text:
from flask import render_template, flash, redirect, url_for, request, g, session
from app import app, mysql, lm, db, babel
from .forms import LoginForm, EditForm, PostForm, SearchForm#, Email_change
from flaskext.mysql import MySQL
from oauth import OAuthSignIn
from flask_login import login_user, logout_user, current_user
from models import User, Post
from flask_security import login_required
from datetime import datetime
from config import POSTS_PER_PAGE, MAX_SEARCH_RESULTS, LANGUAGES
from .emails import follower_notification
from flask_babel import lazy_gettext

lm.login_view = 'index'
lm.login_message = lazy_gettext('Please log in to access this page.')

@babel.localeselector
def get_locale():
  return 'es' #request.accept_languages.best_match(LANGUAGES.keys())

@lm.unauthorized_handler
def unauthorized():
  flash(lazy_gettext('Please log in to access this page before all'))
  return redirect(url_for('index'))

@app.before_request
def before_request():
  g.user = current_user
  if g.user.is_authenticated:
    user.last_seen = datetime.utcnow()
    db.session.add(g.user)
    db.session.commit()
    g.search_form = SearchForm()
  g.locale = get_locale()

@app.route('/search', methods = ['POST'])
@login_required
def search():
  if not g.search_form.validate_on_submit():
    return redirect(url_for('index'))
  return redirect(url_for('search_results', query = g.search_form.search.data))

@app.route('/search_results/<query>')
@login_required
def search_results(query):
  results = Post.query.whoosh_search(query, MAX_SEARCH_RESULTS).all()
  return render_template('search_results.html', query= query, results=results)

def after_login(user):
  if user:
    nickname = user.nickname
    if nickname is None or nickname == '':
      nickname = resp.email.split('@')[0]
    if User.query.filter_by(nickname=nickname).first():
      flash("your default nickname from twitter is already exists:  " + nickname)
      nickname = User.make_valid_nickname(nickname)
      nickname = User.make_unique_nickname(nickname)
    flash("Your new nickname is: " + nickname)
    flash("You can change your nickname name in settings!")
    return nickname

@app.route('/', methods=['GET', 'POST'])
@app.route('/index', methods=['GET', 'POST'])
@app.route('/index/<int:page>', methods = ["GET", "POST"])
def index(page = 1):
  if current_user:
    user = current_user # fake user
  else:
    user = None
  form = PostForm()
  if form.validate_on_submit():
    post = Post(body=form.post.data, timestamp = datetime.utcnow(), author=user)
    db.session.add(post)
    db.session.commit()
    flash("You post is now live!")
    return redirect(url_for('index'))
  if not user.is_anonymous:
    posts = user.followed_posts().paginate(page, POSTS_PER_PAGE, False)
  else:
    posts = None
  return render_template('index.html',title='Home', user=user, form = form, posts=posts, ret = 'index')

@app.route('/user/<nickname>', methods=['GET', 'POST'])
@app.route('/user/<nickname>/<int:page>', methods=['GET', 'POST'])
@login_required
def user(nickname, page = 1):
    user = User.query.filter_by(nickname=nickname).first()
    if user == None:
      flash('User %s not found.' % nickname)
      return redirect(url_for('index'))
    posts = user.posts.paginate(page, POSTS_PER_PAGE, False)
    return render_template('user.html',user=user,posts=posts, ret = 'user')

@app.route('/users_list', methods=['GET', 'POST'])
@login_required
def users_list():
  user = current_user;
  users_list = User.query.all()
  return render_template('users_list.html', user = user, users_list = users_list)

# @app.route('/login', methods=['GET', 'POST'])
# def login():
#     form = LoginForm()
#     if form.validate_on_submit():
#         flash('Login requested for OpenID="%s", remember_me=%s' %
#               (form.openid.data, str(form.remember_me.data)), 'success')
#         return redirect(url_for('index'))
#     return render_template('login.html',
#                            title='Sign In',
#                            form=form,
#                            providers=app.config['OPENID_PROVIDERS'])

@app.route('/logout')
def logout():
  logout_user()
  return redirect(url_for('index'))

@app.route('/edit', methods = ['GET', 'POST'])
@login_required
def edit():
  user = current_user
  form = EditForm(user.nickname, user.email)
  if not user.email:
    flash('Please enter your email!')
  if form.validate_on_submit():
    user.nickname = form.nickname.data
    user.about_me = form.about_me.data
    user.email = form.email.data
    db.session.add(user)
    db.session.commit()
    flash('Your changes have been saved.')
    return redirect(url_for('edit'))
  elif request.method != "POST":
    form.nickname.data = user.nickname
    form.about_me.data = user.about_me
    form.email.data = user.email
  return render_template('edit.html', form = form, user = user)

@app.route('/follow/<nickname>')
@login_required
def follow(nickname):
  user = User.query.filter_by(nickname=nickname).first()
  if user is None:
    flash('user %s not found.' % nickname)
    return redirect(url_for('index'))
  if user == current_user:
    flash("you can\'t follow yourself!")
    return redirect(url_for('user', nickname = nickname))
  u = current_user.follow(user)
  if u is None:
    flash("Cannot follow " + nickname + '.')
    return redirect(url_for('user', nickname = nickname))
  db.session.add(u)
  db.session.commit()
  flash('You are now following ' + nickname)
  follower_notification(user, g.user)
  return redirect(url_for('user', nickname = nickname))

@app.route('/unfollow/<nickname>')
@login_required
def unfollow(nickname):
  user = User.query.filter_by(nickname = nickname).first()
  if user is None:
    flash('user %s not found.' % nickname)
    return redirect(url_for('index'))
  if user == current_user:
    flash("you can\'t unfollow yourself!")
    return redirect(url_for('user', nickname = nickname))
  u = current_user.unfollow(user)
  if u is None:
    flash("Cannot unfollow " + nickname + '.')
    return redirect(url_for('user', nickname = nickname))
  db.session.add(u)
  db.session.commit()
  flash('You have stopped following ' + nickname)
  return redirect(url_for('user', nickname = nickname))

# @app.route("/Authenticate")
# def Authenticate():
#     username = request.args.get('UserName')
#     password = request.args.get('Password')
#     cursor = mysql.connect().cursor()
#     cursor.execute("SELECT * from User where Username='" + username + "' and Password='" + password + "'")
#     data = cursor.fetchone()
#     if data is None:
#      return "Username or Password is wrong"
#     else:
#      return "Logged in successfully"

@app.route('/authorize/<provider>')
def oauth_authorize(provider):
  if not current_user.is_anonymous:
    return redirect(url_for('index'))
  oauth = OAuthSignIn.get_provider(provider)
  return oauth.authorize()

@app.route('/callback/<provider>')
def oauth_callback(provider):
    if not current_user.is_anonymous:
        return redirect(url_for('index'))
    oauth = OAuthSignIn.get_provider(provider)
    social_id, username, email = oauth.callback()
    if social_id is None:
        flash('Authentication failed.')
        return redirect(url_for('index'))
    user = User.query.filter_by(social_id=social_id).first()
    if not user:
        user = User(social_id=social_id, nickname=username, email=email)
        user.nickname = after_login(user)
        db.session.add(user)
        db.session.commit()
        db.session.add(user.follow(user))
        db.session.commit()
    remember_me = False
#     if 'remember_me' in session:
#       remember_me = session['remember_me']
#       session.pop('remember_me', None)
    login_user(user, True) #remember = remember_me)
    flash('User %s login successful.' % user.nickname)
    return redirect(request.args.get('next') or url_for('edit'))

@app.errorhandler(404)
def not_found_error(error):
    return render_template('404.html'), 404

@app.errorhandler(500)
def internal_error(error):
    db.session.rollback()
    return render_template('500.html', user = current_user), 500

redpajama_set_name: RedPajamaGithub


text:
Home » Social Sector » Are the Railways cheating the visually-impaired?
Are the Railways cheating the visually-impaired?
Speed News Desk | Updated on: 5 May 2016, 13:52 IST
The Northern Railways Delhi Division employs a considerable number of visually-impaired workers. While that sounds like a positive move, a report by a daily has shed light on some startling facts about these workers, employed in the sanitation department of the division.
According to a report in Mail Today, the visually-impaired workers who have been employed in the sanitation department, have Masters and B Ed degrees to their name.
The employees had reportedly applied for jobs - under the disability quota - with the division for desk jobs but they were made to sweep floors, lift garbage and clean sewage despite their disability.
According to the report, a few of these workers also travel long distances to reach their workplaces. The daily quotes some of the workers as saying that not only were the tasks difficult to perform, but some of them were also dangerous.
To add their woes, the Railway division has not provided them with appropriate facilities like places to rest. According to Mail Today, some of the workers were found resting under a tree.
A number of requests filed by these workers to shift them to desk jobs have reportedly been turned down by the division.
The daily quoted the authorities as denying the charges. The authorities claim that all placements have been made according to rules framed by railway board.
Mail Today quoted Dimpy Garg, additional divisional railway manager, as saying that since the employees were selected through the process of direct recruitment, they could only be posted as sanitation workers at divisional-level.
Meanwhile Ameen Khan, one of the visually impaired workers, has reportedly decided to take on the authorities. "I will not succumb to their pressure, I will fight for my dignity," Khan toldthe daily.
First published: 5 May 2016, 13:52 IST
indian railways news
Water scarcity likely to follow forest fires in Uttarakhand
Arvind Kejriwal writes to Delhi University V-C, demands to see PM Modi's degree
READ NEXT >> Arvind Kejriwal writes to Delhi University V-C, demands to see PM Modi's degree

redpajama_set_name: RedPajamaCommonCrawl


text:
The Legends and Heroes Tour
Ezra Lusk
LEGENDS AND HEROES HONOREE - ATLANTA SX FEBRUARY 2020
About Ezra Lusk-
A highly touted amateur rider Bainbridge, Georgia's Ezra "Yogi" Lusk burst onto the scene in 1985 with a second-place finish in the 65cc 7-11 Modified class. The Team Green rider backed it up again in 1986 with two class championships. In his seven-year amateur career, Lusk would rack up a total of four class championships and seven podium finishes.
He would make his pro debut as part of the Factory Suzuki team at the Orlando Supercross in 1992, where he would take a second-place finish. He followed that up with seven more top 10 finishes in 1992 and a fifth-place overall finish in the series.
His success would continue in 1993 with a win at the Orlando Supercross and a third-place overall finish in the series standings. 1994 would see Lusk dominate the 125 East Coast Supercross series with ten podium finishes, including five wins the 125 East Coast Supercross Series Championship.
In a professional career that spanned 13 years, Lusk would achieve podium success 80 times with 16 wins in the premier class. He is tied with David Bailey for 16th on the all-time win list. He also had six victories in the 125 Supercross Class to add to his total.
After retiring from the series in 2004, he made a brief comeback to race the US Open in 2009 and then retired from racing again.
In recent years, Lusk could be found working in the background with professional and amateur racers sharing some of the secrets of his success.
Today he still resides in Georgia with his wife Jennifer and sons Hayden and Hudson.
Nancy Sabater-Schlag
Gary Chaplin


Black Leather Wallet - Beautiful and professional for any man.
This is one of those gifts that are practical, pragmatic and pretty awesome. Pulp Fiction years ago made a lasting point about the value of a personalized wallet. You can create a lasting and perhaps more tasteful memory with having your name engraved on it. Please be aware that our production time for engraved products runs between 3 to 5 business days.


text:
19 Times Neil Patrick Harris Embodied #DadGoals
Happy birthday, NPH!
ByCaroline Bologna
There are a lot of fun and funny celebrity dads, but Neil Patrick Harris might be one of the most beloved.
Harris and his husband, David Burtka, have 6-year-old twins, Gideon and Harper. If their social media posts are any indication, they are nailing this fatherhood thing ― from holidays to family trips to everyday adventures at home.
NPH turns 44 today, so in honor of his birthday (and Father's Day on Sunday), here are 19 times he embodied #DadGoals.
1. When he was game for father-daughter thrill rides
A post shared by Neil Patrick Harris (@nph) on Jan 3, 2017 at 4:25pm PST
2. When he helped his kids design their own desserts
A post shared by David Burtka (@dbelicious) on Nov 22, 2015 at 7:54pm PST
3. When he did a hilarious impression of his babies
4. When he made fanny packs cool
A post shared by Neil Patrick Harris (@nph) on Dec 4, 2013 at 10:45am PST
5. When he gushed about fatherhood
6. When he was the master of family Halloween costumes ... year after year
A post shared by Neil Patrick Harris (@nph) on Oct 20, 2013 at 9:52am PDT
Have a spooktacular night! Here's our family Halloween pic (via @projectphotoboo):
Well done, @DavidBurtka! pic.twitter.com/2ThNeFKdYQ
— Neil Patrick Harris (@ActuallyNPH) October 31, 2013
Happy Halloween from Gotham City!! pic.twitter.com/3gzmG44iEf
A post shared by Neil Patrick Harris (@nph) on Oct 31, 2015 at 1:54pm PDT
7. When he nailed basically every holiday
A post shared by Neil Patrick Harris (@nph) on Dec 17, 2014 at 5:02pm PST
A post shared by Neil Patrick Harris (@nph) on Feb 14, 2017 at 11:52am PST
A post shared by Neil Patrick Harris (@nph) on Apr 20, 2014 at 11:48am PDT
A post shared by Neil Patrick Harris (@nph) on Mar 17, 2015 at 9:17am PDT
8. When he summed up taking care of babies
Neil Patrick Harris/Twitter
9. When he managed to make flying with kids seem fun
A post shared by Neil Patrick Harris (@nph) on Aug 31, 2013 at 9:10am PDT
10. When he brought the twins to the 'Frozen' premiere
A post shared by Neil Patrick Harris (@nph) on Nov 21, 2013 at 12:04pm PST
11. When he taught his kids to be philanthropic
A post shared by David Burtka (@dbelicious) on Jun 9, 2016 at 6:57am PDT
12. When he did Disney World right
A post shared by Neil Patrick Harris (@nph) on Nov 30, 2016 at 8:52am PST
A post shared by Neil Patrick Harris (@nph) on Nov 28, 2016 at 11:56am PST
13. When he unplugged to spend more time with his family
14. When he passed down his love for magic
15. When he let his kids show off their killer style
A post shared by Neil Patrick Harris (@nph) on Mar 8, 2015 at 2:26pm PDT
A post shared by Neil Patrick Harris (@nph) on Sep 8, 2016 at 6:04am PDT
16. When he shared his kids' favorite jokes
17. When he indulged his daughter's love for villains
Is it weird that our daughter Harper is drawn to the villains more than the princesses? (Thanks, @TrueEnchantment) pic.twitter.com/mm1BT8LOEr
18. When he showed the world that love is love
A post shared by Neil Patrick Harris (@nph) on Mar 7, 2014 at 8:14am PST
19. When he summed up what parenthood is all about
Never mind. I've got the two best gifts in the world sitting right in front of me. pic.twitter.com/XQnhaJ4k6L
— Neil Patrick Harris (@ActuallyNPH) June 15, 2013
celebrity childrenNeil Patrick Harris
Caroline Bologna
Culture & Parenting Reporter, HuffPost


text:
OFFICIAL PRESS RELEASE: Flutie Foundation Distributes Over $405,000 in Signature Grant Cycle
Contact: Nick Savarese
The Doug Flutie Jr. Foundation for Autism
508-270-8855 | info@flutiefoundation.org
Flutie Foundation Distributes Over $405,000 to Autism-serving Organizations Through its Largest Grant Cycle of the Year
Boston, Mass. – (December 2020)- The Doug Flutie Jr., Foundation for Autism (The Flutie Foundation), a leading non-profit in the Autism community, distributed over $405,000 to 43 non-profit organizations as part of its annual grant cycle. The announcement came on Wednesday, December 16th during their Flutie Family Holiday Spectacular virtual broadcast event, where each organization was surprised on camera. In light of the extreme challenges that many people with Autism are facing this year, and thanks to the generosity of so many donors, the Flutie Foundation increased their overall funding in 2020 as compared to recent years.
"We are proud to increase our funding this year and invest in an amazing group of resilient organizations through our grant cycle," said Doug Flutie, president and co-founder of The Doug Flutie Jr. Foundation for Autism. "These grants will help them continue their critical work of helping people and families affected by autism live life to the fullest."
The distribution will support families and organizations who represent at least seven different primary languages – English, Spanish, Portuguese, Mandarin, Vietnamese, Arabic and American Sign Language (ASL) across eight states. The funding will directly aid programs that support work, school, safety, social activities, and a wide range of other activities for individuals with Autism and their families.
"During a pandemic, we had to change everything we do," said Ashlee Bronson at The Summit Center. "The Summit Center is excited for this opportunity because it will greatly impact the people we serve with autism to boost their physical fitness and social opportunities during respite care. More than ever, these caregivers of people with autism need a break!"
A full list of the grant recipients for this year's grant cycle are listed here:
Amego Inc.
Asperger/Autism Network, Inc.
Autism Alliance, a program of Advocates -
Autism Families CONNECTicut
Autism Insurance Resource Center
Autism Sprinter
Boston Chinatown Neighborhood Center, Inc. -
Boys & Girls Clubs of Dorchester
Bryce's Journey, Inc.
Community Autism Resources
Extraordinary Ventures-
Friendship Home
Gnome Surf Therapy
Harmony Farms, Inc.
Horace Mann Educational Associates, Inc. (HMEA)
House of Possibilities (HOPE)
Italian Home for Children
J. Arthur Trudeau Memorial Center -
League School of Greater Boston
Melmark New England
NEADS Inc.
Northeast Arc Autism Support Center
POPCORN FOR THE PEOPLE
Promise Inc. -
REED Foundation for Autism
School for Exceptional Studies
Scituate C.O.R.S.E. Foundation
SHARP Musical Experiences
Tech Kids Unlimited
The Arc of Bristol County
The Arc of Massachusetts
The Arc of the South Shore
The Circle of Vietnamese Parents Inc
The North East Educational and Developmental Supports Center Inc.
The Northern Lighthouse, Inc.
The Special Needs Support Center of the Upper Valley
The Sports Museum of New England, Inc.
The Summit Center
Vermont Adaptive Ski & Sports
Vermont Family Network
Waypoint Adventure Inc.
YMCA of The North Shore
ABOUT THE FLUTIE FOUNDATION
Former NFL Quarterback, Doug Flutie, and his wife Laurie started the Doug Flutie, Jr. Flutie Foundation for Autism in 1998 after their son, Dougie, was diagnosed with autism at the age of three. Their personal experience of raising a son on the autism spectrum inspired them to help others on an equally long and challenging journey. Over its 20-year history, the Flutie Foundation has distributed over $15 million to schools and organizations who provide clinical therapies, respite services, recreational programs, social skills training, job supports and more for people affected by autism. The Flutie Foundation also provides education technology tools, adaptive camp scholarships, safety equipment, and direct family support through its partnerships and special initiatives.


My Goal: To make you look like a rockstar. Creative and stylish are two words I would love to be used to describe my craft. Evocative and energetic to add a few more. A real photojournalist captures a moment, he doesn't orchestrate it. My goal is to harness energy in my stills and elicit an emotional response. Every person is different so every photo of every person should different and unique. I love discovering people and what they are passionate about. My job is done and I am happy when I capture the real essence of a person in a photograph.


text:
The Rhythm Boys – amerykański zespół muzyczny, śpiewający głównie muzykę jazzową, działający w latach 1927–1930. Tworzyli go: piosenkarz i aktor Bing Crosby (1903–1977), kompozytor i wokalista Al Rinker (1907–1982) oraz pianista i autor tekstów Harry Barris (1905–1962).

Kariera i rozpad grupy

Crosby i Rinker zaczęli występować razem już w 1925 roku. Harry Barris dołączył do nich w 1927 i oficjalnie powstała grupa muzyczna The Rhythm Boys, która dokonała wielu nagrań wraz z Paulem Whitemanem i jego orkiestrą. Razem wystąpili w filmie Król jazzu (ang. King of Jazz) w 1930 roku, gdzie wykonali kilka piosenek, m.in. So the Bluebirds and the Blackbirds Got Together.

Zespół rozpadł się po tym jak Bing Crosby zdecydował się go opuścić i rozpocząć swoją karierę jako solowy wykonawca. Od tego momentu rosła jego popularność, dlatego uważa się, że jest to jedyny członek tego zespołu, któremu udało się osiągnąć taki sukces po jego opuszczeniu.

The Rhythm Boys spotkali się jeszcze w 1943 roku i pojawili się w audycji radiowej Paul Whiteman Presents, gdzie zaśpiewali Mississippi Mud.

Dyskografia

Single

 1927: Side by Side
 1927: I'm Coming, Virginia
 1930: Them There Eyes
 1932: I'll Take An Option On You
 1932: I've Got To Sing A Torch Song
 1950: Side by Side
 1950: Magnolia

Albumy

 1988: A Recently Discovered 1930 Radio Broadcast

Przypisy

Amerykańskie zespoły muzyczne
Amerykańskie zespoły jazzowe

redpajama_set_name: RedPajamaWikipedia


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What is a practical Sandpit?
It will be an internet-based resource for creating virtual practical resources from basic modules. This approach will make virtual experiments truly open in that instructors can create and tailor resources according to their own practise and students' needs.
Through my participation in the JISC sponsored "Skills for Scientists" project, I created online experiment resources for the JorumOpen repository under Creative Commons licensing. However, making resources "open" does not mean they will be used, and personal experience as a laboratory coordinator suggests that academic creativity in practical learning is vital. It is therefore apparent that although "open" in terms of being accessed, virtual experiment OERs are "closed" in the sense of creative re-use. The practical sandpit will therefore provide a modular means for creating virtual experiments with minimum complexity in programming and resources.
How will we use this in practice. If I have an experiment resource, how do I put it into the sandpit so others can access it?
This is something I'm thinking about in the light of how the sandpit wll finally work. My current thinking would involve resources or assets being placed in an open repository, properly tagged and documented with their purpose and means of use.


At Guthrie's we fabricate rings that reflect our clients' unique relationships – our jewellery is is a symbol of your love for each other and we aim to achieve a one of a kind ring each and every time.
Of course, with such an investment we must also create a design that will stand the test of time – we believe good design is worth the added effort and work tirelessly to provide this for you.
The finer details in an item of jewellery go a long way – while this may not be first apparent, these details are what takes our jewellery from plain to extraordinary.
We love adding a secret touch, often something just for her, to our rings and we always go the extra mile to add this finishing touch to our jewellery.
We offer a unique experience to our customers, we also have many friends when it comes to providing a unique proposal, birthday, anniversary or other special events to you.
Enquire now to see what amazing offers we often have included with our purchases this month.


To encourage a deeper appreciation for Singapore's marine biodiversity, NParks has developed Singapore's first Dive Trail located at Pulau Subar Laut or the Big Sister's Island, Sisters' Island Marine Park.
Two separate trails with varying depths have been established to showcase the variety of marine biodiversity and reed features present in Singapore's waters. Approximately 100m in length, the shallow trail will take divers around a loop to a maximum depth of 6m. The deep trail will reach a maximum depth of 15m.
Divers will be guided through 20 stations marked by signs which will bring their attention to the variety of marine biodiversity and reef features present in Singapore's waters, as well as messages on conservation and responsible diving. Some stations will also engage divers in simple biodiversity or water quality surveys as part of NParks' efforts to encourage participation in our Citizen Science programmes. Learn more about the Dive Trail.
To protect marine biodiversity, the Dive Trail is planned for divers with relevant dive experience and a certification beyond entry level from reputable international training organisations. This certification can be obtained by signing up with certified dive centres. Divers are required to have logged at least 20 dives with one local dive within the past two years. Good buoyancy control is essential to ensure that corals are not broken accidentally when diving. To ensure long term sustainability of the dive trails, training dives will not be allowed at the Dive Trail.
Dive windows will be made available based on the conditions of the marine habitat and periods when currents are suitable for diving. To minimise damage to the reefs and prevent overcrowding, a maximum of 8 divers are allowed for each trail at any given time.
To ensure dive safety while safeguarding the reef habitat, only dive operators that meet all necessary criteria and adhere to regulations established for the Dive Trail are approved to conduct the guided dives.
The cost of the dive trips will be set independently by operators and may differ based on the types of packages and the services offered. Divers are encouraged to contact the approved dive operators for more information on their services and select their preferred dive operators.
NParks does not collect any fees from dive operators for conducting guided dives at Sisters' Islands Marine Park Dive Trail.
Interested dive operators may contact NParks to obtain approval for conducting the guided dives.
Please check the table below for the Dive Operator(s) allocated for the respective dive window(s).
*The dive windows will be confirmed with the Dive Operators when we obtain a better tidal stream projection one month before the stated dive dates.


Kozlinac (lat. Astragalus), veliki rod mahunarki rasprostranjen po ciječlom svijetu, osim Australije, gdje su neke vrste uvezene. Postoji oko 3000 vrsta trajnica, polugrmova i grmova.
Astragalus afghanomontanus Širj. & Rech.f.
Astragalus albispinus Širj. & Bornm.
Astragalus anacamptoides Širj. & Rech.f.
Astragalus ardakensis Širj. & Rech.f.
Astragalus asterabadensis Širj. & Rech.f.
Astragalus austroaltaicus Popov ex Knjaz.
Astragalus bezudensis Širj. & Rech.f.
Astragalus biabanensis Širj. & Rech.f.
Astragalus bifoliolatus Širj. & Rech.f.
Astragalus bijugus Širj. & Rech.f.
Astragalus calcareus Širj. & Rech.f.
Astragalus cesarensis Širj. & Bornm.
Astragalus chamanbidensis Maassoumi & Mozaff.
Astragalus chaworth-mustersii Širj. & Rech.f.
Astragalus coelicolor Širj. & Rech.f.
Astragalus confertiformis Širj. & Rech.f.
Astragalus darii Širj. & Rech.f.
Astragalus dekazygus Širj. & Rech.f.
Astragalus delanensis Širj. & Rech.f.
Astragalus discernendus Širj. & Rech.f.
Astragalus djenarensis Širj. & Rech.f.
Astragalus dscheratuensis Širj. & Rech.f.
Astragalus durudensis Širj. & Rech.f.
Astragalus edelbergianus Širj. & Rech.f.
Astragalus elisabethae Širj. & Rech.f.
Astragalus erwinii-gaubae Širj. & Rech.f.
Astragalus faghirensis Širj. & Rech.f.
Astragalus farakulumensis Širj. & Rech.f.
Astragalus farsicus Širj. & Rech.f.
Astragalus genuflexus Freyn & Sint.
Astragalus getschesarensis Širj. & Bornm.
Astragalus gorganicus Širj. & Rech.f.
Astragalus haematosemius Bornm. & Širj.
Astragalus horridissimus Širj. & Bornm.
Astragalus intermixtus (Boriss.) Litv. ex Širj.
Astragalus kandaharensis Širj. & Rech.f.
Astragalus karsicola Širj. & Bornm.
Astragalus khaneradarensis Širj. & Rech.f.
Astragalus khoshjailensis Širj. & Rech.f.
Astragalus longimucronulatus Širj. & Rech.f.
Astragalus managettae Širj. & Rech.f.
Astragalus manucehrii Širj. & Rech.f.
Astragalus microcalycinus Širj. & Rech.f.
Astragalus mikrophyton Širj. & Rech.f.
Astragalus miseriflorus Širj. & Rech.f.
Astragalus myriocladus Širj. & Rech.f.
Astragalus naftabensis Širj. & Rech.f.
Astragalus neubauerianus Širj. & Rech.f.
Astragalus nigritus Širj. & Rech.f.
Astragalus nigrolineatus Širj. & Rech.f.
Astragalus nishapurensis Širj. & Rech.f.
Astragalus orthocarpoides Širj. & Rech.f.
Astragalus ovoideus Širj. & Rech.f.
Astragalus pachyrhachis Širj. & Rech.f.
Astragalus paghmanensis Širj. & Rech.f.
Astragalus panjaoensis Širj. & Rech.f.
Astragalus patulepilosus Širj. & Rech.f.
Astragalus pseudaureus Širj. & Rech.f.
Astragalus pseudobeckii Širj. & Rech.f.
Astragalus pseudobrachystachys Širj. & Rech.f.
Astragalus pseudohofmeisteri Širj. & Rech.f.
Astragalus pseudoindurascens Širj. & Rech.f.
Astragalus pseudokurrumensis Širj. & Rech.f.
Astragalus pseudoparrowianus Širj. & Rech.f.
Astragalus pseudopendulinus Širj. & Rech.f.
Astragalus pseudosquarrosus Širj. & Rech.f.
Astragalus pseudoszovitsii Širj. & Rech.f.
Astragalus quinquejugus Širj. & Rech.f.
Astragalus radschirdensis Širj. & Bornm.
Astragalus rarus Širj. & Rech.f.
Astragalus rosellus Širj. & Rech.f.
Astragalus rubrolineatus Širj. & Rech.f.
Astragalus serpentinicus Širj. & Rech.f.
Astragalus sharifii Širj. & Rech.f.
Astragalus siahderrensis Širj. & Rech.f.
Astragalus skvortsovii Sytin & L.V.Rjaz.
Astragalus stenopterus Širj. & Rech.f.
Astragalus subrosulariformis Širj. & Rech.f.
Astragalus sulfuratus Širj. & Rech.f.
Astragalus talarensis Širj. & Rech.f.
Astragalus talimansurensis Širj. & Rech.f.
Astragalus tarumensis Širj. & Rech.f.
Astragalus thyrsiflorus Širj. & Rech.f.
Astragalus tolgorensis Širj. & Rech.f.
Astragalus truncatoalatus Širj. & Rech.f.
Astragalus turbat-haidariensis Širj. & Rech.f.
Astragalus vicia Širj. & Rech.f.
Ova je stranica posljednji put uređivana dana 21. ožujka 2019. u 14:11.


Last Saturday my neighbor and friend drove to London, Ohio for their annual Strawberry Festival. I don't know about where you live. But Ohio is big on local festivals and fairs. We have a festival and celebration for just about everything under the sun.
We have several strawberry festivals, a banana-split festival, several ice-cream festivals, an apple festival (not to be confused with the Johnny Appleseed festival or the apple butter festival,) a honey festival, a melon festival, a potato festival, a pumpkin festival, a zucchini festival, a sauerkraut festival, and a tomato festival. I better not forget the popcorn festivals and the maple syrup festival.
We have festivals celebrating sunflowers, wooly bears, tall stack ships, wild turkeys and Bratwursts too.
Then there are the non-food themed festivals: railroads, coal, pioneer days, covered bridge festival, hot-air balloon, a Renaissance Fair, and a moonshine festival to name just a few. And that last one has nothing to do with the phases of the moon, just in case anyone was wondering!
The beautiful, historical, and very interesting Mission of San Juan Capistrano in California and their swallows have nothing on Ohio. We have a festival in early spring marking the day when the Hinckley Buzzards come back to Ohio from their southern winter stomping grounds.
Believe it or not there is also a Tobacco Festival featuring a tobacco worm race.
Ohio is also home of a festival honoring twins and their families. Twins Days Festival in, where else? but Twinsburg, Ohio.
The festival I love the most though is the Columbus Jazz & Rib Fest. Smooth music and good eats. Life doesn't get much better than that.
While there was no cool jazz or hot ribs at the Strawberry Festival in London, Ohio Saturday, the air was loud with the sound of car engines roaring and revving-up at the annual Cobra Car Show and charity fundraising event benefiting Cystic Fibrosis.
The Cobra is a British built and designed sports and race car that was produced during the 1960s.The London Strawberry Festival is one of the biggest shows for Cobra enthusiasts nationwide.
What can I say? Ohioans love to party and eat. From strawberries and sports cars to tobacco worms, we find many new and inventive ways to mix it up.
I have two recipes for you today.
This is more technique than recipe. You can make any flavor butter you want. One of my favorites is Lime Butter. I just mix a little lime juice and lime zest with the softened butter and keep it in my freezer. I like to use the Lime Butter on Broiled Salmon and on corn-on-the-cob.
You can also use up any herbs lurking in your fridge by making Herbed Butter. I like the combination of chopped fresh garlic (or onion or shallots), parsley, tarragon, and thyme. But you can use any fresh herb or herbs you like.
Last post I mentioned one of the ways you can save money at the grocery store is to just buy plain frozen vegetables instead of those in butter or seasoned sauces. It is cheaper to make your own. This recipe/technique is how you can do it. Make up several different combinations on flavored butters and keep them in your freezer. Then add a slice or two to your frozen vegetables after they are cooked.
You can keep these flavored butters up to one year in the freezer. If you are going to store them that long though, place the frozen butter in aluminum foil, wrap tightly and place in a plastic bag.
You should be able to get at least 10 slices of butter from 1 roll.
When the butter is soft use the heel of your hand to flatten it some what on a cutting board.
Place the strawberries on top of the butter and using a spoon or a knife mix the two together very well and form into a rough roll.
Make a rough loaf or roll with the butter.
Now place the butter on a piece of waxed paper. Fold the top edge of the waxed paper over and then under the butter. Using your hands and finger smooth the butter into a log-shaped roll. Now smooth it out. At this point I sometimes have to put the butter roll into the freezer for about 5 minutes. Then I can smooth it out and make a nice roll.
Once you have it wrapped in the wax paper you can begin to shape and smooth the roll out.
Seal the roll and turn the ends of the wax paper in opposite directions. Freeze for at least 1 hour.
To use, take the roll out of the freezer about 5-10 minutes before using. Then just slice it into rounds and spread on a hot biscuit or toast.
This recipe is perfect for me, for as you know I am secretly the lazy cook. This is an easy way to make a jam like to spread for bread. Except you don't have to sterilize the jars and all that other time-consuming stuff you have to do to make jam and jelly. I have made jam before. So it is not like I can't do it. It is just that this bread spread is so much quicker and easier. And just as delicious.
Once made you will need to refrigerate this spread. It will keep 3-5 days in the refrigerator. That is why I make just a little of it at a time. If you have a larger family the recipe can be doubled.
Not only can you put this yumminess on bread, you can top ice cream with it, or mix it in hot breakfast cereals like oats or my personal favorite Cream of Wheat.
Today I just used strawberries, but you can mix several fruits together, for instance, strawberries and blueberries and/or blackberries. One of my favorite bread spreads is peaches and raspberries; a little slice of heaven right here on earth.
Place all of the ingredients in a pan over high heat and bring to a boil.
Reduce the heat to medium high and cook for about 15-20 minutes.
Stir occasionally until the mixture reaches a semi-thick or jam like consistency.
Cool and spread on biscuits or other breads with or without butter.
This is for about 10 tablespoons of spread.


text:
Wellness at work
The latest Workspace Business Insights Dinner was bang on trend at The Pill Box in Bethnal Green. The theme was 'wellness at work' and the panel of guest speakers were truly outstanding, if we do say so ourselves.
So how do organisations really help their employees achieve that illusive work/life blend? With more and more businesses making a conscious and determined effort to improve the office environment to benefit and nurture employees' general health, the topics of mental health and wellness are on almost everyone's agenda.
Day to day stresses from working long hours, meeting tight deadlines and busy commutes are being combatted with offices that make space for staff to relax, exercise and eat healthily. The Workspace panellists are all experts in the subject of wellbeing. So after a warm welcome from Workspace Energy & Sustainability Manager, Karen Jamison, Leanne Spencer, CEO of Bodyshot Performance chaired a discussion around the issues of wellness at work.
Kicking off with a quote from Arianna Huffington, who previously ran The Huffington Post, Leanne posed the question of how important it is that an employer or corporation take responsibility for the health and wellbeing of staff:
"This is a tough economy. Stress reduction and mindfulness don't just make us happier and healthier, they're a proven competitive advantage for any business."
The three panellists, James Routledge Co-Founder of Sanctus, Alyssia White, Instructor at House of Voga and John Grummit, Vice President of Diabetes UK and CEO of Changing Health tackled the issue and discussed how important it is that companies recognise that by looking after their staff, they can reduce staff turnover and foster a happier working environment for everyone.
#WBIDinner host @BodyshotPT have released a fantastic podcast, continue exploring the theme of #wellnessatwork: https://t.co/CQbJeMV5K3 pic.twitter.com/T0oTigMGUW
— Workspace (@WorkspaceGroup) 7 August 2017
There was an agreement among the panellists that there is a difference between good stress and bad or chronic stress. So often we only associate the word with negative connotations, but as Leanne reminded the audience "I put myself through an hour of HIIT this morning, now that's stress", but it set her up for the day and had a positive impact.
John added that there is a lot of evidence to suggest that if we expose ourselves to intermittent bouts of stress, it can help us to focus and grow, but if stress is sustained our bodies cease to be able to deal with it properly. It is far easier for the body to cope with moving in and out of stressful situations, such as the daily commute, than to deal with them for overly long periods of time.
Alyssia explained how a Voga class can help channel stress and turn it into something positive. Much like Leanne's workout, Voga is a physical challenge for the body, but includes aspects of yoga to reduce stress, increase calm and balance and improve muscle tone and core strength.
Further to talking about the responsibility an employer has to employees to encourage a healthy work/life balance and improve attitudes towards mental health, the panel discussed an email that recently went viral. Madalyn Parker, a web developer at Olark, recently took a couple days off work, to "focus on my mental health." Olark's CEO replied to her email, and his response was quickly spread across the internet as a signal to others that this is how mental health should be dealt with in the workplace.
James expressed his concern that many managers and businesses see stress and mental health as something to manage or pay lip-service to. His company, Sanctus, are hoping to create the world's first mental health gym. After experiencing burnout in his own career, James knows the importance of being able to talk about mental health freely and without stigma. His brand aims to transform how people perceive these issues and create an environment where people can become the best version of themselves and be happier at work.
Other topics the panel delved into included the power of social media, how much sleep comes into play when it comes to keeping healthy and the little changes we can all begin to make to improve our own health and wellbeing.
Alyssia demonstrated a useful 'power pose' that she finds helps with feeling confident and dealing with difficult situations. She explained "when people first come to a Voga class, they've been used to sitting at their desk and are stuck in the same routine, they can be quite timid. That's why we add music to it, so they can switch off and be freer. When you're free you are more open and you can let yourself go and that's how you can start to build on a new posture or pose you might previously have been struggling with."
Voga encourages participants to take each class as it comes, one step at a time and most of all they want people to have fun and enjoy the time.
Alyssia from @houseofvoga encourages setting small achievable goals to improve your physical health #AlternativeFitness pic.twitter.com/JdReD3Dzae
— Workspace (@WorkspaceGroup) 13 July 2017
When it comes to technology, the panel agreed that it can be a complex matter. On the one hand, being connected to people can help with feeling supported or gaining advice from someone on an issue you might be facing. But they also explored the issue that sometimes social media can be detrimental, especially if we compare our lives to others on platforms such as Instagram.
James said: "our life is full of what the other person down the road's doing… They might not mean to make us feel bad, but they might share a picture of an amazing lunch they just had and you just spent £7 on a sandwich."
He explained that it can feel suffocating to see all the things we 'should' be doing and that social media has in some ways 'flipped our world upside down' and we're not sure what to do about it yet.
Leanne suggested the concept of putting yourself on 'flight mode' which can be as literal as putting your phone to that setting, or a more in-depth thing of making yourself "unreceptive to anything that's incoming" just for a while.
The panel concluded with a chat about sleep and how important it is. They agreed that less emphasis should be placed on the quantity and more on the quality of the rest we get. Then the audience settled down to enjoy some delicious food and had time to chat amongst themselves before the illuminating Q&A portion of the evening.
The Workspace Business Insight Dinner organised by the business network Knowledge Peers provided an opportunity for businesses all over London to come together. There was a lot of positive feedback from the event, with many people relishing the chance to talk to others about how their business could improve when it comes to dealing with mental health.
Farah Qureshi - Jewellery designer and maker based at Record Hall said: "The #WBIDinner was the first event that I have attended as I have just recently moved to Workspace. I enjoyed attending the latest event and learning more about the topics discussed; it was great to meet other businesses and the food was good too."
"Thought-provoking discussion and excellent networking opportunities" said Ben Ellison from Urban Jungle based at Kennington Park.
Make sure you visit Workspace's Facebook page for your chance to watch the Facebook Live stream of the panel discussion and don't forget to 'Like' our page to keep up-to-date on future events. Or, watch a r
If you're a Workspace customer and you'd like to come to our next quarterly Business Insights Dinner, keep your eyes on our Events Calendar which is packed with insightful workshops and seminars held at many of our business centres each week.
With her company, Bodyshot Performance, Leanne has recently launched a podcast. Remove the Guesswork: health, fitness and wellbeing for busy professionals is for people who want to improve their health, fitness and wellbeing, but are also very busy. Each episode is 30 minutes long and focuses on a topic as well as featuring interviews from people who have transformed their health – and how they did it. You can listen and download it, here.


Machine has been chugging away for something along the lines of 30 hours on this batch of downloads. The Mini itself is not too hot, but the external hard drive I'm using is quite warmish. Be glad when this is done, I'd like to give it a break. Gone fairly smoothly once I discussed with Apple techs.


text:
\section{Introduction}

In today's data rich world, we are collecting data on almost every aspect of human activities. Such increasing availability of data is enabling us to discover knowledge and insights that provide an opportunity of making informed decisions in both public and private sectors. While association and model-based prediction help us extract value from data, robust causal inference enables us to identify the effect of potential interventions. Causal inference identifies key insight from data by studying how different actions, treatments, or interventions (or many potential interventions) affect certain desirable outcomes \citep{stuart2010matching}. Estimating such cause-effect relations is the cornerstone of policy decision making. Moreover, in the case of social studies, healthcare, and medicine, the question of causality is often inevitable. In this paper, we develop novel techniques that helps us generating efficient estimates of causal quantities from large-scale, high-dimensional observational data.

Over the last few decades, methodological advances have improved analytical methods for generating unbiased causal effect estimators from observational data \citep{rosenbaum2010design}. Matching, more specifically, Propensity Score Matching is one of the most popular methods for identifying causal effect from observational data \citep{stuart2010matching,brookhart2006variable}. While the usage of propensity score based techniques are increasing in health and social science literature, there is no specific guideline for selecting variables for propensity score model \citep{brookhart2006variable} or other matching methods like Mahalanobis Distance Matching \citep{rubin:1979}, Coarsened Exact Matching \citep{king:2011}, and Mixed Integer Programming Matching \citep{zubizarreta2012using}. In addition, researchers often take a ``throw in the kitchen sink'' approach and control for all available covariates just to make sure that the estimate is free of confounding bias \citep{shortreed2017outcome}. In support to this practice, in the past, several researchers have claimed that the largest set of observed pre-treatment covariates protects against unobserved confounding. However, recent literature \citep{shortreed2017outcome,brookhart2006variable,vanderweele2011new} show that controlling for all variables may not lead to the expected reduction in bias, even if it includes a sufficient set of confounders. On the other hand, it inflates the variance of the estimate of the causal effect.

\subsection{Challenges in Variable Selection for Causal Inference}
While taking the parametric approach, causal effect estimation methods mainly consider two statistical models: outcome model and treatment allocation mechanism, both as functions of pre-treatment variables \citep{ertefaie2018penalization}. Variables that appear in both models are regarded as confounders. Inclusion of such variables in the propensity score model or in the matching mechanism ensures an unbiased estimate of the causal quantity considering all such confounders are observed. Ideally, a causal variable selection process must start with the domain knowledge on the underlying treatment allocation mechanism and the predictors of the outcome. Unfortunately, researchers often do not have the access of the expert knowledge, instead, are burdened with a large set of pre-treatment variables. When domain knowledge is available, the expert selection can result a significantly large set of variables and particularly, include pure predictors of treatment and pure predictors of outcome. Moreover, high-dimensional observational data can become challenging to comprehend for an expert and may contain spurious variables that does not have any relation with treatment or outcome.

Altogether, the variable selection for causal effect estimation poses three major problems. First, inclusion of pure treatment predictors and weak confounders that are strongly related to treatment inflate the variance of the estimate, both asymptotically \citep{rotnitzky2010note} and in finite sample \citep{brookhart2006variable}. In addition, inclusion of such variables can increase bias if the complete set of confounders are not observed \citep{pearl2012class,wooldridge2016should}. In contrast, selecting the outcomes predictors can enhance the efficiency of the causal estimate \citep{brookhart2006variable,rotnitzky2010note}. Second, selecting pure treatment predictors increase the possibility of violating the positivity assumption \citep{stuart2010matching,schnitzer2016ctml}. For example, a strong predictor of treatment can have values for which the probability of receiving treatment is almost zero. Selecting that variable can result in estimating zero probability of receiving treatment in finite sample and lead to positivity violation. \citet{schnitzer2016ctml} call such violation as artificial positivity violation. Finally, inability to discern noise from relevant variable influences the bias and variance of the treatment estimate.

\subsection{Relevant Literature}
To overcome these challenges, the matching mechanism or the Propensity Score (PS) model should include the confounders (predictors of both outcome and treatment) and pure outcome predictors while eliminating pure treatment predictors and noise. There is a vast literature in machine learning that discusses variable selection for prediction purposes, however, there is little work on identifying variables with the objective discussed above.
Bayesian adjustment for confounding (BAC) \citep{wang2012bayesian} is one of the earliest work in variable selection for causal inference. BAC relies on tuning a parameter representing the odds of a variable that is associated to both outcome and treatment. \citet{wilson2014confounder} selects the counfounders using a decision-theoretic approach. First, they select a group of candidate models by utilizing the posterior credible region of parameters that are identified by a fitted Bayesian Regression model. The final model is determined from the candidate set through penalization of models that do not incorporate confounders. However, several simulation studies \citep{stijn2012DiscussionBAC,lin2015regularization} reveal that this approach often includes variables that are only associated with treatment resulting to inflated variance of the causal effect. Motivated by the graphical causal inference framework \citep{pearl2009causality} and Bayesian model averaging, Talbot et al. \citep{talbot2015bayesian} and Talbot and Beaudoin \citep{talbot2020generalized} proposed a Bayesian causal effect estimation (BCEE) method. In BCEE, posterior probabilities of each model are considered as weights in the averaging process while probabilities are identified from conditional outcome model, given treatment and all observed covariates. However, this approach may exclude weak confounders \citep{ertefaie2018penalization}.

Over the years, several researchers proposed machine learning methods to fit either the treatment model \citep{lee2010improving,westreich2010propensity,austin2013using} or the outcome model \citep{wang2021flame}. \citet{brookhart2006variable} and \citet{schnitzer2016ctml} show that such approaches primarily selects variables that are strongly related to either treatment or outcome. Modified penalized regression methods are also utilized in variable selection for causal inference. For instance, Ertefaie et al. \citep{ertefaie2018penalization} used a weighted sum of treatment and outcome model. In addition, they introduced a customized $l_{1}$ penalty function to heavily penalize the treatment predictors. On the other hand, \citep{shortreed2017outcome} uses an adaptive lasso approach which applies less penalty to the outcome predictors. While \citep{ertefaie2018penalization} improves the small sample properties of outcome regularization, the estimated coefficients do not have any causal interpretation and the parameter selection in \citep{shortreed2017outcome} is designed solely for Inverse Probability of Treatment Weighted (IPTW) estimator. Moreover, both approaches \citep{shortreed2017outcome,ertefaie2018penalization} will perform poorly on correlated data due to the inclusion of $l_1$ penalty and many causal inference applications, specially in healthcare and medicine, the covariates are highly correlated.

In causal inference with matching method, the variable selection process is followed by sampling process to match the covariate distributions of treated and control groups. The matched counterparts of the treated units in the control group are interpreted as counterfactuals, and the average treatment effect on treated (ATT) is estimated by comparing the outcomes of every matched pair \citep{stuart2010matching}. One of the popular matching method is the Nearest Neighbor Matching (NNM) \citep{rubin:1973a,rosenbaum:2017} which forms a treated-control pair by matching a treated unit to its nearest control unit based on some prespecified distance metrics. Some commonly used NNM matching methods are Propensity Score Matching (PSM) \citep{rubin:1983,rubin:1985}, Coarsened Exact Matching (CEM) \citep{king:2011}, Mahalanobis Distance Matching (MDM) \citep{rubin:1979}, and Genetic Matching (GM) \citep{sekhon2012matching}. In the case of high dimensional data, these widely used NNM methods perform poorly. For instance, PSM projects the covaiate vector space into one dimension disregarding the neighbourhood structure of the dataset, and the matched units in projected dimension often differ on important covariates in actual dimension. MDM is prone to bias in high dimensions since it imposes parametric assumptions, so does the GM. In the presence of large number of covariates, to obtain a good match, CEM discards a lot of samples. In addition, recent results show that the bias of a NNM estimator increases at a rate of $O(N^{-1/p})$ where $N$ is the number of samples and $p$ is is the number of variables \citep{abadie:2006}. Dimension reduction techniques like Principle Component Analysis (PCA) are commonly have been studied extensively in Machine Learning literature where the main objective is to improve the models' predictive accuracy by retaining the information content.
However, in observational studies using matching methods, the objective is to ensure the local neighborhood structure of the data as a treated sample is essentially matched to a control sample within its locality.

\subsection{Contribution}

In this paper, we propose a unique modeling framework termed as Outcome Adaptive Elastic Net (OAENet) specifically designed for causal inference to select the confounders and outcome predictors for inclusion in the propensity score model or in the matching mechanism.
OAENet provides two major advantages over existing methods: it performs superiorly on correlated data, and it can be applied to any matching methods and any estimates. We compare the performance of the proposed OAENet with state-of-the-art variable selection techniques for causal inference on simulated data. Our preliminary analysis shows that the OAENet achieves less bias, less variance and has better accuracy in selecting variables compared to the benchmark methods. In addition, we plan to develop a policy decision case study from real-world, high dimensional data to show the applicability of the proposed method.

The remainder of the paper is organized as follows. In section \ref{cause_high_dim}, we discuss causal inference under potential outcome framework, objective of variable selection problems causal inference, and review adaptive elastic-net. In section \ref{out_ad_enet}, we outline the Outcome Adaptive Elastic Net approach. We present the empirical performance of the proposed methods and compare it with alternative approaches on both simulated data in section \ref{high_dim_exp}. Finally, we provide the concluding remarks in section \ref{high_dim_conc}.

\section{Causal Inference from High-dimensional Data} \label{cause_high_dim}
In this section, we provide an overview of causal inference under potential outcome framework, common assumptions, variable selection strategies, and adaptive elastic-net.

\subsection{Causality under Potential Outcome Framework}
In this paper, we consider the potential outcome framework \citep{holland1986stat} and matching methods developed under this framework for identifying treatment effect from observational data. In the matching method, an unbiased estimate of causal inference can be achieved if treatment unit $t \in \mathscr{T}$ is exactly matched with a control unit $c \in \mathscr{C}$ in terms of their covariate set $\mathbf{X} \in \mathcal{X}$ \citep{rubin:1983}. However, in most of the applications, it is impossible to achieve exact matching \citep{zubizarreta2012using,rubin:1983,nikolaev2013balance,King:2019}. A wide variety of matching methods are employed to make $(t,c)$ pairs (or subset $C\subset \mathscr{C}$ and $T \in \mathscr{T}$) as similar as possible \citep{zubizarreta2012using,zubizarreta2015stable,rubin:1983,nikolaev2013balance} in terms of the covariate set $\mathbf{X}$. One of the popular methods (if not the most popular) \citep{pearl2010foundations,stuart2010matching,zubizarreta2012using} is the propensity score matching (PSM) method \citep{rubin:1983,rubin:1985} that employs a logistic model to estimate each sample's propensity of receiving treatment and find the $(t,c)$ pairs or subset $T,C$ by minimizing the differences in their propensity scores. The matching process is repeated and evaluated iteratively until the desired quality of the matches achieved.

\subsection{Objective of Variable Selection for Causal Inference} \label{obj_var_sel}
The set of covariates $\mathbf{X}$ can be divided into different sets based on their relations (or the lack of relation) with outcome and treatment. There are different statistical objectives for variable selection in causal inference which use different combinations of these variable sets. For instance, a causal inference expert may decide to include largest possible set of covariates to control for any possible confounding. Another expert may choose to minimize the variance of the estimate and decide to include all the predictors of the outcome which can be done by an conditional outcome model $E(Y|T,X)$. Alternatively, we can choose to reduce the bias and use a propensity score model to choose the predictors of the treatment. This approach is suboptimal in terms of bias as it may exclude weak predictors of treatment. As we mentioned earlier, inclusion of the predictors of treatment through the propensity score model may exclude moderate to weak confounders and artificially inflate the variance of the estimate. On the other hand, considering just the confounders may not achieve minimum variance estimate in finite-sample. Therefore, the ideal objective of variable selection for causal inference should consider the all confounders (variables that are associated with both treatment and outcome) and the predictors of outcome.

\subsection{Adaptive Elastic Net}
Adaptive elastic-net proposed by \citet{zou2009adaptiveEnet} is an extension of the popular model selection and estimation method of Elastic-net \citep{zou2005Enet}. Elastic-net combines the automatic variable selection property of $l_1$ regularization and the stabilizing property of $l_2$ regularization. Adaptive elastic-net further improves the finite sample performance of elastic-net by introducing an adaptive weight in the $l_1$ penalty. Under weak regularity condition, \citet{zou2009adaptiveEnet} showed that the adaptive elastic-net meets the oracle properties which implies that this adaptive procedure selects the right variables with high probability (i.e., consistency) and estimated coefficients are asymptotically normal. An elastic-net estimator takes the following form:
\begin{align}
    \hat{\boldsymbol{\beta}}(enet) = (1+\frac{\lambda_2}{n})\left\{ {\argmin}_{\boldsymbol\beta} \left \Vert\mathbf{y}-\mathbf{X\beta}  \right\Vert^2_2 + \lambda_2\left \Vert\boldsymbol{\beta}\right\Vert_2^2 + \lambda_1 \left \Vert\boldsymbol{\beta}\right\Vert_1^1 \right\} \label{enet}
\end{align}
where, $n$ is the sample size, $\mathbf{X}$ and $\mathbf{y}$ are data matrices, and $\lambda$s are the regularization penalties. Adaptive elastic-net first computes the elastic-net estimator $\hat{\boldsymbol\beta}(enet)$ as defined in\eqref{enet}, and then uses the estimator as the adaptive weights $\hat{w}_j$ for all $p$ variables as the following.
\begin{align}
    \hat{w}_j = \left(|\hat{\beta}_j(enet)|\right)^{-\gamma}, \quad \quad j = 1,2,\cdots,p
\end{align}
Here, $\gamma$ is a positive constant. Now, the adaptive weights $\hat{w}_j$ is multiplied with the $l_1$ penalty to create variable and adaptive penalties among the features. Therefore, the adaptive elastic-net takes the following form:
\begin{align}
    \hat{\boldsymbol{\beta}}(adenet) = (1+\frac{\lambda_2}{n})\left\{ {\argmin}_{\boldsymbol\beta} \left \Vert\mathbf{y}-\mathbf{X\beta}  \right\Vert^2_2 + \lambda_2\left \Vert\boldsymbol{\beta}\right\Vert_2^2 + \lambda_1 \sum_{j=1}^p \hat{w}_j\left \Vert\boldsymbol{\beta}\right\Vert_1^1 \right\} \label{adenet}
\end{align}
As we can see from equation \eqref{adenet}, if we consider zero $l_2$ penalty then we can recover the adaptive lasso proposed by \citet{zou2006adaptiveLas}.

\section{Outcome Adaptive Elastic-net}\label{out_ad_enet}
Lets denote the set of confounders, variables that are associated with both treatment and outcome, as $\mathbf{X}_C$, the treatment predictors as $\mathbf{X}_T$, and the outcome predictors as $\mathbf{X}_P$. In general, high dimensional data includes a large number of spurious variables that are neither associated with treatment nor the outcome. We refer to those variables as $\mathbf{X}_S$. As we mentioned before, the ideal objective for variable selection for causal inference is to select $\mathbf{X}_C$ and $\mathbf{X}_P$, and remove $\mathbf{X}_T$ and $\mathbf{X}_S$. To that end, we modify the adaptive elastic-net to accommodate this objective and propose the Outcome Adaptive Elastic-net. In this project, we confine our focus on the binary treatment.

Adaptive elastic-net provides the opportunity to create variable penalties for different covariates. We use this to connect the two models: outcome model and treatment model. At the first step, we consider the outcome model, however, instead of elastic-net regularization, we use ordinary linear regression to identify the strength of variables' association to the outcome. As we will use the estimated coefficients to reduce the penalty on the variable set $\mathbf{X}_P$ instead of model selection, we replace this step of adaptive elastic-net with ordinary least-square linear regression.
\begin{align}
       \textrm{\textit{Step 1 - Outcome Model:}    } \hat{\boldsymbol{\beta}}(OLS) & = \argmin_{\boldsymbol\beta} \left \Vert\mathbf{y}-\mathbf{X\beta}  \right\Vert^2_2 \\
        & = \argmin_{\beta} l_n(\boldsymbol{\beta}; y, \mathbf{X}) \textrm{ (MLE of $\beta$) }
\end{align}

Lets assume, $\hat{\boldsymbol{\beta}}(OLS)$ is the maximum likelihood estimate for the coefficients of the variables when regressed on outcome $y$: $\hat{\boldsymbol{\beta}}(OLS) = \argmin_{\beta} l_n(\boldsymbol{\beta}; y, \mathbf{X})$. Now, at the second step, we use a logit model with adaptive
elastic-net regularization as our treatment model.

\begin{align}
    \textrm{\textit{Step 2 - Treatment Model:}    } \hat{\boldsymbol{\alpha}}_{(OAENet)} = \left( 1+\frac{\lambda_2}{n} \right) \Biggl [ {\argmin}_{\boldsymbol\beta} \biggl \{ \sum_{i=1}^n  ( -a_i(x_i^T\alpha) +   \mspace{15mu} \notag\\
   log(1+e^{x_i^T\alpha})  ) + \lambda_2\left \Vert\boldsymbol{\alpha}\right\Vert_2^2 + \lambda_1 \sum_{j=1}^p \hat{w}_j \left \Vert\boldsymbol{\alpha}\right\Vert_1^1 \biggr \} \Biggr ] \label{oaenet}
\end{align}
where, $a_i$ is the indicator of treatment status, $\hat{w}_j = (|\hat{\beta}(OLS)_j|)^{-\gamma}$ with $\gamma > 1$. Here, we multiply the inverse of the coefficients of the outcome model with $l_1$ penalty. It apply smaller penalties to variables that are highly associated with the outcome. Therefore, this model is more likely to includes variables that are confounders and outcome predictors.

\section{Numerical Experiment}\label{high_dim_exp}
In this section, we discuss the performance of the proposed Outcome Adaptive Elastic-net on simulated data and compare it with state-of-the-art methods.

\subsection{Simulation Design and Scenarios}
We design the simulation study based on the scenarios designed by \citet{shortreed2017outcome} and \citet{ertefaie2018variable}. We consider two scenarios where covariates are generated from multivariate Gaussian distribution. We confine our focus in the realm of binary treatment and continuous outcomes. For each scenario, we use two correlation structures: independent covariates (correlation = 0) and strongly correlated (correlation = 0.5). For all the scenarios, we simulate $n=1000$ data points, use $\gamma = 3$ and select the penalty parameters with 5-fold cross-validation. For all the scenarios, data is normalized before using it for variable selection.

\textbf{Scenario 1} is adapted from \citet{shortreed2017outcome}. The data generating equations are the following:
\begin{itemize}
    \item Covariates: $X_1, X_2, \cdots,X_{100} \stackrel{iid}{\sim} N(0,1)$ with correlation $\rho = 0$ (Scenario 1A) and $\rho = 0.5$ (Scenario 1B)
    \item Treatment: $A \sim Bernoulli( p = Expit(X_1 + \cdots + X_{10} + X_{21}+\cdots+X_30))$ where, $Expit (x) = \frac{e^x}{1+e^x} $
    \item Outcome: $y = TE + 0.6X_1+0.6X_2+\cdots+0.6X_{20}$ with true treatment effect $TE = 0.5$
\end{itemize}

\textbf{Scenario 2} is adapted from \citet{ertefaie2018variable}. The data generating equations are the following:
\begin{itemize}
    \item Covariates: $X_1, X_2, \cdots,X_{100} \stackrel{iid}{\sim} N(0, \sigma^2=4)$ with correlation $\rho = 0$ (Scenario 2A) and $\rho = 0.5$ (Scenario 2B)
    \item Treatment: $A \sim Bernoulli( p = Expit(0.5X_1-X_2+0.3X_5-0.3X_6+0.3X_7-0.3X_8))$ where, $Expit (x) = \frac{e^x}{1+e^x} $
    \item Outcome: $y = TE + 2X_1+2X_2+5X_3+5X_4$ with true treatment effect $TE = 1.0$
\end{itemize}

We compare the performance of the proposed method with the following techniques:
\begin{itemize}
    \item BCEE: Bayesian Causal Effect Estimation method proposed by \citet{talbot2015bayesian} using the \texttt{R} package \texttt{BCEE} \citep{bcee2015package}
    \item Outcome adaptive lasso (OLas): Outcome adaptive lasso proposed by \citet{shortreed2017outcome}
    \item BAC: Bayesian Adjustment for Confounding proposed by \citet{wang2012bayesian} with the \texttt{R} package \texttt{BACR} \citep{bacr2015package}
    \item Bor(T), Bor(Y): Identifying the treatment predictors - Bor(T) and the outcome predictors Bor(Y) using random forest with the \texttt{R} package \texttt{Boruta} \citep{boruta2020package}
\end{itemize}

In addition to the above mentioned methods, we include following three benchmarks to evaluate the performance of the proposed technique. As we are simulating the dataset, we take the advantage of our knowledge on the true relation between variables, treatment, and outcome.
\begin{itemize}
    \item Target (Targ): Estimating the causal effect with the set of target variables $\mathbf{X}_C$ and $\mathbf{X}_P$.
    \item Confounders (Conf): The set of confounders, variables that are associated with both treatment and outcome ($\mathbf{X}_C$).
    \item Potential confounders (Pot.Conf): Set of potential confounders, variables that are only associated with treatment ($\mathbf{X}_C$ and $\mathbf{X}_T$).
\end{itemize}

\subsection{Discussion}
We evaluate the performance of the Outcome Adaptive Elastic-net in terms of three metrics: bias and variance of the estimate, and the ability to select the target variables. In this experiment, our target is to select the variables that are associated with both treatment and outcome ($\mathbf{X}_C$), and the variables that are associated with only outcome ($\mathbf{X}_P$). For each scenario, we generate 1000 datasets and uses the variable selection techniques to select appropriate variables for the inclusion in the propensity score model. Using the selected variables, we perform nearest neighbor matching \citep{stuart2010matching} and compute the average treatment effect on treated (ATT) from the matched samples. We also compute the proportion of the time a variable is selected by the variable selection techniques.

In figure \ref{fig:att}, we present the box-plot of the ATT computed for all the scenarios. The objective is to estimate the ATT as close as the target (presented as ``Targ" in the figure \ref{fig:att}). As we can see, the proposed Outcome adaptive elastic-net (presented as ``Onet") and BCEE closely follows the target in all scenarios.
\begin{figure}[ht!]
\centering
    \begin{subfigure}[b]{0.45\textwidth}
    \centering
    \includegraphics[width=1\textwidth]{att_1a.png}
    \caption{}
    \end{subfigure}
    \begin{subfigure}[b]{0.45\textwidth}
    \centering
    \includegraphics[width=1.08\textwidth]{att_1b.png}
    \caption{}
    \end{subfigure}
    \begin{subfigure}[b]{0.45\textwidth}
    \centering
    \includegraphics[width=1\textwidth]{att_2a.png}
    \caption{}
    \end{subfigure}
    \begin{subfigure}[b]{0.45\textwidth}
    \centering
    \includegraphics[width=1\textwidth]{att_2b.png}
    \caption{}
    \end{subfigure}
    \caption{Average Treatment effect on the treated (ATT) for (a) Scenario 1A (b) Scenario 1B (c) Scenario 2A (d) Scenario 2B. Horizontal dashed line indicate the true treatment effect.}
    \label{fig:att}
\end{figure}

BAC, Bor(T), and Bor(Y) are omitted in the figure \ref{fig:att}(b) due to the lack of matched pairs. Scenario 1B has 100 variables and high correlation ($\rho =0.5$). In such situations, BAC, Bor(T), and Bor(Y) select most of the variables including the spurious variables. It is almost impossible to find a sufficiently large matched set of sample from 1000 data points. However, the proposed outcome adaptive method along with outcome adaptive lasso and BCEE can identify confounders and the outcome predictors. In addition, recent literature \citep{brookhart2006variable,shortreed2017outcome,ertefaie2018penalization,ertefaie2018variable} suggest that considering only the treatment predictors does not reduce bias but inflate variance of the estimate. Our experiment also shows similar result as we can see in figure \ref{fig:att} (a), (c), and (d), Bor(T) which considers only the treatment predictors has the most variance in ATT among all the variable selection techniques.

In figure \ref{fig:prop}, we present the proportion of the time a variable is selected by the variable selection techniques for different scenarios. In scenario 1A and 1B, our target is to select the confounders (variables $X_1,\cdots,X_{10}$) and the outcome predictors (variables $X_{11},\cdots,X_{20}$). In scenario 2A and 2B, an ideal variable selection algorithm should select variables $X_1,\cdots,X_4$ among which, first two are confounders and the last two are outcome predictors. In almost all the cases, outcome adaptive elastic-net selects the confounders and in majority of the cases, it selects the outcome predictors. BCEE closely follows the target by selecting all the confounders and outcome predictors. However, it often selects some spurious variables in the process. Outcome adaptive lasso also performs well in selecting the confounders but it overly penalizes the outcome predictors. BAC, Bor(T), and Bor(Y) performs poorly in this metric and often selects spurious variables.
\begin{figure}[ht!]
    \centering
    \begin{subfigure}[b]{0.45\textwidth}
    \centering
    \includegraphics[width=1\textwidth]{prop_1a.png}
    \caption{}
    \end{subfigure}
    \begin{subfigure}[b]{0.45\textwidth}
    \centering
    \includegraphics[width=1\textwidth]{prop_1b.png}
    \caption{}
    \end{subfigure}
    \begin{subfigure}[b]{0.45\textwidth}
    \centering
    \includegraphics[width=1\textwidth]{prop_2a.png}
    \caption{}
    \end{subfigure}
    \begin{subfigure}[b]{0.45\textwidth}
    \centering
    \includegraphics[width=1\textwidth]{prop_2b.png}
    \caption{}
    \end{subfigure}

    \caption{Proportion of the time variables selected in (a) Scenario 1A (b) Scenario 1B (c) Scenario 2A (d) Scenario 2B.}
    \label{fig:prop}
\end{figure}

\section{Conclusion}\label{high_dim_conc}
In this paper, we discuss the variable selection problem in causal inference. Variable selection problem is extensively studies in machine learning literature, however, the objective of this problem in the context of causal inference is quite different. Here, we have to select variables that are associated with multiple factors. To that end, we propose an Outcome adaptive elastic-net method that uses a variable penalty function instead of constant penalty function of elastic-net. First, we estimate the strength of association of the variables to the outcome by ordinary least square regression. Then, we use the inverse of these estimates in the elastic-net regularization to create a variable penalty function. This variable regularization ensures that the outcome predictors are less penalized in the treatment model and selects all the confounders and outcome predictors. We evaluate the performance of the proposed techniques on bias, variance and proportion of the time right variables are selected. Our analysis shows that the proposed outcome adaptive technique performs superiorly compared to the state-of-the-art methods. In the future, we plan to perform analysis on the properties of outcome adaptive elastic-net such as the possibility of achieving the oracle properties. In addition, we plan to develop case studies with real-world high dimensional data to present the applicability of the proposed method and additional guidelines for the users.

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The world has ended and the Earth belongs to the dead. A zombie plague has wiped out most of the human race. There is still hope. Pleasant Harbour, Massachusets, stands strong...but it is about to come under attack. Its only defense is 5 teenagers with attitude...but how will they stop the insidious shapeshifting Dopplegangrene?


text:
Home » Action » Elizabeth: The Golden Age (2007)Hindi Dubbed
Elizabeth: The Golden Age (2007)Hindi Dubbed
May 13, 2019 Hindilinks4u Action, Adventure, Animation, Dubbed Movies, DVD, HD, Hollywood Dubbed Movies, Romance No Comment
Elizabeth: The Golden Age (2007)Hindilinks4u Watch Online Hindi Dubbed Full Movie Hindilinks4u:
Director Hindilinks4u:Shekhar Kapur
Producer:Tim Bevan
Production company:Remi Adefarasin
Cast :Abbie Cornish, Cate Blanchett, Clive Owen, Geoffrey Rush, Laurence Fox, Rhys Ifans, Tom Hollander
Elizabeth: The Golden Age (2007)Hindilinks4u Watch Online Hindi Dubbed Full Movie Story:
In 1585, Catholic Spain, ruled by King Philip II of Spain, is the most powerful country in the world. Seeing Protestant England as a threat, and in retaliation for English piracy of Spanish ships, Philip plots to take over England and make his daughter, Isabella, the Queen of England in Elizabeth's place. Meanwhile, Elizabeth I of England is pressured by her advisor, Francis Walsingham, to marry – if she dies without an heir, the throne will pass to her cousin, Mary, Queen of Scots, who is Catholic.
English explorer Walter Raleigh is presented at Elizabeth's court, having returned from the New World. Elizabeth is attracted to Raleigh, enthralled by his tales of exploration, and asks Bess Throckmorton, her most favored lady-in-waiting, to observe him. Bess also finds Raleigh attractive and they begin a secret affair. With tensions strained between England and Spain, Elizabeth seeks guidance from her astrologer, Dr. John Dee.
Jesuits in London conspire with Philip to assassinate Elizabeth and replace her with Mary, in what Philip calls "The English Enterprise", historically known as the Babington Plot. From her imprisonment, Mary sends secret correspondence to the Jesuits, who recruit Anthony Babington to assassinate Elizabeth. Walsingham continues to warn Elizabeth of Spain's rising power and of the Catholics' plots against her, but unlike her predecessor and half-sister Mary I of England, Elizabeth refuses to force her people to share her religious beliefs.
Walsingham's Catholic brother, who knows of the plot against Elizabeth, is jailed, leading Walsingham to reveal Spain's plan to Elizabeth, who angrily confronts the Spanish diplomats. The Spanish ambassador feigns ignorance, accuses Elizabeth of receiving Spanish gold from pirates, and insinuates that she has a sexual relationship with Raleigh. Enraged, Elizabeth throws the Spaniards out of court. Meanwhile, Philip is cutting down the forests of Spain to build the Spanish Armada to invade England. Mary writes letters condoning the plot.
Babington storms into a cathedral where Elizabeth is praying and fires a pistol at her, though Elizabeth is unharmed as there was no bullet in the gun. As Elizabeth learns of Mary's involvement in the plot, Walsingham insists Mary be executed to quell any possible revolt. Elizabeth reluctantly agrees. Mary is tried for high treason and beheaded; Walsingham realizes this was part of the Jesuits' plan all along: Philip never intended for Mary to become queen, but with the Pope and other Catholic leaders regarding Mary as the true Queen of England, Philip uses Mary's death to obtain papal approval for war. The "murder" of the last legitimate Catholic in the line of succession gives Philip the pretext he needs to invade England.
Bess reveals to Raleigh that she is pregnant with his child, and pleads with him to leave. Instead, the couple marries in secret. When Elizabeth confronts Bess, she confesses her pregnancy and that Raleigh is her husband. An infuriated Elizabeth berates Bess, reminding her that she cannot marry without royal consent. She banishes Bess from court and has Raleigh imprisoned for the crime of seducing a ward of the Queen.
As the Spanish Armada begins its approach up the English Channel, Elizabeth forgives Bess and sets Raleigh free to join Sir Francis Drake in the battle. The ships of the Armada vastly outnumber England's, but a storm blows the Armada toward the beaches, endangering its formation and becoming vulnerable to English fire ships. Elizabeth, atop her coastal headquarters, walks out to the cliffs and watches the Spanish Armada sink in flames as the English prevail.
She visits Raleigh and Bess and blesses their child. Elizabeth appears to triumph personally through her ordeal, again resigned to her role as the Virgin Queen and mother to the English people.
Abbie Cornish, Cate Blanchett, Clive Owen, Geoffrey Rush, Laurence Fox, Rhys Ifans, Tom Hollander
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text:
Q: Lark simple sql grammar I'm trying to parse a simple sql via this grammar:
grammar = ```
        program          : stmnt*
        stmnt            : select_stmnt | drop_stmnt
        select_stmnt     : select_clause from_clause? group_by_clause? having_clause? order_by_clause? limit_clause? SEMICOLON

        select_clause    : "select"i selectables
        selectables      : column_name ("," column_name)*
        from_clause      : "from"i source where_clause?
        where_clause     : "where"i condition
        group_by_clause  : "group"i "by"i column_name ("," column_name)*
        having_clause    : "having"i condition
        order_by_clause  : "order"i "by" (column_name ("asc"i|"desc"i)?)*
        limit_clause     : "limit"i INTEGER_NUMBER ("offset"i INTEGER_NUMBER)?

        // NOTE: there should be no on-clause on cross join and this will have to enforced post parse
        source           : joining? table_name table_alias?
        joining          : source join_modifier? JOIN source ON condition

        //source           : table_name table_alias? joined_source?
        //joined_source    : join_modifier? JOIN table_name table_alias? ON condition
        join_modifier    : "inner" | ("left" "outer"?) | ("right" "outer"?) | ("full" "outer"?) | "cross"

        condition        : or_clause+
        or_clause        : and_clause ("or" and_clause)*
        and_clause       : predicate ("and" predicate)*

        // NOTE: order of operator should be longest tokens first

        predicate        : comparison ( ( EQUAL | NOT_EQUAL ) comparison )*
        comparison       : term ( ( LESS_EQUAL | GREATER_EQUAL | LESS | GREATER ) term )*
        term             : factor ( ( "-" | "+" ) factor )*
        factor           : unary ( ( "/" | "*" ) unary )*
        unary            : ( "!" | "-" ) unary
                         | primary
        primary          : INTEGER_NUMBER | FLOAT_NUMBER | STRING | "true" | "false" | "null"
                         | IDENTIFIER

        drop_stmnt       : "drop" "table" table_name

        FLOAT_NUMBER     : INTEGER_NUMBER "." ("0".."9")*

        column_name      : IDENTIFIER
        table_name       : IDENTIFIER
        table_alias      : IDENTIFIER

        // keywords
        // define keywords as they have higher priority
        SELECT.5           : "select"i
        FROM.5             : "from"i
        WHERE.5            : "where"i
        JOIN.5             : "join"i
        ON.5               : "on"i

        // operators
        STAR              : "*"
        LEFT_PAREN        : "("
        RIGHT_PAREN       : ")"
        LEFT_BRACKET      : "["
        RIGHT_BRACKET     : "]"
        DOT               : "."
        EQUAL             : "="
        LESS              : "<"
        GREATER           : ">"
        COMMA             : ","

        // 2-char ops
        LESS_EQUAL        : "<="
        GREATER_EQUAL     : ">="
        NOT_EQUAL         : ("<>" | "!=")

        SEMICOLON         : ";"

        IDENTIFIER.9       : ("_" | ("a".."z") | ("A".."Z"))* ("_" | ("a".."z") | ("A".."Z") | ("0".."9"))+

        %import common.ESCAPED_STRING   -> STRING
        %import common.SIGNED_NUMBER    -> INTEGER_NUMBER
        %import common.WS
        %ignore WS

However, when I call the parser with text,
"""select cola, colb from foo left outer join bar b on x = 1 join jar j on jb > xw where cola <> colb and colx > coly""",
it parses the second join as a term, i.e. as part of the first join's condition. Any thoughts on how to do this correctly?

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2-0 up at half time. Nice!
Just got better 3-0 MCallum 2 and Williamson - WE WANT 4!
What a result! Sounded like a terrific performance from the lads today, a clean sheet and the most goals scored in a game this season at our former bogey ground. Brilliant stuff. What would we give for a similar performance & result next Saturday? A game I'm really looking forward to. 48 away fans is pretty good. Well done & respect to all who travelled.
Superb result; we really are a decent away team. Its all looking up now and hopefully the confidence from recent displays and results will carry us forward for the rest of the season.
Superb result. EFC now sit 4th in the last-10 match form table with only Salford, Orient and Solihull(!!!!) above us. That's a fantastic turn-around from a couple of months ago when a relegation fight was all we could look forward to. Now we are 3 or 4 points off the play-offs.
There are a number of issues to be resolved before we can sleep at night but I think that the management team and board are all making the right noises.
The big issue for me is the goalkeeper situation and whether Max can stay beyond the New Year.
As far as I'm aware the past 2 loan spells for him have been 'emergency loans' which are only allowed to be a maximum of 1 month. So from January we may well see him sign on until the end of the season as it would be under normal loan conditions. I might be slightly wrong but that's pretty close. Fingers crossed that'll happen, he's a fantastic keeper and has certainly played a part in our recent form!
I think Max has been the single biggest factor in our transformation. I would love him to stay with us forever; but I must say if that bloke is not capable of playing league football, i would be amazed. In fact I would go further, I think he could play in the championship. Easily the finest goalkeeper to have ever donned an EFC shirt.
Michael - I've been watching EFC since 1999. So far (and its still early days), I'd rate him among the top 10 of anyone in an Eastleigh shirt in that time. Potential Championship player.
Agreed. If he is number 3 at Sunderland, the other two must be fantastic!
What a superb result. Didn't see us having the game fully wrapped up after 40 odd minutes!
Just 5 goals conceeded in the 9 games he has played with 6 wins, 2 draws and 1 defeat.
and what about a word for Paul McCallum, 9 now this season and he sits 5th on the top scorers chart.


Teams may seek permission from BC Hockey Board of Directors, with input from the appropriate Divisional Coordinator and/or District to participate in Leagues that operate outside BC Hockey. If such permission is granted the team will also require the approval of the BC Hockey Board of Directors, on recommendation of the applicable Program or Divisional Committee, to participate in a BC Hockey Championship.
Members are to clearly indicate where (which Province) they wish to compete for a Branch Championship.
The form is to be submitted to the BC Hockey office.
I trust the above to be in order but please contact me should you have any further questions.


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Actress Surveen Chawla is over the moon with her pregnancy and has been sharing pictures on social media showing just that. The latest.
To help sweeten the deal, we're passing some savings along with a launch promotion – if you're among the first folks to sign up, you can save 30% off the regular .99 monthly plan – join now and only pay .99/mo for life. (Or until you cancel.) Even better, an annual commitment is only .99 right now which could save you a bunch and help us keep the lights on.


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Kate Andrews spoke to Radio 4's Today Programme about the recently announced increase to NHS funding and the knock on effects this will have on other Departments.
Discussing tax rises, Kate argued against an increase to corporation tax as it is a tax paid by ordinary people – pensioners with shares in companies and workers themselves.
She also outlined the problems with raising public borrowing – which is effectively a tax on future generations as it has to be paid back down the road.
Healthcare spending as a proportion of GDP had doubled since the early 1990s as populations age. More money, especially to the tune of £20bn, is too high, especially as the Government has done little to make efficiency savings within the current system.
Ultimately, more money has to go hand in hand with serious reform. A pre-funded system would avoid these crisis situations the NHS faces every year.
Listen here. Segment begins at 2:40:00.
What can the NHS learn from the world?