投稿時間:2021-04-09 04:30:35 RSSフィード2021-04-09 04:00 分まとめ(34件)

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AWS	AWS Compute Blog	Evaluating access control methods to secure Amazon API Gateway APIs	https://aws.amazon.com/blogs/compute/evaluating-access-control-methods-to-secure-amazon-api-gateway-apis/	Evaluating access control methods to secure Amazon API Gateway APIsThis post is written by Bryant Bost Cloud Application Architect There is not a one size fits all approach to access control for Amazon API Gateway Properties of your application such as API type identity provider client access patterns privacy requirements and others influence the design of your access control solution Understanding the types of access control available …	2021-04-08 18:08:11
AWS	AWS Media Blog	How iconik’s media management hub and AI accelerate remote collaboration	https://aws.amazon.com/blogs/media/ptner-iconiks-media-management-hub-ai-accelerate-remote-collaboration/	How iconik s media management hub and AI accelerate remote collaborationAuthored by Parham Azimi CEO at iconik The content and opinions in this post are those of the third party author and AWS is not responsible for the content or accuracy of this post nbsp Video is no longer just for the media and entertainment industry Today prominent businesses in every vertical are becoming “media companies …	2021-04-08 18:40:19
js	JavaScriptタグが付けられた新着投稿 - Qiita	CSSで進捗サークルチャートを描いてJavaScriptで可変	https://qiita.com/akebi_mh/items/744efe3bb4e53a5a19f7	CSSで進捗サークルチャートを描いてJavaScriptで可変backgroundにradialgradientとconicgradientを組み合わせて適用すればできそうな感じだったので試してみました。	2021-04-09 03:52:34
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	(rails×vue)spaで作られたサイトはどの様にして直接アクセスの対策をしているのでしょうか？	https://teratail.com/questions/332324?rss=all	rails×vuespaで作られたサイトはどの様にして直接アクセスの対策をしているのでしょうかvuejsでルーティングにvuerouterを使うとしてconstroutespathapivusersidnameUsercomponentusers以上の様なルーティングになっているとします。	2021-04-09 03:18:00
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	rustのfor文をマルチスレッドにしたい	https://teratail.com/questions/332323?rss=all	rustのfor文をマルチスレッドにしたい私は下記の関数を並列処理に変更したいと考えています。	2021-04-09 03:08:03
Ruby	Rubyタグが付けられた新着投稿 - Qiita	O/Rマッピングって便利	https://qiita.com/yuukinakamura0925/items/158aebbaf9576725a34d	ORマッピングって便利ORマッピングとはORマッピングObjectrelationalmappingについて、Wikipediaには以下のように書かれています。	2021-04-09 03:03:05
Ruby	Railsタグが付けられた新着投稿 - Qiita	O/Rマッピングって便利	https://qiita.com/yuukinakamura0925/items/158aebbaf9576725a34d	ORマッピングって便利ORマッピングとはORマッピングObjectrelationalmappingについて、Wikipediaには以下のように書かれています。	2021-04-09 03:03:05
海外TECH	Ars Technica	The Apple Watch Series 6 is down to its lowest price yet today	https://arstechnica.com/?p=1755345	monitors	2021-04-08 18:25:37
海外TECH	DEV Community	5 Best Resources To Learn JavaScript For Free	https://dev.to/codeloungedev/5-best-resources-to-learn-javascript-for-free-n53	Best Resources To Learn JavaScript For FreeOriginally posted on the codelounge dev blog gt Best Resources To Learn JavaScript For Free There are countless resources to learn JavaScript and that is both a bad and a good thing The good thing is that we have many options to choose from However the bad thing is that we do not know which resource is the best Thus the purpose of this article is to shine some light and guide you towards the best resources to learn JavaScript Especially as a beginner Therefore let us see the best resources FreeCodeCampThe first resource on the list is FreeCodeCamp FreeCodeCamp provides an excellent introduction to JavaScript Moreover it dives into advanced topics as well It takes you from no knowledge to an intermediate level However once you dive into the advanced topics you might have to supplement the learning with additional resources Or with their YouTube channel By the way talking about their YouTube channel it is another excellent resource They have many JavaScript tutorials created by professional developers Thus you can get to an advanced level just by using the FreeCodeCamp platform and their YouTube channel Check FreeCodeCamp curriculumFreeCodeCamp YouTube channel You Don t Know JavaScriptThe book series “You Don t Know JavaScript is one of the best resources to learn JavaScript if not the best This series is split into six books and it takes you from zero knowledge to an advanced level It teaches you about the scope closures “this keyword object prototype types amp grammar async performance and ES amp beyond The series is written by Kyle Simpson who is a very knowledgeable person and an active JavaScript developer The good thing is that like FreeCodeCamp YDKJS series is free Of course you can and you should buy them to support the author if you can afford it The “You Don t Know JavaScript series is my go to manual Whenever I want to refresh concepts or learn new ones I use this resource first It is well written detailed to the point and always following the ECMAScript specification Check You Don t Know JavaScript JavaScriptPractising by building application yourself is critical in programming As a result I want to recommend you JavaScript which focuses exclusively on building JavaScript applications It does not use any frameworks compilers boilerplates and so on It is purely vanilla JavaScript The JavaScript course is for beginners and intermediate It is going to teach you the JavaScript fundamentals and how to work with the Document Object Model DOM Bear in mind you should already have basic JavaScript knowledge before starting the course The purpose of the course is to make you apply that knowledge JavaScript is entirely free as well Until this point you have three excellent resources to skyrocket your JavaScript skills for free Check JavaScript MDN Web Docs JavaScriptMDN cannot miss from this article as it is an excellent website Thus it is no surprise that their JavaScript tutorial is fantastic The online tutorial is split into four parts Complete beginnersJavaScript GuideIntermediateAdvancedAs with the other resource listed in the article you can go from a basic level to an advanced level Besides that the course is written and maintained by professional developers Therefore you can bet on the tutorial provided by MDN It is of the highest quality Check MDN JavaScript JavaScript infoThe last resource I want to mention is JavaScript info which is a superb website for reference It contains information on basic JavaScript advanced concepts and Document Object Model The website is split into three parts The JavaScript Language start from scratch and go on to advanced concepts like OOP The focus is on the language itself here Browser Document Events Interfaces learn how to manage the browser page add elements manipulate their size and position dynamically create interfaces and interact with the visitor Additional Articles a list of extra topics that assume you ve covered the first two parts of the tutorial There is no clear hierarchy here you can read articles in the order you want The information is dense and to the point You can use the website as your primary study guide or you can use it in conjuncture with the other resources Check JavaScript info ConclusionThe list in this article is not exhaustive but it covers some of the best resources to learn JavaScript By using these resources you can get to a pretty advanced level and even get a job offer For the best results try to mix them because you can learn something from all of them And the best thing they are entirely free except for printed copies of YDKJS which you can buy if you want to support the author If you enjoyed the article consider sharing it so more people can benefit from it	2021-04-08 18:45:44
海外TECH	DEV Community	Tolkien character or prescription drug name? Classification using character-level Long Short-Term Memory (LSTM) neural networks	https://dev.to/gbnegrini/tolkien-character-or-prescription-drug-name-classification-using-character-level-long-short-term-memory-lstm-neural-networks-1o4m	Tolkien character or prescription drug name Classification using character level Long Short Term Memory LSTM neural networksAfter trying to read J R R Tolkien s The Silmarillion again for the millionth time I remembered a funny tweet that has been around for a while Geraldo of Rivera️ checarina Antidepressant or Tolkien character AzafenBergilCelebríanCírdanClédialDesyrelEdronaxElendilElrononErestorEskalithFinarfinHaldirIstinilMinalcarNardilNarmacilNarviNorvalOrophinProthiadenSintamil AM Mar Even though I was a casual fan of The Lord of the Rings and having already taken two pharmacology courses in college I had no idea who or what a Narmacil was Should we fear him her by its sword skills or by its dangerous side effects This little trivia prompted me to ask if an artificial neural network ANN could succeed where I and many more have failed Here I show you how to build a special type of ANN called Long Short Term Memory LSTM to classify Tolkien characters and prescription drug names using Keras DatasetThe first step was to build from scratch a combined dataset with names of Tolkien characters and prescription drugs a bunch of them not just antidepressants Tolkien charactersLucky for us the Behind the Name website has a database of the first names of Tolkien characters that we can directly read from the page s HTML using pandas import pandas as pdraw tolkien chars pd read html raw tolkien chars head Name Gender Details Total Adalbert m character Adaldrida f character Adalgar m character Adalgrim m character Adamanta f character tolkien names raw tolkien chars Name tolkien names iloc Gethron Ghân buri Ghân Gildis Gildor Gil galadName Name dtype objectWe can see that some names are hyphenated and have accented letters To simplify the analysis I transformed unicode characters to ASCII removed punctuation marks transformed them to lowercase and removed any possible duplicates import unidecodeprocessed tolkien names tolkien names apply unidecode unidecode str lower str replace processed tolkien names name for name in processed tolkien names str split processed tolkien names pd DataFrame processed tolkien names columns name sort values name drop duplicates processed tolkien names tolkien processed tolkien names name iloc gethron ghan gil gildis gildorName name dtype objectprocessed tolkien names shape Done Now we have different character names Prescription drugsTo get a comprehensive list of drug names I downloaded the medication guide of the U S Food amp Drug Administration FDA raw medication guide pd read csv data raw medication guides csv raw medication guide head Drug Name Active Ingredient Form Route Appl  No Company Date Link Abilify Aripiprazole TABLET ORALLY DISINTEGRATING ORAL OTSUKA Abilify Aripiprazole TABLET ORAL OTSUKA Abilify Aripiprazole SOLUTION ORAL OTSUKA Abilify Aripiprazole SOLUTION ORAL OTSUKA Abilify Aripiprazole INJECTABLE INTRAMUSCULAR OTSUKA drug names raw medication guide Drug Name drug names iloc Chantix Children s Cetirizine Hydrochloride Allergy Chlordiazepoxide and Amitriptyline Hydrochloride Cimzia CimziaName Drug Name dtype objectA similar preprocessing step was repeated for this dataset too processed drug names drug names str lower str replace str replace str replace str replace str replace processed drug names name for name in processed drug names str split processed drug names pd DataFrame processed drug names columns name sort values name drop duplicates processed drug names tolkien processed drug names name iloc chantix children chlordiazepoxide cimzia ciproName name dtype objectprocessed drug names shape Done different drug names We can finally combine the two datasets and move on dataset pd concat processed tolkien names processed drug names ignore index True Data transformationSo now we have a bunch of names but machine learning models don t work with raw characters We need to convert them into a numerical format that can be processed by our soon to be built model Using the Tokenizer class from Keras we set char level True to process each word at character level The fit on texts method will update the tokenizer internal vocabulary based on our dataset names and then texts to sequences will transform each name into a sequence of integers from keras preprocessing text import Tokenizertokenizer Tokenizer char level True tokenizer fit on texts dataset name char index tokenizer texts to sequences dataset name Look how our beloved Bilbo is now print dataset name print char index bilbo Yet this representation is not ideal Having integers to represent letters could lead the ANN to assume that the characters have an ordinal scale To solve this problem we have to Set all names to have the length of the longest name characters here We use pad sequences to add s to the end of names shorter than letters Convert each integer representation to its one hot encoded vector representation The vector consists of s in all cells except for a single in a cell to identify the letter from keras preprocessing sequence import pad sequencesfrom keras utils import to categoricalimport numpy as npchar index pad sequences char index maxlen dataset name apply len max padding post x to categorical char index onehot encodingy np array dataset tolkien x shape We have names Each name has letters and each letter is a one hot encoded vector of size letters of the Latin alphabet padding character Data splitI split the data into train validation and test sets with a ratio using a custom function since sklearn train test split only outputs two sets from sklearn model selection import train test splitdef data split data labels train ratio rand seed x train x temp y train y temp train test split data labels train size train ratio random state rand seed x val x test y val y test train test split x temp y temp train size random state rand seed return x train x val x test y train y val y testx train x val x test y train y val y test data split x y train ratio Let s take a look at the splits from collections import Counterimport matplotlib pyplot as pltdataset count pd DataFrame Counter y train Counter y val Counter y test index train val test dataset count plot kind bar plt xticks rotation plt show print f Total number of samples n dataset count sum axis sum print f Class Samples n dataset count sum axis print f Split Class Samples n dataset count Total number of samples Class Samples dtype intSplit Class Samples train val test There are more Tolkien characters than drug names but it seems like a decent balance LSTM modelLong Short Term Memory is a type of Recurrent Neural Network proposed by Hochreiter S amp Schmidhuber J to store information over extended time intervals Names are just sequences of characters in which the order is important so LSTM networks are a great choice for our name prediction task You can read more about LSTMs in this awesome illustrated guide written by Michael Phi TrainingKeras was used to build this simple LSTM model after some tests and hyperparameter tuning It is just a hidden layer with LSTM blocks one dropout layer to prevent overfitting and one output neuron with a sigmoid activation function to make a binary classification from tensorflow keras import Sequentialfrom tensorflow keras layers import Dense Activation Dropout LSTMfrom tensorflow random import set seedset seed model Sequential model add LSTM return sequences False input shape x shape x shape model add Dropout model add Dense units model add Activation sigmoid model summary Model sequential Layer type Output Shape Param lstm LSTM None dropout Dropout None dense Dense None activation Activation None Total params Trainable params Non trainable params Adam is a good default optimizer and produces great results in deep learning applications Binary cross entropy is the default loss function to binary classification problems and it is compatible with our single neuron output architecture from tensorflow keras optimizers import Adammodel compile loss binary crossentropy optimizer Adam learning rate e metrics accuracy Two callbacks were implemented EarlyStopping to stop the training process after epochs without reducing the validation loss and ModelCheckpoint to always save the model when the validation loss drops from tensorflow keras callbacks import EarlyStopping ModelCheckpointes EarlyStopping monitor val loss verbose patience mc ModelCheckpoint best model h monitor val loss verbose save best only True history model fit x train y train batch size epochs validation data x val y val callbacks es mc Epoch val loss did not improve from Epoch s ms step loss accuracy val loss val accuracy Epoch val loss did not improve from Epoch early stoppingval loss per epoch history history val loss best epoch val loss per epoch index min val loss per epoch print f Best epoch best epoch Best epoch Let s plot the accuracy and loss values per epoch to see the progression of these metrics def plot metrics history plt figure figsize plt subplot plt plot history history accuracy label Training plt plot history history val accuracy label Validation plt xlabel Epoch plt ylabel Accuracy plt legend loc lower right plt grid on plt subplot plt plot history history loss label Training plt plot history history val loss label Validation plt xlabel Epoch plt ylabel Loss plt legend loc upper right plt grid on plot metrics history We can see that the accuracy quickly reaches a good plateau around Visually the model appears to start overfitting after epoch It shouldn t be a problem to use the version saved by ModelCheckpoint at epoch Performance evaluationFinally let s see how our model does with the test dataset from tensorflow keras models import load modelmodel load model best model h metrics model evaluate x x test y y test s ms step loss accuracy print Accuracy f format metrics Accuracy Not bad We can explore the results a little more with the confusion matrix and classification report from sklearn metrics import confusion matrixfrom seaborn import heatmapdef plot confusion matrix y true y pred labels cm confusion matrix y true y pred heatmap cm annot True fmt d cmap rocket r xticklabels labels yticklabels labels plt ylabel True label plt xlabel Predicted label plt show predictions model predict x test threshold y pred predictions gt thresholdplot confusion matrix y test y pred labels Drug Tolkien from sklearn metrics import classification reportprint classification report y test y pred target names Drug Tolkien precision recall f score support Drug Tolkien accuracy macro avg weighted avg Besides the good accuracy the model has almost the same number of false positives and false negatives We can see this reflecting in a balanced precision and recall So just out of curiosity which Tolkien characters could pass as prescription drugs def onehot to text onehot word Reverse one hot encoded words to strings char index np argmax char for char in onehot word word tokenizer sequences to texts char index return join word split test result pd DataFrame test result true y testtest result prediction y pred astype int test result name onehot to text name for name in x test test result head true prediction name supprelin bingo ponstel elidel aubagio test result name loc test result true amp test result prediction ivy camellia celebrindor meriadoc vanimelde finduilas eglantine ruby poppy otto tanta myrtle prisca cottar stybba este daisy tulkas arciryas odovacar tarcil hyarmendacil jago tata ponto landrovalName name dtype object ConclusionSo here we covered how to work with character embeddings and build a simple LSTM model capable of telling apart Tolkien character names from prescription drug names Full code including requirements dataset a Jupyter Notebook code version and a script version can be found at my GitHub repo You can also play around with this popular interactive quiz found on the web Antidepressant or Tolkien I only got right Can you guess better than the LSTM network ReferencesHu Y Hu C Tran T Kasturi T Joseph E amp Gillingham M What s in a Name Gender Classification of Names with Character Based Machine Learning Models arXiv preprint arXiv Bhagvati C Word representations for gender classification using deep learning Procedia computer science Liang X How to Preprocess Character Level Text with Keras	2021-04-08 18:43:25
海外TECH	DEV Community	20 Git Commands You should know 😎	https://dev.to/1nj3ct0r/20-git-commands-you-should-know-4j2p	Git Commands You should know Git is a version control system developed in by Linus Torvalds The creator of the Linux kernel It helps you keep track of the code changes you have made to files in your project It comes with a large number of commands that you can use to manage your source code efficientlyIn this article we ll go over the most commonly used Git commands that every software developer should know Checking the Git Configuration git config lThe above command displays a list of information about your Git configuration including username email default code editor etc Configure your Git Username ‍ ️git config global user email example sample com The above command could be used to configure your email address Replace your email address with example sample com Initialize a Git Repository git initThe above command can be used to initialize That is to create a new Git repository It can be used to convert an existing project to a Git repository The above command creates a new git subfolder in your current working directory that contains all the require metadata for the new repository Adding a single file to the staging area git add FILEThe above command adds a file to the staging area Be sure to replace FILE with the name of the file to be added to the staging area Adding all files to the staging area ️git add The above command adds all files to the staging area Check Git Status git statusThe above commands displays the status of the current repository including the current branch the list of deployed undeployed and untracked files etc Maintain Changes git commitThe above command commits the changes to head When executed it opens a code editor in the terminal where you can write a commit message Fix Changes with a Message ️git commit m YOUR COMMIT MESSAGE This command lets you specify just a short summary for your commit message without opening the code editor ️Replace YOUR COMMIT MESSAGE with your own commit summary which describes the changes in your commit Check Git History git logThe above command displays a list of commit logs Get Branch List Use git branchThe above command to display the list of all created branches in the local repository Delete a Branch ️git branch d BRANCHUse the above command to delete a Git branch Make sure to replace BRANCH with the name of your own branch Also don t forget to add the d flag It tells Git that you want to delete the specified branch Create a New Branch ️git branch BRANCHUse the above command to create a new branch One thing we need to keep in mind is that Git does not automatically switch to this branch you need to do this manually with the checkout command See Change Branches git checkout BRANCHYou can use the above command to switch to a newly created branch or to another branch Create a new branch in Git and switch to it immediately git checkout b BRANCHYou can create and checkout a new Git branch in a single command by adding the b option to the checkout command Adding a Remote Repository in Git ️git add remote https REPO URL The command adds a remote repository to your local repository Make sure to replace REPO URL with the actual URL of the remote and repository Commit your changes to a remote repository in Git git pushYou can use the above command to commit your changes to the remote repository Pulling Changes from a Remote Repository in Git git pullYou can use the above command to pull the latest changes from the remote repository Stowing Changes git stashThe stash command allows you to temporarily park stash your uncommitted changes Both staged and unstaged to save them for later use Undoing Saved Changes git stash popUse the above command to reapply changes parked with the stash commandAnd that s it Those were the Git commands I use most oftenI hope you found this article useful have fun Also Published on C Sharp Corner	2021-04-08 18:28:14
Apple	AppleInsider - Frontpage News	Apple testing search tags in App Store to narrow down results	https://appleinsider.com/articles/21/04/08/apple-testing-search-tags-in-app-store-to-narrow-down-results	Apple testing search tags in App Store to narrow down resultsApple appears to be testing a new tag feature on the App Store to narrow down search results in popular app categories making it easier for users to find specific types of apps Credit AppleInsiderRecently some users have noticed that searching for popular search terms like photos or adventure games brings up additional tags Tapping on these tags curates and focuses the search results In some cases a second tag can be added to narrow down the results even further Read more	2021-04-08 18:09:31
Apple	AppleInsider - Frontpage News	M1 Mac mini steal: 512GB model discounted to $799 ($100 off) at Amazon	https://appleinsider.com/articles/21/04/06/m1-mac-mini-steal-512gb-model-discounted-to-799-100-off-at-amazon	M Mac mini steal GB model discounted to off at AmazonApple s M Mac mini is marked down to at Amazon this Thursday thanks to a hidden in cart discount stacked with an instant cash rebate New M Mac mini dealThe April price drop at Amazon offers shoppers an additional off at checkout on top of a instant discount already in effect bringing the total savings on the GB model with a GB SSD to off Read more	2021-04-08 18:57:16
海外TECH	Engadget	Muslim civil rights group sues Facebook for 'misleading' content moderation claims	https://www.engadget.com/muslim-advocates-sues-facebook-183132654.html	Muslim civil rights group sues Facebook for x misleading x content moderation claimsA new consumer protection lawsuit alleges Facebook executives like Mark Zuckerberg and Sheryl Sandberg have misled Congress and the American public by falsely stating the company removes content that violates its policies	2021-04-08 18:31:32
海外TECH	Engadget	Netflix will get exclusive streaming rights to future Sony films	https://www.engadget.com/sony-netflix-exclusive-deal-182221930.html	Netflix will get exclusive streaming rights to future Sony filmsNetflix will get exclusive streaming rights to all of Sony s upcoming movies including the next installment in the Spider Man franchise	2021-04-08 18:22:21
Cisco	Cisco Blog	Want more optics information? Try this new podcast	https://blogs.cisco.com/sp/want-more-optics-information-try-this-new-podcast	major	2021-04-08 18:04:13
海外ニュース	Japan Times latest articles	Ikee earns Olympic berth in 4×100 freestyle relay with latest win	https://www.japantimes.co.jp/sports/2021/04/08/more-sports/swimming/rikako-ikee-tokyo-olympics/	Ikee earns Olympic berth in × freestyle relay with latest winIkee won in seconds and has now qualified for two Olympic events Her butterfly victory on Sunday at Tokyo Aquatics Centre earned her	2021-04-09 04:00:15
海外ニュース	Japan Times latest articles	Rising star Shoma Sato swimming toward Olympic spotlight	https://www.japantimes.co.jp/sports/2021/04/08/more-sports/swimming/rising-star-shoma-sato/	world	2021-04-09 03:15:51
ニュース	BBC News - Home	Covid: Deaths in England and Wales fall 92% since January peak	https://www.bbc.co.uk/news/uk-56682716	safety	2021-04-08 18:42:44
ニュース	BBC News - Home	Gillingham stabbing: Sir Richard Sutton named as victim	https://www.bbc.co.uk/news/uk-england-dorset-56680636	richard	2021-04-08 18:53:12
ニュース	BBC News - Home	Greensill lobbying row: Rishi Sunak texts to David Cameron released	https://www.bbc.co.uk/news/uk-politics-56681939	calls	2021-04-08 18:45:10
ニュース	BBC News - Home	Belfast: Rioting 'was worst seen in Northern Ireland in years'	https://www.bbc.co.uk/news/uk-northern-ireland-56664868	belfast	2021-04-08 18:45:43
ニュース	BBC News - Home	The Masters 2021: Dustin Johnson holes 'wonderful' birdie chip	https://www.bbc.co.uk/sport/av/golf/56681355	The Masters Dustin Johnson holes x wonderful x birdie chipDefending champion Dustin Johnson holes a wonderful birdie chip on the th to move back to level par in his opening round of the Masters	2021-04-08 18:37:20
ビジネス	ダイヤモンド・オンライン - 新着記事	「死体遺棄」「殺人未遂」に職員を走らせた福祉現場が模索する、再生への道 - 生活保護のリアル～私たちの明日は？ みわよしこ	https://diamond.jp/articles/-/267915	取り組み	2021-04-09 03:50:00
ビジネス	ダイヤモンド・オンライン - 新着記事	「米中・コロナ・五輪」が絡み合う異例の日米首脳会談延期の内幕 - 永田町ライヴ！	https://diamond.jp/articles/-/267716	日米同盟	2021-04-09 03:45:00
ビジネス	ダイヤモンド・オンライン - 新着記事	日本の財政資金の使い方が残念すぎる証拠、借金膨張幅はダントツでも成長率は完敗 - 金融市場異論百出	https://diamond.jp/articles/-/267914	世界経済	2021-04-09 03:40:00
ビジネス	ダイヤモンド・オンライン - 新着記事	半導体不足で膨らむ、製造装置1000億ドルの夢 - WSJ PickUp	https://diamond.jp/articles/-/267964	wsjpickup	2021-04-09 03:35:00
ビジネス	ダイヤモンド・オンライン - 新着記事	自動運転車、普及妨げる心理的バイアスとは - WSJ PickUp	https://diamond.jp/articles/-/267965	wsjpickup	2021-04-09 03:30:00
ビジネス	ダイヤモンド・オンライン - 新着記事	コロナ禍で拡大した「返さなくてOK」な奨学金制度とは - ニュース3面鏡	https://diamond.jp/articles/-/267801		2021-04-09 03:25:00
ビジネス	ダイヤモンド・オンライン - 新着記事	就活生が親にされて「よかったこと」「嫌だったこと」ランキング2021 - 親と子の就活ギャップ	https://diamond.jp/articles/-/267827	大学院生	2021-04-09 03:20:00
ビジネス	ダイヤモンド・オンライン - 新着記事	買ってきたお惣菜をパックのまま 食卓に出すのは、育ちがよくない？ - 育ちがいい人だけが知っていること	https://diamond.jp/articles/-/267741	内容は、マナー講師として活動される中で、「先生、これはマナーではないのですが……」と、質問を受けることが多かった、明確なルールがないからこそ迷ってしまう、日常の何気ないシーンでの正しいふるまいを紹介したもの。	2021-04-09 03:15:00
ビジネス	ダイヤモンド・オンライン - 新着記事	ひろゆきが「とりあえず大企業に行ったほうがいい」と語るワケ - 1％の努力	https://diamond.jp/articles/-/267192	匿名掲示板	2021-04-09 03:10:00
ビジネス	ダイヤモンド・オンライン - 新着記事	【マンガでわかる】「僕にはもう時間がない」と語った若き天才数学者の悲劇 - とてつもない数学	https://diamond.jp/articles/-/267812	【マンガでわかる】「僕にはもう時間がない」と語った若き天才数学者の悲劇とてつもない数学天才数学者たちの知性の煌めき、絵画や音楽などの背景にある芸術性、AIやビッグデータを支える有用性…。	2021-04-09 03:05:00
北海道	北海道新聞	「ホテル住まい」に商機　宿泊１カ月単位、札幌に続々　通院高齢者らに好評	https://www.hokkaido-np.co.jp/article/531206/	札幌市内	2021-04-09 03:11:02