text:
Introducing another LUMC Global PhD Student, Alexandre Marques
Brazilian Alexandre Marques started his Global PhD project in January 2022. His view on research? "Every step we take in research gets us closer to the truth". Let's delve into Alexandre's view on life, research and the project he will be working on.
Alexandre is part of a collaboration between the Leiden University Medical Center (LUMC) and Albert Einstein Israelite Hospital. Different from a lot of other PhD students walking around at the LUMC, Alexandre is not a biologist, biomedical scientist or a student of medicine. Rather, he is a statistician. But it's for this exact reason Alexandre thinks he can bring much to the table. "I think one of the main problems in institutions is that we tend to stick to our own discipline. People do what they do without connecting with each other and each other's knowledge", says Alexandre.
Interdisciplinary research; an essential approach to solving modern research problems
Sticking to your own discipline in research is a crucial mistake, as far as Alexandre is concerned. "Even though I'm a statistician, I can use my knowledge to solve some problems in for instance biology and vice versa. Nowadays, we're dealing with more complex issues that cannot be solved by sticking to your own 'box'. For example, biologists may be unaware of statistic tools they can use to solve new problems, but by working together with other experts, they can widen their view. And the same goes for me too. An interdisciplinary approach to modern problems helps me broaden my own view and come up with solutions I wouldn't have otherwise thought about. It's called 'uni'-versity for a reason."
Why this project
It's for this exact reason Alexandre was so keen to join the LUMC and Albert Einstein Israelite Hospital project. "The LUMC already has this interdisciplinary approach to solve research problems. There's a mix of great scientists in different fields here, complemented by extraordinary data scientists and other experts. We can all learn from each other. I'm very grateful for the opportunity to meet all these different experts and researchers. It's why I was so eager to come to the LUMC, because it's not that easy to find research institutions that approach research the way the LUMC does. Albert Einstein Israelite Hospital in turn helps me develop my knowledge as a statistician further. It really makes for a unique and wonderful project."
Research: a reflection of life itself
"What this experience is showing me is that we're more equal than we think. Despite Brazil still being more of a developing country than the Netherlands, I can now see that some of the presumed gaps aren't nearly as big as I initially thought. We both have excellent research groups and as people, we're more similar than we tend to think. For me it's an eye opener: I can go back to Brazil and tell other people that there's something out there, that it's more attainable to add value in other countries than you think. The LUMC is giving me a chance to break barriers. It's a good stimulus for doing science and constructing myself as a better human being. So far, being abroad at the LUMC has been a humbling experience. It has already taught me so much about looking beyond my own perspective in research and in life", Alexandre beautifully states.
His research
Despite gaining interdisciplinary research experience and valuable life lessons, Alexandre is logically working on a research project with a clear goal. Alexandre: "The goal of the project is to develop computational tools to better understand immune responses and vaccination. We approach the research from the development side and by investigating new applications of tools already available. For the latter, we're trying to determine how we can use already available tools for new issues and what limitations these tools have that we need to address. This combination is important. Developing existing tools revolves around extending the methods of the tools. For developing new tools we create new models and then check where these models can fit.
A concrete example and the necessity for advanced computational tools
I'm currently working on an existing application and see a lot of opportunities to improve it. Usually, we develop a tool for a specific dataset. But now, we want to develop tools that can be applied to multiple datasets. This way, we give someone the means to make comparisons and get answers to several questions. So it needs a generic approach. For example, I'm working on why there's differences in susceptibility for certain diseases between adults and children, like COVID-19. But we could develop tools to look at differences between two groups from any kind, not just adults and kids. But this is a good example of comparing datasets.
And we need more complex tools, because the way we're conducting experiments is changing. 20 years ago, research was completely different than it is now. We have so much more possibilities to gather data. Statistical data analysis' demands are therefore completely different too. Modern data is often too complex for old models. We need to develop these tools to go hand in hand with the complexity of data gathering and complex hypotheses."
Personal goals and future
It's safe to say Alexandre has his work cut out for him. It will help him to become a better professional and it will open doors for him in research worldwide. "A PhD is valuable, especially in a fast-growing field like bio-informatics. Learning about this subject can really help my career." But Alexandre doesn't dwell on the future, he lives in the moment. "I just try to think about today, tomorrow and perhaps the day after. I just try to do the best I can do today and tomorrow. And right now, that means putting in the work every day to push this research forward."


Food appreciation to the max!
We cook, we feed, we share! Cuisine came together two years ago, Spring 2013, to bring that added spice to the AUC student body. As students lucky enough to have our own kitchens and explore our culinary talents, we thought to bring it all together and share our experiences as foodies with others, and to create an inviting path for cookery rookies who might just not know where to start. We'll give you tips, workshops, bake sales and more! What we're aiming for: maybe one day we can print a cookbook with excellent and diverse recipes.
Contact us at cuisine@aucsa.nl or visit our Facebook page.
Alejandra Espinosa, Evi Hadjipieri, Stefan Plug, Maria Veronica Romero, Manon Falces, Deniz Ovalıoğlu and Daria Nicolson.


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Home > Bookshop > Other Subjects > Travel and Places > New Zealand « Back
A Concise History of New Zealand
Smith, Philippa Mein
Concise History of New Zealand/Smith, Philipp/Paper/0521542286/S053-D2
New Zealand was the last major landmass, other than Antarctica, to be settled by humans. In this new account of New Zealand's history, Philippa Mein Smith considers this rugged and dynamic land from its break from Gondwana 80 million years ago to the beginning of the twenty-first century. Mein Smith highlights the effects of the country's smallness and isolation, from late settlement by Polynesian voyagers, very late colonisation and settlement by Europeans, and the interactions that made these people Maori and Pakeha, to struggles over land, and efforts through time to manage global forces. This is a history that places New Zealand in its global and regional context, linked to Britain, immersed in the Pacific, and part of Australasia. Distinctively, this book unravels the ways in which key moments have contributed to the founding of the country's national myths.

redpajama_set_name: RedPajamaCommonCrawl


This humidor can hold up to 40 cigars and includes a cigar humidifier which keeps the humidity consistent (68-70% is recommended) and a hygrometer which shows the current relative humidity levels, ensuring the freshness of the cigar.
The Classico cigar humidor comes in 4 different finishes (Burl, Cherry, Red Walnut and Black Walnut).
Product Dimensions: 10 1/4" x 8 1/2" x 4 1/4"


text:
CENTRAL ILLINOIS COUNCIL
Visit Our Council Site/About Us/Store Locations & Hours
Official Daisy Vest
Official Brownie Vest
Official Junior Vest
Official Cadette, Senior And Ambassador Khaki Vest
The Daisy Girl's Guide To Girl Scouting
The Brownie Girl's Guide To Girl Scouting
The Junior Girl's Guide To Girl Scouting
The Cadette Girl's Guide To Girl Scouting
Welcome to the Girl Scouts of Central Illinois online shop.
We are 17,000 girls and 4,000 adult members strong. We offer girls in 38 counties across Central Illinois exciting, life-changing opportunities that build their courage, confidence, and character, to become tomorrow's leaders. Through engaging programs and activities, we help girls become tomorrow's leaders. Here in our online shop and six Trefoil Boutique locations, you can find all the uniforms, badges, and supplies girls need for their Girl Scout experience, along with plenty of fun merchandise to show your Girl Scout spirit! When you shop with us, you're helping girls be amazing. You're supporting them to attend camp, participate in character-building activities, and take action in their communities.
To join, volunteer, donate, or learn more, visit www.getyourgirlpower.org.
Bloomington Service Center
3 Westport Court, Bloomington, IL 61704
Monday - 9:30 a.m. - 6:00 p.m.
Wednesday - 9:30 a.m. - 5:00 p.m.
Friday - 9:30 a.m. - 12:00 p.m.
First Saturday of the Month - 9:30 a.m. 1:00 p.m.
customercare@girlscouts-gsci.org
Closes from 1:00 p.m. - 2:00 p.m. for lunch.
Closed on observed holidays.
Champaign Service Center
2001 Round Barn Rd. Suite C, Champaign, IL 61820
Decatur Urban Program Center
1170 E. Pershing Rd., Decatur, IL 62526
Monday - 5:00 p.m. - 6:00 p.m.
Tuesday - 9:30 a.m. - 5:00 p.m.
Thursday - 9:30 a.m. - 5:00 p.m.
Friday - 9:30 a.m. - 12:00 p.m
Closes from 1:00 p.m. - 2:00 p.m. Tuesday and Thursday for lunch.
Peoria Service Center
1103 W. Lake Ave., Peoria, IL 61614
Tuesday - Thursday - 9:30 a.m. - 5:00 p.m.
Friday - 9:30 a.m. - 12:00 p.m. First Saturday of the Month - 9:30 a.m. 1:00 p.m.
Quincy Service Center
3837 Easlake Centre Dr., Suite 400B, Quincy, IL 62305
Closes from 1:00 p.m. - 2:00 p.m. Monday and Wednesday for lunch.
Springfield Service Center 3020 Baker Dr., Springfield, IL 62703


There are many Indian families living in the USA, among whom a large number of people follow the Hinduism. The NRIs normally prefer to preserve their original religious and cultural traits even in this foreign land. So these people make sure that all the major Hindu traditions are maintained during the marriages organized in their families. The Hindus living in the US want to exhibit their close relation to their roots, by sending wedding cards with traditional Hindu designs and style.
First of all, it is essential to choose any online wedding card maker, who has prior expertise in creating designer Hindu wedding invitations. There are some well-known wedding card designers available online, who should be chosen based on their reputation in the market and the quality of their sample works uploaded on their websites.
The theme of the wedding should be decided, as many wedding card designers prefer to create the wedding invitations based on the wedding themes. If it is an outdoor wedding or beach wedding, the colors and the designs of the wedding cards are chosen accordingly, to give a hint of the wedding theme to the invited guests.
Many Hindus living in the US prefer the Hindu religious symbols or the images of their deities to be imprinted as the designs of their wedding invitations. Generally, the symbols of Swastik and Om are widely preferred due to the simplicity and close religious association to Hinduism. Likewise, the pictures of the divine couples, like Radha-Krishna, Shiva-Parvati, and Ram-Sita are preferred on the cover pages of Hindu wedding cards.
Some modern Hindu couples may want wedding-related paintings; like the wedding procession or Baarat, a wedding couple exchanging garlands or the images of colorful butterflies. Few wedding couples may wish to further personalize their wedding cards by imprinting their own photos or group photos of both families, for acquainting the invited guests with all. The chosen wedding card makers should have provisions for fulfilling these desires of the new couples.
The desire for association with cultural roots inspires the NRI Hindus to choose red as the primary color to be used on their wedding cards. Moreover, lots of Hindus basically from Gujarat, Rajasthan, and Punjab wish to have all the vibrant colors on their wedding invitation designs. The uses of golden and silver colors are also preferred for adding grandeur to the décor of the wedding cards.
The wedding invitation letters of the Hindus are always undersigned by the eldest member of the family, which should be the names of parents or grandparents of the bride or groom, as per the culture of the Indians. The names of the new couple, their parentage, and a brief family background are added for information, in the content of these invitation letters.
The wedding invitation letters always need to start with any name of Hindu God or Goddess, or any phrase from the Hindu religious scriptures. It shows the reverence of the family towards their religion, in spite of living in a foreign land.
All these factors make the wedding cards of Hindus in the USA to be religiously and culturally special, for their relatives and friends staying in India.
What is the Need for Professional Photographers for Wedding Photography?


"I think I'm naturally born to be a leader," said Kevin Pillar, who is the longest tenured player on a youthful squad.
Watch on MLB.TV: Trent Thornton starts against the NL West squad when the Blue Jays return to Toronto tonight.
It's not rare to see former Major League sons in Major League uniforms, but is it unusual to find four in one clubhouse?
OH MY, LOURDES! It's commercial day for our #PiñaPower bobblehead!
Man! Justin Smoak crushed that high FB. That was a BOMB!!


Fulham lost 2-0 to Liverpool, despite an encouraging performance, thanks to goals from Mo Salah and Xherdan Shaqiri, although they will feel aggrieved that Aleksandar Mitrovic's goal was ruled out for offside.
The game started off tentatively, with Liverpool having the lion's share of possession but not doing much with it until around the 15 minute mark.
Sadio Mane dragged an effort wide, but it was the duel between Mo Salah and Sergio Rico that was most interesting - the Spaniard was on hand to deny Liverpool's number 11 twice with two excellent saves.
Fulham were presented with their best chance of the game 23 minutes in when Ryan Sessegnon was clean through on goal, but he dragged his effort just wide of Alisson's goal.
But Liverpool took the lead in gut-wrenching circumstances - Mitrovic had the ball in the net from a Tom Cairney cross, but judged to be offside despite replays showing different, Liverpool broke up the pitch and Salah finished a brutal counter.
It didn't take the home side anywhere near as long in the second half - Shaqiri popped up at the back post and put away Andy Robertson's brilliant cross-field ball to make it 2-0.
The rest of the half fizzled out, with Fulham defending fairly well but not threatening going forward, while Liverpool had chances but struggled to take them.
Made a couple of very good saves to deny Salah and keep the scores level. Continued that in the second half in a game that has really cemented his place as number one.
Was allowing too much space on his side that Liverpool exploited early on but recovered and had a good first half. Looked much more sound defensively than he has in the past.
Did well alongside Mawson for the most part and looked comfortable with the ball at feet, playing out from the back and into the midfield.
Looked a real asset in the heart of defence in the first half. Strong in the air and in the tackle, you feel that he has to continue at centre half from here on in.
Had a good game down the right and continued to show the fight he showed on Monday night. Got caught a couple of times in possession however.
Played in a holding midfield role and did a pretty good job in the first half, putting himself about the pitch and putting in tackles. I think this was a really positive game for Chambers who did well.
Looked a lot better than on Monday night and looked far more comfortable having Chambers next to him in midfield. At times charged forward with power, which is something Jokanovic has claimed to be missing this season. Didn't do as well in the second half but still encouraging.
Played some nice passes when he got on the ball in a more advanced role. Quietened as the game went on and didn't have much more of a say on proceedings before being taken off at the hour mark. Marking for the second goal left a lot to be desired.
Put himself in good positions but should've scored when he went through on goal with Alisson. Had a quiet second half but worked himself into some good positions.
Had a fairly quiet first half but had one shot of note that Alisson saved easily. Didn't do much at all in the second half, rarely getting the ball. In a team that needs to fight for points, I'm not sure Schurrle is the right fit for the the side.
His hold up play was a joy to watch it the first half - he won almost everything against Gomez and van Dijk. Was unlucky to have his header ruled out for offside but had a decent game by all accounts.
Didn't really get up to the pace of the game and had very little impact after coming on for Cairney in the 60th minute. Poor touch, poor passes and easily shrugged off the ball.
Made a few decent runs but didn't do anything of note after coming on late into the game.
Had no time to affect proceedings when he came on.


Does anybody know what could this be?!
Question: Does anybody know what could this be.?
It's probably a canker sore or inflaimed papillae. I get them all the time.
Since you just drank lemonade, that makes me suspect even more. Try orajel.
Actually, I wouldn't take a vitamin C. They are also slightly burny and can make these things worse. Unless you have a swallowable kind, I am referring to those orange flavored ones you chew.


If you really want to play with one of the dogs, Chance is the one. That boy never gives up on a good old fashion playtime. This is his favorite toy, it is his second one. He loves it and when we added the rope...well, he couldn't get enough of it.
We have a game I like to call our ninja game. I swing the rope in a martial arts fashion and he jumps and tries to catch it. Now Chance is very nimble. I've seen him make incredible leaps, jumps, flips and twist. I have even seen him do a back flip before. These events always seem to happen when Daddy isn't home and so I have no photos at this time. (And it is rather impossible to snap a shot while playing with him, trust me I've tried! lol) So your going to have to trust me until I get some photos and video.
When Chance and I connect during this game, the whole world melts away. No one else exist. We become rather caught up in our fantasy land and find ourselves clicking in tune with each other. Blaze will step back, lay down and watch. She knows this is our time together. There seems to be a certain magic that surrounds us and all our cares and worries are forgotten. It becomes more than a game, it becomes an art, with just the right move or twist, we find ourselves centered within each other. Each of us challenging the other with precision and grace. It is an awesome experience that engages our senses and spirits in a unique union that I feel the need to bow to him after our performance.
In those moments we relate as one to each other. How precious it is to find that special tie with a dog.
Do you have a special game that completely engages you and your dog that unites your bond?
I like to play hide and seek with my little (human) sister. She hides and then I go and find her. She laughs so hard when I find her!
Silver and I play hide-n-seek too, Pip. We love that game.


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"""
Robust location and covariance estimators.

Here are implemented estimators that are resistant to outliers.

"""
# Author: Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD Style.
import warnings
import numpy as np
from scipy import linalg
from scipy.stats import chi2

from . import empirical_covariance, EmpiricalCovariance
from ..utils.extmath import fast_logdet
from ..utils import check_random_state

###############################################################################
### Minimum Covariance Determinant
#   Implementing of an algorithm by Rousseeuw & Van Driessen described in
#   (A Fast Algorithm for the Minimum Covariance Determinant Estimator,
#   1999, American Statistical Association and the American Society
#   for Quality, TECHNOMETRICS)
###############################################################################
def c_step(X, n_support, remaining_iterations=30, initial_estimates=None,
           verbose=False, cov_computation_method=empirical_covariance,
           random_state=None):
    """C_step procedure described in [Rouseeuw1984] aiming at computing the MCD

    Parameters
    ----------
    X: array-like, shape (n_samples, n_features)
      Data set in which we look for the n_support observations whose
      scatter matrix has minimum determinant
    n_support: int, > n_samples / 2
      Number of observations to compute the robust estimates of location
      and covariance from.
    remaining_iterations: int
      Number of iterations to perform.
      According to [Rouseeuw1999], two iterations are sufficient to get close
      to the minimum, and we never need more than 30 to reach convergence.
    initial_estimates: 2-tuple
      Initial estimates of location and shape from which to run the c_step
      procedure:
      - initial_estimates[0]: an initial location estimate
      - initial_estimates[1]: an initial covariance estimate
    verbose: boolean
      Verbose mode
    random_state: integer or numpy.RandomState, optional
        The random generator used. If an integer is given, it fixes the
        seed. Defaults to the global numpy random number generator.

    Returns
    -------
    location: array-like, shape (n_features,)
      Robust location estimates
    covariance: array-like, shape (n_features, n_features)
      Robust covariance estimates
    support: array-like, shape (n_samples,)
      A mask for the `n_support` observations whose scatter matrix has
      minimum determinant

    Notes
    -----
    **References**:

    .. [Rouseeuw1999] A Fast Algorithm for the Minimum Covariance Determinant
       Estimator, 1999, American Statistical Association and the American
       Society for Quality, TECHNOMETRICS

    """
    random_state = check_random_state(random_state)
    n_samples, n_features = X.shape

    # Initialisation
    if initial_estimates is None:
        # compute initial robust estimates from a random subset
        support = np.zeros(n_samples).astype(bool)
        support[random_state.permutation(n_samples)[:n_support]] = True
        location = X[support].mean(0)
        covariance = cov_computation_method(X[support])
    else:
        # get initial robust estimates from the function parameters
        location = initial_estimates[0]
        covariance = initial_estimates[1]
        # run a special iteration for that case (to get an initial support)
        precision = linalg.pinv(covariance)
        X_centered = X - location
        dist = (np.dot(X_centered, precision) * X_centered).sum(1)
        # compute new estimates
        support = np.zeros(n_samples).astype(bool)
        support[np.argsort(dist)[:n_support]] = True
        location = X[support].mean(0)
        covariance = cov_computation_method(X[support])
    previous_det = np.inf

    # Iterative procedure for Minimum Covariance Determinant computation
    det = fast_logdet(covariance)
    while (det < previous_det) and (remaining_iterations > 0):
        # compute a new support from the full data set mahalanobis distances
        precision = linalg.pinv(covariance)
        X_centered = X - location
        dist = (np.dot(X_centered, precision) * X_centered).sum(axis=1)
        # save old estimates values
        previous_location = location
        previous_covariance = covariance
        previous_det = det
        previous_support = support
        # compute new estimates
        support = np.zeros(n_samples).astype(bool)
        support[np.argsort(dist)[:n_support]] = True
        location = X[support].mean(axis=0)
        covariance = cov_computation_method(X[support])
        det = fast_logdet(covariance)
        # update remaining iterations for early stopping
        remaining_iterations -= 1

    # Check convergence
    if np.allclose(det, previous_det):
        # c_step procedure converged
        if verbose:
            print "Optimal couple (location, covariance) found before" \
                "ending iterations (%d left)" % (remaining_iterations)
        results = location, covariance, det, support
    elif det > previous_det:
        # determinant has increased (should not happen)
        warnings.warn("Warning! det > previous_det (%.15f > %.15f)" \
                          % (det, previous_det), RuntimeWarning)
        results = previous_location, previous_covariance, \
            previous_det, previous_support

    # Check early stopping
    if remaining_iterations == 0:
        if verbose:
            print 'Maximum number of iterations reached'
        det = fast_logdet(covariance)
        results = location, covariance, det, support

    return results

def select_candidates(X, n_support, n_trials, select=1, n_iter=30,
                      verbose=False,
                      cov_computation_method=empirical_covariance,
                      random_state=None):
    """Finds the best pure subset of observations to compute MCD from it.

    The purpose of this function is to find the best sets of n_support
    observations with respect to a minimization of their covariance
    matrix determinant. Equivalently, it removes n_samples-n_support
    observations to construct what we call a pure data set (i.e. not
    containing outliers). The list of the observations of the pure
    data set is referred to as the `support`.

    Starting from a random support, the pure data set is found by the
    c_step procedure introduced by Rousseeuw and Van Driessen in
    [Rouseeuw1999].

    Parameters
    ----------
    X: array-like, shape (n_samples, n_features)
      Data (sub)set in which we look for the n_support purest observations
    n_support: int, [(n + p + 1)/2] < n_support < n
      The number of samples the pure data set must contain.
    select: int, int > 0
      Number of best candidates results to return.
    n_trials: int, nb_trials > 0 or 2-tuple
      Number of different initial sets of observations from which to
      run the algorithm.
      Instead of giving a number of trials to perform, one can provide a
      list of initial estimates that will be used to iteratively run
      c_step procedures. In this case:
      - n_trials[0]: array-like, shape (n_trials, n_features)
        is the list of `n_trials` initial location estimates
      - n_trials[1]: array-like, shape (n_trials, n_features, n_features)
        is the list of `n_trials` initial covariances estimates
    n_iter: int, nb_iter > 0
      Maximum number of iterations for the c_step procedure.
      (2 is enough to be close to the final solution. "Never" exceeds 20)
    random_state: integer or numpy.RandomState, optional
        The random generator used. If an integer is given, it fixes the
        seed. Defaults to the global numpy random number generator.

    See Also
    ---------
    `c_step` function

    Returns
    -------
    best_locations: array-like, shape (select, n_features)
      The `select` location estimates computed from the `select` best
      supports found in the data set (`X`)
    best_covariances: array-like, shape (select, n_features, n_features)
      The `select` covariance estimates computed from the `select`
      best supports found in the data set (`X`)
    best_supports: array-like, shape (select, n_samples)
      The `select` best supports found in the data set (`X`)

    Notes
    -----
    **References**:
    .. [Rouseeuw1999] A Fast Algorithm for the Minimum Covariance Determinant
       Estimator, 1999, American Statistical Association and the American
       Society for Quality, TECHNOMETRICS

    """
    random_state = check_random_state(random_state)
    n_samples, n_features = X.shape

    if isinstance(n_trials, int):
        run_from_estimates = False
    elif isinstance(n_trials, tuple):
        run_from_estimates = True
        estimates_list = n_trials
        n_trials = estimates_list[0].shape[0]
    else:
        raise Exception("Bad 'n_trials' parameter (wrong type)")

    # compute `n_trials` location and shape estimates candidates in the subset
    all_estimates = []
    if not run_from_estimates:
        # perform `n_trials` computations from random initial supports
        for j in range(n_trials):
            all_estimates.append(
                c_step(
                    X, n_support, remaining_iterations=n_iter, verbose=verbose,
                    cov_computation_method=cov_computation_method,
                    random_state=random_state))
    else:
        # perform computations from every given initial estimates
        for j in range(n_trials):
            initial_estimates = (estimates_list[0][j], estimates_list[1][j])
            all_estimates.append(c_step(
                    X, n_support, remaining_iterations=n_iter,
                    initial_estimates=initial_estimates, verbose=verbose,
                    cov_computation_method=cov_computation_method,
                    random_state=random_state))
    all_locations_sub, all_covariances_sub, all_dets_sub, all_supports_sub = \
                                     zip(*all_estimates)
    # find the `n_best` best results among the `n_trials` ones
    index_best = np.argsort(all_dets_sub)[:select]
    best_locations = np.asarray(all_locations_sub)[index_best]
    best_covariances = np.asarray(all_covariances_sub)[index_best]
    best_supports = np.asarray(all_supports_sub)[index_best]

    return best_locations, best_covariances, best_supports

def fast_mcd(X, support_fraction=None,
             cov_computation_method=empirical_covariance,
             random_state=None):
    """Estimates the Minimum Covariance Determinant matrix.

    Parameters
    ----------
    X: array-like, shape (n_samples, n_features)
      The data matrix, with p features and n samples.
    support_fraction: float, 0 < support_fraction < 1
          The proportion of points to be included in the support of the raw
          MCD estimate. Default is None, which implies that the minimum
          value of support_fraction will be used within the algorithm:
          [n_sample + n_features + 1] / 2
    random_state: integer or numpy.RandomState, optional
        The generator used to randomly subsample. If an integer is
        given, it fixes the seed. Defaults to the global numpy random
        number generator.

    Notes
    -----
    The FastMCD algorithm has been introduced by Rousseuw and Van Driessen
    in "A Fast Algorithm for the Minimum Covariance Determinant Estimator,
    1999, American Statistical Association and the American Society
    for Quality, TECHNOMETRICS".
    The principle is to compute robust estimates and random subsets before
    pooling them into a larger subsets, and finally into the full data set.
    Depending on the size of the initial sample, we have one, two or three
    such computation levels.

    Note that only raw estimates are returned. If one is intersted in
    the correction and reweighting steps described in [Rouseeuw1999],
    see the MinCovDet object.

    **References**:

    .. [Rouseeuw1999] A Fast Algorithm for the Minimum Covariance
       Determinant Estimator, 1999, American Statistical Association
       and the American Society for Quality, TECHNOMETRICS

    .. [Butler1993] R. W. Butler, P. L. Davies and M. Jhun,
       Asymptotics For The Minimum Covariance Determinant Estimator,
       The Annals of Statistics, 1993, Vol. 21, No. 3, 1385-1400

    Returns
    -------
    location: array-like, shape (n_features,)
      Robust location of the data
    covariance: array-like, shape (n_features, n_features)
      Robust covariance of the features
    support: array-like, type boolean, shape (n_samples,)
      a mask of the observations that have been used to compute
      the robust location and covariance estimates of the data set

    """
    random_state = check_random_state(random_state)

    X = np.asanyarray(X)
    if X.ndim == 1:
        X = np.reshape(X, (1, -1))
        warnings.warn("Only one sample available. " \
                          "You may want to reshape your data array")
    n_samples, n_features = X.shape

    # minimum breakdown value
    if support_fraction is None:
        n_support = int(np.ceil(0.5 * (n_samples + n_features + 1)))
    else:
        n_support = int(support_fraction * n_samples)

    # 1-dimensional case quick computation
    # (Rousseeuw, P. J. and Leroy, A. M. (2005) References, in Robust
    #  Regression and Outlier Detection, John Wiley & Sons, chapter 4)
    if n_features == 1:
        # find the sample shortest halves
        X_sorted = np.sort(np.ravel(X))
        diff = X_sorted[n_support:] - X_sorted[:(n_samples - n_support)]
        halves_start = np.where(diff == np.min(diff))[0]
        # take the middle points' mean to get the robust location estimate
        location = 0.5 * (X_sorted[n_support + halves_start]
                          + X_sorted[halves_start]).mean()
        support = np.zeros(n_samples).astype(bool)
        support[np.argsort(np.abs(X - location), axis=0)[:n_support]] = True
        covariance = np.asarray([[np.var(X[support])]])
        location = np.array([location])

    ### Starting FastMCD algorithm for p-dimensional case
    if (n_samples > 500) and (n_features > 1):
        ## 1. Find candidate supports on subsets
        # a. split the set in subsets of size ~ 300
        n_subsets = n_samples // 300
        n_samples_subsets = n_samples // n_subsets
        samples_shuffle = random_state.permutation(n_samples)
        h_subset = np.ceil(n_samples_subsets * (n_support / float(n_samples)))
        # b. perform a total of 500 trials
        n_trials_tot = 500
        n_trials = n_trials_tot // n_subsets
        # c. select 10 best (location, covariance) for each subset
        n_best_sub = 10
        n_best_tot = n_subsets * n_best_sub
        all_best_locations = np.zeros((n_best_tot, n_features))
        all_best_covariances = np.zeros((n_best_tot, n_features, n_features))
        for i in range(n_subsets):
            low_bound = i * n_samples_subsets
            high_bound = low_bound + n_samples_subsets
            current_subset = X[samples_shuffle[low_bound:high_bound]]
            best_locations_sub, best_covariances_sub, _ = select_candidates(
                current_subset, h_subset, n_trials,
                select=n_best_sub, n_iter=2,
                cov_computation_method=cov_computation_method,
                random_state=random_state)
            subset_slice = np.arange(i * n_best_sub, (i + 1) * n_best_sub)
            all_best_locations[subset_slice] = best_locations_sub
            all_best_covariances[subset_slice] = best_covariances_sub
        ## 2. Pool the candidate supports into a merged set
        ##    (possibly the full dataset)
        n_samples_merged = min(1500, n_samples)
        h_merged = np.ceil(n_samples_merged * (n_support / float(n_samples)))
        if n_samples > 1500:
            n_best_merged = 10
        else:
            n_best_merged = 1
        # find the best couples (location, covariance) on the merged set
        locations_merged, covariances_merged, supports_merged = \
            select_candidates(
                X[random_state.permutation(n_samples)[:n_samples_merged]],
                h_merged, n_trials=(all_best_locations, all_best_covariances),
                select=n_best_merged,
                cov_computation_method=cov_computation_method,
                random_state=random_state)
        ## 3. Finally get the overall best (locations, covariance) couple
        if n_samples < 1500:
            # directly get the best couple (location, covariance)
            location = locations_merged[0]
            covariance = covariances_merged[0]
            support = supports_merged[0]
        else:
            # select the best couple on the full dataset
            locations_full, covariances_full, supports_full = \
                select_candidates(
                    X, n_support,
                    n_trials=(locations_merged, covariances_merged),
                    select=1,
                    cov_computation_method=cov_computation_method,
                    random_state=random_state)
            location = locations_full[0]
            covariance = covariances_full[0]
            support = supports_full[0]
    elif n_features > 1:
        ## 1. Find the 10 best couples (location, covariance)
        ## considering two iterations
        n_trials = 30
        n_best = 10
        locations_best, covariances_best, _ = select_candidates(
            X, n_support, n_trials=n_trials, select=n_best, n_iter=2,
            cov_computation_method=cov_computation_method,
            random_state=random_state)
        ## 2. Select the best couple on the full dataset amongst the 10
        locations_full, covariances_full, supports_full = select_candidates(
            X, n_support, n_trials=(locations_best, covariances_best),
            select=1, cov_computation_method=cov_computation_method,
            random_state=random_state)
        location = locations_full[0]
        covariance = covariances_full[0]
        support = supports_full[0]

    return location, covariance, support

class MinCovDet(EmpiricalCovariance):
    """Minimum Covariance Determinant (MCD): robust estimator of covariance

    Parameters
    ----------
    store_precision: bool
      Specify if the estimated precision is stored
    assume_centered: Boolean
      If True, the support of robust location and covariance estimates
      is computed, and a covariance estimate is recomputed from it,
      without centering the data.
      Useful to work with data whose mean is significantly equal to
      zero but is not exactly zero.
      If False, the robust location and covariance are directly computed
      with the FastMCD algorithm without additional treatment.
    support_fraction: float, 0 < support_fraction < 1
      The proportion of points to be included in the support of the raw
      MCD estimate. Default is None, which implies that the minimum
      value of support_fraction will be used within the algorithm:
      [n_sample + n_features + 1] / 2
    random_state: integer or numpy.RandomState, optional
        The random generator used. If an integer is given, it fixes the
        seed. Defaults to the global numpy random number generator.

    Attributes
    ----------
    `raw_location_`: array-like, shape (n_features,)
        The raw robust estimated location before correction and reweighting

    `raw_covariance_`: array-like, shape (n_features, n_features)
        The raw robust estimated covariance before correction and reweighting

    `raw_support_`: array-like, shape (n_samples,)
        A mask of the observations that have been used to compute
        the raw robust estimates of location and shape, before correction
        and reweighting.

    `location_`: array-like, shape (n_features,)
        Estimated robust location

    `covariance_`: array-like, shape (n_features, n_features)
        Estimated robust covariance matrix

    `precision_`: array-like, shape (n_features, n_features)
        Estimated pseudo inverse matrix.
        (stored only if store_precision is True)

    `support_`: array-like, shape (n_samples,)
        A mask of the observations that have been used to compute
        the robust estimates of location and shape.

    Notes
    -----

    **References**:

    .. [Rouseeuw1984] `P. J. Rousseeuw. Least median of squares regression.
       J. Am Stat Ass, 79:871, 1984.`
    .. [Rouseeuw1999] `A Fast Algorithm for the Minimum Covariance Determinant
       Estimator, 1999, American Statistical Association and the American
       Society for Quality, TECHNOMETRICS`
    .. [Butler1993] `R. W. Butler, P. L. Davies and M. Jhun,
       Asymptotics For The Minimum Covariance Determinant Estimator,
       The Annals of Statistics, 1993, Vol. 21, No. 3, 1385-1400`

    """
    _nonrobust_covariance = staticmethod(empirical_covariance)

    def __init__(self, store_precision=True, assume_centered=False,
                 support_fraction=None, random_state=None):
        self.store_precision = store_precision
        self.assume_centered = assume_centered
        self.support_fraction = support_fraction
        self.random_state = random_state

    def fit(self, X):
        """Fits a Minimum Covariance Determinant with the FastMCD algorithm.

        Parameters
        ----------
        X: array-like, shape = [n_samples, n_features]
          Training data, where n_samples is the number of samples
          and n_features is the number of features.

        Returns
        -------
        self: object
          Returns self.

        """
        self.random_state = check_random_state(self.random_state)
        n_samples, n_features = X.shape
        # compute and store raw estimates
        raw_location, raw_covariance, raw_support = fast_mcd(
                X, support_fraction=self.support_fraction,
                cov_computation_method=self._nonrobust_covariance,
                random_state=self.random_state)
        if self.assume_centered:
            raw_location = np.zeros(n_features)
            raw_covariance = self._nonrobust_covariance(
                    X[raw_support], assume_centered=True)
        self.raw_location_ = raw_location
        self.raw_covariance_ = raw_covariance
        self.raw_support_ = raw_support
        self.location_ = raw_location
        self.support_ = raw_support
        # obtain consistency at normal models
        self.correct_covariance(X)
        # reweight estimator
        self.reweight_covariance(X)

        return self

    def correct_covariance(self, data):
        """Apply a correction to raw Minimum Covariance Determinant estimates.

        Correction using the empirical correction factor suggested
        by Rousseeuw and Van Driessen in [Rouseeuw1984]_.

        Parameters
        ----------
        data: array-like, shape (n_samples, n_features)
          The data matrix, with p features and n samples.
          The data set must be the one which was used to compute
          the raw estimates.

        Returns
        -------
        covariance_corrected: array-like, shape (n_features, n_features)
          Corrected robust covariance estimate.

        """
        X_centered = data - self.raw_location_
        dist = np.sum(
            np.dot(X_centered, linalg.pinv(self.raw_covariance_)) * X_centered,
            1)
        correction = np.median(dist) / chi2(data.shape[1]).isf(0.5)
        covariance_corrected = self.raw_covariance_ * correction
        self._set_estimates(covariance_corrected)
        return covariance_corrected

    def reweight_covariance(self, data):
        """Reweight raw Minimum Covariance Determinant estimates.

        Reweight observations using Rousseeuw's method (equivalent to
        deleting outlying observations from the data set before
        computing location and covariance estimates). [Rouseeuw1984]_

        Parameters
        ----------
        data: array-like, shape (n_samples, n_features)
          The data matrix, with p features and n samples.
          The data set must be the one which was used to compute
          the raw estimates.

        Returns
        -------
        location_reweighted: array-like, shape (n_features, )
          Reweighted robust location estimate.
        covariance_reweighted: array-like, shape (n_features, n_features)
          Reweighted robust covariance estimate.
        support_reweighted: array-like, type boolean, shape (n_samples,)
          A mask of the observations that have been used to compute
          the reweighted robust location and covariance estimates.

        """
        n_samples, n_features = data.shape
        X_centered = data - self.location_
        if self.store_precision:
            precision = self.precision_
        else:
            precision = linalg.pinv(self.covariance_)

        dist = np.sum(
            np.dot(X_centered, precision) * X_centered,
            1)
        mask = dist < chi2(n_features).isf(0.025)
        if self.assume_centered:
            location_reweighted = np.zeros(n_features)
        else:
            location_reweighted = data[mask].mean(0)
        covariance_reweighted = self._nonrobust_covariance(
            data[mask], assume_centered=self.assume_centered)
        support_reweighted = np.zeros(n_samples).astype(bool)
        support_reweighted[mask] = True
        self._set_estimates(covariance_reweighted)
        self.location_ = location_reweighted
        self.support_ = support_reweighted
        return location_reweighted, covariance_reweighted, support_reweighted

redpajama_set_name: RedPajamaGithub


text:
Sports in the real world
Kyle Nodarse
I know the NBA season is not over yet; in fact, it's barely past the halfway point, but I've already got my MVP for the season picked out and crowned. It is not based on statistics though, which gives everyone who reads this article a lot of room for argument to fight with me. That's OK though, because I am sticking to my guns.Consider this: When was the last team to win an NBA championship without a big man? Still thinking? Me too, because as far back as you go in recent history, teams do not win championships without a big man getting boards and making blocks. Big men disrupt the flow of the game by preventing the other team's leading scorer from driving for easy points every possession.
The San Antonio Spurs have been considered a dynasty as of late, and the reason is Tim Duncan. He controls the game down low and allows the Spurs playmaker, Manu Ginobli, to do his job even better. He is the reason the Spurs have been winning championships and consistently getting to the NBA finals.
Think back a few years ago to when the Lakers were the team to beat. Why is that? Kobe Bryant you say? I say you're wrong.
I say that Shaq is the real reason the Lakers were able to win three NBA championships. Want to know why? Who has won championships since they split up? Shaq has with the Miami Heat because he is the big man teams need to win titles.
Kobe may be the best individual talent in the NBA-I can't deny that claim at all. I would not even argue with those who crown Lebron James as the best in the NBA, either. Steve Nash and Paul Pierce can join the argument as well. Yet regardless of all those claims, none of these men can win a championship without a big man.
The Lakers had Kareem, the 76ers had Dr. J., and Kobe and Dwayne Wade both had Shaq. The argument could be made that Michael Jordan did not really have a big man, left with Bill Cartwright and Luc Longley. But Jordan was arguably the greatest player ever to play the game, so I am going to make an exception for him. Without a big man, you cannot win.
That is why my pick for the NBA MVP goes to Dwight Howard. Not only does he do all of the things I have talked about a big man being able to do, he can fly! Did anyone see the All-Star slam-dunk contest?
He literally flew, with a cape and everything. I will not guarantee that the Orlando Magic will win the NBA championships, but I should say that I would be surprised if he does not win the MVP this year, and perhaps many more years to come.


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text:
Q: How long before you can replay a random event? I recently failed one of the numerous random events in Grand Theft Auto 5. I eventually came back to the area a few days later and was able to replay it.
How long does it take for a random event to respond? Does switching characters reduce the wait time?

A: The in-game time that takes the random event to re-appear will vary for each of the encounters:
Characters for heists (Paleto, The Big One).
Example: Patrick McReary and Taliana Martinez
They are usually one time only, because the choice will change the available heisters during the course of the game history.
Characters for activities (Golf, Tenis).
Example: Castro Lagano
In case of a failure (the NPC died or got scared) he will re-appear during the next week.
Characters that give you stock (BAWSAQ & LCN).
Example: Gray Nicholson and Timm
They will reappear, but the time will vary according to the missions played and the distance between the encounter and the player. Most of the time is 72 hours for a failure, 7 days for the rest of the characters and between 1-4 weeks for the same character.
Note that sleeping will not make the random events re-appear faster.

redpajama_set_name: RedPajamaStackExchange


text:
HomeUSA News
Military turns down 80 percent of applicants as armed forces shrink
Reuters / Lucy Nicholson © Reuters
Gone are the post-9/11 recruiting days where the US military would take just about anyone who walked through its doors, issuing waivers for misconduct and health issues. Now 80 percent of applicants are turned away.
US Secretary of Defense Chuck Hagel announced in February of this year that the Obama administration would work to shrink the US Army to its smallest size since before World War II. Citing the federal government's budgetary concerns and the winding down of the wars in the Middle East, Hagel deemed it necessary to reduce the military personnel and lingering, expensive equipment costs.
He said in a speech that cutting the current level of 522,000 soldiers to 490,000 soldiers by autumn of 2015 was necessary "to protect critical capabilities like Special Operations Forces and cyber resources."
And cutting the size of the military means recruiting fewer people. The Army's recruiting goal dropped from 69,000 new enlistments for the 2013 fiscal year to 57,000 for 2014, the US Army Recruiting Command (USAREC) reported. The Navy's goal dropped from 40,112 in 2013 to 33,800 in 2014, according to its recruiting command.
Fewer spaces means tough choices for recruiters. "We're turning down twice as many as before," Army Sgt. 1st Class Terrence Hoard, who supervises the Army recruiting station in Grandview, Mo., told the Kansas City Star. Potential recruits who might have been granted waivers before are now rejected.
"It's not that we have a zero defect mentality, because we don't," Nathan Christensen, a Navy officer in public affairs for the Defense Department, told the Star. "But it is true that the quality of military recruits right now is the highest it's been in 40 years."
Most young Americans are ineligible to join the military, either because they're drug users, obese, medically unfit, failed to graduate high school, or have criminal records. The Pentagon estimates that only 25 percent are qualified.
"There are 30-some million 17- to 24-year-olds out there, but by the time you get all the way down to those that are qualified, you're down to less than a million young Americans," said Maj. Gen. Mark Brilakis, the commander of Marine Corps Recruiting Command, told Federal News Radio.
"Five years ago, if a kid had gotten caught with a (marijuana) joint in his car, that person would be pretty much a shoo-in" to enlist under a waiver, Hoard said to the Star. "Now you're not authorized."
When it comes to education, the Department of Defense considers its crop of new recruits to be 'high quality' if 90 percent have a high school diploma, Federal News Radio reported. Last year's newcomers crushed that goal, with 99.6 percent having graduated.
The economy has been favorable for military enlistments since the Great Recession began in December 2007. As the economy improves, youth unemployment drops. And when fewer young people can't find a job in the civilian sector, the military is less of a draw.
In 2013, data from the Pentagon's Joint Advertising and Market Research Studies program found that only 40 percent of the DoD's target market thought the military offered "an attractive lifestyle," down from 63 percent in 2004. The youth unemployment rate fell to 12.8 percent in April, according to the the Bureau of Labor and Statistics. The youth unemployment rate in the United States averaged 12.32 percent from 1955 until 2014, reaching an all-time high of 19.6 percent in April 2010.
"A weak economy in recent years, coupled with the talented and adequately resourced recruiting force produced the highest quality recruits in Air Force history," Brig. Gen. Gina Grosso, the director of force management policy for the Air Force, told the House Armed Services Committee in January. "However, we recognize this trend will be unsustainable as the economy continues to improve and competition to draw recruits from the small, qualified talent pool, who are alarmingly less inclined to choose military service as a career, increases dramatically."
Now the military is competing with the civilian sector for quality candidates, and they are forced to use the recruiting budgets they never needed to before - right as Congress seeks to cut those budgets.
"All this belt-tightening has caused the Department of Defense to chase after the same successful, highly motivated high school graduates that everyone else is chasing," Mike Byrd, a retired Army lieutenant colonel and an instructor for a Junior ROTC program in Independence, Mo. told the Star.
"It's very expensive to recruit," he added. "We don't have the flexibility we once had to get it wrong with one kid and hope the next one works out."
Military recruiters are hoping Congress will continue to allot money into funding recruitment efforts and paying for expensive advertising and marketing campaigns.
"As private sector career opportunities increase, use of incentives such as enlistment bonuses will help attract recruits with the characteristics necessary for Navy service as a means of getting the right sailor with the right skills to the right place at the right time," Rear Adm. Annie Andrews, the commander of Navy Recruiting Command, told Federal News Radio.
US Military Enlistment Goals and Accessions since FY2003
(Sources: Department of Defense, Government Accountability Office)
Air Force Army Marine Corps Navy Fiscal Year
GoalActualGoalActualGoalActualGoalActual 2003 37,000 37,141
73,800 74,132 32,501 32,530 41,077 41,376 2004 34,080
34,362 77,000 77,587 36,773 36,794 39,592
39,874 2005 18,900 19,222 80,000 73,373* 32,917 32,961 37,635 37,703 2006 30,750 30,889 80,000 80,635 32,301 32,337 36,656 36,679 2007 27,801 27,801 80,000 80,407 35,576 35,603 37,000 37,361 2008 27,800 27,848 80,000 80,517 37,967 37,991 38,419 38,485 2009 31,980 31,983 65,000 70,045 31,400 31,413 35,500 35,527 2010 28,360 28,493 74,500 74,577 28,000 28,041 34,140 34,180 2011 28,515 28,518 64,000 64,019 29,750 29,773 33,400 33,444 2012 29,037 29,037 58,000 60,490 30,500 30,514 36,275 36,329 2013 26,275 26,275 69,000 69,154 32,200 32,215 40,112 40,112
*The Army was the only military to miss an enlistment goal since 2003, and only missed their goal once (in 2005).
ArmynewsChuckHagelnews
Army, Navy offer fast-track US citizenship
War Inc. – Pentagon sucks in American youth
America's front lines clash with bottom lines on obesity
Nazi extremists infiltrate US military
US admits to lack of cybersecurity professionals as war drums beat louder
Too fat to serve: US army gives tons of overweight soldiers the boot
Only one-in-four Americans fit to serve in the military
US Navy nuclear officer cheating scandal grows, dozens decertified
'How did these guys get in the Army?' New study delves into mental illness
US drone pilots are 'stressed' and 'demoralized' – official report


text:
namespace Localwire.Graphinder.Core.Tests.Algorithms.GeneticAlgorithm.Base
{
    using System;
    using System.Collections.Generic;
    using System.Linq;
    using Core.Algorithms.GeneticAlgorithm;
    using Core.Algorithms.GeneticAlgorithm.MutationStrategies;
    using Providers;
    using Providers.TestData;
    using Xunit;

    public abstract class IMutationStrategyTests
    {
        protected IMutationStrategy Strategy;
        protected ITestDataProvider<ICollection<Individual>> IndividualProvider;

        protected IMutationStrategyTests()
        {
            IndividualProvider = new IndividualsProvider();
        }

        [Fact]
        public void IMutationStrategy_Mutate_ThrowsOnNullIndividual()
        {
            Assert.Throws<ArgumentException>(() =>
            {
                Strategy.Mutate(null);
            });

            Strategy.Mutate(IndividualProvider.ProvideValid().FirstOrDefault());
        }
    }
}


text:
stated on August 31, 2020 in a statement:
Says Tony Evers and Mandela Barnes "have participated in rallies that have done more to incite than calm the situation" in Kenosha
By Madeline Heim September 1, 2020
Johnson misses mark with claim Evers, Barnes participated in rallies that incited Kenosha chaos
On Saturday, Aug. 29, Lt. Gov. Mandela Barnes spoke at a rally led by the family of Jacob Blake, who was shot seven times in the back by a Kenosha police officer the previous Sunday.
Near the end of the rally, an unidentified man said, "If you kill one of us, it's time for us to kill one of yours."
But that message was not the message of the day from the peaceful rally where activists, community members and state leaders marched and called for change
Furthermore, the violence in Kenosha occurred days before the rally -- not after -- meaning the lone speaker's message did not "incite" as Johnson claims
What's more, Johnson himself has touted the fact that violence and unrest in Kenosha had been tempered days before this rally.
Wisconsin's elected officials didn't hesitate to point fingers at one another after Kenosha police officer Rusten Sheskey shot Jacob Blake, a 29-year-old Black man, seven times at close range on Aug. 23, 2020.
The shooting and the video of it that went viral ignited days of protest and unrest in the city, which reached a breaking point Aug. 26, 2020 when an Illinois teen — who had joined a group of armed people responding to a Facebook call to protect and defend the city — shot and killed two people, according to criminal charges.
Wisconsin Democrats say their counterparts across the aisle haven't done enough to act on racial disparities and police brutality, criticizing GOP leaders for gaveling out of an Aug. 31, 2020 special session called by Gov. Tony Evers to address such issues in less than 30 seconds.
Republicans, meanwhile, have pointedly blamed Evers, saying the deaths of the two people that Kyle Rittenhouse is accused of shooting could have been avoided if the state had been more aggressive earlier about deploying National Guard troops.
Trump appeared in Kenosha on Sept. 1, 2020 to meet with local law enforcement officers and business owners, despite a plea from Evers and other Democratic state leaders to stay away during a painful time for the city.
U.S. Sen. Ron Johnson, R-Wisconsin, welcomed the visit and in an Aug. 31, 2020 statement blasted Evers and Lt. Gov. Mandela Barnes, saying that instead of working with Trump they "made statements, leapt to conclusions, and have participated in rallies that have done more to incite than calm the situation."
Did Evers and Barnes participate in rallies that have "done more to incite than calm the situation in Kenosha," as Johnson claims?
Barnes attended one rally at request of Jacob Blake's family
Johnson's office did not respond to a request for the evidence that he used to make the claim, but his statement echoes an Aug. 25, 2020 claim from state Sen. Howard Marklein, R-Spring Green, that Evers and Barnes did not "call for peace" or "encourage calm" after Blake was shot.
We rated that claim Mostly False. Evers released a video statement the day prior asking protesters to remain peaceful.
What about the rallies?
Besides a joint news conference on Aug. 27, 2020 in Kenosha, Evers and Barnes have not appeared together publicly to discuss the situation.
Evers spokeswoman Britt Cudaback said the pair have gone to one rally between the two of them, an Aug. 29, 2020 event that Blake's family asked Barnes to attend.
Barnes met with Blake's family, spoke at the event and marched alongside thousands who showed, Cudaback said.
Reports from the rally confirm it was a peaceful one, but the Republican Party of Wisconsin and the Kenosha News seized on a comment near the end of the event in which an unidentified man took the microphone, decried police shootings of Black people and said, "If you kill one of us, it's time for us to kill one of yours."
The remarks were met with a muted response from the crowd.
To be sure, the comment strayed from the message of the event. In the Milwaukee Journal Sentinel's reporting, Blake's father, Jacob Blake Sr., is quoted asking people to be calm and civil, saying, "If we tear it up, we have nothing."
Barnes, meanwhile, spoke on the importance of voting and continued activism, commended Blake's family for their resilience despite "centuries of injustice" that Black people have endured and urged the crowd to demand accountability from police departments.
In any case, Johnson's claim was that the rally did more "to incite than calm the situation."
But examining the timeline of the last week in the city, the violence and property destruction in Kenosha took place in the days before the Saturday rally — not after.
Protests and cases of arson and looting began the night Blake was shot and reached a chaotic point two nights later, on the night Rittenhouse is charged with killing two and injuring one.
The rally Barnes attended was days later.
Indeed, Johnson himself noted during Trump's visit that the situation in Kenosha became much calmer after the night where the protesters were shot. He credits the president. But in praising Trump for bringing calm, he undermines his claim here that an event days later attended by Barnes somehow incited violence again.
In short, it can't be both.
Johnson claimed that Evers and Barnes "have participated in rallies that have done more to incite than calm the situation" in Kenosha.
Barnes was present at a rally where a threat was made, but the event was overall a peaceful one, and reports of violence tied to Blake's shooting came before the rally, not after.
And Johnson himself, while praising Trump's actions, has acknowledged things had been quiet for days before the Barnes speech -- and have remained so in the days since.
We rate the claim False.
Milwaukee Journal Sentinel, "Kyle Rittenhouse, charged in Kenosha protest homicides, considered himself militia," Aug. 26, 2020
Milwaukee Journal Sentinel, "Monday's legislative session on policing policies ended after seconds and GOP leaders aren't promising action later," Aug. 31, 2020
Milwaukee Journal Sentinel, "Assembly Speaker Robin Vos says 2 Kenosha deaths are 'because of Tony Evers' actions'", Aug. 26, 2020
Milwaukee Journal Sentinel, "Wisconsin Gov. Tony Evers urges President Trump to reconsider Kenosha visit," Aug. 30, 2020
U.S. Sen. Ron Johnson, statement on President Trump's visit to Kenosha, Aug. 31, 2020
Politifact Wisconsin, "Claim that Evers, Barnes did not call for peace, calm after Blake shooting goes too far," Aug. 28, 2020
Milwaukee Journal Sentinel, "Lt. Gov. Mandela Barnes says Kenosha now among cities like Charlottesville that have seen death in protests," Aug. 27, 2020
Milwaukee Journal Sentinel, "Thousands march with Jacob Blake family in Kenosha and Lt. Gov. Mandela Barnes, Rep. Gwen Moore call for change," Aug. 29, 2020
Republican Party of Wisconsin, "In case you missed it: Lt. Governor Shares Stage with Radicals Threatening Violence Against Police," Aug. 31, 2020
Kenosha News, "WATCH NOW: Kenosha speaker strays from message at rally," Aug. 29, 2020
Milwaukee Journal Sentinel, "Much of Kenosha's deadly protest shooting was captured in social media videos. Here's what they show," Aug. 26, 2020
New York Times, "Journalist Quits Kenosha Paper in Protest of Its Jacob Blake Rally Coverage," Aug. 31, 2020
Milwaukee Journal Sentinel, "Trump in Kenosha: Ron Johnson credits Trump for peace in Kenosha streets," Sept. 1, 2020


text:
'Happiest Season': Mary Steenburgen, Victor Garber, Aubrey Plaza, Alison Brie, Dan Levy & More Round Out TriStar Comedy
Mel Gibson In Talks To Direct 'Hacksaw Ridge' With Andrew Garfield Starring In War Hero Pic
By Ali Jaafar
Ali Jaafar
More Stories By Ali
Andrea Riseborough To Board BBC's Agatha Christie Adaptation 'The Witness For The Prosecution'
Stephen Frears' 'Victoria And Abdul' Adds Ali Fazal And Eddie Izzard; Working Title-BBC Co-Pro, Focus Co-Fi And Distributing
Dominic West Joins Elisabeth Moss In Ruben Ostlund's 'The Square'
EXCLUSIVE: Mel Gibson is in talks to take on directing duties on Hacksaw Ridge, based on the true story of Desmond T Doss, the first conscientious objector in U.S. history to win the Congressional Medal of Honor. Andrew Garfield is also in discussions to play the lead role.
Doss was a U.S. Army medic who served in World War II during the Battle of Okinawa and refused to kill. President Truman awarded him the Medal of Honor for saving so many lives.
This is not a done deal yet, and Deadline understands that talks are moving forward delicately and slowly. If Gibson does step back into the director's chair, the subject matter would seem to suit his tastes to a tee. Throughout his career in front of and behind the camera, Gibson has shown an abiding fascination with protagonists prepared to suffer for their principles but ultimately prevailing over hardship.
Hacksaw Ridge, if it happens, would seem to offer a return to the type of emotional terrain Gibson explored to such success in previous directorial efforts Braveheart and Passion Of The Christ. The project would reunite him with Braveheart scribe Randall Wallace, who co-wrote the script with Robert Schenkkan and had been attached to direct.
2006's Apocalypto was the last film Gibson directed.
Robert Downey Jr Q&A: On 'The Judge', Turning 50 With A New Baby, And Yes To 'Iron Man 4' - If Mel Gibson Directs It
The project is being led by producers Bill Mechanic (Coraline) through his Pandemonium banner and David Permut (Face Off), through Permut Presentations. Terry Benedict (The Death Of Reasonable Doubt) and Steve Longi (Youth In Revolt) are co-producers. Gregory Crosby is an associate producer. Walden Media has also been developing the project.
Gibson most recently starred in Expendables 3 and is in post on Jean-Francois Richet's Blood Father, in which he plays an ex-con reunited with his 16-year-old daughter who must protect her from drug dealers trying to kill her.
Big Deals Film
'The Karate Kid' In Training For Broadway: Musical Adaptation Has Original Screenwriter On Board
'Martha Marcy May Marlene' Director Sean Durkin Plots Pic On Von Erich Wrestling Family; Tessa Ross & BBC Films Onboard
Lori Rovner Hired By Imagine Entertainment To Be EVP, Business And Legal Affairs
Prosecutor Describes Harvey Weinstein As Sexual Predator & Rapist: "Not A Harmless Old Man"


text:
Бухарестско-Арадская наступательная операция (также известная как Румынская операция; 30 августа — 3 октября 1944) — фронтовая военная операция войск СССР и Румынии против Германии, Венгрии и румынских антикоммунистов в ходе Великой Отечественной войны, в результате которой от немецко-венгерских войск была освобождена почти вся Румыния.

Предыстория и планы сторон

Разгром немецких войск в Ясско-Кишинёвской операции и победа Августовского восстания в Румынии создали благоприятные условия для последующего наступления советских войск на юго-западном стратегическом направлении. Сплошной фронт немецких войск был разорван на большом протяжении и войска противника отступали по расходящимся направлениям: часть немецких войск пыталась с боями уйти в Болгарию и Югославию, часть — в Карпаты. Перед Красной Армией простирался широкий оперативный простор и эту уникальную возможность Ставка Верховного Главнокомандования стремилась максимально использовать.

В самой Румынии наблюдался мощный антифашистский патриотический подъём. Вопреки ожиданиям ряда советских руководителей, румынская армия в целом положительно восприняла выход из войны на стороне Германии и переход на сторону советских войск, противодействие прогермански настроенных офицеров свелось к дезертирству и бегству в расположение германских войск, имелись случаи их самоубийств. И напротив, румынские военнослужащие в массовом порядке и по собственной инициативе оказывали помощь советским войскам. Однако окружение короля Михая I и высшее командование румынской армии стремились уклониться от активных действий против немецких войск, обеспечить им возможность мирно уйти из Румынии. Более того, новое румынское правительство Константина Сэнэтеску прилагало все усилия, чтобы убедить западных союзников перебросить американские и английские войска в Бухарест.

Правительство Болгарии ещё 26 августа спешно объявило о полном нейтралитете, что исключало вступление болгарских войск в войну на стороне Гитлера. 29 августа на почве военных поражений произошёл правительственный кризис в Венгрии, а в Словакии началось Словацкое национальное восстание.

Военная обстановка была следующей: южное крыло немецкой группы армий «Южная Украина» перестало существовать, его остатки в виде разрозненных подразделений бежали в Болгарию, на северном крыле у командования группы армий осталось не более 6 дивизий, часть из которых уже понесла значительные потери, и до 300 самолётов. В Трансильвании находилось 8 венгерских дивизий и бригад, а также 30 пограничных батальонов. Непосредственно в Венгрии находились ещё 8 венгерских дивизий и бригад (из них спешно началось формирование 2-й венгерской армии) и часть войск могла быть переброшена в Румынию из центральных Карпат, где оборонялась 1-я венгерская армия. Из действовавших в Греции и на Балканах групп армий «Ф» и «Е» немецкое командование могло перебросить в Румынию несколько немецких дивизий (более того, такая переброска спешно начала готовиться), но в сложившейся обстановке для этого требовалось время. Остановить советские войска до восстановления сплошного фронта можно было только используя выгодные условия местности в Южных Карпатах, захватив все горные перевалы там.

С другой стороны, на границе с Трансильванией находились 20 румынских дивизий (1-я и 4-я румынские армии), которым новое румынское правительство отдало приказ разоружить все немецкие подразделения, не покинувшие Румынию, а в случае вступления в страну венгерских и немецких войск вступить с ними в бой. Впрочем, эти тыловые армии были укомплектованы в основном неопытными новобранцами и солдатами старших возрастов, очень слабо вооружены и практически не имели танков.

Учитывая обстановку, Ставка Верховного Главнокомандования 29 августа приказала 3-му Украинскому фронту (командующий генерал армии, с 12 сентября Маршал Советского Союза Ф. И. Толбухин) занять румынско-болгарскую границу и готовиться к началу боевых действий против Болгарии.

2-му Украинскому фронту под командованием генерала армии (с 10 сентября — Маршала Советского Союза) Р. Я. Малиновского силами 27-й армии, 53-й армии, 6-й танковой армии обойти Южные Карпаты по придунайской равнине и выйти на границу с Венгрией и Югославией западнее Джурджу, затем наступать в общем направлении на Слатин — Турну-Северин, к 7—8 сентября достигнуть этих городов. Правое крыло фронта (7-я гвардейская армия, 40-я армия) должно было наступать на Сату-Маре, обходя Карпаты с востока. Численность войск 2-го Украинского фронта к началу операции составляла 681 556 человек. Перед правым флангом 2-го Украинского фронта сплошной немецкий фронт был развален, но перед левым крылом и частично перед центром сохранилась устойчивая оборона, опиравшаяся на удобную горно-лесистую местность Карпат.

Германское командование в свою очередь намеревалось восстановить сплошной фронт между группой армий «Южная Украина» и группой армий «F» по линии Восточные и Южные Карпаты — Западные Балканы. Для этого 2-й венгерской армии было приказано не позднее 5 сентября начать наступление из Клуж — Турда в южном направлении с целью овладения перевалами через Южные Карпаты.

Начало операции: бросок на запад
Используя благоприятную обстановку, войска 2-го Украинского фронта на следующий день после завершения Ясско-Кишинёвской операции, с 30 августа 1944 года начали новую наступательную операцию. 31 августа передовой отряд советских войск без боя вступил в Бухарест (почти сразу советские войска были выведены из Бухареста по политическим соображениям). Преследуя разбитые и разрозненные немецкие части, к 5 сентября на направлении главного удара они вышли на линию Кымпулунг — Питешти — Каракал — Зимнича, продвинувшись на 80 — 140 километров, а передовые части 6-й танковой армии вышли на Дунай к границе Югославии у города Турну-Северина. На правом крыле фронта 40-я и 7-я гвардейская армии за это время продвинулись всего на 15-20 километров, прорывая заранее созданные там оборонительные рубежи. Здесь немецкие войска оказали упорное сопротивление.

Действуя по приказу германского командования, утром 5 сентября 2-я венгерская армия (командующий генерал-полковник Лайош Вересс, 6 венгерских и немецких дивизий) атаковала передовые части 4-й румынской армии (командующий корпусной генерал Георге Аврамеску) из района Турды и за двое суток продвинулась к югу на 20—30 километров, а в последующие дни — до 50 километров. Она имела задачу занять проходы через Южные Карпаты и запереть их до подхода советских войск. Основные силы 4-й румынской армии находились значительно южнее, в связи с чем возникла угроза занятия северной и центральной Румынии войсками противника.

Для избежания разобщённости в действиях советских и румынских войск, 6 сентября в оперативное подчинение командованию 2-го Украинского фронта были переданы 1-я и 4-я румынские армии, 4-й и 6-й румынские армейские корпуса (свыше 138 тыс. человек, 580 орудий) и 1-й румынский авиационный корпус (113 исправных самолётов).

Второй этап операции: поворот на север
В этой обстановке командующий 2-м Украинским фронтом Р. Я. Малиновский 6 сентября приказал 6-й танковой армии генерал-полковника танковых войск А. Г. Кравченко круто повернуть на север и к 12 сентября освободить район Деж — Клуж — Шэрмэшел; такой же приказ получила 27-я армия, а 53-я армия должна была повернуть на северо-запад и занять рубеж Брад — Лугож. Эти задачи также были успешно решены — 6-я танковая армия стремительным маршем прошла 250 километров и к вечеру 11 сентября вышла на позиции, где оборонялась 4-я румынская армия. Без оперативной паузы советско-румынские войска перешли здесь в контрнаступление и к 13 сентября отбросили 2-ю венгерскую армию в исходное положение, заняв 12 сентября Клуж. К концу дня 15 сентября на рубеж Тыргу-Муреш — Турда вышли 27-я советская и 4-я румынская армии, но бои на этом рубеже приняли затяжной характер. Западнее 53-я армия прошла за 7 суток 220 километров и заняла район Карансебеша. В Восточных Карпатах 40-я и 7-я гвардейская армии в эти дни смогли сломить сопротивление немецких войск, ускорили темп наступления и продвинулись до 130 километров. Одновременно продолжались бои по уничтожению пытавшихся вырваться из окружения под Яссами разрозненных групп немецких войск.

В целом к 15 сентября 2-й Украинский фронт почти полностью достиг румыно-венгерской границы, установленной Венским «арбитражем». Немецкое командование к тому времени успело создать по ней новую линию фронта, на которой находились 27 немецких и венгерских дивизий и бригад, в том числе 6 танковых и моторизованных дивизий. Более того, в этот день противник силами 6-й немецкой армии (командующий генерал артиллерии Максимилиан Фреттер-Пико) и 3-й венгерской армии (командующий генерал Йозеф Неслени) предпринял новую попытку контрнаступления на Арад и Тимишоару с целью не допустить подходившие войска левого крыла 2-го Украинского фронта в Средне-Дунайскую низменность. Здесь за три дня противник оттеснил части 1-й румынской армии (командующий корпусной генерал Николае Мачичи) на 30—50 километров, занял Арад и вышел на подступы к Тимишоаре.

Третий этап операции: сражения на подступах к Венгрии
Однако Ставка Верховного Главнокомандования по-прежнему оценивала группу армий «Южная Украина» как разгромленную и приказала 2-му Украинскому фронту нанести главный удар в направлении на Клуж — Дебрецен — Мишкольц с задачей выйти на довоенную румыно-венгерскую границу, а в дальнейшем подойти к Тисе на участке Чоп — Сольнок и помочь 4-му Украинскому фронту, наступление которого развивалось медленно (см. Восточно-Карпатская операция), преодолеть Карпаты и овладеть районом Ужгорода.

Но этот план реализовать полностью не удалось. К 15 сентября противнику удалось восстановить сплошной фронт от Украины до Югославии. Хотя советские части перешли в наступление и потеснили противника, но и немецкое командование перебросило в район Клуж — Турда 4 дивизии, в том числе две танковые. Войскам 2-го Украинского фронта противостояли уже 27 немецких и венгерских дивизий и бригад, в том числе 6 танковых и моторизированных дивизий. Обе стороны непрерывно атаковали и контратаковали друг друга. Это сражение завершилось незначительным продвижением советско-румынских войск: они отбросили контратаковавшие части противника и 22 сентября вышли на румыно-венгерскую границу в районе города Мако и к северо-востоку от него. На следующий день 18-й танковый корпус и 243-я стрелковая дивизия вступили в Венгрию. На левом крыле фронта 53-я армия совместно с 1-й румынской армией действовали более успешно, 21 сентября они освободили крупный город Арад и 24 сентября также вышли на старую румыно-венгерскую границу.

В этих условиях Малиновский поставил вопрос об изменении направления главного удара с центра на левое крыло, нанесении отсюда удара в северном направлении и о временном переходе войск фронта к обороне для подготовки новой, Дебреценской наступательной операции. 25 сентября было получено соответствующее разрешение от Ставки. 24 сентября фронт приостановил наступление и приступил к перегруппировке сил (датой окончания операции по работе Кривошеева считается 3 октября 1944 года).

Итоги
В результате операции была освобождена от немецко-венгерских войск почти вся Румыния, последние небольшие её северные районы были освобождены в октябре 1944 года в ходе Дебреценской операции. Были заняты исходные районы для наступления в Венгрию и в Югославию. В руки советских войск почти без разрушений перешли крупные промышленные районы и железные дороги, а также мощные румынские нефтепромыслы. Продвижение войск фронта составило от 250 до 500 километров. Для восстановления фронта противнику пришлось перебрасывать в Румынию 20 дивизий, в том числе 4 танковые и 1 моторизованную, 2 танковые бригады. Советские и румынские войска получили опыт успешного совместного ведения боевых действий. В целом, румынская армия в этой сложной динамичной и напряжённой операции проявила высокие боевые качества, что резко контрастировало с её последними неудачными боевыми действиями против советских войск под руководством Антонеску.

Потери сторон
Потери советских войск в этой операции были относительно невелики: за месяц боёв они составили 8447 человек безвозвратными и 46 839 человек санитарными. Потери румынских войск в этой операции точно неизвестны, но судя по тому, что с 23 августа по 30 октября её потери составили более 58,3 тыс. человек общих потерь, можно предположить что в сентябре 1944 года румынская армия потеряла от 30 тыс. до 40 тыс. человек убитыми и ранеными.

Потери немецко-венгерских войск известны только по данным советского командования: свыше 100 тыс. уничтожено и свыше 18 тыс. пленных. Об их степени даёт представление такой факт, что в сентябре 1944 года в Румынии было убито в боях два немецких генерала, восемь захвачены в плен и один застрелился.

Примечания

Литература
 Великая Отечественная война 1941—1945 гг.: В 12 тт. — Том 5. Победный финал. Завершающие операции Великой Отечественной войны в Европе. Война с Японией. М.: Кучково поле, 2013. — С.112—125.

 Освобождение Юго-Восточной и Центральной Европы войсками 2-го и 3-го Украинских фронтов 1944—1945. — : Наука, 1970.

Операции в Великой Отечественной войне
Сражения Румынии
Сражения Германии
Сражения Венгрии
Сражения в Румынии
Конфликты 1944 года
Август 1944 года
Сентябрь 1944 года
Октябрь 1944 года
Десять сталинских ударов

redpajama_set_name: RedPajamaWikipedia


Why See Joffrey Ballet - Across The Pond?
One of Britain's most influential and celebrated choreographers, Wayne McGregor has brought modernity and new into the mainstream of the British Ballet scene. In this Greek inspired piece, McGregor's signature physicality is brought to life with Max Richter's experimental adaptation of Vivaldi's 'Four Seasons'.
Widely considered one of Ashton's greatest works, this ethereal piece excites the senses and evokes authentic and dazzling portrayals of the turning of the seasons. A true dance classic, Symphonic Variations brings you through the storminess of winter to the bright and carefree joy of summer.
Founder and Artistic Director of London's 201 Dance Company, Andrea Walker has worked with some of the brightest and most world renowned stars. Bringing his beautiful and unique style to the likes of Coldplay and Lady Gaga, Walker now releases his undenied talent onto American audiences. In this never seen before World Premiere catch a glimpse and ignite your tastebuds for Andrea and his work.
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LatinaVIDA is a 501(c)3 public benefit corporation dedicated to inspiring the next generation of Latina executives. We do this through workshops (Rise to the Top), webinars, newsletters, peer mentoring (LV Circles) and coaching. Our focus is to change a statistic that keeps us all up at night: Only 3% of all executives in any sector are Latina. As Latinas grow in number and continue to influence family and community life, we must be seen as more than consumers. The Fortune 500, major nonprofits, universities, and public agencies need Latina executives.
The goal of this campaign is to build our nonprofit's capacity to produce monthly podcasts (one in English/one in Spanish) where we interview and feature a Latina senior executive.
One of the most important factors that is known to interrupt the career ambitions of Latinas is a sense of isolation, bias and lack of mentors and sponsors. LatinaVIDA360 is an opportunity to show case Latina Trailblazers in every sector who have defied the odds and made it to the top. As Latina are exposed to leaders who are just like them, who faced the same questions as they do now–this will help many remain focused on their career goals and reach out to find mentors that guide them.
Any unused portions of campaign funds will be used to support our existing workshop program Rise to the Top.
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text:
Q: $x_{n+1}=\frac{a_{n+1}+x_n}{1+a_{n+1}x_n}$ then value of $lim_{x\rightarrow \infty}x_n$? Let ($a_n$) be a sequence with $a_n \in(\frac{1}{2},1)$ for all $n\geq0$ Define the sequence $(x_n)_{n=1}^{\infty}$ by $x_0=a_0$ and $x_{n+1}=\frac{a_{n+1}+x_n}{1+a_{n+1}x_n}$. Does the sequence diverge? If not what are the possible value of $lim_{x\rightarrow \infty}x_n$?
I can connect the sequence with the hyperbolic tan function but I failed to proceed further

A: $$x_n=\tanh(y_n) ;\ a_n=\tanh(b_n)$$
Then we have $$y_{n+1}=y_n+b_{n+1} $$
Since $b_{n+1}>\tanh^{-1}(1/2)>0$, then $y_{n+1}>y_n+\tanh^{-1}(1/2)$
So $y_n\rightarrow\infty$. Therefore $x_n\rightarrow1$


The Lee family behind South Korea's Samsung Group has topped the list of 50 richest dynasties in Asia, although the rankings are dominated by Chinese and Indian names.
Singapore's Kwek family and their Malaysian cousins, the Queks, shared sixth place on the inaugural list with a net worth of US$18.9 billion (S$26.6 billion), although their operations in the Hong Leong Group are separate from each other.
Hong Kong tycoon Li Ka-shing, sitting on a fortune of US$25 billion, has been excluded from the list, which includes only families whose wealth and businesses have extended to at least three generations.
None of Mr Li's grandchildren have taken serious roles in the family business, said Forbes Asia magazine, which compiled the rankings.
"Nearly half of the richest families in Asia are of Chinese descent, yet none of the inaugural 50 is based in the mainland, where conglomerates are young, run by the first generation," it said.
Families with Indian origins hold 14 of the 50 spots on the list, which also includes those from Japan, Malaysia, Thailand, Indonesia and the Philippines.
Owners of Singapore's Hong Leong topped the list of regional dynasties.
Helmed by executive chairman and second-generation member Kwek Leng Beng, the group ranks among Asia's largest and most successful conglomerates, managing over $40 billion in diversified premium assets, including 13 companies listed in seven stock exchanges globally.
Grandsons of family patriarch Kwek Hong Png, Sherman and Eik Sheng, are part of the senior management team at group companies.
Across the Causeway, cousin Quek Leng Chan chairs the Malaysia business.
According to Forbes, the Ng, Khoo, Wee, Kwee and Hiranandani families, who are all based here, have a combined net worth of US$25.3 billion. The Kuok Group, which is also on the list, has a slew of companies in the region, with patriarch Robert Kuok's Singaporean nephew Kuok Khoon Hong running agribusiness group Wilmar.
Forbes said it sifted through more than 500 families while compiling the list, shortlisting only those with a minimum net worth of US$2.9 billion based on stock prices and exchange rates in September.
Korea's Lee family, with a net worth of US$26.6 billion, came out on top, while the Lee family of Hong Kong, who control Henderson Development, a leading property developer, came next with a fortune of US$24.1 billion.
The third richest are the Ambanis of India's Reliance Group with a combined net worth of US$21.5 billion, followed by Thailand's Chearavanont family with US$19.9 billion generated from the agriculture-based Charoen Pokphand group. The Kwok family, which controls Hong Kong's Sun Hung Kai property empire (US$19.5 billion), rounds out the top five.
Forbes noted that a challenge for these dynasties was to keep wealth in the family, while also keeping it growing - an area which, studies show, family-owned businesses in Singapore are among the least prepared to handle in South-east Asia.
A study by the Economist Intelligence Unit (EIU) in January found that only 58 per cent of Singapore families have prepared for succession. "This research has shown an over-reliance on informal structures, such as family councils, to manage succession issues," said Mr Saiful Bahari Baharom, chief executive of Labuan IBFC, which commissioned the EIU study.
Correction note: In the original article, we stated that Sherman Kwek and Kwek Eik Sheng are grandsons of Quek Leng Chan. This is incorrect. They are grandsons of Kwek family patriarch Kwek Hong Png and the son and nephew respectively of Mr Kwek Leng Beng. We are sorry for the error.


Following the discovery of the Antarctic ozone hole in late 1985, various governments recognized the need for stronger measures to reduce the production and consumption of a number of chlorofluorocarbons (CFCs). CFCs, which are human-made chemicals widely used in manufacturing, have been found to deplete the ozone layer that shields the surface of Earth from harmful forms of solar radiation. During the mid-1980s negotiations began on the Vienna Convention for the Protection of the Ozone Layer—a framework treaty focused on cooperation in research, information exchange, and scientific assessment of the atmospheric ozone (O 3 ) problem—government representatives discussed drafting a protocol controlling the use of CFCs, human-made chemicals widely used in manufacturing that deplete the ozone layer. However, no consensus could be reached. The Executive Director of the United Nations Environmental Programme (UNEP) established a working group to begin drafting such a protocol. The final agreement, which was concluded on September 16, 1987, reflects the contentious nature of the negotiations. For example, by Article V, developing countries with low consumption rates (e.g., Brazil, India, and Vietnam) that feared the protocol would hinder their economic development are allowed a ten-year delay in required compliance with targets and timetables for reducing ozone emissions.
However, countries have generally been aggressive and effective in implementing the protocol. By the time it came into effect on January 1, 1989, countries were already contemplating the protocol's modification and strengthening. Amendments and adjustments were agreed to in London (1990), Copenhagen (1992), Vienna (1995), Montréal (1997), and Beijing (1999). These modifications shortened the timetables for phasing out consumption of listed chemicals, added and funded the Montréal Protocol Fund, established the Implementation Committee, developed noncompliance procedures, and expanded the Technology and Economic Assessment Panels. These panels have addressed new issues as they have arisen, such as recycling and international smuggling of CFCs.
SEE ALSO CFCs (C HLOROFLUOROCARBONS ) ; O ZONE ; T REATIES AND C ONFERENCES .
Benedick, Richard Elliott. (1998). Ozone Diplomacy: New Directions in Safeguarding the Planet. Cambridge, MA: Harvard University Press.
Weiss, Edith Brown. (2000). "The Five International Treaties: A Living History." In Engaging Countries: Strengthening Compliance with International Environmental Accords, edited by Edith Brown Weiss and Harold K. Jacobson. Cambridge, MA: The MIT Press.
Ozone Secretariat of the United Nations Environment Programme. "The Montréal Protocol on Substances That Deplete the Ozone Layer." Available from http://www.unep.ch/ozone .


Tells the story of a young boy Surya who has a rare condition of incognito sensitivity to pain meaning he can not feel pain, and sets out learn martial arts and hunt down muggers.
"Classic martial arts films come to life in this fun caper that stays with you."
"Surya (debutant Abhimanyu Dassani) has a rare (superhero) disorder. He can't feel pain. This makes him a misfit — a social outcast, who grows up to believe that he is Karate man, a self-proclaimed..."
"I'm going with three out of five for Mard Ko Dard Nahin Hota. Vasan Bala hits the high notes frequently, even if the film doesn't quite fall into a seamless symphony."
"A cocktail of gorgeous action sequences, absurd laugh-out-loud moments"
"Its a homage to action movies of the past."
"The hero and the film are alike - they do things that you least expect them to. Mard Ko Dard Nahi Hota is therefore is packed with delightful surprises, not the least of which are the ways in which it..."
"Vasan Bala creates a world so mad yet meticulous and unlike no other in Mard Ko Dard Nahi Hota, raves Sukanya Verma."
"Vasan Balas debut directorial Mard Ko Dard Nahi Hota is an extremely fresh and hilarious interpretation of the heroics we have seen in Hindi cinema."
"A must watch terrific action-comedy that caters to all set of audiences."
"MARD KO DARD NAHI HOTA rests on an interesting idea and is well directed and performed. At the box office, it will need to depend on a strong word of mouth to post decent numbers at the ticket windows..."
"A BIG FAT FILMY (in legend Ameen Sayani's dialect) JEE HAAAAAAAAAAN!!!"
"Abhimanyu Dassanis phenomenal acting takes the spotlight"
"Abhimanyu Dasani debuts with an impressive performance"
"Abhimanyu Dassani debuts with an impressive performance"
"It's one of those -- 'you've to be there' -- kind of films. But within those two hours, you'll surely laugh your head off. At another level, the film is a homage to Hong Kong action films, H..."
"Missing Out On This Crazy, Engaging Ride Would Be A Crime!"


Welcome to Episode 25 of The DPF Doctor TV.
We welcome aboard Mike and his team from Shires Garage Vehicle Services in Langport, Somerset. A great addition to our network and we're delighted to have them on board.
We also talk about a couple of trips we'll be making.
We're really excited to announce that we're launching The DPF Doctor network in Ireland. We're looking for 12 of the best garages we can find. We're also looking for 6 of the best garages in Northern Ireland.
Nine of those spaces have been reserved and once the rest of those spaces are filled we'll launch the whole network complete.
Before then though we're off to see a few garages around Oxford to about joining our network then we're up to Harrogate for what is going to be our biggest training day to date.