| 海外TECH |
DEV Community |
2022, a look back |
https://dev.to/harshhhdev/2022-a-look-back-56dd
|
a look backAs this year has come to an end I thought I d take a moment to reflect on the past year of my life It was an year full of learning growing and sharing Looking back over the past year I m able to remind myself of the challenges I faced remember what I ve learned how I ve grown see what I ve accomplished and most importantly think about what I need to do this year in order to further facilitate this growth This recap isn t just limited to things related to technology but needless to say technology makes up a major chunk of this blog post NumbersI ll start with some raw numbers followers stars etc Mostly meaningless but nevertheless they give me an awkward sense of accomplishment Hit over followers on TwitterHit over followers on GitHubHit over views on my postsHit over stars across my projectsHit over followers on LinkedinHit over new followers on my blogHit over page viewsHit over stars on kmenuHit over followers on InstagramHit over wins in Fortnite 🫠 MiscellaneousLaunched kmenu which reached over stars in under five monthsGave a talk at four different meetupsWon two categories at Bitcamp Won the Astro HackathonMoved to the Dallas Fort Worth areaStarted actively attending in person technology meetupsGot my American citizenshipRedesigned my website four timesWrote twelve blog postsStarted actually earning money with contractsBought the new iPhone and AirPodsRebuilt my entire workspace in TexasBuilt snip place with IbrahimGot into photography a bitVisited over twenty four citiesHeld a snake in my handStarted learning RustBought a new mountain bikePulled of seven all nightersBenched my bodyweightGot my driving permit TravelCities I ve visited this year They re all in the United States Dallas TXFort Worth TXAustin TXSan Antonio TXLittle Rock ARMemphis TNNashville TNLouisville KYCincinnati OHColumbus OHAkron OHCleveland OHBuffalo NYRochester NYSyracuse NYAlbany NYHartford CTNew York NYNewark NJJersey City NJBoston MAPhiladelphia PABaltimore MDWashington D C ConclusionThat s about all I have to share for my year Through many highs and lows this year was filled with memories I hope to cherish one day I try to write a post similar to this annually to retrospect how I ve grown I ll end this post off with a quote by Ferris Bueller Life moves pretty fast If you don t stop and look around once in a while you could miss it Life moves pretty fast If you don t stop and look around once in a while you could miss it Wishing y all the best of luck for the upcoming year Cheers |
2023-01-07 18:50:36 |
| 海外TECH |
DEV Community |
Cricket Match Simulation using Machine Learning [Part 1] |
https://dev.to/ashwinscode/cricket-match-simulation-using-machine-learning-part-1-1hjd
|
Cricket Match Simulation using Machine Learning Part Having watched Cricket as my favourite sport for years I ve recently come to wonder how well the outcomes of games could be predicted Especially in tournaments like the IPL it seems difficult to predict the winner of each game since the difference in quality between sides often seems minimal on paper It would be interesting to see how effective machine learning could be in this task and what certain factors influence the outcomes of games Top Down vs Bottom Up ApproachOne way to predict the outcome of a game is by taking in a list of players from two teams predicting the win percentage of both teams While this approach works well to predict the final outcome of the match it fails to answer any other questions we may have about a game For this reason I have decided to take the bottom up approach of predicting the outcome of each ball in a game which would ultimate predict the final outcome of a game This way we can answer many questions such as Who will score the highest in a game Who will take the most wickets What will they score What happens if we reverse the batting order Predicting the outcome of ballsTo accurately predict the outcome of each ball in a game you would need to take into account two groups of data Data of the current match team score batsman score wickets fallen etc Pre match historical data batter and bowler skill We of course would need data from the current match so that the situation in which the ball is bowled is known In order to make predictions more accurate the actual skill level of the bowler and batsman should be known in order to know who is most likely to have the advantage A players skill level is found out by looking at their data of previous matches For simplicity in this project run outs are counted as a bowler s wicket and extras are counted as the batsman s runs This means each ball outcome can be one of runs run runs runs runs runsWicket The DatasetThe dataset I have is a collection of CSV files from each IPL match from Each CSV file has ball by ball data of their match This collection of CSV files will be used to calculate certain statistics of the players and will be ultimately used as part of the dataset for the machine learning model to predict the outcomes of balls Calculating Player Skill Ratings Batsmen RatingsThe two metrics commonly used to indicate the skill of a batsman are Strike Rate how many runs a batsman scores per balls Average how many runs a batsman scores on average before getting out Adding these together can give a rating for a batsman where a higher number would indicate a more skilled player However there are some limitations when using this as a ratingDifferent batsmen excel in different situations in a game For example finishers are excellent at scoring quick runs near the end of an innings but probably would not do as good a job when opening the innings Strike Rates and Averages do not provide this insight into what role a batsman is best in Strike Rates and Averages do not give an insight into how a batsman scores their runs Two batsman can have a strike rate of and an average of but one can achieve that by frequently running between the wickets while the other can achieve it by hitting boundaries after every few balls Predicting the outcome of a ball can be much more accurate if the type of batsman is considered With these things considered I felt a single number was not enough to capture the skill of a batsman After some thought I decided that each batsman shall have the following ratingsExplosivity RatingQuantifies whether a batsman is a big hitter who likes to accumulate their runs from boundariesTotal no of boundaries hit Total no of balls facedRunning RatingQuantifies whether a batsman likes to score their runs by running hard between the wicketsTotal no of balls where batsman ran for their runs Total no of balls facedFinisher RatingQuantifies how often a batsman is involved in the finish of an inningsTotal no of not outs Total no of balls facedConsistency RatingMeasure of how consistently a batsman performsBatsman s AverageQuick Scorer RatingMeasure of how quickly a batsman gets their runsBatsman s Strike RateAfter each rating above is calculated for each player in the database the ratings will be standardised to acquire the final rating The formula for standardising a data point is z x μσz frac x μ σ z σx μ xx x is the data point in question μμμ is the mean of the whole dataset σσσ is the standard deviation of the whole dataset zz z is the resulting score It shows how many standard deviations above below the average the datapoint is Bowler RatingsThe metrics commonly used to indicate the skill of a bowler areEconomy how many runs they concede per over Strike Rate balls bowled per wicket Average runs conceded per wicket I felt like these metrics were already good enough to indicate the type of bowler The economy measures how good a bowler is at conceding less runs while strike rate measures the wicket taking ability of the bowler A low average would mean a bowler has good both economy and strike rate stats I named the ratings economy ratingwicket taking ratingbowling consistency ratingThese will be standardised using this formula z x μσz frac x μ σ z σx μ xx x is the data point in question μμμ is the mean of the whole dataset σσσ is the standard deviation of the whole dataset zz z is the resulting score It shows how many standard deviations above below the average the datapoint is This is almost the same as the formula used for batting except for the negation at the front Since lower economies strike rates and averages are better the scores are negated so that a higher rating would indicate a more skilled bowler I also decided to use another rating specialist ratingused to measure whether a bowler is a specialist part timer or a non bowlerNumber of balls bowled number of matches playedThis rating will be standardised using the original standardisation formula ExperienceRight now a tailender who has only faced balls in their career and hit runs would be given a high number in most of the batting ratings This obviously should not be the case as a tailender would not be able to keep up those numbers for an extended period of time Similarly a batsman may have bowled a few decent overs in their career and may end up being higher rated than some established bowlers Therefore these ratings need to be adjusted to account for their experience as a batsman and a bowler The number of innings a player has batted in will determine the batting experience The number of balls a player has bowled will determine the bowling experience Both of these will be standardised using the formula from before and their values will be clipped from to so that the ratings don t just favour the players who have played the most Once they have been standardised each player rating will be adjusted as follows If the rating is a batting rating add the batting experience to the rating Otherwise add the bowling experience Re standardise this new number Exploring the ratingsI wrote a script to go through each ball from the dataset and calculate the overall ratings of players and save it to a CSV file You can see a snippet of the data below According to these ratings The top fastest scorers in the IPL have been PN MankadAD RussellV SehwagAB de VilliersGJ MaxwellRR PantCH GayleKA PollardHH PandyaSP NarineYK PathanDA WarnerSR WatsonSA YadavDA MillerThe top most consistent batsmen Iqbal AbdullaKL RahulDA WarnerAB de VilliersCH GayleMS DhoniDA MillerV KohliRR PantF du PlessisJC ButtlerS DhawanJP DuminySPD SmithSK RainaThe top most explosive batsmen highest proportion of their runs scored in boundaries PN MankadB StanlakeRS SodhiV SehwagSP NarineAD RussellCH GayleGJ MaxwellSR WatsonRR PantAB de VilliersSA YadavBB McCullumDR SmithDA WarnerThe top finishers RA JadejaHH PandyaDJ BravoMS DhoniDA MillerHarbhajan SinghJR HazlewoodYK PathanIK PathanIqbal AbdullaMukesh ChoudharyP SahuBA BhattK UpadhyayJE TaylorTop hardest runners highest proportion of runs scored by non boundaries CRD FernandoDP VijaykumarNJ RimmingtonRG MoreSPD SmithDA MillerRA JadejaV KohliAB de VilliersMS DhoniMK PandeyAR PatelAT RayuduKL RahulSK RainaTop most economical bowlers AD RussellSN ThakurGJ MaxwellSR WatsonPP ChawlaJA MorkelJJ BumrahSTR BinnyDL ChaharHV PatelRashid KhanMohammed SirajR Vinay KumarKH PandyaK RabadaTop best wicket takers Sandeep SharmaRP SinghMM PatelUT YadavGJ MaxwellMG JohnsonRashid KhanAR PatelA NehraSN ThakurMM SharmaAB DindaJD UnadkatDL ChaharDJ BravoTop most consistent bowlers MM PatelSandeep SharmaRP SinghA NehraMG JohnsonUT YadavAR PatelAB DindaRashid KhanGJ MaxwellJH KallisMM SharmaHarbhajan SinghR BhatiaSN ThakurObviously there are a few anomalies seen in each rating with some bowlers ranking higher than specialist batsmen despite correcting for experience However these anomalous players do not carry over to the other ratings and the ratings as a whole do make sense Match DataFor each ball along with player skill data the context of the current match will also be considered The following pieces of information will be considered in each ball Ball NumberBatsman s ScoreBalls faced by the batsmanProportion of balls faced by the batsman that resulted in runsProportion of balls faced by the batsman that resulted in runProportion of balls faced by the batsman that resulted in runsProportion of balls faced by the batsman that resulted in runsProportion of balls faced by the batsman that resulted in runsProportion of balls faced by the batsman that resulted in runsRuns conceded by the bowlerNumber of balls bowled by the bowlerNumber of wickets taken by the bowlerProportion of balls bowled by the bowler that resulted in runsProportion of balls bowled by the bowler that resulted in runsProportion of balls bowled by the bowler that resulted in runsProportion of balls bowled by the bowler that resulted in runsProportion of balls bowled by the bowler that resulted in runsProportion of balls bowled by the bowler that resulted in runsProportion of balls bowled by the bowler that resulted in a wicketChasing score if applicable Required run rate if applicable Innings scoreInnings wicketsAll of these will be standardised as done before Building the datasetNow that the data to be used has been decided it is time to process the ball by ball CSV files into the dataset to train on import pandas as pdimport numpy as npimport osimport pickle as pkl standardising formuladef zscore col mean col mean std col std return col mean stddf pd DataFrame will hold the final dataset at the endplayer db pd read csv player db csv need it for player ratingsThe code below goes through each match and adds the relevant data to the dataframe for file in os listdir matches f os path join matches file match df pd read csv f match df match df fillna columns of all the match data needed ball no striker bowler batsman runs batsman balls batsman outcome dists outcome for outcome in bowler economy wicket taking bowler consistency bowler wickets bowler runs bowler balls bowler outcome dists outcome for outcome in range innings score innings wickets chasing req run rate outcome batsmen bowlers score wickets chasing score prev innings for ball in match df iloc batter ball striker bowler ball bowler striker append batter bowler append bowler runs int ball runs off bat runs min runs runs if runs else runs wides int ball wides wicket if ball wicket type else innings int ball innings if innings prev innings chasing score score score wickets prev innings innings if batter not in batsmen this will hold the number of balls faced for each outcome by a batsman batsmen batter if bowler not in bowlers this will hold the number of balls bowled for each outcome by a bowler means wicket bowlers bowler Batting Data batsman balls faced np sum batsmen batter i for i in batsmen batter batsman dist outcome if batsman balls faced else batsmen batter outcome batsman balls faced for outcome in batsmen batter getting proportion of balls faced for each outcome batsman runs scored np sum outcome batsmen batter outcome for outcome in batsmen batter for outcome in batsman outcome dists batsman outcome dists outcome append batsman dist outcome batsman runs append batsman runs scored batsman balls append batsman balls faced Bowling Data bowler balls bowled np sum bowlers bowler i for i in bowlers bowler bowler runs given np sum outcome bowlers bowler outcome if outcome else for outcome in bowlers bowler bowler wickets taken bowlers bowler bowler dist outcome bowlers bowler outcome bowler balls bowled if bowler balls bowled else for outcome in bowlers bowler getting proportion of balls bowled for each outcome for outcome in bowler outcome dists bowler outcome dists outcome append bowler dist outcome bowler runs append bowler runs given bowler wickets append bowler wickets taken bowler balls append bowler balls bowled Innings Data innings score append score innings wickets append wickets chasing append chasing score ball outcome runs if wicket else outcome append ball outcome discrete ball no ball ball discrete ball no int discrete ball no round discrete ball no ball no append discrete ball no rem balls discrete ball no if discrete ball no lt else int discrete ball no req run rate max chasing score score rem balls req run rate append req run rate Update State batsmen batter runs bowlers bowler runs bowlers bowler wicket score runs wickets wicket new match df pd DataFrame new match df striker striker new match df bowler bowler new match df batsman runs batsman runs new match df batsman balls batsman balls for outcome in batsman outcome dists new match df f batsman outcome batsman outcome dists outcome new match df bowler runs bowler runs new match df bowler balls bowler balls new match df bowler wickets bowler wickets for outcome in bowler outcome dists new match df f bowler outcome bowler outcome dists outcome new match df innings score innings score new match df innings wickets innings wickets new match df ball ball no new match df chasing chasing new match df req run rate req run rate new match df outcome outcome frames df new match df df pd concat frames df now contains the relevant match data from every single ball in the datasetThis now adds the player ratings to the collected data and saves the dataset batting ratings player db player explosivity rating consistency rating finisher rating quick scorer rating running rating bowling ratings player db player economy rating wicket taking rating bowling consistency rating specialist rating df df join batting ratings set index player on striker add batsmen s batting ratings to dataframedf df join bowling ratings set index player on bowler add bowler s bowling ratings to dataframedf df drop striker bowler axis remove the striker and bowler columns since they are not part of the features needed to predict the outcome of a ball get the means and standard deviations of each columndf mean df drop outcome axis mean df std df drop outcome axis std save the mean and std of all the columns this would be needed later for preprocessing when predicting new data with open mean std bin wb as f pkl dump mean df mean std df std f standardise all the columns except the ratings and outcomefor col in df columns if col outcome and rating not in col df col zscore df col shuffledf df sample frac split into a training set and a testing set training split int len df take to traintraining df df training split testing df df training split save the datasetstraining df to csv balls train csv index False testing df to csv balls test csv index False The dataset looks something like this Training and testing the modelI have chosen to use a Random Forest Classifier to train on the data which is really simple and quick to train using sklearn import numpy as npimport pandas as pdfrom sklearn ensemble import RandomForestClassifierimport pickle as pklfrom random import choicesdataset pd read csv balls train csv x dataset drop outcome axis y dataset outcome clf RandomForestClassifier max depth n estimators clf fit x y with open clf bin wb as f pkl dump clf f Now to test this model we could see its accuracy on the test set test pd read csv balls test csv predicted clf predict test drop outcome axis print np sum predicted test outcome len test Running this results in the following output An accuracy of does not seem too great However each ball can easily have or reasonable outcomes so using accuracy is not a good measure to capture how the model is performing Instead I thought it would be better to look at the input data of each outcome For each outcome take the inputs that were predicted to have this outcome by the model and also take the inputs in the test set that were labelled with this outcome If these two sets on inputs are similar then the model has predicted this outcome well test pd read csv balls test csv predicted test drop outcome axis preds clf predict predicted predicted outcome predsfor outcome in range print Outcome outcome get rows which were labelled with this outcome in the test data test slice test test outcome outcome get rows which were predicted with this outcome by the model predicted slice predicted predicted outcome outcome a vector of the average values of the inputs that were labelled with the outcome in the test set test slice mean test slice mean a vector of the average values of the inputs that were predicted to have this outcome by the model predicted slice mean predicted slice mean fillna calculates the euclidean distance between the two vectors dist predicted slice mean test slice mean sum print Actual Count len test slice Predicted Count len predicted slice compared how many times this outcome appeared in the test set and how many times it got predicted print Average Distance dist Running this results in the following output Outcome Actual Count Predicted Count Average Distance Outcome Actual Count Predicted Count Average Distance Outcome Actual Count Predicted Count Average Distance Outcome Actual Count Predicted Count Average Distance Outcome Actual Count Predicted Count Average Distance Outcome Actual Count Predicted Count Average Distance Outcome Actual Count Predicted Count Average Distance This shows that the model is performing poorly Its distances are quite large for each outcome except for and considering the magnitudes of the values in the input It has also not been able to match the proportions of outcomes in the test set with the predicted counts being very different to the actual counts The model is clearly biased to predicted and for each ball This does make sense however since those are the most common outcomes in games of Cricket The problem here is how the predictions are made The model outputs a probability distribution of the outcomes for each ball Currently the outcome with the highest probability is taken as the predicted outcome It would make more sense to randomly select an outcome using the probability distribution to weight the random selection Here is the same code but using weighted random selection test pd read csv balls test csv predicted clf predict proba test drop outcome axis preds for weights in predicted outcomes weighted random selection p choices outcomes weights weights preds append p predicted test drop outcome axis predicted outcome predsfor outcome in range print Outcome outcome test slice test test outcome outcome predicted slice predicted predicted outcome outcome a vector of the average values of the inputs that were labelled with the outcome in the test set test slice mean test slice mean a vector of the average values of the inputs that were predicted to have this outcome by the model predicted slice mean predicted slice mean fillna calculates the euclidean distance between the two vectors dist predicted slice mean test slice mean sum print Actual Count len test slice Predicted Count len predicted slice print Average Distance dist Running this results in the following Outcome Actual Count Predicted Count Average Distance Outcome Actual Count Predicted Count Average Distance Outcome Actual Count Predicted Count Average Distance Outcome Actual Count Predicted Count Average Distance Outcome Actual Count Predicted Count Average Distance Outcome Actual Count Predicted Count Average Distance Outcome Actual Count Predicted Count Average Distance Now the model can be seen to be performing very well It matches the test set s proportion of outcomes and the distances between the inputs have reduced to a small range Exploring each featureI thought it would be intriguing to see what each feature of the dataset contributed to the outcome of a ball Shown below are box plots for each feature against each predicted outcome to help visualise the distribution of features against each output This will help in showing how a feature contributes to the outcome Batsman s runs Batsman s balls Batsman s dot ball proportion Batsman s single proportion Batsman s double proportion Batsman s three runs proportion Batsman s four runs proportion Batsman s six runs proportion Runs conceded by the bowler Number of balls bowled by the bowler Wickets taken by the bowler Bowler s dot ball proportion Bowler s single proportion Bowler s double proportion Bowler s three runs proportion Bowler s four runs proportion Bowler s wicket delivery proportion Bowler s six runs proportion Innings score Innings wickets Ball number of the innings Score to chase Required run rate Explosivity rating Consistency rating Finisher rating Quick scorer rating Running rating Economy rating Wicket taking rating Bowling consistency rating Bowling specialist ratingFrom these box plots we can see which features have more of an impact on the outcome Features that produce similar looking boxplots for each outcome do not have much of an effect on the outcome of a ball These features include Batsman Bowler ball outcome proportionsBowler ratingsNumber of wickets taken by a bowlerChasing scoreRequired run rateIt was not surprising to see the chasing score and required run rate features to not have much of an effect on the outcome as half the balls in the dataset wouldn t have had these features applied to them It also was not surprising to see the ball outcome proportion and no of wickets taken features in this list too This is because these can have the same values appear all throughout a cricket match so they are bound to have a wide range of outcomes for the same values I was however surprised to see that bowler ratings did not have much of an impact on the outcome while batting ratings did This can mean a few thingsThe outcome of a game comes more down to how strong a team s batting lineup is rather than their bowling There must be a better way to quantify the skills of a bowlerAfter having a thought about this while I do think it s a mix of both I feel it is mainly due to the first point I believe this is because especially in a competition like the IPL the quality of bowlers does not fluctuate as much as they do for batsmen statistically speaking at least It is much more common to have bowlers non specialist batsmen to bat in games while you would almost never find a batsman bowling in a game and occasionally find a part time bowler The way to quantify a bowler s skill could still be improved however maybe considering some of the following Average pace of the bowlerAverage degree of turn the bowler gets for spinners Adjusting their economy wicket taking ratings to consider the contexts of the matches they bowled inThese pieces of data were outside the scope of the dataset I had but would be interesting to implement to improve this project in the future Nevertheless I am happy with how the modelled has trained aligning itself closely to the test dataset Part To avoid making this part too long I have decided to split this up into two parts Part will involve building the actual match simulator I will use it to see how it does in simulating real games and to answer any question about any hypothetical game situations Thank you for reading |
2023-01-07 18:26:26 |
| Apple |
AppleInsider - Frontpage News |
Xencelabs Graphic Tablet review: a great midrange tablet for artists |
https://appleinsider.com/articles/23/01/07/xencelabs-graphic-tablet-review-a-great-midrange-tablet-for-artists?utm_medium=rss
|
Xencelabs Graphic Tablet review a great midrange tablet for artistsEdit photos digitally paint and mark up documents with Xencelabs Graphic Tablet a fantastic tool for professional artists and hobbyists alike As some of you may already know I ve spoken at length about how my favorite way to draw digitally is with an iPad and Apple Pencil However there are times when that isn t an option If I m doing photo editing I still gravitate toward desktop Photoshop as I m not the biggest fan of Photoshop on the iPad When I m designing in Illustrator I d rather use the desktop version as it s got more of the tools I use in my workflow Read more |
2023-01-07 18:13:28 |
| 海外TECH |
Engadget |
Google asks India’s Supreme Court to block $161.9 million Android antitrust ruling |
https://www.engadget.com/google-escalates-challenge-android-antitrust-ruling-india-supreme-court-184501607.html?src=rss
|
Google asks India s Supreme Court to block million Android antitrust rulingGoogle has turned to India s Supreme Court as a “last hope to block an order that has the potential to reshape the Android ecosystem Last October the Competition Commission of India CCI fined Google million and barred the search giant from requiring OEMs to preinstall Google apps and services on their phones On Wednesday an appeals tribunal rejected the company s request to block the ruling which is scheduled to go into effect on January th According to court documents seen by Reuters Google filed a challenge against the tribunal s decision with the country s top court on Saturday The company reportedly sees the effort as its last and best hope at preventing the CCI s order from impacting its business Google did not immediately respond to Engadget s comment request While the million fine is tiny for Google the order is likely to force the company to change its deals with Android manufacturers dramatically The CCI seeks to prevent Google from including “anti fragmentation clauses that bar Android forks The order would also force the company to allow third party app stores on Google Play and allow users to uninstall first party apps they don t want on their phones India is a critical market for Google The country is home to about million smartphone users and about percent of the phones in India run Android In other words the company can t afford to exit the market |
2023-01-07 18:45:01 |
| 海外TECH |
Engadget |
Roland's 50th Anniversary Concept Piano has flying speakers for some reason |
https://www.engadget.com/rolands-50th-anniversary-concept-piano-has-flying-speakers-for-some-reason-160052643.html?src=rss
|
Roland x s th Anniversary Concept Piano has flying speakers for some reason marked the th anniversary of storied instrument maker Roland But even though we ve switched over to our calendars the company took the opportunity at CES to take one more victory lap by showing off its th Anniversary Concept Piano It s an audacious electronic piano built in collaboration with Japanese furniture maker Karimoku The outside is one piece molded from Japanese Nara oak that hides a degree speaker system nbsp If the elegant curves stuffed to the brim with speakers aren t exciting enough for you well I ve got good news Roland has also built speakers into drones that hover above the piano and can be controlled by the player Unfortunately those couldn t be flown on the show floor at CES so Roland dangled a pair of them from wires Those are combined with a proprietary low latency audio connection and the company s PureAcoustic Ambience tech to create flexible natural sounding reverb that more accurately mimics what you d hear in say a concert hall And I can confirm that even in the cavernous Las Vegas Convention Center fighting against the constant din of people and other exhibitors the concept piano sounded amazing nbsp Terrence O Brien EngadgetAbove the keyboard itself there s a large touchscreen that can be used to stream tutorials video conference with a piano teacher or even run Zenbeats from Roland Cloud turning the instrument into a studio hub There s even USB MIDI and Bluetooth connectivity for interacting with other instruments Of course the Roland th Anniversary Concept Piano is not for sale And never will be This is a creation in the tradition of over the top concept cars Maybe some of its features will eventually make their way to future instruments but for now it s truly a one of a kind Terrence O Brien EngadgetTerrence O Brien Engadget |
2023-01-07 18:00:52 |
| ニュース |
BBC News - Home |
CCTV released in search for missing couple and baby after M61 breakdown |
https://www.bbc.co.uk/news/uk-england-manchester-64200338?at_medium=RSS&at_campaign=KARANGA
|
gordon |
2023-01-07 18:58:29 |
| ニュース |
BBC News - Home |
Congressman Mike Rogers physically restrained over Speaker vote |
https://www.bbc.co.uk/news/world-us-canada-64195940?at_medium=RSS&at_campaign=KARANGA
|
kevin |
2023-01-07 18:30:37 |
| ニュース |
BBC News - Home |
Bournemouth 2-4 Burnley: Championship leaders beat Premier League side in FA Cup |
https://www.bbc.co.uk/sport/football/64167888?at_medium=RSS&at_campaign=KARANGA
|
Bournemouth Burnley Championship leaders beat Premier League side in FA CupAnass Zaroury and Manuel Benson both score twice as Championship leaders Burnley beat Premier League strugglers Bournemouth in the FA Cup third round |
2023-01-07 18:56:27 |
| ビジネス |
ダイヤモンド・オンライン - 新着記事 |
外資系企業で「クビになる人」と「活躍できる人」の決定的な差 - １位思考 |
https://diamond.jp/articles/-/315449
|
|
2023-01-08 03:55:00 |
| ビジネス |
ダイヤモンド・オンライン - 新着記事 |
ダチョウ、ペンギン、エミュー…「飛べない鳥」たちは、なぜ遠いむかしに空を飛ぶことをあきらめたのか？ - 超圧縮 地球生物全史 |
https://diamond.jp/articles/-/315735
|
「地球の誕生」から「サピエンスの絶滅、生命の絶滅」まで全歴史を一冊に凝縮した『超圧縮地球生物全史』は、その奇跡の物語を描き出す。 |
2023-01-08 03:50:00 |
| ビジネス |
ダイヤモンド・オンライン - 新着記事 |
【精神科医が教える】 頑張っているのに空回り…成功する人と結果が出ない人の決定的な差 - 精神科医Tomyが教える 心の執着の手放し方 |
https://diamond.jp/articles/-/315304
|
そんなときの助けになるのが、最新刊の感動小説『精神科医Tomyが教える心の執着の手放し方』ダイヤモンド社だ。 |
2023-01-08 03:45:00 |
| ビジネス |
ダイヤモンド・オンライン - 新着記事 |
【小児科医が教える】骨がゆがむ病気の子どもが5倍に増加！ その理由とは？ - 医師が教える 子どもの食事 50の基本 |
https://diamond.jp/articles/-/315674
|
食材 |
2023-01-08 03:40:00 |
| ビジネス |
ダイヤモンド・オンライン - 新着記事 |
うす暗い部屋で過ごすと、不調が消えるのはなぜ？ 整体プロが教える美と健康の習慣 - すごい自力整体 |
https://diamond.jp/articles/-/315582
|
『すごい自力整体』の著者・矢上真理恵さんは、「不調のほとんどは自力整体で解消できる」と語る。 |
2023-01-08 03:35:00 |
| ビジネス |
ダイヤモンド・オンライン - 新着記事 |
「チャドってどんな国？」2分で学ぶ国際社会 - 読むだけで世界地図が頭に入る本 |
https://diamond.jp/articles/-/315550
|
|
2023-01-08 03:30:00 |
| ビジネス |
ダイヤモンド・オンライン - 新着記事 |
【英語リスニング】絶対覚えておくべき口語表現10選 - 逆転合格90日プログラム |
https://diamond.jp/articles/-/315657
|
逆転 |
2023-01-08 03:20:00 |
| ビジネス |
ダイヤモンド・オンライン - 新着記事 |
【単語さがし】「集中力」が身につく脳トレ - 1分間瞬読ドリル 超かんたん!入門編 |
https://diamond.jp/articles/-/315749
|
【単語さがし】「集中力」が身につく脳トレ分間瞬読ドリル超かんたん入門編「認知症、ボケ予防に役立つ」「記憶力や思考力がアップし、勉強に活かせる」「頭の回転が速くなった」「本が速く読めて、判断スピードがあがった」「モチベーションの向上、習慣化につながる」「持続力が増して途中で投げ出さなくなった」などの声が届いた、くり返し楽しんで使える『分間瞬読ドリル』に、超入門編が登場。 |
2023-01-08 03:15:00 |
| ビジネス |
ダイヤモンド・オンライン - 新着記事 |
「やる気があるのに仕事ができない人」がついやってしまっている習慣とは？ - 1秒で答えをつくる力 お笑い芸人が学ぶ「切り返し」のプロになる48の技術 |
https://diamond.jp/articles/-/315706
|
|
2023-01-08 03:10:00 |
コメント
コメントを投稿