專門為中學生寫的程式語言設計

李家同、劉國有、謝一功、侯冠維、陳庭偉，專門為中學生寫的程式語言設計：強化邏輯思考力，聯經出版公司，2018

《專門為中學生寫的程式語言設計：強化邏輯思考力》採用的是 Python 語言，容易上手學習。目的是教會大家程式語言，可是更重要的任務是要使學生有邏輯思考的能力。只要能把程式語言設計的基礎打好，建立程式運作的邏輯觀念，對於日後要學習資料結構或演算法都相當有幫助，或是之後需要學習其它程式語言，也相當容易。

劉和師，書本範例教學及題解 PPT 檔

口罩生產流程

蔣曜宇，直擊抗疫隱形功臣！台灣躍升全球No.2大口罩生產國，揭一週出貨10台產線秘密，數位時代，2020.02.14

原則上，一台設備產線一分鐘約生產120片口罩，若24小時運作不停歇，一天最理想情況可以生產約17萬片，但扣除補貨等流程，一台設備一天可以生產10萬片左右。 

一個口罩的成形需要有三個步驟。一個外科口罩共有三層布料，外層紡黏不織布用於防水；中層熔噴不織布為靜電濾材，用來吸附病菌及有害物質；內層不織布則有透氣效果、可以吸收口鼻分泌物。 

在產線中第一步驟中，口罩的三層布料透過熔接成形，經過第一個機台「口罩本體機」。此處每分鐘會處理120片口罩。 

後來經過分流，由兩道產線流進兩台耳帶機中掛上耳帶，最後進行封邊即完成。此處一台耳帶機每分鐘會處理60片口罩，兩台加起來是120片。

每日產百萬片 他第一 台灣口罩王 護國之戰-李四端的雲端世界

Netflix Culture (網飛文化)

Netflix Culture (網飛文化): Company website, prior version at slideshare, Netflix’s Reed Hastings: Why culture matters, 給力 (POWERFUL)

警政署【110 視訊報案】app

警政署【110視訊報案】app

好提議改變社會

行政院，公共政策網路參與平臺  (【提點子】我要如何提議？)

看這群工程師，如何3天讓大家用健保卡記名買口罩

管婺媛，鍵盤救國是真的！看這群工程師，如何3天讓大家用健保卡記名買口罩，商周頭條， 2020.02.10

跨部會的資訊相關局處與政院資安處幕僚單位齊聚一堂，大家要共同解決一道考題：如何讓民眾在「快速、便利、公平」3大原則下，買到口罩？ 

這是行政院早在2週半前整合跨部會機關資訊人員所組成的「防疫資訊科技作戰小組」。他們透過大數據輿情系統發現，買不到口罩的民怨聲量越來越高，特別集中在北部與都會地區，外加超商店員也瀕臨崩潰邊緣，為了防止民怨爆炸，他們腦力激盪，擬定3套口罩實名制作戰計畫；其中一案，就是用健保卡。

Covariant Uses Simple Robot and Gigantic Neural Net to Automate Warehouse Picking

Evan Ackerman, Covariant Uses Simple Robot and Gigantic Neural Net to Automate Warehouse, IEEE Spectrum, 29 Jan 2020.

There’s already a huge amount of automation in logistics, but as Abbeel explains, in warehouses there are two separate categories that need automation: “The things that people do with their legs and the things that people do with their hands.” The leg automation has largely been taken care of over the last five or 10 years through a mixture of conveyor systems, mobile retrieval systems, Kiva-like mobile shelving, and other mobile robots. “The pressure now is on the hand part,” Abbeel says. “It’s about how to be more efficient with things that are done in warehouses with human hands.”

極端政治的誕生

陳重亨譯，極端政治的誕生：政客如何透過選舉操縱左右派世界觀的嚴重對立，有方文化，2019
Marc Hetherington and Jonathan Weiler, Prius or Pickup?: How the Answers to Four Simple Questions Explain America’s Great Divide, Houghton Mifflin Harcourt, 2018.

《極端政治的誕生》
為政治兩極對立的現況，提供真正關鍵而精準的解析 

你的車庫停著什麼車？你喝的是哪裡買的咖啡？
你是貓星人還是狗星人？你更喜歡住在城市還是鄉下？ 

喜歡宣稱「政治歸政治，____歸____」？
很遺憾的，你的投票傾向，無一不在你的生活選擇之中被揭露。
我們在日常生活中的選擇，從教養觀、上班地點、飲食品酒，
聽的音樂、看的電影、喜歡的運動，都透露著我們的政治偏好。
正因如此，政治立場與己相左的人似乎更是方方面面不能入眼，
從他的投的票到衣著美學都令人嫌惡。

學習數學的四個層次：(0) 如何學數學

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。

YouTube 影片

2015/12/1 初稿，持續更新中。

數學研究數 (number) 與形 (shape)，被稱為科學之母。

我們從小學數學，到了大學還是要學數學。許多學生不知道為什麼要學數學，甚至深受其苦。接下來的四篇文章期待能夠提供一點點的幫忙，也希望能讓國高中生知道，數學和大學科系的連結和生活中的應用。歡迎指教。

Online Vehicle Routing: The Edge of Optimization in Large-Scale Applications

Dimitris Bertsimas, Patrick Jaillet, and Sébastien Martin, Online Vehicle Routing: The Edge of Optimization in Large-Scale Applications, Operations Research, Vol. 67, No. 1, 2019, Pages:143–162.

With the emergence of ride-sharing companies that offer transportation on demand at a large scale and the increasing availability of corresponding demand data sets, new challenges arise to develop routing optimization algorithms that can solve massive problems in real time. In this paper, we develop an optimization framework, coupled with a novel and generalizable backbone algorithm, that allows us to dispatch in real time thousands of taxis serving more than 25,000 customers per hour. We provide evidence from historical simulations using New York City routing network and yellow cab data to show that our algorithms improve upon the performance of existing heuristics in such real-world settings.

Travel Time Estimation in the Age of Big Data

Dimitris Bertsimas, Arthur Delarue, Patrick Jaillet, and Sébastien Martin, Travel Time Estimation in the Age of Big Data, Operations Research, Vol. 67, No. 2, 2019.

Twenty-first century urban planners have identified the understanding of complex city traffic patterns as a major priority, leading to a sharp increase in the amount and the diversity of traffic data being collected. For instance, taxi companies in an increasing number of major cities have started recording metadata for every individual car ride, such as its origin, destination, and travel time. In this paper, we show that we can leverage network optimization insights to extract accurate travel time estimations from such origin–destination data, using information from a large number of taxi trips to reconstruct the traffic patterns in an entire city. We develop a method that tractably exploits origin–destination data, which, because of its optimization framework, could also take advantage of other sources of traffic information. Using synthetic data, we establish the robustness of our algorithm to high variance data, and the interpretability of its results. We then use hundreds of thousands of taxi travel time observations in Manhattan to show that our algorithm can provide insights about urban traffic patterns on different scales and accurate travel time estimations throughout the network.