheck, and 40 percent of Americans are unable to cover an unexpected expense of $400 without borrowing. ...

Growing healthcare capacity at lightning speed... 

Slowing the demand for critical care ...