投稿時間:2021-09-29 09:44:03 RSSフィード2021-09-29 09:00 分まとめ(52件)

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IT	気になる、記になる…	Apple、｢MacBook｣シリーズのパフォーマンスを向上させる｢ハイパワーモード｣を開発中か − macOSの最新ベータ版から証拠が見つかる	https://taisy0.com/2021/09/29/146717.html	apple	2021-09-28 23:23:38
IT	ITmedia 総合記事一覧	[ITmedia エグゼクティブ] 住友不、省エネ性能標準化　新築マンションを「ZEH」に	https://mag.executive.itmedia.co.jp/executive/articles/2109/29/news074.html	itmedia	2021-09-29 08:19:00
IT	ITmedia 総合記事一覧	[ITmedia News] 日本人は「テレワークだと仕事がはかどらない」　7カ国調査で唯一	https://www.itmedia.co.jp/news/articles/2109/29/news073.html	itmedia	2021-09-29 08:03:00
AWS	AWS Big Data Blog	Coming January 2022: An updated Amazon QuickSight sign-in experience	https://aws.amazon.com/blogs/big-data/coming-january-2022-an-updated-amazon-quicksight-sign-in-experience/	Coming January An updated Amazon QuickSight sign in experienceStarting January the Amazon QuickSight will undergo minor updates such as changes in the sign in domain and a new look and feel while signing in This won t impact your access to QuickSight In this post we walk through changes to expect in the sign in experience and domains to allow on your organization s network to …	2021-09-28 23:15:32
js	JavaScriptタグが付けられた新着投稿 - Qiita	【JavaScript】クロージャを理解する	https://qiita.com/tkmd35/items/3567912380d2eb124561	クロージャとは自分の言葉で簡単に表現してみると「定義した関数の外側のスコープにある変数や関数を参照できる仕組みまたは関数」のことだと理解しました。	2021-09-29 08:31:17
js	JavaScriptタグが付けられた新着投稿 - Qiita	これからReact始めたい人のための今日だけでできるTODO#11 if文をJSX内に書きたい	https://qiita.com/tanimoto-hikari/items/935287874e2304991a63	論理積演算子ampamp条件式の評価がtrueの時だけ処理して欲しい時は論理積演算子を活用すると良いですAppjsreturnltpgtisReactampampHelloReactltpgt論理和演算子条件式の評価がfalseの時だけ処理して欲しい時は論理和演算子を活用すると良いですAppjsreturnltpgtisReactHelloReactltpgt最後に今回記述したコード全体は下記の通りです。	2021-09-29 08:16:20
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	GoogleサイトにGASでコメント欄を追加したい(初心者）	https://teratail.com/questions/361802?rss=all	GoogleサイトにGASでコメント欄を追加したい初心者前提・実現したいこと初心者です。	2021-09-29 08:53:25
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	エクセルVBAを使い関数で表示された値を取得したい	https://teratail.com/questions/361801?rss=all	エクセルVBAを使い関数で表示された値を取得したい前提・実現したいこといつもお世話になっています。	2021-09-29 08:45:53
AWS	AWSタグが付けられた新着投稿 - Qiita	Amazon Linux2におけるsystemdのバージョンは古め	https://qiita.com/thaim/items/fa8ae6ffe82548b840f5	これはリリースなので、RHELのリリースがだったことを考えてもちょっと古め。	2021-09-29 08:27:38
Docker	dockerタグが付けられた新着投稿 - Qiita	Docker	https://qiita.com/skmtydi947/items/56ac073d4196dd978634		2021-09-29 08:22:40
Docker	dockerタグが付けられた新着投稿 - Qiita	apt-key is deprecated. Manage keyring files in trusted.gpg.d instead (see apt-key(8))	https://qiita.com/pei_coffee/items/1407c34fca654d607a58	aptkeyisdeprecatedManagekeyringfilesintrustedgpgdinsteadseeaptkey原因dokcerfileでapekeyを使ったaptkeyは廃止予定なので推奨されてないらしい参考FROMrubyRUNcurlsSaptkeyaddampampechodebstablemainteeetcaptsourceslistdyarnlistampampaptgetupdateqqampampaptgetinstallynodejsyarnampampmkdirmyapp解決法色々あるみたいだけど、エラーメッセージを出ないようにするっていう強引な解決方法に・・・これをRUNの前に書けばOKENV APTKEYDONTWARNONDANGEROUSUSAGE yesおまけcurlとはファイルを送受信するコマンド。	2021-09-29 08:16:33
Ruby	Railsタグが付けられた新着投稿 - Qiita	バリデーションについて	https://qiita.com/skmtydi947/items/6da3a48db63544450fc6		2021-09-29 08:22:18
技術ブログ	Developers.IO	React ベースの Web フォームを国際（多言語）化対応してみた	https://dev.classmethod.jp/articles/tried-i18n-support-for-react-based-web-form/	materialui	2021-09-28 23:28:43
Apple	AppleInsider - Frontpage News	AirTag vulnerability turns tracker into Trojan horse, fix incoming	https://appleinsider.com/articles/21/09/28/airtag-vulnerability-turns-tracker-into-trojan-horse-fix-incoming?utm_medium=rss	AirTag vulnerability turns tracker into Trojan horse fix incomingA recently discovered AirTag weakness allows would be attackers to redirect users to a malicious webpage when the device is scanned in Lost Mode effectively turning the tracker into a Trojan horse Lost Mode is a tentpole AirTag capability that when activated allows anyone with an NFC capable device to scan the tracker and read a programmed discovery message that includes an owner s phone number The feature assists in the return of lost items like car keys if the Find My network fails to locate a lost AirTag Researcher Bobby Rauch has uncovered a vulnerability that turns Lost Mode into a potential attack vector Outlined by Krebs on Security the feature generates a unique URL at where owners can enter a personal message and phone number should the device be found Rauch discovered that Apple s systems do not prevent injection of arbitrary code into the phone number field meaning unsuspecting good Samaritans who scan the device can be sent to a malicious website Read more	2021-09-28 23:38:22
金融	金融総合：経済レポート一覧	FX Daily(9月27日)～ドル円、およそ2ヵ月半ぶりの111円台に続伸	http://www3.keizaireport.com/report.php/RID/469565/?rss	fxdaily	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	米国：金融政策動向（2021年9月FOMC）～テーパリング決定は次回に持ち越し、22年利上げ予想は二分：MRIデイリー・エコノミック・ポイント	http://www3.keizaireport.com/report.php/RID/469573/?rss	三菱総合研究所	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	金融政策決定会合議事要旨（7月15、16日開催分）	http://www3.keizaireport.com/report.php/RID/469574/?rss	日本銀行	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	円LIBOR参照金利スワップの新規取引停止等について	http://www3.keizaireport.com/report.php/RID/469575/?rss	libor	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	アセットオーナーとESG投資～GPIFのESG活動報告を読む：基礎研レター	http://www3.keizaireport.com/report.php/RID/469576/?rss	研究所	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	新興諸国　高齢化の急進展により退職後の生活レベル維持が課題～退職後の生活資金の不足額等の試算：保険・年金フォーカス	http://www3.keizaireport.com/report.php/RID/469577/?rss	新興諸国	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	LIBOR移行対応アップデート―ハイライト（2021年8月16日～8月31日）～非金融企業からの要望に対する米国財務省の回答...	http://www3.keizaireport.com/report.php/RID/469585/?rss	libor	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	レナサイエンス（東証マザーズ）～創薬バイオベンチャーで、基礎から臨床開発まで手掛ける。医薬品から医療機器、AIソリューションまでの多様なモダリティを展開：アナリストレポート	http://www3.keizaireport.com/report.php/RID/469586/?rss	医療機器	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	キャッシュレスを牽引するクレジットカード業；好調？どこで？どのように使われている？ 統計でひもといていきます	http://www3.keizaireport.com/report.php/RID/469595/?rss	経済産業省	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	パブリック・アセットとプライベート・アセットを一つのポートフォリオに統合する意義はあるか？：フォーカス	http://www3.keizaireport.com/report.php/RID/469597/?rss	統合	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	インド株式(SENSEX指数)が初の6万台乗せ：新興国レポート	http://www3.keizaireport.com/report.php/RID/469600/?rss	sensex	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	オーストラリア マーケット動向（2021/9/28）～先週の豪ドルの対円レートは、若干上昇...	http://www3.keizaireport.com/report.php/RID/469602/?rss	三井住友	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	2021年10月の注目イベント～テーパリングに向かう米国、日本では衆議院選挙の準備が本格化	http://www3.keizaireport.com/report.php/RID/469603/?rss	三井住友	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	こちらの「票読み」も重要～ローゼングレン総裁の辞任で変化：Market Flash	http://www3.keizaireport.com/report.php/RID/469608/?rss	marketflash	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	オピニオン：中国グリーン金融月報	http://www3.keizaireport.com/report.php/RID/469610/?rss	日本総合研究所	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	人民元週間レポート【為替取引量が堅調に推移】2021年9月17日	http://www3.keizaireport.com/report.php/RID/469631/?rss	為替取引	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	自民党総裁選～直前のチェックポイント：市川レポート	http://www3.keizaireport.com/report.php/RID/469632/?rss	三井住友	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	大阪の目指す「国際金融都市」の姿～東京との差別化や補完性の視点を含めた戦略が特徴：金融・証券市場・資金調達	http://www3.keizaireport.com/report.php/RID/469640/?rss	大和総研	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	【記者会見要旨】黒田総裁（大阪、9月27日分）	http://www3.keizaireport.com/report.php/RID/469642/?rss	日本銀行	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	官民ファンドの収益面からの分析：規制改革・行政改革担当大臣直轄チーム分析レポートNo.3	http://www3.keizaireport.com/report.php/RID/469646/?rss	官民ファンド	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	【注目検索キーワード】マテリアリティ	http://search.keizaireport.com/search.php/-/keyword=マテリアリティ/?rss	検索キーワード	2021-09-29 00:00:00
金融	金融総合：経済レポート一覧	【お薦め書籍】実務家ブランド論〜あなたのブランディングはなぜ失敗するのか？	https://www.amazon.co.jp/exec/obidos/ASIN/4883355276/keizaireport-22/	日本企業	2021-09-29 00:00:00
金融	日本銀行：RSS	預金種類別店頭表示金利の平均年利率等	http://www.boj.or.jp/statistics/dl/depo/tento/te210929.pdf	預金	2021-09-29 08:50:00
ニュース	BBC News - Home	Real Madrid 1-2 Sheriff Tiraspol: Champions League debutants snatch win	https://www.bbc.co.uk/sport/football/58705912?at_medium=RSS&at_campaign=KARANGA	bernabeu	2021-09-28 23:21:13
ニュース	BBC News - Home	Life at 50C: Mexico's struggle for water	https://www.bbc.co.uk/news/science-environment-58724297?at_medium=RSS&at_campaign=KARANGA	mexico	2021-09-28 23:01:45
ニュース	BBC News - Home	Garden ponds help boost biodiversity	https://www.bbc.co.uk/news/science-environment-58711394?at_medium=RSS&at_campaign=KARANGA	garden	2021-09-28 23:03:06
ニュース	BBC News - Home	Calais: On patrol with the troops looking for migrants	https://www.bbc.co.uk/news/world-europe-58725582?at_medium=RSS&at_campaign=KARANGA	calais	2021-09-28 23:02:32
ニュース	BBC News - Home	Covid grief: 'We didn't want the prime minister's condolences'	https://www.bbc.co.uk/news/uk-politics-58711400?at_medium=RSS&at_campaign=KARANGA	minister	2021-09-28 23:03:25
ニュース	BBC News - Home	Paralysed woman to tackle London Marathon in motorcycle gear	https://www.bbc.co.uk/news/uk-england-london-58598129?at_medium=RSS&at_campaign=KARANGA	lomas	2021-09-28 23:02:02
ニュース	BBC News - Home	German coalition talks: Where Brits see a crisis, Germans find unity	https://www.bbc.co.uk/news/world-europe-58718307?at_medium=RSS&at_campaign=KARANGA	germany	2021-09-28 23:19:07
ニュース	BBC News - Home	Hathras rape case: Prisoners in their own home, lives on hold, a village divided	https://www.bbc.co.uk/news/world-asia-india-58706861?at_medium=RSS&at_campaign=KARANGA	dividedthe	2021-09-28 23:20:19
ニュース	BBC News - Home	CEO Secrets: Don't fire staff for making mistakes	https://www.bbc.co.uk/news/business-58669312?at_medium=RSS&at_campaign=KARANGA	hasty	2021-09-28 23:24:23
LifeHuck	ライフハッカー［日本版］	PCのマイクが使えなくなったときに試したい10の解決法	https://www.lifehacker.jp/2021/09/24250610-ways-to-troubleshoot-your-most-common-mic-issues.html	解決	2021-09-29 08:30:00
GCP	Google Cloud Platform Japan 公式ブログ	Google I/O 向けの WebGL を利用したマップデモの舞台裏	https://cloud.google.com/blog/ja/products/maps-platform/behind-scenes-webgl-powered-maps-demos-google-io/	GoogleIOでのWebGLを利用したマップ機能のベータ版リリースの発表に備え、GoogleMapsPlatformチームは、年からのGoogleCloudパートナーであるUbilabsと協力して、Dレンダリングを地図に導入すると実現できることをデベロッパーに紹介するためのデモを作成しました。	2021-09-29 01:00:00
北海道	北海道新聞	バド、日本が準々決勝進出　男女混合団体のスディルマン杯	https://www.hokkaido-np.co.jp/article/594151/	決勝進出	2021-09-29 08:04:10
北海道	北海道新聞	自民新総裁、午後に選出　河野氏、岸田氏が決選投票へ	https://www.hokkaido-np.co.jp/article/594138/	決選投票	2021-09-29 08:02:05
GCP	Cloud Blog	Monitoring feature attributions: How Google saved one of the largest ML services in trouble	https://cloud.google.com/blog/topics/developers-practitioners/monitoring-feature-attributions-how-google-saved-one-largest-ml-services-trouble/	Monitoring feature attributions How Google saved one of the largest ML services in troubleAn emergency in the largest MLOps at GoogleClaudiu Gruia is a software engineer at Google who works on machine learning ML models that recommend content to billions of users daily In Oct Claudiu was notified by an alert from a monitoring service A specific model feature let us call this feature F had reduced in importance The importance of the feature is measured using the concept of Feature Attribution the influence of the feature on the model s predictions This reduction in importance was associated with a large drop in the model s accuracy The attribution feature importance of the feature F dropped suddenlyIn response to the alert Claudiu quickly retrained the model and the two other features F and F below rose in importance effectively substituting for F eliminating the drop in model quality Had it not been for the alert and Claudiu s quick fix the user experience of a large consumer service would have suffered After the retraining the feature F and F covered the F lossMonitoring without the ground truthSo what happened F was a feature generated by a separate team On further investigation it was found that a certain infrastructure migration caused F to significantly lose coverage and consequently its attribution across examples The easiest way to detect this kind of model failure is to track one or more model quality metrics e g accuracy and alert the developer if the metric drops below a threshold But unfortunately most model quality metrics rely on comparing the model s prediction to ground truth labels which may not be available in real time For instance in tasks such as fraud detection credit lending or estimating conversion rates for online ads the groundtruth for a prediction may lag by days weeks or months  In the absence of the ground truth ML engineers at Google rely on proxy measures of model quality degradations derived using model inputs and predictions as two available observables There are two main measures Feature Distribution monitoring detecting the skew and drift of feature distribution Feature Attribution monitoring detecting the skew and drift of feature importance scoreIn the recent post Monitor models for training serving skew with Vertex AI we explored the first measure Feature Distribution monitoring for detecting any skew and anomalies happening in the feature itself at the serving time in comparison to training or some other baseline In the rest of this post we discuss the second measure Feature Attribution monitoring which has also been successfully used to monitor large ML services at Google Feature Attributions monitoringFeature Attributions is a family of methods for explaining a model s predictions on a given input by attributing it to features of the individual inputs The attributions are proportional to the contribution of the feature to the prediction They are typically signed indicating whether a feature helps push the prediction up or down Finally attributions across all features are required to add up to the model s prediction score Photo by Dlanglois CC BY SA Feature Attributions have been successfully used in the industry and also at Google to improve model transparency debug models and assess model robustness Prominent algorithms for computing feature attributions include SHAP Integrated Gradients and LIME Each algorithm offers a slightly different set of properties For an in depth technical discussion refer to our AI Explanations Whitepaper An Example of Feature AttributionsMonitoring Feature AttributionsWhile feature distribution monitoring is a handy tool it suffers from the following limitations    Feature drift scores do not convey the impact the drift has on the model s prediction There is no unified drift measure that works across different feature types and representations numeric categorical images embeddings etc Feature drift scores do not account for drift in the correlation between features  To address this on September th Vertex Model Monitoring added new functionality to monitor feature attributions In contrast to feature distribution monitoring the key idea is to monitor the contribution of each feature to the prediction i e attribution during serving to report any significant drifts relative to training or some other baseline This has several notable benefits Drift scores correspond to impact on predictions A large change in attribution to a feature by definition means that the feature s contribution to the prediction has changed Since the prediction is equal to the sum of the feature contributions large attribution drift is usually indicative of large drift in the model predictions But there may be false positives if the attribution drifts across features cancel out leading to negligible prediction drift For more discussion on false positives and false negatives please see Note Uniform analysis units across feature representations Feature attributions are always numeric regardless of the underlying feature type Moreover due to their additive nature attributions to a multi dimensional feature e g embeddings can be reduced to a single numeric value by adding up the attributions across dimensions This allows using standard univariate drift detection methods for all feature types   Account for feature interactions Attributions account for the feature s contribution to the prediction both individually and via interactions with other features Thus distribution of feature attributions may change even if the marginal distribution of the feature does not change but its correlation with the features it interacts with changes Monitor feature groups Since attributions are additive we can add up attributions to related features to obtain attribution to a feature group For instance in a house pricing model we can combine the attribution to all features pertaining to the location of the house e g city school district into a single value This group level attribution can then be tracked to monitor for changes in the feature group Track importances across model updates Monitoring attributions across model retraining helps understanding how the relative importance of a feature changes with model retraining For instance in the example mentioned in the beginning we noticed that features F and F stepped up in importance after retraining Enabling the serviceVertex Model Monitoring now supports Feature AttributionsOnce a prediction endpoint is up and running you can turn on skew or drift detection for both Feature Distibution and Feature Attributions by running a single gcloud command like the following no need for any pre processing or extra setup tasks Here are the key parameters emails The email addresses to which you would like monitoring alerts to be sentendpoint the prediction endpoint ID to be monitoredprediction sampling rate This parameter controls the fraction of the incoming prediction requests that are logged and analyzed for monitoring purposesfeature thresholds Specify which input features to monitor Feature Distribution along with the alerting threshold for each feature  feature attribution thresholds Specify which input features to monitor Feature Attributions along with the alerting threshold for each feature  You can also use the Console UI of setup the monitoring when creating a new Endpoint Using Console UI to set up a Feature Attributions and Feature Distribution monitoringFor the detailed instructions on how to set up the monitoring please refer to the documentation  After enabling it you would see some alerts on the console like below whenever any feature attribution skews or drifts are detected and also receive an email for the same The Ops engineer can then take appropriate corrective action Example The feature attribution of cigsPerDay has crossed the alert thresholdDesign choicesLastly we go over two important technical considerations involved in designing feature attributions monitoring Selecting the prediction class for attribution In case of classification models feature attributions are specific to an input and prediction class When monitoring a distribution of inputs which prediction class must be used for computing attributions We recommend using the class that is considered as the prediction decision for the input For multiclass models this is usually the class with the largest score i e “argmax class In some cases there is a specific protagonist class for e g the “fraud  class in a fraud prediction model whose score is considered by downstream applications In such cases it is reasonable to always use the protagonist class for attributions  Comparing attribution distributions There are several choices for comparing distributions of attributions including distribution divergence metrics e g Jensen Shannon divergence and various statistical tests e g Kolmogorov Smirnov test Here we use a relatively simple method of comparing the average absolute value of the attributions This value captures the magnitude of contribution of each feature Since attributions are in units of the prediction score the difference in average absolute attribution can also be interpreted in units of prediction score A large difference typically translates into a large impact on the prediction Next stepsTo get started with Feature Attribution monitoring start trying it with the Model Monitoring documentation Also Marc Cohen created a great Notebook material for learning how to use the functionality with an end to end scenario By incorporating Vertex Model Monitoring and Explainable AI features with the best practices you would be able to experience and learn how to build and operate Google scale production ML systems for supporting mission critical businesses and services Note When Feature Attribution monitoring exposes false positives and false negativesFeature Attribution monitoring is a powerful tool but also has some caveats sometimes it exposes false positives and false negatives as illustrated by the following cases Thus when you apply the method to a production system consider using it in a combination with other methods such as Feature Distribution monitoring for better understanding of the behaviour of your ML models False negative Univariate drift in attributions may fail to capture multivariate drift in features when the model has no interactionsExample Consider a linear model y x xn Here univariate drift in attributions will be proportional to univariate drift in features Thus attribution drift would be tiny if univariate drift in features is tiny regardless of any multivariate drift False negative Drift in features that are unimportant to the model but affect model performance but may not manifest up in the attribution space Example Consider a task y x XOR x and model y hat x Let s say the training distribution is an equal mix of lt gt and lt gt while the production distribution is an equal mix of lt gt and lt gt While feature x has zero attribution and therefore zero attribution drift drift in x has a massive impact on model performance False positive Drift in important features may not always affect model performanceExample Let s say in the XOR example the production distribution consists of just lt gt While there is large drift in the input feature x it does not affect performance Note Combining Feature Distribution and Feature AttributionsBy combining both Feature Distribution and Feature Attributions monitoring we can obtain deeper insights on what changes might be affecting the model The table below provides some potential directions based on combining the observations from the two monitoring methods Related ArticleMonitor models for training serving skew with Vertex AIThis blog post focuses on how Vertex AI enables one of the core aspects of MLOps monitoring models deployed in production for training s Read Article	2021-09-28 23:15:00
GCP	Cloud Blog JA	Google I/O 向けの WebGL を利用したマップデモの舞台裏	https://cloud.google.com/blog/ja/products/maps-platform/behind-scenes-webgl-powered-maps-demos-google-io/	GoogleIOでのWebGLを利用したマップ機能のベータ版リリースの発表に備え、GoogleMapsPlatformチームは、年からのGoogleCloudパートナーであるUbilabsと協力して、Dレンダリングを地図に導入すると実現できることをデベロッパーに紹介するためのデモを作成しました。	2021-09-29 01:00:00