Pat grew up in Missoula and received Bachelors and Juris Doctor Degrees from the University of Montana. Pat was in the private practice of law with the firm of Luxan & Murfitt, PLLP, from 1981 until his retirement in July of 2010. He was outside general counsel to the MBOH for over ten years prior to his retirement. While working as a staff attorney in the Governor's Office of Budget and Program in the mid-1970's, Pat chaired a housing advisory council appointed by Governor Tom Judge and drafted the Montana Housing Act of 1975. This Legislation is the enabling act for the MBOH and the basis for most of its activity.
Jeanette is a former legislator, and has served as development officer of the Montana Historical Society Foundation and as an advisor to the National Trust for Historic Preservation. She's currently a real estate agent in Hamilton and serves on the Daly Mansion Board. She holds a bachelor's degree in elementary education from the University of Montana.
Sheila has been the Executive Director of NeighborWorks Great Falls and NeighborWorks Montana since 2003. In 2005, she was selected for the NeighborWorks America Achieving Excellence Program at Kennedy School of Government at Harvard University. She was selected as the national NeighborWorks' Emerging Leader of the Year in 2007. Sheila holds a BS in Chemistry and an MBA. She served in the Montana House of Representatives in 1991 and 1993 legislative sessions and was a member of the Montana Redistricting Commission in 2000. In 2009, she received HUD's Lightning Rod Mover and Shaker Award. She was appointed to the board of directors for the Montana Board of Housing in 2010.
Bob has worked for the Confederated Salish and Kootenai Tribes for 25 years in various positions, including consumer credit counselor and director of the housing authority. Bob is a consultant and the chairman of Eagle Bank in Polson. He is currently involved full-time in the development and the management of real estate and has developed specialized training and technical assistance for Indian housing resources. Bob has served on many boards, including as a presidential appointee to the Community Development Financial Institutions (CDFI) Advisory Board.
Johnnie McClusky has been with First Interstate Bank since 2000, where he worked in branch management. He is the organization's community development and Community Reinvestment Act officer. Prior to FIB, Johnnie worked for Billings Public Schools in the Indian Education Department as the Title VII Home/School coordinator. McClusky will oversee First Interstate's efforts relating to community development. He is also responsible for coordinating and implementing the Bank's CRA outreach efforts. He is a graduate of Dickinson State University.
Amber is currently the Government Affairs Director for the Billings Association of Realtors. She spends much of her time educating members about the issues and urging them to contact elected officials about issues that are important to the industry. Previously, as public relations director for a Habitat for Humanity chapter in Florida, Amber often organized weekend work crews who pitched in to build homes for families in need. "My job carried a requirement for mandatory vol-unteerism. I worked every Saturday on the Habitat job site leading in constructing homes," Parish said. "With the right team I can side a house in four hours."
Eric has served many national and Montana-based organizations. He is the former CEO of the Montana School Services Foundation, the former Chief Financial Officer at Blue Cross Blue Shield of Montana and former Vice President and Chief Financial Officer of American Chemet Corp. Schindler is an Air Force Veteran and resides in Helena.


text:
Q: Doctrine ORM geting foreign entity by non primary key I am trying to reference foreign entity by no primary key
I have a table I may not alter due to compatibility reason and other out of my control. It is a simple locale/country with primary auto increment, unique code like 'us' and other non-relevant fields.
There was no doctrine on the project until now and some of the tables in the project reference this by id, some by code
I would like to reference this entity like this:
    /**
     * @ORM\ManyToOne(targetEntity="Country")
     * @ORM\JoinColumn(name="country", referencedColumnName="code")
     *
     *  @var Country
     */
    private $countryEntity;

some other entities do use id instead. I cannot use @ORM\Id on both as I would need both to query it. I do not want to use second query to fetch Country entity. I need to preserve both id and code.

A: You can't reference another table by a non primary key field.
https://www.doctrine-project.org/projects/doctrine-orm/en/2.8/reference/limitations-and-known-issues.html#join-columns-with-non-primary-keys
You should migrate your database to use the primary key in all the relations with the country table.
You can migrate your data with a query like this ine
UPDATE target_table SET country_id = (SELECT id FROM country WHERE country.code = target_table.country_entity_code);

redpajama_set_name: RedPajamaStackExchange


text:
Че Банс (,  29 серпня 1975, Окленд) — новозеландський футболіст, захисник.

Клубна кар'єра
На початку ігрової кар'єри був у структурі англійського «Шеффілд Юнайтед», але на поле не виходив. Після цього 1995 року Банс повернувся на батьківщину і два роки виступав за місцевий «Нейпір Сіті Роверс».

1997 року Че перейшов у ісландський «Брейдаблік», де провів три роки.

У 1999 році Че Банс знову повернувся до Нової Зеландії і три сезони грав у новоствореному клубі «Футбол Кінгз», який брав участь у австралійській Національній футбольній лізі, де за три роки він зіграв 55 матчів і забив 2 голи. Надалі Че Банс знову став виступати у Європі, граючи за ірландську «Дрогеду Юнайтед» та данський «Раннерс».

З 2004 по 2006 рік грав за «Ваїкато» в чемпіонаті Нової Зеландії з футболу. На сезон 2006/07 він повернувся до «Футбол Кінгз», який зараз грав під новою назвою «Нью-Зіленд Найтс» в А-лізі. Будучи капітаном, він зіграв у 19 із 21 матчі чемпіонату до того, як команда була розпущена в кінці сезону.

Незабаром після закінчення сезону наприкінці січня 2007 року Банс став гравцем англійського «Ковентрі Сіті», але не зіграв за команду жодної гри до кінця контракту влітку того ж року, після чого недовго пограв за фіджійський клуб «Навуа».

2008 року Че Банс остаточно повернувся на батьківщину, де пограв за клуби «Ваїкато», «Мелвілл Юнайтед» та «Хоукіс Бей Юнайтед», завершивши ігрову кар'єру 2010 року

Виступи за збірну
28 вересня 1998 року дебютував в офіційних матчах у складі національної збірної Нової Зеландії в матчі групового етапу Кубка націй ОФК 1998 року в Австралії проти Вануату (8:1), в якому забив гол. Загалом зігравши у трьох матчах на турнірі — проти Вануату, Фіджі та фіналу проти Австралії, Банс з командою здобув золоті нагороди турніру. Перемога дозволила новозеландцям представляти федерацію на Кубку конфедерацій 1999 року, вперше у своїй історії. На турнірі в Мексиці Че зіграв у одному матчі — з Бразилією, але його команда програла всі три гри і не вийшла з групи.

На черговому Кубку націй ОФК 2000 року у Французькій Полінезії Банс знову був гравцем основи, зігравши у трьох з чотирьох іграх, але цього разу його команда поступилась австралійцям у фіналі і здобула лише «срібло».

Останнім великим турніром для Банса став Кубок націй ОФК 2004 року в Австралії (де Че зіграв у чотирьох з п'яти матчів групового етапу і забив один гол у ворота Фіджі), за результатами якого його команда сенсаційно посіла третє місце і не вийшла у фінал.

Востаннє зіграв за збірну 24 березня 2007 року в товариській грі проти Коста-Рики (0:4). Всього протягом кар'єри у національній команді провів у її формі 29 матчів, забивши 2 голи.

Титули і досягнення
 Володар Кубка націй ОФК: 1998
 Срібний призер Кубка націй ОФК: 2000
 Бронзовий призер Кубка націй ОФК: 2004

Примітки

Посилання

новозеландські футболісти
Гравці збірної Нової Зеландії з футболу
Гравці кубка націй ОФК з футболу 1998
Гравці розіграшу Кубка конфедерацій з футболу 1999
Гравці кубка націй ОФК з футболу 2000
Гравці кубка націй ОФК з футболу 2004
Футболісти «Шеффілд Юнайтед»
Футболісти «Нейпір Сіті Роверс»
Футболісти «Брєйдабліка»
Футболісти «Нью-Зіленд Найтс»
Футболісти «Дрогеда Юнайтед»
Футболісти «Раннерса»
Футболісти «Ковентрі Сіті»
Футболісти «ВейБОП Юнайтед»
Футболісти «Хоукіс Бей Юнайтед»
новозеландські футбольні легіонери
Футбольні легіонери в Англії
Футбольні легіонери в Ісландії
Футбольні легіонери в Австралії
Футбольні легіонери в Ірландії
Футбольні легіонери в Данії
Футбольні легіонери на Фіджі
Уродженці Окленда

redpajama_set_name: RedPajamaWikipedia


text:
8 Favorite Destinations of 2016
by adam on December 28, 2016
Below are my 8 favorite destinations of 2016 in no particular order.
Cueñca, Ecuador: For New Years, 2015/2016, my anthropologist friend and I spent 3-nights in the colonial city in the Austro (Southern Andes). The city is known for its 16th-century churches—Iglesia de El Sagrario and Catedral Metropolitana de la Inmaculada Concepción are highlights. But also known for its colorful indigenous markets and increasing amount of ex-pat retirees. But Cueñca's unloved modernist architecture is not to be overlooked, and styles that pay homage to Le Corbusier, Brutalism and various whimsical mid-century flairs reflect the city's 20th century boom when roads first linked it other parts of the country. Cuenca also offers a close connection to nature and a relaxed quality of life, neither of which I experienced in bustling and sprawling Quito. The tranquil Tomebamba River flows right through the city center, and its green riverbanks make a great place to sit in the sun and reflect on Andean life. Stay tuned for my forthcoming Hummingbird Journey in the WSJ.
Emilia-Romagna, Italy: The more I explore the boot—my southern neighbor—the more I adore it. Emilia-Romagna left a big impression on me this year. To eat here in Italy's pantry is to experience the heart of Italian food. This oft overlooked northeastern region stretches from the earthy and untouristed Apennine Mountains to the mega beaches of Rimini. Between them is one of Italy's biggest food producing valleys. It's also home to Parma, Bologna, Ravenna and Modena which produce Parmesan cheese, Po Delta Rice, Modena Balsamic Vinegar, Lambrusco, Parma ham, prosciutto, and lesser known foods like Borgataro mushrooms, culatello, and Mantua melon. But getting off the path in Emilia-Romagna was equally rewarding. The villages of Savigno and the peaceful mountain town of Sant'Agata Feltria are great places to understand Italy a bit deeper, with nary "grazie-ing" tourist in sight.
Lauterbrunnen, Switzerland: My hiking trips across my adopted homeland of Switzerland are some of my most rewarding, because time spent in the Alps gives me a clarity and a calmness that is needed more than ever. My one night in Lauterbrunnen really delivered the quintessential alpine experience. The scenic village sits in a valley bed who mossy, rocky ledges are laced with waterfalls both grandiose and tinkling. The village was the inspiration for J.R. Tolkien's Rivendell, the elvish wonderland in The Lord of The Rings,' and is every bit as inspiring. A 3-4 hour hike from Mürren was an excellent way to enjoy the area various vantages.
Mt. Hagen, Papua New Guinea Highlands: My 10 days in PNG was exhilarating. It's home to the nicest people I've ever met anywhere. There are no McDonalds, no Starbucks, no KFCs. At least none that I saw. In fact there were no recognizable brands anywhere. PNG is home to 700 cultures and 838 languages. Bio-geeks will go ballistic over the 3,000 species of orchids, and 708 bird species, 43 of them elaborate birds-of-paradise. See my Papua New Guinea stories in National Geographic and T+L SE Asia.
Barcelona, Spain: Like many travelers, I've been to Barcelona a few times, but on my four night visit in April it felt like the city was entering an especially exciting period. The economic collapse of 2008 saw many of Barcelona's young people leave to take jobs overseas, but many are returning for the city's indisputable quality of life (sunny weather, excellent food, amazing art and architecture), and they're bringing new international ideas with them. Barcelona, and particularly the neighborhood of Gràcia, is teeming with happy youthful energy, third wave coffee spots, and exciting new restaurants like Catalan-Japanese, and Vietnamese tapas.
Faroe Islands: Cast adrift in the mighty swells of the wild North Atlantic, this forlorn 779 piece smattering of islands, islets, and skerries has all the antiquity and modernity of its Danish motherland, yet the autonomous, verdant green islands rest in the Norwegian Sea halfway between Scotland and Iceland. I spent five nights on the islands in May, in awe of the rigged, treeless landscape, the waterfall-laced mountains that plunge thousands of meters to craggy fjords below. The islands are also home to a rich avifauna that's nothing short of an seabird lover's mecca. They include puffins, skuas, guillemots, fulmars who make good use of the Gulf Stream warmed waters and fill the air with their rich haunting cries that add a melancholic element to the already lonely landscape. Read my Faroe Islands story in the NYT, National Geographic and Cadillac Magazine.
Aarhus, Denmark: Copenhagen gets the ink, but Denmark's second city Aarhus, is not to be overlooked. The lively university hub is home to 319,000 residents, but dominated by students. It's situated on the Jutland Coast of Denmark and has been designated a 2017 European Cultural Capital, while surrounding Central Denmark has been awarded the European Region of Gastronomy 2017. To see it now is to witness a city undergoing a complete transformation, as new street food markets, light rail links, living libraries, refurbed hotels, and value-forward restaurants have altered the mechanical workings of a Danish city. But Aarhus' makeover has been in the works for the past several years. In 2009 it announced plans to go carbon neutral by 2030, and has stayed on track since, despite several impressive new developments every year. The city has been relying on 70+ new green technologies to meet the reductions and assesses its growth every year, including by shifting from coal to biomass in local heat production, using co-generators, solar and wind power, promising to become a new model in old Europe. You can read about my Aarhus stories on BBC Travel and New York Times and a forthcoming story in National Geographic Travel.
Kinosaki Onsen, Japan:I spent two nights in the 8th century onsen town located on the Sea of Japan in Japan's under-visited Hyogo Prefecture in Kansai Region. The willow lined Otani-gawa River that runs through the town is the source of the thermal waters that feed the town's various baths. Evenings see visitors klip klop through town wearing yukata (Japanese robes) and geta (wooden sandals) hopping from onsen to onsen. Legend says that Kinosaki was founded in the 8th century by Dochi-shonin, a Buddhist saint. Today town is also known for its delicacies like crab, sake, and its excellent ryokan, some of the best in all of Japan.
Tokyo, Japan Greener and quiter than you think.
Dubrovnik, Croatia Walk the medieval wall in the morning
Soca Valley, Slovenia Glittering rivers and pristine Alps.
Southwest England (Devon and Dorset) Sunny England with beaches, cliffs, and moors
Assisi, Italy Home of St. Francis is spiritual even for atheists
Stoos, Switzerland Quiet Swiss Alps in a nutshell and close to Zurich
Gruyere, Switzerland Eat fondue in a fortified medieval castle town
Sud Tirol, Italy Excellent food and skiing, in bald mountains of German-speaking Italy
Vejle, Denmark Underrated gem hidden in Denmark's hills
Previous post: Destinations I Visited In 2016:
Next post: Noteworthy Meals of 2016: Part 1

redpajama_set_name: RedPajamaCommonCrawl


text:
What is the definition of atheism? How do atheists define atheism? How do dictionaries, standard and online, define atheism? There is some disagreement about the definition of atheism and it is interesting to note that most of that disagreement comes from theists – atheists themselves tend to agree on what atheism means. Christians in particular dispute the definition used by atheists and insist that atheism means something very different.
Myth: Agnosticism is the Suspension of Belief; Atheism Asserts a Belief
One popular tactic used by religious theists (usually Christians) is to confuse the differences between agnosticism and atheism in order to insist that atheists aren't 'really' atheists, but agnostics. There seems to be the hope that by misrepresenting both as two points along a continuum with theism, then atheists can be convinced that they are 'closer' to theism than they realized, thus making the entire evangelization effort easier. Evangelization through deception is far too common.
Myth: Defining Atheism as a 'Lack of Belief in God' is a Cop Out
I've written a number of articles about how religious theists, but especially Christians, try to argue against the broad definition of atheism as simply the absence of belief in gods. This myth explains why this is so important to some theists: if atheism is just the absence of belief in gods, then it's not making any claims that all atheists must defend, and therefore the only burden of proof lies with religious theists themselves.
Myth: Atheists Cannot 'Lack Belief in God' After Learning About God
Christians' objections to defining atheism as simply the absence of belief in gods can extend to blatant misrepresentation of what should be a simple concept in order to divert attention from what atheism really is. In the case of this myth, we find that people will inexplicably distort the simple nature of "not believing the truth of a proposition" in order to pretend that it's not possible to merely lack belief in the truth of someone's claims. This makes the myth easy to refute.
Atheism & Naturalism: Atheists Should Focus on Naturalism & Science
Many atheists object to the labels 'atheist' and 'atheism"' for who they are and what they believe. They don't object because such labels inaccurate – they don't dispute that they don't believe in any gods. They object to the labels because they don't regard disbelief in gods as important enough to warrant its own label or any special attention. It is argued that it would be better if they were described based on what they do believe: specifically their naturalistic approach to life.
How are Atheism & Theism Different? How are Atheism & Theism Similar?
Given the constant debates between atheists and theists, the differences between atheism and theism should be obvious. The truth is that there are so many misconceptions which both sides have about the other that the facts can get lost. The difference is ultimately very simple: theists believe in at least one sort of god. How many gods, the nature of these gods, and why the belief exists is irrelevant to the concept. Atheists lack belief in the existence of any gods external to human minds
Is Atheism an Ism? Atheism is No Religion, Philosophy, Ideology, Belief System
Because of atheism's long-standing association with freethought, anti-clericalism, and dissent from religion, many people seem to assume that atheism is the same as anti-religion. This, in turn, seems to lead people to assume that atheism is itself a religion – or at least some sort of anti-religious ideology, philosophy, etc. This is incorrect. Atheism is the absence of theism; by itself, it isn't even a belief, much less a belief system, and as such cannot be any of those things.
Defining Atheism as Not Worshipping Gods: Atheism Is About Worship, Not Belief?
Some have argued that rather than define atheism as the absence of belief in gods, it makes more sense to define atheism as not worshipping any gods. There are advantages and disadvantages to this idea. A principle advantage is the fact that there is no need to go through contortions when it comes to apparently "real" gods, like a tree or Caesar. A principle disadvantage is the fact that it allows for a person to be an atheist but to believe that some god exists — an apparent contradiction.
What is Atheism? Overview of How Atheism is Defined
It seems strange that atheism would be so difficult to understand, but it must be – otherwise, there wouldn't be so many arguments over what it means, what it entails, and what significance it has for society. Because the same questions and same arguments keep coming up over and over again, it's important to have a central resource where people can inform themselves or to which they can refer others.
What Is Atheism? Strong vs. Weak Atheism
What is the definition of atheism? How do atheists define atheism? How do dictionaries, standard and online, define atheism? There is some disagreement about the definition of atheism and it is interesting to note that most of that disagreement comes from theists – atheists themselves tend to agree on what atheism means.
How Modern Dictionaries Define Atheism
What is the definition of atheism? How do atheists define atheism? How do dictionaries define atheism? A common theme throughout the definitions here is the primary use of 'disbelieve' when defining atheism. Some dictionaries drop this, but most comprehensive dictionaries do not.
Online Dictionary Definitions of Atheism
When debating atheism online, people often rely on various online dictionaries. These are references which everyone has equal access to, unlike printed dictionaries which people may not have at all or may not have immediate access to. So, what do these online sources have to say about atheism?
Definition of Atheism in Reference Books
The works cited here are all specialized reference materials designed not to provide general information for a general audience but, rather, information on specific topics like religion, sociology, or other social sciences. Their value here is in the fact that they provide insight into what scholars from different fields think of when it comes to the concept of atheism.
Definition of Atheism for Early Freethinkers
Atheists and freethinkers have defined atheism relatively consistently over the past couple of centuries. Although a few have focused solely on the sense of 'strong' atheism, many more have differentiated between 'weak' and 'strong' atheism. Included here are definitions of atheism from nonbelievers and freethinkers from the early 20th century and before.
Definition of Atheism for Modern Atheists
A few contemporary atheists restrict the definition of atheism to just the sense of 'strong' atheism, but most do not. Most instead point out the difference between 'weak' atheism and 'strong' atheism, arguing that the former is the broader and more commonly found form of atheism. Included here are quotes and definitions from nonbelievers from the latter part of the 20th century.
Definition of Atheism from Theologians
Although misunderstandings about the definition of atheism have tended to come from theists, it is also true that many theists have recognized that atheism has a broader sense than simply 'denial of the existence of gods.' Included here are quotes from a few of them.


E ach day I wake up I have to choose to exercise. Of course most days I don't feel like getting up early in the morning and exercising, but for my health, I do it anyway. I get up early to exercise, just in case my day goes left and I can't workout later. Most of the time I am able to get 2 workout sessions in (one in the morning and one in the evening). As a parent, I am trying to incorporate exercise routines with my child as well. As a young child, exercising on a daily basis was not emphasized. I believed we played outside enough and also had physical education at school, therefore we never really had to worry about getting in the proper amount of exercise. Fitness and nutrition has been proven to relieve a lot of ailments and disease. Although we are not immune from getting sick, when you are eating and exercising properly, your body will be able to combat most of it. You at least give your body a fighting chance to get well. I challenge you today to move your body more often. I can almost guarantee you will feel better.


AJ's Kitchen in Pearl River may have moved and expanded — its new incarnation opened March 16 on North Main Street — but its mission is the same.
The motto, according to co-owner AJ Servidio — he owns the 40-seat restaurant with his brother, Doug Faehndrich — is "Culinary Old School" with an emphasis on "fresh, local and scratch made." Everything from the chocolate chip cookies to the hand-sliced potato chips to the wide assortment of dressings is made that day and to order.
"This allows us to customize any meal, especially if a customer has an allergy," said Servidio. Gluten and dairy free options are available daily. There are also specials that change seasonally depending upon what's available. Using what's local matters. That means craft beer, thanks to relationships with area breweries such as Industrial Arts and Defiant. The restaurant also showcases Finger Lake wines, local coffee roasters and local artisanal tea makers.
On the menu: A large array of sandwiches, wraps and salads such as a Southwest Chicken Sandwich (Blackened Chicken, Cheddar-Jack Cheese, Bacon, Lettuce & Chipotle Mayo), Multi Grain Flat Bread Panini (Grilled or Fried Eggplant, Roasted Peppers, Fresh Mozzarella & Balsamic Glaze), and Baby Arugula Salad, as well as larger mains such as Shepherd's Pie, Eggplant Parmesan & Pasta Marinara, Grilled Salmon, and Homemade Mac-N-Cheese with Bacon & Peas. There's also a kid's menu.
The point, said Faehndrich, is to provide a great tasting meal in a friendly environment that encourages folks to make friends and linger over a glass of wine just like you would in your friend's kitchen.
Many in the area may already know chef Servidio, a Culinary Institute of America grad who started AJ's Catering and Take Out six years ago on Pearl River's Brightwood Avenue (now closed) where Faehndrich also worked.
But the two wanted bigger and dreamed of owning a full service restaurant. Which is why, when the opportunity to move the business and expand presented itself, they jumped. The new place was about a year in the making and honors the history of the butcher shop that preceded it, albeit with a more urban ambiance.
The chair rail along the concrete wall, for example, was made from refurbished pallets that Faehndrich put together. He also built the bar using reclaimed wood from the butcher's box. And the lights above the bar are from old meathooks, also from the butcher.


text:
Bonehead Or Brilliant? $1 Million Maybach To Be Followed By Super Luxury SUV
Agent009 submitted on 7/8/2014 Auto Spies Photos Timestamp: 9:27:00 AM
Views : 27,709 | Category: New Cars | Source: | SOURCE: www.motoring.com.au
Tag Links: #Maybach, #Luxury, #s-class, #GL, #Pullman
Mercedes-Benz is planning a 'super-luxury' version of its GL-Class seven-seat SUV (pictured) as part of the launch of the Maybach sub-brand. That's the clear message behind comments from senior Mercedes-Benz insiders speaking at last week's Italian launch of the new S-Class coupe.
The move sees Maybach set to become to luxury, what AMG is for performance for the Daimler brand. And SUVs appear key to the Maybach 'comeback'.
Although not a return to models badged Maybach in their own right, the plan will see a number of models offered at super-luxury specification levels and/or standalone chassis configurations.
The first new Maybach-badged model will be a stretched long-wheelbase S-Class. One of six configurations built on the new W222 series S-Class architecture, the 'S-Class by Maybach' will remain a two-row car, unlike the three-row Pullman ultra-long-wheelbase that will top-off the big Benz sedan line-up.
Honor thy error as a hidden intention.
— Agent009 (View Profile)


Jack communicates using his current AAC device, which costs around $9000. VocaliD estimates that voices will cost only $1249, making them significantly more affordable.
When 7-year-old Jack tries to speak, he can't make the words come out.
Jack was born with a rare genetic disorder called ornithine transcarbamylase (OTC) deficiency. As a result, he suffered hyperammonemia immediately after his birth. This ultimately caused neurological insults [acquired brain injury] that resulted in hypotonia and apraxia, a lack of coordination of muscle movement. This makes simple actions such as chewing, swallowing, and speaking difficult for Jack.
For Jack, this means using a "talker" — a device that reads aloud whatever message Jack composes on screen.
Jack's computer-generated "voice" resembles that of renowned physicist Stephen Hawking. While this system works at home with Jennifer, this artificial voice has created some complications in other areas of Jack's life.
After years of using an AAC device that was far from perfect, Jack now has a chance to dramatically improve the way he communicates through an organization called VocaliD.
Ever since giving a TED talk in late 2013 entitled "Synthetic voices, as unique as fingerprints," Patel has worked toward funding the development of more natural-sounding voices for AAC users.
The startup campaign culminated with a successful web-based Indiegogo fundraiser in June, which was 172 percent funded by the time it closed.
In a matter of months, the first few "trailblazer" recipients will have their new custom voices, and many more will move away from mechanical or pre-recorded voices toward a more personalized alternative to traditional communication.
In addition to the custom nature of the voice, another revolutionary aspect is the cost.
Because VocaliD utilizes volunteer voice donors, is very affordable when compared to other AAC devices. The current estimated cost of each VocaliD voice is around $1249.
"It costs VocaliD money to save and store people's voices, so we need people to send the most high-quality recordings, not just care about getting through the task," said Patel. This way, even more savings can be passed on to the voice recipients.
Those who wish to donate their time and voices to VocaliD's Human Voicebank can make an account on VocaliD.co and record a complete set of 3500 sentences. The website is compatible with Google Chrome and requires a quiet recording environment to ensure quality voice donations.
After contributing to the Human Voicebank, donors will receive a certificate for seven hours of community service. More importantly, donors could change the lives of those like Jack.
Carlmont will be hosting a VocaliD "Voicedrive" for all who are interested in making a difference and helping everyone find their voice. Signups will continue through early November, and people can join the Facebook group or talk to math teacher Andrew Ramroth to get involved.


If there is one thing that students of today are so happy about, it has to be the fact that there is so much available at their disposal, so much that they can use to help them overcome the hurdles that they normally experience in writing research papers. It is common sense that writing a research paper is part and parcel of learning, but in the event that you are not able to do it well, chances are high that you might not really fall in love with the entire experience. Writing a research paper is not supposed to be one of the hardest things that you have to do. It is actually one of the easiest things, just as long as you know where to start looking for useful information.
If you want to find some really cool research papers, how about you consider checking out the internet? This is a sure way to get the finest papers ever, especially because of the variety that is available at your disposal. So many students today have access to a lot of websites from where they can get all the information that they need for their papers. As a matter of fact, go right ahead and bookmark some of your favorite sites and you can always visit them whenever you are in need.
One of the traditional means that still applies to date is visiting the library. Make sure that you get some time and visit your library so that you are able to have a chance at some real learning. As a word of caution though, make sure you only get to visit the library when you are pretty sure you are into or you can handle some research. It never is an easy task flipping through all the books trying to find the right one for you. However, if you have the guts for it, nothing should stop you.
The one thing that you have to make sure when you are doing all of this is to ascertain the legitimacy of whichever piece of information you are using. If you are accessing any website, make sure that the information you are getting from there is legitimate. There are so many students who throw caution to the wind in an attempt to find some free research papers and it all comes back to haunt them in the long run.


Close collaboration with the manufacturing supply chain is enabling one major distributor of electronic, electrical, and industrial components to improve its value-added offering to electrical engineers.
Following a visit to the new HELLERMANNTYTON ACADEMY, RS Components is working even more closely with the cable management specialist to devise and deliver bespoke solutions to its customers throughout the UK and the rest of the world. Already the two companies are collaborating on products such as luminous cable ties for specialist industries, printed metal content cable ties for the food and beverage industry, as well as bespoke specialist solutions for the utilities industry.
Despite HellermannTyton being a key supplier to RS Components for more than 35 years, supplying in excess of 1,500 product lines to the business from its UK manufacturing sites, Mike Williams, Regional Sales Manager, admits that it is difficult for its team to have an in-depth understanding of HellermannTyton's extensive product range. This is where the HELLERMANNTYTON ACADEMY is playing a vital role for electrical distributors, delivering interactive training sessions on the HellermannTyton range of products, enabling businesses to get to grips with its vast range of manufactured solutions.
Mike Williams commented: "We've always worked with HellermannTyton's core range of cable ties, but were previously unaware of how they could support our more general cable management requirements. The ACADEMY opened our eyes to this and showed us some of the more bespoke applications that the business has the capability to develop.
Since its visit to the ACADEMY, RS Components has partnered with HellermannTyton on client visits, and is planning to utilise its academy as part of its ongoing sales development strategy. The business has dedicated sales teams covering seven areas of the UK, with three of these already visiting the HELLERMANNTYTON ACADEMY and working more closely with the manufacturer. The other sales teams will also attend the ACADEMY between now and the rest of the year, with annual visits planned for all teams.
Claire Philpott at HellermannTyton commented: "We understand that electrical distributors such as RS Components have two primary business aims; not only do they want to advise customers on the best products for their requirements, but they also want to be able to upsell and provide more bespoke solutions. This is why we created the HELLERMANNTYTON ACADEMY, so that we can advise contractors and wholesalers on the vast range of solutions we can assist them with, so that they can deliver even greater value to their own customers.


text:
The Cousin Collector
Genealogy and Cemetery Project
Donald David Leroy Addington
Personal Information | Sources | All
Name Donald David Leroy Addington [1, 2, 3, 4]
Born 7 Apr 1913 Missouri [1, 2, 3, 4]
Died 10 Sep 1915 Saint Joseph, Buchanan County, Missouri [1, 2, 3, 4]
Cause: accidental gunshot wound
Buried 10 Sep 1915 Rochester Cemetery, Andrew County, Missouri [1, 2]
Person ID I11030 CCBasic
Father William Samuel Addington, b. 16 Nov 1889, Cosby, Andrew County, Missouri , d. 28 Apr 1974, San Bernardino County, California (Age 84 years)
Mother Grace Hector, b. 22 Jun 1896, Rochester, Andrew County, Missouri , d. 5 May 1943, Tucson, Pima County, Arizona (Age 46 years)
[S81] Eshelman, Monica Schirmer Eshelman -- Personal Research (Pact Burials), 9 Mar 2018.
[S64] Missouri -- Death Certificates 1910-1969, Missouri Bureau of Vital Records, Missouri Secretary of State, (http://www.sos.mo.gov/mdh/ :n.d.), death certificate no. 26884 (1915), Donald David Leroy Addington, accessed 28 April 2018., 28 Apr 2018.
[S86] Missouri, Gentry County, King City -- The King City Democrat Newspaper (Online), Missouri., "Donald Addington," 24 September 1915, p. 6, col. 4; digital images : accessed 28 April 2018)., 28 Apr 2018.
[S186] Missouri, Gentry County, Albany -- The Albany Capital Newspaper (Online), Missouri., "Child Killed by Mother," 23 September 1915, p. 2, col. 3; digital images : accessed 28 April 2018)., 28 Apr 2018.
USGenWeb County Projects | Blog


text:
\section*{Abstract}
The stochastic block model is able to generate different network partitions, ranging from traditional assortative communities to disassortative structures. Since the degree-corrected stochastic block model does not specify which mixing pattern is desired, the inference algorithms, which discover the most likely partition of the networks nodes, are likely to get trapped in the local optima of the log-likelihood. Here we introduce a new model constraining nodes' internal degrees ratios in the objective function to stabilize the inference of block models from the observed network data. Given the regularized model, the inference algorithms, such as Markov chain Monte Carlo, reliably finds assortative or disassortive structure as directed by the value of a single parameter. We show experimentally that the inference of our proposed model quickly converges to the desired assortative or disassortative partition while the inference of degree-corrected stochastic block model gets often trapped at the inferior local optimal partitions when the traditional assortative community structure is not strong in the observed networks.
\\
\newline
\textbf{Keywords:} community detection, stochastic block model, Markov chain Monte Carlo, modularity, assortative structures
\\

\section*{Introduction}
The study of modular structure in networks has a long history in the literature \cite{parnas1984modular,newman2004finding,newman2003structure,fortunato2010community}. The primary focus of this line of work has been the discovery of the assortative community structures where the edges inside each module are generally more numerous than the edges across them. In complex networks, the modular structures in networks include not only such assortative community structures but also other mixing patterns, like the core-periphery structures~\cite{borgatti2000models} and bi-partite structures. These various structures found in networks demand a more comprehensive class of community detection models than the assortative ones, targeted by the {\it modularity}-based approaches~\cite{newman2004finding}. Modularity maximization is perhaps the most widely used approach for community detection. It aims at maximizating the modularity of the network partitions, which is a broadly accepted quality metric comparing the number of edges observed in each community to the expected number in a random graph with the same degree sequence. As shown in~\cite{newman2016equivalence}, modularity maximization is, in fact, equivalent to the maximum likelihood estimation of the degree-corrected planted partition model, a special case of the degree-corrected stochastic block model~\cite{karrer2011stochastic}. Therefore, the stochastic block model can be considered more general than the traditional modularity-based community detection models because it can generate a wide variety of different network structures, which include not only the traditional assortative community structures, but also the disassortative structures such as the core-periphery structures~\cite{borgatti2000models}. Exceeding the limitations of modularity maximization approaches, the statistical inference of stochastic block model, which maximizes the likelihood of generating the observed network data, is capable of discovering of both assortative and disassortative structures in networks. The stochastic block model and its variants have gained significant attention in the past decade.

In the following, when we discuss the nodes' block assignment, for any node $l$, its block assignment is denoted by $g_l$. The standard stochastic block model is a variant of the generative random graph model which assumes the probability of connecting two nodes in a graph is defined exclusively by their block assignments. More precisely, this probability follows \textit{Bernoulli} distribution with the mean $\omega_{g_i,g_j}$. Hence, the model is fully specified by the block assignments of nodes $\{g_i\}$ and the mixing matrix $\Omega=\{\omega_{rs}\}$ governing the probabilities of observing one edge between each pairs of nodes from blocks $r$ and $s$. If the diagonal elements $\omega_{rr}$ in the mixing matrix are larger than the off-diagonal elements, then the networks generated by the stochastic block model have the traditional assortative communities. When the off-diagonal elements $\omega_{rs}$ for $r \neq s$ are larger than the diagonal elements, the generated network contains disassortative mixing patterns, such as the structures observed in the core-periphery graphs~\cite{borgatti2000models} and the bi-partite graphs. In general, the inference of the stochastic block model aims at discovering the mixing matrix and nodes block assignment which maximize the likelihood of generating the observed network. It does not impose any constraint on which type of structure is discovered. Thus, the inference of the stochastic block model is more general than the traditional modularity maximization because it may recover a set of assortative or disassortative structures from the observed network.

Given the nodes' block assignments of the standard stochastic block model, the nodes in the same block are by definition statistically indistinguishable, and they all have the same expected node degrees. Therefore, the most likely block assignments often cluster the nodes of similar degrees into a block, resulting in clusters with homogeneous node degrees, rather than heterogeneous node degrees in each block, typical for the traditional community structures. The degree-corrected stochastic block model~\cite{karrer2011stochastic} defines the expected number of edges between nodes $i$ and $j$ as $\lambda_{ij} = \omega_{g_i,g_j} \beta_i \beta_j$ where for each node $l$, $\beta_l$ is a model parameter associated with it. Note that $\lambda_{ij}$ is defined as the expected number of edges and not as customary the probability, assuming multi-edges (multiple edges between a pair of nodes) are allowed in this definition~\cite{karrer2011stochastic}. The expected node degrees in the degree-corrected stochastic block model, given the maximum likelihood estimates of these model parameters, are equal to the degrees observed in the input network. Hence, the degree-corrected stochastic block model allows a community to have a wide range of node degree distribution.

The simple yet effective degree-correction modification improves the performance of statistical inference of community structure in complex networks. When the nodes in an assortative community have a wide range of degree distribution, the statistical inference of the standard stochastic block model is likely to detect the sub-optimal disassortative structures, while the statistical inference of the degree-corrected model is more likely to discover the sought-after assortative structures. Yet, the degree-corrected stochastic block model does not impose any constraints on the mixing pattern of the resulting block assignments, thus the return of the assortative structures is not guaranteed.

As suggested in~\cite{peel2017ground}, both the traditional assortative community structures and the other disassortative structures are potentially good fits to the degree-corrected stochastic block models. Hence, depending on the starting point, the inference algorithm like Markov chain Monte Carlo tends to get trapped in the local optimum of the log-likelihood. Therefore, in this paper, we propose a regularized stochastic block model which provides an extra regularization term to guide the discovery of assortative or disassortative structure by the statistical inference of the stochastic block model. Unlike the modularity maximization algorithm which always attempts to find traditional assortative communities, the inference of this regularized stochastic block model controls the mixing patterns discovered in the given network.

\section*{Results}
\subsection*{Degree-corrected stochastic block model}
The degree-corrected stochastic block model~\cite{karrer2011stochastic} is a generative model of the graph in which the edges are randomly placed between nodes. The probability of observing an edge between two nodes depends exclusively on the block assignments of its two endpoints. In the degree-corrected stochastic block model, the nodes in the same block are statistically indistinguishable from each other. The degree-corrected stochastic block model assumes the number of edges between nodes $i$ and $j$ is independently distributed Poisson random variable with the mean $\lambda_{g_i,g_j}$.

Let $\bf A$ be the adjacency matrix whose component $A_{ij}$ denotes the number of edges between nodes $i$ and $j$ in an unweighted undirected multigraph. Multi-edges (multiple edges between a par of nodes) and self-loop edges (the edges connecting a node to itself) are practical in certain networks such as the web network where a web page may contain multiple hyperlinks pointing to other pages and to itself. Such edges are less common in social networks. However, most social networks are very sparse, so the impact of multi-edges and self-loop edges is negligible in such networks. Let a node $i$ with $k$ self-loop edges be represented by the diagonal adjacency matrix element $A_{ii} = 2k$ and the number of edges between two different nodes $i$ and $j$ follows the Poisson distribution with mean $\lambda_{ij}$ while the number of self-loop edges at node $l$ follows the Poisson distribution with mean $\frac{1}{2}\lambda_{ll}$.

Given the parameters $\{\lambda_{ij}\}$, the likelihood of generating $\bf A$ is
\begin{equation} \label{eq:ll0}
    \log P({\bf A}|\{\lambda_{ij}\}) = \prod_{i} \frac{(\frac{1}{2}\lambda_{ii})^{A_{ii}/2} }{(\frac{1}{2}A_{ii})!} e^{-\lambda_{ii}/2} \prod_{i<j} \frac{\lambda_{ij}^{A_{ij}} }{A_{ij}!} e^{- \lambda_{ij}}.
\end{equation}
Note that $\lambda_{ij}$ is defined as the number of edges here and not as customary the probability, because multi-edges are allowed in this definition. In an unweighted undirect network, after ignoring all terms independent of the $\lambda_{ij}$s, the log-likelihood above simplifies to
\begin{equation} \label{eq:ll}
    \log P({\bf A}|\{\lambda_{ij}\}) = \frac{1}{2} \sum_{ij} \Big(A_{ij} \log \lambda_{ij} - \lambda_{ij} \Big).
\end{equation}
In the degree-corrected stochastic block model, the model parameter $\lambda_{ij}$ is defined as $\lambda_{ij} = \omega_{g_i,g_j} \beta_i \beta_j$ where for each node $l$, $\beta_l$ is a parameter associated with this node. Given a partition of the network, i.e., its block assignments $\{g_i\}$, reference~\cite{karrer2011stochastic} obtains the maximum likelihood estimates of the model parameters as $\hat{\beta}_i = \frac{k_i}{\kappa_{g_i}}$ and $\hat{\omega}_{rs} = m_{rs}$, where $k_i$ is the degree of node $i$, $\kappa_{r}$ is the sum of the degrees of all nodes in a block $r$, and $m_{rs}$ is the total number of edges between blocks $r$ and $s$, or, if $r=s$, twice the number of edges in $r$. Note that each parameter $\omega_{rs}$ in the degree-corrected stochastic block model is not the probability of observing an edge between endpoints in blocks $r$ and $s$. Instead, it is a model parameters that depends exclusively on the block assignments $r$ and $s$.

As Eq.~\ref{eq:ll} indicates, the degree-corrected stochastic block model does not specify the mixing pattern of the resulting block assignments. It is possible that the Poisson means $\lambda_{ij}$ for the pairs of nodes in the same block are much smaller than the corresponding value between different blocks, which corresponds to the disassortative mixing pattern. Hence, the disassortative mixing pattern can also be a good fit to the degree-corrected stochastic block model. This observation inspires us to propose a regularization term for the degree-corrected stochastic block model, so it controls the mixing patterns discovered in the given network.

\subsection*{Assortative and disassortative structures}

As defined in previous literatures~\cite{newman2004finding,newman2003structure,fortunato2010community}, the assortative structures correspond to the traditional community structures, nodes are more frequently connected with each other inside communities than with the rest of the network. The disasortative structures do not satisfy this condition. For example, the core-periphery structures~\cite{borgatti2000models} divides the networks nodes into the core part, where nodes are often the hubs of the networks, and the periphery part, where nodes of low-degree connect to the core nodes. This is an advantage of the degree-corrected stochastic block model because it allows the discovery of the structures with different mixing patterns. However, when searching for the weak assortative community structures, the inference algorithm may still return the disassortative structure\cite{decelle2011asymptotic} instead.

We generate synthetic networks using the degree-corrected stochastic block model with a block assignment $\{g_i\}$ and the parameters $\omega_{rs}$ chosen for the assortative communities as follows
\begin{equation} \label{eq:synthetic}
\omega_{rs}=
    \begin{cases}
    &\gamma \omega_{0} \quad \quad  \text{ if  } r=s,\\
    &\omega_{0} \quad \quad \quad  \text{if  } r\neq s.
    \end{cases}
\end{equation}
where a large value of $\gamma$ results in strong community structure while $\omega_{0}$ controls the sparsity of the network. The degree sequence is drawn from a power-law distribution with exponent $2.5$, and the block assignments are randomly assigned, but each block is of the same size.

\begin{figure*}[!htb]
    \centering
    \subfloat[Local optima of log-likelihood]{\includegraphics[width=9cm]{opt_figure_dcsbm.jpg}}
    \subfloat[Convergence]{\includegraphics[width=7cm]{dcsbm_converge2.png}}
    \caption{The convergence of 20 Markov Chain Monte Carlo (MCMC) trials for the degree-corrected stochastic block model. The local optimum $A$ found by MCMC represents the assortative communities, whereas there are other local optima representing disassortative structures such as point B. \textbf{(a)} Multiple locally optimal partitions discovered by the MCMC inference for degree-corrected stochastic block model; \textbf{(b)} 2 out of the 20 MCMC trials find the most likely and sought-after assortative partition, while the other trials get trapped at the local optima. The matrix $M_{rs}$ indicates the number of edges between every pair of blocks.}
    \label{fig:dcsbm_3dscatter}
\end{figure*}

Given the synthetic networks produced by the degree-corrected stochastic block model, we infer the block assignment $\{g_i\}$, recovering the parameters used for its generation. Specifically, the number of communities is set as two for both the generation and recovery. To generate samples, the degree-corrected stochastic block model uses $\omega_{0}=0.01$ and $\gamma=10$. Each of the two blocks contains $10$ nodes. We scatter the sampled partitions on the x-y plane in Figure~\ref{fig:dcsbm_3dscatter} and the z-axis indicates the log-likelihood of the corresponding partitions. We adapt the Markov chain Monte Carlo (MCMC) algorithm~\cite{nasrabadi2007pattern,peixoto2014efficient} to infer the block assignment of the stochastic block model and its extensions. The MCMC algorithm~\cite{peixoto2014efficient} samples the distribution of all possible block assignments in the network partitions space. For each trail, the algorithm starts from a random partition and iteratively, with certain probability, traverse to an adjacent partition. An entire MCMC sweep of all nodes in the network requires $O(E)$ operations where $E$ is the number of edges in the networks~\cite{peixoto2014efficient}. The mixing time of the MCMC algorithm depends on the specific input network and the starting point. The details of the MCMC algorithm are discussed in the Supplementary Information.

Figure~\ref{fig:dcsbm_3dscatter} shows the existence of multiple local optima in the log-likelihood of degree-corrected stochastic block model. The inference process finds three local optima here: (i) partition A which corresponds to the assortative structure matching the ground truth block assignment used for its generation; (ii) partition B which corresponds to a disassortative structure; and (iii) another disassortative partition which is not explicitly marked in the Figure~\ref{fig:dcsbm_3dscatter}(a). Under the degree-corrected stochastic block model, the MCMC inference starting from random initial partition finds the most suitable partition A in only 2 out of 20 trials, whereas the other attempts are trapped at the local optima as shown in Figure~\ref{fig:dcsbm_3dscatter}(b).

Since there are multiple local optima in the log-likelihood of the degree-corrected stochastic block model, the inference algorithm may converge to any of them nondeterministically. Specifically, the type of the discovered structure depends on the trial starting point and inference algorithm parameters. To avoid such nondeterministic outcomes, we introduce a novel approach called Regularized Stochastic Block Model (RSBM) applicable to any inference algorithm. RSBM constrains nodes' internal degree ratios, each of which is defined in the objective function as the fraction of a node's neighbors inside the same community. The resulting algorithm reliably finds assortative or disassortive structures as directed by the value of a single parameter.

\subsection*{Regularized stochastic block model}

We extend the formulation of the expected number of edges between nodes $i$ and $j$, determined by the \textit{Poisson} rate $\lambda_{ij}$, in the degree-corrected stochastic block model by defining it as
\begin{equation} \label{eq:theta}
    \lambda_{ij} =
    \begin{cases}  \omega_{g_i,g_j} I_i I_j    & \mbox{if} \quad g_i=g_j\\
                                 \omega_{g_i,g_j} O_i O_j  & \mbox{otherwise}
    \end{cases}
\end{equation}
where any node $l$ has two associated parameters $I_l$ and $O_l$. Given Eq.~\ref{eq:ll}, the log-likelihood of generating graph $G$ by this regularized stochastic block model can be written as
\begin{align} \label{eq:logl_IO}
    \mathcal{L}(G|{\bf g,\omega, I, O})  = 2\sum_i \Big( k_i^+ \log I_i + k_i^- \log O_i \Big) + \sum_{rs} m_{rs} \log \omega_{rs} - \omega_{rs} \Lambda_{rs}
\end{align}
where $k_i^+$ is the number of neighbors of node $i$ which are inside the same block given the block assignment $\bf g$ and $k_i^- = k_i - k_i^+$. Thus, we get
\begin{align}
   \Lambda_{rs} = \begin{cases}  (\sum_{i \in r} I_i)^2   & \mbox{if } r=s \\
 \sum_{i \in r} O_{i}  \sum_{i \in s} O_{i} & \mbox{if } r\neq s \end{cases}
\end{align}
To simplify, we write $i\in r$ if $g_i = r$. For block assignment $\bf g$, the maximum-likelihood values of $\omega_{rs}$ are
\begin{equation} \label{eq:mle_omega}
    \hat{\omega}_{rs} = \frac{m_{rs}}{\Lambda_{rs}}
\end{equation}
Dropping the constants and substituting using Eq.~\ref{eq:logl_IO}, we obtain
\begin{align} \label{eq:important}
    \mathcal{L}(A|{\bf g,I,O})
    &=  \sum_{rs} m_{rs} \log \frac{m_{rs}}{\Lambda_{rs}} + 2\sum_i \Big( k_i^+ \log I_i + k_i^- \log O_i \Big)
\end{align}

Note that if we set $I_i = O_i = 1$ here, the log-likelihood above reduces to the definition of standard stochastic block model with $\Lambda_{rs} = n_r n_s$ which is exactly the product of the sizes of two blocks $r$ and $s$. When $I_i = O_i = k_i$, the second sum on the right hand side (RHS) becomes irrelevant to the maximum likelihood estimation (MLE) result. Hence, the log-likelihood reduces to the definition of degree-corrected stochastic block model in Eq.~\ref{eq:important} with $\Lambda_{rs} = \kappa_r \kappa_s$, i.e., the product of the sums of degrees of nodes in two blocks $r$ and $s$. Hence, by introducing two sets of parameters ${\bf I} = \{I_i\}$ and ${\bf O} = \{O_i\}$ in the edge probability, we obtain a more generalized definition of stochastic block model here.

\subsection*{Regularization by prior in-degree ratios }
For alternative formulation of our model, we define the prior in-degree ratio $f_i = I_i / (I_i + O_i)$ and $\theta_i = I_i + O_i$ for each node $i$. By rewriting the second summation on the RHS of Eq.~\ref{eq:important}, we get
\begin{align} \label{eq:important2}
    \mathcal{L}(G|{\bf g,I,O})
    &=  \sum_{rs} m_{rs} \log \frac{m_{rs}}{\Lambda_{rs}} - 2\sum_i k_i H(\frac{k_i^+}{k_i},f_i) + 2 \sum_i k_i \log \theta_i
\end{align}
where $H(\frac{k_i^+}{k_i},f_i) = -\frac{k_i^+}{k_i} log f_i - \frac{k_i^-}{k_i} log (1 - f_i)$ represents the cross entropy between the observed and prior in-degree ratio. Therefore, the prior in-degree ratios $\{f_i\}$ regularizes the in-degree ratios $\{\frac{k_i^+}{k_i}\}$ in the resulting partition by maximizing Eq.~\ref{eq:important2}.

In real networks, the low degree nodes are more likely to have neighbors inside a block than the high degree nodes are. Suppose $f_i$ depends only on the degree of node $i$, i.e. $f_i = f(k_i)$. Then, the function $f(k): \mathbb Z_+ \to [0, 1] $ should be strictly decreasing. In an assortative partition of the network, we have
\begin{itemize}
    \item $f(1) = 1$ because a node with degree one must connect to the community it belongs to;
    \item for $k\approx |V|$, $f(k) \ll 1$ because a super-hub eventually does not belong to any community as its degree is of the order of the number of nodes in the entire network.
\end{itemize}
A simple function $\{f(k)\}$ satisfying this requirement is of the form
\begin{equation}
  f(k) = \alpha + \frac{(1 - \alpha)}{k}
\end{equation}
where $\alpha$ is the only extra parameter we introduce to the regularized stochastic block model (RSBM). Alternatively, we can select a constant $f\in(0,1)$  such that
\begin{equation}
    f(k)= max(f, \frac{1}{k})
\end{equation}
The impact of different choices of $f_i$ on the discovered block assignment is discussed in the following two subsections presenting experimental results. It is worth noting that, for either choice of the prior in-degree ratio $\{f_i\}$, there is only one extra parameter, i.e. $\alpha$ or $f$, introduced here in addition to the original parameters of the degree-corrected stochastic block model.

\subsection*{Experimental Results}
We generate synthetic networks with the assortative communities using the degree-corrected stochastic block model. The parameters $\omega_{rs}$ in the network generation process are specified by Eq.~\ref{eq:synthetic}. By selecting a small value of $\gamma > 1$ in Eq.~\ref{eq:synthetic}, the generated community structures are relatively weak, thus, it is more difficult to detect them using the statistical inference of the degree-corrected stochastic block model.

We adapt the Markov chain Monte Carlo (MCMC) algorithm~\cite{nasrabadi2007pattern} to infer the block assignment of the stochastic block model and its extensions. Figure~\ref{fig:gsbm_3dscatter}(a) shows there is only one unique local optimum, the partition C, found by 20 MCMC trials under the regularized stochastic block model. Therefore, all 20 MCMC trials converge to this unique local optimum. As shown in Figure~\ref{fig:gsbm_3dscatter}(b), the Markov Chain Monte Carlo inference finds the correct block assignment within only 150 steps. The regularization terms made it possible to avoid the unsuitable local optima during the inference.

\begin{figure*}[!htb]
    \centering
    \subfloat[Local optimum of log-likelihood]{\includegraphics[width=9cm]{opt_figure_gsbm.jpg}}
    \subfloat[Convergence]{\includegraphics[width=7cm]{gsbm_converge3.png}}
    \caption{The convergence of 20 Markov Chain Monte Carlo (MCMC) trials for the regularized stochastic block model (RSBM) introduced here. All trials converge to the local optimum C which represents an assortative structure. \textbf{(a)} One local optimal partition observed during the MCMC inference for our model. \textbf{(b)} All twenty MCMC trials find the sought-after assortative structure.}
    \label{fig:gsbm_3dscatter}
\end{figure*}

We use the networks including Karate club network of Zachary~\cite{zachary1977information}, the Dolphin social network of Lusseau et al.~\cite{lusseau2003bottlenose} and the network of fictional characters' interactions in the novel Les Miserables by Victor Hugo~\cite{newman2004finding} to demonstrate the performance of the regularized model introduced here. The details of each network are presented in the Supplementary Materials. For Karate club network, we evaluate the effect of the regularization terms on the resulting partitions using Markov chain Monte Carlo (MCMC) as the inference algorithm. For every node $i$ in the network, we set the parameter $\theta_i = k_i$ and $f_i = max(f, 1/k_i)$ for our regularized stochastic block model defined by Eq.~\ref{eq:important2} where $k_i$ is the degree of node $i$ and $f_i$ represents the prior in-degree ratio for regularization. Figure~\ref{fig:karate_figure} shows the most likely partition of the Karate club network found by MCMC using different $f$ values. The color represents the block assignment, and the black dashed line divides the network into two parts in the ground truth partition. As shown in Figure~\ref{fig:karate_figure}, when $f=0.14$, the inference algorithm outputs a core-periphery structure which clusters high-degree nodes into the blue block and the remaining low-degree nodes into the red block. This is because the sum of cross entropy terms serves as a regularization term which penalizes those partitions that assign adjacent nodes into the same block. As the value of $f$ grows, the inference algorithm is more likely to detect assortative structure. When $f=0.85$, the inferred block assignment matches the ground truth partition of the Karate club network with the exception of one single red node. However, this node has only one connection to each block; thus, it is quite arguable to which block this node should belong.

\begin{figure*}[!htb]
    \centering
    \subfloat[$f=0.14$]{\includegraphics[width=5cm]{s014.PNG}}
    \subfloat[$f=0.35$]{\includegraphics[width=5cm]{s035.PNG}}
    \subfloat[$f=0.56$]{\includegraphics[width=5cm]{s056.PNG}} \\
    \subfloat[$f=0.68$]{\includegraphics[width=5cm]{s068.PNG}}
    \subfloat[$f=0.81$]{\includegraphics[width=5cm]{s081.PNG}}
    \caption{The partition of Karate network inferred by Markov Chain Monte Carlo under different parameter settings where nodes of the same color belong to the same partition. The black dotted line represents the ideal partition in the ground truth. A small $f$ results in the core-periphery partition of the network while a large $f$ leads to assortative partitions. The values of the $f$ parameter are shown in the corresponding sub-figures captions.}
    \label{fig:karate_figure}
\end{figure*}

\begin{figure*}[!htb]
    \centering
    \subfloat[Karate club]{\includegraphics[width=6cm]{karate_scatter_real.pdf}}
    \subfloat[Dolphin social]{\includegraphics[width=6cm]{dolphins_scatter_real.pdf}}
    \subfloat[Les Miserables]{\includegraphics[width=6cm]{lesmis_scatter_real.pdf}}
    \caption{The \textit{coverage} of partitions in real networks as a function of average node degree. The optimal partitions are inferred by the Markov Chain Monte Carlo algorithm under degree-corrected stochastic block model (DCSBM), marked by circles, and its regularized extension (RSBM) introduce here, marked by crosses. \textbf{(a)} Karate club network~\cite{zachary1977information}; \textbf{(b)} Dolphin social network~\cite{lusseau2003bottlenose}; \textbf{(c)} Characters interaction network of Les Miserables~\cite{newman2004finding}}
    \label{fig:gsbm_real}
\end{figure*}

The results in Figure~\ref{fig:gsbm_real} show that the MCMC inference of our RSBM model on these networks finds partitions with higher \textit{coverage} than the ones inferred by the degree-corrected stochastic block model. The \textit{coverage} of a partition~\cite{fortunato2010community} is defined as the ratio of the number of edges with both endpoints in the same block to the total number of edges in the entire network
\begin{equation}
    \text{coverage({\bf g})} = \frac{|\{(i,j)\in E| g_i = g_j\}|}{|E|}
\end{equation}
A low \textit{coverage} indicates that the resulting partition is disassortative. An ideal assortative partition of the network, where all clusters are disconnected, yields a \textit{coverage} of 1.

Figure~\ref{fig:gsbm_real} shows the \textit{coverages} of the partitions found in the three real networks mentioned above. We randomly remove edges in these networks to further increase the sparsity of these networks. And the numbers of blocks used in trials are set to their values for these real networks well-accepted in the literature. The results in Figure~\ref{fig:gsbm_real} indicate that, under degree-corrected stochastic block model (DCSBM), the inference algorithm is likely to miss the assortative structures and returns instead the disassortative partitions of the network, which also fit the model in such cases. In contrast, the MCMC inference of our regularized stochastic block model (RSBM) almost deterministically produces the assortative structures. Interestingly, the inference of degree-corrected stochastic block model produces the partitions with the \textit{coverage} values distributed at two levels. There is no partition with a \textit{coverage} between these two levels found by the inference algorithm. This is similar to the case of the synthetic network in Figure~\ref{fig:dcsbm_3dscatter} where both the assortative communities and disassortative structures fit the degree-corrected stochastic block model. Which structure is found is determined by random sampling of the potential partitions. In other words, there is no way to guide whether the disassortative structures or the assortative communities are preferred, so, two consecutive MCMC trials may return completely different structures.

In contrast, the inference of the introduced here regularized stochastic block model using the prior in-degree ratio $f_i = 0.8 + 0.2 / k_i$ is very robust. It only produces partitions with a high \textit{coverage}, which are generally distributed at the same level as the assortative partitions under degree-corrected stochastic block model. Moreover, the sparsity of the networks does not have an obvious impact on the resulting partitions under our regularized model.

\begin{figure*}[!htb]
    \centering
    \subfloat[Karate club]{\includegraphics[width=5cm]{karate_coverage_modularity.pdf}}
    \subfloat[Dolphin social]{\includegraphics[width=5cm]{dolphins_coverage_modularity.pdf}}
    \subfloat[Les Miserables]{\includegraphics[width=5cm]{lesmis_coverage_modularity.pdf}}
    \caption{The \textit{coverage} and modularity  of partitions in real networks as a function of parameter $f$. The optimal partitions are inferred by Markov Chain Monte Carlo algorithm under degree-corrected stochastic block model (DCSBM) and its regularized extension (RSBM) introduced here. The networks used for evaluation include \textbf{(a)} Karate club network~\cite{zachary1977information}; \textbf{(b)} Dolphin social network~\cite{lusseau2003bottlenose}; \textbf{(c)} Characters interaction network of Les Miserables~\cite{newman2004finding}}.
    \label{fig:gsbm_real2}
\end{figure*}

We evaluate the modularity~\cite{newman2006modularity} of resulting partitions of the real networks as a function of parameter $f$.
A high modularity indicates the strong community structure. In our experiments, we use a constant $f$ such that $f_i=f$ for each
node $i$ in the network with degree $k_i>1/f$, otherwise $f_i=1/k_i$. We start with a small $f$ value and increase it in each iteration.
The MCMC inference uses as a starting point the original network initially, and the network partition found in the previous iteration subsequently. Figure~\ref{fig:gsbm_real2} shows that, as the value of $f$ increases, in general both the modularity and the \textit{coverage} grow. When $f$ is close to 0, the value of $f$ does not have any effect on the network partition. However, as $f$ becomes larger, then at a certain threshold of about 0.75, the increase of $f$ leads to a critical transition from disassortative partitions to assortative communities. Then, a larger $f$ value does not further increase the modularity  of the partition. These results indicate that, with one single parameter $f$, the Markov Chain Monte Carlo inference gains the flexibility to choose between assortative communities resulting from clustering dense modules of nodes together or disassortative structure, such as the core-periphery structure from clustering high-degree and low-degree nodes into separate blocks. With a choice of high $f$, the inference algorithm is likely to detect assortative communities.

\section*{Discussion}
The stochastic block model is able to produce a wide variety of different network structures, including traditional assortative communities and different from them disassortative structures. In theory, it should be possible to guide the inference algorithm which type of structures is preferred when both types of structures fit the stochastic block model and its degree-corrected variant well for the input network. However, the existence of multiple local optima of the log-likelihood traps the inference algorithms in one of them. Although there were efforts to enable community detection at different levels of granularity, cf.~\cite{fortunato2018multi,lu2019asymptotic}, the need for controlling assertiveness of the solution has not been addressed so far.
Here, we apply a simple yet effective constraint on nodes' internal degree ratio in the objective function. This approach is independent of the inference algorithm. The resulting algorithm reliably finds assortative or disassortive structure as directed by the value of a single parameter $f$. We validated the model experimentally testing its performance on several real and synthetic networks. The experiments show that the inference of degree-corrected stochastic block model quickly converges to the sought-after assortative or disassortative structure. In contrast, the inference of degree-corrected stochastic block model gets often trapped in the inferior local optimal partitions.

\section*{Methods}
\subsection*{Proving properties of the Regularized Stochastic Block Model}

\begin{theorem} \label{thm:1}
When $f_i = 1/2$ for each node $i$ in the network, the MLE of our RSBM model defined by Eq.~\ref{eq:important2} becomes the MLE of degree-corrected stochastic block model\footnote{The proofs of Theorem~\ref{thm:1},\ref{thm:2} and \ref{thm:3} are provided in the Supplementary Material.}.
\end{theorem}

\begin{figure*}[!htb]
    \centering
    \includegraphics{sbm_type.pdf}
    \caption{A unified view on the variants of stochastic block models. The standard stochastic block model (SSBM) is represented by the dark blue point $(1, 1)$ since it sets $I_i=O_i=1$ for each node $i$. The degree-corrected stochastic block model (DCSBM) requires $I_i = O_i$, which corresponds to the light blue line with a slope $f_i=1/2$ in the 2D coordinate plane. The introduced here regularized stochastic block model (RSBM) allows choosing $I_i$ and $O_i$ on the line $I_i + O_i = k_i$ where $k_i$ is the node degree. So our model is represented by a set of points on the parallel gray lines each of which intersects the x and y-axis at integer values corresponding to the degree of a node in the range $[k_{\text{min}}, k_{\text{max}}]$.}
    \label{fig:sbm_type}
\end{figure*}

According to Theorem~\ref{thm:1}, if we add a constraint $I_i = O_i$ for every $i$, the model introduced here reduces to the degree-corrected stochastic block. Figure~\ref{fig:sbm_type} illustrates the relationship between the most commonly used variants of stochastic block models. If we plot the constraints of these models on a 2D plane with $I_i$ and $O_i$ as the $x$ and $y$ axes, respectively, the standard stochastic block model (SSBM) sets $I_i=O_i=1$ for each $i$, which maps to the point (1,1) in the 2D coordinate plane. The constraint $I_i = O_i$ which represents the degree-corrected stochastic block model (DCSBM) maps onto the line with a slope $f_i=1/2$ in the 2D coordinate plane. Here, we extend the stochastic block model in two different directions: (i) using a constraint on $\theta_i = I_i + O_i$; (ii) choosing $I_i$ and $O_i$ on the line with a customized slope $f_i$. It turns out the second choice, like degree-corrected
stochastic block model, preserves the degree sequence of the network.

\begin{theorem} \label{thm:2}
Given any customized $\{f_i\}$ and the corresponding maximum-likelihood estimator $\hat{\theta}_i$, our model defined by Eq.~\ref{eq:important2} preserves the degree sequence of a network, so the expected degree of node $i$ generated by the RSBM model with $\hat{\theta}_i$ is
\begin{equation}
    \sum_j \lambda_{ij} = k_i
\end{equation}
\end{theorem}

\subsection*{Information-theoretic interpretations}

While the maximum likelihood estimator $\hat{\theta}_i$ preserves the degree sequence in the observed network, we find that the closed-form analytic expression of such estimator is hard to obtain. However, when we impose constraints that for each $i$, $\theta_i = k_i$ and look for the MLE of $f_i$ for every $i$, the log-likelihood of the model introduced here has an interesting interpretation uncovered by the following theorem.

\begin{theorem} \label{thm:3}
When $\theta_i = k_i$ for every node $i$, maximizing the log-likelihood of Eq.~\ref{eq:important2} is equivalent to maximizing the target function
\begin{align} \label{eq:meaning0}
    \mathcal{L} = \mathbb{D}_{KL}(p_{\text{degree}}(r,s) || p_{\text{null}}(r,s)) - 2 \mathbb{E}_{k_i}[ H(\frac{k_i^+}{k_i}) ]
\end{align}
where the first term represents the Kullback–Leibler divergence between the edge distribution under the block model and the corresponding distribution under the null model which randomizes the edges inside and across blocks respectively, while the second term is twice the expectation of the binary entropy of in-degree ratio $k_i^+/k_i$.
\end{theorem}

Imposing the constraint $I_i + O_i = k_i$, we obtain a new model which involves a regularization term of the expected entropy of in-degree ratio $k_i^+/k_i$. According to Theorem~\ref{thm:3}, maximizing the log-likelihood of this model has a physical interpretation: the MLE of the RSBM model increases the KL divergence between the edge distribution under the block model and the corresponding edge distribution under the \textit{null} model, and, at the same time, it decreases the expected entropy of the in-degree ratio $k_i^+/k_i$. Intuitively, the \textit{null} model here separates the edges inside a block and the edges across different blocks. It assumes all the edges inside blocks are statistically equivalent, and so are all the edges across different blocks. In contrast, the \textit{null} model of the degree-corrected stochastic block model~\cite{karrer2011stochastic} mixes all the edges, assuming all the edges are statistically equivalent - the KL divergence term in its \textit{null} model does not distinguish the edges inside the same block and those across different blocks. On the other hand, the sum of entropy function $H(k_i^+/k_i)$ controls the identification of the edges' types. In Eq.~\ref{eq:meaning0}, $H(k_i^+/k_i)$ is the entropy function which achieves its minimum when $k_i^+/k_i \to 0$ or $k_i^+/k_i \to 1$. Therefore, the MLE of this model is likely to classify the edges of a node as either all in-edges or all out-edges -- the former case is likely to detect assortative communities and the latter case infers disassortative structure as the most probable block assignment.

For community detection problems, it is rarely observed that all neighbors of a node are not located in the same block, especially for those nodes with low degrees. Unlike traditional community detection algorithms, the definition of stochastic block model and its variants mentioned above does not explicitly control the portion of neighbors in different blocks, rather they rely on the specific statistical inference to determine the block assignments. This observation inspires us to regularize the traditional stochastic block model on the in-degree ratio. Specifically, our \textbf{r}egularized \textbf{s}tochastic \textbf{b}lock \textbf{m}odel (\textbf{RSBM}) introduced here maximizes the objective function of Eq.~\ref{eq:important2} with $\theta_i = k_i$ and $f_i$ defined by the prior in-degree ratio. As explained above, the KL divergence of RSBM has similar meaning as that of degree-corrected stochastic block model. The expected cross-entropy term serves as a regularization term to control the resulting partition. Intuitively, when $f_i$ is close to one, then the inference algorithm tends to cluster nodes in a network into dense modules; when $f_i$ is close to zero, then disassortative partitions such as the core-periphery structure are likely to be discovered because the regularized block model tends to assign adjacent nodes into different blocks to decrease $k_i^+$.

\section*{Data availability statement}
The network data sets used for the evaluation of our proposed model are available at \url{http://konect.uni-koblenz.de/networks/}

redpajama_set_name: RedPajamaArXiv


text:
Hardik Patel held road show in Saurashtra, received hero's welcome
Patel Quota Agitation Leader Hardik Patel, Whose 48-hour Deadline To Leave Gujarat Ends On Sunday, Today Held Road Shows At Several Places In Saurashtra Region And Got A Grand Welcome In His Home Town Viramgam In Ahmedabad District.
PTI | Updated on: 16 Jul 2016, 09:50:58 PM
Ahmedabad:
The 22-year-old Patidar leader, who will leave for Udaipur in Rajasthan tomorrow, where he will stay for the next six months, told reporters at a road show that he was not interested in politics and working for his community is what matters to him the most.
After stepping out of the Lajpore Jail in Surat around 11 am yesterday, Hardik had said that he would continue his agitation to get reservation for his community under the OBC quota. During his road shows today, he once again made it clear and said they have prepared a plan to revive the stir.
After his arrival in Ahmedabad from Surat last night, he was greeted by hundreds of supporters. Aam Aadmi Party (AAP) members and leaders, including Ashutosh and Kanubhai Kalsariya, tried to meet Hardik, but could not do so due to the huge crowd around him.
"Our attempt to meet Hardik was not successful due to huge crowd that turned out to greet him late night as he reached Ahmedabad," an AAP member said. This morning, Hardik reached his hometown Viramgam, where he offered prayers at the temple of his family deity and met his family members. He was given a hero's welcome by the members of his community.
Hundreds of supporters accompanied him as he left for Sarangpur in Botad district, where he prayed at Lord Hanuman temple. At Lathidad village on the way, villagers weighed him in donated blood on a weighing scale as a show of solidarity. They also gifted him a sword. Speaking to reporters on the way, Hardik, who was nine months in jail in connection with two sedition and one arson- related casea during the agitation, said he is not a political person.
"We are receiving amazing support. Thousands of people have turned up. I carry the trust of our community members and I am working for their cause...I am not a political person and politics is not important for me, what matters is our work for our community members," he said.
First Published : 16 Jul 2016, 09:42:00 PM
Hardik Patel Saurashtra


text:
May call for action on migrants
Home Secretary Theresa May faces tough talks over the Mediterranean migrant crisis (MoD/PA)
The migrant crisis in the Mediterranean is "reaching to our borders", Theresa May said as she called for European action to tackle the criminals behind the boatloads of people risking the voyage.
The Home Secretary, who was attending talks with European Union counterparts aimed at responding to the emergency, stressed that the UK was playing a role in the search and rescue mission and the effort to target the gangs "making a profit out of human lives".
European home affairs ministers are meeting in Luxembourg to try to hammer out a joint strategy for dealing with the flow of people crossing the Mediterranean.
The European Commission has proposed redistributing around 60,000 Syrian and Eritrean asylum seekers from Italy and Greece to other member states.
But the quota plan is being resisted by a number of countries in the borderless Schengen zone, while Britain has indicated it will use an opt-out to avoid taking part.
Save The Children stepped up the pressure on UK ministers by calling for Britain to agree to take in 1,500 unaccompanied youngsters who had made the crossing to Europe.
Arriving in Luxembourg Mrs May said: "Of course the crisis in terms of migrants who cross the Mediterranean is a problem in two ways. First of all, obviously, lives are being put at risk but secondly, as we see in Calais and elsewhere, it's putting great pressure on European towns and cities which is even reaching to our borders, although we are not part of the borderless Schengen area.
"To deal with this issue in the long term we need to go after the criminal gangs who are plying a terrible, callous trade in human lives.
"We also need to break the link between people getting into the boats and reaching Europe. That means returning people to North Africa or elsewhere, or to their home countries; so that they see that there is no merit in this journey.
"It also means development work in the countries of origin so that we can ensure people no longer have the same incentive to try to get into these boats and risk their lives."
Asked about the British response Mrs May said: "The UK is working with other European countries in a number of ways. We are putting effort into the search and rescue that is taking place - HMS Bulwark but also two Border Force cutters, they are doing that work alongside others.
"But we are also working to deal with these terrible criminal gangs who are making a profit out of human lives and human misery. That is essential work.
"Of course as the UK we also put effort into development aid to help these countries, to stabilise them, to provide the economy that means there is no longer the incentive for the journey."
Italian prime minister Matteo Renzi upped the ante yesterday by suggesting he could begin granting temporary residence permits, which would allow the arrivals free movement into other Schengen states.
In an interview with the Corriere della Sera newspaper, Mr Renzi insisted it was unacceptable that his country was having to bear the brunt of the migration.
"If the European Council chooses solidarity, then good. If it doesn't we have a Plan B ready but that would be a wound inflicted on Europe," he said.
Interior minister Angelino Alfano, who was attending the talks with Mrs May, told Sky TG24: "I will say with great clarity: kids, either we do equal distribution of migrants in Europe, or we organise refugee camps in Libya, or we organise a serious policy of repatriation."
A similar move by Rome in 2011 saw residence permits handed to around 25,000 Tunisian asylum seekers - prompting furious protests from Germany and France.
Prime Minister David Cameron is expected to discuss the issue with Mr Renzi when they meet in Milan tomorrow. No final decisions are likely before the European Council summit later this month.
A Downing Street spokeswoman said: "We do not support relocation. We think our approach should be focused on not creating further pull factors for migrants."
Save The Children urged the Government to take in a "fair share" of youngsters who make the crossing to Europe and increase the number of families fleeing Syria that are offered sanctuary in the UK.
The charity said that more than 5,000 children, many of them travelling without their parents, were among the 54,000 people who have landed in Italy so far this year.
The charity's chief executive Justin Forsyth said: "Britain showed real leadership by deploying HMS Bulwark to help restart rescue operations in the Mediterranean - the Navy is doing incredible work, rescuing nearly 3,000 people in the last weeks alone.
"Without the rescue boats, children would potentially have drowned off the shores of Europe. We ask that people all over Britain join us in saying a heartfelt thank you to the crew of HMS Bulwark for their life-saving work.
"But European leaders must acknowledge the problem doesn't go away when people are brought to shore. For many migrants, including lone children at risk from people traffickers, their ordeal isn't over when they land in Europe. Italy and Greece cannot be expected to bear the brunt of this issue - this is an Europe-wide problem that requires an Europe-wide solution."
"It's up to the Government to decide how they deal with this issue, whether unilaterally or through a joint EU process, but we know that they must act. There are actions that the UK could take now, including committing properly to the existing scheme to resettle the most vulnerable Syrian refugee families and offering sanctuary to a share of the unaccompanied children who have arrived alone on Europe's shores."


Indivisible: Conejo's Issue Action Team on Education encourages members to call our Senators today, urging them to vote NO on the Senate version of a House bill that would gut accountability rules for states evaluating the performance of their public schools. Civil rights advocates warn that the bill would endanger the prospects for disadvantaged children, and students with disabilities, to receive an adequate education.
House Joint Resolution 57, passed in that chamber last week, rolls back a regulation that was written by the Department of Education as part of implementing the Every Student Succeeds Act (ESSA). That law, the successor to No Child Left Behind, passed with bipartisan majorities -- and many of its provisions, including those concerning school accountability, focus on historically underserved students such as those from low-income families and those with disabilities. Now, however, Republicans want to narrow the federal government's role in its implementation. The rule in question defines the ways in which states must determine which schools are serving students well, and which are struggling. Without it, civil rights groups say, individual states will be able to make their own rules -- with some likely to use that freedom to hide the academic performance of disadvantaged and disabled students, in an effort to get around ESSA requirements that all students be given equal opportunities to receive a good education.
In other words, without this rule, states will have more latitude to leave behind those children who don't learn as easily -- poorer students, students with Down Syndrome, and children with other physical and mental disabilities. Let's urge our Senators to reject this bill that places simplistic right-wing ideology over the right of every child to a good education.
Hello, my name is _______. I am one of Sen. ____________'s constituents in ____________, California. I'm calling to urge her to vote NO when the Senate takes up House Joint Resolution 57. America's commitment to public education -- and the bipartisan will of Congress when it passed the Every Student Succeeds Act -- demands that every public school provides equal opportunities for every student to receive a good education, including disadvantaged children and those with Down Syndrome and other disabilities. Thank you!


text:
Leo Chadburn
Leo Chadburn's work straddles the borders between notated music and electronica, pop and experimental music, spoken word and music for gallery installations. Unifying concerns in his diverse practice include the use of voices, examination of text, and collaborative work with visual artists. His music has been broadcast on BBC Radio 1, 3 and 6Music.
He is best known for his albums under the pseudonym Simon Bookish, the most recent of which was Everything/Everything (2008), a 'big-band song cycle about science and information' for the Cologne-based record label Tomlab. His discography also includes remixes for Owen Pallett, Grizzly Bear and Franz Ferdinand, and an album of music for malfunctioning Casio synthesizers, Epigram/Microgram (2013).
Notable non-pop works include two pieces for Apartment House, X Chairman Maos, (2011) and Freezywater (2016); The Indistinguishables, written for Quatour Bozzini in 2014; and Affix Stamp Here, for voices, sine waves and postcards, for EXAUDI (2016).
As a performer, he played the role of 'The Singer' in the National Theatre's high-tech, critically-acclaimed 2007 production of Brecht's Caucasian Chalk Circle and appeared at the London Contemporary Music Festival in 2013 and 2014 performing (as vocalist) Frederic Rzewski's Coming Together and Gavin Bryars' 1-2, 1-2-3-4. His has presented his own work internationally and undertook a UK tour in Spring 2015 supported by SAM called Leo Chadburn presents The Text Message, performing works by composers with an interest in the idea of 'found text.'
He has collaborated with artists including Cerith Wyn Evans, (Imagination Dead Imagine, 2013, for the Irish Museum of Modern Art), Richard Grayson (Nothing Can Stop Us Now, 2014, for Dilston Grove / Matt's Gallery) and Jennet Thomas (The Unspeakable Freedom Device, 2015, for The Grundy Gallery, Blackpool).
He frequently writes about innovative and creative pop and classical music for publications including The Wire, The Quietus, Frieze, TEMPO, Sinfini and The New Statesman and has been commissioned to write programme notes for the Barbican Centre and the London 2012 Festival, and a set of talks for The Rest is Noise festival at the Southbank Centre, 2013.
ANTICLOCK
Five Loops For The Bathyscaphe
Affix Stamp Here
Freezywater
The Indistinguishables
Vapour Descriptors
Brown Leather Sofa
X Chairman Maos
Everything / Everything
The Reflecting Pool
www.simonbookish.com
Music by Simon Bookish on SoundCloud
Music by Leo Chadburn on SoundCloud
Music by Leo Chadburn / Simon Bookish on Bandcamp
Composer-Performers
the-indistinguishables-2014


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US Open – Follow the Tennis match between Kveta Peschke / Anna-Lena Grönefeld and Martina Hingis / CoCo Vandeweghe live with Eurosport. The match starts at 16:00 on 1 September 2016. Our live coverage lets you follow all the key moments as they happen.
Head-to-head: see historical stats and visit our detailed profiles for Kveta Peschke / Anna-Lena Grönefeld vs Martina Hingis / CoCo Vandeweghe. Get all the latest on Tennis: fixtures, results and tables.