投稿時間:2021-10-22 16:46:42 RSSフィード2021-10-22 16:00 分まとめ(58件)

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TECH	Engadget Japanese	Chromebookのおすすめ5選。学校や企業への導入が急増中。個人ユーザー向けモデルも品揃えは豊富！	https://japanese.engadget.com/best-selection-chromebook-061601367.html	Chromebookのおすすめ選。	2021-10-22 06:16:01
TECH	Engadget Japanese	新型MacBook ProのSDカードスロット、最大転送速度は250MB/sと判明	https://japanese.engadget.com/sd-cardslot-macbookpro-uhs-ii-250-mbs-060055203.html	macbookpro	2021-10-22 06:00:55
IT	ITmedia 総合記事一覧	[ITmedia PC USER] 手のひらサイズのミニPC「Pantera Pico PC」がCAMPFIREで限定販売	https://www.itmedia.co.jp/pcuser/articles/2110/22/news121.html	campfire	2021-10-22 15:43:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] ダウンロード不要、ワクチンパスポートアプリ「スマートコロナパス」運用開始	https://www.itmedia.co.jp/business/articles/2110/22/news120.html	itmedia	2021-10-22 15:32:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 全世界、移動するだけでマイルがたまるアプリ「Miles」　正式ローンチから24時間で10万人突破	https://www.itmedia.co.jp/business/articles/2110/22/news117.html	appstore	2021-10-22 15:22:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 航空券の最安値は出発日の何日前？　エクスペディアが「2022年の旅行節約術」を公開、需要喚起へ	https://www.itmedia.co.jp/business/articles/2110/22/news112.html	itmedia	2021-10-22 15:04:00
python	Pythonタグが付けられた新着投稿 - Qiita	わかったつもりのχ二乗をもう一度ちゃんと理解する	https://qiita.com/Qwertyutr/items/fb7a1884ac7dd3ccffbc	さすがにχ二乗の確率密度関数は覚えるものではないw加法性χ二乗は分散との共通性質もあり、例えば、χ自由度nχ自由度nであるとき、χχχの自由度はnnとなりますつ以上でもOKχiは独立t分布との関係性χ二乗もt分布も標本から計算される数値を扱っているため、共通点を持っています。	2021-10-22 15:53:30
python	Pythonタグが付けられた新着投稿 - Qiita	【スクレイピングツール】scrape.doの使い方	https://qiita.com/hiro_pppp/items/c640a88341a3935029db	scrapedoのポイント高度なカスタマイズを可能とするWebスクレイピングAPISPAサイトもスクレイピング可能リクエストごとにIPアドレスをローテーションJavaScriptをレンダリングし、CAPTCHAを処理可能ターゲットWebサイトへの失敗したリクエストを自動的に再試行リクエストごとにMBの応答サイズ制限がありますscrapedoの無料枠サインアップすると、毎月のAPIリクエストを含む無料枠がありますので、安心して試すことができます。	2021-10-22 15:51:30
python	Pythonタグが付けられた新着投稿 - Qiita	【Python】for文で辞書(dictionary)繰り返し処理	https://qiita.com/PuchiCorner/items/ebb5a021026e3135cee8	【Python】for文で辞書dictionary繰り返し処理辞書また、辞書型変数dictionaryはkeyvalueで格納されるデータです。	2021-10-22 15:45:09
python	Pythonタグが付けられた新着投稿 - Qiita	データ分析	https://qiita.com/tk-tatsuro/items/561e9fc657422e05f0f7	この考察を元に、次回前処理を行なっていきます。	2021-10-22 15:27:13
js	JavaScriptタグが付けられた新着投稿 - Qiita	Native File System API でテキストエディタを作る	https://qiita.com/yubais/items/b02d5d4f7a9edf4b0725	NativeFileSystemAPIでテキストエディタを作る先日公開されたvscodedevを触った際、なんかNativeFileSystemAPIというのを使えばブラウザから普通にローカルファイルを扱えると知ったので、試しにブラウザで動くテキストエディタを作ってみた。	2021-10-22 15:23:03
js	JavaScriptタグが付けられた新着投稿 - Qiita	axiosのinterceptorsでリクエストに共通のパラメータを付与する	https://qiita.com/ysugimo/items/e8741f90ef24c72d48d2	axiosのinterceptorsでリクエストに共通のパラメータを付与するaxiosを利用してAPIリクエストを送る際に、全リクエストに同じパラメータを付与したいパターンがありました。	2021-10-22 15:03:49
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	python DataFrameのindexを振り直せない	https://teratail.com/questions/365699?rss=all	pythonDataFrameのindexを振り直せないDataFrameに行を追加し、最後に追加された行を削除するDataFrameの行数を一定に保ちたいようにしたいです。	2021-10-22 16:00:10
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Pythonでｋ近傍法のプログラムを作成する	https://teratail.com/questions/365698?rss=all	Pythonでk近傍法のプログラムを作成する前提・実現したいことPythonでk近傍法のプログラムを作成したいです。	2021-10-22 15:42:06
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	API Gatewayにて「Missing Authentication Token」返ってくる。	https://teratail.com/questions/365697?rss=all	リソースのURLが存在しない。	2021-10-22 15:32:03
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	PHPの正規表現に関して	https://teratail.com/questions/365696?rss=all	PHPの正規表現に関してPHPでテンプレートエンジンを開発している者です。	2021-10-22 15:31:36
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	特定のテキストを出力したい	https://teratail.com/questions/365695?rss=all	特定のテキストを出力したい前提・実現したいことhuggingfaceからDLしたTextnbspClassificationnbsponnbspGLUEというものを、UNIXのローカルターミナルで実行し、結果として表示されるevallossだけを出力し、学習曲線を作成しようとしております。	2021-10-22 15:29:49
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	VMware Player ホストOSのコンピューターロック時、ゲストOSと通信ができなくなる	https://teratail.com/questions/365694?rss=all	しかしながらホストOSをロックこのコンピューターをロックしますすると、ゲストOSと通信ができなくなります。	2021-10-22 15:29:41
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	rubyワンライナー で文字列処理を複数行うには	https://teratail.com/questions/365693?rss=all	rubyワンライナーで文字列処理を複数行うにはrubyのワンライナーを使ってみようとして勉強しています。	2021-10-22 15:25:19
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	WPの独自検索バー（searchform.php）が反映されない	https://teratail.com/questions/365692?rss=all	WPの独自検索バーsearchformphpが反映されない前提WordPressのオリジナルテーマを作っています。	2021-10-22 15:24:57
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	HTMLでjavascriptを使ってクリックイベントを行いたい	https://teratail.com/questions/365691?rss=all	HTMLでjavascriptを使ってクリックイベントを行いたい前提・実現したいことHTMLを試しにやってみたのですが、クリックしたときテキストの内容を変えるという処理が動きません。	2021-10-22 15:16:32
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	【Xamarin】SqlCommandBuilderのTypeLoadExceptionについて	https://teratail.com/questions/365690?rss=all	再起動、NuGetのアンインストールインストール・別のソリューションを作成しても同様の結果となります。	2021-10-22 15:14:47
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	flexのspace-betweenで3列に並べた要素の文頭を揃えたい	https://teratail.com/questions/365689?rss=all	flexのspacebetweenで列に並べた要素の文頭を揃えたい前提・実現したいこと画像のように、二行の二列目の文頭を一行目と揃えたいのですがうまくいきません。	2021-10-22 15:08:27
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	【jQuely】index()メソッドを利用して、クリックした要素とスライドショーを連動させる方法	https://teratail.com/questions/365688?rss=all	【jQuely】indexメソッドを利用して、クリックした要素とスライドショーを連動させる方法実装したいものFlowItemというクラスをクリックしたら、swiperslide正しい順番で表示されるようにしたい。	2021-10-22 15:06:10
AWS	AWSタグが付けられた新着投稿 - Qiita	Fargateスケジュールタスクがたまに実行されないことがある	https://qiita.com/flatnyat/items/6e3029804513b4e328e8	参考結論年月現在、タスク起動時に環境変数をSから取得するようにタスク定義を設定している場合は、タスクの実行に失敗することがあります。	2021-10-22 15:23:26
Docker	dockerタグが付けられた新着投稿 - Qiita	【スクレイピングツール】scrape.doの使い方	https://qiita.com/hiro_pppp/items/c640a88341a3935029db	scrapedoのポイント高度なカスタマイズを可能とするWebスクレイピングAPISPAサイトもスクレイピング可能リクエストごとにIPアドレスをローテーションJavaScriptをレンダリングし、CAPTCHAを処理可能ターゲットWebサイトへの失敗したリクエストを自動的に再試行リクエストごとにMBの応答サイズ制限がありますscrapedoの無料枠サインアップすると、毎月のAPIリクエストを含む無料枠がありますので、安心して試すことができます。	2021-10-22 15:51:30
Ruby	Railsタグが付けられた新着投稿 - Qiita	rails ActiveRecordでjsonの値を取り出す	https://qiita.com/namasho0606/items/15d51561d7a8660db5df	railsActiveRecordでjsonの値を取り出すActiveRecordでjsonの値を取り出すmongoDBqueryembeddeddocumentsとかを検索ワードにして調べてみました。	2021-10-22 15:55:57
Ruby	Railsタグが付けられた新着投稿 - Qiita	Rasil ja.ymlについてまとめてみた 【human_attribute_name】【Model.model_name.human】の使い分け!!	https://qiita.com/mm_st/items/9faefcd74bd9b4b4ca69	Rasiljaymlについてまとめてみた【humanattributename】【Modelmodelnamehuman】の使い分け概要jaymlについてまとめる前提設定してある部分configlocalesviewsjaymljadefaultssubmit送信loginログインregister登録logoutログアウトusersnewtitleユーザー登録tologinpageログインページへconfiglocalesactiverecordjaymljaactiverecordmodelsuserユーザーattributesuseremailメールアドレスpasswordパスワードviewsjaymlconfiglocalesviewsjayml再記jadefaultssubmit送信loginログインregister登録logoutログアウトusersnewtitleユーザー登録tologinpageログインページへ呼び込み方ターミナルでやってみるrbmaingtIntdefaultslogingtログインこんな感じで使用できるviewで使用するとviewsusersnewhtmlerbltdivclasstextcentergtltlinktottologinpagloginpathgtltdivgtこんな感じで使用できる。	2021-10-22 15:24:02
海外TECH	DEV Community	Competitive self-play with Unity ML-Agents	https://dev.to/joooyz/competitive-self-play-with-unity-ml-agents-1nh6	Competitive self play with Unity ML Agents An overview of self playCompetitive self play involves training an agent against itself It was used in famous systems such as AlphaGo and OpenAI Five Dota By playing increasingly stronger versions of itself agents can discover new and better strategies In this post we walk through using competitive self play in Unity ML Agents to train agents to play volleyball This article is also part of the series A hands on introduction to deep reinforcement learning using Unity ML Agents The case for self playWe previously trained agents using PPO with the following setup Symmetric environmentBoth agents shared the same policyObservations velocity rotation and position vectors of the agent and ballReward function for hitting the ball over the netThis resulted in agents that were able to successfully volley the ball back and forth after M training steps You can see that the agents make easy passes by aiming the ball towards the centre of the court This is because we set the reward function to incentivize keeping the ball in play Our aim now is to train competitive agents that are rewarded for winning i e landing the ball in the opponent s court We expect this will lead to agents that learn interesting strategies and make passes that are harder to return Self play setup in ML AgentsTo follow along this section you will need Unity ML Agents Release getting started instructions The latest version of the Ultimate Volleyball repo or you can use your own volleyball environment if you ve been following the tutorial series Step Put the agents on opposing teamsOpen the Ultimate Volleyball environment in UnityOpen Assets gt Prefabs gt PVolleyballArea prefabSelect either the PurpleAgent or BlueAgent objectIn Inspector gt Behavior Parameters set TeamId to the actual value doesn t matter as long as the PurpleAgent and BlueAgent have different Team ID s Step Set up the self play reward functionOur previous reward function was for hitting the ball over the net For self play we ll switch to to the winning team to the losing teamOpen VolleyballEnvController cs and add the rewards to the ResolveEvent method case Event HitBlueGoal blue wins blueAgent AddReward f purpleAgent AddReward f turn floor blue StartCoroutine GoalScoredSwapGroundMaterial volleyballSettings blueGoalMaterial RenderersList f end episode blueAgent EndEpisode purpleAgent EndEpisode ResetScene break case Event HitPurpleGoal purple wins purpleAgent AddReward f blueAgent AddReward f turn floor purple StartCoroutine GoalScoredSwapGroundMaterial volleyballSettings purpleGoalMaterial RenderersList f end episode blueAgent EndEpisode purpleAgent EndEpisode ResetScene break Remove AddReward from the other casesYou can also set penalties for hitting the ball out of the court in case Event HitOutOfBounds From my experience this may take longer for the agents to learn to hit the ball Step Add self play training parameters to the trainer configCreate a new yaml file and copy in the following behaviors Volleyball trainer type ppo hyperparameters batch size buffer size learning rate beta epsilon lambd num epoch learning rate schedule constant network settings normalize true hidden units num layers vis encode type simple reward signals extrinsic gamma strength keep checkpoints max steps time horizon summary freq self play window play against latest model ratio save steps swap steps team change Explaining self play parametersDuring self play one of the agents will be set as the learning agent and the other as the fixed policy opponent Every save steps steps a snapshot of the learning agent s existing policy will be taken Up to window snapshots will be stored When a new snapshot is taken the oldest one is discarded These past versions of itself become the opponents that the learning agent trains against Every swap steps steps the opponent s policy will be swapped with a different snapshot The snapshot is sampled with a probability of play against latest model ratio that it will play against the latest policy i e the strongest opponent This helps to prevent overfitting to a single opponent playstyle After team change steps the learning agent and opponent teams will be switched Feel free to play around with these default hyperparameters more information available in the official ML Agents documentation Training with self playTraining with self play in ML Agents is done the same way as any other form of training Activate the virtual environment containing your installation of ml agents Navigate to your working directory and run in the terminal mlagents learn lt path to config file gt run id VB time scale When you see the message Start training by pressing the Play button in the Unity Editor click   within the Unity GUI In another terminal window run tensorboard logdir results from your working directory to observe the training process Self play training resultsIn a stable training run you should see the ELO gradually increase In the diagram below the three inflexion points correspond to the agent Learning to serve Learning to return the ballLearning more competitive shotsCompared to our previous training results I found that even after M steps the agents trained using self play don t serve or return the ball as reliably However they do learn to hit some interesting shots like hitting the ball towards the edge of the court If you discover any other interesting playstyles let me know Wrap upThanks for reading I hope you found this post useful If you have any feedback or questions feel free to post them on the Ultimate Volleyball Repo	2021-10-22 06:47:04
海外TECH	DEV Community	Enlist major features of Kotlin?	https://dev.to/mayankquora/enlist-major-features-of-kotlin-4akb	Enlist major features of Kotlin Kotlin was developed to make it feature rich in mind so there are many features that make Kotlin a unique programming language Below are some major Features of Kotlin Programming Language Extension functions In Kotlin you can add some extra functionality to the existing component to make it much more versatile Higher Order Function You can pass a function as an argument to a method or return a function as a result of the method in Kotlin Smart Cast Smart Cast checks for some type and then it will allow performing all the operations allowed for that particular type Destruction Declaration With this it is possible to return more than one value from a function Default and Named arguments You can assign a default value to the parameter whom you want to make optional and while calling that method there is no bound on giving values to the default parameters You can easily change the sequence of the parameter at the time of calling with Named arguments It is done by writing the name of the argument and then assigning the value to it	2021-10-22 06:33:17
海外TECH	DEV Community	API Testing - Setting up API Tests for different environments like Dev, Prod,...	https://dev.to/dheerajaggarwal/api-testing-setting-up-api-tests-for-different-environments-like-dev-prod-h69	API Testing Setting up API Tests for different environments like Dev Prod This video tutorial explains how you may set up multiple test environments for the same set of API tests in the vREST NG Application vREST NG is a script less API test automation tool It helps you to write maintainable test cases quickly for functional and regression testing of APIs You can download and install the vREST NG application directly on Windows OSX and Linux via our website Video Link API Testing Tutorials Playlist Important Links vREST NG WebsiteCommunity ChatBook a Live DemoPlease do like and share if you found this video helpful and let the voice heard by the testing community Also let us know your feedback by commenting on this post	2021-10-22 06:29:10
海外TECH	DEV Community	1 simple way to implement variable-Length Pattern Matching	https://dev.to/lisahui/nebula-graph-how-variable-length-pattern-matching-is-implemented-1pb5	simple way to implement variable Length Pattern MatchingAt the very heart of openCypher the MATCH clause allows you to specify simple query patterns to retrieve the relationships from a graph database A variable length pattern is commonly used to describe paths and it is Nebula Graph s first try to get nGQL compatible with openCypher in the MATCH clause As can be seen from the previous articles of this series an execution plan is composed of physical operators Each operator is responsible for executing unique computational logics To implement the MATCH clause the operators such as GetNeighbors GetVertices Join Project Filter and Loop which have been introduced in the previous articles are needed Unlike the tree structure in a relational database the execution process expressed by an execution plan in Nebula Graph is a cyclic graph How to transform a variable length pattern into a physical plan in Nebula Graph is the focus of the Planner In this article we will introduce how variable length pattern matching is implemented in Nebula Graph Problem Analysis Fixed Length PatternIn a MATCH clause a fixed length pattern is commonly used to search for a relationship If a fixed length pattern is considered a special case of the variable length pattern that is a pattern describing a path of a specified length the implementations of both can be unified Here are the examples Fixed length pattern MATCH v e v Variable length pattern MATCH v e v The preceding examples differ from each other in the type of the e variable In the fixed length pattern e represents an edge while in the variable length one e represents a list of edges of length Connecting Variable Length PatternsAccording to the syntax of openCypher a MATCH clause allows you to specify a combination of various patterns for describing complicated paths For example two variable length patterns can be connected as follows MATCH v e v ee v The pattern combination in the preceding example is extendable which means by connecting variable length and fixed length patterns in different ways various complicated paths can be queried Therefore we must find a pattern to generate an execution plan to iterate the whole process recursively The following conditions must be considered The following variable length path depends on the preceding one The variables in the following pattern depend on the preceding pattern Before the next traversal step the starting vertex must be de duplicated From the following example you can see that as long as an execution plan can be generated for the part of like m n combinations and iterations may be applied to generate plans for the subsequent parts like m n like k l Pattern Pattern Pattern Execution PlanIn this section we will introduce how the like m n part in the preceding example is transformed into a physical execution plan in Nebula Graph This pattern describes a graph of a minimum of m hops and a maximum of n hops In Nebula Graph a one step traversal is completed by the GetNeighbors operator To implement a multi step traversal each traversal step must call the GetNeighbors operator again on the basis of the previous step and when the traversal of all the steps are completed all the retrieved vertices and edges are connected end to end to form a single path What users need is the paths of m to n relationships However in the execution process paths of length to length n are queried and are stored for output or for the next traversal but only the paths of length m to n are retrieved One Step TraversalLet s see what the one step traversal looks like In Nebula Graph the source vertex is stored together with its outgoing edges so retrieving them does not need to access data across partitions However the destination vertex and its incoming edges are stored in different partitions so GetVertices is necessary for retrieving the properties of the vertex In addition to avoid replicated scanning of Storage the source vertices must be de duplicated before the traversal The execution plan of a one step traversal is shown as follows Multi Step TraversalThe process of a multi step traversal is the repetition of one step traversal However we can see that the GetNeighbors operator can retrieve the properties of an edge s source vertex so the GetVertices operator can be omitted in the previous step Here is an execution plan of a two step traversal Storing PathsThe paths retrieved in each traversal step may be needed at the end of the traversal so all the paths must be stored The paths for a two step traversal are connected by the Join operator In the result of the example e like m n e represents a list of data edges so Union is needed to merge the results of each traversal step The execution plan will be evolved further as follows Connecting Variable Length PatternsAfter the implementations of the preceding process a physical plan will be generated for the e like m n pattern If multiple similar patterns are connected together such a process is iterated However before the iteration the results of the previous process must be filtered to get the paths of length m to length n The retrieved dataset of the previous process involves the paths of length to length n so filtering them by path length is needed When the variable length patterns are connected together the execution plan becomes as follows After the step by step decomposition of the patterns the expected execution plan for the MATCH clause is finally generated As you can see it takes a lot of effort to transform a complicated pattern into the underlying interfaces for a traversal Of course the execution plan can be optimized such as the multi step traversal can be encapsulated by using the Loop operator and the sub plan of a one step traversal can be reused which will not be detailed in this article If you are interested please refer to the source code of Nebula Graph SummaryThis article demonstrated the process of generating an execution plan for a MATCH clause with a variable length pattern While reading the article you may have this question Why such a basic and simple path query will generate such a complicated execution plan in Nebula Graph It s not like Neoj where only a few operators are needed to complete the same job In Nebula Graph complicated directed acyclic graphs DAG are generated The answer is that in Nebula Graph the operators are closer to the underlying interfaces and there is a lack of semantic abstractions for higher level graph operations The operator granularity is too fine so too many details need to be considered to implement the optimization of the upper layer We will further study the execution operators to gradually improve the functionality and the performance of the MATCH clause If you encounter any problems in the process of using Nebula Graph please refer to Nebula Graph Database Manual to troubleshoot the problem It records in detail the knowledge points and specific usage of the graph database and the graph database Nebula Graph	2021-10-22 06:06:50
海外TECH	DEV Community	5 proverbs for rapid development	https://dev.to/aatmaj/5-proverbs-for-rapid-development-2o4o	proverbs for rapid development Let us see five proverbs which fit perfectly to the rapid development scenario An ounce of protection is worth a pound of cure A little precaution before a crisis hits is better than lot of firefighting afterwards Many times developers neglect risk management Risk management identification and prevention are more critical than we think Unidentified risks cause a lot of trouble in the later stages of software development This is why preventing such risks is wiser than to fix them after they get worsened Identifying risks and taking precautions against them that is protecting the project against the risk is a key to rapid development A stich in time saves nineCorrection at early stages saves work later In a software development lifecycle doing right things at the right time is very important If work is delayed in the earlier stages it can causes much trouble afterward Example a flaw in the design time is not fixed is bound to cause a disaster And one flaw in the implementation time if not fixed will take almost triple the time to fix at the debugging phase Cross the stream where it is shallowest Don t complicate things unnecessarily Many developers believe that the more complicated things will be made the better the project will execute But that s not the case In reality complicated practices stringent methodologies actually lengthen the process rather than making it faster This is why many times the simple is the best Complex design patters complex code leads only to increasing the clutter of the program Yes this doesnt mean that one must nor use complex patterns or code This means that one must always strive for a simpler solution to the problem Watch the doughnut and not the hole Focus on what you have rather than at what you don t In a software development lifecycle one cannot get all the aspects perfect In that case you need to maximize what you have over what you don t For example you might not have trained personnel or say might not have enough time But in such cases you must focus on what you have and try to give the best possible Focus on your strengths and try to win the match Too many cooks spoil the broth If too many people are involved in a task or activity it will not be done well This is often the case with software development scenarios Too many people who are experienced and have their own different viewpoints While difference in perspectives always leads to better ideas there must be only one decision maker whome everyone must follow Disagree but commit must be the case with those who are the subordinates If too many people are decision makers then it will lead to chaos	2021-10-22 06:06:40
医療系	医療介護 CBnews	保健所から入院調整本部への依頼が減少-東京都がコロナモニタリング会議の専門家意見公表	https://www.cbnews.jp/news/entry/20211022154723	新型コロナウイルス	2021-10-22 15:55:00
医療系	医療介護 CBnews	22年4月からの新たな福祉用具貸与、上限価格など公表-厚労省	https://www.cbnews.jp/news/entry/20211022151755	介護保険	2021-10-22 15:30:00
医療系	医療介護 CBnews	第4次募集を開始、感染症対策実地研修-厚労省	https://www.cbnews.jp/news/entry/20211022151123	介護保険	2021-10-22 15:25:00
金融	JPX マーケットニュース	[東証]マザーズから市場第二部への変更：（株）ビーアンドピー	https://www.jpx.co.jp/listing/stocks/transfers/01.html	東証マザーズ	2021-10-22 15:30:00
金融	JPX マーケットニュース	[OSE]特別清算数値（2021年10月第4週限）：日経225	https://www.jpx.co.jp/markets/derivatives/special-quotation/	特別清算	2021-10-22 15:15:00
金融	ニッセイ基礎研究所	コロナ禍でテレワークが増えたのはどんな人か？ (5) －属性別のテレワーク頻度の変化のまとめ	https://www.nli-research.co.jp/topics_detail1/id=69094?site=nli	目次ーはじめにー推定したモデルについてー産業分類と企業の規模情報通信業および大企業に勤める人のテレワークが拡大ー雇用形態と職種正社員および管理職・専門職等のテレワークが拡大ー地域と通勤時間手段南関東在住者、長時間通勤者・公共交通機関での通勤者のテレワークが拡大ー男女、年代、年収男女差見られず、若年者、年収が高い人のテレワークが拡大ーおわりにコロナ禍でテレワークの頻度が増えたのはどのような人なのか。	2021-10-22 15:02:24
ニュース	ジェトロ ビジネスニュース（通商弘報）	トルドー・カナダ首相、国家レベルで標準化したワクチンパスポート導入発表	https://www.jetro.go.jp/biznews/2021/10/86965ee207512ed8.html	標準	2021-10-22 06:30:00
ニュース	BBC News - Home	Social care: Staff shortages will leave many without help - report	https://www.bbc.co.uk/news/health-58997811?at_medium=RSS&at_campaign=KARANGA	commission	2021-10-22 06:54:55
ニュース	BBC News - Home	Covid-19: Effects on social care and rape case delays revealed	https://www.bbc.co.uk/news/uk-59003779?at_medium=RSS&at_campaign=KARANGA	coronavirus	2021-10-22 06:23:54
ニュース	BBC News - Home	Biden says US will defend Taiwan if China attacks	https://www.bbc.co.uk/news/world-asia-59005300?at_medium=RSS&at_campaign=KARANGA	ambiguity	2021-10-22 06:19:42
ニュース	BBC News - Home	UK shop sales continue to fall in September	https://www.bbc.co.uk/news/business-59006619?at_medium=RSS&at_campaign=KARANGA	statistics	2021-10-22 06:51:37
ニュース	BBC News - Home	A decade on - remembering the day that changed the face of football in Manchester	https://www.bbc.co.uk/sport/football/58995303?at_medium=RSS&at_campaign=KARANGA	A decade on remembering the day that changed the face of football in ManchesterSaturday marks exactly years since Manchester s football landscape changed for City and United when the so called noisy neighbours produced a stunning win at Old Trafford	2021-10-22 06:09:14
ビジネス	不景気.com	鴨川グランドホテルの22年第2四半期は3億円の営業赤字へ - 不景気.com	https://www.fukeiki.com/2021/10/kamogawa-ground-hotel-2022-2q-loss.html	鴨川グランドホテル	2021-10-22 06:34:09
LifeHuck	ライフハッカー［日本版］	iPhoneの液晶と有機ELを比較｜メリット・デメリットは？	https://www.lifehacker.jp/2021/10/the-difference-between-lcd-and-oled-screens-and-wh.html	iphone	2021-10-22 16:00:00
北海道	北海道新聞	中止のプロ野球	https://www.hokkaido-np.co.jp/article/603066/	神宮	2021-10-22 15:14:00
北海道	北海道新聞	ＮＦＬ、ブラウンズが４勝目　ブロンコスに１７―１４	https://www.hokkaido-np.co.jp/article/603065/	勝目	2021-10-22 15:14:00
北海道	北海道新聞	国学院大が５勝目　東都大学野球第５週	https://www.hokkaido-np.co.jp/article/603059/	国学院大	2021-10-22 15:03:00
IT	週刊アスキー	JAPANNEXT、31.5型曲面のフルHD液晶ディスプレー「JN-315VCG240FHDR」発売	https://weekly.ascii.jp/elem/000/004/072/4072844/	japan	2021-10-22 15:30:00
IT	週刊アスキー	世界のラッキーモチーフのアートがそのままスイーツに！　キンプトン新宿東京で「いちごフォーチュンアフタヌーンティー」が11月1日から提供開始	https://weekly.ascii.jp/elem/000/004/072/4072847/	提供開始	2021-10-22 15:30:00
IT	週刊アスキー	自慢の庭を公開しよう！ 「第10回港北オープンガーデン」参加会場・協賛企業等を募集	https://weekly.ascii.jp/elem/000/004/072/4072850/	開催	2021-10-22 15:30:00
IT	週刊アスキー	エネボルト、ラベルにユニバーサルデザインを採用し見やすくわかりやすいパッケージに	https://weekly.ascii.jp/elem/000/004/072/4072854/	月日	2021-10-22 15:30:00
IT	週刊アスキー	ライトアップされた冬の横浜を見に行こう！ 「ヨコハマミライト2021～みらいを照らす、光のまち〜」11月11日スタート	https://weekly.ascii.jp/elem/000/004/072/4072846/	色鮮やか	2021-10-22 15:10:00
IT	週刊アスキー	本日10月22日20時から！『オクトパストラベラー 大陸の覇者』公式生放送「第1回」が配信	https://weekly.ascii.jp/elem/000/004/072/4072849/	開始予定	2021-10-22 15:10:00
マーケティング	AdverTimes	高遠ダムをロゴカラーでライトアップ 長野県企業局60周年企画で	https://www.advertimes.com/20211022/article366127/	情報発信	2021-10-22 06:18:32
マーケティング	AdverTimes	広聴の視点でオウンドメディアを活用する	https://www.advertimes.com/20211022/article366114/	企業ブランディング	2021-10-22 06:04:28