Misreading Chat

夏なので古のレイトレ方程式を森田が復習します。感想などはハッシュタグ #misreading か hello@misreading.chat にお寄せください。

• Kajiya: The rendering equation – Google Scholar
• Rendering equation – Wikipedia
  • Bidirectional reflectance distribution function – Wikipedia
• SIGGRAPH 2019: Home
• What Makes a (Graphics) Systems Paper Beautiful
• ACM Transactions on Graphics (TOG) – Special Issue On Production Rendering and Regular Papers
• Amazon | Physically Based Rendering: From Theory to Implementation
  • オンライン HTML 版
  • 紹介ウェブページ
  • GitHub
• 2014 | Oscars.org | Academy of Motion Picture Arts and Sciences

Follow up

• Ten Years of Erlang

エッジデバイスを寄せ集めて分散機械学習をする Federated Learning を向井が紹介します。感想などはハッシュタグ #misreading か hello@misreading.chat にお寄せください。

• [1602.05629] Communication-Efficient Learning of Deep Networks from Decentralized Data
• [1610.05492] Federated Learning: Strategies for Improving Communication Efficiency

Follow-up

• Arrow

Netflix の Chaos Engineering 最新事情っぽい論文を森田が冷やかします。感想などはハッシュタグ #misreading か hello@misreading.chat にお寄せください。

• [1905.04648] Automating chaos experiments in production
• Netflix TechBlog
  • Four Reasons We Choose Amazon’s Cloud as Our Computing Platform (2010)
  • 5 Lessons We’ve Learned Using AWS – Netflix TechBlog – Medium (2010)
  • The Netflix Tech Blog: Chaos Monkey Released Into The Wild (2012, from Internet Archive)
  • FIT: Failure Injection Testing – Netflix TechBlog – Medium (2014)
  • Chaos Engineering Upgraded – Netflix TechBlog – Medium (2015)
  • Netflix Chaos Monkey Upgraded – Netflix TechBlog – Medium (2016)
• OSS Projects
  • Netflix/Hystrix: Hystrix is a latency and fault tolerance library designed to isolate points of access to remote systems, services and 3rd party libraries, stop cascading failure and enable resilience in complex distributed systems where failure is inevitable.
  • Netflix/zuul: Zuul is a gateway service that provides dynamic routing, monitoring, resiliency, security, and more.
• Distributed Tracing
  • AWS X-Ray – Distributed Tracing System
  • Stackdriver Trace – Distributed Tracing from Google – Google Cloud Platform  |  Stackdriver Trace  |  Google Cloud
• Principles of Chaos Engineering
• Game day – AWS Well-Architected Framework
• Resilience Engineering: Learning to Embrace Failure – ACM Queue
• Weathering the Unexpected – ACM Queue
• Gremlin: Chaos Engineering Tools to Break Things on Purpose

 

関数型言語業界ではやっているらしい Algebraic Effects の論文を向井が紹介します。感想などはハッシュタグ #misreading か hello@misreading.chat にお寄せください。

• [1203.1539] Programming with Algebraic Effects and Handlers
• Algebraic Effects for Functional Programming
• Programming and Reasoning with Algebraic Effects and Dependent Types
• yallop/effects-bibliography: A collaborative bibliography of work related to the theory and practice of computational effects
• Eff Programming Language
• Koka – Microsoft Research
• Episode 41 – IDRIS — Systems Programming Meets Full Dependent Types – Misreading Chat

follow-up

• What Spectre and Meltdown Mean For WebKit | WebKit
• IBM Research | Technical Paper Search | Let’s Not Speculate: Discovering and Analyzing Speculative Execution Attacks(Search Reports)

Joe Armstrong 自身が書いた Erlang の歴史を森田が読みます。感想などはハッシュタグ #misreading か hello@misreading.chat にお寄せください。

• A History of Erlang
• Programming Erlang (2nd edition) by Joe Armstrong | The Pragmatic Bookshelf (初版日本語訳)
• Erlang Garbage Collector | Erlang Solution blog
• HPACK: the silent killer (feature) of HTTP/2
• Concurrent Programming in Erlang (2nd Edition)
• erlang nine nines – Google Search
• The Pragmatic Programmer, 20th Anniversary Edition: your journey to mastery by David Thomas, Andrew Hunt
• Programming Elixir 1.6: Functional |> Concurrent |> Pragmatic |> Fun by Dave Thomas (初版日本語訳)
• Nintendo Switch(TM)向け プッシュ通知システム 「NPNS」 (PDF)
• voluntas/japanese-erlang-elixir-companies: 日本で Erlang/OTP や Elixir を利用している会社一覧

JS 処理系 V8 の Spectre 対策について向井が話します。感想などはハッシュタグ #misreading か hello@misreading.chat にお寄せください。

• [1902.05178] Spectre is here to stay: An analysis of side-channels and speculative execution
• A year with Spectre: a V8 perspective · V8

GBDT の実装である XGBoost とかのコードを森田が読みます。感想などはハッシュタグ #misreading か hello@misreading.chat にお寄せください。

• • [1603.02754] XGBoost: A Scalable Tree Boosting System
  • dmlc/xgboost: Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow
  • Sparse matrices (scipy.sparse) — SciPy v1.2.1 Reference Guide
  • u++ on Twitter: “[#misreading episode 58] 自分にとっては新鮮な視点が多かった。GBDTのFrameworkとして終始「XGBoost」に言及があるけど、Kaggleでの現在の主流は「LightGBM」なので2019年の情報としては誤解を招きそう。あとアンサンブルの例としてバギングとランダムフォレストを横並びで列挙するのは微妙かも。”
  • Microsoft/LightGBM: A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
  • LightGBM: A Highly Efficient Gradient Boosting Decision Tree – Microsoft Research
  • How to train Boosted Trees models in TensorFlow – TensorFlow – Medium

 

Follow-up

• Cloud Run documentation  |  Cloud Run  |  Google Cloud
• 今シーズンはこのエピソードでおしまいです。またしばらくお休みします。