API¶
Contents
The API describes all functionalities implemented for this exercise. Of particular interest is the main
script, which can be used to validate an implementation (see testing for how to properly verify your implementation).
The modules lib.spiking_functions
and lib.spiking_layer
contain the missing functionalities that have to be implemented. There is a new theory question in week 2. Don’t forget to answer the theory question in the file theory_question.txt
and submit the completed .txt file with your code.
- Throughout, the following notation is used:
: The membrane potential.
: The post-synaptic current.
: The spiking activity.
Controller for simulations (main
)¶
The module main
is an executable script that controls the simulations
(i.e., the training and testing of MNIST digit classification tasks).
For more usage information, check out:
$ python3 main.py --help
|
Trains the given network on the MNIST dataset. |
|
Tests a trained network by computing the classification accuracy on the test set. |
|
Runs the script. |
-
main.
train
(args, device, x, y, net)[source]¶ Trains the given network on the MNIST dataset.
The
main.train
method takes data (x, y) and a spiking neural net, puts the net in training mode, and sets up the optimiser. Then, for each epoch, it runs through the whole MNIST dataset once, updating the weights once every mini-batch, after the images in this mini-batch have been converted to spike trains. Note, theFunction
lib.spiking_functions.loss_on_spikes()
is used to compute the loss.- Parameters
args (argparse.Namespace) – The command-line arguments.
device (torch.device) – The PyTorch device to be used.
x (torch.Tensor) – The training inputs.
y (torch.Tensor) – The training targets.
net (lib.snn.SNN) – The spiking neural network.
-
main.
test
(args, device, x, y, net)[source]¶ Tests a trained network by computing the classification accuracy on the test set.
- Parameters
(...) – See docstring of function
train()
.x (torch.Tensor) – The testing inputs.
y (torch.Tensor) – The testing targets.
- Returns
The classification accuracy for the test data (x, y) when using the network
net
. Note, theFunction
lib.spiking_functions.accuracy_on_spikes()
is used to compute the accuracy.- Return type
(float)
Implementation of a spiking multilayer perceptron (lib.snn
)¶
The module lib.snn
implements a fully-connected spiking neural network.
Internally, it will make use of Functions
implemented in module
lib.spiking_layer
to define the spiking dynamics of all layers of the
network.
|
Implementation of a fully-connected spiking neural network. |
Compute the outputs of the network. |
-
class
lib.snn.
SNN
(args, n_in=1, n_out=1, n_hidden=[10])[source]¶ Bases:
torch.nn.modules.module.Module
Implementation of a fully-connected spiking neural network.
The
SNN
is implemented as atorch.nn.Module
, which is a convenient object for building neural networks, sinceModules
can contain otherModules
.Modules
can be instantiated multiple times, as with multiple instances of the same type of layers. Submodules, (which are themselvesModules
) can easily be manipulated together as a wholeModule
. For example, theparameters
for aModule
includes all theparameters
attributes of its submodules which you can feed to the optimiser together.The
Module
built here is a spiking neural network, constructed from layers of spiking neurons defined by in thespiking_layer
script.-
spiking_layers
¶ A container for your spiking layers.
- Type
- Parameters
n_in (int) – Network input size.
n_out (int) – Network output size.
n_hidden (list) – Size of each hidden layer of the network. This argument implicitly defines the
depth
of the network.args (argparse.Namespace) – The command-line arguments.
-
forward
(x)[source]¶ Compute the outputs of the network.
- Parameters
x (torch.Tensor) – A tensor of shape , where is mini-batch size, is number of timesteps, and is the dimension of a flattened MNIST image (i.e. 784).
- Returns
Tuple containing:
- U_layers (list): A list of tensors of membrane potentials in
each layer(other than the input), each with shape , where is mini-batch size, is number of timesteps, and is the number of neurons in the layer.
- S_layers (list): A list of tensors of spiking activities in
each layer (other than the input), each with shape , where is mini-batch size, is number of timesteps, and is the number of neurons in the layer.
- Return type
(tuple)
-
Implementing, training, and evaluating a spiking neural network (lib.spiking_functions
)¶
The module lib.spiking_functions
contains custom functions that should
be used for running, training and evaluating spiking networks. Specifically,
you must implement the surrogate gradient for the spiking nonlinearity, as well
as the functions computing the loss and the accuracy on the spike trains of the
output neurons. You will also implement a function that calculates a
regularization loss term on the spiking activities.
New functionality can be added to
autograd by creating a subclass of class torch.autograd.Function
.
If you have pasted your answer to lib.spiking_functions.spike_function()
from last week, then the forward pass
torch.autograd.Function.forward()
is already implemented for you in
lib.spiking_functions.SurrogateSpike.forward()
.
As discussed in the lecture and in the exercise session, we will use the
derivative of the hard-sigmoid function for the surrogate gradient. The
code for the backward pass torch.autograd.Function.backward()
,
with normalisation and scaling, is already
implemented for you in lib.spiking_functions.SurrogateSpike.forward()
.
What is missing is the function that calculates the surrogate
gradient: lib.spiking_functions.derivative_hard_sigmoid()
. You must derive
and implement the gradient of the hard-sigmoid function.
You will also implement the loss function
lib.spiking_functions.loss_on_spikes()
, the accuracy function
lib.spiking_functions.accuracy_on_spikes()
and the function that
computes the regularization loss term
lib.spiking_functions.spike_regularizer()
.
The analytic derivative of the hard-sigmoid function. |
|
Spike non-linearity function. |
|
A class to house the functions for the forward and backward passes of a spiking nonlinearity. |
|
Computes the output of a spiking layer. |
|
Computes the surrogate gradient of the spiking layer. |
|
Computes cross entropy loss based on the spike trains of the output units. |
|
Computes classification accuracy of the spiking network based on the spike trains of the output units. |
|
Calculate the regularization loss term for the spiking activity. |
-
lib.spiking_functions.
derivative_hard_sigmoid
(x)[source]¶ The analytic derivative of the hard-sigmoid function.
This function implements the derivative of the hard-sigmoid function with respect to the input , , defined as
(1)¶
- Parameters
x (torch.tensor) – Input on which to compute the derivative.
- Returns
The derivative of the hard-sigmoid on x.
- Return type
(torch.tensor)
-
lib.spiking_functions.
spike_function
(D)[source]¶ Spike non-linearity function.
This function takes as input, which is the amount by which the membrane potential of neurons is above the membrane threshold . There are neurons in a layer and minibatch size is , hence .
This function computes the spiking nonlinearity, which should produce a spike when a neuron’s membrane potential exceeds or is equal to the membrane threshold potential i.e. when .
The spiking nonlinearity we use here is the simple Heaviside step function, , defined as
(2)¶
Last week, you coded the
spike_function()
method to take as input and compute elementwise for each entry in the matrix. This is the same function. You should paste the code you wrote last week for that function in this week’s function.- Parameters
D – A matrix of shape representing , the difference between the membrane potential of each of the neurons in each of the images of the mini-batch.
- Returns
The output spikes, obtained by applying (defined in eq. (2)) elementwise to D.
-
lib.spiking_functions.
loss_on_spikes
(S, T)[source]¶ Computes cross entropy loss based on the spike trains of the output units.
Takes a set of output spikes in form of a tensor , where denotes the size of the mini-batch, the number of timesteps during which each mini-batch is presented, and the number of output units. Additionally, it takes a set of target labels , indicating the true class of each image in the current mini-batch.
The loss calculated here is the cross-entropy loss applied to the average number of spikes over all timesteps for each output neuron for each image.
It is calculated as follows:
Let be the total number of spikes over all timesteps for each output neuron for each image , divided by the total number of timesteps. Then calculate the cross entropy loss:
You may wish to refer to the pytorch documentation for its native
torch.nn.CrossEntropyLoss
class and use it.- Parameters
S – The output spike trains, i.e., the matrix .
T – The target activations, i.e., the matrix .
- Returns
The cross entropy loss on the average of number of spikes.
- Return type
(float)
-
lib.spiking_functions.
accuracy_on_spikes
(S, T)[source]¶ Computes classification accuracy of the spiking network based on the spike trains of the output units.
Takes a set of output spikes in form of a matrix , where denotes the size of the mini-batch, the number of timesteps during which each mini-batch is presented, and the number of output units. Additionally, this
Function
requires a set of targets , indicating the correct classes of the current mini-batch.Using these two arguments, it finds the output neurons that have the highest membrane spiking rate for each image, and compares these with the target labels to compute the accuracy.
Letting be the total number of spikes over all timesteps for each output neuron for each image , divided by the total number of timesteps,
where is the indicator function.
- Parameters
S – The output spike trains, i.e., the matrix .
T – The target classes, i.e., the vector .
- Returns
The classification accuracy of the current batch.
- Return type
(float)
-
lib.spiking_functions.
spike_regularizer
(spikes_list)[source]¶ Calculate the regularization loss term for the spiking activity.
Biological neurons usually do not fire at rates greater than around 20Hz. In order to regularise our networks so that the neurons do not fire at grossly unphysiological rates, we calculate an additional term to add to the training loss.
Many different types of regularization could work. Here you should implement a regularization term that has two components:
The first component sums all the spikes over all hidden layers, batch elements, timesteps and neurons, i.e.
where is the number of neurons in the hidden layer . This corresponds to an L1 regularization on the total number of spikes at the population, thus inducing sparsity at a population level.
The second component regularizes the firing of individual neurons. It takes the mean squared sum of spikes for individual neurons (summed across batch elements and timestes), summed over all hidden layers, i.e.
This corresponds to an L2 norm in the firing rate of individual neurons, thus inducing neurons to have low firing rates.
Finally, the regularization loss term returned by this function consists of the sum of these two components.
Please note that the inputs to this function only correspond to hidden spiking activity. Therefore you do not have to exclude any layers within this function because it is already done for you.
-
class
lib.spiking_functions.
SurrogateSpike
[source]¶ Bases:
torch.autograd.function.Function
A class to house the functions for the forward and backward passes of a spiking nonlinearity.
Because this class is an instance of
torch.autograd.Function
, we are able to use all of PyTorch’s autograd functionality. It has two components: the forward function and the backward function.If you have pasted your answer to
lib.spiking_functions.spike_function()
from last week, then the forward passtorch.autograd.Function.forward()
is already implemented for you.For the backward pass, typically we take the partial derivative of the forward nonlinearity with respect to each element of the inputs that is tagged as requiring the gradient. However, the gradient of the step function (and many other spiking non-linearities) is zero everywhere except at 0 where it is ill-defined. Therefore it is necessary to use the gradient of a different function for the backward pass. This is the ‘surrogate gradient’.
Here we will take the derivative of the hard-sigmoid function with respect to its input as the surrogate gradient, as implemented in
lib.spiking_functions.derivative_hard_sigmoid()
.-
static
forward
(ctx, D)[source]¶ Computes the output of a spiking layer.
In the forward pass we compute a step function of the input Tensor and return it. This directly applies your
spike_function()
implemented already for last week’s tutorial.- Parameters
ctx – A context. Should be used to store activations that are needed in the backward pass. To achieve this we use the ctx.save_for_backward method.
D – A matrix of shape representing , the difference between the membrane potential of each of the neurons in each of the mini-batches.
- Returns
The output spikes, obtained by applying (defined in eq. (2)) elementwise to D.
-
static
backward
(ctx, grad_output)[source]¶ Computes the surrogate gradient of the spiking layer.
In the backward pass we receive a Tensor D we need to compute the surrogate gradient of the loss with respect to the input, which represents the difference between the membrane potential of the layer and the membrane threshold: . Here we use the partial derivative of the hard-sigmoid function with respect to all input tensors that are flagged to require gradients.
- Parameters
ctx – A context.
- Returns
- The gradient of the loss with respect to the
input.
- Return type
grad (torch.tensor)
-
static
A spiking layer module that maintains its own parameters (lib.spiking_layer
)¶
The module lib.spiking_layer
contains the implementation of a single
spiking layer. The goal is to utilize the custom
Functions
implemented in module lib.spiking_functions
and to provide a wrapper that takes care of managing the parameters
() of such a layer. The layers defined here will then be used in
lib.snn
to define a multi-layer spiking network.
In biological networks, the electrical activity from a pre-synaptic spike leads to changes in the membrane potential of a post-synaptic neuron. In nature, this is a process involving many ions and channels. Here we will use a simplified model: the leaky integrate-and-fire model for spiking neurons. Such models are composed of 1) a description of the dynamics of the membrane potential, and 2) a mechanism for triggering spikes.
In our implementation, the dynamics of the membrane potential, together with the dynamics of the current and spiking variables, are updated at each timestep based on a discrete implementation of a set of differential equations described below.
The ODEs that you will implement sequentially update the membrane potentials , the auxiliary variable for the alpha function , and the current . The equation for the membrane potential of neuron is:
(3)¶
Most commonly, post-synaptic currents resulting from spiking inputs from
pre-synaptic neurons are modeled as exponential decay functions, where a spike
causes an instantaneous increase in the post-synaptic membrane potential, which
then decays exponentially with time i.e. , assuming the
pre-synaptic spike occurs at , and a resting potential of 0.
A more biologically plausible model is an alpha-shaped post-synaptic current,
where the post-synaptic current following a pre-synaptic spike has a finite rise
time. In this case we would have . In this tutorial, we
ask you to implement alpha-shaped post-synaptic currents by filling the methods
lib.spiking_layer.update_H()
and lib.spiking_layer.update_I()
.
These are based on the following equations:
(4)¶
(5)¶
All equations were derived during the tutorial session, and can be found in the tutorial slides. Note, however, that only a discrete version for the case of exponential-shaped post-synaptic currents was derived, and not for the alpha-shaped case, which is the one you need here. Therefore you will need to figure out how to turn the ODEs into code appropriately.
|
Implements a single spiking layer. |
Updates the membrane potential. |
|
Updates the post-synaptic current. |
|
Updates the state of the auxiliary variable for alpha-shaped post-synaptic current. |
|
Computes the output activation of a spiking layer. |
-
lib.spiking_layer.
surrogate_spike_fn
()¶
-
class
lib.spiking_layer.
SpikingLayer
(in_features, out_features, args)[source]¶ Bases:
torch.nn.modules.module.Module
Implements a single spiking layer.
The
lib.SpikingLayer
class contains all the parameters and variables necessary to implement a single spiking layer. It will be a submodule of thetorch.nn.Module
instance namedlib.snn.SNN
. You will use the classlib.SpikingLayer
to define your hidden layer and your output layer.-
weights
¶ The weight matrix of the layer.
- Type
torch.nn.Parameter
-
compute_spikes
¶ Spike non-linearity function.
- Type
func
- Parameters
in_features (int) – Size of the pre-synaptic layer.
out_features (int) – Size of the current layer.
args (argparse.Namespace) – The command-line arguments.
-
update_U
(U, I, S)[source]¶ Updates the membrane potential.
Updates the membrane potential given the current and past states of the network as specified in eq. (3). As a discretized version of eq. (3) please use the following:
Note that the
lib.SpikingLayer
has an attribute called that corresponds to . For details on how to refer these derivations, please refer to theDiscretizedODEs.pdf
file in moodle.- Parameters
U – The membrane potential .
I – The post-synaptic current .
S – The spiking activity .
- Returns
The updated membrane potential of the neurons in the layer.
-
update_H
(H, inputs)[source]¶ Updates the state of the auxiliary variable for alpha-shaped post-synaptic current.
Implementation of eq. (4). Please note that the pre-synaptic inputs have already been multiplied by the weights at the beginning of the forward method. Therefore, you should only make sure you give the correct inputs argument to this function. As a discretized version of eq. (4) please use the following:
Note that the
lib.SpikingLayer
has an attribute called that corresponds to .- Parameters
H – The auxiliary variable for the alpha-shaped post-synaptic currents .
inputs – The inputs (weighted spikes) to the layer in the current time step.
- Returns
The updated auxiliary current variable for the neurons in the layer.
-
update_I
(I, H)[source]¶ Updates the post-synaptic current.
Updates the post-synaptic current given the current and past states of the network as specified in eq. (5). As a discretized version of eq. (5) please use the following:
Note that the
lib.SpikingLayer
has an attribute called that corresponds to .- Parameters
I – The post-synaptic current .
H – The auxiliary variable for the alpha-shaped post-synaptic currents .
- Returns
The updated post-synaptic current of the neurons in the layer.
-
forward
(X)[source]¶ Computes the output activation of a spiking layer.
This method computes the membrane potential and spiking activity of the current layer across all time steps given the pre-synaptic spiking activity. For this, the state of the layer is updated time step by time step; i.e. the post-synaptic current, membrane potential and spiking activity are computed in each time step. The states are updated based on the computational graph provided in Figure 2 in Neftci et al. (2019), and when filling in the missing lines you should pay extra attention and make sure that the values you provide to the update methods belong to the right time step according to this computational graph.
Note that since we deal with alpha-shaped post-synaptic currents here (and not exponential decay post-synaptic currents), the computational graph has an extra variable that is updated based on the inputs in the previous time step, and its own value in the previous time step. in a given time step is then used to compute the post-synaptic current in the following time step. Notice that for this extra equation, the
lib.SpikingLayer
class has an attribute that governs the decay rate of the variable . For further discussion seelib.spiking_layer
.- Parameters
X – The spiking activity of the previous layer.
- Returns
Tuple containing:
U: The membrane potential for all neurons of the layer in all time steps.
S: The spiking activity of all neurons of the layer in all time steps.
- Return type
(tuple)
-
A collection of helper functions (lib.utils
)¶
The module lib.utils
contains several general purpose utilities and
helper functions.
The functions utils.current2firing_time()
and
utils.sparse_data_generator()
are taken directly from Friedemann Zenke’s
Spytorch tutorial:
A collection of helper functions ( |
|
|
Converts MNIST pixel values to latency-coded spikes. |
|
A generator that takes mini-batches in analog format and transforms them to spike trains as sparse tensors. |
|
Plot histogram of the initial and trained weights in each layer. |
-
lib.utils.
current2firing_time
(x, tau=20, thr=0.2, tmax=1.0, epsilon=1e-07)[source]¶ Converts MNIST pixel values to latency-coded spikes.
Computes first firing time latency for a current input x assuming the charge time of a current based LIF neuron. Images to spikes using a spike latency code, i.e. the higher the input intensity, the earlier the first spike will be fired.
- Parameters
x (numpy.ndarray) – The “current” values for each pixel in each image. Shape: (samples, 784)
- Keyword Arguments
- Returns
- Time to first spike for each “current” x.
Shape: (samples, 784)
- Return type
T (numpy.ndarray)
-
lib.utils.
sparse_data_generator
(x, y, args, shuffle=True)[source]¶ A generator that takes mini-batches in analog format and transforms them to spike trains as sparse tensors.
- Parameters
x – The data ( sample x event x 2 ) the last dim holds (time,neuron) tuples, (samples, 28 x 28)
y – The labels (samples,)
args (argparse.Namespace) – The command-line arguments.
shuffle (boolean) – Whether batches should be shuffled.
- Yields
(tuple) –
Tuple containing:
X_batch: Spiking mini-batch.
y_batch: Target classes for the current mini-batch.
-
lib.utils.
load_MNIST
(path='data/')[source]¶ (Down)Loads data for a classification task on MNIST images.
The Torchvision library provides methods to load the MNIST dataset from a local directory if the data have been downloaded previously, or to download and load the data if they cannot be found locally.
The data are split into train and test sets and are preprocessed to convert the pixel values from [0,255] to [0,1]. Each 28 x 28 image is flattened to a 784 vector.
-
lib.utils.
plot_weight_hist
(parameters, initial_weights)[source]¶ Plot histogram of the initial and trained weights in each layer.
For each layer, a different subplot with overlapping weight distributions before and after training will be shown.
- Parameters
parameters – The set of weights after training.
initial_weights (list) – The set of initial weights.


User Guide
Before Using Snail Mail
Before using Snail Mail, you should have some addresses entered into your Mac OS X Contacts (application distributed with Mac OS X), otherwise you will only be able to manually enter addresses each time you want to print an envelope. Preferably, you should have one of the entries in Contacts marked as "My Card" (choose "Go to My Card" from Contacts's "Card" menu to see if it's set, or choose "Make This My Card" from Contacts's "Card" menu to set it for the selected entry). Snail Mail, by default, uses your card in Contacts for the return address.
Selecting a Return Address
The return address is automatically set to the first address of the "My Card" record in the Mac OS X Contacts, by default.
If you have more than one address entered in Contacts's "My Card" record, you can choose your return address from the address selection popup menu (next to the "Return Address:" popup menu), which automatically contains all of your "My Card" addresses. This selection will be remembered next time you use Snail Mail.
It is also possible to enter a return address manually (see "Manual Return Address" below) or to have no return address printed (see "No Return Address" below). To change back to having your Contacts automatically kept up to date with Contacts, choose "Contacts" from the "Return Address" popup menu.
Selecting a Delivery Address (ie Addressing the Envelope)
Select a delivery address (to whom the envelope is to be sent) by clicking on an address in the list of addresses (which contains all of the addresses from your Contacts)
OR
Type part of the name of the delivery address you want in the "Search" field, and the list of names and addresses automatically updates to show those which match the search text you type.
Printing Single Envelopes
To print immediately (no dialogue) just click the "Print" button in the toolbar. Snail Mail will attempt to print 1 copy of the envelope to the printer that was last used in Snail Mail (using the same print settings last used in Snail Mail).
OR
Press command-P for a standard print dialogue to choose another printer, enter number of copies, or change other print settings.
Refining the List of Displayed Addresses
Click on a group from the Groups list to display only the addresses from that group as defined in Contacts (to add, delete or modify groups, use Contacts application).
Enter text in the "Search" field to have the list of addresses automatically updated to show those who's names match the search text you type.
Printing Batches of Envelopes
To print a batch of envelopes select all of the persons or addresses to be printed (using SHIFT-click or COMMAND-click, or "Select All" from the "Edit" menu) and choose the "Print Selected Envelopes..." command from the "File" menu.
Depending on settings in preferences this will either print envelopes for all of the selected addresses (default), OR, print one envelope for each of the selected persons with an address matching the label specified in preferences (eg, "Home", "Work").
Manual Return Address
If you want to use a return address that is not automatically included from Contacts, you can override the automatic entry of the Return Address by selecting "Manual" from the "Return Address:" popup menu. When this option is selected, the text of the Return Address on the envelopes will no longer be updated automatically from information in Contacts database. Now click in the return address area in the envelope window, and enter the text (and/or images) for return address you want.
Turning on this setting also allows saving of images in the return address or no return address at all - see below.
Images in Return Address
Images can be pasted into the return address text on the envelope window. To have the images remembered between uses of Snail Mail, select "Manual" from the "Return Address:" popup menu. If more precise formatting of images is required, the entire return address can be saved as an image (using some other image processing application) and then copy/pasted or drag/dropped into Snail Mail's return address area on the Envelope window.
To include images without using manual return address, see "Background Text and Images" below.
No Return Address
If you do not want to have any return address on your envelopes, you can select "None" from the "Return Address" popup menu. The return address selection popup menu will then be disabled, but it's selection will still be respected for country hiding and barcode display.
Background Text and Images
The entire background of the envelope can have any arbitrary text added with the exception of the margins themselves (which are outside the printable area). To edit text in this area, simply click on the envelope window to bring it to the front, then click anywhere in the envelope window apart from the return address, recipient address, or margins areas (these areas are easier to see if you turn on margin guidelines in the format menu). Then type and edit your text in the normal way.
Graphics can also be added to this background area by copy/paste or drag/drop.
NB: Because this background area overlaps with the return address and recipient address areas, be careful not to add text or graphics that will interfere with the text in these areas. It may be necessary to press RETURN or TAB (or both) a few times to avoid typing visible text directly behind the return address, for example.
Tip: Using images in the envelope background allows company logos to be placed behind the return address area and be used in conjuction with an automatically placed address from Contacts. Because the left margin of the return address and the left margin of the background area are usually the same an extra step is necessary if you wish to have a graphic placed to the left of the return address (as is often the case for a company logo, for example). To do this, open the "Text Attributes" window using the toolbar button, or the command in the "Format" menu, then select all the text in the Return Address Fields area, and choose "Show Ruler" from the "Format" menu. Using this ruler, drag the left line starting markers to the right enough to make room for the desired graphic, then click "OK".
Envelope Profiles and Page Setup
An envelope profile is simply a set of standard "Page Setup" settings and Snail Mail "Margins" settings. Snail Mail is initially configured with one default envelope profile (#10 envelope with appropriate margins) called "Default".
Add, Delete or Rename Envelope Profile
Choose "Edit Profiles..." from the "Envelope Setup" poup menu in the Snail Mail window. This will display the "Envelope Profiles" window. From here you can click "New" to add a profile, "Delete" to delete the selected profile or double-click a profile name to change its name.
Note: You cannot delete the last remaining profile (there must always be at least 1 profile), and you cannot have 2 profiles with the same name.
Edit an Envelope Profile's Envelope Setup (Page Setup and Margins)
To change the settings for an Envelope Profile, choose the profile from the "Envelope Setup" popup menu, then use the "Page Setup..." command in the "File" menu to change paper size, orientation, etc, and use the "Margins..." command in the "Format" menu to change margins settings (see "Margins" below).
Margins
Use the "Margins..." command in the "Format" menu to change the margin settings for both the return address text area and the delivery address text area on the envelope.
There is also a "Margin Guidelines" command in the "Format" menu which toggles grey lines which can help you in seeing exactly where the margins are on the envelope window (these lines can be left on, if desired, without being printed).
Default Text Attributes
Snail Mail can remember the text attributes (eg, font, colour, size, style) that you prefer for both delivery address and return address. You can set different attributes for different fields (ie, The name can be larger and/or in a different font or colour to the rest of the address).
To set the default text attributes, click the "Text Attributes" button in the toolbar, or choose the "Address Text Attributes" command from the "Format" menu, then in the window that appears, select the field(s) for which you wish to change attributes, then use the standard "Font" panel (which is also on screen) to change the attributes for the selected field(s).
Inter-Application Features
System Services
The following 3 Snail Mail services are available to other applications. Please note that Snail Mail must be installed in a standard Applications location (eg /Applications) for these to work. Additionally, these services will not be available until after the next login after installation. You may also need to enable them in System Services->Keyboard->Services->Text.
Selected Text to Snail Mail Envelope - Places the selected text from the current application into the delivery address text area in the Snail Mail envelope window.
Snail Mail Envelope Text to Current Document - Places the text from the Snail Mail Envelope window's delivery address text area into the current application's front-most window, at the insertion point (replacing any selected text).
Selected Text to Snail Mail Print - Places the selected text from the current application into the delivery address text area in the Snail Mail envelope window, and invokes Snail Mail's print command. ie, address and print an envelope using any text from any Services-Compliant application.
Other Inter-Application Features
Many Mac OS X Applications were not services-compliant (ie, all Carbon applications, as of OS X 10.2.x). Therefore to supply similar functionality to these applications there are "Copy Address" and "Paste Address" toolbar buttons in Snail Mail's main window to copy/paste text quickly between the envelope window's delivery address text area and the system clipboard (where it can be copied/pasted into any other application). This is a little faster than selecting the text in the envelope window and manually copying and pasting text using the Edit menu.
Preferences
Using the "Preferences..." command in the "Snail Mail" menu, the behaviour of Snail Mail can be altered as described for each setting below. All of these settings are off by default.
Hiding Fields
Select the checkbox(es) for any "Hide..." field(s) to avoid having those fields displayed in addresses.
Enter text in the prefix or suffix field(s) to have that text printed before/after the address(es) on the envelope. For example, a useful return address prefix might be, "If undeliverable, return to:"; or a useful addresse prefix might be, "Attn: Accounting Dept".
Local Country Variants:
Some countries can be referred to by several different names (eg, "United Kingdom", "UK", "U.K."), so in order for Snail Mail to be able to tell that these different names all refer to the same country as the return address, enter the names in this field (each on a separate line). Snail Mail will treat each of these as though it was the same as the return address country, for the purposes of country hiding and barcoding.
Return Address Popup Includes:
By default, the Return Address popup menu in the main Snail Mail window contains all addresses in the special "My Card" record in Contacts (if it exists). If you want to be able to choose return addresses from some other Contacts record, choose either "All Addresses", to have every address listed in the Return Address popup, or choose a group (as defined in Contacts) to show every address from that group.
Use Barcodes:
Choose a barcode scheme to have those type of barcodes displayed/printed on your envelopes, or choose "(None)" to stop using barcodes. NB: Barcodes will only be printed if Snail Mail thinks the return address and delivery address are for the same country. You can add other countries for which barcodes should always be printed in the "Local Country Variants" (see above).
Print Selected Envelopes
By default, the "Print Selected Envelopes" command ("File" menu) prints one envelope for each selected address, however, if some persons records in Contacts have several addresses (eg, one for work and one for home), you may want to automatically choose home addresses only, or work addresses only.
To only print addresses that match a particular label (eg, "Home", "Work") enter the label into the "Only Print Label" field in the Preferences window. Using this setting, one envelope is printed for each selected person who has a matching address (the address actually selected in the main window is ignored, only the selected person is used).
If some records do not have an address that matches the label you've entered (eg, "Home", "Work"), you can select the "Print Other Address if No Matching Label" checkbox to print an envelope for another address (if the record has another address). This way, you can be certain to print an envelope for every person with an address, even if some of the person records do not have an address that matches the label you've entered.
Editing Delivery Addresses
You can make one-off changes by editing the delivery address text directly in the envelope window, but such changes will not be saved in Contacts.
To make permanent changes to an address, double-click the address in the address list, and Contacts will open that record in edit mode. To add or remove addresses, click on Snail Mail's "Contacts" button to open Contacts in browse mode. (see Contacts documentation for more information on editing, adding and deleting addresses).


Blackgate is a dead simple reverse proxy for microservices. You are currently looking at the documentation of the development version.


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title=Rest in Peace Ravi Shankar date=2012-12-12 type=post tags=blog status=published ~~~~~~
|
I'm in India (Bangalore and Hyderabad) and the news today is of the passing of Ravi Shankar, the sitar virtuoso who's music influenced the Beatles in the 60s and changed the genre in some ways. The first introduction to him that I had was through the music of his daughters on Concert for George (Harrison), a tribute concert to honor the late Beatle. From there, I explored his other music and in particular his influence on the Beatles and really have a lot of respect for the man. He was an amazingly talented musician, and all walks of music owe him a debt of gratitude. RIP Ravi Shankar. |


THE RUSSIA HOUSE A film review by Frank Maloney Copyright 1990 Frank Maloney
THE RUSSIA HOUSE (I may slip and call this the Russian House somewhere in the course of this notice--I once lived in a Russian-language-only institute call Russian House and the habit is ingrained, I fear) stars Sean Connery and Michelle Pfeiffer with Klaus Maria Brandauer and Roy Scheider inter alia; it is based on a novel by John LeCarre of the same name.
This is touted as LeCarre's first post-Cold War suspense novel-cum-movie, and so it may be; but alas Cold War (pre-, post-, or amid) thriller have never been my preferred reading genre and I have not had the pleasure of any of LeCarre's efforts. However, I have seen movies based on his works, THE SPY WHO CAME IN FROM THE COLD staying especially in the memory. And my memory is that as stories they were pretty well muddled affairs and my impression has been that they did not translate the novels into film well.
I think it is possible LeCarre and his readers have been better served in THE RUSSIA HOUSE. The screenplay was written by Tom Stoppard, a very respectable writer in his own right. Certainly, I was left with none of the "well, somebody did something to somebody, that much is clear at any rate" (as Alice said after hearing the "Jabberwocky") feeling I've felt in the past in the face of this cloak-and-dagger tomfoolery.
Here, instead, I have a pretty straightforward kind of story, with a beginning, a middle, and an end. This is not to say it doesn't have its surprises, twists, turns, and suspense. It's just that here the theme is crystal clear throughout: loyalty. People's loyalties are tested, defined, and put the test. The final loyalty, the one the film endorses ultimately, is typical of our post-CW sensibility, and I for one am glad to see it.
As to the performances, Connery, Pfeiffer, and company do good solid, yeomanlike work. I don't think anybody really sails here, but no one does a pratfall either. In some ways, Connery's performance could be the most suspect insofar as he turns one of his standard curmudgeons-with-a-heart-of-gold interpretations; but the man has such presence, such command of the screen, that it is hard not to just bathe in it gratefully even though I did not find him nearly as disreputable in the beginning as he should have been, nor his transformation nearly so graduated as it could have been.
Pfeiffer in so ways takes more chances with her role as Katya, the go-between, the Russian woman who loves both Brandauer the great scientist and Connery the sodden British publisher. For one thing, she does a pretty creditable Russian accent and speaks a little Russian here and there in a not embarrassing way as near as this former student of Russian can detect.
Among the supporting players, Brandauer and Scheider are the most familiar to me. For those of us who have seen COLONEL REDL, all other Brandauer performances seem footnotes; but I can't think of film that hasn't been better for his presence (especially the worse ones, like OUT OF AFRICA). I was just wondering to myself the other day what Roy Scheider was doing these days. Here he plays the CIA bigshot and does a good job of being alternately (or simultaneously) a jerk and a gentleman. James Fox plays his British counterpart (at Russia House) and does a fine turn. His real purpose appears to be slimy in just such a civil way as puts the upstart Yanks in their place.
One of the real draws of THE RUSSIA HOUSE is the wonderful location photography, especially that in Moscow and Leningrad. This must be the first Western thriller that was shot in the Soviet Union instead of using Helsinki as a stand-in (see GORKY PARK, for example). Fascinating, revealing and gorgeous, used to great effect throughout. Fred Schepisi, the director, and the production people, especially the director of photography, Ian Baker, ought to be in line for some appropriate recognition in this area.
I have no problem in recommending THE RUSSIAN HOUSE to anyone who doesn't mind quite a lot of rough language. There's no nudity, no open sex, no graphic violence, but oh the language.
-- Frank Richard Aloysius Jude Maloney
.
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.
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Apurva Gupta
Kickass Kung Fu Panda Style Designer
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 When a person goes to a web
page like www.google.com, their computer sends
a
HTTP Request to a
server. The
server finds the appropriate HTML document and sends
it back to the user's computer where a
web browser
interprets the page and displays it on the user's screen.
This videodoes a good job of explaining.
HTML stands for
Hypertext Markup Language. HTML documents form the majority of the
content on the web. HTML documents contain
text content which describes "what you see"
and
markup which describes "how it looks".
This video gives a good overview.
HTML documents are made of HTML
elements. When writing HTML, we tell browsers the type of each element by using HTML
tags.
This videoexplains the distinction well.


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Convert a Video to a GIF entirely in your browser.
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@@include('./analytics-code.html') @@include('./gtm-code.html')
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Exercise 5 :
1) Create a new sketch and select the XY-plane as the sketch plane
2) Firstly, I use a two point rectangle with dimensions "40mm" by"130mm
3) Next, I go to the "Create" tab and selected arc > 3point-arc to draw
4) Afterwards, I go the "Create" tab once again to select slot > center to center slot with distance "40mm" and diameter "20mm"
5) Once done, click on the "Extrude" command and change the extrude distance to "10mm"
6) Create a second sketch on the top face of the object
7) Use a "line" command and draw the sketch with dimensions "10mm" by "50mm"
8) Once done, click on the "Extrude" command and change the extrude distance to "30mm"
9) Create a third sketch on the front face of the object and using the "circle" command to draw diameter "30mm" and "50mm"
10) Once done, click on the "Extrude" command and change the extrude distance to "-10mm"
11) Create the final sketch on the front face of the object and draw a "30mm" diameter circle in the center
12) Once done, click on the "Extrude" command and change the extrude distance to "-10mm"
13) Afterwards, do the same for the other side and we are DONE modelling Exercise 5


ANDRE A film review by James Berardinelli Copyright 1994 James Berardinelli
Rating (0 to 10): 6.3
Date Released: 8/17/94 Running Length: 1:36 Rated: PG (Mature themes)
Starring: Keith Carradine, Tina Majorino, Chelsea Field, Keith Szarabajka Director: George Miller Producers: Annette Handley and Adam Shapiro Screenplay: Dana Baratta based upon A SEAL CALLED ANDRE by Harry Goodridge and Lew Dietz Cinematography: Thomas Burstyn Music: Bruce Rowland Released by Paramount Pictures
Dogs have always been man's best friend and, as such, it's only fitting that a sizable number of movies have had their foundation laid in this popular human/pet relationship. How else is it possible to explain the countless incarnations of Lassie and Benji? Every once in a while, however, filmmakers decide to throw a bone to the other creatures of this planet--cats (one of three stories in HOMEWARD BOUND), horses (BLACK BEAUTY), whales (FREE WILLY), and now--with the arrival of ANDRE--seals.
Really, Andre isn't much more than Lassie with flippers. He does a lot of the same cute antics and, of course, is ready to come to the rescue when his foolish human friends need help. Some might argue that there's a little more to this film than that, but all of the peripheral stuff is only so much excess baggage. It's developed and presented with little flair and a high degree of obviousness. Those seeking hard- hitting drama had best look elsewhere. The closest ANDRE gets to that sort of thing is a couple of smarmy scenes that preach the need for tolerance.
The film opens, as we're told by narrator Annette O'Toole, in Rockport, Maine during the spring of 1962.  Much to the delight of his daughter Toni (Tina Majorino), he brings the pup home to care for it, braving the ire of local fisherman Billy Baker (Keith Szarabajka), who blames seals for the season's poor catches. The return to health is a slow process, but eventually the seal--named Andre by Toni--is ready to return to its natural habitat. When that day arrives, the nine year old girl is heartbroken--but Andre still has a surprise or two in store for her.
Considering ANDRE as light family entertainment is the only way to form a satisfying opinion of the movie. On a strictly clinical level, it really isn't very good--the cliche-riddled plot is hopelessly predictable, the characters are broadly realized, and the acting is merely serviceable. However, this, much like FREE WILLY, is the kind of movie that kids enjoy. It moves with enough snap not to threaten boredom, and there aren't many deep meanings for children to furrow their brows over. The villains are suitably nasty, the heroes are nicely heroic and, of course, the seal is cute and funny. Also, ANDRE has a couple of none-too-subtle messages that everyone will understand.
Tina Majorino is a busy young actress--this is her third film of the year (in addition to WHEN A MAN LOVES A WOMAN and CORRINA, CORRINA). She's an appealing performer who shows promise, but she still has some rough edges to file down. When she smiles, she lights up the screen, but her expressions of sadness and grief are less overwhelming. Nevertheless, she's considerably more credible than many actors of her age.
Those with children can feel confident that ANDRE will present an enjoyable ninety-six minutes for their offspring. There's a lot of saccharine here--perhaps too much for the "intellectual" movie-goer. ANDRE will appeal most strongly to the under-thirteen crowd, or at least to those who are of that age in heart, if not in body.
- James Berardinelli<EMAIL_ADDRESS>.
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


runSimulation wraps around setModel, the phenotype component functions (genFixedEffects, genBgEffects, noiseBgEffects, noiseFixedEffects and correlatedBgEffects), rescales each component and combines them into the final phenotype. For details to all parameters, see the respective functions.
runSimulation(N, P, genVar = NULL, h2s = NULL, theta = 0.8, h2bg = NULL, eta = 0.8, noiseVar = NULL, rho = NULL, delta = NULL, gamma = 0.8, phi = NULL, alpha = 0.8, tNrSNP = 5000, cNrSNP = 20, SNPfrequencies = c(0.1, 0.2, 0.4), genotypefile = NULL, format = "delim", genoFilePrefix = NULL, genoFileSuffix = NULL, genoDelimiter = ",", skipFields = NULL, header = FALSE, probabilities = FALSE, chr = NULL, NrSNPsOnChromosome = NULL, NrChrCausal = NULL, kinshipfile = NULL, kinshipHeader = FALSE, kinshipDelimiter = ",", standardise = TRUE, distBetaGenetic = "norm", mBetaGenetic = 0, sdBetaGenetic = 1, pTraitsAffectedGenetics = 1, pIndependentGenetic = 0.4, pTraitIndependentGenetic = 0.2, keepSameIndependentSNPs = FALSE, NrFixedEffects = 1, NrConfounders = 10, distConfounders = "norm", mConfounders = 0, sdConfounders = 1, catConfounders = NULL, probConfounders = NULL, distBetaConfounders = "norm", mBetaConfounders = 0, sdBetaConfounders = 1, pTraitsAffectedConfounders = 1, pIndependentConfounders = 0.4, pTraitIndependentConfounders = 0.2, keepSameIndependentConfounders = FALSE, pcorr = 0.8, corrmatfile = NULL, meanNoiseBg = 0, sdNoiseBg = 1, nonlinear = NULL, logbase = 10, expbase = NULL, power = NULL, customTransform = NULL, transformNeg = "abs", proportionNonlinear = 0, sampleID = "ID_", phenoID = "Trait_", snpID = "SNP_", seed = 219453, verbose = FALSE)
| N | Number [integer] of samples to simulate. |
|---|---|
| P | Number [integer] of phenotypes to simulate. |
| genVar | Proportion [double] of total genetic variance. |
| h2s | Proportion [double] of genetic variance of genetic variant effects. |
| theta | Proportion [double] of variance of shared genetic variant effects. |
| h2bg | Proportion [double] of genetic variance of infinitesimal genetic effects; either h2s or h2bg have to be specified and h2s + h2bg = 1. |
| eta | Proportion [double] of variance of shared infinitesimal genetic effects. |
| noiseVar | Proportion [double] of total noise variance. |
| rho | Proportion [double] of noise variance of correlated effects; sum of rho, delta and phi has to be equal 1. |
| delta | Proportion [double] of noise variance of non-genetic covariate effects; sum of rho, delta and phi has to be equal 1. |
| gamma | Proportion [double] of variance of shared non-genetic covariate effects. |
| phi | Proportion [double] of noise variance of observational noise effects; sum of rho, delta and phi has to be equal 1. |
| alpha | Variance [double] of shared observational noise effect. |
| tNrSNP | Total number [integer] of SNPs to simulate; these SNPs are used for kinship estimation. |
| cNrSNP | Number [integer] of causal SNPs; used as genetic variant effects. |
| SNPfrequencies | Vector of allele frequencies [double] from which to sample. |
| genotypefile | Needed when reading external genotypes (into memory), path/to/genotype file [string] in format specified by format. |
| format | Needed when reading external genotypes, specifies the format of the genotype data; has to be one of plink, oxgen, genome, bimbam and delim when reading files into memory, or one of oxgen, bimbam or delim if sampling genetic variants from file; for details see readStandardGenotypes and getCausalSNPs. |
| genoFilePrefix | Needed when sampling cuasal SNPs from file, full path/to/chromosome-wise-genotype-file-ending-before-"chrChromosomeNumber" (no '~' expansion!) [string] |
| genoFileSuffix | Needed when sampling causal SNPs from file, following chromosome number including fileformat (e.g. ".csv") [string] |
| genoDelimiter | Field separator [string] of genotypefile or genoFile if format == delim. |
| skipFields | Number [integer] of fields (columns) in to skip in genoFilePrefix-genoFileSuffix-file. See details in getCausalSNPs if format == delim. |
| header | [logical] Can be set to indicate if genoFilePrefix-genoFileSuffix file has a header for format == 'delim'. See details in getCausalSNPs. |
| probabilities | [bool]. If set to TRUE, the genotypes in the files described by genoFilePrefix and genoFileSuffix are provided as triplets of probablities (p(AA), p(Aa), p(aa)) and are converted into their expected genotype frequencies by 0*p(AA) + p(Aa) + 2p(aa) via probGen2expGen. |
| chr | Numeric vector of chromosomes [integer] to chose NrCausalSNPs from; only used when external genotype data is sampled i.e. !is.null(genoFilePrefix) |
| NrSNPsOnChromosome | Specifies the number of SNPs [integer] per entry in chr (see above); has to be the same length as chr. If not provided, lines in genoFilePrefix-genoFileSuffix file will be counted (which can be slow for large files). |
| NrChrCausal | Number [integer] of causal chromosomes to chose NrCausalSNPs from (as opposed to the actual chromosomes to chose from via chr ); only used when external genotype data is sampled i.e. !is.null(genoFilePrefix). |
| kinshipfile | path/to/kinshipfile [string]; if provided, kinship for simulation of genetic backgound effect will be read from file. |
| kinshipHeader | [boolean] If TRUE kinship file has header information. |
| kinshipDelimiter | Field separator [string] of kinship file. |
| standardise | [boolean] If TRUE genotypes will be standardised for kinship estimation (recommended). |
| distBetaGenetic | Name [string] of distribution to use to simulate effect sizes of genetic variants; one of "unif" or "norm". |
| mBetaGenetic | Mean/midpoint [double] of normal/uniform distribution for effect sizes of genetic variants. |
| sdBetaGenetic | Standard deviation/extension from midpoint [double] of normal/uniform distribution for effect sizes of genetic variants. |
| pTraitsAffectedGenetics | Proportion [double] of traits affected by the genetic variant effect. For non-integer results of pTraitsAffected*P, the ceiling of the result is used. Allows to simulate for instance different levels of pleiotropy. |
| pIndependentGenetic | Proportion [double] of genetic variant effects to have a trait-independent fixed effect. |
| pTraitIndependentGenetic | Proportion [double] of traits influenced by independent genetic variant effects. |
| keepSameIndependentSNPs | [boolean] If set to TRUE, the independent SNPs effects always influence the same subset of traits. |
| NrFixedEffects | Number [integer] of different non-genetic covariate effects to simulate; allows to simulate non-genetic covariate effects from different distributions or with different parameters. |
| NrConfounders | Number [integer] of non-genetic covariates; used as non-genetic covariate effects. |
| distConfounders | Vector of name(s) [string] of distributions to use to simulate confounders; one of "unif", "norm", "bin", "cat_norm", "cat_unif". |
| mConfounders | Vector of mean(s)/midpoint(s) [double] of normal/uniform distribution for confounders. |
| sdConfounders | Vector of standard deviation(s)/extension from midpoint(s) [double] of normal/uniform distribution for confounders. |
| catConfounders | Vector of confounder categories [factor]; required if distConfounders "cat_norm" or "cat_unif". |
| probConfounders | Vector of probability(ies) [double] of binomial confounders (0/1); required if distConfounders "bin". |
| distBetaConfounders | Vector of name(s) [string] of distribution to use to simulate effect sizes of confounders; one of "unif" or "norm". |
| mBetaConfounders | Vector of mean(s)/midpoint(s) [double] of normal/uniform distribution for effect sizes of confounders. |
| sdBetaConfounders | Vector of standard deviation(s)/extension from midpoint(s) [double] of normal/uniform distribution for effect sizes of confounders. |
| pTraitsAffectedConfounders | Proportion(s) [double] of traits affected by the non-genetic covariates. For non-integer results of pTraitsAffected*P, the ceiling of the result is used. |
| pIndependentConfounders | Vector of proportion(s) [double] of non-genetic covariate effects to have a trait-independent effect. |
| pTraitIndependentConfounders | Vector of proportion(s) [double] of traits influenced by independent non-genetic covariate effects. |
| keepSameIndependentConfounders | [boolean] If set to TRUE, the independent confounder effects always influence the same subset of traits. |
| pcorr | Correlation [double] between phenotypes. |
| corrmatfile | path/to/corrmatfile.csv [string] with comma-separated [P x P] numeric [double] correlation matrix; if provided, correlation matrix for simulation of correlated backgound effect will be read from file; file should NOT contain an index or header column. |
| meanNoiseBg | Mean [double] of the normal distributions for the simulation observational noise effects. |
| sdNoiseBg | Standard deviation [double] of the normal distributions for the simulations of the observational noise effects. |
| nonlinear | nonlinear transformation method [string]; one exp (exponential), log (logarithm), poly (polynomial), sqrt (squareroot) or custom (user-supplied function); if log or exp, base can be specified; if poly, power can be specified; if custom, a custom function (see for details). Non-linear transformation is optional, default is NULL ie no transformation (see details). |
| logbase | [int] base of logarithm for non-linear phenotype transformation (see details). |
| expbase | [int] base of exponential function for non-linear phenotype transformation (see details). |
| power | [double] power of polynomial function for non-linear phenotype transformation. |
| customTransform | [function] custom transformation function accepting a single argument. |
| transformNeg | [string] transformation method for negative values in non linear phenotype transformation. One of abs (absolute value) or set0 (set all negative values to zero). If nonlinear==log and transformNeg==set0, negative values set to 1e-5 |
| proportionNonlinear | [double] proportion of the phenotype to be non- linear (see details) |
| sampleID | Prefix [string] for naming samples (will be followed by sample number from 1 to N when constructing sample IDs); only used if genotypes/kinship are simulated/do not have sample IDs. |
| phenoID | Prefix [string] for naming traits (will be followed by phenotypes number from 1 to P when constructing phenotype IDs). |
| snpID | Prefix [string] for naming SNPs (will be followed by SNP number from 1 to NrSNP when constructing SNP IDs). |
| seed | Seed [integer] to initiate random number generation. |
| verbose | [boolean]; If TRUE, progress info is printed to standard out |
Named list of i) dataframe of proportion of variance explained for each component (varComponents), ii) a named list with the final simulated phenotype components (phenoComponentsFinal), iii) a named list with the intermediate simulated phenotype components (phenoComponentsIntermediate), iv) a named list of parameters describing the model setup (setup) and v) a named list of raw components (rawComponents) used for genetic effect simulation (genotypes and/or kinship, eigenvalues and eigenvectors of kinship)
Phenotypes are modeled under a linear additive model where Y = WA + BX + G + C + Phi, with WA the non-genetic covariates, BX the genetic variant effects, G the infinitesimal genetic effects, C the correlated background effects and the Phi the observational noise. For more information on these components look at the respective function descriptions (see also) Optionally the phenotypes can be non-linearly transformed via: Y_trans = (1-alpha) x Y + alpha x f(Y). Alpha is the proportion of non- linearity of the phenotype and f is a non-linear transformation, and one of exp, log or sqrt.
setModel, geneticFixedEffects, geneticBgEffects, noiseBgEffects, noiseFixedEffects, correlatedBgEffects and rescaleVariance.
# simulate phenotype of 100 samples, 10 traits from genetic and noise # background effects, with variance explained of 0.2 and 0.8 respectively genVar = 0.2 simulatedPhenotype <- runSimulation(N=100, P=5, cNrSNP=10, genVar=genVar, h2s=1, phi=1)#> Warning: The genetic model does not contain infinitesimal genetic effects but the total number of SNPs to simulate (tNrSNP: 5000 ) is larger than the number of genetic #> variant effects SNPs (cNrSNP: 10 ). If genotypes are not needed, consider setting #> tNrSNPs=cNrSNPs to speed up computation


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What did we learn from first week of UK election campaign?
by John Paul GingThe first full week of the UK's General Election campaign is drawing to a close - and it proved to be an eventful one.
There was a potentially game-changing development when Brexit Party leader Nigel Farage stood down hundreds of Brexit Party candidates in Conservative-held seats.
But Prime Minister Boris Johnson faced accusations of not doing enough to help flood-hit - and perhaps electorally crucial - areas.
And, despite doctors' pleas, the National Health Service was subject to claim and counter-claim by the main parties.
Watch the full report in the player above
Journalist name • John Paul Ging
Video editor • John Paul Ging


Wilderness Encounter Generator - Stars Without Number Toolbox: Revised Edition
Click here
for more, or
click here
to return to the toolbox.
Weather:
Nature:
Friendlies:
Range:
Hostiles:
Features:


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Lorem ipsum dolor sit amet, consectetur adipisicing elit. Sunt ut voluptatum eius sapiente, totam reiciendis temporibus qui quibusdam, recusandae sit vero unde, sed, incidunt et ea quo dolore laudantium consectetur!
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Sunt ut voluptatum eius sapiente, totam reiciendis temporibus qui quibusdam, recusandae sit vero unde, sed, incidunt et ea quo dolore laudantium consectetur!
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Sunt ut voluptatum eius sapiente, totam reiciendis temporibus qui quibusdam, recusandae sit vero unde, sed, incidunt et ea quo dolore laudantium consectetur!
In 2004 my brother—stage name “the Latino Saint,” or “Saint” for short— released a crunk rap album entitled, Half Breed. As far back as I can remember, his music has pretty much always been about partying and taking pride in Mexican and Latino identities; this album is no exception. Half Breed takes its name from the mixed Mexican and white backgrounds from which we both come. As with most crunk music, much of the content is centered around alcohol, dancing and celebrating life with friends and family. Yet, in spite of the negative connotation of the term “half breed” itself, Saint delivers these club songs from an unapologetically Mexican perspective, reclaiming both the term and the concept of “half breed” and using it to assert his own position as a Mexican MC from the Midwest.
Furthermore, Forbes’ argument also points out an inherent irony (read: prejudice) in the concept of mestizaje, which is that to say you are mestizo is to say nothing at all, because all cultures are always already mixing with one another. As he explicates, “[p]eoples categorized as ’half-caste’ or ‘mestizo’ tend to…reside in areas subjected to recent European colonialism…” and “they seldom possess the power or resources to determine their own destiny” (179). If all cultures are always under a process of transformation and mixing in response to constantly changing set of circumstances, yet colonized peoples are primarily the only populations in the world deemed to be “mestizo,” it would follow that there is something inherently wrong, less, or incomplete about those colonized people—from the perspectives of colonizers.
While my brother uses the colonizer’s terminology to name his album, Half Breed, I think his artistic approach is actually in line with Forbes’ activist and scholarly position on mestizaje. Saint avoids employing a perspective that is half of, mixed with, or less than what might be considered a “full bred” Mexican. He doesn’t use words like “Indigenous” or “indio” to describe himself in the lyrics, but there is also no lyrical indication that he is interested in denying these roots or in claiming Spanish heritage. This is different from many academic examples and arguments about mestizaje in which there is there is an obsession to either name something as Indigenous or non-Indigenous. Saint is not as concerned with this label as he is presenting his Mexicanness in a positive way—Forbes notes that before 1821, “Mexican” was used to refer to Nahua speaking people, primarily the Aztec or the Mexica.
When he uses the term “half-breed” he is not referring to his mixed indio and Spanish bloodline, but to his lived experience with a white mother and a Mexican father. For this reason, I do believe it is possible to utilize mestizaje in a way the affirms Indigeneity, because for many mixed people, we do have a concrete reality in which being a part of two distinct culture is an everyday reality. From an early age, this was the conflict me and my siblings were tasked with: navigating the reality of our actual mixed-race experience at home—a white side of the family and a Mexican side of the family—with the fictional rhetoric of “half breed” that tells us we are only part Mexican, part Latinx, or part Indigenous. Thus, the concept of “half breed” can affirm my one’s Indigeneity, but used in another way, it disrupts the wholeness of Mexicanness. Mestizaje is both a bridge and barrier.


--- layout: post title: 'College Entrance Exams: Raising Scores' date: 2012-10-27 10:23:58.000000000 -07:00 type: post parent_id: '0' published: true pd: '' status: publish categories: - Classroom Talk - College Prep - Guest Posts tags: - College Prep - free college prep - raising test scores - SAT author: login: k email: k@k display_name: mcary first_name: '' last_name: '' ---
College Entrance Exams: Raising Scores with Free Prep Courses
Guest Post by Kate Wilson
Recent SAT test scores show that the average critical reading section score is the lowest it has been since 1972. The high school graduating class of 2012 received an average score of 496, out of a possible 800. The average score for writing has also dropped by six points since College Board, the organization that administers the test, first began testing for it in 2006. In addition to these numbers, the overall SAT score has continued to remain below average in several states throughout the years, and this year didn’t show much improvement.
These reports raise serious concerns among teachers and parents who see poor college entrance exam results as proof that the education system in the United States is not succeeding. Unfortunately, it is nearly impossible to find one solid reason for these consistently poor test scores. In fact, there are several reasons why students are not doing well, so instead of discussing all the possible roots of the problem, let’s look at one possible solution.
According to past articles written on the subject, most students believed that completing a SAT prep course helped significantly increase their test scores. On the flip side, there are also articles that say prep classes aren’t always worth the money, because they only increase scores by a marginal amount. However, even if a course only helps raise a student’s score by a few points, those few points could make the difference between college admittance and rejection. The general consensus seems to be that prep courses are beneficial, but only if the sign-up fee is inexpensive. As for proof that courses do increase scores, College Board and the National Association of College Admission Counseling have conducted studies that show they do.
Some would argue that most students are already taking prep courses, and that the continued weakness in scores proves that these courses do not work. This may be true, in some school districts. However, it is also true that many more students all across the nation cannot afford courses or choose not to attend, because they do not believe the benefits will outweigh the costs.
Students who do not attend courses to prepare for college entrance exams often choose to study for the test independently by using free example tests on the SAT’s official website, and some do nothing to prepare. Although students who choose to prepare independently do succeed, courses discuss each test section and effective test-taking methods on a much wider scale. They also give students the chance to take multiple example tests that are timed and conducted just as the real test is.
So, while success rates for various SAT prep methods vary, one thing is certain; prep courses can and do give students an advantage. When discussing solutions to the low score problem, schools should consider this advantage and understand the unfairness of charging a high fee for SAT prep classes. Already, schools across the country are beginning to see the importance of offering free prep courses after school. For example, T.C. Williams High School in Alexandria, Virginia, offered free ACT and SAT prep programs to juniors for the first time this year. The classes were taught by an outside company and were voluntary.
Because college applicants do not have a choice on whether or not to take a college entrance exam, high schools need to find a way to provide every student with all the resources they need to succeed. Money should not be an issue, and the resources should not be difficult to attain. In addition to providing free prep courses, schools should also provide up-to-date prep guides in their libraries and inform students on where to find more information about signing up and preparing for the SAT or ACT. Once more schools begin to take an active role in preparing students for not only college entrance exams but college in general, the results will follow, and I believe they will be positive.
Kate Willson is a researcher/writer for www.CollegeCrunch.org, a great online resource for all things related to higher education and campus life. Her articles cover several topics related to learning, including trends in online schooling, advice for new college students and recent grads and college preparation for high school students. Please leave any questions or comments for Kate below.


This class defines a hierarchical state that has a single sub-network. You attach the state to the state engine by specifying the state network manager pointer (of type stateMgr). If this is the topmost (root) state in the statechart, you pass in undefined (and then use the stateEngine's setStateNetwork() to attach it to the engine).
Before defining the sub-network, create the state manager. You attach states by specifying the manager.
new hState(name, id, stateMgrPtr);
name String name of the state. Used in trace output
id Typically numeric, this is the ID of the state (must be unique among other states in the same network level)
stateMgrPtr Pointer to the state manager for the local network in which the state resides (not to be confused with the sub-network this state manages), or undefined for the root state
// state id is 0 myState0 = new hState("State Name", 0, myMgrPtr);
Variable names with the suffix "_hState" are recognized for the sake of codehinting, as hStates.


{% extends "layout.html" %} {% block cont %} {% if val%}
Here's your question :
{{logicq}}
Now imagine the answer as some position.
Enter the word at that position, from the list given below:
{% for item in wordq %} {{item}} {% if not loop.last %},{% endif %} {% endfor %}
CAPTCHA has been verified!
redirecting....
CAPTCHA not verified! Please try again
{% endif %} {% endif %} {% endblock%}


Hello there. Welcome to my home page,I am going to present some interesting storys for you,here is:
Below is a picture from my windows phone, guess where it is photoed


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Controversies that have beset the royal family in 2019
Andrew’s troubles are just the lastest development in a difficult year.
The Duke of York has been forced to step down from public duties due to his friendship with convicted sex offender Jeffrey Epstein, with one royal claiming it was another development in a “disastrous year” for the royal family.
Royal author Penny Junor told the PA news agency: “I would think the Queen is horrified … I think it’s been a disastrous year.”
But social historian Professor Judith Rowbotham said the difficulties the royal family, including the Duke and Duchess of Sussex, had faced in 2019 were not as bad as the Queen’s annus horribilis of 1992.
This has been a ‘few weeks horribilis'Judith Rowbotham
“Back in 1992, it was an annus horribilis.
“This has been a ‘few weeks horribilis’,” she suggested.
Here, PA looks at the controversies that have dogged the royal family in 2019.
– Duke of Edinburgh car troubles, January 17
He said he was “somewhat shaken after the accident” and wished the woman who was a passenger in a car hit by his Land Rover a “speedy recovery”.
– Hong Kong royal access, August 10
Sarah, the Duchess of York, was also found to be receiving payments from Johnny Hon’s firm.
– Sussexes versus the press, October 1
Harry says: “My wife has become one of the latest victims of a British tabloid press that wages campaigns against individuals with no thought to the consequences.”
– Brotherly rift, October 21
– Eyes on Andrew, November 21
The Duke of York gives an interview to BBC in which he is criticised for his apparent lack of empathy towards victims of Epstein.
Sponsors of the royal’s organisations and charities cut ties, leading Andrew to step away from public life.


Index: mgmt/tm
mgmt/tm/analytics
mgmt/tm/apm
mgmt/tm/auth
mgmt/tm/cli
mgmt/tm/cm
mgmt/tm/gtm
mgmt/tm/ilx
mgmt/tm/ltm
mgmt/tm/net
mgmt/tm/pem
mgmt/tm/security
mgmt/tm/sys
mgmt/tm/util
mgmt/tm/vcmp
mgmt/tm/wam
mgmt/tm/wom


Psychic Game
Do you think you can read my mind?
Guess what letter I'm thinking of... May the odds be ever in your favor!


ValidatorServerHttpConfig
validatorServer
$httpProvider
Configure $http to add a interceptor for trigger a $broadcast for bad request.


What we do...
This Minecraft club is a club for kids who love Minecraft and want to game design/build.
We meet weekly and make new minigame ideas and build minigame maps.
We also host special events for special occasions and have multiple completed project.
Our Goals
-
To socialize while playing Minecraft
-
To game design
-
To build maps
-
To host events for special occasions
-
To help people learn Minecraft


private class TouchTime.Engine
extends android.support.wearable.watchface.CanvasWatchFaceService.Engine
| Modifier and Type | Field and Description |
|---|---|
private static float |
CENTER_GAP_AND_CIRCLE_RADIUS |
private static float |
HOUR_STROKE_WIDTH |
private boolean |
mAmbient |
private android.graphics.Bitmap |
mBackgroundBitmap |
private android.graphics.Paint |
mBackgroundPaint |
private boolean |
mBurnInProtection |
private java.util.Calendar |
mCalendar |
private float |
mCenterX |
private float |
mCenterY |
private android.graphics.Bitmap |
mGrayBackgroundBitmap |
private android.graphics.Paint |
mHourPaint |
private static float |
MINUTE_STROKE_WIDTH |
private boolean |
mLowBitAmbient |
private android.graphics.Paint |
mMinutePaint |
private boolean |
mMuteMode |
private android.graphics.Rect |
mPeekCardBounds |
private boolean |
mRegisteredTimeZoneReceiver |
private float |
mSecondHandLength |
private android.graphics.Paint |
mSecondPaint |
private android.graphics.Paint |
mTickAndCirclePaint |
private android.content.BroadcastReceiver |
mTimeZoneReceiver |
private android.os.Handler |
mUpdateTimeHandler |
private int |
mWatchHandColor |
private int |
mWatchHandHighlightColor |
private int |
mWatchHandShadowColor |
private static java.lang.String |
ON_TAP |
private static float |
SECOND_TICK_STROKE_WIDTH |
private static int |
SHADOW_RADIUS |
private float |
sHourHandLength |
private float |
sMinuteHandLength |
private static java.lang.String |
VIBRATE_MESSAGE |
| Modifier | Constructor and Description |
|---|---|
private |
Engine() |
| Modifier and Type | Method and Description |
|---|---|
private void |
handleUpdateTimeMessage()
Handle updating the time periodically in interactive mode.
|
private void |
initGrayBackgroundBitmap() |
void |
onAmbientModeChanged(boolean inAmbientMode)
Called when device enters / exits ambient mode
|
void |
onCreate(android.view.SurfaceHolder holder)
Initialize watch face
|
void |
onDestroy()
Destroy watch face
|
void |
onDraw(android.graphics.Canvas canvas,
android.graphics.Rect bounds)
Draw the watch face with hour, minute, and second hands
|
void |
onInterruptionFilterChanged(int interruptionFilter)
Called when the user changes interruption filter.
|
void |
onPeekCardPositionUpdate(android.graphics.Rect rect) |
void |
onPropertiesChanged(android.os.Bundle properties)
Get device features(burn-in, low-bit ambient)
|
void |
onSurfaceChanged(android.view.SurfaceHolder holder,
int format,
int width,
int height)
Updates the watch face if the surface canvas (physical watch parameters) have changed
|
void |
onTapCommand(int tapType,
int x,
int y,
long eventTime)
Captures tap event (and tap type).
|
void |
onTimeTick()
changed time
|
void |
onVisibilityChanged(boolean visible)
Called to inform you of the watch face becoming visible or hidden
|
private void |
registerReceiver() |
private boolean |
shouldTimerBeRunning()
Returns whether the
mUpdateTimeHandler timer should be running. |
private void |
unregisterReceiver() |
private void |
updateTimer()
Starts/stops the
mUpdateTimeHandler timer based on the state of the watch face. |
private void |
updateWatchHandStyle() |
private void |
vibrate(long[] vibPattern)
Uses the given vibration pattern to output vibrations from the device.
|
invalidate, onSurfaceCreated, onSurfaceRedrawNeeded, postInvalidate
getInterruptionFilter, getNotificationCount, getPeekCardPosition, getUnreadCount, isInAmbientMode, onCommand, onComplicationDataUpdate, onNotificationCountChanged, onStatusChanged, onTouchEvent, onUnreadCountChanged, setActiveComplications, setDefaultComplicationProvider, setDefaultSystemComplicationProvider, setTouchEventsEnabled, setWatchFaceStyle
private static final float HOUR_STROKE_WIDTH
private static final float MINUTE_STROKE_WIDTH
private static final float SECOND_TICK_STROKE_WIDTH
private static final float CENTER_GAP_AND_CIRCLE_RADIUS
private static final java.lang.String ON_TAP
private static final java.lang.String VIBRATE_MESSAGE
private static final int SHADOW_RADIUS
private final android.graphics.Rect mPeekCardBounds
private final android.os.Handler mUpdateTimeHandler
private java.util.Calendar mCalendar
private final android.content.BroadcastReceiver mTimeZoneReceiver
private boolean mRegisteredTimeZoneReceiver
private boolean mMuteMode
private float mCenterX
private float mCenterY
private float mSecondHandLength
private float sMinuteHandLength
private float sHourHandLength
private int mWatchHandColor
private int mWatchHandHighlightColor
private int mWatchHandShadowColor
private android.graphics.Paint mHourPaint
private android.graphics.Paint mMinutePaint
private android.graphics.Paint mSecondPaint
private android.graphics.Paint mTickAndCirclePaint
private android.graphics.Paint mBackgroundPaint
private android.graphics.Bitmap mBackgroundBitmap
private android.graphics.Bitmap mGrayBackgroundBitmap
private boolean mAmbient
private boolean mLowBitAmbient
private boolean mBurnInProtection
public void onCreate(android.view.SurfaceHolder holder)
onCreate
in class android.support.wearable.watchface.WatchFaceService.Engine
public void onDestroy()
onDestroy
in class android.support.wearable.watchface.CanvasWatchFaceService.Engine
public void onPropertiesChanged(android.os.Bundle properties)
onPropertiesChanged
in class android.support.wearable.watchface.WatchFaceService.Engine
properties
- public void onTimeTick()
onTimeTick
in class android.support.wearable.watchface.WatchFaceService.Engine
public void onAmbientModeChanged(boolean inAmbientMode)
onAmbientModeChanged
in class android.support.wearable.watchface.WatchFaceService.Engine
inAmbientMode
- private void updateWatchHandStyle()
public void onInterruptionFilterChanged(int interruptionFilter)
onInterruptionFilterChanged
in class android.support.wearable.watchface.WatchFaceService.Engine
interruptionFilter
- public void onSurfaceChanged(android.view.SurfaceHolder holder, int format, int width, int height)
onSurfaceChanged
in class android.support.wearable.watchface.CanvasWatchFaceService.Engine
holder
- format
- width
- height
- private void initGrayBackgroundBitmap()
public void onTapCommand(int tapType, int x, int y, long eventTime)
onTapCommand
in class android.support.wearable.watchface.WatchFaceService.Engine
tapType
- x
- y
- eventTime
- public void onDraw(android.graphics.Canvas canvas, android.graphics.Rect bounds)
onDraw
in class android.support.wearable.watchface.CanvasWatchFaceService.Engine
canvas
- bounds
- public void onVisibilityChanged(boolean visible)
onVisibilityChanged
in class android.support.wearable.watchface.WatchFaceService.Engine
visible
- public void onPeekCardPositionUpdate(android.graphics.Rect rect)
onPeekCardPositionUpdate
in class android.support.wearable.watchface.WatchFaceService.Engine
private void registerReceiver()
private void unregisterReceiver()
private void updateTimer()
mUpdateTimeHandler
timer based on the state of the watch face.private boolean shouldTimerBeRunning()
mUpdateTimeHandler
timer should be running. The timer
should only run in active mode.private void handleUpdateTimeMessage()
private void vibrate(long[] vibPattern)
vibPattern
-


Copied verbatim from https://gist.github.com/1216850
Copied verbatim from https://gist.github.com/1216850


A Test Case that can automatically generate a number of Tests.
Return a name for the check_fn for an individual test. (NB: must start with “test”).
Generate an individual test, and add it to this TestCase.
Stats
URI
Enter search terms or a module, class or function name.


– movies/webseries I plan to watch –
0001 | Godfather part 2
0002 | Schindler’s list
0003 | Cosmos(WebSeries)
0004 | Sense 6
0005 | All eyes on me (2pac biography)
0006 | The game
0007 | Stuart life backward
0008 | Infinity
0009 | Little Miss sunshine
0010 | 2001-The space time odyssey
0011 | Elizabethtown
0012 | Lost in translation
0013 | Our Friend
0014 | The machinist
0015 | Brothers
0016 | Hello my name is doris
0017 | Eyimofe(This is my desire)
0018 | Man in the high castle
0019 | The usual suspects
0020 | Dark
0021 | Dunkirk
0022 | A few good men
0023 | 12 angry men
0024 | The card counter


We exchange mices!
- reintermediate impactful solutions
- recontextualize one-to-one platforms
- unleash killer partnerships
- revolutionize seamless architectures
- optimize leading-edge technologies
- target visionary relationships
- engage real-time eyeballs
- whiteboard efficient initiatives
- incubate strategic paradigms
- optimize cutting-edge communities


Hi! So this week we learned a little bit about CSS. I say a little bit because well, in the grand scheme of it there is way more that I don't know about than I do. With that being said, I am proud of all that I did learn! So I want to write a little bit today about margins, border, and padding.
So a webpage is broken down into elements, with each element having it's own allocated space taken up on the webpage. This is determined by height/width parameters, but we won't get into that. Just know that there is a finite amount of space each element takes up. Each one of these elements has 4 sections to it. First is the content, which is the stuff you actually see on the webpage, be it a picture or some text or links. The content is the stuff you care about, since it what you are displaying for the whole world to see. So consider the content a crock-pot that you ordered from amazon for your mom's birthday (which you forgot was tomorrow so you had to pay for overnight shipping, way to go!). It's going to be placed inside of the box, which we can liken to the element you are maneuvering around.
This image helped me to visualize how the other 3 things (margin, border, padding) work in relation to each other. Margin forms the outer buffer between other elements on the page outside of your border. So if you want somet space between your content and things outside your element, such as the edges of the page. You can use margin to provide a little bit of breathing room. The margin would be like if amazon wrapped the outside of your package in 3-inches of tempered steel... okay fine, how would you open that? We can change it to bubble-wrap, you 'realist'. The bubble-wrap would provide a space on the outside between your crock-pot and the other packages, just like the border provides spacing between it's element and neighboring elements.
The border provides the line between your padding and your margin. If you want to box in your content, whether for aesthetics or to help you while you're setting up your elements, you would use your border. Now sometimes your border can seem like it's crowding your content, so this is where the padding comes in. The border would relate to the thin cardboard your wonderful mom's birthday present is going to come in. It lets you know exactly the space your element is occupying.
The padding comes between the content and the border. The padding provides an extra buffer once you have your elements aligned with their neighbors the way you want them to. You may still think that your content is too close together, so you can use the padding to manuever your content how you'd like. The padding is much like everyone's favorite part of ordering things online, the bubble-wrap on the inside of the package. It provides a layer between your border (cardboard box) and your content (beautiful $20 crock-pot). You can do this to avoid cramming your content next to another element, or if your margins are where you want, and it still feels a little cramped for the content.
Margin, border, and padding is a fairly basic concept of spacing, but believe me when I say when you're working with it it's tough to keep everything in it's place. You definitely want to have a firm understanding of this concept before you start experimenting with it yourself, but I will say that experimenting with it is the only way to get an actual understanding of it. Hope you learned a little something today! I also hope you realized that your mom already has a crock-pot... that you gave her for her birthday 3 years ago... and Christmas 4 years before that... and that she already had one before you bought the first one. Goodspeed, code warrior!


There are many different forms of heart disease. The most common cause of heart disease is narrowing or blockage of the coronary arteries, the blood vessels that supply blood to the heart itself. This is called coronary artery disease and happens slowly over time. It's the major reason people have heart attacks.
Other kinds of heart problems may happen to the valves in the heart, or the heart may not pump well and cause heart failure. Some people are born with heart disease.
You can help reduce your risk of heart disease by taking steps to control factors that put you at greater risk:


get.colvector.Rd
Given a vector of class labels, return a color vector corresponding to the class values. There is an option to set a different color for NA
. A vector with corresponding color values and a key specifying color value of each class label is returned.
get.colvector(labels,col, NA.flag=FALSE, NA.col="grey")
| labels | vector, labels for which color vector is desired. Can be |
|---|---|
| col | vector, a character vector containing colors for each unique class label. Also accepts colors from |
| NA.flag | logical, a logical flag where |
| NA.col | character, color value specifying how |
if the vector labels
has names
, the returned color coded vector will also have names
.
A list, with following elements -
labels
key : a matrix, a 2 x unique class labels
character matrix showing how values in labels
have mapped to col
#> x #> a b c d #> 3 7 8 2#> $labels.col #> [1] "green" "green" "green" "blue" "green" "blue" "blue" "blue" #> [9] "green" "red" "orange" "blue" "blue" "red" "blue" "green" #> [17] "orange" "red" "green" "green" #> #> $key #> [,1] [,2] [,3] [,4] #> "a" "b" "c" "d" #> col "red" "blue" "green" "orange" #>#lets add some NAs x[c(1,4,5)] = NA x.col<-get.colvector(x,c("red","blue","green","orange"), NA.flag=TRUE, NA.col="grey") x.col#> $labels.col #> [1] "grey" "green" "green" "grey" "grey" "blue" "blue" "blue" #> [9] "green" "red" "orange" "blue" "blue" "red" "blue" "green" #> [17] "orange" "red" "green" "green" #> #> $key #> [,1] [,2] [,3] [,4] [,5] #> "a" "b" "c" "d" NA #> col "red" "blue" "green" "orange" "grey" #>


FoodKhan is Bangladeshi’s own fast food restaurants chain offering Burgers, French fries, Milkshakes, Smoothies and so much more.
FoodKhan is Bangladeshi’s own fast food restaurants chain offering Burgers, French fries, Milkshakes, Smoothies and so much more. We are known for our delicious taste, superior quality
Food Khan is a restaurant, bar and coffee roasters located on a busy corner site in Farrington's Exmouth Market.
Food Khan is a gret Restaurant from the University of Texas at Austin has been researching the benefits of frequent testing and the feedback leads to. He explains that in the history of the study.
Food Khan is a gret Restaurant from the University of Texas at Austin has been researching the benefits of frequent testing and the feedback leads to. He explains that in the history of the study.
Food Khan is a gret Restaurant from the University of Texas at Austin has been researching the benefits of frequent testing and the feedback leads to. He explains that in the history of the study.
Food Khan is a gret Restaurant from the University of Texas at Austin has been researching the benefits of frequent testing and the feedback leads to. He explains that in the history of the study.
Food Khan is a gret Restaurant from the University of Texas at Austin has been researching the benefits of frequent testing and the feedback leads to. He explains that in the history of the study.
Food Khan is a gret Restaurant from the University of Texas at Austin has been researching the benefits of frequent testing and the feedback leads to. He explains that in the history of the study.
Food Khan is a gret Restaurant from the University of Texas at Austin has been researching the benefits of frequent testing and the feedback leads to. He explains that in the history of the study.
Food Khan is a gret Restaurant from the University of Texas at Austin has been researching the benefits of frequent testing and the feedback leads to. He explains that in the history of the study.


I suffered from depression for several years (before I even knew it was depression that I was feeling). When I heard about what depression really was I figured that must be what was wrong with me. So I told my mom about how I was feeling and she took me to get evaluated.
I was diagnosed with a typical case of massive depression, and I also suffered from insomnia. I was prescribed with Remeron, which is an antidepressant that serves also as a sleep aid (thus it should be taken at nighttime about an hour before you want to go to bed). I was also prescribed Effexor, which is another antidepressant that can help you feel more energetic (thus by itself it can cause insomnia). This effect is most likely caused by its mild effect on dopamine, which aside from its role in depression is also the chemical that cocaine works on to cause its effects (hence the mild stimulation).
The Remeron immediately helped me sleep better (causing lethargy actually for the first few days, I fell asleep at school a good bit in the first week of treatment). It's essential to take it fairly early at night (7:00 to 8:30 if you take 30 mg, possibly later if you have a lower dose) that way you won't be tired the next day.
After the lethargic effects of the Remeron subsided, I began feeling quite energetic. I had already begun feeling better soon after beginning treatment. For the first few weeks of treatment I was lightheaded and out of it a little, though I found the feeling to be quite enjoyable especially after being depressed for so long.
The only noticeable side effects from Effexor were dry mouth and trouble reaching orgasm, which took some months of gradual lessening to go away completely, though this wasnít completely a bad thing. The main side effects from Remeron were the initial drowsiness, adding to the dry mouth (I often carried a bottle of water around), and extreme appetite right after dosage (comparable to the munchies and usually the more I ate, the harder I slept and vice versa).
I no longer take Effexor currently, though several times during treatment I would stop taking it for short periods for various reasons. I had some mild withdrawal symptoms whenever I did so. I would feel the same lightheadedness and some dizziness (which was quite enjoyable actually). It's possible that my withdrawal symptoms were mild due to still taking Remeron or possibly just due to my personal body chemistry.
Now, I realize, of course, some individuals have negative reactions to particular medications, but I believe that in general Effexor is a very effective antidepressant, especially in combination with Remeron, which will counter some of the side effects of Effexor like insomnia and the lack of appetite (Remeron can increase overall appetite).
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.


Design responsibilities
- Ensure your product content is structurally organized.
- The visual styling of the different heading levels also helps to organize the content.
- Visual styling needs to be coded semantically, that is, the code communicates the level of the headings to non-visual users. This can be a shared responsibility with developers. However if the design team
owns
the CSS, that could force a developer to use a non-semantic code element to get the appearance the designer has specified. - Coordinate with SEO teams to balance heading requirements against the search ranking component afforded by headings
Designer tips
Design the headings in the same font and consistent color scheme with descending font sizes based on the heading level. Making sure the font sizes and colors align with the overall design of the website is essential to bringing consistency and cohesion to the design.
User testing and meaningful involvement
- Can the user perceive the different content sections delineated by headings if they are using assistive technology and cannot see the headings?
- Ask a user to find a specific piece of content by only reviewing a list of headings.
- Can the understand the different content sections delineated by headings?
- Can the user move to a heading deeper in the content hierarchy without waiting for the content from an earlier heading to announce?


Revalidation and Alerts
A
B
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valid
must be one character long
Constraint not valid
must be one character long


Gamification
Alex Lakatos,
@lakatos88
or
<EMAIL_ADDRESS>What is Gamification
Gamification is the use of game elements and game design techniques in non-game contexts.
Games
Game Thinking
Game Elements
Motivation & Psychology
Gamification Design Framework
Design Choices
Enterprise Gamification
Social Impact & Behavior Change
Criticisms and Risks
Beyond the Basics
https://class.coursera.org/gamification-003/lecture/preview
Thanks
Alex Lakatos @lakatos88
Slides
: alexlakatos.github.io/slides/gamification
Social Impact & Behavior Change