投稿時間:2022-01-03 02:17:30 RSSフィード2022-01-03 02:00 分まとめ(22件)

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IT	気になる、記になる…	Apple、｢Pro Display XDR｣の約半値の新型外付けディスプレイを開発中か	https://taisy0.com/2022/01/03/150350.html	apple	2022-01-02 16:01:22
python	Pythonタグが付けられた新着投稿 - Qiita	VSCodeでipynbファイルをHTML出力した時に等幅フォントにならない時の対処法	https://qiita.com/lyulu/items/2ebe787b7d2820d4a166	VSCodeでipynbファイルをHTML出力した時に等幅フォントにならない時の対処法結論CodeMirrorpreのCSSにfontfamilyCourierNewConsolasmonospaceを加える。	2022-01-03 01:07:22
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	[GAS,TwitterAPI]定義しているはずなのに未定義とかえされてしまいます。	https://teratail.com/questions/376421?rss=all	GASTwitterAPI定義しているはずなのに未定義とかえされてしまいます。	2022-01-03 01:59:28
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Reactの「MUI-v5」と「ReactHookForm-v7」を組み合わせたときにhandleSubmitにて正しくデータが取得できない	https://teratail.com/questions/376420?rss=all	Reactの「MUIv」と「ReactHookFormv」を組み合わせたときにhandleSubmitにて正しくデータが取得できない下記のように実装をしています。	2022-01-03 01:57:26
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	mask-imageが表示されない	https://teratail.com/questions/376419?rss=all	maskimageが表示されないHTMLで表示させた画像にCSSでmaskimageをあてて、切り抜き表示させたいのですがうまくいきません。	2022-01-03 01:53:43
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Twitter API　人気のツイートを取得したい	https://teratail.com/questions/376418?rss=all	twitter	2022-01-03 01:16:40
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	CentOS stream 9上のサイトのSSL化ができなくて困っています	https://teratail.com/questions/376417?rss=all	CentOSstream上のサイトのSSL化ができなくて困っています前提・実現したいことCentOSnbspstreamnbspにApacheで立てたサイトをSSL化したいと思っています。	2022-01-03 01:09:52
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	pythonの結果をHTMLで表示したい(PHP,JSでも可)	https://teratail.com/questions/376416?rss=all	htmlphpjs	2022-01-03 01:04:54
AWS	AWSタグが付けられた新着投稿 - Qiita	AWS データレイクハンズオン - Lab4 -	https://qiita.com/glaciermelt00/items/cf673165b975e6930f88	AWSデータレイクハンズオンLab背景データ分析にAWSを使えることを知ったので、AWSデータレイクハンズオンを試して、実際に分析パイプラインを構築してみる。	2022-01-03 01:05:33
海外TECH	DEV Community	Excelize 2.5.0 Released - Go language API for spreadsheets (Excel) files	https://dev.to/xuri/excelize-250-released-go-language-api-for-spreadsheets-excel-files-hmg	Excelize Released Go language API for spreadsheets Excel filesExcelize is a library written in pure Go providing a set of functions that allow you to write to and read from XLSX XLSM XLTM files Supports reading and writing spreadsheet documents generated by Microsoft Excel amp tradde and later Supports complex components by high compatibility and provided streaming API for generating or reading data from a worksheet with huge amounts of data GitHub github com xuri excelizeWe are pleased to announce the release of version Featured are a handful of new areas of functionality and numerous bug fixes A summary of changes is available in the Release Notes A full list of changes is available in the changelog Release NotesThe most notable changes in this release are CompatibilityClose spreadsheet and row s iterator required the user should be close the stream after using the row s iterator and close the spreadsheet after opening an existing spreadsheetChange ReadZipReader as an implementation of the File extract spreadsheet with given options and support extract in memory or touching the filesystemRemove unnecessary exported variable XMLHeader we can using encoding xml package s xml Header instead of itRemove unused exported error variable ErrToExcelTime Notable FeaturesNew API SetRowStyle support for set style for the rows related issue New API GetCellType support for get the cell s data type related issue and New API SetAppProps and GetAppProps support to set and get document application properites related issue GetCellValue GetRows GetCols Rows and Cols support to specify read cell with raw value related issue New support formula functions ACCRINT ACCRINTM ADDRESS AMORDEGRC AMORLINC AVEDEV AVERAGEIF CHIDIST CONFIDENCE CONFIDENCE NORM COUNTIF COUNTIFS COUPDAYBS COUPDAYS COUPDAYSNC COUPNCD COUPNUM COUPPCD DATEVALUE DAY DAYS DELTA DEVSQ DISC DURATION ERF ERF PRECISE ERFC ERFC PRECISE GEOMEAN GESTEP IFNA IFS INDEX INTRATE ISFORMULA ISLOGICAL ISREF ISOWEEKNUM MATCH MAXA MAXIFS MDURATION MINIFS MINUTE MONTH ODDFPRICE PERCENTILE EXC PERCENTRANK EXC PERCENTRANK INC PERCENTRANK PRICE PRICEDISC PRICEMAT PV QUARTILE EXC RANK RANK EQ RATE RECEIVED RRI SHEETS SLN STANDARDIZE STDEV P STDEVP SWITCH SYD TBILLEQ TBILLPRICE TBILLYIELD TEXTJOIN TIME TRANSPOSE TRIMMEAN VALUE VAR VAR S VARA VARPA VDB WEEKDAY WEIBULL WEIBULL DIST XIRR XLOOKUP XNPV XOR YEAR YEARFRAC YIELD YIELDDISC YIELDMAT Z TEST ZTEST related issue Formula calculation engine support nested calc for IF formula related issue Formula calculation engine support get shared formula related issue Formula calculation engine support text comparison related issue Support specify the formula in the data validation range related issue Support specified unzip size limit on open file options avoid zip bombs vulnerability attackSetCellFormula now support set the shared formulaUpdateLinkedValue will skip macro sheet related issue Fix AddPicture created duplicate image in some cases caused by incorrect internal relationships ID calculation related issue AddShape support set line width of add the shape related issue New options UnzipXMLSizeLimit have been added support to specifies the memory limit on unzipping worksheet and shared string table in bytesAn error will be returned if given an invalid custom number format when creating a new style related issue Now support set row style in the stream writerStream writer will create a time number format for time type cells related issue Now support specify compact and outline for the pivot table related issue Support get current rows columns and total rows columns in the stream reader related PR Now support time zone location when set cell value related issue Export errors so users can act differently on different type of errors Improve the CompatibilityImprove compatibility with row element with r attributePreserve XML control characterImprove the compatibility of style settings with Apple Numbers related issue Support multi byte language on set header footer related issue Preserve horizontal tab character when set the cell value related issue Bug FixesFix the data validation deletion failed resolve issue Fix set data validation drop list failed in some cases resolve issue Fix formula calculation engine LOOKUP doesn t handle array form correctly resolve issue Fix formula calculation engine LOOKUP can only find exact match resolve issue Fix formula percentages calculated incorrectly resolve issue Fix panic caused by incorrect cell read on some caseFix conditional format bottom N not workingFix time parse accuracy issue resolve issue and Fix build in scientific number format failed resolve issue Fix small float parse error in some case resolve issue Fix worksheet deletion failed in some caseFix build in time number format parse error resolve issue Fix NewStyle returned incorrect style ID in some caseFix merged cell range error after row column insert deletion in some corner case PerformanceMerge cell time cost speed up time cost decrease over Improve streaming reading performance unzip shared string table to system temporary file when large inner XML memory usage decreased about related issue Worksheet list read speed upMerge column styles to reduce spreadsheet size resolve issue MiscellaneousThe dependencies module has been updatedUnit tests and godoc updatedDocumentation website with multilingual Arabic German Spanish English French Russian Chinese Japanese and Korean which has been updated	2022-01-02 16:38:44
海外TECH	DEV Community	Machine Learning Best Practices in Healthcare and Life Sciences | AWS White Paper Summary	https://dev.to/awsmenacommunity/machine-learning-best-practices-in-healthcare-and-life-sciences-aws-white-paper-summary-49ob	Machine Learning Best Practices in Healthcare and Life Sciences AWS White Paper Summary IntroductionThis whitepaper describes how AWS approaches machine learning ML in a regulated environment and provides guidance on good ML practices using AWS products The pharmaceutical industry which is sometimes slow to adopt the latest technologies is witnessing a massive change The industry is looking to technologies such as artificial intelligence machine learning AI ML Internet of Things IoT blockchain and other Industry technologies With this adoption of new technology comes regulatory challenges Machine Learning in particular has garnered some focus recently with the publishing of a discussion paper by the FDA It explores the use of AI ML in the context of medical devices but many of the same topics arise when bringing up ML adoption with executive leadership in any company How do you trust ML models to not make important business decisions based on erroneous or unstable values How do you know you have good hygiene in managing your ML environments Are you prepared for or capable of a retrospective analysis if anything goes wrong One particularly strong example is seen in pharmaceutical companies who must abide by mandatory reporting standards for any patient relevant “Adverse Events that are viewed by any employee or ingested by the company This means that creating a new Twitter account for a particular market or releasing a digital therapeutic app for patient health can result in a surge of new natural language data that needs to be reviewed adjudicated and in some cases reported to the FDA This flood of data can completely overwhelm manual review teams and risk delays in reporting to the FDA within the mandated time limits resulting in the potential for formal warnings or even legal action ML has the potential to address the immediate needs of scale in these scenarios and triage obvious problem cases from innocuous cases However before deploying these models you may need to evaluate a few requirements relevant to your stakeholders or regulators such as model reproducibility model explainability decision support tooling and how this all ties into templatized ML workflows Benefits of machine learningRegulatory agencies such as the FDA acknowledge that ML based technologies hold the potential to transform healthcare through their ability to derive new and important insights from vast amounts of data One of the technology s greatest strengths is its ability to continually learn from real world data and its capability to improve its performance The ability for AI ML software to learn from real world feedback training and improve its performance adaptation makes these technologies uniquely situated among software as a medical device SaMD To help customers realize the benefits of ML this whitepaper covers the points raised by the FDA while also drawing from AWS resources The Life Sciences industry encompassing but not exclusively bio pharma genomics medical diagnostics and medical devices is governed by a set of regulatory guidelines called Good Laboratory Practice Good Clinical Practice Good Manufacturing Practice and Good Machine Learning Practices commonly referred to as “GxP The GxP Systems on AWS whitepaper provides information on how AWS approaches GxP related compliance and security and provides customers guidance on using AWS products in the context of GxP The AWS Well Architected Framework helps you understand the pros and cons of decisions you make while building systems on AWS Using the Framework enables you to learn architectural best practices for designing and operating reliable secure efficient and cost effective systems in the cloud The Machine Learning Lens whitepaper focuses on how to design deploy and architect your ML workloads in the AWS Cloud This lens adds to the best practices included in the Well Architected Framework but also covers ML scenarios and identifies key elements to ensure that your workloads are architected according to best practices Life sciences at AWSAWS started its dedicated Genomics and Life Sciences Practice in in response to the growing demand for an experienced and reliable life sciences cloud industry leader The AWS Genomics and Life Sciences practice serves a large ecosystem of life sciences customers including some of the largest enterprise pharmaceutical biotechnology medical device genomics start ups university and government institutions as well as healthcare payers and providers In addition to the resources available within the Genomics and Life Science practice at AWS you can also work with AWS Life Sciences Competency Partners to drive innovation and improve efficiency across the life sciences value chain including cost effective storage and compute capabilities advanced analytics and patient personalization mechanisms AI ML in the life sciences industry fall under five main categories Research and discovery ーUse cases include molecular structure prediction antibody binding affinity prediction outcome prediction for patients with certain biomarkers patient data enrichment and search the classification of tissue samples and so on Clinical development ーUse cases include forecasting and optimization of trial timelines optimization of inclusion exclusion criteria and site selection protocol document enrichment and so on Manufacturing and supply chain ーUse cases include predictive maintenance of equipment computer vision for effective line clearance optimization of manufacturing process staging vial inspection and so on Commercial ーUse cases include predicting healthcare providers with relevant patient bases identifying next best action for sales and marketing annotation and management of existing promotional materials and so on Post market surveillance and patient support ーUse cases include forecasting patient cost automation of adverse event detection from social media or call centers patient outcome prediction and so on Current regulatory situationTraditional Computer Systems Validation CSV is a point in time exercise where the resultant computer system was tested against the requirements to verify that the system satisfies its intended use Whenever there was a change the system went through revalidation However the FDA s view is that AI ML based SaMD exist on a spectrum from locked to nearly continuously learning and changing The FDA proposes a Total Product Lifecycle TPLC approach which enables the evaluation and monitoring of a software product from its premarket development to postmarket performance along with continued demonstration of the organization s excellence With this approach the FDA will assess the following Culture of quality and organizational excellence ーto gain a reasonable assurance of the high quality of the organization s software development testing and performance monitoring capabilitiesPre market assurance of safety and effectivenessReview of SaMD pre specifications and algorithm change protocolReal world performance monitoringReference Implementation of Good machine learning practices This framework relies on the principle of a “predetermined change control plan The predetermined change control plan includes the types of anticipated modifications SaMD Pre Specifications based on the retraining and model update strategy and the associated methodology Algorithm Change Protocol being used to implement those changes in a controlled manner that manages risks to patients SaMD Pre Specifications SPS ーA SaMD manufacturer s anticipated modifications to “performance or “inputs or changes related to the “intended use of AI ML based SaMD These are the types of changes the manufacturer plans to achieve when the SaMD is in use The SPS draws a “region of potential changes around the initial specifications and labeling of the original device This is what the manufacturer intends the algorithm to become as it learns Algorithm Change Protocol ACP ーSpecific methods that a manufacturer has in place to achieve and appropriately control the risks of the anticipated types of modifications delineated in the SPS The ACP is a step by step delineation of the data and procedures to be followed so that the modification achieves its goals and the device remains safe and effective after the modification The preceding figure provides a general overview of the components of an ACP This is how the algorithm will learn and change while remaining safe and effective Challenges to support AI ML enabled GxP workloadsDeveloping AI ML enabled GxP workloads raise the following challenges Building a secure infrastructure that complies with a stringent regulatory process working on the public cloud and aligning to the FDA framework for AI MLSupporting an AI ML enabled solution for GxP workloads covering ReproducibilityTraceabilityData integrityMonitoring the Machine Learning model with respect to various changes to parameters and dataHandling model uncertainty and confidence calibrationMaking AI ML models interpretable Provision a secure and GXP compliant machine learning environmentFor healthcare and life sciences customers the security and privacy of an ML environment is paramount It is therefore strongly recommended that you protect your environment against unauthorized access privilege escalation and data exfiltration Common considerations when you set up a secure ML environment include Platform qualificationCompute and network isolationAuthentication and authorizationThese considerations are detailed in the following sections Platform qualificationThe validated state of ML models may be brought into question if the underlying IT infrastructure is not maintained in a demonstrable state of control and regulatory compliance Similarly data integrity can also be affected by problems related to IT infrastructure Therefore to support a culture of quality and operational excellence it is important to qualify your underlying platform and then maintain it under a state of control Details about platform qualification and the approach taken by many AWS customers are described in GxP whitepaper Customers will often provide a qualified MLOps platform to their teams along with a selection of pre qualified building blocks to support their specific needs Compute and network isolationA well governed and secure ML workflow begins with establishing a private and isolated compute and network environment The virtual private cloud VPC that hosts Amazon SageMaker and its associated components such as Jupyter notebooks training instances and hosting instances should be deployed in a private network with no internet connectivity Connectivity between SageMaker and other AWS services such as Amazon Simple Storage Service Amazon S should be established using VPC endpoints or even AWS PrivateLink Additionally when creating a VPC endpoint you can attach an endpoint policy to further control access to specific resources for which you are connecting Authentication and authorizationAfter you create an isolated and private network environment the next step is to ensure that only authorized users can access the appropriate AWS services AWS Identity and Access Management IAM can help you create preventive controls for many aspects of your ML environment including access to SageMaker resources access to your data in S and access to API endpoints You can access AWS using a RESTful API and every API call is authorized by IAM You grant explicit permissions through IAM policy documents which specify the principal who the actions API calls and the resources such as S objects that are allowed as well as the conditions under which the access is granted Access to AWS Glue and Amazon SageMaker resources is also governed by IAM While each organization has different authentication and access requirements you should configure permissions in line with IAM best practices and your own internal policies and controls by granting least privilege access or granting only the permissions required to perform a particular task Role based access control RBAC is an approach used commonly by customers in financial services for ensuring only authorized parties have access to desired system controls Creating roles based on job function policies and using AWS Config to monitor and periodically audit IAM policies attached to users is a recommended best practice for viewing configuration changes over time AWS Config offers conformance packs which provide a general purpose compliance framework designed to enable you to create security operational or cost optimization governance checks using managed or custom AWS Config rules and AWS Config remediation actions Data encryptionBecause ML environments can contain sensitive data and intellectual property one of the considerations for a secure ML environment is data encryption AWS recommends that you enable data encryption both at rest and in transit For at rest encryption AWS provides tools for creating an encrypted file system using open standard algorithms For instance customers can encrypt data stored in Amazon S and the data stored in Amazon Elastic Block Store Amazon EBS volumes Some healthcare customers require the use of AWS KMS keys Additionally AWS recommends enabling Amazon S default encryption so that all new objects are encrypted when they are stored in the Amazon S bucket Machine learning lifecycleBuilding and operating a typical ML workload is an iterative process and consists of multiple phases We identify these phases loosely based on the open standard process model for Cross Industry Standard Process Data Mining CRISP DM as a general guideline CRISP DM is used as a baseline because it s a proven tool in the industry and is application neutral which makes it an easy to apply methodology that is applicable to a wide variety of ML pipelines and workloads The end to end ML process includes the following phases Business goal identification ML problem framingData collectionData integration and preparationFeature engineeringModel trainingModel validationBusiness evaluationProduction deployment model deployment and model inference Below section presents a high level overview of the various phases of an end to end ML lifecycle which helps frame our discussion around security compliance and operationalization of Amazon Web Services ML best practices in healthcare and life science The machine learning lifecycle process More Details about various phases of an end to end Ml lifecycle can be found here Best practices for ML lifecycle stagesData collectionData integration and preparationFeature engineeringModel trainingModel validationAdditional considerations for AI ML complianceAuditabilityTraceabilityReproducibilityModel interpretabilityModel monitoringOperationalize AI ML workloadsMore Details about best practices for Different Lifecycle stages can be found here Reference architectures Training pipelineThe following shows a specific model training and tracking architecture that leverages AWS native tools and services Model training and tracking architectureStep Train Model Handler Lambda Train Model Queue SQS ーModel training is initiated training could be initiated in multiple ways Manually follows human in the loop design Automated by scheduling relevant jobs Steps AWS Glue ーReads and processes data from multiple sources Intermediate files like train test validation datasets are stored in a training repository S Steps Amazon SageMaker ーTrains the ensemble models and saves the model file and train and test results into S and DynamoDB training results tablesStep Training Results Queue SQS ーUpon completion of model training a notification is sent to an analyst data scientist to review the model performance results based on the decision made by the reviewer that an action would be takenStep Endpoint Update Handler Lambda ーDepending on the decision of the reviewer eitherThe endpoint is updated with the new model orThe existing endpoint is not deleted or reusedStep After the endpoint is updated an email notification will be sent to the client Inference pipelineThe following figure shows a specific model inference architecture that leverages AWS native tools and services Specific model inference architecture that leverages AWS native tools and servicesStep When the input data is available ingest handler is invoked and necessary preprocessing steps are performed Data is stored in S and a message is passed to Records QueueStep Inference Handler is involved by the Records Queue and data is read from SStep Inference Handler performs inference using Amazon SageMaker resourcesStep After performing inference the results are saved in the DynamoDB results table and the message is passed to the Results QueueStep Results Handler then publishes the results to the Client application or a QuickSight dashboard OrchestrationGeneral guidelines for creating pipelines include using an orchestration layer such as AWS CodePipeline To incorporate traceability use the following services listed in the following architecture Amazon SageMaker a fully managed ML service that lets data scientists and developers build train and deploy ML models to productionAmazon S a highly scalable reliable service to store and retrieve any amount of data at any time from anywhere on the webAmazon DynamoDB a fully managed key value and document database that delivers single digit millisecond performance at any scaleAWS Lambda a serverless compute service to run code without provisioning or managing serversAWS Glue a fully managed data preparation service for ETL operationAWS CodePipeline a fully managed nearly continuous delivery service that helps you automate your release pipelines for fast and reliable application and infrastructure updatesThe following figure highlights only the key components for tracking data and model lineage Key components for tracking data and model lineageLatest code changes commit id can be extracted from AWS CodeCommit or any GitHub toolkit AWS Glue Crawlers AWS Glue jobs or any ETL services can be used to extract raw data run preprocessing split data into train test validation datasets and export data to S and metadata to DynamoDB tables Amazon SageMaker can be used for model development and evaluation steps Necessary model training results and model artifacts can be sent to S Necessary metadata can be sent to DynamoDB tables Model performance results can be reviewed by data scientists or other human reviewers to decide if the model can be promoted to the production environment in which case the management table model version status can be set to ACTIVE After data is scored input data predicted probability predicted label and the model version used for performing inference can be saved in a DynamoDB inference results table and necessary results can be sent to S Orchestration for SageMaker jobsAmazon SageMaker Pipelines can automate various tasks across the ML lifecycle All infrastructure is fully managed SageMaker Pipelines have the ability to Orchestrate SageMaker jobs and author reproducible ML pipelinesDeploy custom build models for inference in real time with low latency or offline inferences with Batch TransformMonitor lineage of objectsThrough a simple interface you can implement sound operating practices for deploying and monitoring production workflows model artifact deployment and artifact lineage tracking while adhering to safety and best practice paradigms for ML application creation Certain use cases may necessitate a single larger end to end pipeline that can handle anything Other use cases such as the following can necessitate multiple pipelines One pipeline for ETL and data transformation stepsOne pipeline for model training tuning lineage and depositing into the Model RegistryPossibly another pipeline for specific inference scenarios such as real time vs batch One pipeline for triggering retraining by using SageMaker Model Monitor to detect model drift or data drift and trigger retraining Some of the main components in Amazon SageMaker Pipelines include pipelines Model Registry and MLOps templates Pipelines Model building pipelines are defined with a simple Python SDK They can include any operation available in Amazon SageMaker such as data preparation with Amazon SageMaker Processing or Amazon SageMaker Data Wrangler model training model deployment to a real time endpoint or batch transform You can also add Amazon SageMaker Clarify to your pipelines to detect bias prior to training or after the model has been deployed Likewise you can add Amazon SageMaker Model Monitor to detect data and prediction quality issues After they are launched model building pipelines are run as CI CD pipelines Every step is recorded and detailed logging information is available for traceability and debugging purposes You can also visualize pipelines in Amazon SageMaker Studio and track their different runs in real time Model Registry The Model Registry lets you track and catalog your models In SageMaker Studio you can easily view model history list and compare versions and track metadata such as model evaluation metrics You can also define which versions may or may not be deployed in production You can even build pipelines that automatically trigger model deployment after approval has been given You ll find that the Model Registry is very useful in tracing model lineage improving model governance and strengthening your compliance posture MLOps templates SageMaker Pipelines includes a collection of built in CI CD templates published via AWS Service Catalog for popular pipelines build train deploy deploy only and so on You can also add and publish your own templates so that your teams can efficiently discover them and deploy them Not only do templates save lots of time they also make it easier for ML teams to collaborate from experimentation to deployment using standard processes and without having to manage any infrastructure Templates also enable Ops teams to customize steps as needed and give them full visibility for troubleshooting Directed acyclic graph of steps that orchestrate SageMaker jobsThe preceding figure is a Directed Acyclic Graph DAG of steps and conditions that orchestrate SageMaker jobs and resource creation Sample notebooks are available to get you started ConclusionThis whitepaper discusses security and GxP compliance considerations for operationalizing AI ML workloads in the life sciences industry It references Good Machine Learning Practices GMLP as referenced by the FDA and details best practices for implementing ML workflows However you should work with your company s regulatory and compliance team and understand your company s policies and regulatory responsibilities before implementing these solutions which may impact your use of ML ReferenceOriginal paper	2022-01-02 16:04:00
Apple	AppleInsider - Frontpage News	Apple's 2022 monitor rumored to be half the price of the Pro Display XDR	https://appleinsider.com/articles/22/01/02/apples-2022-monitor-expected-to-ship-at-half-the-price-of-the-pro-display-xdr?utm_medium=rss	Apple x s monitor rumored to be half the price of the Pro Display XDRApple s rumored external display will launch in according to predictions on Apple s launch plans with the model still expected to be half the price of the existing Pro Display XDR Rumors have put forward the idea that Apple will be offering consumers a lower priced monitor option to the Pro Display XDR In predictions published on Sunday about the release schedule it is forecast that such a display will be released Included in theBloomberg Power On newsletter Mark Gurman says he is hoping Apple s next external monitor will launch in the coming year Gurman also writes that it is destined to be about half the price of the Pro Display XDR Read more	2022-01-02 16:27:39
Apple	AppleInsider - Frontpage News	Smaller Mac Pro with Apple Silicon to join Mac mini refresh in 2022	https://appleinsider.com/articles/22/01/02/smaller-mac-pro-with-apple-silicon-to-join-mac-mini-refresh-in-2022?utm_medium=rss	Smaller Mac Pro with Apple Silicon to join Mac mini refresh in An Apple Silicon version of the Mac Pro is on the way a report predicts with Apple also expected to release an updated Mac mini in Apple is pushing to release Apple Silicon hardware to replace Intel based Macs as part of an aggressive two year transition schedule It seems that will see Apple complete the shift with it finally offering a high end Mac aimed at enterprise users According to predictions made in Mark Gurman s Power On newsletter for Bloomberg on Sunday an Apple Silicon Mac Pro will launch in the year Gurman reckons that the model will be a smaller counterpart to the existing Mac Pro design while also packing considerable performance gains from using Apple s own chip design Read more	2022-01-02 16:27:15
Apple	AppleInsider - Frontpage News	2022 'iPhone 14' rumored to ditch screen notch, new iPhone SE will have 5G	https://appleinsider.com/articles/22/01/02/updated-iphone-14-forecast-to-lose-the-notch-from-the-screen?utm_medium=rss	x iPhone x rumored to ditch screen notch new iPhone SE will have GApple s smartphone releases will include the iPhone with a hole punch display that eliminates the notch according to Mark Gurman s predictions for alongside an updated iPhone SE with G connectivity Apple will launch new iPhone models during since it has done so as part of its regular release schedule for over a decade In a rumor roundup for the those models the new models could include a version that cuts out the infamous notch from the design According to Mark Gurman s Power On newsletter for Bloomberg Apple will be bringing a big design change to some versions of the iPhone Instead of a notch Gurman says Apple will introduce a hole punch screen that creates a compact window for a camera to shine through without needing to use a full notch Read more	2022-01-02 16:28:17
Apple	AppleInsider - Frontpage News	New MacBook Air predicted to have 'marginally faster' Apple Silicon M2 processor	https://appleinsider.com/articles/22/01/02/new-macbook-air-predicted-to-have-marginally-faster-apple-silicon-m2-processor?utm_medium=rss	New MacBook Air predicted to have x marginally faster x Apple Silicon M processorAs part of Apple s slate and after a series of rumors throughout Mark Gurman has predicted that a new MacBook Air will indeed arrive at some point in MacBook Air renders via FrontPageTech In his weekly Power On newsletter Bloomberg writer Mark Gurman has amplified predictions for a MacBook Air refresh Alongside many others Gurman is expecting Apple to refresh the MacBook Air lineup with new chips and a redesign sometime in Read more	2022-01-02 16:29:06
海外TECH	Engadget	Twitter permanently bans Marjorie Taylor Greene's personal account	https://www.engadget.com/twitter-marjorie-taylor-greene-permanent-ban-160838201.html?src=rss	Twitter permanently bans Marjorie Taylor Greene x s personal accountTwitter isn t shying away from banning more high profile US politicians As The New York Timesreports the social network has permanently banned Georgia Representative Marjorie Taylor Greene s personal account after a fifth quot strike quot for spreading COVID misinformation Her official account is still active as of this writing because it hasn t run afoul of Twitter s rules It s not clear what immediately precipitated the ban Twitter spokeswoman Katie Rosborough said only that the company had quot been clear quot it would issue permanent suspensions for quot repeated violations quot of its COVID misinformation policy Greene had faced an escalating series of bans over her inaccurate claims She falsely claimed in July that COVID wasn t dangerous unless you were over or obese and in August incorrectly maintained vaccines were quot failing quot against the new coronavirus Delta variant The posts respectively led to hour and one week suspensions In a statement Greene said technology companies and Democrats quot can t stop the truth quot and accused Twitter of hypocrisy in dealing with Democrats calling Twitter an quot enemy to America quot However she signalled no intentions to challenge the ban and said only quot we don t need them quot The crackdown comes just under a year after Twitter permanently banned former President Trump and reflects the social media giant s less forgiving attitude toward politicians in the past year ーit s willing to ban officials for violations rather than apply warning labels and otherwise avoid direct action And while ban targets like Trump can start their own social networks or jump to anything goes alternatives there s little doubt they ll lose some online influence by getting kicked off mainstream platforms	2022-01-02 16:08:38
Linux	OMG! Ubuntu!	Is Ubuntu Finally Taking Linux Gaming Seriously? This Suggests It Is	https://www.omgubuntu.co.uk/2022/01/ubuntu-is-looking-for-a-desktop-gaming-manager	Is Ubuntu Finally Taking Linux Gaming Seriously This Suggests It IsA new job listing at Canonical shows that the company is taking gaming on Ubuntu seriously Choosing graphics drivers is among tasks the new job requires This post Is Ubuntu Finally Taking Linux Gaming Seriously This Suggests It Is is from OMG Ubuntu Do not reproduce elsewhere without permission	2022-01-02 16:08:06
ニュース	BBC News - Home	E-scooter rider, 74, dies in crash with parked cars	https://www.bbc.co.uk/news/uk-england-manchester-59854204?at_medium=RSS&at_campaign=KARANGA	rider	2022-01-02 16:03:51
ニュース	BBC News - Home	Leeds United 3-1 Burnley: Hosts move eight points clear of drop	https://www.bbc.co.uk/sport/football/59792554?at_medium=RSS&at_campaign=KARANGA	fears	2022-01-02 16:24:59
ニュース	BBC News - Home	Fans stand for first time in English top flight since 1994	https://www.bbc.co.uk/sport/football/59848850?at_medium=RSS&at_campaign=KARANGA	ground	2022-01-02 16:48:34
ニュース	BBC News - Home	Brighton win five-goal thriller with Benitez's Everton	https://www.bbc.co.uk/sport/football/59792556?at_medium=RSS&at_campaign=KARANGA	goodison	2022-01-02 16:21:13
北海道	北海道新聞	新千歳空港　終日混雑　振り替えに数日も　１日からの降雪の影響	https://www.hokkaido-np.co.jp/article/629600/	振り替え	2022-01-03 01:16:11