In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
#data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
inputs = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name = 'input')
z = tf.placeholder(tf.float32, (None, z_dim), name = 'z')
lr = tf.placeholder(tf.float32, name = 'learning_rate')
return inputs, z, lr
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False, alpha = 0.2):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
with tf.variable_scope('discriminator', reuse=reuse):
x1 = tf.layers.conv2d(images, filters=64, kernel_size=5, strides=2,padding='SAME')
#no batch normalization on 1st layer
relux1 = tf.maximum(alpha*x1, x1)
#14x14x64
x2 = tf.layers.conv2d(relux1, filters=128, kernel_size=5, strides=2,padding='SAME')
batchx2 = tf.layers.batch_normalization(x2, training=True)
relux2 = tf.maximum(alpha*batchx2, batchx2)
#7x7x128
x3 = tf.layers.conv2d(relux2, filters=256, kernel_size=5, strides=2,padding='SAME')
batchx3 = tf.layers.batch_normalization(x3, training=True)
relux3 = tf.maximum(alpha*batchx3, batchx3)
#4x4x256
x4 = tf.reshape(relux3, (-1, 4*4* 256 ))
logits = tf.layers.dense(x4, 1)
output = tf.sigmoid(logits)
return output, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
alpha=0.2
reuse = not is_train
with tf.variable_scope('generator', reuse=reuse):
x1 = tf.layers.dense(z, 7*7*512)
x1 = tf.reshape(x1, (-1, 7,7,512))
x1 = tf.layers.batch_normalization(x1, training=is_train)
relux1 = tf.maximum(alpha*x1, x1)
#7 x 7 x 512 now
x2 = tf.layers.conv2d_transpose(relux1, filters=256, kernel_size=5, strides=1, padding='SAME')
x2 = tf.layers.batch_normalization(x2, training=is_train)
relux2 = tf.maximum(alpha*x2, x2)
# 7 x 7 x 256 now
x3 = tf.layers.conv2d_transpose(relux2, filters=128, kernel_size=5, strides=2, padding='SAME')
x3 = tf.layers.batch_normalization(x3, training=is_train)
relux3 = tf.maximum(alpha*x3, x3)
# 14 x 14 128 now
# Output layer, 28x28xout_channel_dim
logits = tf.layers.conv2d_transpose(relux3, filters=out_channel_dim, kernel_size=5, strides=2, padding='SAME')
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
g_model = generator(input_z, out_channel_dim)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# Get weights and bias to update
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
# Optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
_, img_width, img_height, channels = data_shape
input_real, input_z, lr = model_inputs(img_width, img_height, channels, z_dim)
d_loss, g_loss = model_loss(input_real, input_z, channels)
d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr, beta1)
steps = 0
generator_output_every = 100
print_output_every = 2
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
#images given are between -0.5 to 0.5. for tanh should be rescaled to -1,1
batch_images = batch_images * 2
steps += 1
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
_ = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
if steps % print_output_every == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epochs),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
if steps % generator_output_every == 0:
show_generator_output(sess, 16, input_z, channels, data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
print(mnist_dataset.shape)
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


|
Elektra
0.9.5
|
Again we managed to release with many new features, many fixes and also other improvements.
getenv(3) is one of the most popular ways to retrieve configuration, even though it has many known problems:
With elektrify-getenv we wrote a solution which solves most of the problems. We use the LD_PRELOAD
technique to additionally retrieve values from Elektra, and not only the environment.
You simply can do:
This will set the HTTP_PROXY
environment variable to http://my.proxy:8080
. Configuration can be retrieved with kdb get
:
Or using the man pages:
Will use MANWIDTH 40 for this invocation of man man. This feature is handy, if an option is only available by environment, but not by command-line arguments, because sometimes environment variables are not trivial to set (e.g. in Makefiles).
Some more examples:
So is this the final solution for configuration and manual elektrification of applications is not needed anymore?
The answer is: no and yes.
It is quite satisfactory for configuration that is inherently sharable (not different from one application to another) and needs the environment semantics, i.e. some subprocesses should have different configuration than others, e.g. in a specific terminal.
But it might not be a good solution for your own application, because libgetenv(3) implies many architectural decision, that other elektrified applications would decide differently, e.g.:
char*
and has no support for other data typesFor more information see https://git.libelektra.org/blob/master/src/libs/getenv/README.md "src/libgetenv/README.md"
As always, the API and API is fully forward-compatible, i.e. programs compiled against an older 0.8 versions of Elektra will continue to work.
Because keyUnescapedName
and keyGetUnescapedNameSize
is added in this release, it is not backward-compatible, i.e. programs compiled against 0.8.13, might not work with older 0.8 libraries.
The function keyUnescapedName
provides access to an unescaped name, i.e. one where /
and \\
are literal symbols and do not have any special meaning. NULL
characters are used as path separators. This function makes it trivial and efficient to iterate over all path names, as already exploited in all bindings:
Other small changes/additions in bindings:
Doxygen 1.8.8 is preferred and the configfile was updated to this version.
The symbols of nickel (for the ni plugin) do not longer leak from the Elektra library. As such, old versions of testmod_ni won't work with Elektra 0.8.13. A version-script is now in use to only export following symbols:
kdb*
key*
ks*
libelektra*
for module loading systemelektra*
for proposed and other functions (no ABI/API compatibility here!)In this release, ENABLE_CXX11 was changed to ON
by default.
Note that in the next release 0.8.14 there will be two changes:
By not having to care for pre-C++11 compilers, we hope to attract more developers. The core part is still in C99 so that Elektra can be used on systems where libc++ is not available. Many new plugins are still written in C99, also with the purpose of not depending on C++.
A technical preview of python3 and python2 plugins has been added.
With them its possible to write any plugin with python scripts.
Note, they are a technical preview. They might have severe bugs and the API might change in the future. Nevertheless, it is already possible to, e.g. develop storage plugins with it.
They are not included in ALL
plugins. To use it, you have to specify it:
Thanks to Manuel Mausz for to this work on the plugins and the patience in all the last minute fixes!

The commandline tool kdb
also got some improvements. Most noteworthy is that kdb get -v
now gives a complete trace for every key that was tried. This is very handy if you have a complex specification with many fallback and override links.
It also shows default values and warnings in the case of context-oriented features.
Furthermore:
-v
for setmetaAs Michael Haberler from machinekit pointed out it was certainly not easy for someone to get started with Elektra. With the documentation initiative we are going to change that.
| more,thanks | to Pino Toscano |
@
and not \\
.| more,thanks | to Pino Toscano |
Any further help is very welcome! This call is especially addressed to beginners in Elektra because they obviously know best which documentation is lacking and what they would need.
kdb-full
and kdb-static
work fine now for Windows 64bit, thanks to Manuel Mausz. The wresolver is now more relaxed with unset environment.
All issues for macOS were resolved. With the exception of elektrify-getenv everything should work now, thanks to Mihael Pranjic:
and thanks to Daniel Bugl:
kdb
worksfurthermore:
__FUNCTION__
to __func__
(C99), thanks to Pino ToscanoThanks to Manuel Mausz for to testing and improving portability!
cmake_parse_arguments
instead of parse_arguments
, thanks to Manuel Mausztype
to command -v
and avoid echo -e
) thanks to Pino ToscanoThere are some misconceptions about Elektra and semi structured data (like XML, JSON). Elektra is a key-value storage, that internally represents everything with key and values. Even though, Elektra can use XML and JSON files elegantly, there are limitations what XML and JSON can represent. XML, e.g., cannot have holes within its structure, while this is obviously easily possible with key-value. And JSON, e.g., cannot have non-array entries within an array. This is a more general issue of that configuration files in general are constrained in what they are able to express. The solution to this problem is validation, i.e. keys that does not fit in the underlying format are rejected. Note there is no issue the other way round: special characteristics of configuration files can always be captured in Elektra’s metadata.
You can download the release from here and now also here on github
This release tarball now is also available signed by me using gpg
already built API-Docu can be found here
Subscribe to the RSS feed to always get the release notifications.
For any questions and comments, please contact the Mailing List the issue tracker on github or by mail elekt. ra@m arkus -raa b.org
For more information, see https://libelektra.org
Best regards, Markus


|
SeeSharper
1.0
Drawing App
|
Presenter class - manages the presentation logic of the application More...
Public Member Functions | |
| Presenter (IModel model, IView view) | |
| Constructor of the Presenter class More... | |
| void | MouseMoved (int x, int y) |
| Called to notify of a mouse move event More... | |
| void | MouseStateChanged (int x, int y) |
| Called when the mouse button changes its state to DOWN or UP More... | |
| void | SaveDrawing () |
| Saves the current state of the View drawing to the model More... | |
| void | Redo () |
| Sets the state of the View drawing to revert the last "Undo" operation More... | |
| void | Undo () |
| Sets the state of the View drawing to revert the last recorded change More... | |
| void | ColorChanged (Color color) |
| Applies the new color to the current drawing strategy More... | |
| void | FillColorChanged (Color color) |
| Applies the new fill color to the current drawing strategy More... | |
| void | ThicknessChanged (float thickness) |
| Changes the thickness of the current tool More... | |
| void | ChoosePaintingTool (PaintingTool paintingTool, Color borderColor, Color fillColor, float thickness) |
| Updates the current strategy to match the selected tool More... | |
| void | LoadDrawing (string filename) |
| Sets the View drawing to the one loaded in the model More... | |
| string | GetCurrentStrategyDescription () |
| Returns a description for the last change made by the selected tool More... | |
Presenter class - manages the presentation logic of the application
Constructor of the Presenter class
| model | The model of the MVP architecture |
| view | The view of the MVP architecture |
| void Presenter.Presenter.ChoosePaintingTool | ( | PaintingTool | paintingTool, |
| Color | borderColor, | ||
| Color | fillColor, | ||
| float | thickness | ||
| ) |
Updates the current strategy to match the selected tool
| paintingTool | The new selected painting tool |
| borderColor | The color of the border of the painting tool |
| fillColor | The fill color of the painting tool |
Implements Shared.IPresenter.
| void Presenter.Presenter.ColorChanged | ( | Color | color | ) |
Applies the new color to the current drawing strategy
| color | The new color |
Implements Shared.IPresenter.
| void Presenter.Presenter.FillColorChanged | ( | Color | color | ) |
Applies the new fill color to the current drawing strategy
| color | The new color |
Implements Shared.IPresenter.
| string Presenter.Presenter.GetCurrentStrategyDescription | ( | ) |
Returns a description for the last change made by the selected tool
Implements Shared.IPresenter.
| void Presenter.Presenter.LoadDrawing | ( | string | filename | ) |
Sets the View drawing to the one loaded in the model
| filename | The path to the file on the disk |
Implements Shared.IPresenter.
| void Presenter.Presenter.MouseMoved | ( | int | x, |
| int | y | ||
| ) |
Called to notify of a mouse move event
| x | The x coordinate of the mouse cursor |
| y | The y coordinate of the mouse cursor |
Implements Shared.IPresenter.
| void Presenter.Presenter.MouseStateChanged | ( | int | x, |
| int | y | ||
| ) |
Called when the mouse button changes its state to DOWN or UP
| x | The x coordinate of the mouse cursor |
| y | The y coordinate of the mouse cursor |
Implements Shared.IPresenter.
| void Presenter.Presenter.Redo | ( | ) |
Sets the state of the View drawing to revert the last "Undo" operation
Implements Shared.IPresenter.
| void Presenter.Presenter.SaveDrawing | ( | ) |
Saves the current state of the View drawing to the model
Implements Shared.IPresenter.
| void Presenter.Presenter.ThicknessChanged | ( | float | thickness | ) |
Changes the thickness of the current tool
| thickness | The new thickness |
Implements Shared.IPresenter.
| void Presenter.Presenter.Undo | ( | ) |
Sets the state of the View drawing to revert the last recorded change
Implements Shared.IPresenter.


Sjasm can include files as text or as binary. If you include the file as text it will be processed as if it was part of the source file. If you include the file as binary it will be put in to the output without any processing.
With INCBIN you can include any file. The file is not processed in any way, and directly put into the output as-is. You can specify the offset and the length, if you don't the whole file will be included.
incbin gfx.plet6 ; include the whole file incbin boe.bin,7 ; include boe.bin but skip the first 7 bytes incbin rantab,,1024 ; include the first 1024 bytes incbin a.bat,1,2 ; include the second and the third byte
You can use INCBIN.LIST instead of just INCBIN to list the included data in the list file.
You can use INCLUDE to include another source file into the current.
include more.i include "more.i"
If the file cannot be found in the current directory (the current directory is the directory the current file comes from), the file will be searched for in the directories specified at the command line or specified with INCDIR. When angle brackets are used, the command line directories are searched before the current directory:
include <vdp.i>
you can specify the directories to look in at the command line, or with the INCDIR command:
incdir sourcedir


The standard Java libraries fail to provide enough methods for manipulation of its main components. The Lang Package provides these extra methods. There are other classes which might justifiably be included in java.lang someday, this package also provides for them.
This proposal is to create a package of Java utility classes for the classes that are in java.lang's hierarchy, or are considered to be so standard as to justify existence in java.lang. The Lang Package also applies to primitives and arrays.
Lang relies only on standard JDK 1.2 (or later) APIs for production deployment. It utilizes the JUnit unit testing framework for developing and executing unit tests, but this is of interest only to developers of the component. Lang will be a dependency for several existing components in the open source world.
No external configuration files are utilized.
The initial classes came from the Commons.Util subproject.
The proposed package name for the new component is
org.apache.commons.lang
.
lang
in the
jakarta-commons
CVS repository.The initial committers on the Lang component shall be as follows:


SETTINGS
DONE
OPENi
My Media
Let apps use my media.


Configuration
Ikube has two parts to the configuration. A base configuration, that doesn’t need to be changed or modified unless the volumes of data processed scales over multiple machines and deployment topology becomes more complex to cope with trillions of events. However this is a general overview of the configuration, each of the configuration files generally has comments describing the options above the module elements.
The configuration files must be in a folder named ikube in the user directory, i.e. where the server was started.
Base configuration
Security
Spring is the base for configuration. The web part contains two spring configuration files, one for security and one for Spring MVC. The base security configuration is file based. However Spring allows integration with LDAP or OAuth. For details on how to integrate Spring with other forms of security please consult the Spring security web site.
The rest web services are by default not secured. To secure these the spring-security.xml file in the WEB-INF/spring directory in the war application needs to be changed ro use ROLE_USER,ROLE_ADMIN rather than IS_AUTHENTICATED_ANONYMOUSLY.
The Spring MVC configuration is for the web services(rest) and tiles configuration, and typically does not need to be modified.
The rest of the configuration files must be placed in the $TOMCAT_HOME/bin/ikube directory as explained in the installation instructions.
Actions
Actions that Ikube performs, like indexing and processing data for analytics are in the spring-actions.xml file.
Interceptors
Ikube has several interceptors, for transaction management and intercepting the actions to perform rules. The rules need to be executed atomically, as such the cluster is locked before the rules are executed.
General
The file spring-beans.xml contains the database details, threading functionality, the services that users have access to like the analytics service and the search service. Ikube actions are based on schedulers, and these are also configured in the beans file. There other functionality like mailing and spelling checking that are also defined in this file. Importantly the handlers for various types of data sources, like database and file system are also defined in this file. This is where advanced users would define and plug in their own handlers for specific types of data sources. There are also strategies for enriching data for geospatial searches, and detecting language defined in this file. These concepts are described in more detail in the use cases, with examples of usage.
Database
Ikube uses an embedded database, H2. However for more robust users this can be changed to Oracle or DB2 for example by modifying the properties in the spring.properties file by changing the details for the database using the examples given, looking for the property ikube.dataSource.
Grid
The grid is Hazelcast at the time or writing. This can be changed to GridGain or any other provider, by implementing an interface and changing the configuration file to use the new provider. The configuration for Hazelcast is in the spring-hazelcast.xml file. This typically does not need to be changed, however the properties in the spring.properties file relating to the grid may be changed to suit the user. Specifically and importantly the interface needs to be changed to the ip address of the machine that it is running on, the property hz-tcp-ip-interface in the spring.properties file.
Users
Security for the web console are defined here, and optionally for the web services. Users can be added to this file to allow access, however for larger installations where multiple applications need access it is recommended that an LDAP be integrated to provide the user access and administration.
Index configuration
Indexes and processing pipelines are configured in Spring format. This will be familiar to developers that have a Spring background, but will also be understandable for technical people.
Index context
Configurations start with a context, an IndexContext. Each context must have a unique identifier, which is the name. Each context can be viewed as a collection of resources that are indexed for searching or processing viw analytics. As ikube is based on schedules, the maximum age of the context will trigger he processing jobs, as such the maxAge property is important to determine when processing will start. For all the properties related to contexts please refer to the properties page.
Please refer to the spring-auto.xml configuration in the configuration folder production. The analyzer is important for realising the results. In ths autocomplete index the analyzer is an EdgeNgramAnalyzer. This analyzer will break the words into tokens, smaller than the whole. The effect of this is that the index will be tolerant of spelling errors by the users. The index however will be larges by a factor, and the performance will be less than that of a direct index using a standard analyzer.
Data source resources
Please refer to the spring-geo.xml configuration file. This index is a database index. There are two tables that contain the geonames data, but only one is indexed. The context specifies the name of the index and some other parameters that are described on the properties page, like the path where the index will be stored etc. This index definition also contains geoname-strategies.
The context contains a list of indexables which are the resources that will be indexed and processed. In this case the indexable is the geoname indexable. It is a table indexable, meaning it defines a table in a database that will be indexed. Similarly the indexable needs a unique identifier, the name and several properties, also described on the properties page. Interesting in this index is the address property, which indicates that this resource will contain physical addresses, and can be enhanced with geospatial data, specifically a longitude and latitude will be discovered in the data, or the address will be looked up in the database. We define the number of threads that will handle the table, bearing in mind the volumes of data in the database and the hardware that the database is on. The data source is defined in the nonXaDataSourceDb2 file, spring-db2-jdbc.xml. This configuration file contains the url for the database, and importantly the user name and password. Database connections will be retrieved from the database using this datasource configuration. The properties for the database are quite self explanatory, and the defaults can be used in most cases.
The geoname context also contains strategies. Strategies are pre and post processing logic. Strategies can be chained, executed in order, enhancing the index with various information like language detection or analytics processing. In this case the strategy is the GeospatialEnrichmentStrategy, which will add geospatial information to the index. Specifically it will look for co-ordinates in the data, latitude and longitude, then generate a geohash for the co-ordinates. Fields in the table called longitude or latitude will be used to generate the geohash. Failing that, the data will be concatenated and geocoder will be used to estimate the location of the resource.
Adding a geohash to the index will allow geospatial searches. The results will be limited to a location or a radius around a
location, and they will be sorted according to distance from the point specified in the search. For an example of results from the
geoname index please go to the production instance os ikube at search page. Select
the geospatial index from the drop down list. Then input hotel in the name field. In the longitude and latitude fields input -33.9693580
and 18.4622110 respectively. Click on the Go! button at the bottom and wait for the results. They will be displayed on the map
on the right of the page as in the following screen shot
Full data source
Todo… ==== File system Todo… ==== Internet Todo… ==== Email Todo… ==== Csv Todo… ==== Log Todo… ==== Svn Todo… ==== Table Todo… ==== Twitter Todo…


Introduction
There are numerous prediction markets1 which are implemented to trade the outcome of events like if Donald Trump will win the 2020 presidential election. These markets are different from traditional financial markets because they usually have a defined end point where the bet settles to either 0 or 1 (similar to sports betting). Due to the nature of the settlement, there is extreme volatility as new information gets processed and odds rapidly shift. Currently, the most popular prediction markets are the political events like the recent 2020 presidential election; in fact this election has generated the most volume of any event in the history of many bookies. Other less popular markets include world events like "if Kim Jung Un will still be North Korea's leader by the end of this year" and if "2020 will be the hottest year on record". They can range from "if Kim Kardashian and Kayne West will get a divorce in 2020" or "if Airbnb will begin publicly trading in 2020".
Some of the most popular prediction betting sites include2 are PredictIt or the decentralized crypto alternatives Augur and Polymarket. Oftentimes these sites will have similar markets, so arbitrage is possible between the sites or even within the various markets in the site. Before betting it is important to read the fine print as to how the bet settles as they could slightly differ from site to site. In addition different sites have different betting and payout fees.
- PredictIt has a 5% withdraw fee and a 10% winnings fee.
- Augur uses base layer Etherum and will have expensive gas fees along with around 1-2% of winning fees.
- Polymarket charges around 2% of trades because it uses an AMM (Automated Market Maker) similar to Uniswap and its bonding curve. 3
It is worth noting that PredictIt is capped at $850 dollar max risk per bet in order to comply with regulations and users need to KYC. They try to get around this by offering multiple ways to bet the same outcome like "Will Donald Trump win the 2020 presidential election" along with "Will the republican nominee win the 2020 presidential election". Augur and Polymarket do not suffer from these constraints as they are decentralized, but they have less volume and liquidity than PredictIt.
Election Primer
It is shocking how much volume and liquidity there is for these some of these events, because they are often mispriced relative to other correlated bets as well as in absolute probability. Below is a screenshot of the Polymarket presidential market and the correlated markets taken November 6th 2020. Note that a yes of $0.12 means the yes outcome has a 12% chance of occurring.
Joe Biden, the democratic contender, is on the eve of winning the election and the only ballots left to count are the mail-in ballots and provisional ballots which heavily lean democrat. At this point, most of the battle ground states (Florida, Michigan, Wisconsin) had been called4, or about to be called. For all intensive purposes, Joe Biden wins this election if he wins either Georgia, Arizona, or Pennsylvania whereas Trump would need all three states.
Election Modeling
Relative Probabilities
Instantly it is obvious that something is wrong with these odds. If we use relative probabilities implied from the state odds, then we can calculate Biden's chances as follows:
Assuming the probabilities of winning each state are independent, the probability that Biden wins the election is the reciprocal of the probability that he losses all the states. Nonetheless, some simple hard arbitrage betting strategies can be created like buying Biden winning the presidential election and Trump winning Pennsylvania. In this scenario, if Biden wins Pennsylvania, he automatically wins the presidential election and the market is hedged, we make $.12 off Biden's presidential victory and lose $.12 off Trump's Pennsylvania victory. If Biden losses Pennsylvania, then we make $.88 off Trump's Pennsylvania victory and can potentially still win the presidential election, but even if he loses the loss is still only $.12 for a total profit of $.74.
Absolute Probabilities
Pollsters often use advanced models to try and predict the probabilities of the election race. Some of the more popular/famous ones include Nate Silver, who famously predicted all of the races correctly in 2012 and 49/50 correctly in 2008. How these models build fundamental predictions from the bottom up is more advanced than the scope of this blog, but the gist is that they take state polls and try to adjust for various polling biases like education and race. Here is a video of him calling the Pennsylvania race 1/20,000 chance for Trump making both the Biden Presidency and Biden Pennsylvania lines very mispriced. At this point, Biden was ahead of Trump with only mail-in and provisional ballots left and Nate Silver like most political analysts saw no chance for Biden to lose, but the market had Biden only at 88% to win the election.
Conclusion
There is a lot of edge in these prediction markets both from an absolute value perspective and from a relative value perspective. These markets have started to generate a lot of interest and I expect volume and liquidity on these platforms to grow in the coming years. I see modeling these markets as no different than sports betting because they both have defined payouts at fixed ending times.
- Sports betting is a subsection of prediction markets. ↩
- Other than the sports betting sites ↩
- I will dictate a future series of posts to AMM's as they have very interesting principles. ↩
- Biden's margin of victory in Wisconsin was below the automatic threshold so there would be an automatic recount triggered by state law, but in the history of recounts those have not changed the final tally by over one thousand whereas his margin of victory in Wisconsin was over twenty thousand. ↩


--- title: resume permalink: "resume/index.html" layout: default ---
I'm Ruby On Rails developer. At this time I write code at Shakuro
- Ruby on Rails
- RSpec
- JavaScript
- EmberJS
- HTML, CSS, Bootstrap
- OSX, UNIX
- Vim
- Heroku
2011 – 2014 Adyax - Automation Engineer
2014 – 2015 KodoLabs - Ruby on Rails developer
Since 2015 Shakuro - Ruby on Rails developer
linkedin: https://linkedin.com/in/gorodnichy
twitter: https://twitter.com/dgorodnichy
email: gorodni4ij@gmail.com


| | |
- lobpy.models.loblinear.OrderVolumeMeanRev(lobpy.models.loblinear.LOBLinearTwoFactor)
-
- PriceModel
class PriceModel(lobpy.models.loblinear.OrderVolumeMeanRev) |
| |
This class provides the price models which are induced by the linear SPDELOB models in loblinear.py
---------
fields:
modelid: identifier string
profile_bid: bid profile, LOBProfile object
profile_ask: ask profile, LOBProfile object
dynamics_bid: bid dynamics of depth, LOBFactorProcess object
dynamics_ask: ask dynamics of depth, LOBFactorProcess object
rho: correlation between bid and ask side |
| |
- Method resolution order:
- PriceModel
- lobpy.models.loblinear.OrderVolumeMeanRev
- lobpy.models.loblinear.LOBLinearTwoFactor
- lobpy.models.loblinear.LOBLinear
Methods defined here:
- __init__(self, modelid='PriceModelID', num_levels=2, p0=0.0, tick_size=0.01)
- Optional parameter for initialization:
modelid="PriceModel", name of the price model
num_levels=2, number of levels considered for depths modeling
p0=0. initial price level
- get_depth(self)
- Returns the initial depth
- get_vol(self)
- Returns the mid price volatility.
- prob_upmove(self, t1, threshold=0.0)
- Returns the probability that the price is larger than the threshold at time point t1
- set_depth(self, depth_bidask)
- Set the initial depths on bid and ask side
arg: depth_bidask = (depth_bid, depth_ask)
- set_p0(self, p0)
- Set mid price p0
- simulate(self, dt, num_tpoints, out=None, ret_depths=False)
- Simulate a trijectory of the price, with grid size dt and using num_tpoints future time points. Optionally one can give a numpy array out of length num_tpoints + 1 in which the path will be stored. If ret_depths is True then also the simulated depths processes for bid and ask side are returned.
- simulate_depth(self, dt, num_tpoints, return_bmincr=False)
- Simulate a trijectory of the depth for bid and ask side using Euler scheme. This function calls simulate_dynamics from OrderVolumeMeanRev.
----------
args:
dt grid size
num_tpoints number of time points on the grid
return_bmincr if True, then also the sample paths of the ind. BMs used for simulation will be returned
output:
path_bid, path_ask numpy arrays with the simulated paths
or
path_bid, path_ask, dw1t, dw2t
Methods inherited from lobpy.models.loblinear.OrderVolumeMeanRev:
- get_gamma(self)
- get_modelpar_dict(self)
- returns the model parameters in dict format.
Touples are formatted as (bid_value, ask_value)
- get_profilefct(self)
- Not implemented
- get_profilefct_ask(self)
- Not implemented
- get_profilefct_bid(self)
- Not implemented
- init_leastsq(self, profile_data_bid, profile_data_ask)
- Not implemented
- init_tv_argmax(self, profile_data_bid, profile_data_ask)
- Not implemented
- init_tv_leastsq(self, profile_data_bid, profile_data_ask)
- Not implemented
- init_tv_rmax1(self, profile_data_bid, profile_data_ask)
- Not implemented
- set_modelpar_dict(self, pardict)
- sets model parameter to the parameter given in dict format
Touples are formatted as (bid_value, ask_value)
Methods inherited from lobpy.models.loblinear.LOBLinearTwoFactor:
- __str__(self)
- get_modelid(self)
- get_mu(self)
- get_nu(self)
- get_rho(self)
- get_sigma(self)
- get_z0(self)
- Returns bid and ask values for z0
- loadf(self, filename)
- Creates and loads a model from a file in json format with name filename.lobm
- savef(self)
- Saves model in a file with name modelid.lobm using json
- set_gamma(self, gammas)
- set_modelid(self, modelid)
- set_mu(self, mus)
- set_nu(self, nus)
- set_rho(self, rho)
- set_sigma(self, sigmas)
- set_z0(self, z0s)
- Set bid and ask initial values for z0
- simulate_dynamics(self, dt, num_tpoints, return_bmincr=False)
- Simulate a trijectory of the depth for bid and ask side using Euler scheme.
----------
args:
dt grid size
num_tpoints number of time points on the grid
return_bmincr if True, then also the sample paths of the ind. BMs used for simulation will be returned
output:
path_bid, path_ask numpy arrays with the simulated paths
or
path_bid, path_ask, dw1t, dw2t
| |


HTTP_STATUS_CODE
Oops, something blew up
We're sorry. Try refreshing the page.
If you keep seeing this,
shout at us on Slack
.


From Miss Stella Robinson ABIDJAN,IVORY COAST WEST AFRICA,
Dear Chosen One, How are you and your activities hope all is fine . Dear even if we never knew each other before, I believe firmly that on the bases of the right of humanitarian assistance that a real confidence can be built from our communication and equally a good partnership between us.
I wish to request for your assistance in a financial transaction. I wish to invest in Manufacturing and real estate management in your country.
I have fifteen Million United State Dollars. USD($<IP_ADDRESS>) to invest in the extablishment and I will require your assistance in receiving this fund in your account and extablishing in your country.This fund was deposited in the Security Company by my late father before his sudden death. Your percentage in assisting me is negociable as we proceed. I would like to count on your human, virtuous and professional qualities. Please reply as soon as possible in this email address
Thanks and God bless you for your kind heart to help and Orphan Like me.
Yours friendly,
Miss. Stella Robinson


{% extends "mails/messages/activity_base.html" %} {% load i18n %} {% block content %}
{% blocktrans context 'email' %}We are very curious to know what impact you managed to make with your activity. Please share your results via your activity page.{% endblocktrans %}
{% endblock %}


*There are inconsistencies between the quantities in this visualization and the search results on the catalog. These are due to a number of factors including inconsistent legacy metadata in the catalog itself and a lack of flexibility in the search string of the url. The next version will use API calls to read accurate quantity information and build more precise search urls.


Weclcome to the Vanessa Talcott-Kendrick Dressage Website!
VTK Dressage services are located in Orlando, Oviedo, and Central Florida.
VTK provides private lessons on your horse or dressage schoolmaster.
Vanessa has over 20 years experience working, training, and teaching the art of dressage.
Please enjoy browsing our website!
Vanessa is a Brazilian-American with dual citizenship having been eduacted in Brazil and the United States.
Having trained and competed early in her life with Hunters/Jumpers, she switched over fully to
Dressage in the year 2000. She has successfully trained and competed through Prix St.
George and has worked with many accomplished professionals in both the Hunter/Jumper world and Dressage.
In her past, she has been a working student for many Dressage greats,
and has been teaching and training since 1994, for over 23 years.
Teaching and training is her passion, and as she continues to grow as a horsewoman,
believes when you stop wanting to learn, you stop all possibilites of progressing.
Good horsemanship crosses the disciplines.


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---
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Previous
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Kayaking on the Sudbury River! Concord MA, July 2019
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Kayaking on the Sudbury River! Concord MA, June 2018
Sometimes, we have fun! Some photos from those moments:
Kayaking on the Sudbury River! Concord MA, June 2018
Yi-Fen and Artem, a QTIM alumnus now working at the Center for Clinical Data Science, go candlepin bowling.


Vai's Blog
Emotional Intelligence
Date: 17th April 2016
What learnings did you take from the chapter?
The most important leaning for me from the chapter is that EQ is trainable. Why this is important to me is because I feel that I need to really work on improving my EQ and the faith/ the conviction that it is trainable is hence, very important.
I have to work on developing high resolution awareness of developing awareness as soon as they arise. And for this I have to practice Mindfulness.
- I shall start by paying more attention to my thoughts and emotions.
- I will consciously try to keep my mind in the present in all my day to day activities.
- If I have any distractions then I will try and recover from them promptly.
- Develop emotional stabilisation by trying to reflect on doing it before I do it.
How did you find the two minutes of mindfulness?
I tried meditation and it was not easy for me. I wanted mind brain to just be as is where is and not to think about anything but as you can figure out it didn’t work well for me... I kept on going back to thoughts that I haven’t spoken to mom and dad today, what is that I’ll make for dinner, and how did my sisters exam go today….
Then I thought instead if aiming for the blank I’ll start by focussing on my breathing, and to make sure that I don’t waver I started counting my breathing and kept my closed eye focused on my nose (if that’s even possible... ). I think I must have counted till about 70 and then I don’t know why I stopped counting but I was in a vacuum and sometime after that my timer went off and that’s when I felt that it went of pretty fast and I would have liked to be in that state for some more time.
I am going to try and practice this every day from now on…


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OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EDX | | edx_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EDX | | efg | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EFG | | efg_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EFG | | efg_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EFG | | efm | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EFM | | efm_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EFM | | efm_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EFM | | efn | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EFN | | efn_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EFN | | efn_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EFN | | egr | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EGR | | egr_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EGR | | egr_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EGR | | eii_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EII | | eii_ImageObjectName | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EII | | eii_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EII | | eim | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EIM | | eim_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EIM | | eim_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EIM | | emm | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EMM | | emm_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EMM | | emm_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EMM | | emo | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EMO | | emo_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EMO | | emo_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EMO | | eng | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.ENG | | eng_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.ENG | | eng_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.ENG | | eoc | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EOC | | eoc_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EOC | | eoc_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EOC | | eog | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EOG | | eog_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EOG | | eog_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EOG | | epg | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EPG | | epg_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EPG | | epg_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EPG | | epm | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EPM | | epm_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EPM | | epm_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EPM | | eps | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EPS | | eps_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EPS | | eps_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EPS | | ept | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EPT | | ept_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EPT | | ept_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.EPT | | er | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.ER | | er_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.ER | | er_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.ER | | erg | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.ERG | | erg_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.ERG | | erg_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.ERG | | esg | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.ESG | | esg_ | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.ESG | | esg_Type | OpenAFP, OpenAFP.Records, OpenAFP.Records.AFP, OpenAFP.Records.AFP.ESG |
|


What is paired programming?
Pair programming has gained in popularity among companies as enabling developers to learn quickly and make fewer mistakes, but it's not as easy as it may seem. Pair programming can enable both partners to learn and grow if communication is open, friendly, and based on mutual trust and respect.
Why paired programming?
There are so many reasons, but I’ll discuss the top three:
Learn faster: How would you like to learn Java from James Gosling or C# from Anders Hejlsberg? Pairing is the best way to learn a new platform (wait, you didn’t think it was lecture-style did you?). By placing you at the same keyboard and monitor as the Java wizard who sits in the corner, both driver and navigator benefit. The navigator (also well-versed in software engineering, just not Java) brings the same benefits to the driver as a second set of eyes, constantly probing and questioning, “why are things are done that way?” Inevitably the old rules and ineffective habits of the driver are broken. The navigator learns Java faster than they could in the classroom, faster than books and faster than on their own.
Transfer domain knowledge: The old adage used to be that engineers should focus on one area of specialty and it was rare to find engineers who were good at more than one thing (i.e. like an engineer who was great at both JavaScript and back-end database optimization). Pairing dispels this old paradigm. Pair a front-end engineer (Anisha) with a back-end engineer (Brian) for a few months and you’ll see Anisha cranking out prepared statements and Brian working on HTML5 optimizations in no time. Don’t believe me? Come and pair on a project in a system you’ve never worked on before.
Intensity: It’s so hard to concentrate these days. With Twitter/Facebook/email/YouTube/Tumblr/IM/etc, I’m not sure how any work gets done. What if I could put a super-sharp engineer beside you to help you focus on the tasks at hand? Now what if the two of you could bounce ideas off each other and unblock each other faster than looking up answers on Google? No, those services aren’t blocked, it’s just with two people working together the urge to check personalized services (including email) usually waits until a creative break. i.e. more time on task.
A short comparison between Driver & Navigator roles
A sucessful pair programming session is heavlily dependent on the sucess of the team's roles as driver and naviagtor.
The driver is the programmer with his or her hands on the keyboard, listening for directions from the navigator. This distinction is important because it eliminates the confusion and mess of both programmers typing at once. This allows for collaboration and a flow of ideas between the pair and an effiecnt way to keep the work process effiecnt. By only having the driver type, the navigator is freed up to search for erros within the code, research topics, and view rescoures like wireframes, etc. The open dialouge between driver and navigator makes the pair programming process smooth and easy. To make sure both programmers are expirencing both roles, switiching between driver and navigator is encouraged and expected.


Cholesterols are chemicals in the blood that help our cells make essential hormones.
Eating an excess of fatty foods increases our chances of having too much bad cholesterol; this increases our risk of heart disease (OCR; see Section 3.3).
Exercise for health
Exercise helps us to keep healthy by using energy, building muscle mass and raising our metabolic rate.
Regular exercise reduces our overall levels of blood cholesterol and helps us to maintain an ideal weight.
When we exercise our good cholesterol increases and our bad cholesterol decreases.
Our heart also strengthens with exercise: it can pump the same amount of blood around the body with fewer beats per minute (see ‘heart rate’, Section 3.3).
Inherited factors
Inherited factors in our bodies include the proportion of muscle to fat, and our metabolic rate.
Our livers produce cholesterol from the fat we eat; the balance of good and bad cholesterol produced is inherited.
A very small proportion of the population has high bad cholesterol no matter what they eat; this is an inherited problem.


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Question 1
What motivates you to get out of bed in the morning?
Question 2
Do you like to go out every night?
Question 3
Do you know to dance?
Question 4
Do you enjoy to go to the beach?
Question 5
Would you like to parachute?
Question 6
You know how to cook?
Question 7
Do you like adventures like walking around the world?
Question 8
Do you like to spend time with your family?
Question 9
Do you have many friends?
Question 10
Do you enjoy solitude?


Adobe Creative Suite
| Located: | Charlotte, NC. | |
| Phone: |<PHONE_NUMBER> | |
| Email: |<EMAIL_ADDRESS>|
Web developer with more than 3 years of well-rounded experience as a Certified Full Stack Web Deveopler. A degree Graphic Design and Marketing, extensive knowledge of modern Web techniques with the love for Hot Chocolate. Seeking an opportunity to work full-time with an talented and passionate group of developers/desingers creating something awesome everyday. I believe in great leadership and teamwork, as well as being involved in an organization that believes in gaining a competitive edge and giving back to the community.
| Located: | Charlotte, NC. | |
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Adobe Creative Suite
JavaScript:
Word Press:
HTML5/CSS3
Eat DemBurger App Desktop
Fascinating Friend Finder App
MovieNow App


There are two datasets for the TREC 2016 Dynamic Domain Track: Ebola Dataset and Polar Dataset.
Basic statistics of these datasets are shown in the Table 1-1. These
datasets contains records in forms of web pages, scientific data, PDFs, and tweets. All the datasets are
formatted using the Common Crawl Architecture schema from the DARPA MEMEX project, and stored as
sequences of CBOR objects. Detailed Description, as well as some links to sample code, of each dataset
is given in the following subsections.
Table 1-1 Datasets Statistics
| Datasets |
Size of Data on Disk |
Number of Records |
| Ebola |
12.6 GB |
682,157 |
| Polar |
158 GB |
1,741,530 |
1.1 Ebola Domain
This data is related to the Ebola outbreak in Africa in 2014-2015. The dataset comprises tweets
relating to the outbreak, web pages from sites hosted in the affected countries as well as PDF
documents from websites such as The World Health Organization, Financial Tracking Service and The
World Bank. Such information resources are designed to provide information to citizens and aid
workers on the ground.
- Company/POC for crawl: Juliana Friere and Kien Pham, NYU (juliana dot freire at nyu dot
edu); Peter Landwehr
peter dot landwehr at giantoak dot com); Lewis McGibbney, JPL (Lewis dot J dot Mcgibbney at
jpl dor nasa dot gov)
- Version: 1.0
- Purpose of data: represents an emerging humanitarian assistance situation.
- Four disjoint parts:
- Ebola-web-01-2015: web pages crawled during January 2015.
- Ebola-web-03-2015: web pages crawled during March 2015. These two parts are
primarily information from NGO's, relief agencies, and news organizations.
- Ebola-pdfs: PDF documents collected from West African government and other sources.
- Ebola-tweets: Tweets that originate from West African regions involved in the Ebola
outbreak.
- Time frame: January - March 2015
- Geographic area: global, primarily West Africa
- Size of data on disk: 12.6 GB
- Format: Gzipped sequence of CBOR records; sequence of tweet pointers
- Number of items: 497,362 web pages, 19,834 PDFs, 164,961 tweets.
- Schema notes: The web and PDF subsets only contain the raw crawled data. The tweets subset
as distributed only contains pointers to tweets, because Twitter does not allow
redistribution of tweets. You will need to use the Twittertools crawler (link here) to get
the actual tweets. The output of that crawler includes both the HTML pages from Twitter, as
well as extracted tweet data in the 'features' block.
1.2 Polar Domain
This dataset is a set of web pages, scientific data, zip files, PDFs, images, and science code
related to the polar sciences and available publicly from the NSF funded Advanced Cooperative Artic
Data and Information System (ACADIS), NASA funded Antarctic Master Directory (AMD), and National
Snow and Ice Data Center (NSIDC) Arctic Data Explorer. More information about this dataset can be
found
here.
- Company/POC for crawl: Chris Mattman, JPL (chris dot a dot mattmann at jpl dot nasa dot
gov)
- Version: 1.0
- Purpose of data: represents a domain for open science and scientific data search.
- Description: Web pages, data files, zip archives, PDFs, images, and code.
- Number of items: 1,741,530 records.
- Time frame: September 2014 - May 2015.
- Geographic area: global
- Size of data on disk: 158GB.
- Format: Gzipped sequence of CBOR records, encrypted with the KBA StreamCorpus key. Crawled
data were put into Common Crawl Format, acording to Memex format, using the CommonCrawlDataDumper.
The CommonCrawlDataDumper is an Apache Nutch tool that can dump Nutch segments into Common
Crawl data format, mapping each crawled-by-Nutch file on a JSON-based data structure.
CommonCrawlDataDumper dumps out the files and serialize them with CBOR encoding, a data
representation format used in many contexts.Each contributed web crawl has an accompanying
JSON file that lists the total records, by mimeType. A program, aggregate.py, aggregates all
of the JSON files.
The documents in each dataset are stored in a structured format called the Common Crawl Architecture
(CCA), developed as part of the DARPA MEMEX project. Records in this schema follow this format:
{ 'key': 'ebola-03cad6ee34e9dc0aeb77e4c5d31aad2aa41f6ad819f23b8504612d6e6de8a18c',
'request': { 'body': None,
'client': { '...': '...' },
'headers': [ [ 'Accept-Language': 'en-US,en' ], [ '...', '...' ] ],
'method': 'GET'
},
'response': { 'body': '<!DOCTYPE html> <html lang=\'en\' class=\'js-disabled\'> <head> ... </html>',
'status': '200',
'headers': [ ['Content-Type', 'text\/html' ], [ '...', '...'] ],
},
'timestamp':<PHONE_NUMBER>L,
'url': 'http://www.nature.com/news/ebola-1.15750',
'indices': [
{ 'key': 'crawl', 'value': 'ebola' },
{ 'key': '...', 'value': '...'},
],
'features': [
{ '...': '...' }, { '...': '...' }
],
}
More or fewer fields are present in the different datasets depending on how they were created. Every
record in every dataset has a 'key' field (the "document number"), a 'response.body' with raw content,
the timestamp, and the source URL. They should also have an 'indices.key=crawl' field indicating which
dataset the document goes with. The 'features' block is meant to contain extracted or derived data. In
the Ebola tweets subset to hold a structured representation of the tweet, automatically extracted from
the raw Twitter HTML contained in the 'response.body'. In the Illicit-goods dataset, the features block
contains extracted posts and thread/post metadata from the raw HTML thread.
The documents are stored in files that each contain a stream of
CBOR
records that follow the CCA format above. CBOR is a variation of JSON that supports binary data and a
has more efficient encoding than text.
Here is an example
using Python and the cbor library. More
example code in Python and Java can be found at
the TREC DD Github site.
To get access to the collections, you must be a TREC 2016 participant. Complete the
TREC DD
Organizational User Agreement (password protected, use the TREC participant password) and email it
as a scanned PDF or a high-quality digital photo to Angela (dot) Ellis (at) NIST (dot) gov, including
your TREC participant ID. Within a week you should receive access credentials to download the data, as
well as the decryption key to read the local-politics documents.
Local users of the data must complete the
Individual
User Agreement and return it to the organizational point of contact who will maintain those records.


{% block css %} {# additional css #} {% endblock %} {% block content %}
404 Panda Not Found
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Use the Wikipedia Tool Labs to display statistics about a wikipedia page over time, including contributors
On any Wikipedia page's history tab is a link to external resources.
This page takes you directly there, and links you to the Top Editors section.
Highlighted link to the XTools resource
This page is not affiliated with Wikipedia, the Wikimedia Foundation, or Wikipedia Tools


EditingOperation
问题描述 :
There are two strings: A and B. We define an editing operation as “Insert a character”, “Delete a character” or “Replace a character”.
So your task is to calculate how many editing operations do we need to make B become the substring of A? If the answer is more than the given limitation, just output -1.
So your task is to calculate how many editing operations do we need to make B become the substring of A? If the answer is more than the given limitation, just output -1.
输入:
Multiple test cases.
There are 3 lines in each test case:
String A is given in the first line, and the length of A is from 0 to 1000000.
String B is given in the second line, and the length of B is from 0 to 1000.
In the third line, there is an integer LIM. （0<= LIM <=30）
There are 3 lines in each test case:
String A is given in the first line, and the length of A is from 0 to 1000000.
String B is given in the second line, and the length of B is from 0 to 1000.
In the third line, there is an integer LIM. （0<= LIM <=30）
输出:
Multiple test cases.
There are 3 lines in each test case:
String A is given in the first line, and the length of A is from 0 to 1000000.
String B is given in the second line, and the length of B is from 0 to 1000.
In the third line, there is an integer LIM. （0<= LIM <=30）
There are 3 lines in each test case:
String A is given in the first line, and the length of A is from 0 to 1000000.
String B is given in the second line, and the length of B is from 0 to 1000.
In the third line, there is an integer LIM. （0<= LIM <=30）
样例输入:
annealing annual 3
样例输出:
1
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最主要是收了我大几十还是一百来块。再也不去惠普售后，我自己拆坏了也不拿去了。
最主要是收了我大几十还是一百来块。再也不去惠普售后，我自己拆坏了也不拿去了。
最主要是收了我大几十还是一百来块。再也不去惠普售后，我自己拆坏了也不拿去了。
最主要是收了我大几十还是一百来块。再也不去惠普售后，我自己拆坏了也不拿去了。
多谢指出，已改正
题目需要求解的是最小值，而且没有考虑可能存在环
[…] 答案：http://www.acmerblog.com/interview-2-1549/ […]
网站做得很好看，内容也多，全。前段时间在博客园里看到有人说：网页的好坏看字体。觉得微软雅黑的字体很好看，然后现在这个网站也用的这个字体！nice!
if(j){
int ans=a ;
for(int x=j-1;x>=0;x–){
if(!a ) break;
ans=min(ans,a );
sum+=ans;
}
}
求解释，，dp的思路是什么呢？
21行中，j<10可改为j<10-i，减少比较次数
顺利测试通过了，多谢
居然看懂了
代码不对，仔细对比一下输入输出， 怎么会有‘‘printf("The winning moves are:");’’这种输出呢？
有限自动机在ACM中是必须掌握的算法，实际上在面试当中几乎不可能让你单独的去实现这个算法，如果有题目要用到有限自动机来降低时间复杂度，那么这种面试题应该属于很难的级别了。
第23行：
hash = -1是否应该改成hash[s ] = -1
因为是要把从字符串s的start位到当前位在hash中重置
修改提交后能accept，但是不修改居然也能accept


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|
BSL430.NET
1.2.1
|
Type that holds device information for GetDeviceInformation method. Used with FT_GetDeviceInfo and FT_GetDeviceInfoDetail in FTD2XX.DLL More...
Public Attributes | |
| UInt32 | Flags |
| Indicates device state. Can be any combination of the following: FT_FLAGS_OPENED, FT_FLAGS_HISPEED More... | |
| FT_DEVICE | Type |
| Indicates the device type. Can be one of the following: FT_DEVICE_232R, FT_DEVICE_2232C, FT_DEVICE_BM, FT_DEVICE_AM, FT_DEVICE_100AX or FT_DEVICE_UNKNOWN More... | |
| UInt32 | ID |
| The Vendor ID and Product ID of the device More... | |
| UInt32 | LocId |
| The physical location identifier of the device More... | |
| string | SerialNumber |
| The device serial number More... | |
| string | Description |
| The device description More... | |
| IntPtr | ftHandle |
| The device handle. This value is not used externally and is provided for information only. If the device is not open, this value is 0. More... | |
Type that holds device information for GetDeviceInformation method. Used with FT_GetDeviceInfo and FT_GetDeviceInfoDetail in FTD2XX.DLL
| string FTD2XX_NET.FTDI.FT_DEVICE_INFO_NODE.Description |
The device description
| UInt32 FTD2XX_NET.FTDI.FT_DEVICE_INFO_NODE.Flags |
Indicates device state. Can be any combination of the following: FT_FLAGS_OPENED, FT_FLAGS_HISPEED
| IntPtr FTD2XX_NET.FTDI.FT_DEVICE_INFO_NODE.ftHandle |
The device handle. This value is not used externally and is provided for information only. If the device is not open, this value is 0.
| UInt32 FTD2XX_NET.FTDI.FT_DEVICE_INFO_NODE.ID |
The Vendor ID and Product ID of the device
| UInt32 FTD2XX_NET.FTDI.FT_DEVICE_INFO_NODE.LocId |
The physical location identifier of the device
| string FTD2XX_NET.FTDI.FT_DEVICE_INFO_NODE.SerialNumber |
The device serial number
| FT_DEVICE FTD2XX_NET.FTDI.FT_DEVICE_INFO_NODE.Type |
Indicates the device type. Can be one of the following: FT_DEVICE_232R, FT_DEVICE_2232C, FT_DEVICE_BM, FT_DEVICE_AM, FT_DEVICE_100AX or FT_DEVICE_UNKNOWN