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Our Town Coming To Stage 9
"Our Town" Coming to Stage 9
STAGE 9 Welcomes You to OUR TOWN – January 21 & 22, 2017
Hutchinson, Kan. – Stage 9 opens its 2017 season with a special production of the classic drama by Thornton Wilder, Our Town. Stage 9 presents a staged reading of the classic Our Town for two performances only, Saturday, January 21 at 7:30 p.m. and Sunday, January 22 at 2:00 p.m. Performances will be at the theatre at 9 S. Main, Hutchinson, Kansas.
Bringing Our Town to life once again in this one-weekend only production, Stage 9 will visit the residents of Grover's Corners between the years 1901 to 1913. This American masterpiece of the stage is framed around Emily Webb and George Gibbs when their romance blossoms among a town of colorful characters. And, in a twist that left the American Theatre changed forever, we get the rare opportunity to look at life after death. Stage 9 revisits this Pulitzer Prize Winning American classic for another look at its timeless themes which touch the human spirit as no other play can.
Our Town follows the lives of Emily, George, their families and other denizens of Grover's Corners as they experience life's day-to-day activities. Throughout the play, the Stage Manager directs us to various time periods in this town for glimpses of life, love, and death. Wilder makes us aware of the importance of relationships and staying connected. He points to the fleeting existence we all share on earth and encourages us to live in the moment and not take life for granted.
Our Town is directed by Stage 9 Producing Artistic Director Charles Johnston. The cast of Our Town includes Joel Perkins, Katy Carlin, Jackson Swearer, Amy Thompson, Dell Marie Shanahan Swearer, Jason Probst, Jocelyn Woodson, Alice Jane Hayes, Bob Colladay, Sarah Winkler, Nick Kennedy, Anita Poovey, Ellen McCarley, Jo Stropes and Lindsey Fields.
Performances for Our Town will occur Saturday, January 22, 2017 at 7:30 p.m. and Sunday, January 23, 2017 at 2:00 p.m. The ticket price for this special event is $20. Children under 8 are not permitted. All seating is general admission. Stage 9 seats 85 people and seats are available on a first come, first served basis. To check performance availability, please contact Stage 9 by email at info@stage9hutch.com or by phone at 316-350-PLAY (7529).
Our Town is presented with special permission by Samuel French, Inc. All seating is general admission.
For more information about Our Town, auditions or to schedule interviews with the director, contact Charles Johnston at 646-628-4353 or charles@stage9hutch.com. You may also contact Johnston for information about program advertising or sponsorship for Stage 9's 2017 Season.
Memberships for the 2017 season are now available for patrons wishing to contribute to the ongoing success of Stage 9. Memberships may be purchased online at www.stage9hutch.com or by contacting Producing Artistic Director Charles A. Johnston at charles@stage9hutch.com or 646-628-4353.
Stage 9's mission is to excite minds, move hearts, and change lives through theatrical performance, arts education, and live entertainment in Hutchinson, Reno County, and south central Kansas. A 501(c)3 organization, Stage 9 strives to produce quality entertainment and provide opportunities for artistic expression, specifically focused on ages 18 and up. At home at 9 S. Main in downtown Hutchinson, Stage 9's 2017 Season features Our Town, by Thornton Wilder, the hard-hitting modern drama Time Stands Still by Donald Margulies, The 25th Annual Putnam County Spelling Bee by Rebecca Feldman, a musical comedy about an eclectic group of pubescent spellers, Neil Simon's classic comedy The Odd Couple (Female Version) pitting polar opposite roommates, a staged reading of The Laramie Project by Moises Kaufman that examines the tragedy of the murder of Matthew Shepard, the groundbreaking musical Godspell by John-Michael Tebelak and Stephen Schwartz, a loose retelling of the Gospel story, Stage 9's haunting annual fundraiser Talking Tombstones, and the romantic tales of John Cariani's Almost, Maine.
Category: (none)
CBS Sports Network To Air 2019 NJCAA DI Basketball Championship
A Gentleman's Guide to Love and Murder at Stage 9
Hutchinson To Continue Hosting the NJCAA DI Volleyball Championship
KCAC Inks Two-Year Contract for Volleyball Tournaments To Remain In Hutchinson


How Many Figures of *blank* Does Oldphan Have? Fun new videos up on our Youtube Channel!
Well that's one! Click below to see the grand total!
Greetings everyone! There's some fun new videos up on our Youtube channel, that I thought I should let everyone know about. In a fit of insanity, our very own Oldphan decided to go through her collection, and see just how many of a single character she has! So far there are two videos like this, the first one showing off all the Gohans, (With a whopping total of about 120 Gohan figures!!!) and another for Vegeta! More videos like this on the way, but be sure to let us know what character you think Oldphan should do next, on either our forum or anywhere on our Youtube channel. Thanks to everyone for watching, and more news on the way soon!
Click here to see the Gohan Collection!
Click here to see the Vegeta collection!


For informal matters we can follow our internal complaints procedures.
Make a complaint using the Orkney Islands Council's formal complaints procedures.
At any time you can make a complaint to the Care Inspectorate at East Bank, East Road, Kirkwall, Orkney, KW15 1LX. Telephone:01856870535. Alternatively you could contact the Care Inspectorate National Enquiry line on 08456009527. A link to the Care Inspectorate website can be found in the related sites section of this page.
Please contact us for further information on making a complaint.
Care InspectorateExternal link to the Care Inspectorate website.


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3.5 Character Sheet
Common Language 5e (5th Edition) for Dnd
October 2, 2021 October 2, 2021 by Prakash
In dungeons and dragons 5th edition game our character can speak some default 5e languages which are indicated by your race and also your character background might offer you the access to urge one or some additional languages depends on your choice. you want to use the D&d 5e character sheet to notice these languages.
If you would like to settle on any of d&d 5e languages then you would like to settle on the language which is common in your campaign otherwise you'll choose any of your wishing languages from the d&d 5e Standard languages table. But whenever we would like to settle on a language for our character from the d&d 5e Exotic languages table you want to have the permission of the GM's, so with this permission, you furthermore may have an opportunity to urge the key language like thieves can't or tongue of druids.
Common 5e (5th edition)
From these Dungeons & Dragons, 5e languages are basically families of languages that have many dialects, as an example, the primordial language has included the Terran dialects, Auran, Aquan, Ignan and one for every one of the four elemental planes. The creatures which have the knowledge to talk in several dialects of an identical language can communicate easily with each other.
Actually, there's no separate category for the simplest 5e languages and it comes from your experience together with your character and the way many 5e backgrounds you've got used and also races,..etc with of these aspects you'll gather some 5e languages, in order that they are going to be the simplest and useful 5e languages in your campaign.
We can get numerous languages once we have the below specifications with us and once you chose the new 5e language then you'll easily interact together with your families before of your enemies. So allow us to start the steps to urge a replacement language for your d&d character.
To begin "First Level" we've to understand a minimum of any of the "Two 5e Languages"
Your Character Default language is going to be indicated by your "Race"
If you've got picked up "Half-Elf" or the other different "Race" then there you'll access more languages.
For instance, you've picked up the "Half-Elf", so you'll approach the "Dungeon Master" and he will provide some "Additional Learning Languages". You no got to worry about your "INT Score" because it won't "Affect".
With your "Background" also you'll get access to chose one or more "Extra Languages". But it does use for the "Particular Task"
It's mandatory to take care of the "Character Sheet" to write down the small print of that "Initial Languages" after you've selected
"Speak Language Skill" is best to get than getting the rank through any skill you've got.
The forgotten realms human languages are available within the Swords Coast Adventurer's Guide only, so you'll all the below languages during this book but the most aspect you've got to recollect is that the Game Master or the Dungeon Master permission is mandatory. These are the simplest 5e languages and you'll get the above-mentioned languages like exotic and standard categories. We hope with the above-mentioned sources like players' handbook and Swords Coast Adventurer's Guide and also all other sources.
Categories Languages Post navigation
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5e Learning Languages & How To Learn More ?
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redpajama_set_name: RedPajamaCommonCrawl


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Prison Decongestion: SGF inaugurates committee
The Secretary to the Government of the Federation, Boss Mustapha, on Tuesday in Abuja, inaugurated the Presidential Advisory Committee on Prerogative of Mercy to decongest prisons nationwide.
Mustapha, who was represented by Abubakar Malami, Attorney-General of the Federation, said that the committee was to assist President Muhammadu Buhari in discharging his constitutional responsibilities.
He added that the constitutional responsibility entailed granting pardon to deserving inmates and ex-convicts and ensuring a decongested prisons in the country.
He said: "The PACPM is an administrative committee established to assist the president in granting pardon to inmates and ex-convicts.
"The reconstitution of the committee as approved by Mr. President will go a long way in bringing about drastic reduction in the population of prisoners in the nation's prison system that has become a blot on the nations' criminal justice system."
He noted that the responsibility of the committee was sensitive and enormous, urging them to start by visiting prisons across the country.
According to him, their Terms of Reference was statutory as constituted in the Nigerian Constitution, Section 175 (1) Prerogative of Mercy.
"(1) The President may –
(a) grant any person concerned with or convicted of any offence created by an Act of the National Assembly a pardon, either free or subject to lawful conditions;
"(b) grant to any person a respite, either for an indefinite or for a specified period, of the execution of any punishment imposed on that person for such an offence;
"(c) substitute a less severe form of punishment for any punishment imposed on that person for such an offence; or
"(d) remit the whole or any part of any punishment imposed on that person for such an offence or of any penalty or forfeiture otherwise due to the State on account of such an offence."
The Inspector General of Police, Ibrahim Idris, who spoke on behalf of the members of the committee, assured the government that they would deliver on their mandate.
The I-G, who was represented by Shehu Lawal, Force CID, stated that at the end of their assignment, Nigerian prisons would be decongested.
Idris said: "We will take this assignment seriously, ensuring that the rule of law is applied. We will do our job assiduously and deliver as expected of us."
The News Agency of Nigeria reports that the committee was made up of 11 members and it is for a four-year tenure with Abubakar Malami as the Chairman.
The members of the committee were drawn from various organisations, such as the Christian Association of Nigeria, Nigerian Prisons Service, National Human Rights Commission, Office of the Secretary to the Government of the Federation among others.
Tags: Abubakar MalamiBoss MustaphaMuhammadu BuhariPrison decongestion
11 in contention for $100,000 The Nigeria Prize for Literature 2019


They started in early 2014 as a duo and released their first album Big City Blues in June 2015. Now they are a trio and bring songs that carry the soul and vibe of the good days of the 20s in any way instrumentally and 'Big City Blues' is a good example. They also covered some classic songs like the 'St. James Infirmary', which they transformed into their trademark style of ragtim , blues and roots.
The music of The Burying Ground flows out of their love for the early country, blues, ragtime and jazz and inspired them to create original songs that emulate old ones as well as to play renditions of songs from the 1920's and 30's.
If you listen to St. Cider you'll hear a nice harmony between a whole lot of roots instruments. But above all it is the harmonious voices of ST. Cider that grab your attention. "Double Yellow Blues' is a perfect example for the bands repertoire, It is a fantastic song, I really dig the great vocals and the instrumental jam that lift this song to a climax.
"Greenbelt Blues" is a humble song more folkloric and, slower then other songs in their repertoire. "You Aint No City Dog" is a catchy that will make you dance. The music reminds me of the New Orleans swing, blues and jazz of the pre-war era. Guys like Professor Longhair who was picked up from the street to make music again, Or Frank Stokes who performed with his duo the Beale street Sheiks outdoors.
St. Cinder is comprised of six travelers. On their Facebook page they write that "We were all individually moving along the line from town to town busking for our fare and seeing the sights and smelling the smells. Our lines crossed in southern Oregon and we hit the ground running".
In the North East of Texas lays the town of Big Sandy. It is a small town near to the Sabine River, Folk Bluesman Henry Thomas was born here. In 1928 he recorded the catchy folk blues songs 'Fishin'Blues' which is inspiring for ragtime guitar enthusiasts.
In When We Were Good: The Folk Revival by Robert Cantwell, he described that the song may have been written differently. Because in blues fishin', like a hound dog refers to a female figure. The songs could have a more sexual context. The recorded version nevertheless is about fishin' for fish in the river at one o'clock in the afternoon.
Yes, you've been fishin' all the time.
I bet your life your lovin' wife.
Catch more fish than you.
Any fish bite, got good bait.
Here's a little somethin' I would like to relate.
Any fish bite, you've got good bait.
Blind Boy Chocolate & The Milk Sheiks is a band that plays blues, ragtime, jazz or bluegrass in the style of the jug bands of the early twentieth century. These Guys started in the summer of 2009 as a three piece band with Dwight Hawkins, Nicholas Marshall and Antone Costa primarily featuring the wood saw, mandolin and guitar respectively.
The love for old blues, jazz and ragtime brought these guys together, their love for music would soon be turned into performing together. A show of these guys, wherever it is in your local pub or on the streets is full of good energy swinging rhythm and please focus on the wood saw which adds a pervasive melody.
After a half a year of heavy busking, the band gained momentum and began to play inside venues as well as on the sidewalk. Since then, Alex Brady has come into the ranks on the gut bucket bass, though other members have come in and out of the band as time and circumstances permitted.


For the past two years, a large swath of the media engaged in a mass act of self-deception and partisan group think. Perhaps it was Watergate envy, or bitterness over Donald Trump's victory, or antagonism toward Republicans in general—or, most likely, a little bit of all the above.
But now that special counsel Robert Mueller has delivered his report on Russian collusion, it's clear that political journalists did the bidding of those who wanted to de-legitimatize and overturn Trump's election.
The natural disposition of journalists—even opinion journalists—should be skepticism. Like him or not, the notion that the president of the United States, a wealthy showman who's been in the limelight for decades, and ran one of the most chaotic major political campaigns in history, had been secretly conspiring with Russia to steal a 50-state election should immediately have been deemed too good to be true by any decent journalist.
No one, of course, will take responsibility for two years of panic-driven coverage. Margaret Sullivan, a media columnist at The Washington Post, argues that mainstream journalists—not the ones who were rightly dubious about the collusion narrative—should be "proud" of the exemplary job they did.
Sullivan might not be aware that it was The Washington Post that helped ignite the Russia scare with two "scoops" that turned out to be completely false in early 2017: the first, a piece on Russia hacking a Vermont power grid and the other a breathless story about the reach of Putin's fake news operations.
He would know. Not only was feverish speculation regularly batted around on CNN panels, but also most of the network's anchors allowed guests to perpetuate conjecture without any pushback.
In reality, The New York Times and other outlets have hurt their ability to expose the actions of the powerful, because, even when they're right, many Americans will have a difficult time believing them. And the powerful will certainly have an a lot easier time dismissing them as tools of the Democratic Party.
Polls show that a majority of Democrats still believe Russians tampered with their votes. The 2016 election wasn't Pearl Harbor. It isn't the worst attack on America since 9/11. You have free will. No one determines your vote but you. In truth, Russia didn't really "meddle" in the election. Russia, as it has always done and always will, tried to meddle in the American election.
Must-See Moments: Is the Media Making Much Ado About Nothing?


Today's e-session comes to us by the fabuloso and incredibly energetic husband and wife team of Cathy and David Photography. The first part of the session involves Summer and Ryan's love of all things Indiana U... the stadium of their alma mater makes a great session locale for this sports-loving couple! But the session doesn't end there... as nighttime befalls, urban details and ambient lighting take over and even though the sun has set, the heat has risen!
I hope you find these e-shots as cool and inspiring as I do... ENJOY!
to see ALL of the shots... head on over to Cathy and Davi'd blog for more!
Oooh, I love the stadium shot!! :) So awesome to see an e-session take place at night. I don't see that very often.
FH and I are IU alums too... how awesome. If we were closer to bloomington, we'd definitely go back there for pics... that gazebo... i had a friend propose to his girlfriend our first semester there :) Super sweet! Brings back a rush of GREAT memories!


Complex password requirements. Those wonderful things which motivate users to write their passwords on sticky notes and place them under the keyboard, or store them in text files on their desktop. 2 uppercase, 2 lowercase, 2 numbers, 2 special characters, and a minimum length of 10. This is a complex standard used by many organizations as the minimum requirement for user passwords. What's the best way to crack these complex passwords? Brute forcing would be infeasible given a time limit, most word lists are full of patterns which don't meet the criteria, and none of JTR's built-in filters and rule sets are designed to specifically attack complex passwords. That was the discussion that Mark Baggett of PaulDotCom and I were having when we decided to write our own JTR filters to build word lists for cracking complex passwords. Props to Korelogic for their kick as "Crack Me If You Can" password generation rules which we used as our baseline. Here's how it went.
First, Mark pulled a rule out of the Korelogic rule set and began to rebuild it to meet specs. He passed it on to me and I finished the particulars. Below is the resulting filter. We added it to the bottom of our john.conf file.
This gave us a list of 988,057 passwords. Much better. Time to crack some hashes. But... not so fast. Unfortunately, neither of us currently have legal authority to pentest a network which enforces a complex standard such as this. Therefore, if anyone reading this does, or has a decent set of hashes to run it against, I would be interested to hear about the effectiveness of this technique.
Next up, developing our own custom rule set which will intelligently guess the character patterns of complex passwords.


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Hurricane products entrepreneur gives $4M to college
The Rist Family Foundation gave another significant gift in 2020 to the Daveler & Kauanui School of Entrepreneurship.
By Business Observer
| 8:31 a.m. October 28, 2021
Charlotte–Lee–Collier
FORT MYERS — The Rist Family Foundation recently committed to a multiyear, $4 million donation to the Daveler & Kauanui School of Entrepreneurship at Florida Gulf Coast University in Fort Myers.
The donation follows a $1 million gift in 2020 toward construction of the entrepreneurship school's building, Lucas Hall, with a portion designated for the Newton Ethical Leadership Initiative to help expand ethics-based education. The foundation is the charitable arm from Brian Rist and his wife Kim. Brian Rist grew Fort Myers-based hurricane, storm and home protection business Storm Smart into a $73 million company over 25 years. Storm Smart reached record annual sales in 2020 before Rist sold a controlling interest in the business to a Maryland-based private equity firm.
The school's institute, according to a statement, will now be known as the Rist Family Foundation Institute for Entrepreneurship, in recognition of the gift. The institute is responsible for the Daveler & Kauanui School of Entrepreneurship's outward-facing community engagement, including the Runway Program and the Veterans Florida Entrepreneurship Program. The funds will be distributed over several years, focusing on specific needs within the school, the release adds.
"I got involved with the university almost a decade ago," said Brian Rist. "I've watched it grow. I've watched the things they've done and how they've changed Southwest Florida. That motivates my family and me to get more involved."
The Rist Family's contributions to the Daveler & Kauanui School include the Rist Family Foundation Maker Space, designed to help students create products for their businesses. The Maker Space has 3D printers, a laser cutter and virtual reality development kits.
"Support like Brian's is what makes our school so unique," says Sandra Kauanui, director of the school, in a statement. "He is providing the resources that will last a very long time. It will continue to support the school for many years. Having his name on the institute gives us the drive to be as successful as he's been."
The Daveler & Kauanui School of Entrepreneurship recently celebrated the opening of Lucas Hall.
GULF COAST 500: International Smart
Teaching by example
Stoked on entrepreneurship
College's new $10M home for entrepreneurs has bright future
School for entrepreneurs — finally — gets a place to call home
Nonprofit adds trio to business hall of fame
Construction begins on $8 million college entrepreneurship center
A storm-worthy business model


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Q: Which prepositions to use with airline and flight number when speaking of arrival and departure? I was filling a certain application form and wasn't sure which preposition to use:

Arrival via Lufthansa LH 470
Arrival with Lufthansa LH 470
Arrival by Lufthansa LH 470

I tried googling and all of them seem to be used equally.
Which preposition should be used here? Can the same preposition be used with Departure?

A: I would use on. For example:

Departed on BA flight 372, returning on Lufthansa LH470.

redpajama_set_name: RedPajamaStackExchange


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Surprise, Surprise! 5 CIA Challenge Coin Mottos
Jesse Daugherty - Aug 20, 2019
If you have ever seen or received a CIA challenge coin, you already know the various mottos, special projects, and intelligence initiatives these coins can reflect.
While many of these projects are classified, and as such won't ever be on a coin, with a little searching, you shouldn't have any trouble finding a CIA coin on sale or display on the internet.
However, given the nature of the organization and what it does, many of these coins may seem a little bit cryptic. Today we take a look at a few slogans and mottos we've seen on CIA coins.
"The Work of a Nation. The Center of Intelligence."
This is the official CIA motto. The slogan reflects the core principles of the Central Intelligence Agency. These principles are also reflected in the vision, found on the CIA website: "One Agency. One community. An Agency unmatched in its core capabilities, functioning as one team, fully integrated into the Intelligence Community."
The official CIA motto and vision reflect the agency's place in the American political system. It's a reasonably well-known motto; however, the CIA's unofficial motto is perhaps even more famous.
"And ye shall know the truth, and the truth shall make you free."
The CIA's unofficial motto is a quote from the Bible – John 8:32, to be specific. The verse features heavily in CIA memorabilia, even engraved on the wall at the CIA headquarters in Langley, Virginia.
The verse is an appropriate motto for the CIA, which expends plenty of time, resources, and manpower bringing the truth to light. When the headquarters was undergoing construction in the 1950s and '60s, Director of Central Intelligence Allen Dulles insisted on including the verse on the wall.
Since then, the verse has greeted CIA employees and visitors and displays the ideals of the agency proudly for everyone to see.
"We accomplish what others cannot accomplish and go where others cannot go."
This phrase is part of the CIA's mission statement. It has become a common sight on CIA challenge coins. The phrase showcases the CIA's role in world politics and what it stands for as an agency.
The CIA handles sensitive missions and hazardous tasks that are not suited for civilians or the military. Agents do tasks that others are not able to do, in areas that are dangerous or closed off to most individuals.
This statement adds an aura of mystery and intrigue to the agency that many major Hollywood films or popular television shows aim to capture.
"Nowhere to Hide."
A coin featuring this particular slogan was the topic of a fairly popular Reddit post a few years back.
The phrase "Nowhere to Hide" emphasizes the reach and influence the CIA has around the globe. The CIA is known for having spies embedded in many countries, while also using top-of-the-line surveillance technology. There truly is nowhere to hide, no matter where you go!
"We Shall Reap What Ye Have Sown."
This is undoubtedly one on the cryptic side, especially when paired with a Punisher skull in a jester hat. This CIA challenge coin, as shown in a Washington Post article.
We interpret the phrase to mean that no matter what you do or where you go, the CIA can find out. Regardless of what you "sow," the CIA will "reap" it!
These are just a few examples of CIA challenge coins, though we've heard stories of even more coins out there. It's no surprise that people tend to hold on to a CIA coin when they receive one. They are rare and difficult to come by. Do you have a CIA challenge coin you'd like to share? Share it on Facebook! We'd love to see it!
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LAnnapolis Symphony Orchestra (ASO), con sede ad Annapolis, nel Maryland, è operativa dal 1962. Il suo fondatore fu Kenneth W. Page, un rispettato leader cittadino nell'area di Annapolis negli anni '60, che fu anche direttore musicale del Banda della scuola superiore di Annapolis. L'ASO ha ospitato ospiti come il violinista cubano Guillermo Perch e Charlie Byrd. Ha ispirato il compositore David Ott a creare lAnnapolis Overture, che fu eseguita in anteprima nel 1993. José-Luis Novo è il direttore musicale dalla stagione 2005-2006.

Per 50 anni l'Annapolis Symphony Orchestra è stata la principale organizzazione di arti dello spettacolo nella capitale del Maryland. Costituita nel 1962 l'ASO comprende ora 70 musicisti professionisti che eseguono una varietà di musica sinfonica per il pubblico di tutte le età. È la più grande organizzazione artistica nella contea di Anne Arundel. Attualmente l'ASO produce e presenta cinque concerti della Lexus Classic Series, quattro concerti Education, due concerti Family, un concerto Holiday Pops e un concerto gratuito al Park Community Concert. I Pops in the Park e due concerti congiunti con la United States Naval Academy portano l'ASO all'attenzione di migliaia di nuovi membri del pubblico ogni anno. L'ASO sponsorizza numerosi programmi di educazione e sensibilizzazione premiati nelle scuole della comunità, raggiungendo migliaia di bambini delle scuole di diversa estrazione. Con un budget di $ 1 milione all'anno, l'ASO è governata da un Consiglio di fondazione volontario di 25 membri e gestito da uno staff professionale di cinque dipendenti a tempo pieno e guidato dal famoso direttore musicale, José-Luis Novo. I precedenti direttori musicali sono stati Leslie Dunner (1998-2003), Gisele Ben-Dor (1991-1997), Peter Bay (1983-1990) e Leon Fleisher (1970-1982).

Bibliografia

Collegamenti esterni

Gruppi musicali costituitisi nel 1962
Orchestre statunitensi
Annapolis

redpajama_set_name: RedPajamaWikipedia


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For further information about members of our leadership team, or about Pearson College UWC, please visit our Media Kit or contact: communications@pearsoncollege.ca.
Interim President and Head of College Tyrone Pile
Tyrone (Ty) Pile, CMM, MSC, CD, was appointed Interim President and Head of College effective January 1, 2020. During this appointment, he retains his role as Vice-President, Operations and Administration, which he assumed in June 2016. As Vice-President, Ty leads all internal non-academic operations including administration, strategic and organizational planning, finance, legal, infrastructure maintenance and renewal, information technology, facilities revenue and strategic and operational planning. Over a 35-year career with the Royal Canadian Navy, he led at every level from Captain at sea to Flag Officer and Member of Armed Forces Council, the most senior executive body at Canada's Department of National Defence. In his most senior posts, he served as Chief of Military Personnel, responsible for all Human Resources in the Canadian Forces, as well as Commander Canadian Fleet Atlantic (2003-5) and Commander Pacific Naval Forces (2007-10), which included joint responsibility with the RCMP for public security and safety for the Vancouver 2010 Olympic and Paralympic Games.


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Home Video 360° Bryce Canyon
Bryce Canyon National Park is a National Park located in southwestern Utah in the United States. The major feature of the park is Bryce Canyon, which despite its name, is not a canyon, but a collection of giant natural amphitheaters along the eastern side of the Paunsaugunt Plateau.
Bryce Canyon National Park is a National Park located in southwestern Utah in the United States. The major feature of the park is Bryce Canyon, which despite its name, is not a canyon, but a collection of giant natural amphitheaters along the eastern side of the Paunsaugunt Plateau. Bryce is distinctive due to geological structures called hoodoos, formed by frost weathering and stream erosion of the river and lake bed sedimentary rocks. The red, orange, and white colors of the rocks provide spectacular views for park visitors. Bryce sits at a much higher elevation than nearby Zion National Park. The rim at Bryce varies from 8,000 to 9,000 feet (2,400 to 2,700 m).
The Bryce Canyon area was settled by Mormon pioneers in the 1850s and was named after Ebenezer Bryce, who homesteaded in the area in 1874. The area around Bryce Canyon became a National Monument in 1923 and was designated as a National Park in 1928. The park covers 35,835 acres (55.992 sq mi; 14,502 ha; 145.02 km2) and receives substantially fewer visitors than Zion National Park (nearly 4.3 million in 2016) or Grand Canyon National Park (nearly 6 million in 2016), largely due to Bryce's more remote location. In 2016, Bryce Canyon received 2,365,110 recreational visitors, representing an increase of 35% from the prior year.
SOURCENational Geographic / Youtube
First sensor of Earth's magnetic field in an animal
A look into the evolution of the eye
'Monstrous' new Russian saber-tooth fossils clarify early evolution of mammal lineage
Researchers Uncover Formation Mechanism of Giant Aktogai Cu Deposit
Fossil discovery: Extraordinary 'big-mouthed' fish from Cretaceous Period
Earthquake risk elevated with detection of spontaneous tectonic tremor in Anza...
Tell-Tale Toes Point to Oldest-Known Fossil Bird Tracks from Australia
New 'Embryonic' Subduction Zone Found


MI6 has articles about the new James Bond movie, a new version of Casino Royale, without Pierce Brosnan.
It's a bit unfortunate that Brosnan didn't have a clear "last movie" -- and it's also unfortunate that the recent movies haven't quite been able to live up to their potential. James Bond shouldn't really need to be the effects driven action movie -- there needs to be wit and cleverness. Fortunately, Goldeneye is one of the better Brosnan ones, so while we don't have Brosnan anymore, there's hope that we'll have another fresh start that will be good for the franchise.
Oddly, I felt for a while that James Bond was becoming increasingly difficult to justify in a modern setting, but I'm not sure. It's difficult to say how he actually fits into the modern era. Sometimes I wonder if doing them as period 1950s or 1960s pieces wouldn't be more interesting.


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Hispanic Business Owners "˜Leading the Charge'
Rosa RamirezNational Journal
Hispanic business owners are front and center during this election cycle, and the pattern continues as presidential nominee Mitt Romney speaks on Monday at the U.S. Hispanic Chamber of Commerce's annual convention in Los Angeles, the largest gathering of Hispanic business leaders.
These entrepreneurs, who are starting small businesses at twice the rate of the national average, have become part of mainstream America, said former Commerce Secretary Carlos Gutierrez, who served under the George W. Bush administration between 2005 and 2009.
One Forbes article projects that Hispanic business owners will have contributed nearly 8 percent each year to the economy over the 10-year span ending in 2015. In Florida, New Mexico, and Texas, Latino-owned enterprises account for 20 percent of all businesses.
"In many ways, they're sort of leading the charge," Gutierrez said. He added that Hispanic business owners are concerned about tax increases, more regulations, and how the Affordable Care Act may affect their businesses. "If they reach the point when they have 50 employees, they will be subject to onerous regulations. They're saying, "˜Look there's no incentive to become larger.' "
"The No. 1 issue [for Hispanic business owners] is the economy, jobs, education," Gutierrez said during a phone interview on Saturday, shortly after a town-hall meeting targeted to Hispanic business owners in North Carolina. An estimated 60 to 70 people attended the event, he said.
A recent PNC Bank survey found that nine of 10 Hispanic business owners say they are optimistic about the future of their companies. Nearly 70 percent said they feel their business is meeting or exceeding their expectations; 77 percent said they expect their businesses to grow in the next two years.
But a large number — seven of 10 — said they also plan to save money or reduce capital spending in the next six months due to the uncertain economy.
"Hispanic business owners are taking a conservative approach to managing their business as they hunker down in the current economy," Jesus Munoz, vice president of business banking at PNC, said in a statement. About 15 percent of Hispanic business owners surveyed said they planned to reduce staffing.
Gutierrez, along with Romney's son Craig and Hispanic business leaders, has been touring the country to sway Hispanics to vote for Romney.
Below are some insights from the Bureau of Business Research into Hispanic-owned businesses in Texas.
Business growth explosion
Hispanic-owned businesses in Texas grew at rapid pace. Between 2002 and 2007, these enterprises increased by 40.2 percent to 447,589. Still, these Hispanic companies lag in receipt sales, employment, and payrolls. Average gross sales receipts for Hispanic businesses was $134,287, significantly lower than those of mainstream firms at $546,228.
Management and communication skills gap
What limits growth of many Hispanic business owners in Texas is a lack of management and communications skills. As a result, many of their businesses remain small. About 59 percent have no more than nine workers (47 percent had four employees; 22 percent five to nine workers).
Hispanics build their negocios from scratch
The vast majority of Hispanic businesspeople in Texas (80 percent) said they started their firms from the ground up. About 9 percent said they purchased their businesses; 3 percent indicated they inherited it.
Most businesses are in service sectors
While 22 percent of Hispanic-owned businesses are in the construction industry, more than 40 percent are in the service sector, such as professional, scientific, and technical services, retail trade, and health care.
Source: Bureau of Business Research report, "Survey of Texas Hispanic-Owned Businesses with Paid Employees."


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New Hampshire Concealed Weapon License Legislation
New Hampshire removes license requirement to carry concealed weapon
Posted February 24, 2017 by Brian L. Champion & filed under Alerts.
On Wednesday, February 22, 2017, Governor Chris Sununu of New Hampshire signed legislation removing the requirement that people get a license to carry concealed guns in the State of New Hampshire, according to a report in the Portland Press Herald. The article goes on to state that the legislation now makes New Hampshire the 12th state, which include Vermont and Maine, that does not require a license or permit to carry concealed guns in most cases.
Most state laws, according to the article, bar anyone who has a restraining order against them or a felony conviction from possessing a gun. The article goes on to state that federal law goes further, also barring anyone convicted of a crime punishable by imprisonment exceeding one year, drug users, certain people with mental illness, or someone convicted of misdemeanor domestic violence, among several other categories in barring concealed gun authorizations.
Employers are still free however, to carefully draft policies prohibiting guns in the workplace during working hours and should give careful consideration to reviewing their current policies to see that they are compliant with the new legislation in New Hampshire and with federal legislation in this regard.


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Good On Paper: Game of Column Inches for Trap's Ireland
Filed under The Blog Reel
IRELAND'S quest for victory in Europe no doubt begins with a great deal of goodwill from the worldwide footballing family.
The fall-out from Paris generated a tremendous amount of sympathy towards the Irish team and fans, most notably from the French people.
However, the saying goes that sentiment means nothing in sport and no one will be more aware of that than Giovanni Trapattoni.
In contrast to his predecessors, Brian Kerr, and especially Steve Staunton, he appears unfazed by the attempts of Ireland's footballing media to ruffle his feathers.
Kerr bore the brunt of our hacks' transformation since the purging in Saipan.
Sworn in on a wave of euphoria, the relationship between the international team manager and the media cooled most spectacularly and culminated in a ludicrous story about Robbie Keane socialising in Dublin on the eve of a vital qualifier against, ironically, France.
Never mind that Keane had management's permission for recreational free-time in his home city, five days before the game; it represented a new low for the press corps.
The vilification and despising of celebrity had firmy taken root in Ireland by this stage, and our football team were still public enemy number one for the journalists who had cast objectivity aside long before by siding either against, or in the main with, Roy Keane, the great torch bearer for the country's rise above mediocrity.
Keane's opinion was, and still is, glorified in the Irish media, and with the FAI gaining no credit for airing its dirty laundry in public with the infamous Genesis report, Irish football was an easy target,
Starved of on-the-field success, it has remained so ever since.
Steve Staunton may have been out of his depth but an outstanding servant to the game in this country deserved more than to be photoshopped as Kermit the Frog under the headline 'Muppet'.
Irish tabloid journalism had take another giant step towards emulating its British counterparts, (remember the Graham Taylor turnip?). Only this week, the British Sun depicted Fabio Capello with donkey's ears below the banner 'Jackass.' Charming.
No doubt the work-experience recruit who was dispatched to the Irish team hotel in Malahide dressed as Miss Piggy to garner a quote from Stan, considers it a career defining moment.
Giovanni Trapattoini will lead his side out against Armenia to embark on what may well be his last job in football, though he has the aura of a man who will never give up the game.
In two year's time, success will be gauged on whether Ireland takes its place among Europe's top nations in Poland and Ukraine.
It may be a dirty, unforgiving, and hostile campaign ahead, and that's regardless of what awaits on the football pitch.
Trapattoni's arrival has not quite shaken off the air of under-achievement surrounding the national side. His style of football has been criticised with the great sages of the RTE panel wavering in their opinion of the Italian after originally championing his appointment.
Throughout the World Cup 2010 odyssey, the Montrose mob were repeatedly accused of being out of touch with the opinion of the viewer, as the team began to win over the hearts of the disaffected. The play-off game against France finally brought the national team back into the bosom of Irish public adoration.
Written off by everybody, including a football media seemingly revelling in their misfortunes and inability to raise the interest of the nation, the Boys In Green went to Paris and did what no one said they could do; they beat France.
Yet the renowned Tom Humphries' wrote:
We're not going to South Africa and as such we won't have to suffer the sight of our botoxed, ruined selves in the mirror
"SO, LISTEN. We ain't going to South Africa. All through the qualifier series we rode our luck like hobos on a freight train to the sun. Giovanni's side got tighter and more organised and we all came to believe in miracles. And then the ball plinked into the Irish net on Saturday night from a deflection off Sean St Ledger's derriere. Ah well"
Humphries wrote this after the first leg. Half-time.
The cynics will say he was right but his attitude was all wrong. The statistics will say Ireland lost the game, but sport is not about numbers. That's for accountants.
Trapattoni now leads a squad that commands greater attention.
The perfomance in extra-time, regardless of the infamous Hand of Gaul, stirred the passion of the Irish people once more. The fighting spirit, so derided by football journalists and mouth-pieces as a meaningless platitude, but the ultimate accolade when praising our GAA and rugby stars, has always been what inspires followers of Irish sport.
An ageing squad and a lack of ambition could make life very difficult for Ireland, wrote Dion Fanning this week, setting his stall out for the tone he will adopt this year.
A minor health scare had the Irish Indepdent writing Trapattoni's obituary.
"Even if there is absolute confidence that the problem will be sorted, it's a worrying state of affairs for his employers. There is always an element of risk associated with appointing a senior citizen"
The same newspaper cannot get over its apparent hard-on for the never-ending Stephen Ireland saga.
Ireland hints at return
"THE door remains open for Stephen Ireland's return to international football, but the Aston Villa new boy failed to give the impression that he fancies a fresh start with his country as the topic sprung back onto the agenda yesterday."
So basically, he didnt hint at a return at all.
Two days later, the headline read;
Ireland's refusal to answer call won't trouble Trap's sleep
Remember this is a story that had Trapattoni rolling his eyeballs at the very mention of the player's name at press conferences and Liam Brady having to step in and for all the world ask, "are you still going on about Stephen Ireland?
Eamon Dunphy in his anyalysis of his British counterparts recently suggested that in Ireland, the fans are treated with respect because they are knowledgable and intelligent.
It would be nice if we saw some more of that line of thought this time around.
Eamonn Sweeney gives us hope with his anaylsis of the Stephen Ireland question.
"I'm embarrassed by the fact that this time last year I thought the long-term future of the Irish team was bound up with the whim of Irish sport's most talented toddler.
Good luck at Villa, Stephen. Get back to us when you grow up."
Let's hope, along with our players, some others also mature on the road ahead.
If objectivity means nothing, then we should be all on the same team, right?
First posted, Sept 2010
Brian Kerr Dion Fanning Eamonn Sweeney Media Comment Roy Keane RTE panel Stephen Ireland Tom Humphries Trapattoni


This beautiful, but very, very rough river is a little bit isolated, but all the more wildly lovely for it. If you can enjoy the scenery, that is, while your raft leaps these mad rapids.
The stretch runs from Highway 32 to Route 99. The put in is near Red Bluff.


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is Rythem's fifteenth (sixteenth overall) single and the duo's first double-A side and collaboration single (with the J-pop duo, Kimaguren). It was released on July 23, 2008 under Sony Music Entertainment Japan label. Love Call is used as an ending theme for the Nippon TV's drama Sukkiri!! making this the group's second tie-in with the said drama. Akari no Arika is also used as an ending theme for TV Tokyo's drama entitled .

The single comes in a limited (with DVD containing the PV for Love Call) and regular edition (CD only). The item's stock numbers are AICL-1953 (Limited Edition) and AICL-1954 (Regular Edition).

Limited Edition Track listing
Love Call
Composition: Yui Nītsu & ISEKI
Arrangement: TAICHI MASTER
Lyrics: Yui Nītsu
Akari no Arika
Composition/Lyrics: RYTHEM
Arrangement: Shin Kouno
Love Call (instrumental)
Akari no Arika (instrumental)

Charts and sales

2008 singles
Rythem songs
2008 songs
Sony Music Entertainment Japan singles


Emami E et al. Int J Dent 2013; 2013:498305.
UNFPA and HelpAge International. Ageing in the twenty-first century: a celebration and a challenge. Available at: https://www.unfpa.org/sites/default/files/pub-pdf/Ageing%20report.pdf [last accessed February 2018].


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Spotlight Powerpoint Template Free spotlight powerpoint template free spotlight. spotlight powerpoint template free free spotlight powerpoint templates myfreeppt download. spotlight powerpoint template free download stadium lights powerpoint template 000019 buy printable. spotlight powerpoint template free smart city powerpoint template poweredtemplate authorstream. spotlight powerpoint template free free spotlight picture free download clip art free clip art printable.


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MAFS' Cathy Evans Reveals Incredible Weight Loss on Keto Diet
By Bradley Bell
The reality star is proud of her body transformation after losing a staggering 16kg!
The 27 year-old's weightless took place before her Married at First Sight TV debut.
She told OK! magazine that her weight was up to 78kg before she embarked on her Keto diet.
Sharing that she is only 152cm tall, Cathy said, "I was that weight [78kg] for around nine months. So it was a lot for me."
Photo by: @summertanx / Instagram
Cathy says she put on the weight when she moved to Australia from New Zealand after a bad break-up.
The infamous high fat, low-carb Ketogenic diet is the key to her success.
The Ketogenic diet is known for putting your body in a metabolic state of ketosis allowing the body to burn fat and not carbohydrates.
The logistics investigator says the Keto diet helped her with the initial weight loss but she had to work hard at regular exercise to maintain her ideal weight.
"I walk to work and I take the stairs rather than a lift where possible. I also eat lots of vegetables with every meal," Cathy shared.
After losing her goal weight, she embarked on a bodybuilding journey, getting down to a mere 49kg while entering competitions!
The gorgeous Kiwi is 'married' to 28-year-old truck driver, Josh Pihlak on Married At First Sight.
https://www.instagram.com/p/B8H06mbDkNs/
Love this? You'll love this bit from Will & Woody – catch the boys weekdays from 4PM on KIIS 1065
Cathy Evans MAFS married at first sight


I live in California with clients in Central and Eastern time zones, so I often wake up and feel like I need to catch up. On these mornings, I wake up with a to-do list already running through my head of all of the things that need to get done today.
I get up, brew a pot of coffee and jump back into bed. Then I grab my laptop (which is never more than a foot away from me – sick, I know) and I get to work. I start with my eastern-most client, and work my west, pausing my Freshbooks timer to put ponytails in hair, refill my coffee and sign off on homework.
I LOVE being able to work from bed home.
So, what's on my to-do list today?
Find an affordable fan page designer – do you know someone?


text:
package org.apache.cloudstack.api.response;

import com.google.gson.annotations.SerializedName;

import org.apache.cloudstack.api.BaseResponse;

import com.cloud.serializer.Param;

public class SuccessResponse extends BaseResponse {
    @SerializedName("success")
    @Param(description = "true if operation is executed successfully")
    private Boolean success = true;

    @SerializedName("displaytext")
    @Param(description = "any text associated with the success or failure")
    private String displayText;

    public Boolean getSuccess() {
        return success;
    }

    public void setSuccess(Boolean success) {
        this.success = success;
    }

    public String getDisplayText() {
        return displayText;
    }

    public void setDisplayText(String displayText) {
        this.displayText = displayText;
    }

    public SuccessResponse() {
    }

    public SuccessResponse(String responseName) {
        super.setResponseName(responseName);
    }
}

redpajama_set_name: RedPajamaGithub


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Disentangling Data Science, every Tuesday
paulvanderlaken.com
Tag: clustering
Visualizing the inner workings of the k-means clustering algorithm
Originally, I wrote this blog to share this interactive visualization of the k-means algorithm (wiki) which I was all enthusiastic about.
However, then I imagined that not everybody may be familiar with k-means, hence, I wrote the whole blog below.
Next thing I know, u/dashee87 on r/datascience points me to these two other blogs that had already done the same… but way better! These guys perfectly explain k-means, alongside many other clustering algorithms. Including interactive examples and what not!!
Seriously, do not waste your time on reading my blog below, but follow these links. If you want to play, go for Naftali's apps. If you want to learn, go for David's animated blog.
Naftali Harris built this fantastic interactive app where you can run the k-means algorithm step by step on many different datasets. You can even pick the clusters' starting locations yourself! Next to this he provides a to-the-point explanation of the inner workings. On top of all this, he built the same app for the DBSCAN algorithm (wiki), so you can compare the algorithms' performance… Insane!
David Sheehan (yes, that's dashee) is more of a Python guy and walks you through the inner workings of six algorithmic clustering approaches in his blog here. Included are k-means, expectation maximization, hierarchical, mean shift, and affinity propagation clustering, and DBSCAN. David has made detailed step-wise GIF animations of all these algorithms. And he explains the technicalities in a simple and understandable way. On top of this, David shared his Jupyter notebook to generate the animations, along with a repository of the GIFs themselves. Very well done! Seriously one of the best blogs I've read in a while.
Let me walk you through what k-means is, why it is called k-means, and how the algorithm interally works, step by step.
What is K-means?
K-means (wiki) is a clustering algorithm.
Let me dissect that sentence for you, starting at the back.
Simply put, an algorithm (wiki) is a set of task instructions to be followed. Often algorithms are perform by computers, and used in calculations or problem-solving. Basically, an algorithm is thus not much more than a sequence of task instructions to be followed by a computer — like a cooking recipe.
In machine learning (wiki), algorithmic tasks are often divided in supervised or unsupervised learning (let's skip over reinforcement learning for now).
The learning part reflects that algorithms try to learn a solution — learn how to solve a problem.
The supervision part reflects whether the algorithm receives answers or solutions to learn from. For supervised learning, examples are required. In the case of unsupervised learning, algorithms do not learn by example but have figure out a proper solution themselves.
Clustering (wiki) is essentially a fancy word for grouping. Clustering algorithms seek to group things together, and try to do so in an optimal way.
Group things. As long as we can represent things in terms of data, clustering algorithms can group them. We can group cars by their weight and horsepower, people by their length and IQ, or frogs by their slimyness and greenness. These things we would like to group, we often call observations (depicted by the letter N or n).
Optimal way. Clustering is very much an unsupervised task. Clustering algorithms do not receive examples to learn from (that's actually classification (wiki)). Algorithms are not told what "good" clustering or grouping looks like.
Yet, how do clustering algorithms determine the optimal way to group our observations then?
Well, clustering algorithms like k-means do so by optimizing a certain value. This value is reflected in an algorithm's so-called objective function.
k-means' objective function is displayed below. In simple language, k-means seeks to minimize (= optimize) the total distance of observations (= cases) to their group's (= cluster) center (= centroid).
Fortunately, you can immediately forget this function. For now, all you need to know is that algorithms often repeat specific steps of their instructions in order for their objective function to produce a satisfactory value.
K-means objective function (via)
Why is k-means called k-means?
Hurray, we can now move to the k-means algorithm itself!
Why is it called k-means in the first place?
Let's start at the front this time.
k. The k in k-means reflects the number of groups the algorithm is going to form. The algorithm depends on its user to specify what number of k should be. If the user picks k = 2 for instance, the k-means algorithm will identify 2 group by their 2 means.
As I said before, clustering algorithms like k-means are unsupervised. They do not know what good clustering looks like. In the case where the used specified k = 2, the algorithm will seek to optimize its objective function given that there are 2 groups. It will try to put our observations in 2 groups that minimizes the total distance of the observations to their group's center. I will how that works visually later in this blog.
means. The means in k-means reflects that the algorithm considers the mean value of a cluster as its cluster center. Here, mean is a fancy word for average. Again, I will visualize how this works later.
You might have guessed it already: there are indeed variations of the k-means algorithm that do not use the cluster means, but, for instance, the cluster medians (wiki) or mediods (wiki). These algorithms are only slight modifications of the k-means algorithm, and simply called the k-medians (wiki) and k-medoids algorithms (wiki).
How does k-means work?
Finally, we get to the inner workings of k-means!
The k-means algorithm consists of five simple steps:
Obtain a predefined k.
Pick k random points as cluster centers.
Assign observations to their closest cluster center based on the Euclidean distance.
Update the center of each cluster based on the included observations.
Terminate if no observations changed cluster, otherwise go back to step 3.
Easy right?
OK, I can see how this may not be directly clear.
Let's run through the steps one by one using an example dataset.
Example dataset: mtcars
Say we have the dataset below, containing information on 32 cars.
We can consider each car a separate observation. For each of these observations, we have its weight and its horsepower.
These characteristics — weight and horsepower — of our observations are the variables in our dataset. Our cars vary based on these variables.
Mazda RX4 1188 110
Mazda RX4 Wag 1304 110
Datsun 710 1052 93
Hornet 4 Drive 1458 110
Hornet Sportabout 1560 175
Valiant 1569 105
Duster 360 1619 245
Merc 240D 1447 62
Merc 230 1429 95
Merc 280 1560 123
Merc 280C 1560 123
Merc 450SE 1846 180
Merc 450SL 1692 180
Merc 450SLC 1715 180
Cadillac Fleetwood 2381 205
Lincoln Continental 2460 215
Chrysler Imperial 2424 230
Fiat 128 998 66
Honda Civic 733 52
Toyota Corolla 832 65
Toyota Corona 1118 97
Dodge Challenger 1597 150
AMC Javelin 1558 150
Camaro Z28 1742 245
Pontiac Firebird 1744 175
Fiat X1-9 878 66
Porsche 914-2 971 91
Lotus Europa 686 113
Ford Pantera L 1438 264
Ferrari Dino 1256 175
Maserati Bora 1619 335
Volvo 142E 1261 109
Visually, we can represent this same dataset as follows:
Step 1: Define k
For whatever reason, we might want to group these 32 cars.
We know the cars' weight and horsepower, so we can use these variables to group the cars. The underlying assumption being that cars that are more similar in terms of weight and/or horsepower would belong together in the same group.
Normally, we would have smart or valid reasons to expect a specific number of groups among our observations. For now, let's simply say that we want to put these cars into, say, 2 groups.
Well, that's step 1 already completed: we defined k = 2.
Step 2: Initialize cluster centers
In step 2, we need to initialize the algorithm.
Everyone needs to start somewhere, and the default k-means algorithm starts out super naively: it just picks random locations as our starting cluster centers.
For instance, the algorithm might initialize cluster 1 randomly at a weight of 1000 kilograms and a horsepower of 100.
We can use coordinate notation [x; y] or [weight; horsepower] to write this location in short. Hence, the initial random center of cluster 1 picked by the k-means algorithm is located at [1000; 100].
Randomly, k-means could initialize cluster 2 at a weight of 1500kg and a horsepower of 200. It's intitial center is thus at [1500; 200].
Visually, we can display this initial situation like the below, with our 32 cars as grey dots in the background:
Now, step 2 is done, and our k-means algorithm has been fully initialized. We are now ready to enter the core loop of the algorithm. The next three steps — 3, 4, and 5 — will be repeated until the algorithm tells itself it is done.
Step 3: Assignment to closest cluster
Now, in step 3, the algorithm will assign every single car in our dataset to the cluster whose center is closest.
To do this, the algorithm looks at every car, one by one, and calculate its distance to every of our cluster centers.
So how would this distance calculating thing work?
The algorithm starts with the first car in our dataset, a Mazda RX4.
This Mazda RX4 weighs 1188kg and has 110 horsepower. Hence, it is located at [1188; 110].
As this is the first time our k-means algorithm reaches step 3, the cluster centers are still at the random locations the algorithm has picked in step 2.
The k-means algorithm now calculates the Euclidean distance (wiki) of this Mazda RX4 data point [1188; 110] to each of the cluster centers.
The Euclidean distance is calculated using the formula below. The first line shows you that the Euclidean distance is the square root of the squared distance between two observations. The second line — with the big Greek capital letter Sigma (Σ) — is a shorter way to demonstrate that the distance is calculated and summed up for each of the variables considered.
Again, please don't mind the formula, the Euclidean distance is basically the length of a straight line between two data points.
So… back to our Mazda RX4. This Mazda is one of the two observations we need to input in the formula. The second observation would be a cluster center. We input both of the location of our Mazda [1188; 110] and that of a cluster center –, say cluster 1's [1000; 100] — in the formula, and out comes the Euclidean distance between these two observations.
The Euclidean distance of our Mazda RX4 to the center of cluster 1 would thus be √((1118 – 1000)2 + (110 – 100)2), which equals 192.2082 — or a rounded 192.
We need to repeat this, but now with the location of our second cluster center. The Euclidean distance from our Mazda RX4 to the center of cluster 2 would be √((1118 – 1500)2 + (110 – 150)2), which equals 314.5537 — or a rounded 314.
Again, I visualized this situation below.
You can clearly see that our Mazda RX4 is closer to cluster center 1 (192) than to cluster center 2 (314). Hence, as the distance to cluster 1's center is smaller, the k-means algorithm will now assign our Mazda RX4 to cluster 1.
Subsequently, the algorithm continues with the second car in our dataset.
Let this second car be the Mercedes 280C for now, weighing in at 1560 kg with a horsepower of 153.
Again, the k-means algorithm would calcalute the Euclidean distance from this Mercedes [1560; 153] to each of our cluster centers.
It would find that this Mercedes is located much closer to cluster 2's center (560) than cluster 1's (65).
Hence, the k-means algorithm will assign the Mercedes 280C to cluster 2, before continuing with the next car…
and the next car after that…
and the next car…
… until all cars are assigned to one of the clusters.
This would mean that step 3 is completed. Visually, the situation at the end of step 3 will look like this:
Step 4: Update the cluster centers
Now, in step 4, the k-means algorithm will update the cluster centers.
As a result of step 3, there are now actual observations assigned to the clusters. Hence, the k-means algorithm can let go of its naive initial random guesses and calculate the actual cluster centers.
Because we are dealing with the k-means algorithm, these centers will be based on the mean values of the observations in each group.
So for each cluster, the algorithm takes the observations assigned to it, and calculates the cluster's mean value for every variable. In our case, the algorithm thus calculates 4 means: the average weight and the average horsepower, for each of our two clusters.
For cluster 1, the average weight of its cars is a rounded 939 kg. Its average horsepower is approximately 84. Hence the cluster center is updated to location [939; 84]. Cluster 2's mean values come in at [1663; 171].
With the cluster centers updated, the k-means algorithm has finished step 4. Visually, the situation now looks as follows, with the old cluster centers in grey.
Step 5: Terminate or go back to step 3.
So that was actually all there is to the k-means algorithm. From now on, the algorithm either terminates or goes back to step 3.
So how does the k-means algorithm know when it is done?
Earlier in this blog post I already asked "how do clustering algorithms determine the optimal way to group our observations?"
Well, we already know that the k-means algorithm wants to optimize its objective function. It seeks to minimize the total distance of observations to their respective cluster centers. It does so by assigning observations to the cluster whose center is nearest according to the Euclidean distance.
Now, with the above in mind, the k-means algorithm determines that it has reached an optimal clustering solution if, in step 3, no single observation switches to a different cluster.
If that is the case, then every observation is assigned to the group whose center values best represent its underlying characteristics (weight and horsepower), and the k-means algorithm is thus satisfied with the solution given this number of groups (= k). These groups centers now best describe the characteristics of the individual observations at hand, given this k, as evidenced that each observations belongs to the cluster whose center values are closest to their own values.
Now, the k-means algorithm will have to check whether it is done somewhere in its instructions. It seems most logical to directly do this in step 3: quickly check whether every observations remained in its original cluster.
This is not so difficult if you have only 32 cars. However, what if we were clustering 100.000 cars? We would not want to check for 100.000 cars whether they remained in their respective cluster, right? That's a heavy task, even for a computer.
Potentially there is an easier way to check this? Maybe we could look at our cluster centers? We update them in step 4. And if no observations have changed clusters, then the locations of our cluster centers will for sure also not have changed.
Even in our simple example it is less work to see check whether 2 cluster centers have remained the same, than comparing whether 32 cars have not changed clusters.
So basically, that's what we will do in this step five. We check whether our cluster centers have moved.
In this case, they did. As can be seen in the visual at the end of step 4.
This is also to be expected, as it is very unlikely that the algorithm could have randomly picked the initial cluster centers in their optimal locations, right?
We conclude step 5 and, because the cluster center locations have changed in step 4, the algorithm is sent back to step 3.
Let's see how the k-means algorithm continues in our example.
Step 3 (2nd time): Assignment to closest cluster
In step 3, the algorithm reassigns every car in our dataset to the cluster whose center is nearest.
To do this, the algorithm has to look at every car, one by one, and calculate its distance to every of our cluster centers.
Our cluster centers have been updated in the previous step 4. Hence, the distances between our observations and cluster centers may have changed respective to the first time the algorithm performed step 3.
Indeed two cars that were previously closer to the blue cluster 2 center are now actually closer to the red cluster 1 center.
In this step 3, again all observations are assigned to their closest clusters, and two observations thus change cluster.
Visually, the situation now looks like the below. I've marked the two cars that switched in yellow and with an exclamation mark.
The k-means algorithm now again reaches step 4.
Step 4 (2nd time): Update the cluster centers
In step 4, the algorithm updates the cluster centers. Because we are dealing with the k-means algorithm, these centers will be based on the mean values of the observations in each group.
For cluster 1, both the average weight and the average horsepower have increased due to the two new cars. The cluster center thus moves from approximately [939; 84] to [998; 94].
Cluster 2 lost two cars from its cluster, but both were on the lower end of its ranges. Hence its average weight and horsepower have also increased, moving the center from approximately [1663; 171] to [1701; 174].
With the cluster centers updated, step 4 is once again completed. Visually, the situation now looks as follows, with the old centers in grey.
Step 5 (2nd time): Terminate or go back to step 3.
In step 5, the k-means algorithm again checks whether it is done. It concludes that it is not, because the cluster centers have both moved once more. This indicates that at least some observations have changed cluster, and that a better solution may be possible. Hence, the algorithm needs to return to step 3 for a third time.
Are you still with me? We are nearly there, I hope…
In step 3, the algorithm assigns every car in our dataset to the cluster whose center is nearest. To do this, the algorithm looks at every car, one by one, and calculates its distance to each of our cluster centers. As our cluster centers have been updated in the previous step 4, so too will their distances to the cars.
After calculating the distances, step 3 is once again completed by assigning the observations to their closest clusters. Another car moved from the blue cluster 2 to the red cluster 1, highlighted in yellow with an exclamation mark.
Step 4 (3rd time): Update the cluster centers
In step 4, the algorithm updates the cluster centers. For each cluster, it looks at the values of the observations assigned to it, and calculates the mean for every variable.
For cluster 1, both the average weight and the average horsepower have again increased slightly due to the newly assigned car. The cluster center moves from approximately [998; 94] to [1023; 96].
Cluster 2 lost a car from its cluster, but it was on the lower end of its range. Hence, its average weight and horsepower have also increased, moving the cluster center from approximately [1701; 174] to [1721; 177].
With the renewed cluster centers, step 4 is once again completed. Visually, the situation looks as follows, with the old centers in grey.
Step 5 (3rd): Terminate or go back to step 3.
In step 5, the k-means algorithm will again conclude that it is not yet done. The cluster centers have both moved once more, due to one car changing from cluster 2 to cluster 1 in step 3 (3rd time). Hence, the algorithm returns to step 3 for a fourth, but fortunately final, time.
Step 3 (4th time): Assignment to closest cluster
In step 3, the k-means algorithm assigns every car in our dataset to the cluster whose center is nearest. Our cluster centers have only changed slightly in the previous step 4, and thus the distances are nearly similar to last time. Hence, this is the first time the algorithm completes step 3 without having to reassign observations to clusters. We thus know the algorithm will terminate the next time it checks for cluster changes.
Step 4 (4th time): Update the cluster centers
In step 4, the k-means algorithm tries to update the cluster centers. However, no observations moved clusters in step 3 so there is nothing to update.
Step 5 (4th): Terminate or go back to step 3.
As no changes occured to our cluster centers, the algorithm now concludes that it has reached the optimal clustering and terminates.
The clustering solution
With the clustering now completed, we can try to make some sense of the clusters the k-means algorithm produced.
For instance, we can examine how the observations included in each cluster vary on our variables. The density plot below is an example how we could go about exploring what the clusters represent.
We can clearly see what the clusters are made up of.
Cluster 1 holds most of the cars with low horsepower, and nearly all of those with low weights.
Cluster 2, in contrast, includes cars with horsepower ranging from low through high, and all cars are relatively heavy.
We could thus name our clusters respecitvively low horsepower and low weight cars, for cluster 1, and medium to high weight cars, for cluster 2.
Obviously, these clustering solutions will become more interesting as we add more clusters, and more variables to seperate our clusters and observations on.
A word of caution regarding k-means
Normalized input data
In our car clustering example, particularly the weight of cars seemed to be an important discriminating characteristic.
This is largely due to the fact that we didn't normalize our data before running the k-means algorithm. I explicitly didn't normalize (wiki) for simplicity sake and didactic purposes.
However, using k-means with raw data is actually a really easily-made but impactful mistake! Not normalizing data will cause k-means to put relative much importance on variables with a larger variance. Such as our variable weight. Cars' weights ranged from a low 686 kg, to a high 2460 kg, thus spreading almost 1800 units. In contrast, horsepower ranged only from 52 hp to 335 hp, thus spreading less than 300 units.
If not normalized, this larger variation among weight values will cause the calculated Euclidean distance to be much more strongly affected by the weights of cars. Hence, these car weights will thus more strongly determine the final clustering solution simply because of their unit of measurement. In order to align the units of measurement for all variables, you should thus normalize your data before running k-means.
No categorical data
The need to normalize input data before running the k-means algorithm also touches on a second important characteristic of k-means: it does not handle categorical data.
Categorical data is discrete, and doesn't have a natural origin. For instance, car color is a categorical variable. Cars can be blue, yellow, pink, or black. You can really calculate Euclidean distances for such data, at least not in a meaningful way. How far is blue from yellow, further or closer than it is from black?
Fortunately, there are variations of k-means that can handle categorical data, for instance, the k-modes algorithm.
No garuantees
The k-means algorithm does not guarantee to find the optimal solution. k-means is a fairly simple sequence of tasks and its clustering quality depends a lot on two factors.
First, the k specified by the user. In our example, we arbitrarily picked k = 2. This makes the algorithm seek specifically for solutions with two clusters, whereas maybe 3- or 4-cluster solutions would have made more sense.
Sometimes, users will have good theories to expect a certain number of clusters. At other times, they do not and are left to guess and experiment.
While there are methods to assess to some extent the statistically optimal number of clusters, often the decision for k will be somewhat subjective, though strongly affect the clustering solution..
Second, and more imporant maybe, is the influence of the random starting points for the initial cluster centers picked by the algorithm itself.
The clustering solutions that the algorithm produces are very sensitive to these initial conditions.
Due to its random starting points, it is also very likely that every time you run the k-means algorithm you will get different results, even on the same dataset.
To illustrate this, I ran the k-means visualization algorithm I wrote run a dozen of times.
Below is solution number 3 to which the k-means algorithm converged
for our cars dataset. You can see that this is different from our earlier solution where the car at [1450; 270] belonged to the blue cluster 2, whereas here it is assigned to the red cluster 1.
Most k-means algorithms I ran on this dataset of cars resulted in approximately the same solution, like the one above and the one we saw before.
However, the k-means algorithm also produced some very different solutions. Like the one below, number 9. In this case cluster 2 was randomly initiated very muhc on the high end of the weight spectrum. As a result, the cluster 2 center remained all the way on the right side of the graph throughout the iterations of the algorithm.
Or this take this long-lasting iteration, where the k-means algorithm randomly located both cluster centers all the way in the bottom left corner, but fortunately recovered to the same solution we saw before.
One the one hand, the k-means algorithm sequences above illustrate the danger and downside of the k-means algorithm employing its random starting points. On the other hand, the mostly similar solutions produced even by the vanilla algorithm illustrate how the fantasticly simple algorithm is quite sturdy. Moreover, many smart improvements have fortunately been developed to avoid the random stupidity produced by the default algorithm — most notably the k-means++ algorithm (wiki). With these improvements, k-means continues to be one of the simplest though most popular and effective clustering algorithms out there!
Thanks for reading this blog!
While I intended to only share the link to the interactive visualization, I got carried away and ended up simulating the whole thing myself. Hopefully it wasn't time wasted, and you learned a thing or two
Do reach out if you want to know more or are interested in the code to generate these simulations and visuals. Also, feel free to comment on, forward, or share any of the contents!
The cars dataset you can access in R by calling mtcars directly in your R console. Do explore it, as it contains many more variables on these 32 cars.
Some additional k-means resources
Here are some pages you can browse if you're looking to learn more.
k-means Wikipedia page
Basic k-means code in Python
Another k-means implementation in Python
k-means R CRAN manual
Determining the optimal k for k-means in R
Determining the optimal k for k-means in Python
Please suggest any other resources that you found valuable!
Paul van der Laken
affinitypropagation
dbscan
euclidean
expectationmaximization
ggplot2
meanshift
mtcars
unsupervised
unsupervisedlearning
Sentiment Analysis of Stranger Things Seasons 1 and 2
Jordan Dworkin, a Biostatistics PhD student at the University of Pennsylvania, is one of the few million fans of Stranger Things, a 80s-themed Netflix series combining drama, fantasy, mystery, and horror. Awaiting the third season, Jordan was curious as to the emotional voyage viewers went through during the series, and he decided to examine this using a statistical approach. Like I did for the seven Harry Plotter books, Jordan downloaded the scripts of all the Stranger Things episodes and conducted a sentiment analysis in R, of course using the tidyverse and tidytext. Jordan measured the positive or negative sentiment of the words in them using the AFINN dictionary and a first exploration led Jordan to visualize these average sentiment scores per episode:
The average positive/negative sentiment during the 17 episodes of the first two seasons of Stranger Things (from Medium.com)
Jordan jokingly explains that you might expect such overly negative sentiment in show about missing children and inter-dimensional monsters. The less-than-well-received episode 15 stands out, Jordan feels this may be due to a combination of its dark plot and the lack of any comedic relief from the main characters.
Reflecting on the visual above, Jordan felt that a lot of the granularity of the actual sentiment was missing. For a next analysis, he thus calculated a rolling average sentiment during the course of the separate episodes, which he animated using the animation package:
GIF displaying the rolling average (40 words) sentiment per Stranger Things episode (from Medium.com)
Jordan has two new takeaways: (1) only 3 of the 17 episodes have a positive ending – the Season 1 finale, the Season 2 premiere, and the Season 2 finale – (2) the episodes do not follow a clear emotional pattern. Based on this second finding, Jordan subsequently compared the average emotional trajectories of the two seasons, but the difference was not significant:
Smoothed (loess, I guess) trajectories of the sentiment during the episodes in seasons one and two of Stranger Things (from Medium.com)
Potentially, it's better to classify the episodes based on their emotional trajectory than on the season they below too, Jordan thought next. Hence, he constructed a network based on the similarity (temporal correlation) between episodes' temporal sentiment scores. In this network, the episodes are the nodes whereas the edges are weighted for the similarity of their emotional trajectories. In that sense, more distant episodes are less similar in terms of their emotional trajectory. The network below, made using igraph (see also here), demonstrates that consecutive episodes (1 → 2, 2 → 3, 3 → 4) are not that much alike:
The network of Stranger Things episodes, where the relations between the episodes are weighted for the similarity of their emotional trajectories (from Medium.com).
A community detection algorithm Jordan ran in MATLAB identified three main trajectories among the episodes:
Three different emotional trajectories were identified among the 17 Stranger Things episodes in Season 1 and 2 (from Medium.com).
Looking at the average patterns, we can see that group 1 contains episodes that begin and end with neutral emotion and have slow fluctuations in the middle, group 2 contains episodes that begin with negative emotion and gradually climb towards a positive ending, and group 3 contains episodes that begin on a positive note and oscillate downwards towards a darker ending.
– Jordan on Medium.com
Jordan final suggestion is that producers and scriptwriters may consciously introduce these variations in emotional trajectories among consecutive episodes in order to get viewers hooked. If you want to redo the analysis or reuse some of the code used to create the visuals above, you can access Jordan's R scripts here. I, for one, look forward to his analysis of Season 3!
afinn
igraph
jordandworkin
textmining
tidytext
tidyverse
Text Mining: Pythonic Heavy Metal
This blog summarized work that has been posted here, here, and here.
Iain of degeneratestate.org wrote a three-piece series where he applied text mining to the lyrics of 222,623 songs from 7,364 heavy metal bands spread over 22,314 albums that he scraped from darklyrics.com. He applied a broad range of different analyses in Python, the code of which you can find here on Github.
For example, he starts part 1 by calculated the difficulty/complexity of the lyrics of each band using the Simple Measure of Gobbledygook or SMOG and contrasted this to the number of swearwords used, finding a nice correlation.
Lyric complexity relates positive to swearwords used.
Furthermore, he ran some word importance analysis, looking at word frequencies, log-likelihood ratios, and TF-IDF scores. This allowed him to contrast the word usage of the different bands, finding, for instance, one heavy metal band that was characterized by the words "oh yeah baby got love": fans might recognize either Motorhead, Machinehead, or Diamondhead.
Using cosine distance measures, Iain could compare the word vectors of the different bands, ultimately recognizing band similarity, and song representativeness for a band. This allowed interesting analysis, such as a clustering of the various bands:
However, all his analysis worked out nicely. While he also applied t-SNE to visualize band similarity in a two-dimensional space, the solution was uninformative due to low variance in the data.
He could predict the band behind a song by training a one-vs-rest logistic regression classifier based on the reduced lyric space of 150 dimensions after latent semantic analysis. Despite classifying a song to one of 120 different bands, the classifier had a precision and recall both around 0.3, with negligible hyper parameter tuning. He used the classification errors to examine which bands get confused with each other, and visualized this using two network graphs.
In part 2, Iain tried to create a heavy metal lyric generator (which you can now try out).
His first approach was to use probabilistic distributions known as language models. Basically he develops a Markov Chain, in his opinion more of a "unsmoothed maximum-likelihood language model", which determines the next most probable word based on the previous word(s). This model is based on observed word chains, for instance, those in the first two lines to Iron Maiden's Number of the Beast:
Another approach would be to train a neural network. Iain used Keras, which ran on an amazon GPU instance. He recognizes the power of neural nets, but says they also come at a cost:
"The maximum likelihood models we saw before took twenty minutes to code from scratch. Even using powerful libraries, it took me a while to understand NNs well enough to use. On top of this, training the models here took days of computer time, plus more of my human time tweeking hyper parameters to get the models to converge. I lack the temporal, financial and computational resources to fully explore the hyperparameter space of these models, so the results presented here should be considered suboptimal." – Iain
He started out with feed forward networks on a character level. His best try consisted of two feed forward layers of 512 units, followed by a softmax output, with layer normalisation, dropout and tanh activations, which he trained for 20 epochs to minimise the mean cross-entropy. Although it quickly beat the maximum likelihood Markov model, its longer outputs did not look like genuine heavy metal songs.
So he turned to recurrent neural network (RNN). The RNN Iain used contains two LSTM layers of 512 units each, followed by a fully connected softmax layer. He unrolled the sequence for 32 characters and trained the model by predicting the next 32 characters, given their immediately preceding characters, while minimizing the mean cross-entropy:
"To generate text from the RNN model, we step character-by-character through a sequence. At each step, we feed the current symbol into the model, and the model returns a probability distribution over the next character. We then sample from this distribution to get the next character in the sequence and this character goes on to become the next input to the model. The first character fed into the model at the beginning of generation is always a special start-of-sequence character." – Iain
This approach worked quite well, and you can compare and contrast it with the earlier models here. If you'd just like to generate some lyrics, the models are hosted online at deepmetal.io.
In part 3, Iain looks into emotional arcs, examining the happiness and metalness of words and lyrics.
When applied to the combined lyrics of albums, you could examine how bands developed their signature sound over time. For example, the lyrics of Metallica's first few albums seem to be quite heavy metal and unhappy, before moving to a happier place. The Black album is almost sentiment-neutral, but after that they became ever more darker and more metal, moving back to the style to their first few albums. He applied the same analysis on the text of the Harry Potter books, of which especially the first and last appear especially metal.
27 September 2017 3 October 2017
cosinedistance
degeneratestate
emotionalarcs
heavymetal
languagemodels
latentsemanticanalysis
loglikelihoodratio
markov
markovchain
maximumlikelihood
metalness
naturallanguageprocessing
textanalytics
tfidf
Quantifying Gastronomy
A statistical analysis of 4000 recipes and their ingredients: Quantifying Gastronomy
Hierarchical clustering of ingredients. The more to the right an ingredient is, the more common it is.
8 September 2017 16 September 2017
Summarizing our Daily News: Clustering 100.000+ Articles in Python
Andrew Thompson was interested in what 10 topics a computer would identify in our daily news. He gathered over 140.000 new articles from the archives of 10 different sources, as you can see in the figure below.
The sources of the news articles used in the analysis.
In Python, Andrew converted the text of all these articles into a manageable form (tf-idf document term matrix (see also Harry Plotter: Part 2)), reduced these data to 100 dimensions using latent semantic analysis (singular value decomposition), and ran a k-means clustering to retrieve the 10 main clusters. I included his main results below, but I highly suggest you visit the original article on Medium as Andrew used Plotly to generate interactive plots!
Most important words per topic (interactive visual in original article)
The topics structure seems quite nice! Topic 0 involves legal issues, such as immigration, whereas topic 1 seems to be more about politics. Topic 8 is clearly sports whereas 9 is education. Next, Andres inspected which media outlet covers which topics most. Again, visit the original article for interactive plots!
Media outlets and the topics they cover (interactive version in original article)
In light of the fake news crisis and the developments in (internet) media, I believe Andrew's conclusions on these data are quite interesting.
I suppose different people could interpret this data and these graphs differently, but I interpret them as the following: when forced into groups, the publications sort into Reuters and everything else.
Every publication in this dataset except Reuters shares some common denominators. They're entirely funded on ads and/or subscriptions (Vox and BuzzFeed also have VC funding, but they're ad-based models), and their existence relies on clicks. By contrast, Reuters's news product is merely the public face of a massive information conglomerate. Perhaps more importantly, it's a news wire whose coverage includes deep reporting on the affairs of our financial universe, and therefore is charged with a different mandate than the others — arguably more than the New York Times, it must cover all the news, without getting trapped in the character driven reality-TV spectacle that every other citizen of the dataset appears to so heavily relish in doing. Of them all, its voice tends to maintain the most moderate indoor volume, and no single global event provokes larger-than-life outrage, if outrage can be provoked from Reuters at all. Perhaps this is the product of belonging to the financial press and analyzing the world macroscopically; the narrative of the non-financial press fails to accord equal weight to a change in the LIBOR rate and to the policy proposals of a madman, even though it arguably should. Every other publication here seems to bear intimations of utopia, and the subtext of their content is often that a perfect world would materialize if we mixed the right ingredients in the recipe book, and that the thing you're outraged about is actually the thing standing between us and paradise. In my experience as a reader, I've never felt anything of the sort emanate from Reuters.
This should not be interpreted as asserting that the New York Times and Breitbart are therefore identical cauldrons of apoplexy. I read a beautifully designed piece today in the Times about just how common bioluminescence is among deep sea creatures. It goes without saying that the prospect of finding a piece like that in Breitbart is nonexistent, which is one of the things I find so god damned sad about that territory of the political spectrum, as well as in its diametrical opponents a la Talking Points Memo. But this is the whole point: show an algorithm the number of stories you write about deep sea creatures and it'll show you who you are. At a finer resolution, we would probably find a chasm between the Times and Fox News, or between NPR and the New York Post. See that third cluster up there, where all the words are kind of compressed with lower TfIdf values and nothing sticks out? It's actually a whole jungle of other topics, and you can run the algorithm on just that cluster and get new groups and distinctions — and one of those clusters will also be a compression of different kinds of stories, and you can do this over and over in a fractal of machine learning. The distinction here is not the only one, but it is, from the aerial perspective of data, the first.
It would be really interesting to see whether more high-quality media outlets, like the New York Times, could be easily distinguished from more sensational outlets, such as Buzzfeed, when more clusters were used, or potentially other text analytics methodology, like latent Dirichlet allocation.
28 August 2017 16 September 2017
businessindsider
nationalreview
newyorkpost
newyorktimes
plotly
singularvaluedecomposition
talkingpointsmemo
topicmodelling
Beer-in-hand Data Science
Obviously, analysing beer data in high on everybody's list of favourite things to do in your weekend. Amanda Dobbyn wanted to examine whether she could provide us with an informative categorization the 45.000+ beers in her data set, without having to taste them all herself.
You can find the full report here but you may also like to interactively discover beer similarities yourself in Amanda's Beer Clustering Shiny App. Or just have a quick look at some of Amanda's wonderful visualizations below.
A density map of the bitterness (y-axis) and alcohol percentages (x-axis) in the most popular beer styles.
A k-means clustering of each of the 45000 beers in 10 clusters. Try out other settings in Amanda's Beer Clustering Shiny App.
The alcohol percentages (x), bitterness (y) and cluster assignments of some popular beer styles.
Modelling beer's bitterness (y) by the number of used hops (x).
amandadobbyn
densityplot
rstats
AI algorithm application artificialintelligence data datascience datavisualization dataviz deeplearning ggplot2 GIF hr interactive javascript learning machinelearning maps neuralnetwork programming python r research science statistics text textmining tidyverse Tutorial twitter visualization
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Looking for info on Diploma validation in Greece (a.k.a. DIKATSA)?
Athens News Agency: News in English, 07-05-09
Athens News Agency: News in English Directory - Previous Article - Next Article
[01] Papandreou meeting with trade union leaders
[02] Europe Day celebrated
A joint delegation of the General Confederation of Workers of Greece (GSEE) and the civil servants' union (ADEDY) met with main opposition PASOK leader George Papandreou on Wednesday, hours after a similar meeting with new Labour and Social Protection Minister Vassilis Magginas.
During his meeting with the delegation, Papandreou repeated his charge of a "raid" against state-run pension funds' reserves, as the ongoing "bonds affair" dominated talks between the two sides.
The unionists presented their plan for the annulment of a controversial structured bond purchased by the civil servants' supplementary fund, which garnered Papandreou's approval.
Finally, he again called on the government to release a detailed list on similar bond purchases.
Following the joint GSEE-ADEDY meeting with the new minister, Magginas said his goal is to employ a policy of dialogue and understanding with the trade unions based on solving wage-earners' and pensioners' problems.
Magginas was also presented with the unions' plan to return the funds spent for the bond's purchase.
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Greek leadership on Wednesday emphasised Athens' support of EU candidate-countries' course towards Europe, with Prime Minister Costas Karamanlis citing the specific point during events in the Greek capital to mark Europe Day.
Karamanlis made the comments during a ceremony in Athens' Zappeion Hall -- a venue that hosted the accession ceremony for Greece nearly three decades ago and only a stone's throw from the Greek Parliament -- on the occasion of the 50th anniversary of the signing of the Rome Treaty.
"Our country's position is one of support for the consolidation, the entrenchment of stability, cooperation, development and prosperity in our neighbourhood," he said, adding:
"Today we celebrate Europe Day. Over these past 50 years the European Union has demonstrated that peace and cooperation can replace conflict and suspicion, with spectacular results for all."
The premier noted that that proof of Europe's attraction lies in the fact that "every country in our wider region wants desperately to become a member."
On his part, Deputy Foreign Minister Yiannis Valinakis, who served as the host of the commemorative event, said the Treaty of Rome laid the foundations for the spectacular course that followed, "which continues as spectacularly and provocatively in the present day, and where European nations put an end to the use of war and violence."
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