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。」