Optimal Transport

Professor Cédric Villani, “Optimal Transport Theory”, New Frontiers in Mathematics, 2018/1/17.

Making Better Fulfillment Decisions on the Fly in an Online Retail Environment

Jason Acimovic and Stephen C. Graves, Making Better Fulfillment Decisions on the Fly in an Online Retail Environment, Manufacturing & Service Operations Management, Volume 17, Issue 1, Winter 2015. (2017 M&SOM Best Paper Award)

We develop a heuristic that makes fulfillment decisions by minimizing the immediate outbound shipping cost plus an estimate of future expected outbound shipping costs (*). These estimates are derived from the dual values of a transportation linear program (LP). In our experiments on industry data, we capture 36% of the opportunity gap assuming clairvoyance, leading to reductions in outbound shipping costs on the order of 1%. These cost savings are achieved without any deterioration in customer service levels or any increase in holding costs.

東北角偏鄉小校 雲端教育大爆發

商業周刊，東北角偏鄉小校 雲端教育大爆發，2020年4月24日

Chip Placement with Deep Reinforcement Learning

Azalia Mirhoseini, et al., Chip Placement with Deep Reinforcement Learning, arXiv:2004.10746.

In this work, we present a learning-based approach to chip placement, one of the most complex and time-consuming stages of the chip design process. Unlike prior methods, our approach has the ability to learn from past experience and improve over time. In particular, as we train over a greater number of chip blocks, our method becomes better at rapidly generating optimized placements for previously unseen chip blocks. To achieve these results, we pose placement as a Reinforcement Learning (RL) problem and train an agent to place the nodes of a chip netlist onto a chip canvas. To enable our RL policy to generalize to unseen blocks, we ground representation learning in the supervised task of predicting placement quality. By designing a neural architecture that can accurately predict reward across a wide variety of netlists and their placements, we are able to generate rich feature embeddings of the input netlists. We then use this architecture as the encoder of our policy and value networks to enable transfer learning. Our objective is to minimize PPA (power, performance, and area), and we show that, in under 6 hours, our method can generate placements that are superhuman or comparable on modern accelerator netlists, whereas existing baselines require human experts in the loop and take several weeks.

How to motivate yourself to change your behavior

Tali Sharot, How to motivate yourself to change your behavior, TEDxCambridge, 2014/10/28

How to do it in 3 steps: social incentives, immediate reward, and progress monitoring (at 15:22)

COS116: The Computational Universe

Sanjeev Arora, Computer Science 116 The Computational Universe, Princeton University, 2006.

幾年前，我教資管系一下的網際網路應用。心想計算機概論已經教通訊軟體的操作，所以網際網路應用改教重要的概念，取材來自幾本書，並簡化內容，例如資訊經營法則、Networked Life、Mining of Massive Datasets。當時有許多學生棄選，不知道是不是英文字 (部分有中譯) 太多？還是概念比較抽象？方程式太多？ 

Prof. Arora 這門課的授課對象是任何學系的學生，可以視為台灣的通識教育。最近使用 Amazon Kindle (電子書閱讀器) 閱讀其講義，發現講得更生活化，例如以圖書館員取書的例子，說明計算機結構中快取 (cache) 的概念。再一次呼應了我之前的說法，世界級的研究型大學，教學也是令人驚艷。

另外一點，美國的學費驚人，而且有廣大的社會捐獻和好的投資績效，所以經費充足、課程的支援較好。這門課有實驗，大都是助教負責寫講義和上課。但是，台灣的大學學費凍漲，自然無法和其相比。 

給青年知識追求者的十封信

朱敬一，給青年知識追求者的十封信，聯經出版，2018

經過十餘年的沉潛，走在求知路上的朱敬一院士，始終心繫台灣的青年學子。 

為什麼要閱讀經典

王汎森，為什麼要閱讀經典？，風傳媒，2019-11-16  

林肯正式的教育背景很淺，可是他的文章和演講卻極為動人，我認為他主要的精神及思想資源是不斷地精讀包括《聖經》在內的經典。這些經典深入他的精神層次，除了源源不斷地提供許多精彩的養分之外，也形成了一個規模、一個框架、一種內在的雄渾的氣氛與節奏，再加上他在適當的時候套引經典，使得他的演講及文字非常動人。...

外交官的涵養與應變

朱敬一，外交官的涵養與應變，風傳媒，2020-04-30

什麼是「外交」，不需要我來定義；維基百科裡對於外交的歷史緣起、功能特色、正式與非正式外交、外交豁免等，都有詳盡的介紹。但是「外交是什麼」與「如何做好外交」，是兩個不同層面的問題。我是經濟學研究者，算是熟悉國際經濟體系與經濟學理論；我做過科技首長、中央研究院副院長，了解科技發展與產業，也對台灣官僚體系的運作了然於心。但是如何將經濟學與科技產業的知識背景「融入」外交場域，卻不是一件容易的事。這裡可以先說結論：我認為做好外交官，只有四個字，涵養、應變。仔細一點分析，應變又來自於涵養。所以外交官的本事，就是以「涵養」二字為根基。