The Semi-Supervised VAE¶
Introduction¶
Most of the models we’ve covered in the tutorials are unsupervised:
We’ve also covered a simple supervised model:
The semi-supervised setting represents an interesting intermediate case where some of the data is labeled and some is not. It is also of great practical importance, since we often have very little labeled data and much more unlabeled data. We’d clearly like to leverage labeled data to improve our models of the unlabeled data.
The semi-supervised setting is also well suited to generative models, where missing data can be accounted for quite naturally—at least conceptually. As we will see, in restricting our attention to semi-supervised generative models, there will be no shortage of different model variants and possible inference strategies. Although we’ll only be able to explore a few of these variants in detail, hopefully you will come away from the tutorial with a greater appreciation for the abstractions and modularity offered by probabilistic programming.
So let’s go about building a generative model. We have a dataset \(\mathcal{D}\) with \(N\) datapoints,
where the \(\{ {\bf x}_i \}\) are always observed and the labels \(\{ {\bf y}_i \}\) are only observed for some subset of the data. Since we want to be able to model complex variations in the data, we’re going to make this a latent variable model with a local latent variable \({\bf z}_i\) private to each pair \(({\bf x}_i, {\bf y}_i)\). Even with this set of choices, a number of model variants are possible: we’re going to focus on the model variant depicted in Figure 1 (this is model M2 in reference [1]).
For convenience—and since we’re going to model MNIST in our experiments below—let’s suppose the \(\{ {\bf x}_i \}\) are images and the \(\{ {\bf y}_i \}\) are digit labels. In this model setup, the latent random variable \({\bf z}_i\) and the (partially observed) digit label jointly generate the observed image. The \({\bf z}_i\) represents everything but the digit label, possibly handwriting style or position. Let’s sidestep asking when we expect this particular factorization of \(({\bf x}_i, {\bf y}_i, {\bf z}_i)\) to be appropriate, since the answer to that question will depend in large part on the dataset in question (among other things). Let’s instead highlight some of the ways that inference in this model will be challenging as well as some of the solutions that we’ll be exploring in the rest of the tutorial.
The Challenges of Inference¶
For concreteness we’re going to continue to assume that the partially-observed \(\{ {\bf y}_i \}\) are discrete labels; we will also assume that the \(\{ {\bf z}_i \}\) are continuous.
If we apply the general recipe for stochastic variational inference to our model (see SVI Part I) we would be sampling the discrete (and thus non-reparameterizable) variable \({\bf y}_i\) whenever it’s unobserved. As discussed in SVI Part III this will generally lead to high-variance gradient estimates.
A common way to ameliorate this problem—and one that we’ll explore below—is to forego sampling and instead sum out all ten values of the class label \({\bf y}_i\) when we calculate the ELBO for an unlabeled datapoint \({\bf x}_i\). This is more expensive per step, but can help us reduce the variance of our gradient estimator and thereby take fewer steps.
Recall that the role of the guide is to ‘fill in’ latent random variables. Concretely, one component of our guide will be a digit classifier \(q_\phi({\bf y} | {\bf x})\) that will randomly ‘fill in’ labels \(\{ {\bf y}_i \}\) given an image \(\{ {\bf x}_i \}\). Crucially, this means that the only term in the ELBO that will depend on \(q_\phi(\cdot | {\bf x})\) is the term that involves a sum over unlabeled datapoints. This means that our classifier \(q_\phi(\cdot | {\bf x})\)—which in many cases will be the primary object of interest—will not be learning from the labeled datapoints (at least not directly).
This seems like a potential problem. Luckily, various fixes are possible. Below we’ll follow the approach in reference [1], which involves introducing an additional objective function for the classifier to ensure that the classifier learns directly from the labeled data.
We have our work cut out for us so let’s get started!
First Variant: Standard objective function, naive estimator¶
As discussed in the introduction, we’re considering the model depicted in Figure 1. In more detail, the model has the following structure:
\(p({\bf y}) = Cat({\bf y}~|~{\bf \pi})\): multinomial (or categorical) prior for the class label
\(p({\bf z}) = \mathcal{N}({\bf z}~|~{\bf 0,I})\): unit normal prior for the latent code \(\bf z\)
\(p_{\theta}({\bf x}~|~{\bf z,y}) = Bernoulli\left({\bf x}~|~\mu\left({\bf z,y}\right)\right)\): parameterized Bernoulli likelihood function; \(\mu\left({\bf z,y}\right)\) corresponds to
decoder
in the code
We structure the components of our guide \(q_{\phi}(.)\) as follows:
\(q_{\phi}({\bf y}~|~{\bf x}) = Cat({\bf y}~|~{\bf \alpha}_{\phi}\left({\bf x}\right))\): parameterized multinomial (or categorical) distribution; \({\bf \alpha}_{\phi}\left({\bf x}\right)\) corresponds to
encoder_y
in the code\(q_{\phi}({\bf z}~|~{\bf x, y}) = \mathcal{N}({\bf z}~|~{\bf \mu}_{\phi}\left({\bf x, y}\right), {\bf \sigma^2_{\phi}\left(x, y\right)})\): parameterized normal distribution; \({\bf \mu}_{\phi}\left({\bf x, y}\right)\) and \({\bf \sigma^2_{\phi}\left(x, y\right)}\) correspond to the neural digit classifier
encoder_z
in the code
These choices reproduce the structure of model M2 and its corresponding inference network in reference [1].
We translate this model and guide pair into Pyro code below. Note that:
The labels
ys
, which are represented with a one-hot encoding, are only partially observed (None
denotes unobserved values).model()
handles both the observed and unobserved case.The code assumes that
xs
andys
are mini-batches of images and labels, respectively, with the size of each batch denoted bybatch_size
.
[ ]:
def model(self, xs, ys=None):
# register this pytorch module and all of its sub-modules with pyro
pyro.module("ss_vae", self)
batch_size = xs.size(0)
# inform Pyro that the variables in the batch of xs, ys are conditionally independent
with pyro.plate("data"):
# sample the handwriting style from the constant prior distribution
prior_loc = xs.new_zeros([batch_size, self.z_dim])
prior_scale = xs.new_ones([batch_size, self.z_dim])
zs = pyro.sample("z", dist.Normal(prior_loc, prior_scale).to_event(1))
# if the label y (which digit to write) is supervised, sample from the
# constant prior, otherwise, observe the value (i.e. score it against the constant prior)
alpha_prior = xs.new_ones([batch_size, self.output_size]) / (1.0 * self.output_size)
ys = pyro.sample("y", dist.OneHotCategorical(alpha_prior), obs=ys)
# finally, score the image (x) using the handwriting style (z) and
# the class label y (which digit to write) against the
# parametrized distribution p(x|y,z) = bernoulli(decoder(y,z))
# where `decoder` is a neural network
loc = self.decoder([zs, ys])
pyro.sample("x", dist.Bernoulli(loc).to_event(1), obs=xs)
def guide(self, xs, ys=None):
with pyro.plate("data"):
# if the class label (the digit) is not supervised, sample
# (and score) the digit with the variational distribution
# q(y|x) = categorical(alpha(x))
if ys is None:
alpha = self.encoder_y(xs)
ys = pyro.sample("y", dist.OneHotCategorical(alpha))
# sample (and score) the latent handwriting-style with the variational
# distribution q(z|x,y) = normal(loc(x,y),scale(x,y))
loc, scale = self.encoder_z([xs, ys])
pyro.sample("z", dist.Normal(loc, scale).to_event(1))
Network Definitions¶
In our experiments we use the same network configurations as used in reference [1]. The encoder and decoder networks have one hidden layer with \(500\) hidden units and softplus activation functions. We use softmax as the activation function for the output of encoder_y
, sigmoid as the output activation function for decoder
and exponentiation for the scale part of the output of encoder_z
. The latent dimension is 50.
MNIST Pre-Processing¶
We normalize the pixel values to the range \([0.0, 1.0]\). We use the MNIST data loader from the torchvision
library. The testing set consists of \(10000\) examples. The default training set consists of \(60000\) examples. We use the first \(50000\) examples for training (divided into supervised and un-supervised parts) and the remaining \(10000\) images for validation. For our
experiments, we use \(4\) configurations of supervision in the training set, i.e. we consider \(3000\), \(1000\), \(600\) and \(100\) supervised examples selected randomly (while ensuring that each class is balanced).
The Objective Function¶
The objective function for this model has the two terms (c.f. Eqn. 8 in reference [1]):
To implement this in Pyro, we setup a single instance of the SVI
class. The two different terms in the objective functions will emerge automatically depending on whether we pass the step
method labeled or unlabeled data. We will alternate taking steps with labeled and unlabeled mini-batches, with the number of steps taken for each type of mini-batch depending on the total fraction of data that is labeled. For example, if we have 1,000 labeled images and 49,000 unlabeled ones, then we’ll
take 49 steps with unlabeled mini-batches for each labeled mini-batch. (Note that there are different ways we could do this, but for simplicity we only consider this variant.) The code for this setup is given below:
[ ]:
from pyro.infer import SVI, Trace_ELBO, TraceEnum_ELBO, config_enumerate
from pyro.optim import Adam
# setup the optimizer
adam_params = {"lr": 0.0003}
optimizer = Adam(adam_params)
# setup the inference algorithm
svi = SVI(model, guide, optimizer, loss=Trace_ELBO())
When we run this inference in Pyro, the performance seen during test time is degraded by the noise inherent in the sampling of the categorical variables (see Figure 2 and Table 1 at the end of this tutorial). To deal with this we’re going to need a better ELBO gradient estimator.
Interlude: Summing Out Discrete Latents¶
As highlighted in the introduction, when the discrete latent labels \({\bf y}\) are not observed, the ELBO gradient estimates rely on sampling from \(q_\phi({\bf y}|{\bf x})\). These gradient estimates can be very high-variance, especially early in the learning process when the guessed labels are often incorrect. A common approach to reduce variance in this case is to sum out discrete latent variables, replacing the Monte Carlo expectation
with an explicit sum
This sum is usually implemented by hand, as in [1], but Pyro can automate this in many cases. To automatically sum out all discrete latent variables (here only \({\bf y}\)), we simply wrap the guide in config_enumerate()
:
svi = SVI(model, config_enumerate(guide), optimizer, loss=TraceEnum_ELBO(max_plate_nesting=1))
In this mode of operation, each svi.step(...)
computes a gradient term for each of the ten latent states of \(y\). Although each step is thus \(10\times\) more expensive, we’ll see that the lower-variance gradient estimate outweighs the additional cost.
Going beyond the particular model in this tutorial, Pyro supports summing over arbitrarily many discrete latent variables. Beware that the cost of summing is exponential in the number of discrete variables, but is cheap(er) if multiple independent discrete variables are packed into a single tensor (as in this tutorial, where the discrete labels for the entire mini-batch are packed into the single tensor \({\bf y}\)). To use this parallel form of config_enumerate()
, we must inform Pyro
that the items in a minibatch are indeed independent by wrapping our vectorized code in a with pyro.plate("name")
block.
Second Variant: Standard Objective Function, Better Estimator¶
Now that we have the tools to sum out discrete latents, we can see if doing so helps our performance. First, as we can see from Figure 3, the test and validation accuracies now evolve much more smoothly over the course of training. More importantly, this single modification improved test accuracy from around 20%
to about 90%
for the case of \(3000\) labeled examples. See Table 1 for the full results. This is great, but can we do better?
Third Variant: Adding a Term to the Objective¶
For the two variants we’ve explored so far, the classifier \(q_{\phi}({\bf y}~|~ {\bf x})\) doesn’t learn directly from labeled data. As we discussed in the introduction, this seems like a potential problem. One approach to addressing this problem is to add an extra term to the objective so that the classifier learns directly from labeled data. Note that this is exactly the approach adopted in reference [1] (see their Eqn. 9). The modified objective function is given by:
\begin{align} \mathcal{J}^{\alpha} &= \mathcal{J} + \alpha \mathop{\mathbb{E}}_{\tilde{p_l}({\bf x,y})} \big[-\log\big(q_{\phi}({\bf y}~|~ {\bf x})\big)\big] \\ &= \mathcal{J} + \alpha' \sum_{({\bf x,y}) \in \mathcal{D}_{\text{supervised}}} \big[-\log\big(q_{\phi}({\bf y}~|~ {\bf x})\big)\big] \end{align}
where \(\tilde{p_l}({\bf x,y})\) is the empirical distribution over the labeled (or supervised) data and \(\alpha' \equiv \frac{\alpha}{|\mathcal{D}_{\text{supervised}}|}\). Note that we’ve introduced an arbitrary hyperparameter \(\alpha\) that modulates the importance of the new term.
To learn using this modified objective in Pyro we do the following:
We use a new model and guide pair (see the code snippet below) that corresponds to scoring the observed label \({\bf y}\) for a given image \({\bf x}\) against the predictive distribution \(q_{\phi}({\bf y}~|~ {\bf x})\)
We specify the scaling factor \(\alpha'\) (
aux_loss_multiplier
in the code) in thepyro.sample
call by making use ofpoutine.scale
. Note thatpoutine.scale
was used to similar effect in the Deep Markov Model to implement KL annealing.We create a new
SVI
object and use it to take gradient steps on the new objective term
[ ]:
def model_classify(self, xs, ys=None):
pyro.module("ss_vae", self)
with pyro.plate("data"):
# this here is the extra term to yield an auxiliary loss
# that we do gradient descent on
if ys is not None:
alpha = self.encoder_y(xs)
with pyro.poutine.scale(scale=self.aux_loss_multiplier):
pyro.sample("y_aux", dist.OneHotCategorical(alpha), obs=ys)
def guide_classify(xs, ys):
# the guide is trivial, since there are no
# latent random variables
pass
svi_aux = SVI(model_classify, guide_classify, optimizer, loss=Trace_ELBO())
When we run inference in Pyro with the additional term in the objective, we outperform both previous inference setups. For example, the test accuracy for the case with \(3000\) labeled examples improves from 90%
to 96%
(see Figure 4 below and Table 1 in the next section). Note that we used validation accuracy to select the hyperparameter \(\alpha'\).
Results¶
Supervised data |
First variant |
Second variant |
Third variant |
Baseline classifier |
|---|---|---|---|---|
100 |
0.2007(0.0353) |
0.2254(0.0346) |
0.9319(0.0060) |
0.7712(0.0159) |
600 |
0.1791(0.0244) |
0.6939(0.0345) |
0.9437(0.0070) |
0.8716(0.0064) |
1000 |
0.2006(0.0295) |
0.7562(0.0235) |
0.9487(0.0038) |
0.8863(0.0025) |
3000 |
0.1982(0.0522) |
0.8932(0.0159) |
0.9582(0.0012) |
0.9108(0.0015) |
Table 1: Result accuracies (with 95% confidence bounds) for different inference methods
Table 1 collects our results from the three variants explored in the tutorial. For comparison, we also show results from a simple classifier baseline, which only makes use of the supervised data (and no latent random variables). Reported are mean accuracies (with 95% confidence bounds in parentheses) across five random selections of supervised data.
We first note that the results for the third variant—where we summed out the discrete latent random variable \(\bf y\) and made use of the additional term in the objective function—reproduce the results reported in reference [1]. This is encouraging, since it means that the abstractions in Pyro proved flexible enough to accomodate the required modeling and inference setup. Significantly, this flexibility was evidently necessary to outperform the baseline. It’s also worth emphasizing that the gap between the baseline and third variant of our generative model setup increases as the number of labeled datapoints decreases (maxing out at about 15% for the case with only 100 labeled datapoints). This is a tantalizing result because it’s precisely in the regime where we have few labeled data points that semi-supervised learning is particularly attractive.
Latent Space Visualization¶
We use T-SNE to reduce the dimensionality of the latent \(\bf z\) from \(50\) to \(2\) and visualize the 10 digit classes in Figure 5. Note that the structure of the embedding is quite different than that in the VAE case, where the digits are clearly separated from one another in the embedding. This make sense, since for the semi-supervised case the latent \(\bf z\) is free to use its representational capacity to model, e.g., handwriting style, since the variation between digits is provided by the (partially observed) labels.
Conditional image generation¶
We sampled \(100\) images for each class label (\(0\) to \(9\)) by sampling different values of the latent variable \({\bf z}\). The diversity of handwriting styles exhibited by each digit is consistent with what we saw in the T-SNE visualization, suggesting that the representation learned by \(\bf z\) is disentangled from the class labels.
Final thoughts¶
We’ve seen that generative models offer a natural approach to semi-supervised machine learning. One of the most attractive features of generative models is that we can explore a large variety of models in a single unified setting. In this tutorial we’ve only been able to explore a small fraction of the possible model and inference setups that are possible. There is no reason to expect that one variant is best; depending on the dataset and application, there will be reason to prefer one over another. And there are a lot of variants (see Figure 7)!
Some of these variants clearly make more sense than others, but a priori it’s difficult to know which ones are worth trying out. This is especially true once we open the door to more complicated setups, like the two models at the bottom of the figure, which include an always latent random variable \(\tilde{\bf y}\) in addition to the partially observed label \({\bf y}\). (Incidentally, this class of models—see reference [2] for similar variants—offers another potential solution to the ‘no training’ problem that we identified above.)
The reader probably doesn’t need any convincing that a systematic exploration of even a fraction of these options would be incredibly time-consuming and error-prone if each model and each inference procedure were coded up by scratch. It’s only with the modularity and abstraction made possible by a probabilistic programming system that we can hope to explore the landscape of generative models with any kind of nimbleness—and reap any awaiting rewards.
See the full code on Github.
References¶
[1] Semi-supervised Learning with Deep Generative Models
, Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed, Max Welling
[2] Learning Disentangled Representations with Semi-Supervised Deep Generative Models
, N. Siddharth, Brooks Paige, Jan-Willem Van de Meent, Alban Desmaison, Frank Wood, Noah D. Goodman, Pushmeet Kohli, Philip H.S. Torr


bindable_view.js |
|
|---|---|
Duckbone.BindableViewThis mixin enables any view to selectively data-bind to changes in the model. Many developers prefer this approach over re-rendering an entire view on each model change event. UsageMix Duckbone.BindableView into any View that needs to use declarative data-binding to model attributes. Data BindingData bindings should be defined in the
BindableView also mixes in Duckbone.ViewlifecycleExtensions to assist in creating
and tearing down bindings. If you are using the lifecycle, then bindings will be
created and destroyed for you. If not, call Weak BindingBindableView uses “weak bindings” which are intended to be unbound when the view is
removed. This prevents zombie callbacks from wreaking havoc as a model gets passed from
view to view. All of the bindings defined in |
(function(){
Duckbone.BindableView = { |
Mixin |
|
property isBindableViewIdentifies a view that has BindableView mixed in. |
isBindableView: true, |
function includedAlso mix in Duckbone.ViewLifecycleExtensions |
included: function() {
if (!this.hasViewLifecycleExtensions) {
Duckbone.include(this, Duckbone.ViewLifecycleExtensions);
}
}, |
function bindAttributes
Establishes the bindings declared in |
bindAttributes: function(model) {
if (!this.attributeChanges) return;
model = model || this.model || new Backbone.Model();
for (var attr in this.attributeChanges) {
this.bindAttribute(model, attr, this.attributeChanges[attr]);
}
}, |
function bindAttribute
Bind a single attribute to the supplied model. The binding param can either be a string selector, in which case the html of that selector is replaced with the model value or a callback function, which is simply executed, in the context of the view. If the view is already rendered, then initial values will be set to the current state of the model at the time of this call. |
bindAttribute: function(model, attr, binding) { |
|
Get model |
model = model || this.model || new Backbone.Model();
var attrValue = Handlebars.Utils.escapeExpression(model.get(attr));
var handler;
if (typeof binding == "function") { |
|
The binding is a callback function. Set value now. |
binding.call(this, attrValue);
handler = function() {
binding.call(this, Handlebars.Utils.escapeExpression(model.get(attr)));
}
} else if (typeof binding == "string") { |
|
The binding is a selector. Set value now. |
$(this.el).find(binding).html(attrValue);
handler = function () {
$(this.el).find(binding).html(Handlebars.Utils.escapeExpression(model.get(attr)));
}
}
this._attributeBindings = this._attributeBindings || [];
var e = 'change:'+attr;
this._attributeBindings.push([model, e, handler]);
this.weakBindTo(model, e, handler, this);
}, |
function unbindAttributesUnbind all previously-bound attributes. Used internally to clean up for re-rendering a template |
unbindAttributes: function() {
if (this._attributeBindings) {
_(this._attributeBindings).each(function(binding) { |
|
There is no individual unbind for weakBindings. The housekeeping to clean up the metadata would be more expensive than attempting to unbind all at teardown for most use cases, so we just do a raw unbind. |
binding[0].unbind(binding[1], binding[2]);
});
delete this._attributeBindings;
}
}, |
function bindLiveTimestamps
If you are using the {{live_timestamp}} helper, then call this function to begin updating those timestamps once per minute. |
bindLiveTimestamps: function(seconds) {
seconds = seconds || 60;
var updateTimestamps = _.bind(function() {
$(this.el).find('span[data-live-timestamp]').each(_.bind(function(i, span) {
var millis = parseInt($(span).attr('data-live-timestamp'));
var pretty = Duckbone.helpers.dateToPrettyTimeAgo(new Date(millis));
$(span).html(pretty);
}, this))
}, this);
updateTimestamps();
this._updateLiveTimeStampsInterval = setInterval(updateTimestamps, seconds * 1000);
}, |
unbindLiveTimestampsStop updating timestamps. |
unbindLiveTimestamps: function() {
clearInterval(this._updateLiveTimeStampsInterval);
}
}; |
helper {{live_timestamp}}Creates a partial that can be used to insert a live timestamp. Bindable view can use this to update it once per minute. The timestamp must be in a format natively parseable by Date. usage {{livetimestamp “createdat”}} |
if (Handlebars) {
Handlebars.registerHelper("live_timestamp", function(timestamp) {
var stamp = Duckbone.Handlebars.getAttr(this, timestamp);
if (!stamp) return "";
var date = new Date(stamp);
if (!date) return "";
return new Handlebars.SafeString(
'' +
Duckbone.helpers.dateToPrettyTimeAgo(date) + ''
);
});
}
}).call(this); |


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The Level 3 BLAS specifications [40] specify the input, output and calling sequence for each routine, but allow freedom of implementation, subject to the requirement that the routines be numerically stable. Level 3 BLAS implementations can therefore be built using matrix multiplication algorithms that achieve a more favorable operation count (for suitable dimensions) than the standard multiplication technique, provided that these ``fast'' algorithms are numerically stable. The simplest fast matrix multiplication technique is Strassen's method, which can multiply two n-by-n matrices in fewer than operations, where .
The effect on the results in this chapter of using a fast Level 3 BLAS implementation can be explained as follows. In general, reasonably implemented fast Level 3 BLAS preserve all the bounds presented here (except those at the end of subsection 4.10), but the constant p(n) may increase somewhat. Also, the iterative refinement routine xyyRFS may take more steps to converge.
This is what we mean by reasonably implemented fast Level 3 BLAS. Here, ci denotes a constant depending on the specified matrix dimensions.
(1) If A is m-by-n, B is n-by-p and
is the computed
approximation to C=AB, then
(2)
The computed solution
to the triangular systems TX=B,
where T is m-by-m and B is m-by-p, satisfies
For further details, and references to fast multiplication techniques, see [27].


AFFLICTION
Reviewed by Harvey Karten, Ph.D. Lions Gate Films Director: Paul Schrader Writer: Paul Shrader (novel by Russell Banks) Cast: Nick Nolte, James Coburn, Willem Dafoe, Sissy Spacek, Mary Beth Hurt
The production notes for "Affliction" advise us that if a kitten does not have human contact during the first two weeks of its life, it will never EVER respond to human touch. It will become a feral beast. This may sound harsh and inspire disbelief, but as an animal lover I can affirm that the statement is absolutely true. Imagine, then, that the feline is not simply ignored by people but is actually kicked around and thrown out of windows by wanton boys, and perhaps then you can accept this fact of nature.  Not only will the lad likely turn into an alcoholic himself: he will bear the imprint of this misuse throughout his days. Though most people will probably repress the memory and go on to live seemingly normal, if vaguely unhappy lives, Wade Whitehouse (Nick Nolte), the principal character in writer-director Paul Schrader's "Affliction," is unable to curb his rage. Based on a novel by Russell Banks and thematically similar to that author's more appealing short story, "The Sweet Hereafter," "Affliction" cannot sustain its thematic weight. Mr. Schrader is intent on dramatizing not just a textbook case of abnormal psychology but on portraying, Greek-tragic style, the affects of a strongly macho culture on generation after generation of men, propelling the narrative into epic import. This has a legendary impact that could well work on the page but is too ponderous, sluggish, and alien to the virtues of the screen.
The chief flaw in this movie occurs at the very beginning. Wade's brother Rolfe (Willem Dafoe) delivers an off-screen narration, giving away a key point of the plot.  By giving away the store during the initial minutes, Rolfe has destroyed most of the tension that the story could have developed.
The story takes place in a bleak, snowed-in, economically depressed rural area of northern New Hampshire (actually filmed near Montreal), the sort of rustic "paradise" that people with an ounce of smarts would make tracks from, and pronto. Wade, however, has remained, despite the failure of his two marriages to Lillian (Mary Beth Hurt), sustained by odd jobs for businessman-selectman Gordon Lariviere (Holmes Osborne) and a laughable job as a traffic cop with no crimes to pursue. When rich union leader Evan Twombley (Sean McCann) is killed in a hunting accident, Wade suspects foul play, convinced that the man was murdered by his hunting guide and Wade's friend, Jack (Jim True), at the orders of Twombley's son-in-law, Mel Gordon (Steve Adams).  Though he has gained the devotion of the local waitress Margie (Sissy Spacek), he cannot dispel the memories of his abusive dad, Pop Whitehouse (James Coburn), whom he conjures us in his memories of childhood days and whom he visits as well in the present. Though his brother Rolfe (Willem Dafoe) has successfully escaped to the big city by becoming a professor at Boston University-- determined never to return unless tragedy were to strike his family in Laverne, New Hampshire--Wade cannot break the bonds of his cruel and offensive dad and sets up a self- destructive scenario.
There is much to admire in the performances. Nick Nolte gives one of the finest of his career as a man who successfully covers up his emotional scars, particularly in the affectionate way he relates to his daughter, Jill, whom he has taken for the weekend Halloween festivities. You've got to admire his patience. Jill is the sort of youngster who could try anyone's patience, a disagreeable, insulting, hostile creature who from the moment she arrives at the party insists on being taken home to her mother. One can see, as well, a certain fondness he holds for his girl friend Margie, though he throws hints that he is proposing marriage to her only as a way to help gain custody of his daughter. James Coburn as Wade's dad is precise in his depiction of a man who is anything but politically correct, one who has apparently learned his lessons well at his own father's knee: that women should know their place and sons should show unmitigated respect and devotion toward their dads.
There are few people in this harsh environment to sympathize with. We get the feeling that Schrader, and novelist Banks before him, are eager to get this story to the public as an antidote to feel-good movies like "Pleasantville" and "The Truman Show," which celebrate the virtues of small-town life. This is to the good: an acerbic edge is welcome if only to deconstruct the false homilies we've been brought up to believe. But despite fine performances and ambient music to match the dismal milieu, "Affliction" is too novelistic, dependent on outright narration, too much in the principal character's head, and, thanks to Willem Dafoe's opening narrative which gives away the design, too lacking in suspense.
Rated R. Running Time: 114 minutes.(C) 1998 Harvey Karten
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
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Related links: index of all rec.arts.movies.reviews reviews


| DOSE: |
3 hits |
smoked |
Cannabis |
(plant material) |
Let me preface this by saying I'm a Wiccan, so some of the stuff I saw might not be understood by people who aren't.
Anyway, it was a Sunday night and I had just eaten and my friends and I got together in my dorm room and started smoking from a water pipe. In the background, I was playing Brian Eno and an Egyptian group called Musicians of the Nile. Then we smoked - I got 3 hits from a water pipe over the course of about half an hour. Then my friends left and I sat alone in my room listening to music.
Listening to the music, I felt like I was one of the drummers and could see myself in their group, playing. Then I felt waves of energy rippling up and down my body – I mean I could really FEEL it, like it was causing me to move with it. Then I briefly felt like I was dying … I could feel my body going quiet and my mind drifting off, but I decided I didn’t want to do that, so I sort of jumped and focused on the room, and did a little grounding meditation.
Then the interesting stuff happened. I sat down on my bed and dimmed the lights, and I felt like all the forests and fields were pulsing with life, and that energy was coming up into me, and I felt like my whole body was throbbing, and then I felt like I was the Green Man, or Pan, or some similar God, and I was alive with the spirit of nature.
Then I saw myself flowing into the Goddess – she appeared to me as a huge tree and simultaneously a woman dressed in white, reaching towards me and embracing me, and saying “All is Well”, and then, I saw idealized figures of both the Goddess and the God, and they sort of “morphed” into my own mother and father, and I felt indescribable joy.
Another example of why leafy green vegetables are an important part of any balanced diet.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


The SingleSelectList component can take a list of options from a data container. For this purpose, the optionsContainer attribute is used.
SingleSelectList
optionsContainer


Wag The Dog
Chad'z rating: *** (out of 4 = good)
1997, R, 95 minutes [1 hour, 35 minutes]
[satire]
starring: Dustin Hoffman (Stanley Motss), Robert De Niro (Conrad Brean), Anne Heche (Winifred Ames), Denis Leary (Fad King), written by Hilary Henkin, David Mamet; produced by Robert De Niro, Barry Levinson, Jane Rosenthal; directed by Barry Levinson; based on the novel "American Hero" by Larry Beinhart.
Satirical films usually fall into one of two categories: 1) long-term satire where everything, including the jokes, is somehow related on a large scale (i.e. "Dr. Strangelove"; 2) situation satire in which the comedy and themes are derived moment by moment and scene by scene (i.e. "Men In Black"). What's unique about "Wag The Dog" is that it doesn't necessarily fit either of these descriptions, and yet is both at the same time. The result is a clever comedy based on an original, smart premise, even if the focus does tend to wander a bit.
If there's anything that affects Americans the most it's politics and showbusiness. In this age of information and technology, we've been so conditioned by the media and the entertainment industry in our ways of perceiving things, it's gotten to the point that we are practically controlled by it. We need the media to bring us the most up-to-date news, but we also need the escapism of Hollywood to counter all that thinking. This film starts off well by quickly establishing these ideas, and then presents an interesting scenario: if politics, the media coverage of politics, and the entertainment industry are so closely related, couldn't someone with the power and authority to manipulate one of the aspects manipulate them all? That's the idea Conrad Brean (De Niro), a professional Washington spin doctor, comes up with in order to restore the president's good name who has been accused of child molestation just weeks before the election.
Brean, along with the president's top advisor, Winifred Ames (Heche), fly to California to meet up with hot-shot movie producer Stanley Motss (Hoffman). They pitch him an idea to "produce" the image of a war with Albania (it's a country no one's heard of so they people will buy it). Soon the wheels are in motion and the film's riffing satire comes into play. We're inundated with the Hollywood mind set as Motss brings in a number of experts to help manage the situation, such as a folk singer, visual and sound effects technicians, and a modern style expert known as the Fad King (Leary).
The initial atmosphere is rich with numerous pop culture references where everything somehow relates to something on TV, in the movies, or in the news. The characters' dialogue is witty and funny, although some of the actual jokes and gags seem rather sitcom-esque. For example, one scene shows Motss and Brean's production crew filming an actress portraying a poor Albanian refugee. Motss ordered a kitten for the girl to carry, but instead wound up with a dozen different dogs. Since it's all computer-enhanced anyway, she ends up cradling a bag of Tostitos with the kitten to be inserted later. This demonstrates the film's ability to take a relatively ordinary situation and make it smart and funny just through the premise. But then there's the gags and one-liners that, although are humorous, seem a little gratuitious in the long run such as Motss not being able to remember the actress's name, or, whenever a problem emerges, always responding with,"This is nothing!"
By the time the film makes it to the midpoint, everything that is going to happen in terms of plot happens. The first act is quickly paced, while the second act contains most of the jokes and is the core of the film. Every ten minutes or so a new problem crops up that Motss and Brean must deal with, and it's always fascinating to see what they do and how they do it. The screenplay provides good characterization and actually deals with most of the problems head-on in realistic manners. However, it does have a tendency to avoid things and shift its focus from the election campaign and false war smokescreen to something a bit off the subject.
As the third act rolls around the satirical nature becomes more concerned with the situations and less so with the big picture. What had started off as a brilliant, somewhat harmless conspiracy to fool the general public begins to seem more like something that would happen in a movie. Still, the situation comedy is quite funny, especially a montage showing the entire country flinging old shoes into trees to honor Sgt. Willie Schumann - a war hero Motss and Brean concocted.
Truly great satire manages to create for an entertaining story that makes you laugh at the time, but think about afterwards and "Wag The Dog" at least succeeds in this respect. However, in retrospect it seems like so much more could have been done, namely by extending the running time and filling in the plot holes. Then again, it's a valiant effort.
Please visit Chad'z Movie Page @ http://members.aol.com/ChadPolenz - over 180 new and old films reviewed in depth, not just blind ratings and quick capsules. Also, check out The FIRST Shay Astar Web Page @ http://members.aol.com/ChadPolenz/ShayAstar.html
e-mail<EMAIL_ADDRESS>(C) 1998 Chad Polenz
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


"""Program to draw Mandelbrot fractals: the Mandelbrot algorithm.
Author: Lars van den Haak and Tom Verhoeff
Copyright (c) 2020 - Eindhoven University of Technology, The Netherlands
This software is made available under the terms of the MIT License.
* Contributor 1: Harry Verspagen
* TU/e ID number 1: 1484575
* Contributor 2: Sander Debets
* TU/e ID number 2: 1252402
* Date: 04-05-2020
"""
from typing import List, Tuple, Sequence
import math
#: The type of 2D points.
Point = Tuple[float, float]
#: TODO: define this function
[docs]def mandel_seq(x: float, y: float, n: int = 100) -> Sequence[Point]:
"""Return the mandel sequence for the input point (x, y), using n as upper bound.
Assumptions:
* start the sequence at (u_0, v_0) = (0, 0)
* The coordinate (x, y) will have mandel number n,
when the sequence starts diverging at (u_n, v_n)
:param x: x-coordinate of the point for which the sequence is computed
:param y: y-coordinate of the point for which the sequence is computed
:param n: upper bound to detect divergence
:return: the mandel sequence for the point (x, y)
:examples:
>>> mandel_seq(1, 0)
[(0.0, 0.0), (1.0, 0.0), (2.0, 0.0), (5.0, 0.0)]
>>> mandel_seq(0, 0, n = 1)
[(0.0, 0.0), (0.0, 0.0)]
"""
return []
#: TODO: define this function
[docs]def mandel_number(x: float, y: float, n: int = 100) -> int:
"""Return the mandel-number of point (x, y).
This is the smallest index of the mandel sequence at which u_n^2 + v_n^2 > 4.
Assumptions:
* the sequence diverges when u_n^2 + v_n^2 > 4
:param x: x-coordinate of the point for which the Mandel number is computed
:param y: y-coordinate of the point for which the Mandel number is computed
:param n: upper bound to detect divergence
:return: the mandel-number of point (x, y).
:examples:
>>> mandel_number(1, 0)
3
>>> mandel_number(0, 0, n = 10)
10
"""
return 0
#: The type of colors
Color = Tuple[int, int, int]
#: The standard color
STDC: Color = (140, 0, 255)
#: Black
BLACK: Color = (0, 0, 0)
#: Grey
GREY: Color = (128, 128, 128)
#: White
WHITE: Color = (255, 255, 255)
#: Red
RED: Color = (255, 0, 0)
#: Green
GREEN: Color = (0, 255, 0)
#: Blue
BLUE: Color = (0, 0, 255)
#: TODO: define this function
#: TODO: document this
[docs]def convert_pixel(x: int, y: int) -> Point:
return (0, 0)
#: TODO: define this function
#: TODO: document this
[docs]def generate_mandel_nums() -> List[List[int]]:
return []
#: TODO: define this function
[docs]def color_mandel(px: int, py: int, n: int = 100, c1: Color = GREEN) -> Color:
"""Return the colour of the given pixel.
Assumptions:
* pixels that that diverge immediately -> BLACK
* pixels that do not diverge after n iterations -> BLACK
* colour scaled based on the squared root of the mandel number
:param: px: pixel x-coordinate
:param: py: pixel y-coordinate
:param: n: maximum number of iterations, after which the pixel is coloured BLACK
:return: Color(as a defined Tuple - lines 48) for the input pixel
:examples:
TODO: doctests
"""
return (0, 0, 0)


Compressing and UnCompressing Protobuf items in C#
Part of a project i am working on required sending large amounts of data between different instances. To get this to work efficially, we started using the ProtoBuf using ProtoBuf-net in .NET. but the files where still quite large (17mb, give or take). So, we looked into compression… here is some examples of how we managed to compress the protobuf files. We got some decient compression: 3mb files, down from 17mb.
Read more →GIT tips and tricks
I use GIT a lot for different things, including this blog. so, here are a few tips and tricks i have found useful over the while… Using Dropbox as a local GIT/Mercurial Repository: my own post from a while back. Twelve GIT curated Tips and Workflows from the Trenches Using GIT with Subversion: tips that will make your life easier: The office has an SVN server, but i like GIT.
Read more →Symform - P2P Backup
I have previously posted about CrashPlan as my Backup System. I also, a long time ago, talked about Backing up SQL, MySQL and other stuff on my other blog. Well, CrashPlan is all good, but there are 2 “niggly” bits with it… Its not FREE (well, this year i got it Free on Black Friday…) but it is cheap ($120 a year to backup 10 machines to the cloud aint bad.
Read more →enabling VNC from SSH on OSX
I have a Late 2008 MacBook Pro in the house, and SSH is enabled on it. I usually leave it on when in work. Anyway, SSH is enabled, but VNC access is not… I found the following command on Ryan’s Tech Notes allowing you to enable it though SSH: ** NOTE ** Change mypasswd to your own password!!! If you need to disable it, use the following: Also, of note, I am using RealVNC client for Windows to connect.
Read more →moving your TMG SQL server Logs DB and other TMG tips
In house, I have been using Microsoft TMG 2010 Server for a while now. I use it as a firewall for some of the machines on the network, and also as a proxy for most, if not all, machines. When acting as a Firewall, all traffic flows though the machine, be it HTTP/HTTPS, SMTP/POP3/IMAP, or anything for that matter. You can also lock down ports on the box, which is a feature of most firewalls, but i like TMG due to its relitive ease of use…
Read more →RouterOS Dynamic IP Updates
I have been using a MikroTik RouterBoard RB750 for a while now, and i love it! Over the weekend, i upgraded it to a RB1100. Its the same software running the device, but the device is faster (800Mhz PowerPC chip VS MIPS-BE at 680Mhz), has more memory (512Mb upgradable to 1.5Gb vs 32Mb) and more storage (think its 512Mb on board, plus 4Gb MicroSD card vs 32mb…). It also has more ports (13 GigE VS 5) and 2 Switch Groups, which i have no idea what they do just yet…
Read more →Custom MSDeploy OverWrite Rules
I have a project which we are trying to automate the deployment system. The plan is to automatically deploy the project to a staging server anytime the build succeeds from SVN. I have had a few problems with this, but here are some of the links which may come in handy for you. MSDeploy skip rules when using MSBuild PublishProfile with Visual Studio 2012 Web Deploy : Customizing a deployment package How to set MSDeploy settings in .
Read more →WANProxy and Squid with Upstream Servers
In my previous post on WANProxy, i did not really go into detail about what it actually was. The direct quote from their site is WANProxy is a free, portable TCP proxy which makes TCP connections send less data, which improves TCP performance and throughput over lossy links, slow links and long links. This is just what you need to improve performance over satellite, wireless and WAN links. This is something that has interested me for a while, so i have been looking into it, and so far so good… In my last post i mentioned i was proxying Squid traffic, in todays post, i still am, but with some tweaks.
Read more →Building WANProxy on Ubuntu 12.04
[Updated 2016/04/09] I had to use this today to build on Debian 8.3 for 2 different boxes. So, making minor changes (url to git proxy, and where you build from) to make sure this works now. I have been looking into WANProxy for a while now, but never successfully got it to build… I have been more successfull reciently, so here is what you need to do. ** NOTE **: I built this on Ubuntu 12.
Read more →Boot Raspberry Pi with SSH Enabled, Enable Wifi on Boot, and more
Some interesting links for the Raspberry Pi today: So, you just bought a new Raspberry Pi and need to enable SSH, but dont have a screen? Prepare for SSH without a Screen shows you what to do! How to Bring up WLAN0 on boot shows you how to start your Wifi adapter when you boot your Pi
Read more →About
homepage of Tiernan OToole, Geek, photographer, GeekPhotographer, and lazy git.
Recent Posts
- Fixing CID (Caller ID) on incoming calls with 3CX
- Posts
- Network Update Info April 2019
- Adding a Netgear LB2120 to the homelab
- Finally going all in on VoIP
- Auto deploying to multiple servers with GitHub and Webhooks
- Moving the site to Hugo
- Playing with AMD’s Epyc
- AS204994, Own IP Space and Anycast
- Blogging on an iPad Pro


Orange Cabs Terminology:
Orange Cabs is a reliable metered taxi service, giving rides to passengers in and around the Gauteng area. Our service is safe and reliable, with the convenience of booking your ride via our mobile app, desktop, or by simply giving us a call.
Since its inception in 2012, Orange Cabs has grown from a 3 vehicle operation, to a full metered taxi service with multiple passenger vehicles. We offer our riders anything from single trip rides to meetings and malls to rides for groups of up to 8 passengers. All of our vehicles are less than 5 years-old and all of our drivers have valid Operators Licences. With us, you can sit back, relax and enjoy the ride, because every ride with Orange Cabs is covered by Public Liability Insurance. Our rides a quick and reliable and available to you, at the click of a button. 24/7 – 365...
Find us daily at The Boulders Mall (Midrand), Entrance 3 and Centurion Mall, Entrance 3. We are active in Johannesburg and Pretoria daily – call us, App us or hail us .
Schedule your booking for the date and time that you need your ride.
We will be there waiting to take you to your destination.
Score free rides and get specials and discounts when you use our App.
Why drive when you can ride along? Send us your schedule,
and we can be your personal chauffeur.
Getting you to your destination on time.
Download our user-friendly app now,
and follow the easy screen prompts to register and book!
We give your value for your miles! We won’t take you for a ride when it comes to your money.
Our competitive rates ensure
that you have an awesome and reliable experience
when taking a ride with us.
Register Free ON OUR APP
R8.50 PER KM
R15 PER KM
VAN 8-SEATER Hyundai H1 Mercedes BenzTAILOR-MADE TRIPS
Want to go a little further for longer?Drop us a line, or send us a note! We want to hear from you, again, and again...
0860 700 222


FortuneCookie sends an alert with a random fortune from the cookie whenever you shake your device. Don’t forget to enable FortuneCookie in the settings though.
There are over 200 hundred different fortunes in the cookies. They get randomized, so you may get the same fortune more than once or you may get several different fortunes.
To add custom sounds, just place the sounds in /Library/FreeFall and select them in Settings -> FreeFall. Simple like that.
FortuneCookie has several different fortunes, some are inspirational and uplifting, while others are dark and insulting. There’s also a few references there, see if you can get them.


|
QuantLib: a free/open-source library for quantitative finance
Fully annotated sources - version 1.30
|
#include <ql/methods/finitedifferences/utilities/fdmdividendhandler.hpp>
Public Member Functions | |
| FdmDividendHandler (const DividendSchedule &schedule, const ext::shared_ptr< FdmMesher > &mesher, const Date &referenceDate, const DayCounter &dayCounter, Size equityDirection) | |
| void | applyTo (Array &a, Time t) const override |
| const std::vector< Time > & | dividendTimes () const |
| const std::vector< Date > & | dividendDates () const |
| const std::vector< Real > & | dividends () const |
| Public Member Functions inherited from StepCondition< Array > | |
| virtual | ~StepCondition ()=default |
| virtual void | applyTo (Array &a, Time t) const=0 |
Private Attributes | |
| Array | x_ |
| std::vector< Time > | dividendTimes_ |
| std::vector< Date > | dividendDates_ |
| std::vector< Real > | dividends_ |
| const ext::shared_ptr< FdmMesher > | mesher_ |
| const Size | equityDirection_ |
Definition at line 37 of file fdmdividendhandler.hpp.
| FdmDividendHandler | ( | const DividendSchedule & | schedule, |
| const ext::shared_ptr< FdmMesher > & | mesher, | ||
| const Date & | referenceDate, | ||
| const DayCounter & | dayCounter, | ||
| Size | equityDirection | ||
| ) |
Implements StepCondition< Array >.
Definition at line 68 of file fdmdividendhandler.cpp.
| const std::vector< Time > & dividendTimes | ( | ) | const |
Definition at line 56 of file fdmdividendhandler.cpp.
| const std::vector< Date > & dividendDates | ( | ) | const |
Definition at line 60 of file fdmdividendhandler.cpp.
| const std::vector< Real > & dividends | ( | ) | const |
Definition at line 64 of file fdmdividendhandler.cpp.
|
private |
Definition at line 52 of file fdmdividendhandler.hpp.
|
private |
Definition at line 54 of file fdmdividendhandler.hpp.
|
private |
Definition at line 55 of file fdmdividendhandler.hpp.
|
private |
Definition at line 56 of file fdmdividendhandler.hpp.
|
private |
Definition at line 57 of file fdmdividendhandler.hpp.
|
private |
Definition at line 58 of file fdmdividendhandler.hpp.


How to play: Use your arrow keys to move the pictures around. When two pictures with the same person touch, they change into another person! Much wow!
This is a modified version of Gabriele Cirulli's 2048, that was itself based on 1024 by Veewo Studio and is conceptually similar to Threes by Asher Vollmer.
These are the people whose photos were used in the game:
All of them are proud members of HE:labs. You can see the whole team here.
Modified by Israel Ribeiro at 2014.


The Akan people of Ghana frequently name their children after the day of the week they were born and the order in which they were born. These "day names" have further meanings concerning the soul and character of the person. Middle names have considerably more variety and can refer to their birth order, twin status, or an ancestor's middle name.
The Akan, culturally and linguistically related tribes in West Africa, do the same as other African and Jamaican ethnic groups: Name their children after the respective day they were born. Over time, the Akan have defined seven names per gender, each referring to a day of the week.


---
layout: fullpage
title: Ashland BRT
pagetype: story map
bgcolor: FFFFFF
published: true
extrapadding: 100px
section: chicago
image: ashland1
---
## Bus Rapid Transit (BRT) is like a subway on the street.
![Ashland BRT](img/ashland_brt.png)
## Buses have their own lanes and elevated stations. They can turn stoplights green, to slice through rush hour traffic.


Lady of the Lake:
Finally I meet the one who bears a holy soul.
I've been waiting.
"I've been waiting to meet you, too."


| public VOID cdClass() |
| Constructor. Initializes the docObject type to CLASS. |
Member Data |
| public vector<string> mFType - List of member function types public vector<string> mFAcces - List of member function accesses public vector<string> mFName - List of member function names public vector<string> mFDesc - List of member function descriptions public vector<string> mDType - List of member data types public vector<string> mDAccess - List of member data accesses public vector<string> mDName - List of member data names public vector<string> mDDesc - List of member data descriptions |


- New: Spanish localization (thanks to darklinkpower)
- New: Russian localization (thanks to evgdovg1996)
- New: Indonesia localization (thanks to operamaniac)
- New: Hardware acceleration disabled by default
- New: Added social links to main menu
- Fix: Some UI strings can't be translated
- Fix: Restored Steam friends button for Classic theme
- Fix: Improved game name detection (primarily for ROM files)
- Fix: Game details panel doesn't scroll properly
- Fix: Filters stop working sometimes
- Fix: Crash While Downloading Metadata
- Fix: Crash when editing multiple games


SIGSUSPEND
Section: Linux Programmer's Manual (2)Updated: 2008-08-29
Index Return to Main Contents
NAME
sigsuspend - wait for a signalSYNOPSIS
#include <signal.h>int sigsuspend(const sigset_t *mask);
Feature Test Macro Requirements for glibc (see feature_test_macros(7)):
sigsuspend(): _POSIX_C_SOURCE >= 1 || _XOPEN_SOURCE || _POSIX_SOURCE
DESCRIPTION
sigsuspend() temporarily replaces the signal mask of the calling process with the mask given by mask and then suspends the process until delivery of a signal whose action is to invoke a signal handler or to terminate a process.If the signal terminates the process, then sigsuspend() does not return. If the signal is caught, then sigsuspend() returns after the signal handler returns, and the signal mask is restored to the state before the call to sigsuspend().
It is not possible to block SIGKILL or SIGSTOP; specifying these signals in mask, has no effect on the process's signal mask.
RETURN VALUE
sigsuspend() always returns -1, normally with the error EINTR.ERRORS
- EFAULT
- mask points to memory which is not a valid part of the process address space.
- EINTR
- The call was interrupted by a signal.
CONFORMING TO
POSIX.1-2001.NOTES
Normally, sigsuspend() is used in conjunction with sigprocmask(2) in order to prevent delivery of a signal during the execution of a critical code section. The caller first blocks the signals with sigprocmask(2). When the critical code has completed, the caller then waits for the signals by calling sigsuspend() with the signal mask that was returned by sigprocmask(2) (in the oldset argument).
See sigsetops(3) for details on manipulating signal sets.
SEE ALSO
kill(2), pause(2), sigaction(2), signal(2), sigprocmask(2), sigwaitinfo(2), sigsetops(3), sigwait(3), signal(7)COLOPHON
This page is part of release 3.22 of the Linux man-pages project. A description of the project, and information about reporting bugs, can be found at http://www.kernel.org/doc/man-pages/.
Index
This document was created by man2html, using the manual pages.
Time: 05:33:05 GMT, December 24, 2015


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Neuroplasticity refers to the brain's ability to change itself. It's beneficial to understand the principles of neuroplasticity to know that you have the capability to form new pathways within your brain by learning new behaviours and developing them. I can engage in the principles of neuroplasticity through repetition training and by learning about things that are meaningful to me. By doing this I know that my brain is adapting and changing to form new neurons which will benefit me by rewiring my brain with these new skills and forming connections through my learning journey. Throughout my experience I can increase my neuroplasticity by learning new skills, thinking about different things and experiencing different emotions. By doing this I will be forming new pathways throughout my brain and can develop these further by connecting them as I continue throughout my study. Explanation on how neuroplasticity works
Growth mindset is believing that your brain has the capability to learn and develop new skills. It's the idea that as long as you apply yourself that you can always learn more and further your intelligence. It is relevant to have the belief that if you keep at something and practice that you will eventually learn the skill and have an understanding of what you are learning. Something that surprised me during this learning was the concept of 'not yet' and how this isn't applied throughout high school learning. It really blew my mind as I feel that when a student fails at some point it's so easy for them to get discouraged and stop trying. This is the perfect way to ensure that everyone understands that applying yourself will eventually give you the results you strive for.  I will be integrating growth mindset into my learning journey by believing in the 'not yet' strategy. Knowing that on my journey as I learn all this new information that at anytime if I am confused by it or feel that I haven't understood it enough to put it into action that I simply haven't practiced or learnt enough to know what I am doing fully yet. The Power of Yet


Mama used a Choice eye of round roast. I remember that she cut little slits in the roast and inserted slivers of garlic. Then she smeared the top of the roast with a good mustard, seasoned it with salt and pepper and sprinkled it with freshly chopped parsley. Aunt Rosemary suggests cooking it in a 325° oven about 30 min. per lb. Place it in a roasting pan on a rack. Rosemary also suggests putting a little liquid in the roasting pan. Leslie makes a very good roast and she uses a meat thermometer . I think this is really safer than any mathematical formula. One of Mama’s secrets was that she could really carve!! She cooked the beef medium rare -NEVER more, and sliced it as thinly as possible.
For a 12 - 15 lb turkey:
Ingredients: 1 lb. good quality pork sausage, fried and drained
1 small stalk celery with leaves, sliced thinly
2 or 3 onions, chopped
1/2 lb. fresh mushrooms, sliced
1/2 cup parsley, minced
1 bag Pepperidge Farms stuffing mix* or the equivalent rye bread
1 egg
Water or turkey broth to moisten bread
*Rosemary uses the stuffing mix but I remember Mama soaking good rye bread and then crumbling it into the mixture. (Probably before the days of stuffing mix). In same pan, in which you have browned sausage, sauté the vegetables in 2 or 3 Tbsp. of the drippings. Mix the vegetables, sausage, bread and egg with sufficient liquid to moisten. Season to taste with salt, pepper and sage. Stuff turkey.
Optional: I recall Mama put all of her ingredients through an old-fashioned food grinder and her stuffing had the texture of a pate. You could get the same result by putting the ingredients in a food processor before stuffing the bird,
Ingredients: 5 lb. boneless beef rump roast (she used tenderizer on this)
1/3 cup flour
2 Tbsp. oil Accent, salt and pepper
3 Tbsp wine vinegar
Pinch celery seed
1/8 tsp. oregano
1 onion, sliced
2 cups whole small white onions
8 small carrots; pared
3 Tbsp. flour
1/2 cup water
1 Roll beef in 1/3 cup flour to coat on all sides. In Dutch Oven, brown over med. heat 15 - 20 min. Sprinkle with Accent, salt and pepper as you turn it. 2. Add celery seeds, oregano, vinegar and sliced onion. Cover tightly. Simmer turning occasionally 2 3/4 - 3 1/2 hrs 3. Add the vegetables, tucking them around the roast. Simmer until the beef is fork tender and vegetables are done. 4. Remove the meat and vegetables to a warm platter. Add sufficient water to Dutch Oven to make 2 1/2 cups liquid. Mix flour with 1/2 cup water and stir into liquid to thicken gravy.
Ingredients: 3 cups sifted all-purpose flour
3/4 tsp. ea. baking soda, baking powder, salt, mace, ground allspice, ground cardamom
1/4 tsp. ground black pepper
1/4 tsp. anise seeds
1 cup honey
3 Tbsp. shortening
1 egg
Re-sift flour with next 8 ingredients. Heat honey until hot but do not boil. Add shortening to honey, let cool. Beat in egg. Stir in dry ingredients, being careful not to over-mix. Let dough stand 10 min. to stiffen for easy handling. Shape into 1" balls. Place on lightly greased baking sheet. Bake 10 - 15 min. at 350°. Cool and frost.
Ingredients: for Frosting:
1 egg white, unbeaten
2 Tbsp. honey
1/4 tsp. ground anise seed
1 1/2 cups sifted confectioners sugar
Combine first 3 ingredients in mixing bowl. Gradually beat in confectioners sugar with electric mixer. Beat thoroughly. Place 12 - 14 cookies in a bowl. Add 2 Tbsp. frosting. Stir to coat all sides. Lift out with a fork onto wire rack. I.et frosting harden. Makes about 50 cookies. May be stored in a tin box or cookie jar.
Ingredients: 2 eggs, lightly beaten
4 Tbsp. honey
2 cups sugar
1 tsp. cinnamon
1/4 tsp. ginger
1/16 tsp. ea. pepper, cloves, nutmeg
4 Tbsp. cocoa
1/2 lemon, rind grated plus juice
3 1/4 cups sifted all-purpose flour
1/2 tsp. soda
1/4 tsp. cardamom
10 Tbsp. ground hazelnuts (may substitute)
Mix all ingredients thoroughly. Mixture will be very crumbly. Pat onto a cookie sheet with a spoon. Bake at 300° 15 - 20 min. Remove from oven and while still warm, cut into diamond shapes. Glaze with a mixture of 2 cups confectioners sugar and the juice of 1 lemon.
Ingredients: 3 eggs, beaten until light
3 cups light brown sugar
2 Tbsp. anise seeds
1 tsp. salt
1 tsp. baking soda dissolved in 2 Tbsp. hot water
4 1/2 cups flour
Add sugar to eggs, beating continuously until light. Add seeds, salt and soda. Mix in flour. Form hickory nut sized balls. Roll in granulated sugar and place on greased cookie sheet. Bake 10 min. in 350° oven. Allow to remain on cookie sheet until cool. These improve with age.
Ingredients: 1/4 lb. sweet butter
1 cup sugar
3 eggs
Grated rind of 1 lemon
2 cup sifted flour
1 tsp. baking powder
3/4 tsp. baking soda
1 cup sour cream *
1 cup raisins or currants, dredged in flour
1 tsp. vanilla
1/2 tsp. almond extract
Cream butter and sugar. Beat in eggs, one at a time. Sift flour with baking powder and soda; add alternately with sour cream. Add raisins and flavorings. Pour into a greased Bundt cake pan. Bake at 350° for 30 - 40 min. or until tester comes out clean. Let stand 10 min. on wire rack before removing from pan. If difficult to remove, place a damp towel over the pan. May be glazed with the same glaze as the lebkuchen.
*Mama omitted the amount of sour cream from the recipe she sent me so I’m guessing. The batter should be the consistency of a layer cake mix.
Ingredients: 1 recipe favorite pastry or 1/2 pkg. mix
2 1/4 cups sugar
4 Tbsp. cornstarch
4 Tbsp. flour
1/4 tsp. salt
2 cups water
4 eggs, separated
1 Tbsp. grated lemon rind
6 Tbsp. fresh lemon juice
2 Tbsp. butter
1/4 tsp. lemon extract
This is just the best lemon pie you’ll ever eat!
I’ll never forget the first time I tasted Pecan Pie. It was in 1941 on our first trip from Pennsylvania to Florida. I thought I’d died and gone to heaven. Jan Willis makes one that duplicates that experience.
Ingredients: 1 recipe pastry prepared as for Lemon Pie but left unbaked
3 eggs
1 cup Dark Karo syrup
1 cup pecan halves 1 tsp. vanilla
1 cup sugar
1/2 cup melted butter
1/4 tsp. salt
Beat eggs until light. Add sugar and beat again. Add butter, Karo, salt and vanilla. Mix well. Pour into unbaked pie shell and bake 45 min in preheated 350° oven. Custard should be set. Test with knife. It should come out clean.


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def | property |
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def | direction |
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def | clear_direction |
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def | has_direction |
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def | MergeFrom |
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def | Equals |
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def | IsInitialized |
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def | ByteSize |
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def | Clear |
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def | TryMerge |
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int | ASCENDING = 1 |
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int | DESCENDING = 2 |
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tuple | Direction_Name = classmethod(Direction_Name) |
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int | has_property_ = 0 |
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string | property_ = "" |
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int | has_direction_ = 0 |
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int | direction_ = 1 |
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The documentation for this class was generated from the following file:
- code/googleappengine-read-only/python/google/appengine/datastore/datastore_v3_pb.py


Dear Friend, Warmest greetings in the name of ALLAH,the most gracious. I pray that he may grant you the wisdom to understand the pathetic story i am about to tell and the courage to offer the much needed support i solicite for in this letter. I am a mother of two and the wife of late General YOUSSEF EL HADIDI of the defuct Republican Guard of ousted President Saddam Hussein . During the hay days of ex-president Saddam rulership in iraq my late husband was head of secret Army's procurement unit of the Republican Guard. His position and resposibility availed him with so much power and financial liquidity that he was able to amass substantial amount of money some of which he wisely move to secret custody out side iraq. Having forseen the caputilation of Saddam Hussein government my late husband quietly moved his family (my two kids & I) to Europe and tried to desert the Iraqi Army to join us in Europe, but was cought up with by the dangerosu secret security mechinary of Saddam. He was brutally murdered on the order of Saddam himself and all our properties and estate including bank account were consficated by the infamosu regime. Ever since my kids and i have lived in pennary until we uncovered through the help of his life -long solicitor some funds he left in a safe custody in London here. It is for the purpose of entrusting and investing this huge fund with little or no trace to our family that i send you this propostition. The funds in question is $35 Million (Thirty Five Million) and is presently deposited in a trust company awaiting directives from me as my husband next of kin to move it. It is true that i have never known you but i am desprately in need of somebody to trust, it has become a matter of expediency and total surrended to the will of God , the Almighty. If you do understand my story willing and capable of handling this trustteeship proposition, kindly oblige me by sending your personal and business profile including all contact details for us to move forward without delay. You can reach me direclt on my fax +447092041337. Awaiting your response, Yours truly, Jumai Hadidi Tel/fax:<PHONE_NUMBER>337