Feature Toggles (aka Feature Flags)
Feature Toggles (often also refered to as Feature Flags) are a powerful technique, allowing teams to modify system behavior without changing code. They fall into various usage categories, and it's important to take that categorization into account when implementing and managing toggles. Toggles introduce complexity. We can keep that complexity in check by using smart toggle implementation practice
MolGAN: An implicit generative model for small molecular graphs
Deep generative models for graph-structured data offer a new angle on the problem of chemical synthesis: by optimizing differentiable models that directly generate molecular graphs, it is possible to side-step expensive search procedures in the discrete and vast space of chemical structures. We introduce MolGAN, an implicit, likelihood-free generative model for small molecular graphs that circumve
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虚淵玄(ニトãƒãƒ—ラス)×霹é‚社 å°æ¹¾ã®ä¼çµ±èŠ¸èƒ½ã€Œå¸ƒè¢‹åŠ‡ã€ã§æã‹ã‚Œã‚‹æ¦ä¾ ファンタジー人形劇『Thunderbolt Fantasy Project(サンダーボルトファンタジープãƒã‚¸ã‚§ã‚¯ãƒˆ)ã€ã€ä»Šã“ã“ã«é–‹æ¼”ï¼ ã€Žé”法少女ã¾ã©ã‹â˜†ãƒžã‚®ã‚«ã€ã€ŽPSYCHO-PASS サイコパスã€ã®è™šæ·µçŽ„(ニトãƒãƒ—ラス)ã¨ã€ã€Œéœ¹é‚布袋劇ã€ã§çŸ¥ã‚‰ã‚Œã‚‹å°æ¹¾ã®åˆ¶ä½œä¼šç¤¾ãƒ»éœ¹é‚國際多媒體股份有é™å…¬å¸(略称：霹é‚社)ãŒå¥‡è·¡ã®ã‚³ãƒ©ãƒœãƒ¬ãƒ¼ã‚·ãƒ§ãƒ³ã€‚ 圧倒的ãªæ˜ åƒè¡¨ç¾ã¨å¿ƒèºã‚‹ã‚¹ãƒˆãƒ¼ãƒªãƒ¼ã‚’ã€ãœã²ã”覧ãã ã•ã„ï¼
Using AWS Application Load Balancer and Network Load Balancer with EC2 Container Service
Warning: This article is from 2017. For a more up-to-date version see the following ready to deploy reference architectures, which have step by step instructions and CloudFormation for deploying an Application Load Balancer integrated with ECS: Serverless public facing website hosted on AWS FargatePublic facing website hosted on EC2 instances Amazon Web Services recently released new second genera
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Mar 13, 2018 Ethereum上ã®ãƒˆãƒ¼ã‚¯ãƒ³é¡žåž‹ã¨æ–°ã‚¢ã‚¤ãƒ‡ã‚¢ã€ŒRe-FungibleTokenã€ã«ã¤ã„ã¦ï½œBlockchainTopic ã“ã®è¨˜äº‹ã®ãƒã‚¤ãƒ³ãƒˆ Ethereumã§ã¯ã‚¹ãƒžãƒ¼ãƒˆã‚³ãƒ³ãƒˆãƒ©ã‚¯ãƒˆã«ã‚ˆã£ã¦ãƒˆãƒ¼ã‚¯ãƒ³ã‚’発行ã§ãã¾ã™ã€‚ トークンã«ã¯ã€ä»£æ›¿ãƒ»åˆ†å‰²ãŒå¯èƒ½ãªFungibleTokenã¨ä¸å¯èƒ½ãªNon-FungibleTokenã¨ãŒã‚ã‚Šã€ãã‚Œãžã‚Œã®ãƒ¦ãƒ¼ã‚¹ã‚±ãƒ¼ã‚¹ãŒå¤§ããç•°ãªã‚Šã¾ã™ã€‚ FungibleTokenã¯ã€ä¾¡æ ¼ã‚’ç™ºè¡Œè€…ã‚„å¸‚å ´åŽŸç†ã«å§”ããšã€æº–備金ã¨å¯¾å¿œã•ã›ã‚‹æœ¬ä½åˆ¶ãƒ¢ãƒ‡ãƒ«ã§ç™ºè¡Œã§ãã¾ã™ã€‚ã“れをBondedCurvedTokenã¨ã„ã„ã¾ã™ã€‚ Non−FungibleTokenã‚’ã€BondedCurvedTokenã«ç´ä»˜ã‘ã‚‹ã“ã¨ã§ã€åˆ†å‰²ä¸å¯èƒ½ãªè³‡ç”£ã®ãƒ¦ãƒ¼ã‚¹ã‚±ãƒ¼ã‚¹ã‚’増やã™ã®ãŒRe-FungibleTokenã®ã‚¢ã‚¤ãƒ‡ã‚¢ã§ã™ã€‚ ã¯ã˜ã‚ã« ã“ã‚“ã«ã¡ã¯ã€GincoMagazine編集部ã§ã™
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Continue: Web Applications in Racket
How do we make dynamic web applications? In this tutorial, we show how to use Racket to achieve this goal. We explain how to start up a web server, how to generate dynamic web content, and how to interact with the user. Our working example will be a simple web journal—a “blog.†This tutorial is intended for students who have read enough of How to Design Programs to know how to use structures, high
æ±å¤§æ¾å°¾ç ”ã€ã€ŒDeep Learning基礎講座ã€ã®æ¼”習コンテンツを無償公開
æ±äº¬å¤§å¦æ¾å°¾ç ”究室（æ¾å°¾è±Šç‰¹ä»»å‡†æ•™æŽˆï¼‰ã¯1月24æ—¥ã€æ±äº¬å¤§å¦ã®å…¬é–‹è¬›åº§ã€ŒDeep Learning基礎講座ã€ã§å®Ÿéš›ã«ä½¿ã£ã¦ã„る演習コンテンツã®ç„¡å„Ÿå…¬é–‹ã‚’始ã‚ãŸã€‚GPUを使ã£ã¦ãƒ¢ãƒ‡ãƒ«ã‚’å¦ç¿’ã™ã‚‹å®Ÿè·µçš„ãªå†…容ã§ã€å€‹äººãƒ»éžå•†ç”¨ã«é™ã£ã¦ç„¡æ–™ã§åˆ©ç”¨ã§ãる。 Jupyter Notebookå½¢å¼ã§ä½œæˆã•ã‚ŒãŸç ”究者å‘ã‘演習コンテンツã§ã€Jupyter環境ãŒã‚ã‚Œã°åˆ©ç”¨å¯èƒ½ã€‚GPUを利用ã—ã€å®Ÿéš›ã«ãƒ¢ãƒ‡ãƒ«ã‚’å¦ç¿’ã•ã›ãªãŒã‚‰æŠ€è¡“ã‚’ç¿’å¾—ã§ãる。線形代数や機械å¦ç¿’ãŒå‰æ知è˜ã¨ã—ã¦å¿…è¦ã€‚コードã¯TensorFlowã¨ã€Numpyã€Scipyã€Scikit-learnãªã©æ¨™æº–çš„ãªãƒ©ã‚¤ãƒ–ラリã§æ§‹æˆã—ãŸã€‚公開ã—ãŸã®ã¯æ¼”習パートã®ã¿ã§ã€è¬›ç¾©ãƒ‘ートã®ã‚³ãƒ³ãƒ†ãƒ³ãƒ„ã¯åˆ¥ã€‚ 個人ã§å¦ç¿’ã™ã‚‹ç›®çš„ã®ã¿ã§ç„¡å„Ÿã§åˆ©ç”¨ã§ãã€è¬›ç¿’会や教室ãªã©ã§ã®åˆ©ç”¨ã¯ä¸å¯ã€‚クリエイティブ・コモンズã®ã€ŒCC-BY-NC-NDã€ï¼ˆè¡¨ç¤º -改変ç¦æ¢- éžå–¶åˆ©-一般）ãŒé©ç”¨
10 open-source Kubernetes tools for highly effective SRE and Ops Teams
10 open-source Kubernetes tools for highly effective SRE and Ops Teams If you are running workloads in Kubernetes, your site reliability engineering (SRE) and operations (Ops) teams need right kind of tooling to ensure the high-reliability of the Kubernetes cluster and workloads running in it. Here we present a list of 10 open-source Kubernetes tools to make your SRE and Ops teams more effective t
Amazon SageMaker BlazingText: Parallelizing Word2Vec on Multiple CPUs or GPUs | Amazon Web Services
AWS Machine Learning Blog Amazon SageMaker BlazingText: Parallelizing Word2Vec on Multiple CPUs or GPUs Today we’re launching Amazon SageMaker BlazingText as the latest built-in algorithm for Amazon SageMaker. BlazingText is an unsupervised learning algorithm for generating Word2Vec embeddings. These are dense vector representations of words in large corpora. We’re excited to make BlazingText, the
Amazon SageMaker – Accelerating Machine Learning | Amazon Web Services
AWS News Blog Amazon SageMaker – Accelerating Machine Learning Machine Learning is a pivotal technology for many startups and enterprises. Despite decades of investment and improvements, the process of developing, training, and maintaining machine learning models has still been cumbersome and ad-hoc. The process of incorporating machine learning into an application often involves a team of experts
cri-o
Designed Optimized for Kubernetes Stable Committed to passing Kubernetes tests Any Image, Any Registry Pull from any compliant registry; run any OCI-compliant container Try Now Minikube: Use CRI-O as the container runtime kubeadm: Checkout this kubeadm guide to setup kubeadm to use CRI-O Kubic: Configured to use CRI-O out of the box in both its kubeadm and microOS system roles Join #crio on Kubern
コãƒã‚¯ãƒˆãƒ¼ãƒ をシーケンサーã«æŽ›ã‘ãŸã„ - fantom_zona’s diary
今年èªã‚“ã 一番好ããªè«–æ–‡2017ã®12月21æ—¥ã®è¨˜äº‹ã§ã™ã€‚ åˆã‚ã¾ã—ã¦ã€‚kira_kira_worldã§ã™ã€‚åˆæˆç”Ÿç‰©å¦ã¨åŸºç¤ŽåŒ»å¦ãŒå¥½ããªå¦éƒ¨ç”Ÿã§ã™ã€‚ã“ã®ä¼ç”»ã¯ä»¥å‰ã‹ã‚‰è¦‹ã¦ã„ã¦ã€ãšã£ã¨è¦‹ã‚‹å°‚ã ã£ãŸã‚“ã§ã™ãŒã€ä»Šå¹´ã¯æ™¯å“欲ã—ã•ã«ç™»éŒ²ã—ã¦ã¿ã¾ã—ãŸã€‚ 紹介論文 紹介ã™ã‚‹è«–æ–‡ã¯ã€"Using high-throughput barcode sequencing to efficiently map connectomes"ã§ã™ã€‚ãƒãƒ¼ã‚³ãƒ¼ãƒ‰æŠ€è¡“(後述)を使ã£ã¦ã‚³ãƒã‚¯ãƒˆãƒ¼ãƒ を決定ã™ã‚‹æ–¹æ³•ã‚’考ãˆã¾ã—ãŸã£ã¦ã„ã†è«–æ–‡ã§ã™ã€‚ å‰ç½®ã Wikipediaã®"コãƒã‚¯ãƒˆãƒ¼ãƒ "ã¨ã„ã†ãƒšãƒ¼ã‚¸ã‚’引用ã—ã¾ã™ã¨ã€ 「コãƒã‚¯ãƒˆãƒ¼ãƒ （connectome）ã¨ã¯ã€ç”Ÿç‰©ã®ç¥žçµŒç³»å†…ã®å„è¦ç´ （ニューãƒãƒ³ã€ãƒ‹ãƒ¥ãƒ¼ãƒãƒ³ç¾¤ã€é ˜é‡Žãªã©ï¼‰ã®é–“ã®è©³ç´°ãªæŽ¥ç¶šçŠ¶æ…‹ã‚’表ã—ãŸåœ°å›³ã€ã¤ã¾ã‚Šç¥žçµŒå›žè·¯ã®åœ°å›³ã®ã“ã¨ã€‚〠ã¨ã®ã“ã¨ã§ã™ã€‚今回ã®è«–æ–‡ã®ä¸ã§ã¯ã€ã‚³ãƒã‚¯ãƒˆãƒ¼ãƒ ã¨
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今年èªã‚“ã 一番好ããªè«–æ–‡2017ã«ã‚¨ãƒ³ãƒˆãƒªãƒ¼ã—ãŸã®ã§ã€è«–文紹介ã®è¨˜äº‹ã‚’ブãƒã‚°ã«æ›¸ã„ã¦ã¿ã‚ˆã†ã¨æ€ã„ã¾ã™ã€‚ adventar.org ã“ã®ãƒ–ãƒã‚°ã§è«–文紹介もã—ãŸã„ãªã‚ã¨æ€ã£ã¦ã„ãŸã‚“ã§ã™ã‘ã©ã€å°±æ´»ã¨ç ”究ã§è‰²ã€…å¿™ã—ãã£ã¦æ›¸ã‘ã¦ãªã‹ã£ãŸã®ã§ã“れを機ã«ã‚‚ã†ã¡ã‚‡ã£ã¨æ›¸ããŸã„ã§ã™ã€‚ ブãƒã‚°ã§ã¯ImageJã®ã“ã¨ã¨ã‹Pythonã®ã“ã¨ã¨ã‹æ›¸ã„ã¦ã„ã¾ã™ãŒã€åƒ•ã®å°‚é–€ã¯ã‚¿ãƒ³ãƒ‘ク質ã¨ã‹ãªã®ã§ã€ç´¹ä»‹ã™ã‚‹ã®ã‚‚タンパク質ã®ãŠè©±ã«ãªã‚Šã¾ã™ã€‚ ã•ã¦ã€ä»Šå›žç´¹ä»‹ã—ãŸã„ã®ã¯ã€ã“ã®è«–æ–‡ã§ã™ã€‚ Evolutionary drivers of thermoadaptation in enzyme catalysis Science 20 Jan 2017: Vol. 355, Issue 6322, pp. 289-294 DOI: 10.1126/science.aah3717 science.sciencemag.org 進化ã®æ´å²
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Genomic Analysis with Hail on Amazon EMR and Amazon Athena | Amazon Web Services
AWS Big Data Blog Genomic Analysis with Hail on Amazon EMR and Amazon Athena UPDATE: Hail version 0.2 (stable) represents over a year of great work by the community.  It includes new capabilities, numerous fixes and improved performance. We’ve updated the CloudFormation template in this post to allow you to launch an Amazon EMR cluster with Hail v0.2. Genomics analysis has taken off in recent year
Earth on AWS
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Setting up Lisp & Hunchentoot on a web server – @v0idPtr
The Demon-Haunted World - Wikipedia
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