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Model Interpretation and Feature Selection

10/28/2022

Robert Utterback (based on slides by Andreas Muller)

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Model Interpretation (post-hoc?)

NOT causal inference

Does x cause y?

That's NOT what model interpretation is giving us!

Don't want to get too into 'causal inference', but just be careful

Can still inform how to better model the data or do better exploration

Possibly useful for feature selection, feature engineering, model
debugging, explaining predictions.

Alternative: use a model that is easy to interpret.

Alt: Derive a simpler explainable model from a non-explainable one (model compression)

BUT: if your model is bad, it doesn't make sense to try and interpret it!

Types of explanations

Explain model globally

How does the output depend on the input?
Often: some form of 'marginals'

Explain model locally

Why does it classify this point this way?

Explanation could look like a 'global' one but be different for each point

"What is the minimum change to classify it differently?"

Marginals: how does the prediction change as a function of a particular feature?

Explaining the model \(\ne\) explaining the data

model inspection only tells you about the model

the model might not accurately reflect the data

"Features important to the model"?

Naive:

coef_ for linear models

feature_importances_ for tree-based models

feature_importances_ kind of unfair since it comes from the training data!

(why not compute it with the test set?)

Linear Model coefficients

Relative importance only meaningful after scaling

Correlation among features might make coefficients completely uninterpretable

L1 regularization will pick one at random from a correlated group

Any penalty will invalidate usual interpretation of linear coefficients

With regularization, the magnitude and sign of the coefficients
might still tell you something, but it doesn't have the same
interpretation as unregularized

Still tells you about the model, just not as much about the data (not as statistically valid)

Drop Feature Importance

\[ I^{drop}_i = Acc(f,X,y) - Acc(f',X_{-i},y) \]

def drop_feature_importance(est, X, y):
    base_score = np.mean(cross_val_score(est, X, y))
    scores = []
    for feature in range(X.shape[1]):
        mask = np.ones(X.shape[1], 'bool')
        mask[feature] = False
        X_new = X[:, mask]
        this_score = np.mean(cross_val_score(est, X_new, y))
        scores.append(base_score - this_score)
    return np.array(scores)

Doesn't really explain model (refits for each features

Can't deal with correlated features well

Very slow

Can be used for feature selection

With correlated features, could find that both are not important,
even though you had a model which was very successful and originally
(with both features) used only one feature

On its own not very useful, we'll see it later rebuilds many models,
not explaining this particular model.

Permutation Importance

Idea: measure marginal influence of one feature
\[ I^{perm}_i = Acc(f,X,y) - E_{x_i}[Acc(f(x_i,X_{-i}),y)] \]

def permutation_importance(est, X, y, n_repeat=100):
  baseline_score = estimator.score(X, y)
  for f_idx in range(X.shape[1]):
      for repeat in range(n_repeat):
          X_new = X.copy()
          X_new[:, f_idx] = np.random.shuffle(X[:, f_idx])
          feature_score = estimator.score(X_new, y)
          scores[f_idx, repeat] = baseline_score - feature_score

Applied on a validation set given trained estimator.

Also kind of slow.

instead of dropping a feature, replace with an 'uninformative variant'

Shuffle a particular feature's values

Same as prediction made if I average over all possible feature
values, taking expectation over data distribution

We're NOT refitting the model – doing this on a validation set!

Telling you: how much does this model rely on this particular feature

Better than, e.g., tree feature importances

from sklearn.inspection import permutation_importance

LIME

Build sparse linear local model around each data point

Explain prediction for each point locally

Paper: "Why Should I Trust Y ou" explaining the predictions of any classifier

Implementation: https://github.com/marcotcr/lime

Point is to have local explanations

Model built complicated decision boundary shown

Builds a sparse linear model around some given point

Randomly sample some points, build an L1-regularized model, look at the coefficients (repeat)

SHAP

Build around idea of Shapley values

Roughly: does drop-out importance for every subset of features

Intractable, requires sample approximations

Fast variants for linear and tree-based models

Cool visualizations and tools: https://github.com/slundberg/shap

Can give local and global explanations

Case Study

Toy Data

100,000 points, 8 features

Features 0 and 1 are informative but ANTI-correlated

Features 2 and 3 are informative but anti-correlated

Features 4-7 are random (nothing to do with target)

Pretty nasty!

Can trip up regression models

Maybe we can remove correlated features first? (We'll look at this in a minute)

We might expect an L1-penalized model to pick out one of {0,1} and one of {2,3} and do nothing else

Any other model probably uses all of these (but not 4-7) and ideally
assigned similar importances to each pair

Models on lots of data

lasso = LassoCV().fit(X_train, y_train)
lasso.score(X_test, y_test)

0.545

ridge = RidgeCV().fit(X_train, y_train)
ridge.score(X_test, y_test)

0.545

lr = LinearRegression().fit(X_train, y_train)
lr.score(X_test, y_test)

0.545

param_grid = {'max_leaf_nodes': range(5, 40, 5)}
grid = GridSearchCV(DecisionTreeRegressor(), param_grid, cv=10, n_jobs=3)
grid.fit(X_train, y_train)
grid.score(X_test, y_test)

0.545

rf = RandomForestRegressor(min_samples_leaf=5).fit(X_train, y_train)
rf.score(X_test, y_test)

0.542

Coefficients and default feature importance

Lasso makes sense, and so does Ridge

LR is going crazy (the bars actually go way up)

But actually the values of 0 and 1 cancel each other out to some degree (correlation!)

(Maybe easier to see for 2 and 3)

For trees, importances will always be positive (two different things on the same plot…)

Tree is a little odd - picks 0 over 1, but you'd expect the same

Lasso (and to some degree tree) selected randomly among correlated

But a single tree is pretty unstable, so ok

Random Forest makes more sense b/c it has less variance

Also RF assigns some importance to useless features, which reflects
why some people don't like tree-based feature importances

Permutation Importances

Always positive, more comparable

Makes more sense

SHAP values

Does give directionality (can be negative)

Don't look too much into this (?)

More model inspection

Kind of coarse-grained: just tells you how important

How about telling us exactly how a feature interacts with a given model/prediction

Partial Dependence plots

Marginal dependence of prediction on one (or two) features
\[ f^{pdp}_i(x_i) = E_{X_{-i}}[f(x_i,x_{-i})] \]

Idea: get marginal predictions given feature

How? "integrate out" other features using validation data

Fast methods available for tree-based models (doesn't require validation data)

Nonsense for linear models (or just show obvious linearity?)?

Gives 1D function for each feature

For each feature i, what is the expected prediction if I average out all the other features

Example: look at first feature

It has range 0-10

First replace with 0, then 1, then 2, etc., averaging results

Partial Dependence

from sklearn.ensemble.partial_dependence import plot_partial_dependence
boston = load_boston()
X_train, X_test, y_train, y_test = \
    train_test_split(boston.data, boston.target,
                     random_state=0)

gbrt = GradientBoostingRegressor().fit(X_train, y_train)

fig, axs = \
    plot_partial_dependence(gbrt, X_train,
                            np.argsort(gbrt.feature_importances_)[-6:],
                            feature_names=boston.feature_names, n_jobs=3,
                            grid_resolution=50)

But there's also something else that's quite interesting, which is
called Partial Dependence Plots that's actually possible for all
trees. Unfortunately, in scikit-learn, it's available only for the
gradient boosting. So the idea here is to not only see what parameters
are important but how will they influence the target. And so after you
fit your model, I'm using the Boston data set to the gradient boosting
regressor, there's a thing called plot partial dependence, which gets
the model and the training data set and then the features for which I
want to make the partial dependence plots.

For trees, don't actually need validation set (but in general you do!)

Trees actually have a faster way that doesn't require the brute force

Plots shown backwards (RM is most important)

This is about what the model learned, not necessarily about the data

So here, I'm looking at the six most important features. The feature
importance is important according to the trees. So I sort this and
take the six most important ones.

And so this is what the partner dependence plots look like. So this is
the most important feature, the second most important feature and so
on. This is sort of the marginal contribution of each
feature. Basically, in each tree, you're summing out the contribution
of all the other features, and you look only at what is the
contribution of this particular feature. In general, this would be
hard to compute. For tree-based models you can compute is a very
efficient way because you can basically just sum up over the whole
space by traversing the tree.

Here with increased room size, the price increases and you can see how
it increases and you can see that there's like a big step function
here. And you can see that for increased LSTAT, the price
decreases. For the others, there aren't any drastic effect. There
seems to be some threshold for NOX. And something funky going on DIS.

The question is, so I said for random forest, you can't find out the
direction of which way the feature influences. But that was for the
eature importance. So the feature importance tells the impurity
decrease. So here I also look at the feature importance and they're
just numbers so they don't give you the direction. For both gradient
boosting and random forest, you can look at partial dependency plot,
and they will give you a non-parametric model of how a single feature
influences it. Unfortunately, it's not available in scikit-learn. You
could do the same thing for random forests. Because in the end, the
way they predict is quite similar.

Bivariate Partial Dependence Plots

plot_partial_dependence(
    gbrt, X_train, [np.argsort(gbrt.feature_importances_)[-2:]],
    feature_names=boston.feature_names,
    n_jobs=3, grid_resolution=50)

You can also do this in bivariate. So here, looking at the two most
important features in a bivariate plot, I'm actually not giving it a
list of features, but I'm giving it a list of list of features. And so
the two most important features, LSTAT, and ROOM.

You can see there's no very complex interaction. As you can see, the
effect of these two features taking into account all the other
features.

This is something you can usually do for linear model easily. If you
do this for the linear model, you would have lines everywhere but for
more complicated models this is very hard to do. Way more tricky to do
in neural networks.

Partial Dependence for Classification

from sklearn.ensemble.partial_dependence import plot_partial_dependence
for i in range(3):
    fig, axs = \
        plot_partial_dependence(gbrt, X_train, range(4), n_cols=4,
                                feature_names=iris.feature_names,
                                grid_resolution=50, label=i)

We can do the same for classification. Here is a partial dependency
plot for the iris dataset.

As I said, for classification we're usually using One Versus Rest. So
here, we have three classifiers.

One classifier for sertosa, one for Versicolor and one for virginica.

You can see that for the first two features they're like flat. But for
sertosa the decision function is high for small part petal length,
medium peddling for versicolor and large for virginca. These are all
put through logistic function and then normalized.

PDP Caveats

Feature doesn't seem important (very small scale)

But look at data (x-axis feature, y-axis target)

Clearly two groups, one has positive effect, other negative

Not shown in partial dependence plot

ICE (individual conditional expectation) plots

Like partial dependence plots, without averaging

See https://scikit-learn.org/stable/modules/partial_dependence.html

Each line is a data point (row) in the original dataset

What happens to it if I replace the feature with a different value

How common is this in the real world? I have no idea!

(Automatic) Feature Selection

Why Select Features?

Avoid overfitting (?)

Faster prediction and training

Less storage for model and dataset

More interpretable model

Can avoid overfitting, although in practice this doesn't help that
much (other methods, e.g. penalization) But the others are important,
especially the last.

Types of Feature Selection

Unsupervised vs Supervised

Univariate vs Multivariate

Model based or not

Supervised will consider the target.
Univariate – looks at each feature by itself, multivariate looks at interactions.
Model will build an ML model.

Unsupervised Feature Selection

Unsupervised Feature Selection

May discard important information

Variance-based: 0 variance or few unique values

Covariance-based: remove correlated features

PCA: remove linear subspaces

Small variance? Don't remove! Probably data is just on a small scale.

Covariance-based: maybe the diff. between two correlated features was
the only way to distinguish between two classes! In general a problem
with unsupervised; it's not considering the results, so the
information you throw away might be the most important!

PCA derived "linearly independent" features from original
features. Principal Components Analysis.

Covariance

from sklearn.preprocessing import scale
boston = load_boston()
X, y = boston.data, boston.target
X_train, X_test, y_train, y_test = \
    train_test_split(X, y, random_state=0)
X_train_scaled = scale(X_train)
cov = np.cov(X_train_scaled, rowvar=False)

Look at which features are most correlated with others, and drop
those.  Here the lighter values are positively correlated. But this
plot is hard to read.

from scipy.cluster import hierarchy
order = np.array(hierarchy.dendrogram(
    hierarchy.ward(cov),no_plot=True)['ivl'], dtype="int")

Here I've reordered columns and rows to create "blocks" that are
correlated or uncorrelated. Can look at this manually or you can
create algorithms that look at these values and based on some
threshold can remove some features.

Note that that code doesn't create this nice picture. Look at
matplotlib's pcolor, imshow, or matshow

Supervised Feature Selection

Univariate Statistics

Pick statistic, check p-values !

f_regression, f_classsif, chi2 in scikit-learn

from sklearn.feature_selection import f_regression
f_values, p_values = f_regression(X, y)

Use a statistical test, look at each feature and see if it's
significantly related to the target. This is using boston again. Ex:
rooms and lstat are most important here. CHAS is a binary model and
this is using a regression model, and linear models aren't very good
at using binary features (really, this linear test will not put a lot
of emphasis on a binary feature).

Assumes it's a linear model!

from sklearn.feature_selection import \
    SelectKBest, SelectPercentile, SelectFpr
from sklearn.linear_model import RidgeCV
select = SelectKBest(k=2, score_func=f_regression)
select.fit(X_train, y_train)
print(X_train.shape)
print(select.transform(X_train).shape)

(379, 13)
(379, 2)

all_features = make_pipeline(StandardScaler(), RidgeCV())
np.mean(cross_val_score(all_features, X_train, y_train, cv=10))

0.718

select_2 = make_pipeline(StandardScaler(),
                         SelectKBest(k=2,
                                     score_func=f_regression),
                         RidgeCV())
np.mean(cross_val_score(select_2, X_train, y_train, cv=10))

0.624

Select k best features, percentile, Fpr controls for false positive
rate of thinking features are significantly important. Basically does
the multiple hypothesis testing adjustments to make sure that your
false discovery rate of thinking the features are important is low.

Need to set some score function, regression or classification. First
try selects just 2/13 features.

Second part is just with standard scaler.

Third: Fitting a model with only 2 best features. It overselected –
two is not enough features, it got worse.

Mutual Information

from sklearn.feature_selection import mutual_info_regression
scores = mutual_info_regression(X_train, y_train,
                                discrete_features=[3])

A little more complicated method that uses a non-parametric
model. Works on discrete and continuous, but must tell it which are
discrete.

Plot is comparing mutual info scores vs. F values. Kind of similar but
there are differences. If you think there are non-linear interactions
between features, this might catch that, although this takes
a lot longer to run. Still univariate though.

Model-Based Feature Selection

Get best fit for a particular model

Ideally: exhaustive search over all possible combinations

Exhaustive is infeasible (and has multiple testing issues)

Use heuristics in practice.

Find the subset of features on which this model performs best, i.e., a
search algorithm. Exhaustive search is too long and also might overfit
(pick up on noise).

Model based (single fit)

Build a model, select "features important to model"

Lasso, other linear models, tree-based Models

Multivariate - linear models assume linear relation

from sklearn.linear_model import LassoCV
X_train_scaled = scale(X_train)
lasso = LassoCV().fit(X_train_scaled, y_train)
print(lasso.coef_)

[-0.881 0.951 -0.082 0.59 -1.69 2.639 -0.146 -2.796 1.695 -1.614 -2.133 0.729 -3.615]

Fit a model, discard the features it doesn't think are important.

Changing Lasso alpha

from sklearn.linear_model import Lasso
X_train_scaled = scale(X_train)
lasso = Lasso().fit(X_train_scaled, y_train)
print(lasso.coef_)

[-0. 0. -0. 0. -0. 2.529 -0. -0. -0. -0.228 -1.701 0.132 -3.606]

Different results if you have a different alpha. (this one is penalizing more)

SelectFromModel

from sklearn.feature_selection import SelectFromModel
select_lassocv = SelectFromModel(LassoCV(), threshold=1e-5)
select_lassocv.fit(X_train, y_train)
print(select_lassocv.transform(X_train).shape)

(379,11)

pipe_lassocv = make_pipeline(StandardScaler(),
                             select_lassocv, RidgeCV())
np.mean(cross_val_score(pipe_lassocv, X_train, y_train, cv=10))
np.mean(cross_val_score(all_features, X_train, y_train, cv=10))

0.717
0.718

# could grid-search alpha in lasso
select_lasso = SelectFromModel(Lasso())
pipe_lasso = make_pipeline(StandardScaler(), select_lasso, RidgeCV())
np.mean(cross_val_score(pipe_lasso, X_train, y_train, cv=10))

0.671

Takes any model that gives you feature importances, and uses it to do
feature selection. Notice the threshold value.

Can then put this into a pipeline. Performance is about the same actually.

Iterative Model-Based Selection

Fit model, find least important feature, remove, iterate.

Or: Start with single feature, find most important feature, add, iterate.

Importance of features might change after we remove a feature! So we
should do this iteratively! Start with removing features…

Recursive Feature Elimination

Uses feature importances / coefficients, similar to "SelectFromModel"

Iteratively removes features (one by one or in groups)

Runtime: (n_features - n_feature_to_keep) / stepsize

Fit model, discard least important feature(s), repeat until you have
as many features as you want. Can use any models which give you a
measure of feature importance. (hard to get with SVMs).

Expensive b/c we must train a model many times.

from sklearn.linear_model import LinearRegression
from sklearn.feature_selection import RFE
# create ranking among all features by selecting only one
rfe = RFE(LinearRegression(), n_features_to_select=1)
rfe.fit(X_train_scaled, y_train)
rfe.ranking_

array([ 9, 8, 13, 11, 5, 2, 12, 4, 7, 6, 3, 10, 1])

Implemented in sklearn. using LR to decide which features are
important. Notice using RFE ranking is slightly different than LR by
itself. The red dots are LR coefficients for one run of the model. But
RFE is doing this iteratively and then ranking which coefficients are
important. In this case the difference in not huge, but YMMV.

This is selecting only one feature at a time, so along the way it's
training models with n, n-1, n-2, …, 5, 4, 3, 2, 1 models. So if you
wanted to grid search the best number of features to use it would be
wasting a lot of effort if you passed in RFE to it!

RFECV

from sklearn.linear_model import LinearRegression
from sklearn.feature_selection import RFECV
rfe = RFECV(LinearRegression(), cv=10)
rfe.fit(X_train_scaled, y_train)
print(rfe.support_)
print(boston.feature_names[rfe.support_])

[ True  True False  True  True  True False  True  True  True  True  True True]
['CRIM' 'ZN' 'CHAS' 'NOX' 'RM' 'DIS' 'RAD' 'TAX' 'PTRATIO' 'B' 'LSTAT']

pipe_rfe_ridgecv = make_pipeline(StandardScaler(),
                                 RFECV(LinearRegression(),
                                       cv=10),
                                 RidgeCV())
np.mean(cross_val_score(pipe_rfe_ridgecv, X_train, y_train, cv=10))

0.710

Efficient CV for n_features_to_keep. This is RFE inside
cross-validation. Going from all features to just one feature, uses
crossval to select best num. In this case it only dropped 2 features,
the eleven it kept are shown.

If we want to predict with the same model as used for selection, RFECV
can be used as the prediction step. Could also use RFECV as
transformer and use any other model!

pipe_rfe_ridgecv = make_pipeline(StandardScaler(),
                                 RFECV(LinearRegression(),
                                       cv=10),
                                 RidgeCV())
np.mean(cross_val_score(pipe_rfe_ridgecv, X_train, y_train, cv=10))

0.710

from sklearn.preprocessing import PolynomialFeatures
pipe_rfe_ridgecv = make_pipeline(StandardScaler(),
                                 PolynomialFeatures(),
                                 RFECV(LinearRegression(),
                                       cv=10),
                                 RidgeCV())
np.mean(cross_val_score(pipe_rfe_ridgecv, X_train, y_train, cv=10))

0.820

If you do feature selection this way, your model for feature selection
could be different than model for prediction. Just an additional
degree of freedom. So I might do something very complicated for
feature selection, but use a more interpretable model for prediction
so I can explain it to my boss.

Wrapper methods

Can be applied for ANY model!

Shrink / grow feature set by greedy search

Called Forward or Backward selection

Run CV / train-val split per feature

Complexity: n_features * (n_features + 1) / 2

Implemented in mlxtend

Expensive, but can be applied to any model. Basically just do a greedy
search over features, either adding most important or removing least
important feature at each step.

Ex: Start with all features. Leave out first feature, build model,
then do crossval to get an accuracy feature. Do this for each
feature, leaving it out and building a model. After this, drop the
feature that gave you the highest accuracy/\(R^2\). Then repeat!

Each iteration requires training an crossval on many models. VERY slow.

Not in sklearn, but in mlxtend, but has same interface as sklearn,
so you can use these in a pipeline.

SequentialFeatureSelector

from mlxtend.feature_selection import \
    SequentialFeatureSelector
sfs = SequentialFeatureSelector(LinearRegression(),
                                forward=False, k_features=7)
sfs.fit(X_train_scaled, y_train)

Features: 7/7

print(sfs.k_feature_idx_)
print(boston.feature_names[np.array(sfs.k_feature_idx_)])

(1, 4, 5, 7, 9, 10, 12)
['ZN' 'NOX' 'RM' 'DIS' 'TAX' 'PTRATIO' 'LSTAT']

sfs.k_score_

0.725

Forward: start with 0, add the feature that gives the best 1-feature model, and continue from there.
Backward: start with all features and remove 1 at a time. k_features is how many to keep.

redpajama_set_name: RedPajamaGithub


text:
Florida's Total Cases Have Nearly Tripled In One Month
July 14, 2020	Coronavirus, Government
Orlando's NBC News affiliate reports:
Florida health officials on Tuesday reported a record 132 new coronavirus-related deaths among residents and more than 9,100 new cases of COVID-19.
The 132 deaths is the biggest single-day spike Florida has seen since the beginning of the pandemic, however that doesn't mean all of these people died in the past 24 hours.
Another 9,194 cases of coronavirus brings the statewide total to 291,629. The death toll is now 4,409.
Reuters reports:
Florida has been averaging nearly 10,000 new cases per day for the last week. On 12 July, the state broke the national record by reporting 15,300 cases in a single day. A Reuters analysis on 12 July found if Florida were a country, it would be fourth in the world for most new cases in a day.
Critics say the number of deaths is probably higher, as Florida does not like other states report deaths suspected as having been caused by Covid-19.
The state also has yet to report daily hospital admission rates, though officials have promised to do so. Florida only hit the 100,000 case mark on 22 June, according to data from Johns Hopkins University. Just 13 days later, that number had doubled. Now, there are more than 287,000 cases.
Tags coroanvirus Florida pandemic Ron DeSantis
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redpajama_set_name: RedPajamaCommonCrawl


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Theoretically, it is December. I should be huddled in layers, slipping and sliding on ice and snow, and drinking innumerable cups of tea. Only the last is true, but that is always true. Right now it is 60 degrees outside and I only need a sweater. If this keeps up things are going to start blooming. I am not complaining, it is just strange.
I am reading like it is winter though. It gets dark so early that I don't really want to go anywhere. I just want to curl up with a book and an afghan. I am just finishing up Suite Francaise by Irene Nemirovsky.
I did not realize when I bought it that it was unfinished. It was supposed to be a series of five novels, but only two were finished before she was arrested and sent to Auschwitz where she died. Her daughters kept the notebook her writing was in but never read it until the late 1990s. They had always thought it was a journal and that it would be too painful to read. They were going to donate her writings and decided to read them first. That is when they discovered what they really had.
Suite Francaise begins with the Nazi occupation of Paris in 1940 and draws a picture of a country in chaos. It does not really tell the story of individuals, instead it uses the stories of people to tell the story of a country.
I really enjoy her way with words and how she can use them to make a subtle point. Here is one quote I liked that highlights the attitude of the townspeople toward the refugees.
"But they were all right, time would pass, soldiers would fight while the ironmonger on the main street and Mlle Dubois, the hatmaker, would continue to sell saucepans and ribbons; they would eat hot soup in their kitchens and every evening close the little wooden gates that separated their gardens from the rest of the world."
That little line about closing "the wooden gates that separated their gardens from the rest of the world" says it all.
Nemirovsky emphasized the effects of the occupation itself. I found that so interesting. The French and Germans were enemies, they fought each other, but then the Germans came in and took over a town and the two peoples got used to each other. There was a constant pull between the individual and the nation.
"War...yes, everyone knows what war is like. But occupation is more terrible in a way, because people get used to each other. We tell ourselves, "They're just like us, after all," but they're not at all the same. We're two different species, irreconcilable, enemies forever."
"There had been no men in the village for so long that even the soldiers, the invaders, seemed in their rightful place. The invaders felt it too; they stretched out in the sunshine. The mothers of prisoners or soldiers killed in the war looked at them and begged God to curse them, but the young women just looked at them."
It is obvious this is an unfinished work, some things don't flow and there are inconsistencies. However, it is an unusual and beautifully written work. It is such a tragedy that it was never able to be completed.
I know a movie was based on this. I am debating whether or not I want to see it. I have a conflicted relationship with movies based on books. Has anyone seen it and if so, do you recommend it?
It is so strange isn't it about the weather? But like you I'm not complaining after the winter we had last year. That book looks interesting. Have you read 'Those Wonderful Rumours' by May Smith? It's a wartime diary of an Emglish schoolteacher and has little snippets of information about daily life during the war. It's not the best written but quite enjoyable I think.
Thank you for the recommendation. I am fascinated by wartime diaries and tend to collect them. I haven't read Those Wonderful Rumours, I will have to look for it.


The White Eagle Chalet is located at Ste-Émélie-de-l'Énergie, at only 80 minutes from Montreal. It is facing the lake Beaudoin (no motor engines) and has a view of the lake and woods due to its many windows and large balcony. The total comfort in a beautiful urban design chalet with fun activities at the Lake and nearby!
Spring/Summer: SPA on site, Kayak, Paddle boat, swimming, fishing (trout & bass), exterior fireplace, hiking or Activities at Arbraska or SPA La Source at Rawdon or Golf at St-Jean-de-Matha, etc..
-Outdoor fireplace + wood log supplied, 2 Terrace with picnic table and lounger chairs.
-Dining room (8 seats) with balcony facing the lake and Fully equipped kitchen with its island including dishwasher, microwave, dishes, cutlery, pots, toaster, fondue set, Keurig coffee machine (bring your coffee pods), drinking water from our well, high chair for baby, etc..
-2 closed bedrooms with Queen size bed & wardrobe.
Bedding (bed sheets and duvets) & all cleaning products are supplied.
Optional service: Housekeeping at the end of your stay ($).
You must be 21 years of age or older to rent the cottage.
In addition, a security deposit of $ 500 is payable 5 days before arrival at the cottage, via Interac transfer or PayPal service. Your security deposit will be fully refunded 48 hours after your departure if no damage is found and if the cottage is returned to us in the same condition as on arrival. Additional costs will be charged in case of excessive cleaning, furniture moved or transported outside.
In case of cancellation more than 30 days before the booking date, Gestion Chalets Inc. will reimburse you 50% of your payment.
For cancellations less than one week before your booking date no amount will be refunded.
All our chalets are strictly non-smoking.
It is absolutely forbidden to exceed the number of persons authorized in the cottage and strict monitoring of the number of occupants is carried out.
The tranquility of the premises must be respected at all times. Please note that it is important to keep calm and respect the neighborhood after 10pm. We will not hesitate to retain the entire security deposit in case of excessive noise after 10pm and / or nighttime noise.
The arrival time is usually 5 pm and the departure time is 12 pm.


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text:
Urmas Sutrop (born 7 June 1956) is an Estonian linguist.

He graduated from high school in 1974 and in 1984 from University of Tartu with a biology degree. The topic of his thesis was "Ardisia crispa A. DC. lehe baktersõlme siseste taimerakkude peenehitus." Near fifteen years after his first dissertation, he was awarded by University of Konstanz with a PhD in philosophy.

Ummi is probably best known for being the director of Institute of the Estonian Language, a position he held from 2000 until 2015. He is president of the Anthropology and Ethnolinguistics departments, and he is besides professor at General Linguistics department. Since 2006 he has been also professor at University of Tartu.

Bibliography
This is a partial list. Most of the texts appointed by this enumeration were written in Estonian, excepting those that are properly indicated:
"La langue estonienne", Eesti Instituut, 2002
"Eesti viipekeel", Eesti Keele Sihtasutus, 2003 (co-author)
"Estonian language", Eesti Instituut, 2004 (English)
"Die Estnische Sprache", Eesti Instituut, 2005 (German)
"Estniska", Eesti Instituut, 2007
"Viron kieli", Eesti Instituut, 2010
"Värvinimede raamat" Eesti Keele Sihtasutus, 2011 (co-author)

Articles
 "Bertrand Russell in Estonia" (English)
 "Uuenev keel"
 "Trahvidega keelt paremaks ei muuda"
 "Eesti keel pole kunagi oma ajaloos olnud nii tugev kui praegu"
 "Erakorralistest valimistest"
 "Holokaust, kurjus ja meie"
 "Wittgenstein's Tractatus 3.333 and Russell's paradox"

References

1956 births
Living people
Academic staff of the University of Tartu
20th-century linguists
Recipients of the Order of the White Star, 4th Class

redpajama_set_name: RedPajamaWikipedia


Kelly Osbourne leaves restaurant Catch LA in West Hollywood with male friends, asked about Robbie Williams. Apparently 'He's here,' but we never see him. Kelly wears a suit and black hat over her purple hair.


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The Citation Longitude is equipped to use LinxUs®. LinxUs and LinxUs Air work with the Central Diagnostics Maintenance system (CDMS) and function as a fault-isolation system that monitors the aircraft 100 percent of the time. In the event of an on board issue, actionable answers are provided in real time, resulting in faster turnaround to get the aircraft back in the air.
The Longitude is equipped to use LinxUs® – a technology designed to save time and money. Working with the Central Diagnostics Maintenance System (CDMS) LinxUs and LinxUs Air, both function as a fault-isolation system that monitors the aircraft 100 percent of the time. In the event of an on-board issue, actionable answers are provided in real time, resulting in faster turnaround to get the aircraft back in the air.
LinxUs Air – reports issues in flight via satellite.


text:
César Gelabert Piña (born 31 October 2000) is a Spanish footballer who plays as an attacking midfielder for CD Mirandés.

Club career
Born in Palencia, Castile and León, Gelabert joined Real Madrid's La Fábrica in 2015, from Hércules CF. He made his senior debut with the reserves on 25 February 2018 at the age of just 17, coming on as a late substitute in a 3–0 Segunda División B home win against Pontevedra CF.

Gelabert was definitely promoted to Castilla in June 2018, but suffered a knee injury which kept him sidelined for the most of the season. After subsequently overcoming another injury, he scored his first senior goal on 3 November 2019, netting Castilla's first in a 2–4 away loss against Sporting de Gijón B.

After attracting interest from several clubs across Europe during the 2020 summer, Gelabert opted to stay, but was unable to establish himself as a regular starter during the campaign. On 5 August 2021, he signed a two-year deal with Segunda División side CD Mirandés.

Gelabert made his professional debut on 16 August 2021, starting in a 0–0 away draw against Málaga CF.

International career
Gelabert represented Spain at under-17 level in the 2017 UEFA European Under-17 Championship and the 2017 FIFA U-17 World Cup squads, winning the former competition. Initially called up with the under-19s for the 2019 UEFA European Under-19 Championship, he was forced to miss out the competition due to a broken clavicle.

References

External links
Real Madrid profile

2000 births
People from Palencia
Sportspeople from the Province of Palencia
Spanish footballers
Footballers from Castile and León
Association football midfielders
Segunda División players
Segunda División B players
Real Madrid Castilla footballers
CD Mirandés footballers
Spain youth international footballers
Living people


Wind speed error statistics in the computation of the +03h forecast average wind speed for Schiphol airport. The verification period is November 2001 - February 2002.
Although advancements in Numerical Weather Prediction (NWP) modeling have been substantial over the last decade, these models have not reached a state where clouds and precipitation can be resolved at the spatial and temporal resolutions needed for airport weather forecasts. Aviation forecasters compensate for these deficiencies using the model data in combination with detailed observation information regarding recent and current weather developments, and topographical information of the airport and its vicinity. In particular, the quality of very short-term forecasts, up to two hours ahead and provided in the form of TREND bulletins, highly depends on the availability of local and upstream observations.
AUTOTREND aims at the development of methods, which objectively integrate available observations and topographical information with existing NWP model data. The main purpose is to develop and implement the methods in an operational environment, and use them to provide detailed numerical guidance on changes in the local weather conditions, such as winds, visibility, clouds and precipitation, that are forecast to occur and that affect air traffic at civil airports in the Netherlands. Basically, two new methods have been developed and evaluated for this purpose, and they are used to produce 1) A numerical TREND guidance based on statistical and physical postprocessing of NWP model data and observations, and 2) A high-resolution spatial wind forecast based on refining grid-box averaged NWP model winds to local values.


I hope everyone enjoyed last night's screening. I had forgotten that the 35mm print didn't have the same quality of subtitling as the DVD. For instance, it didn't translate the titles of the three stories — which corresponded to the three central characters. In the first story, the little girl is called 'Hava', which is a version of 'Eve', the 'first woman'. In the second story, the young woman is called 'Ahoo' and this is also the Farsi word for 'deer' — which are shown twice darting across the landscape as the husband shouts out. In the third story, the old woman is called 'Houra', which (presumably as a deliberate irony) means 'nymph'. The English language script is on the Makhmalbaf website on the film's own page.
The excellent web essay by Adrian Danks on the Makhmalbaf Film House that I quoted in my introduction is on the Senses of Cinema website. There is also an earlier posting on this blog about the Makhmalbafs.
This entry was posted in Films by women. Bookmark the permalink.
In answer to your post – I loved this film!I guess we'll discuss it in more depth in next week's session (can you tell I am beginning to give up on anyone else on the course posting in a discussion on here?!!), but I do want to say that although this film covers issues that in themselves could make you despair, I found the film itself to be a real heart-warmer.
I totally agree. I admit that the final shot of Houra on the makeshift raft has become my screensaver! There was just an indescribable pleasure in the idea of taking pleasure for yourself when life has not gone out of its way to provide you with any.Thanks for the explanation about 'deer' in the second film – it somehow seems to explain her expression as she is riding, startled and vulnerable by so intent on escape. That was the potentially heart-breaking tale – did anyone else have to 'cheat' and avoid misery by choosing the more positive story of the outcome that's offered by the two girls in the final tale?


At this point I hope you are thinking, wait, what? Why are Halifax senior staff and council discussing what's a reasonable number of people to get mown down on city streets?
By all appearances, the 20 per cent goal is just an arbitrary number, picked out of the sky, and the discussion around the committee table last week reinforced that impression. With the number starting at only 15 per cent, Councillor Shawn Cleary proposed upping it to 20 per cent, pointing out that the Swedes managed a 42 per cent reduction in fatalities in 10 years, so maybe we could do 20 per cent in five.
Even considering the inherent problems of any numerical goal short of 100 per cent, that's not actually my biggest concern about Halifax's nearly-approved SRSP. Twenty per cent is just a short term goal, after all. And while it features too prominently in this plan, we probably shouldn't get obsessed with what is essentially an arbitrary number based on some sense of achievability.
What deserves more scrutiny is the vision of the plan, and the basic principles behind what it promises to actual deliver on our streets.
The vision for Halifax's SRSP is: "Moving Towards Zero Fatalities and Injuries for People Using Any Mode of Transportation." It sounds appropriately aspirational, but what you need to notice is that the writers have specifically chosen "Towards Zero" instead of naming it "Vision Zero," the Swedish safety program that revolutionized road safety in that country, and made the Swedes experts in safe street building.
But let's back up a moment and take a closer look at Vision Zero, just so we understand what it is we are leaving on the table.
Just a partial collection of Vision Zero's representation on Google Images. Vision Zero is being adopted by a growing number of North American cities and towns, but not Halifax.
Let's create a system for the humans instead of trying to adjust the humans to the system.
The Swedes' big innovation in traffic safety is to accept human error as a given, and still design a system that won't kill or maim people. This is why they were bold enough to name their ultimate goal of zero right in the program name: Vision Zero.
Under Vision Zero, if a mistake ends up killing or seriously injuring someone, then that death or injury is a public systems failure, and a problem to be addressed. And it is generally addressed in how the street is designed.
When Goodyear asked Belin about some of the most successful measures the Swedes had implemented, his answer was rooted in basic physics.
From a 2016 presentation by Matts-Åke Belin in Toronto.
And so the biggest control we have over deaths and injuries is vehicle speed.
"If you have places in your system where you have unprotected road users and protected road users, according to Vision Zero you can't allow a higher speed than 30 kilometers per hour," Belin told Goodyear. Under Vision Zero, the upper speed limit is not set according to how people feel comfortable driving (the way our limits are set now) but rather according to the physical realities of what happens when cars hit cars or cars hit people. It's not unlike a health or environmental regulation setting an upper limit on the amounts to be tolerated of some toxic substance or other, Belin explains. It's based on simple, hard science.
Where is the simple, clear, overarching goal of reducing vehicle speeds wherever cars and people mix? It's not there.
We know that Halifax council has written to the province to ask that default limits in residential areas be dropped to 40 km/hr. So where is our matching commitment to actually build our roads to match this new lower limit? (We do know that limits alone don't work, after all. They require accompanying design changes.) This Strategic Road Safety Plan is the ideal place for such a commitment, but it's not there.
You will find measures listed throughout the plan that will have some speed reduction effect, along with some that are, preposterously, aimed at improving traffic flow. But there's no overarching vision on speed reduction, the key component of what turns accidents into catastrophes.
Although the plan pays lip service to Vision Zero, it's almost as if our senior staff either don't understand the paradigm shift that Vision Zero actually requires, or simply don't have the fortitude to require it.
Luckily, Councillor Waye Mason saw fit to ask for that overarching vision, right after approving the plan that should have already included it. Last week, Mason gave notice that at the next monthly transportation committee meeting he will be asking for a report on whether HRM can just start building streets to a lower (40 km/hr, in Mason's case) speed standard.
This is progress. Even if you think Mason should be asking for a report on 30 km/hr standards (based on simple physics), you can still acknowledge that a 40 km/hr across the board design standard would be progress.
But when I look at the timeline for the current SRSP (it originated from a council meeting back in January 2016, over two years ago) and I think forward to the timeline where Mason's modest 40 km/hr standard might one day become HRM policy, I just think: that is too damn long.
There was an opportunity here for people like director of transportation and public works Bruce Zvaniga and manager of traffic management Taso Koutrolakis to steal from the best in the world and transform the city's attitude and expectations of road safety. Instead, they stole the word "zero" and left the rest of the Swedish approach on the table. They've spent two years essentially summing up Halifax's current strategy and calling it new. And now we are debating whether we aim to save three lives or four. It's profoundly disappointing.
Very interesting read. Europe uses ingenuity and high taxes to create beautiful streets that slow traffic . HRM has resorted to snow plows and negligence to render my street hazardous at speed. I suppose it's accomplishing the same thing in the end.
My conspiracy theorist mind would argue that for the same reason the bulk of HRMs councillors couldn't care less about HRM by design they also don't care about car/pedestrian crash avoidance because they live/represent elsewhere. If the only thing their constituents do is commute into Halifax to work, they want them to do so as fast as possible. Slowing that drive to 30kph will not get them re-elected. Particularly if they're coming from Ecum Secum.
Ideally, like Europe we'd have public transit so good it would be foolish to drive. As it stands no one wants to spend the tax dollars to do it because it's still fast/cheap enough to drive to work. It's a hard sell to only offer to slow traffic without the public transit. I definitely agree that the emphasis on education/enforcement will do nothing for safety. The engineering (or abandonment of road maintenance) is the only thing that will slow.
Montreal (with all their snow) has a tone of speed bumps for their crazy drivers. Halifax makes it prohibitive to try to put them in family neighbourhoods. See conspiracy theory above.
The injury/fatal collisions per year from 2007 to 2014 ranges from 1277 to 1574. It has swung 15% or more in either direction over that time frame as it is!
Excellent analysis of a disappointing plan.
The HRM engineering department's active transportation plan was once part of their traffic calming plan. The first HRM cycling route pilot project on Brunswick St, from the bridge head to Spring Garden Rd. was considered a success.
This was not because it was a striped cycling route as it was little used, but because it completely eliminated the huge volume of commuter car and small truck traffic exiting the bridge onto the residential and heritage areas of Brunswick St..
Using active transportation as a traffic calming tool is effective, but painting stripes on busy streets without traffic calming for cyclists has no safety value, and may add to the deaths and injuries caused by commuters.
Further, the HRM has no intention of treating the current commuter accident problem with transportation demand designs. The HRM engineers know these all work very effectively, as shown by the Brunswick St TDM design.


Wednesday was a holiday here in Hawaii. Having a friend visiting from the mainland we struck out for day of sun and fun on West side of the Island.
As we pulled into a parking spot at the beach, we noticed a naked two year old girl wondering around the edge of the parking lot. A large family was camped nearby, but they seemed unaware that that the girl was wandering a little too far away. The sun was out and the pavement was hot, so we kept an eye on the little girl as we unloaded the car.
Predictably, the girl began to cry and call out to her mother. It was windy and loud and the family seemed distracted by the other children. My wife went over to the little girl and asked if she was okay. The girl said she wanted her mother. My wife then asked the girl if she wanted to be carried and the girl's arms shot up. My wife picked up the girl and walked over to the family and children. They took the child and said that the girl's mother was down by the water.
Certainly this was a moment of parental oversight and/or poor judgment. Parenting is difficult. Ideally, we, as community members, should think of ourselves as a sort of parenting safety net. We should be that extra pair of eyes or that extra helping hand when parents and their children need little bit of help. After all, in some sense, all children are our responsibility. Children will shape the future. If we want a healthy and a safe society then we must work together to ensure that today's children are raised in safe and nurturing environments. We, personally, need to be willing to go that extra mile for the benefit of families and children.
Often the extra mile is nothing more than a kind word, a willingness to listen, or taking the time to help a child in distress.


text:
Typhlodromips sessor är en spindeldjursart som först beskrevs av De Leon 1962.  Typhlodromips sessor ingår i släktet Typhlodromips och familjen Phytoseiidae. Inga underarter finns listade i Catalogue of Life.

Källor

Spindeldjur
sessor


text:
Kumakura (written: 熊倉) is a Japanese surname. Notable people with the surname include:

, Japanese actor, voice actor and theatre director
, Japanese rower
 Shino Miyaso (née Shino Kumakura), Japanese women's professional shogi player
, Japanese volleyball player

Japanese-language surnames


A contemporary gospel-rock pairing of "Angels We Have Heard on High" and "O Come All Ye Faithful;" this will be an energized; accessible arrangement for choirs with a limited rehearsal period. Available separately: 3-Part Mixed; 2-Part; ShowTrax CD. Duration: ca. 2:40.


text:
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Interesting but Random


World 8-1 is the first course of World 8 in Puzzle & Dragons: Super Mario Bros. Edition. It is a wooded swamp level with enemies consisting of Goombas, Wigglers, Flame Chomps and Yellow Koopa Troopas.
Taking the path on the left, the player faces four battles before the boss fight with two 2-Goomba Towers.
Taking the blue Warp Pipe down, the player enters an underground area and faces a battle, then finds a ? Block. After opening (or failing to open) the ? Block, the player faces another battle before taking a purple Warp Pipe back to the main path and directly to the boss fight at the end.
The player has Water, Wood, Light, Dark and Heart Orbs for this course.
★World 8-1 is a difficult version of World 8-1 from ★World 8 with Fire Orbs enabled on the Orb field.
This page was last edited on August 15, 2017, at 22:49.


text:
Q: The link to share a document isn't valid I have been sharing document links for years but now when I copy the link to share a document the link is no longer valid because there are extra characters on the end.
Used to look like:
https://company.sharepoint.com/Sales/Shipping%20Library/Lead%20Times/GH-%20%2004-12%20-%20LT.pdf
Now looks like:
https://company.sharepoint.com/Sales/Shipping%20Library/Lead%20Times/GH-%20%2004-12%20-%20LT.pdf?d=wc39ecc2480bf480da8ce4319bf12d8bc

redpajama_set_name: RedPajamaStackExchange


It's no secret that people are living longer. Falling fertility rates indicate that, on average, global populations are getting older. This "demographic tsunami" has already hit Japan, and continental Europe is not far behind. In 1990, the group of countries that now form the euro zone had one older person (65+) for nearly five people of working age. The World Bank forecasts that by 2040, these countries will have one older person for every two of working age.1 And if that happens, I believe bank share price performance in the countries that are most affected could take a significant hit.
Why? Let's first look at the correlation between aging populations and bank stocks.
Since 1990, price performance of bank stocks in each of the Group of Seven (G7) nations, which includes the largest and most advanced economies,2 has mirrored demographic trends in each country. For example, Canadian and US bank stocks have performed the strongest of the group. I believe that has been helped by the fact that both countries have the lowest old-age dependency ratio,3 and have had the highest population growth over that period. At the other end of the scale, Japanese banks have performed the worst, down by over 80% during the same period. Of the G7, Japan has the highest old-age dependency ratio, and has had the lowest population growth since 1990. Aging populations are associated with lower GDP growth and lower interest rates3—a combination that provides a difficult backdrop for bank stocks.
Thankfully, people are not just living longer, they are also staying healthier. This should enable workers to extend their income-earning years beyond the traditional retirement age. Consider that in 2018, more than 1,000 people over 65 years old ran the New York City Marathon—roughly five times the number who ran in 1990.4 Who hasn't admired aging rock stars who, in their 70s, remain as agile as ever, prancing and pouting on stage singing "Jumpin' Jack Flash" or "My Generation" while on global tour?
An aging population isn't necessarily all bad for banks, but to meet customer needs, they will likely have to offer older populations a better mix of products like low-cost savings products and clear financial advice. Banks may also have to adapt to lower demand for borrowing, and more demand for investments. I think banks and other financial services companies that adapt their business models to changing demographic trends should do well over time.
Over the long term, I think bank share prices in countries with higher population growth and less aging should continue to perform relatively better than their counterparts in countries that are aging more rapidly. That should bode well for US and UK banks. According to World Bank projections, these two countries will have the lowest old-age dependency ratios in 2040, and the smallest increase in old-age dependency ratios from now until 2040 among the G7. Meanwhile banks in Japan, Italy and Germany could continue to face relatively severe demographic headwinds.
But I could be wrong. If my 2040 forecasts turn out to be incorrect, don't bother me. I'll be too busy skateboarding.


When a rule throws an exception, the proxy disables the rule.
If an encryption rule condition or action throws an exception, the exception is caught and the encryption rule is disabled. The proxy continues to evaluate the remaining rules in order as if the rule that threw the exception is not present. The exception is logged in the localhost log with a stack trace identifying the root cause of the problem.


All 4 Democratic members of the Gang of 8 — which has received the most classified briefings on the Russia investigation out of anybody in Congress — condemned Attorney General Bill Barr's comments about intelligence officials "spying" on the Trump campaign in 2016.
The backdrop: Barr on Wednesday told a Senate Appropriations subcommittee that he believes spying by law enforcement officials on the 2016 Trump campaign "did occur," and that he would be reviewing "both the genesis and conduct" of the Russia investigation. The so-called "spygate" scandal, which relates to alleged FISA abuses by the intelligence community, has been frequently promoted by defenders of President Trump. It has not been corroborated.
When pressed toward the end of the hearing, Barr clarified his remarks: "I am not saying that improper surveillance occurred. I am saying that I am concerned about it and I'm looking into it."
House Speaker Nancy Pelosi said in an interview with the AP that Barr's testimony calls into question his independence, adding that Barr is "not the attorney general of Donald Trump. He is the attorney general of the United States. ... I don't trust Barr. I trust Mueller."
Senate Minority Leader Chuck Schumer: "AG Barr admitted he had no evidence to support his claim that spying on the Trump campaign "did occur." AG Barr must retract his statement immediately or produce specific evidence to back it up. Perpetuating conspiracy theories is beneath the office of the Attorney General."
House Intelligence Chairman Adam Schiff: "The casual suggestion by the nation's top law enforcement officer of "spying" may please Donald Trump, who rails against a "deep state coup," but it strikes another destructive blow to our democratic institutions. The hardworking men and women at the DOJ and FBI deserve better."
Senate Intelligence Ranking Member Mark Warner: "Mr. Barr knows how counter-intel investigations work. He knows there was ample evidence of Russian attempts to infiltrate the Trump campaign and that the FBI took lawful action to stop it. Giving a wink and a nod to this long-debunked "spying" conspiracy theory is irresponsible."
The other side: The 4 Republican members of the Gang of 8 — Senate Majority Leader Mitch McConnell, House Minority Leader Kevin McCarthy, Senate Intelligence Chairman Richard Burr and House Intelligence Ranking Member Devin Nunes — have not addressed Barr's comments.
The big picture: Barr plans on submitting a redacted version of special counsel Robert Mueller's report by next week. He has said that once it becomes public, he is happy to negotiate with the Senate and House Judiciary chairmen to determine what other material can be released. On the question of releasing grand jury materials, however, Barr has said that he is bound by the law and has no intention of asking a judge to unseal anything.


If your interested in your own piece of Paradise, consider  one of the most affordable timeshares on Cape Cod.
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Developed in the mid 1980's, Mariner's Point is successfully sold as a timeshare resort with deeded fixed intervals.
We at Mariner's Point welcome you to inquire about purchasing a vacation interval. Mariner's Point is affiliated with Interval International Vacation Exchange Network and has honored timesharing guests from as far away as Japan.
That's easy! Vacation ownership provides the advantages of owning your own vacation home - plus the assurance of great accommodations, and a possible second home, (U.S. tax deduction) - without the hassles.
You pay only for the time you actually need. (Most people can only vacation a week or two a year, so why pay for the 50 weeks you're chained to a desk?) Finally, you're pre-paying for future vacations at today's prices. You make a one-time purchase at a fixed price, then pay an annual fee for maintenance and/or real estate taxes.
The basic timeshare concept is simple: you pay a onetime purchase fee that entitles you to a week every year. Instead of renting your vacations, you now own them.
Lots and lots of people - more than three million families now own timeshare. What's more, people spent almost $5 billion last year alone to join the growing number of vacation owners.
What do vacation owners actually own?
Ownership types vary, but generally, vacation owners own the right to enjoy accommodations and facilities at a resort for a specified period of time each year. Mariner's Point ownership is "deeded", which means you are buying real property. You can give it away, will it to your heirs, rent it, sell it.
Will I be locked in to the same place every year?
Only if you want to be. That's where we come in. Most resorts belong to an exchange network such as Interval International so their owners can trade their week for one that belongs to someone else. You can exchange for another time, another location, or both. And Interval International has more than 860,000 members who own at resorts in 65 countries, so exchange privileges literally offer a world of vacation opportunities. If unexpected circumstances arise, or you simply cannot take your vacation. You can bank your timeshare to use in future years with gives you even more flexibility.
So the question is, why wouldn't you own a vacation?
Stay at our beautiful resort or exchange your interval for a week almost anywhere in the world through Interval International. Our owners are our biggest fans and you will be too after visiting our resort!
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text:
Q: How to protect my application data in any operating system? I have an application which generates few files and get stored locally on the system. I need to protect that generated files from getting deleted by any user in the system. By setting the permission to that particular folder containing files, still I cannot stop Admin from deleting the files. Is there any way to achieve this in Windows/Mac/Linux.

A: No, there is no practical way to prevent someone with sufficient credentials from deleting the file. You can make it difficult, but you can't make it impossible unless you patch the OS and/or firmware yourself. And without trying to sound too condescending, is the type of thing where if you have to ask how to do it, you won't be able to do it.
This is as it should be: the user owns the computer, you don't. They should have complete control over it.


DEAN LOSES HIS MEMORY – Sam (Jared Padalecki) enlists Rowena's (guest star Ruth Connell) help to track down an old world, powerful family of witches after Dean (Jensen Ackles) gets hit by a spell that is rapidly erasing his memory. John Badham directed the episode written by Meredith Glynn (#1211). Original airdate 2/9/2017.


text:
ACCEPTED

#### According to
Index Fungorum

#### Published in
Syn. gen. fung. (Paris) [1] (1972)

#### Original name
Rechingeriellaceae Locq.

### Remarks
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text:
Home Company Profile Company Profile: Dayton Machine Tool Company
Company Profile: Dayton Machine Tool Company
Founded half a century ago, and with a global customer base, this company draws strength from its past with an eye constantly trained toward the future.
Since it was founded more than 50 years ago, Dayton Machine Tool—located in the Ohio city from which it takes its name—has steadily grown into one of the premier machine tool modernization companies in the country. Its physical footprint alone makes a powerful statement, according to Rich Alden, the company's COO.
"We have nearly 45,000 square feet of space in total, including the shop area and administrative offices," he says, "and four bays that are each large enough for a semi to enter. And two of those bays are equipped with 20-ton cranes, which can be ganged up to lift parts and machinery as heavy as 40 tons, so we've yet to run into anything too big for us to handle."
From this site Dayton Machine Tool—widely known as DMT—ships to locations throughout the United States, Canada, and Mexico, in addition to customers as far away as England, France, and Turkey. While the majority of those shipments involve the company's "modernized" equipment—namely those that have been remanufactured, rebuilt, retrofitted, and/or repaired—some are new machines, such as DMT's 1200 Series Gantry CenterTM and Multi-Axis Heads, which can be equipped with as many as three heads. It has also built seven blade-tip grinding machines for the power generation industry.
Employing some 25 individuals, who represent decades of experience rebuilding machine tools, DMT specializes in working with Fortune 500 companies, upgrading equipment that's currently in service to the customer's exact specifications. While this sometimes involves taking the machine out of service, completing the rebuild, and then returning it to productivity, the company often utilizes creative means of keeping its clients working.
"Quite often we'll purchase a used machine that replicates the one our customer has in their plant and wants us to upgrade, but can't do without," according to Tom Forsyth, general manager. "We'll get a purchase order from them in advance, and then we'll go ahead and remanufacture it, do a runoff, and then deliver it. On selective machines we'll even take their old machine on trade, especially when it's a high-production machine like a centerless grinder, a multi-spindle machine, boring mills, and some gear-cutting equipment."
He describes a current project in which a customer has five gear-generating machines on its floor. DMT purchased a used machine on the open market, completed its modernization, and then took the first of the client's five machines offline, replacing it with the newly refurbished unit. "That way they will experience no downtime whatsoever," Forsyth says. "We'll continue working our way through their equipment, and at the end of the project we will have a carcass left to either rebuild or break down for parts."
Known for its reliability, DMT sometimes finds itself helping a customer recover from a bad purchase made elsewhere. "For instance, we're working on a machine right now that a customer had bought from someone else, thinking it was in working condition," Alden says. "Well, not only had it been left out in the weather, but parts had been removed. We took it down to bare castings and we're building it back up with a brand-new CNC system and attachments that he wants, like a grinding wheel balancer. So we're adding significant functionality to the machine at the same time we're rebuilding it for him. The price was a great deal higher than he'd originally expected to pay, of course—although it was still less than half the cost of a new machine—but once he realized the added value we could provide he said 'I've got to have the machine, and you're the right outfit to do it, so let's go to work.'"
This willingness to go the extra mile cements DMT's relationship with its customers, as do services including mobile metrology. "We have a fellow who's a 30-year expert in the field of in-plant laser inspection," Alden says, "and he's often called out by some very large manufacturers such as aerospace companies to calibrate or verify the alignment of their grinders and machining centers. We've invested heavily in laser equipment, in fact, and we've just purchased a $45,000 attachment that allows us to conduct the full range of rotary and linear checks."
And he doesn't just do a check and run a graph, Forsyth adds. "He'll actually point out the source of any problem he detects, with suggestions as to how it can be corrected easily."
Looking to the future, Dayton Machine Tool is considering quite a number of new initiatives, including buying and remanufacturing machine tools on spec and perhaps representing OEMs that complement its business model. It's primary concern, however, involves continuing to burnish the reputation for quality it has worked to achieve over the past half century.
"I've been with this company for two decades, and as far as I can recall we've never delivered a project that wasn't satisfactory to our customer," Forsyth says. "And we frequently get calls from people who've had a machine running on their floor for the past 15 or 20 years that they're ready to upgrade. They'll see the DMT nameplate on the side of the machine, and that's all they need to know."
Call (937) 222-6444, send e-mail to info@dmtnet.com, or go to [www.dmtnet.com].
Previous articleQ&A with Tom Treuden
Next articleChris Chlon: Buy Laws
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Achieving Gear Design Success
Q&A with Brett Froats


Nike sent its top designers to Durham to work with ZIon Williamson after the injury and then had those designers go out to China to personally oversee the production of his new sneaker that hopefully would not explode upon impact. In Zion's first game back he was dominant, with 29 points and 14 rebounds. Nike has to lock down Zion or else New Balance will and you'll see the explosion video in every NBA commercial break for the next 10 years.
Zion is playing tonight's game in a pair of custom Kyrie 4s.


Aerial Yoga is a new concept in exercise. It combines the best elements of Yoga, Fitness and Aerial Circus to give a unique full body work out. We use a specialised fabric swing system to get the heart pumping and improve strength, flexibility and postural alignment whilst sculpting and defining muscle tone. This innovative concept combines fitness with fun, acrobatics and flying.
Gentle inversion techniques can improve posture and alignment giving the spine the care and attention it deserves.
The height of the swing and stirrups can be altered to each students range giving the best possible workout.
The swing allows you to perform assisted neck, head, hand and shoulder stands which decompresses the spine in a safe and effective way.
The swing increases balance and stability, core strength, muscle strength and endurance and much more.
Enhanced mood and calmness of mind as inverting offsets the Parasympathetic Nervous System – the part that tells us to take it easy.


Close to UMO and all it has to offer. Ranch Style home includes 3 bedrooms one bathroom with 2 Car Garage. Public Transportation feet from your door or enjoy your back yard. Some updates included, newer roof, Tile and some flooring.


Dealstan presents you the Lenovo usb Speaker M0420 from amazon at very low price –Rs 495 ONLY. Its sound quality is really fantastic and completely free of any distortions at maximum volume. This speakers plays music as well as movies at maximum volume and in VLC Player at 200% volume. Absoluetely flawless. Most laptop speakers give up midway to maximum volume but this remains consistent even beyond Maximum. This speaker is small in looks but it is absolutely portable. Buy this great featuring speakers at amazingly low price.
How to get this USB Speaker at discounted price of Rs 495?
Make your home Disk with your loud music and enjoy your fun. Enjoy the superb sound quality with deep frequencies of bass on these speakers anywhere and everywhere you go as they are easy to carry. Party Hard !!!


The following is an excerpt from a 2010 conversation between Stanley Hainsworth and Robert Scoble. Hainsworth shares his path to storytelling and his agency Tether. Special thanks to Scoble and Rackspace for hosting the video. Watch the full conversation here.
I always equate brands as people. You could describe what Apple is as a person. Because of the personality they've created.
So with companies, the first thing I do is say lets figure out what your story is. What sets you apart in the marketplace from anyone else? Yeah, you have a product or service, but what colors that? What are you like as a person?
It's the same way with a brand or product. A product needs to have a brand associated with it. If there is a buzz about a new app that came out, people say well who made that?
The name of that company - what does that stand for now? If that company wants to start creating other products, they need to start creating their story. Then everything they do will link back to that story.
It's all part of that brand personality. He's realized that me, as a person, I want to associate myself with things that complement my story. That help define who I am.
So when I buy a computer, I want that thing to be beautiful. Inside and out. Backwards and forwards.
He [Steve Jobs] realized that early on. That a phone or computer is actually an accessory to you. It's an appendage of you. Our phones, iPads, and computers have all become our appendages - part of our personality.
His attention to detail and the beauty of it, really ties to his story. Some people called it snobbery in the beginning, but it's a key thing of their success.
How do you get to being a loved brand?
You have to have an authentic story and you have to be true to that.
When you start getting away from it, like Nike went through that period. And Starbucks went through that period.
When you're very successful, you can slap on swoosh on anything and sell it. Hey, lets sell lunch boxes and school supplies - what does that have to do with being an athlete?
Starbucks started getting into that too. How about movies? We start promoting that in our stores? It was like that doesn't have anything to do with the coffee house experience. Music? Yeah. Books? Yeah. Coffee? Yeah. Movies? That doesn't work.
The consumer will correct you - by leaving you to go somewhere else. That's not who I signed up for. That's not the brand I signed up for - you're someone else now.
The computers can't come up with the ideas for us. The technology can't generate the ideas. I just finished a book about how ideas come to life - it's called Idea-ology.
I wrote it because I was disturbed by how people were using technology to get ideas. It seemed like you don't have to come up with original ideas anymore.
I would observe people when given a challenge - the first thing they would do is go on a web browser and start searching for inspiration. What they're doing is feeding off other people.
And they start flipping through what other people have done on the web. It felt like we're kind of feeding of each other, kind of cannibalistic.
In one way it's cool, kind of mashing up a bunch of ideas. But where is your original idea? In the old days, when you couldn't browse everything to be inspired from it or borrow from it or whatever you want to call it - you had to actually go for a run, walk along the beach, go for a hike, or meditate in your chair and actually come up with something original.
So that's what the book is - how do you come up with an original idea, make it yours, bring your life experiences into it.
That's what great companies do. They'll read your soul and give you something you don't even know you need.
Every startup should have their one-year plan, but even more important is there five-year plan.
How do you envision yourself five or 10 years from now?
That's the first thing I did when I started Tether. I did a FAQ with myself. I interviewed myself and I shared it with my team here when I started.
This is where I see us in five years. Because I interviewed myself five years from now and how our company is. It's been our business plan, our guiding soul.
When I was at Starbucks, I noticed the photography wasn't looking good. It's because there was like 5 clients with the art director and photographer.
As the head of design, I could have gone in and kind of cleansed the temple. And sent everyone out, and said this is our job and we're doing this. Instead I got everyone together and I showed beautiful photography. And it was right before lunch, it was dripping things and lucious things.
Everyone said, wow - could we do that?
I said, oh that's a good idea. Let me try it.
We went out a did a little test shoot and shot some stuff. They said that is so beautiful.
I said you know how we get this? A photographer is like an artist or film director.
They said like no one on the set? I said yeah - it's not a strict facsimile of your product. It's an emotional interpretation of it.
From then on, they never came and I never had to tell them not to come.
I found using that method to be very successful in a collaborative environment. Whether it's a company you own with 10 people or Starbucks with thousands of people.
There are ways to treat people. Respect who they are, and what their opinions are, but still get your points across.


Phone Number in Winterville. Find a Phone Number, Address or other Contact Detail, like Email Address and Profiles of people or friends who live in Winterville, Georgia, USA. Results of Phone Numbers and Addresses in any Street.


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Coronavirus Update: New York May Already Be At Or Near Its Apex Of Cases, Cuomo Says; Deaths Drop For First Time
Filed Under:Andrew Cuomo, Coronavirus, COVID-19, Local TV, New York
ALBANY, N.Y. (CBSNewYork) — New York may already be at or near its apex of cases, Gov. Andrew Cuomo said Sunday.
"We're looking at this seriously now, because by the data we could be either very near the apex, or the apex could be a plateau, and we could be on that plateau right now," Cuomo said. "We won't know until we see the next few days — does it go up, does it go down — but that is what the statisticians will tell you today."
WATCH: Gov. Cuomo's Daily Coronavirus Briefing
Cuomo said there's an "interesting blip" in the data as the total number of new hospitalizations is 574, "which is obviously much lower than previous numbers."
WEB EXTRA: Gov. Andrew Cuomo's Presentation (.pdf)
More than 302,280 people have been tested for the coronavirus and at least 122,031 are positive in the state, Cuomo said. There are 16,479 patients currently hospitalized and 4,376 people in the ICU.
At least 12,187 patients have been discharged.
Resources, Hotlines, Unemployment & Covering Bills
Remote Learning Tools For Parents Teaching At Home
Ask Dr. Max Your Health Questions
How To Avoid Psychological Isolation
Talking To Your Kids About Anxiety
Make Your Own Hand Sanitizer
There have been 4,159 deaths, up 594 from the day before.
"But the number of deaths over the past few days has been dropping for the first time. What is the significance of that? It is too early to tell," Cuomo said.
As for where the cases are occurring?
"There is a shift to Long Island. Upstate New York is basically flat. And as Long Island grows, the percentage of cases in New York has reduced," Cuomo said.
CORONAVIRUS: NY Health Dept. | NY Call 1-(888)-364-3065 | NYC Health Dept. | NYC Call 311, Text COVID to 692692 | NJ COVID-19 Info Hub | NJ Call 1-(800)-222-1222 or 211, Text NJCOVID to 898211 | CT Health Dept. | CT Call 211 | Centers for Disease Control and Prevention
Cuomo said 1,000 medical personnel being sent by the federal government will be deployed first to New York City public hospitals.
Cuomo said New York has adopted a "surge and flex" approach to the hospital system: Surging the amount of hospital beds, staff and equipment, and flexing the entire system to be used in a different way than it has been before.
"The operational challenge for the health care system is impossible, because the system is over capacity all across the board. It's just over capacity," Cuomo said. "They are being asked to do the impossible.
"The only way we can make this work is if we flex the system so that we take all hospitals, all hospital networks… and we work together as one system, which has never been done," Cuomo added.
Patients may be taken from hospitals that are overcapacity to ones that still have room, but may not be in their community, Cuomo said.
"The concept here that people have to get is that nobody can handle this alone. Nobody," Cuomo said.
Cuomo said he believes that is the model the nation will ultimately take as well.
PHOTO GALLERY: A Look Inside NYC's Viral 'Warzone'
"There's not enough in the federal stockpile to take care of New York and Illinois and Texas and Florida and California. It's just not an option. The only option I see is there's a national deployment. Everyone says this is wartime. It is a war, and the virus is the enemy. Help New York today… and then nationally, we shift the resources to the next place that is most impacted," Cuomo said.
"I think you see the return to normalcy when we have an approved rapid testing program that can be brought to scale," Cuomo said. "It's under development now… that is going to be the answer, I believe."
Cuomo discussed the cabin fever that comes along with people being stuck at home.
"It's a real thing. Think about it. It's only been one month," Cuomo said.
He said he's going to resume running as a "positive intervention" for cabin fever.
With the holidays of Easter and Passover upon us, the governor is urging New Yorkers not to gather with family members who they do not live with.


U.S. industrial policy should aim to identify competitive dynamics and limits of offshoring, and feed this understanding back into the educational and research grant system.
Note I don't say, "stop offshoring," or "do more offshoring." Offshoring is just a plaything of Adam Smith, like the electric motor. It happens, and you plan around it. All you can do is build a society that is competitive in the new circumstances.
For example, suppose — and this is just one possible direction — suppose that we're on a trajectory in which 99% of software development ends up in well-educated developing countries. This might mitigate toward U.S. universities teaching abstract system design instead of implementation. It might imply creating new, extremely high leverage programming languages like Ruby. It might imply new modes of industrial organization such that a substantial part of the value chain is still captured here.
« Rivals aren't enemies. Enemies aren't rivals.
This entry was posted on Monday, June 20th, 2005 at 12:17 pm and is filed under Policy.	You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.


text:
.PHONY: all fetch unpack patch build install clean distclean

PREFIX?=/usr
DESTDIR?=
VERSION?=3.0
FETCH?=curl -L --retry 3

all: build

fetch: bison-$(VERSION).tar.gz
unpack: bison-$(VERSION)/
patch: unpack
prepare: bison-$(VERSION)/
	mkdir build/
	@touch $@

configure: prepare
	cd build && ../bison-$(VERSION)/configure --prefix=$(PREFIX)
	@touch $@

build: configure
	$(MAKE) -C build

install: build
	$(MAKE) -C build install DESTDIR=$(DESTDIR)

clean:
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	$(RM) -r bison-$(VERSION)/

distclean: clean
	$(RM) bison-$(VERSION).tar.gz

bison-$(VERSION).tar.gz:
	$(FETCH) ftp://ftp.gnu.org/gnu/bison/bison-$(VERSION).tar.gz > $@

bison-$(VERSION)/: bison-$(VERSION).tar.gz
	tar xzf $<

.PHONY: Version
Version:
	@echo $(VERSION) > $@