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There is no online voting facility for any category of voter. Overseas Indians may submit application for enrolment in form 6A online at nvsp.in or by using Voter helpline mobile app. To Cast the vote on the date of poll, an overseas elector may come to his designated Polling station with his Passport as document for identification.
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Performance benchmarks of COSMO¶
The performance benchmarks is used to monitor and track performance changes to the cosmo model. The plan fails if either the timing are 5% above the reference, or if there is a change in the model output (check for bit identity). The plan also checks if differences compare to the operational namelist have been made, in which case it will issue a warning.
The performance benchmarks use a local git repository (no remote) to track changes.
When you make changes or update something, please create a commit so others can
see what you did. environment/perf-benchmarks
uses a separate git repository with remotes.
Each testcase has a reference_float
folder with reference files. This makes it possible
to check whether the current files are OK. reference_float
should be kept up
to date when changes are made to testcases.
TIMINGS_float.cfg
defines the reference timings and thresholds. TOLERANCE_float
defines the tolerance when checking the output values.
Because we have regularly issues with file access permissions, you should use
fix_permissions.sh
before and after you made changes.
Folders & Git repos:
Performance benchmarks location:
/project/g110/benchmarks/COSMO-ORG_performance_benchmark/
Results location:
/project/g110/benchmarks/cosmo-org_benchmarks_results/
Jenkins plan: http://jenkins-mch.cscs.ch/view/cosmo/job/COSMO-ORG_performance_benchmark_daily/
Git perf-benchmarks repo: https://github.com/C2SM-RCM/perf-benchmarks


WITNESS LIST Public Education Committee (Subcommittee on Efficiency of Public Schools) August 25, 1998-1:30P Curriculum Management Audits On: Bell, Ellen (TASA) Hinojosa, Michael (Hays CISD) McLendon, Dan (TASA) Montgomery, Margret (TASA) Veselka, Johnny (TASA) Teacher Shortage Issues On: Haynes, Lynda (Austin ISD) Hinojosa, Michael (Hays CISD) Presley, Robert (Hays CISD) Stripling, Rosanne (Waco ISD) Tackett, Pamela (SBEC) Teacher Training Technology On: Thomas, Michael (Nat. Semi-Conductor)


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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'
data_dir = '/input'
# 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_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='inputs_real')
inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='inputs_z')
learning_rate = tf.placeholder(tf.float32, None, name='learning_rate')
return inputs_real, inputs_z, learning_rate
"""
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):
"""
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)
"""
# TODO: Implement Function
k_init = tf.contrib.layers.xavier_initializer()
#k_init = tf.random_normal_initializer(stddev=0.02)
alpha=0.2
with tf.variable_scope('discriminator', reuse=reuse):
#images shape is 28x28x3
x1 = tf.layers.conv2d(images, 64, 4, strides=2, padding="same", kernel_initializer=k_init)
bn1 = tf.layers.batch_normalization(x1, training=True)
relu1 = tf.maximum(alpha * bn1, bn1)
#print('Relu1 shape is ', relu1.shape)
#relu1 shape is 14x14x64
x2 = tf.layers.conv2d(relu1, 128, 4, strides=2, padding="same", kernel_initializer=k_init)
bn2 = tf.layers.batch_normalization(x2, training=True)
relu2 = tf.maximum(alpha * bn2, bn2)
#print('Relu2 shape is ', relu2.shape)
#7x7x128
x3 = tf.layers.conv2d(relu2, 256, 4, strides=2, padding="same", kernel_initializer=k_init)
bn3 = tf.layers.batch_normalization(x3, training=True)
relu3 = tf.maximum(alpha * bn3, bn3)
#print('Relu3 shape is ', relu3.shape)
#relu3 is 4x4x256
#flatten
flat = tf.reshape(relu3, (-1, 4*4*256))
logits = tf.layers.dense(flat, 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
"""
# TODO: Implement Function
k_init = tf.contrib.layers.xavier_initializer()
#k_init = tf.random_normal_initializer(stddev=0.02)
reuse = not is_train
alpha=0.2
with tf.variable_scope('generator', reuse=reuse):
#First Fully connected layer
x = tf.layers.dense(z, 4*4*512)
#print('x shape is ',x.shape)
#reshape
x1 = tf.reshape(x, (-1,4,4,512))
bn1 = tf.layers.batch_normalization(x1,training=is_train)
relu1 = tf.maximum(alpha * bn1, bn1)
#print('relu1 shape is',relu1.shape)
x2 = tf.layers.conv2d_transpose(relu1, 256, 4, strides=1, padding="valid", kernel_initializer=k_init)
bn2 = tf.layers.batch_normalization(x2,training=is_train)
relu2 = tf.maximum(alpha * bn2, bn2)
#print('relu2 shape is',relu2.shape)
x3 = tf.layers.conv2d_transpose(relu2, 128, 4, strides=2, padding="same", kernel_initializer=k_init)
bn3 = tf.layers.batch_normalization(x3,training=is_train)
relu3 = tf.maximum(alpha * bn3, bn3)
#print('relu3 shape is',relu3.shape)
#x4 = tf.layers.conv2d_transpose(relu3, 32, 2, strides=1, padding="same", kernel_initializer=k_init)
#bn4 = tf.layers.batch_normalization(x4,training=is_train)
#relu4 = tf.maximum(alpha * bn4, bn4)
#print('relu4 shape is',relu4.shape)
#Output layer: logits & tanh activated output
logits = tf.layers.conv2d_transpose(relu3, out_channel_dim, 4, strides=2, padding="same", kernel_initializer=k_init)
#print('logits shape is',logits.shape)
output = tf.tanh(logits)
return output
"""
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)
"""
# TODO: Implement Function
smooth = 0.1
g_model = generator(input_z, out_channel_dim, is_train=True) #fake
d_model_real, d_logits_real = discriminator(input_real, reuse=False)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
#print(input_real)
#print(input_z)
#print(d_logits_real.shape)
#print(d_logits_fake)
real_labels = tf.ones_like(d_model_real) * (1 - smooth)
# Calculate losses
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.ones_like(d_model_fake)))
#labels=tf.ones_like(d_logits_fake)))
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
labels= real_labels))
#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)))
#labels=tf.zeros_like(d_logits_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)
"""
# TODO: Implement Function
t_vars = tf.trainable_variables()
g_vars = [var for var in t_vars if var.name.startswith('generator')]
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
all_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
g_update_ops = [var for var in all_update_ops if var.name.startswith('generator')]
d_update_ops = [var for var in all_update_ops if var.name.startswith('discriminator')]
with tf.control_dependencies(g_update_ops):
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
with tf.control_dependencies(d_update_ops):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_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.
import time
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")
"""
# TODO: Build Model
step = 0
#inputs
inputs_real, inputs_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
#loss
#print('Data shape[3] is ',data_shape[3])
d_loss, g_loss = model_loss(inputs_real, inputs_z, data_shape[3])
#optimization
d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
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):
# TODO: Train Model
start_time = time.time()
step += 1
batch_images = batch_images * 2
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
#print('batch_z shape=', batch_z.shape)
# Run optimizers
_ = sess.run(d_train_opt, feed_dict={inputs_real: batch_images, inputs_z: batch_z, lr:learning_rate})
_ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z, lr:learning_rate})
if step % 80 == 0:
train_loss_d = d_loss.eval({inputs_z:batch_z, inputs_real: batch_images})
train_loss_g = g_loss.eval({inputs_z:batch_z})
print("Epoch {}/{} Step {}...".format(epoch_i+1, epoch_count, step),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g),
"... Time spent={:.4f}".format(time.time() - start_time))
if step % 200 == 0:
show_generator_output(sess, 25, inputs_z, data_shape[3], 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.
batch_size = 64
z_dim = 100
learning_rate = 0.0001
beta1 = 0.2
"""
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 = 0.001
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 3
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.


An R wrapper for 'libnabo', an exact or approximate k nearest neighbour library which is optimised for low dimensional spaces (e.g. 3D). 'libnabo' has speed and space advantages over the 'ANN' library wrapped by package 'RANN'. 'nabor' includes a knn function that is designed as a drop-in replacement for 'RANN' function nn2. In addition, objects which include the k-d tree search structure can be returned to speed up repeated queries of the same set of target points.
| Version: | 0.5.0 |
| Depends: | R (≥ 3.0.2) |
| Imports: | Rcpp (≥ 0.11.2), methods |
| LinkingTo: | Rcpp, RcppEigen (≥ <IP_ADDRESS>.0), BH (≥ 1.54.0-4) |
| Suggests: | testthat, RANN |
| Published: | 2018-07-11 |
| Author: | Stephane Mangenat (for 'libnabo'), Gregory Jefferis |
| Maintainer: | Gregory Jefferis <jefferis at gmail.com> |
| BugReports: | https://github.com/jefferis/nabor/issues |
| License: | BSD_3_clause + file LICENSE |
| Copyright: | libnabo is copyright 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland <stephane at magnenat dot net> |
| URL: | https://github.com/jefferis/nabor https://github.com/ethz-asl/libnabo |
| NeedsCompilation: | yes |
| Citation: | nabor citation info |
| Materials: | README NEWS |
| CRAN checks: | nabor results |
| Reference manual: | nabor.pdf |
| Package source: | nabor_0.5.0.tar.gz |
| Windows binaries: | r-devel: nabor_0.5.0.zip, r-release: nabor_0.5.0.zip, r-oldrel: nabor_0.5.0.zip |
| OS X binaries: | r-release: nabor_0.5.0.tgz, r-oldrel: nabor_0.5.0.tgz |
| Old sources: | nabor archive |
| Reverse imports: | angstroms, filling, mgwrsar, nat, nat.nblast, rcss, STMedianPolish, stplanr |
| Reverse suggests: | nat.templatebrains |
Please use the canonical form https://CRAN.R-project.org/package=nabor to link to this page.


Counterfeit goods to the tune of R7m seized in multidisciplinary operation in Port Elizabeth
by Kamva SomdyalaPort Elizabeth police are continuing their crackdown on counterfeit goods after their latest raid recovered items to the value of R7m.
On Friday, the police conducted a multidisciplinary operation at a store in Port Elizabeth.
According to police spokesperson Colonel Sibongile Soci, border police officers at the Port of Ngqura acted on information about a store trading in counterfeit goods.
"During the operation, counterfeit perfumes and branded clothing to the value of R7m were identified and confiscated.
"All the seized items were handed over to the brand attorney's representatives for further investigation," said Soci.
READ: Counterfeit goods valued at over R1m confiscated by police in Port Elizabeth
'Credible information'
He added the store's owners were warned about the consequences of dealing in counterfeit goods and their documentation was verified by the Department of Home Affairs.
Provincial police commissioner Lieutenant General Liziwe Ntsinga said: "Tough times are ahead for criminals who continue to undermine the authority of the state in the Eastern Cape. We have committed ourselves to stamp the authority of the state and will not allow our province to be a hideout for illegal traders fleeing from their provinces."
Ntshinga thanked members of the public for identifying and sharing "credible information with the South African Police Service".


var terah = {
name: "Terah",
age: 32,
height: 66,
weight: 130,
hairColor: "brown",
eyeColor: "brown"
}
var adam = {}
adam.name = "Adam"
adam.spouse = terah
terah.spouse = adam
terah.weight = 125
delete terah.eyeColor
var terah.children = {}
terah.children.carson = {}
terah.children.carson.name = "Carson"
terah.children.carter = {}
terah.children.carter.name = "Carter"
terah.children.colton = {}
terah.children.colton.name = "Colton"
adam.children = terah.children
var terah = {
name: "Terah",
age: 32,
height: 66,
weight: 130,
hairColor: "brown",
eyeColor: "brown"
}
var adam = {
name: "Adam",
spouse: terah,
}
terah.spouse = adam;
terah.weight = 125;
delete terah.eyeColor;
terah.children = {
carson: {name: "Carson"},
carter: {name: "Carter"},
colton: {name: "Colton"},
}
adam.children = terah.children;


My name is Muhammad Azis Husein or usually called 'Azis'. I am an ordinary man that always curious about new things. I love to learn more about technology and politics. When I was in school, I have read a book about Mr. Habibie's biography. Since that time, I promise to myself to be a better person in order to make Indonesia greater.
My responsible is to held action and do regeneration. Beside that, my task is to research more about political issues. I have some program such as Obrolan Santai, Syntax Error, Sekolah BEM Fasilkom, and some incidental action if needed. Obrolan Santai is like a talkshow and disussion about politics with the fun way. Syntax Error is a forum discussion through Line group that congest Fasilkom families who have interest in politics. Last, Sekolah BEM Fasilkom or usually callsed SBF is a program for our junior to internship in our departement where we could learn more about this departement.
Salam UI is stand for Nuansa Islam Mahasiswa UI, which means Salam UI is an Islamic institution on UI. My responsible is to hold all of stakeholders of Salam UI in national. Some of our stakeholders are Islamic community organization such as Nahdlatul Ulama (NU), Muhammadiyah, etc and/or Islamic institution from another college.


Xirsys Group Video Chat Example
Uses Xirsys ICE service to create a group video chat.
Copy and Share this URL:
Force TURN


HALFAOUINE: CHILD OF THE TERRACES A film review by Thomas E. Billings Copyright 1991 Thomas E. Billings
Synopsis: A kind-hearted story of the coming-of-age of a 12-year-old boy in modern Tunisia. Provides a rare look into sensuality and sexuality in modern Arab societies. Charming, frequently witty and funny, and very entertaining.
Tunisia/France (English subtitles), color, 1990, 98 minutes. Director/Writer: Ferid Boughedir
The story centers on Noura, a 12 year old boy who is small for his age. Because of his size and appearance, he is still permitted to accompany his mother to the women's bathhouse.  (I wonder whether the film was screened uncensored, i.e., including nudity, in Tunisia?)
However, Noura is growing up, and he will soon be banished from the women's baths, and the society of women, into the much harsher society of men. The story line centers on Noura's exploits as he explores his new and growing sexual desires. Typical of "hormone comedies," he has (two) male friends who are a bad influence on him, and encourage him to engage in various types of mischief. Noura's entry into the world of men is helped by two women: his mother's cousin, and a beautiful young woman hired as a maid in Noura's household.
Because of religious sensitivities, films from Arab countries usually cannot be as frank about sexuality and sensuality as Western films. In some ways that can be good, because it can force the filmmakers to use humor or wit to compensate. An example is the film THE CITADEL, an extremely funny and witty film about polygamy and the effects of uncontrolled sexual desire. What makes HALFAOUINE interesting is the direct view it provides of sexuality and sensuality in an Arab culture. The view is generally kind-hearted, and relatively upbeat.
In an interview published in the San Francisco International Film Festival Program Guide, the director, Ferid Boughedir explains his treatment of sexuality: "It has always fascinated me how everyday sensuality has always belied the inflexibility of official dogma.... At a time when cliches about the Arab culture are becoming as arbitrary as ever, I am tempted to talk about a Mediterranean society that can still be experienced today, a society that is luxuriant and tender, where humor and eroticism are always prevalent together with tolerance -- a word rather difficult to brandish about today."
The film has been compared to MY LIFE AS A DOG, another story of a young boy's coming-of-age. However, HALFAOUINE is more upbeat, and does not have the melancholy spirit of MY LIFE AS A DOG. Of the two films, I strongly prefer HALFAOUINE, for its pleasant humor and wit.
Because of its pleasant humor and wit, I recommend the film to a general audience. Overall, the movie rates in the very good/excellent range.
Print Source: Ervin, Cohen & Jessup; 9401 Wilshire Blvd; 9th Floor; Beverly Hills, CA 90064. (Note: a screening is scheduled for 5/9/91, at the Aquarius Twin Cinema, in Palo Alto, CA).
Reviewer contact<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


{#
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements. See the NOTICE file distributed with
this work for additional information regarding copyright ownership.
The ASF licenses this file to You under the Apache License, Version 2.0
(the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
#}
{% extends 'admin/model/edit.html' %}
{% block tail %}
{{ super() }}
{% endblock %}


UK weather forecast: Freezing fog to descend during weekend of torrential rain
The chilly weather is going to hit coastal regions as the sub-zero temperature brings freezing fog patches at the start of the day - while 89 flood warnings remain active
by Tiffany LoBritain will be hit by a freezing fog bringing sub-zero temperatures before a weekend of torrential downpours.
There are 89 flooding warnings that remain active following the wet weather - with 13 of them on the areas along River Avon in the south-west and 19 along the River Severn.
Met Office meteorologist Aidan McGivern said: "(There will be a) grey start to Saturday for many with further showers.
"Some brightness first thing along with the frost and some fog patches but for the rest of Scotland lots of cloud cover particularly across eastern and southern Scotland and some showers.
"Cloud for northern England, into much of Wales and here again there will be showers especially near coastal areas."
The most persistent showers on Saturday will be across south-east Scotland, north-east England and then in a zone from western Scotland into western coasts of Wales as well as Cornwall.
"By the start of Sunday a frost in places as well as some freezing fog patches that could be slow to clear," Mr McGivern added.
The Midlands will start the day with sunny spells that will spread across western Scotland and Northern Ireland.
The temperature will rise and get warmer over the next few days.
Five-day forecast...
Today
Most of southern UK and Scotland dry with sunny spells. Some heavy showers in the far west of Wales and Cornwall. Many other regions cloudier with some rain at times, especially northeast England. Less windy than yesterday and feeling chilly.
Tonight
Southern UK and much of Scotland and Northern Ireland cold with frost and fog patches. Elsewhere, cloudier and less cold with rain at times for some, mainly northern England
Sunday
Much of Scotland and Northern Ireland brightening up after early rain. Southern UK mostly fine again. In-between, cloudier with rain at times, mainly parts of northeast England and southeast Scotland.
Outlook for Monday to Wednesday
Rain clearing the east then Monday largely fine and dry. A widespread overnight frost followed by rain into the west Tuesday. Becoming windier and milder Wednesday but for many dry.


The Ten Best STEM Toys to Give as Gifts in 2019
Stretch young learners’ minds with everything from card games to robotic spheres
by Michelle Z. DonahueIn today’s computer- and data-centric world, coding is king. Yet it is no longer the sole domain of programmers in office cubicles, converted warehouses or frowsy basements; stringing together computer commands that make it all work is truly for everyone, it seems.
At any rate, that’s the impression one gets while leafing through the 2019 edition of Purdue University’s Engineering Gift Guide. Produced by Purdue’s INSPIRE Research Institute for Pre-College Engineering, the guide is now in its sixth year, and editor-in-chief and project lead Elizabeth Gajdzik says coding-focused items are definitely a strong trend in STEM-oriented products across the board.
“People are thinking about it for younger ages, as well—baby books, preschool toys to introduce coding and programming concepts,” Gajdzik says. She adds that companies are also increasingly responding to parents’ desire for more screen-free, unplugged toys—card games, block- and tile-based puzzles, and building kits, for example.
With so many toys branded as “STEM,” how can parents discern what is truly educational, or just an attempt to cash in on the buzzy catchword?
“The good companies explain what STEM skills or concepts their products teach,” Gajdzik says. On the box, in the instructions, or somewhere on the product website, look for key phrases: logic, problem solving, spatial reasoning, working collaboratively and computational, critical or creative thinking.
“Parents also need to know that a STEM toy doesn’t have to be something totally complicated,” Gajdzik adds. “Traditional wooden blocks, Lincoln Logs—LEGOs, all those things are awesome. But there are companies that add a twist and additional context.”
These are Gajdzik’s recommendations for the ten best new STEM toys (and a few honorable mentions) across different age levels.
0-4 Years
Honorable Mention:
5-7 Years
Honorable Mention:
Ages 8+
SimpleRockets2
One of a handful of apps that show up on this year’s list, SimpleRockets is available for PC, Mac and Android as well as mobile devices. Budding engineers learn about rocketry components, and how to design a successful launch vehicle. Want your rocket to be traveling 750 meters per second by the time it reaches an altitude of 10 kilometers, but be no more than 2 degrees off vertical? SimpleRockets2 has you covered. (SimpleRockets.com, $4.99)
Honorable Mention:
Best Overall STEM Toy
Having trouble seeing our list of STEM toys?


{% include "stweb/views/devices/display_device.html" %}
{% include "stweb/attributes/display_boxes.html" %}
{% include "stweb/views/devices/display_device_associations.html" %}
{% include "stweb/views/devices/display_devices_extended.html" %}
{% include "stweb/views/devices/display_networks.html" %}
{% include "stweb/passwords/display_passwords.html" %}
{% include "stweb/views/devices/display_rack.html" %}
{% else %}
{% include "stweb/views/devices/display_device.html" %}
{% include "stweb/attributes/display_boxes.html" %}
{% include "stweb/views/devices/display_rack.html" %}
{% include "stweb/views/devices/display_device_associations.html" %}
{% include "stweb/views/devices/display_devices_extended.html" %}
{% include "stweb/views/devices/display_networks.html" %}
{% include "stweb/passwords/display_passwords.html" %}
{% endif %}
{% include "stweb/attributes/display_attributes.html" %}
{% include "stweb/attributes/display_boxes.html" %}
{% if device_config_list %}
{% include "stweb/views/devices/display_config_list.html" %}
{% endif %}
{% if device_config_template_list %}
{% include "stweb/views/devices/display_config_template_list.html" %}
{% endif %}
{% include "stweb/configs/display.html" %}
{% include "stweb/permissions/display_permissions.html" %}
{% if device_password_associations.all %}
{% include "stweb/views/devices/display_password_associations.html" %}


Package com.jme.util
Class NativeObject
- java.lang.Object
-
- com.jme.util.NativeObject
-
- All Implemented Interfaces:
java.lang.Cloneable
- Direct Known Subclasses:
AudioData
,BufferObject
,Filter
,FrameBuffer
,Image
,Shader
,Shader.ShaderSource
,VertexBuffer
public abstract class NativeObject extends java.lang.Object implements java.lang.Cloneable
Describes a native object. An encapsulation of a certain object on the native side of the graphics or audio library. This class is used to track when OpenGL and OpenAL native objects are collected by the garbage collector, and then invoke the proper destructor on the OpenGL library to delete it from memory.
-
-
Field Summary
Fields Modifier and Type Field Description protected java.lang.Object
handleRef
A reference to a "handle".protected int
id
The ID of the object, usually depends on its type.static int
INVALID_ID
protected NativeObjectManager
objectManager
The object manager to which this NativeObject is registered to.protected static int
OBJTYPE_AUDIOBUFFER
protected static int
OBJTYPE_AUDIOSTREAM
protected static int
OBJTYPE_BO
protected static int
OBJTYPE_FILTER
protected static int
OBJTYPE_FRAMEBUFFER
protected static int
OBJTYPE_SHADER
protected static int
OBJTYPE_SHADERSOURCE
protected static int
OBJTYPE_TEXTURE
protected static int
OBJTYPE_VERTEXBUFFER
protected boolean
updateNeeded
True if the data represented by this GLObject has been changed and needs to be updated before used.
-
Constructor Summary
Constructors Modifier Constructor Description NativeObject()
Creates a new GLObject.protected
NativeObject(int id)
Protected constructor that doesn't allocate handle ref.
-
Method Summary
All Methods Instance Methods Abstract Methods Concrete Methods Modifier and Type Method Description void
clearUpdateNeeded()
Internal use only.protected NativeObject
clone()
This should create a deep clone.abstract NativeObject
createDestructableClone()
Creates a shallow clone of this GL Object.protected void
deleteNativeBuffers()
Deletes any associated nativebuffers
.abstract void
deleteObject(java.lang.Object rendererObject)
Deletes the GL object from the GPU when it is no longer used.void
dispose()
Reclaims native resources used by this NativeObject.int
getId()
abstract long
getUniqueId()
Returns a unique ID for this NativeObject.boolean
isUpdateNeeded()
Internal use only.abstract void
resetObject()
Called when the GL context is restarted to reset all IDs.void
setId(int id)
Sets the ID of the NativeObject.void
setUpdateNeeded()
Internal use only.java.lang.String
toString()
-
-
-
Field Detail
-
INVALID_ID
public static final int INVALID_ID
- See Also:
- Constant Field Values
-
OBJTYPE_VERTEXBUFFER
protected static final int OBJTYPE_VERTEXBUFFER
- See Also:
- Constant Field Values
-
OBJTYPE_TEXTURE
protected static final int OBJTYPE_TEXTURE
- See Also:
- Constant Field Values
-
OBJTYPE_FRAMEBUFFER
protected static final int OBJTYPE_FRAMEBUFFER
- See Also:
- Constant Field Values
-
OBJTYPE_SHADER
protected static final int OBJTYPE_SHADER
- See Also:
- Constant Field Values
-
OBJTYPE_SHADERSOURCE
protected static final int OBJTYPE_SHADERSOURCE
- See Also:
- Constant Field Values
-
OBJTYPE_AUDIOBUFFER
protected static final int OBJTYPE_AUDIOBUFFER
- See Also:
- Constant Field Values
-
OBJTYPE_AUDIOSTREAM
protected static final int OBJTYPE_AUDIOSTREAM
- See Also:
- Constant Field Values
-
OBJTYPE_FILTER
protected static final int OBJTYPE_FILTER
- See Also:
- Constant Field Values
-
OBJTYPE_BO
protected static final int OBJTYPE_BO
- See Also:
- Constant Field Values
-
objectManager
protected NativeObjectManager objectManager
The object manager to which this NativeObject is registered to.
-
id
protected int id
The ID of the object, usually depends on its type. Typically returned from calls like glGenTextures, glGenBuffers, etc.
-
handleRef
protected java.lang.Object handleRef
A reference to a "handle". By hard referencing a certain object, it's possible to find when a certain GLObject is no longer used, and to delete its instance from the graphics library.
-
updateNeeded
protected boolean updateNeeded
True if the data represented by this GLObject has been changed and needs to be updated before used.
-
-
Method Detail
-
setId
public void setId(int id)
Sets the ID of the NativeObject. This method is used in Renderer and must not be called by the user.- Parameters:
id
- The ID to set
-
getId
public int getId()
- Returns:
- The ID of the object. Should not be used by user code in most cases.
-
setUpdateNeeded
public void setUpdateNeeded()
Internal use only. Indicates that the object has changed and its state needs to be updated.
-
clearUpdateNeeded
public void clearUpdateNeeded()
Internal use only. Indicates that the state changes were applied.
-
isUpdateNeeded
public boolean isUpdateNeeded()
Internal use only. Check ifsetUpdateNeeded()
was called before.
-
toString
public java.lang.String toString()
- Overrides:
toString
in classjava.lang.Object
-
clone
protected NativeObject clone()
This should create a deep clone. For a shallow clone, use createDestructableClone().- Overrides:
clone
in classjava.lang.Object
-
deleteNativeBuffers
protected void deleteNativeBuffers()
Deletes any associated nativebuffers
. This is necessary because it is unlikely that native buffers will be garbage collected naturally (due to how GC works), therefore the collection must be handled manually. Only implementations that manage native buffers need to override this method. Note that the behavior that occurs when a deleted native buffer is used is not defined, therefore this method is protected
-
resetObject
public abstract void resetObject()
Called when the GL context is restarted to reset all IDs. Prevents "white textures" on display restart.
-
deleteObject
public abstract void deleteObject(java.lang.Object rendererObject)
Deletes the GL object from the GPU when it is no longer used. Called automatically by the GL object manager.- Parameters:
rendererObject
- The renderer to be used to delete the object
-
createDestructableClone
public abstract NativeObject createDestructableClone()
Creates a shallow clone of this GL Object. The deleteObject method should be functional for this object.
-
getUniqueId
public abstract long getUniqueId()
Returns a unique ID for this NativeObject. No other NativeObject shall have the same ID.- Returns:
- unique ID for this NativeObject.
-
dispose
public void dispose()
Reclaims native resources used by this NativeObject. It should be safe to call this method or even use the object after it has been reclaimed, unlessNativeObjectManager.UNSAFE
is set to true, in that case native buffers are also reclaimed which may introduce instability.
-
-


欢迎您的到来
| 主机名(host) | url地址 | cookie | User-agent | 状态码 | 操作 |
|---|---|---|---|---|---|
| http://localhost | http://localhost/bysj/index/index | 560001 | Tanmay | 200 | 手动注入 自动注入 盲注 |
| http://www.baidu.com | http://www.baidu.com | 560001 | Tanmay | 200 | 手动注入 自动注入 盲注 |
| 主机名(host) | url | service | 符合要求的链接 | 不符合要求 | 其他链接 | 操作 |
|---|
| 名称 | 城市 | 邮编 |
|---|---|---|
| Tanmay | Bangalore | 560001 |
| Sachin | Mumbai | 400003 |
| Uma | Pune | 411027 |
SQL injection system design
SQL injection name to know lots of people, but few really know people, people just know something about SQL injection of probably the most obvious cognition, cognition is also the most simple.On the other hand, because the employees don't attach importance to and do not understand, SQL injection is still one of the largest invasion threat website way.Based on the present situation, a website based on PHP + redis queue SQL injection system arises at the historic moment.
This system is a secondary attack system and learning system, based on BS architecture, the back-end using python injection tests, PHP test injection, according to the results and the front desk for Taiwan before and after using the bootstrap + AJAX interactions, using Apache server as a web server, is dedicated to helping people to understand, learn and use the SQL injection.
This article first introduces the background of the construction of the system, as well as the development of the technology used by the system, and then to the system requirements analysis and general design, system and database design.Finally listed the system implementation of the code, and test results, along with the summary of the development of the system.
SQL injection system based on the HTML page provides people with a platform of study and work, through the system can learn the knowledge of SQL injection, site information, scan the website loophole and SQL injection attacks.At the same time, because of their learning SQL injection attacks, can very good protection on their website.


Climate Change Research
EPA research improves knowledge of the health and environment effects of climate change and provides sustainable solutions for communities to effectively manage and reduce the impacts of a changing climate.
EPA research improves knowledge of the health and environment effects of climate change and provides sustainable solutions for communities to effectively manage and reduce the impacts of a changing climate.


Grilled peaches with vanilla ice cream and honey is a
perfect, simple and scrumptious summertime dessert.
(Instructions)
Last updated 3 mins ago
Prep Time: 45 mins
Cook Time: 30 mins
Total Time: 75 mins
Servings: 4
1.In a small bowl add the butter and stir until smooth. Add
the cinnamon sugar, granulated sugar and salt and mix until
combined.
2.Heat grill to high. Brush peaches with oil and grill
until golden brown and just cooked through. Top each with a
few teaspoons of the butter and garnish with mint leaves.
Honey Kasutera (Honey Castella)-fine textured Japanese
sponge cake raised solely by egg foam.
(Instructions)
Last updated 3 mins ago
Prep Time: 45 mins
Cook Time: 30 mins
Total Time: 75 mins
Servings: 4
1. Line a 7½ in x 3½in (9cm x 19cm) loaf pan with aluminum
foil followed by parchment paper. Preheat oven to 325°F
(165°C).
2. Place egg whites in mixing bowl and beat at high
speed (speed 10*) for 30 seconds until slightly foamy. Add
sugar in 2 to 3 increments until firm peaks form, about 4
minutes.
3. Add yolks one at a time at medium low
speed (speed 4*) until well combined, about 1 minute. Add
sifted bread flour and beat until just combined.
4.
Pour in honey mixture and continue to beat for 1 minute.
5. Pour batter through a sieve into cake pan. Press
with a spatula to help batter go through sieve. Tap pan on
the counter to remove air bubbles. Using a spatula, smoothen
the top of batter to remove any remaining bubbles.
6.
Bake in preheated oven for 45 minutes or until top is evenly
browned. Toothpick inserted into cake should come out clean.
7. Remove pan from oven and drop it from a one foot
height onto the counter to prevent shrinkage
8. When
cake is cool enough to handle, remove from pan. Peel off
parchment paper and turn it upside down onto a fresh piece
of parchment paper. Wrap with plastic wrap and leave
overnight at room temperature** to preserve moisture in the
cake and for flavor to develop.
9. To serve, cut off
edges on all sides except the top and bottom with a very
sharp knife. Cut into thick slices using a sawing motion.
These Honey Mustard Pretzel Pieces are bursting with flavor
and perfectly crunchy! This sweet and salty combination is
addictive!
(Instructions)
Last updated 3 mins ago
Prep Time: 45 mins
Cook Time: 30 mins
Total Time: 75 mins
Servings: 4
1. Preheat oven to 250 degrees.
2. Combine melted butter, honey, Dijion mustard, onion
powder, garlic powder and dried mustard. Pour over pretzel
pieces and stir until coated.
3. Pour onto a cookie sheet and spread out evenly. Bake for
1 hour and stir every 15 minutes.
These Honey Mustard Pretzel Pieces are bursting with flavor
and perfectly crunchy! This sweet and salty combination is
addictive!
(Instructions)
Last updated 3 mins ago
Prep Time: 45 mins
Cook Time: 30 mins
Total Time: 75 mins
Servings: 4
1. Preheat oven to 250 degrees.
2. Combine melted butter, honey, Dijion mustard, onion
powder, garlic powder and dried mustard. Pour over pretzel
pieces and stir until coated.
3. Pour onto a cookie sheet and spread out evenly. Bake for
1 hour and stir every 15 minutes.
These Honey Mustard Pretzel Pieces are bursting with flavor
and perfectly crunchy! This sweet and salty combination is
addictive!
(Instructions)
Last updated 3 mins ago
Prep Time: 45 mins
Cook Time: 30 mins
Total Time: 75 mins
Servings: 4
1. Preheat oven to 250 degrees.
2. Combine melted butter, honey, Dijion mustard, onion
powder, garlic powder and dried mustard. Pour over pretzel
pieces and stir until coated.
3. Pour onto a cookie sheet and spread out evenly. Bake for
1 hour and stir every 15 minutes.
Grilled peaches with vanilla ice cream and honey is a
perfect, simple and scrumptious summertime dessert.
(Instructions)
Last updated 3 mins ago
Prep Time: 45 mins
Cook Time: 30 mins
Total Time: 75 mins
Servings: 4
1.In a small bowl add the butter and stir until smooth. Add
the cinnamon sugar, granulated sugar and salt and mix until
combined.
2.Heat grill to high. Brush peaches with oil and grill
until golden brown and just cooked through. Top each with a
few teaspoons of the butter and garnish with mint leaves.
Grilled peaches with vanilla ice cream and honey is a
perfect, simple and scrumptious summertime dessert.
(Instructions)
Last updated 3 mins ago
Prep Time: 45 mins
Cook Time: 30 mins
Total Time: 75 mins
Servings: 4
1.In a small bowl add the butter and stir until smooth. Add
the cinnamon sugar, granulated sugar and salt and mix until
combined.
2.Heat grill to high. Brush peaches with oil and grill
until golden brown and just cooked through. Top each with a
few teaspoons of the butter and garnish with mint leaves.


Tuesday, 12 November 2019
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I'm currently a software engineer living in Seattle building something new.
I was previously an engineer for Amazon/AWS.
I like to learn, read books, and code.


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The below visualization contains data about the flights that happened in June 1994. Clicking on one of the edge nodes will change the visualization to only include that airport and any flights that go in and out of that airport. Due to a high number of flights and a relatively large dataset, colors are not shown until you dive deeply enough into the dataset(less 60 items).
Scroll through this list to be select airports that you want to explore! Hovering over one of the edges of the diagram will highlight which airport you are hovering over. Airports are listed in order clockwise from 12:00.


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Brain Hemispheres
15 Sep 2014Messed around as much as I could with Unity's built-in image effects. Once I found the Edge Detect Effect Normals filter and saw it's effects, I figured this would be a good time to make a game where there are two cameras side by side, each one corresponding to the left and right eye, and make them view the world in different ways. Lateralization of Brain Function is a fun fact of the brain that pop-psychology has fallen in love with in the past few years. The left hemisphere is mathematical and logical, the right hemisphere is creative and artsy.
The right hemisphere actually controls the left side of the body, and vice versa, so I reversed the left-right split. Scientific accuracy and realism are very important principles in this simulation, as you can clearly see.
The right camera is using only the edge detect script, with one important variable maxed out to the point where everything is whited out except for edges, while the left camera is using just about every other filter in existence. Namely, Glow Effects, Noise and Grain, Sun Shafts, and Vignetting. I also experimented with Camera Motion Blur, Bloom, and a few others, but they were either redundant or wonky.
I also played with camera culling to make certain objects only viewable with one eye. Not sure how a game would actually use this. The challenge would be something like, "How quickly can you dart your eyes back and forth?  Do you have what it takes notice differences? Try Left-And-Right Brain Simulator and find out... if you've got the balls."
I'm pretty sure this would sell out in seconds.


TacoPig
TacoPig is a simple, object-oriented Gaussian process MATLAB toolbox.
Model
Structure
Figure 1 illustrates the
structure of the Gaussian process model used by TacoPig.
Figure 1: TacoPig's Gaussian
process model structure.
At the modelís centre is a
Gaussian process class instance which contains the key functions for inference
and learning as well as the properties such as the training data. Typical
examples of this type of class are the tacopig.gp.Regressor
and tacopig.gp.ClassifierLaplace (not ye implemented)
which perform regression and classification, respectively.
The GP class instance needs 4 plug-ins to operate.
1.† Mean Function
2. Covariance Function
3. Noise Function
4. Objective Function
A Gaussian process is defined
completely by a mean function and a covariance function. A Gaussian Process can
be written as
Where the function f is
distributed as a Gaussian process with mean function m(x) and covariance
function k(x,x*).
Noise is often present in real
data and needs to be accounted for to enable the model to learn the true
underlying function.
Learning the hyperparameters
of the Gaussian process is often done by minimising some objective function.
Getting
Started
Add the TacoPig
tool box to MATLABís path.
Demos can be found in +tacopig/+demos
Open the demos and run them to
see how the Gaussian process model is initialised, trained and queried.
Creating a
Gaussian Process Instance
TacoPig consists of a number of classes that can be
combined to create a Gaussian process instance in a similar manner to that
illustrated in Figure 1. Figure 2 shows the range of classes implemented in
Version 0.1 of TacoPig.
Figure 2: The classes implemented in TacoPig Version 0.1
A Gaussian process instance can
be initialised by combining at least one block from each of the 5 class groups
(Gaussian Processes, Mean Functions, Covariance Functions, Noise Functions and Objective
Functions)
For example, Figure 3 shows the
structure of a Gaussian process regressor
instantiation with a stationary mean and noise functions. Its covariance
function is the squared exponential and during parameter/hyperparameter
learning, the optimiser attempts to minimise the negative log marginal
likelihood.
Figure 3: Structure of a Gaussian process
regression with stationary mean and noise functions, a squared exponential
covariance function and the negative log marginial
likelihood function is minimised when learning the parameters.
GP = tacopig.gp.Regressor;
†
% Plug in the components
GP.MeanFn† = tacopig.meanfn.StationaryMean();
GP.CovFn†† = tacopig.covfn.SqExp();
GP.NoiseFn = tacopig.noisefn.Stationary();
GP.objective_function = @tacopig.objectivefn.NLML;
Figure 4 shows a more complicated
structure of a Gaussian process regressor
instantiation with a fixed mean function (its parameters will not change during
the learning phase) and a LogStationary noise
function with certain parameters clamped (in the code below, the first
parameter of the LogStationary noise function is
clamped to 1.2). The covariance function consists of a summing of the
exponential covariance function and a Matern5 covariance function with some of
its hyperparameters remapped (in the code below the
first and second hyperparameters are remapped to
always have the same value, the third hyperparameter
is independent.). Cross-validation is used by the optimiser during the learning
phase.
†
Figure 4: Structure of a Guassian
process regressor with a fixed mean function, a clamp
logstationary noise function, a covariance function
that is the sum of an exponential cov function &
a remapped Matern5 cov function. Cross val is used by the optimiser.
GP = tacopig.gp.Regressor;
†
% Plug in the components
GP.MeanFn† = tacopig.meanfn.FixedMean();
GP.CovFn†† = tacopig.covfn.Sum(tacopig.covfn.Exp(),tacopig.covfn.Remap(tacopig.covfn.Mat5(),[1
1 2]));
GP.NoiseFn = tacopig.noisefn.Clamp(tacopig.noisefn.LogStationary(),[1],[1.2]);
GP.objective_function = @tacopig.objectivefn.CrossVal;
Code
Documentation
To view the code documentation,
type:
doc tacopig
into the MATLAB command line after you have added
the toolbox to MATLABís path.
Contributing
to TacoPig
Bugs
----
Please submit bug reports using
the GitHub Issues feature. Be sure to first check
that the bug exists in the latest release, as it may have been fixed since you
last downloaded TacoPig. We strongly encourage you to
include code to reproduce the error if possible.
Contributing
Code
-----------------
We welcome contributions to TacoPig in the form of GitHub
pull requests. Before you create a pull request, please ensure you have
included the following:
1. Descriptive commit messages
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
†Describe what functionality you have added. If
you have fixed a bug,†
refer to the issue number using a hash: e.g. "fixed
#453". The standard format for a commit message is a short one-line
description of the commit, then a blank line, then a more detailed paragraph if
necessary.
2. Documentation
~~~~~~~~~~~~~~~~
Comments should be integrate with the MATLAB doc and help commands. Use the
comments included in the MATLAB scripts of TacoPig v0.1 as templates.
3. Test cases
~~~~~~~~~~~~~
The majority of TacoPig
contributions will add covariance, mean or noise function objects to the
code. A standard testing suite (tacopig.tests.standardtestsXML) is provided
for testing these plugin objects, and will produce an XML report if a
filename is provided as the first argument. standardtestsXML will
automatically detect new plugin objects provided they are placed in the
appropriate sub-package folders.
This standard test suite checks for common problems such as outputs that are
the wrong size, covariance functions that are not positive definite, or
gradients that do not agree with numerical differentiation.
However, if you are contributing a plugin with non-standard functionality, or a
new type of GP core object, then it will be neccessary to provide explicit
additional testing. Additional scripts should be placed in
+tacopig/+tests.
4. Your name as a contributor in the AUTHORS
file
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Contact
Alistair
<EMAIL_ADDRESS>Simon
<EMAIL_ADDRESS>Lachlan
lachlan.mccalman@nicta.com.au


Preparation time
1-2 hours
Cooking time
30 mins to 1 hour
Serves
Serves 4
Pork belly poached then fried with lots of Chinese flavourings, served here with quick pickled cucumbers.
For the pork, pour 700ml/1¼ pints water into a large pan, add the pork and bring to the boil. Boil for 30 minutes. Drain and leave to cool.
Transfer the meat to the fridge for an hour, to firm up.
Meanwhile, for the pickled cucumber, combine the garlic, sugar, vinegar, chilli bean paste, chilli oil and sesame oil in a bowl.
Add the cucumbers and leave to marinate in the fridge for 20 minutes.
Remove the cucumbers from the fridge and add the fresh chilli.
Remove the meat from the fridge and slice into 5mm/¼in thick slices.Heat a wok over high heat and add the groundnut oil, then the pork. As the pork starts to brown, add the rice wine (or sherry) and cook until the pork has browned and the skin is slightly crisp.
Add the chilli bean paste, yellow bean sauce and fermented black beans and stir-fry for a minute.
Add the spring onion (or leek) if using, and stir-fry for less than a minute until well mixed.
Add both soy sauces and the sugar, season and serve.
To serve, place a spoonful of rice onto the plate, spoon the pork over the top then the pickles alongside.


Smarter news
Google's Assistant can now present you a curated selection of audio news
Hey Google, play me the news.
by Muhammad Jarir KanjiWhat you need to know
- Google is adding a new way to get your daily news fix.
- The company is implementing an algorithm that automatically curates audio news based on your interests and profile.
- You can trigger Your News Update — what Google calls the feature — by saying, "Hey Google, play me the news."
Google sees a huge opportunity in developing audio content for its users — its latest blog ever refers to the 'audio web' — and it's making a rather significant change to how it provides audio news to users. You could already access audio feeds from a variety of publishers through Assistant, a feature that was added last year, but what's changed now is that Assistant can intelligently sort and curate the news for you.
Previously, you had to select specific publishers to subscribe to, and when you asked Assistant to play you the news, it'd simply play the latest hour-long segment from each publisher, in order. Think of that as listening to an entire album from an artist, before proceeding to a whole album from another artist. With 'Your News Update' — what Google's calling the new way of consuming audio news — Google takes all that audio and uses its machine learning chops to figure out the different topics an hour-long broadcast of NPR may touch upon. It then extracts the different stories from that whole and plays you short clips of only what it believes are the most relevant subjects to you, based on your interests, location, user history, and preferences. Think of this as a Spotify playlist made specifically for you, combining songs from all your favorite artists.
Google's also looking to make sure you don't just stay in your bubble, as the revamped news format will no longer be restricted to only the news sources you follow. It'll also source stories from the entire breadth of content from its many official partners — Al Jazeera, South China Morning Post, CNN, Wired, Reuters, and more — to identify the most important developments at the moment and include them in your feed, even if you aren't subscribed to a particular news source.
You can enable the new format by navigating to the 'You' tab in Assistant's settings. There, select 'News' and change your News playlist format to 'Your News Update.' If you're in the U.S. — Google says international expansion is coming next year — you should now be able to access the new audio experience by asking Assistant to "play the news." It'll start off with shorter stories to quickly bring you up to date with the latest developments in the world before proceeding to longer deep-dive content thereafter. Before each story, Assistant will identify the publisher from which it is sourced.
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| 1803 |
Convict colony in Tasmania (Van Diemen's Land) founded. |
| 1825 |
Convict colony in Brisbane (Moreton Bay) founded. |
| 1828 |
Census: 36,598, of whom 5,000 were actually not convicts. |
| 1922 |
A chemist is employed by the Fred Walker Company (later bought by Kraft) to develop a spread from brewer's yeast. The result is the foul product Vegemite. |
| 27 Jul 1953 |
Yahoo Serious born as Greg Pead, Hunter Valley, New South Wales Australia. |
| 11 Jul 1979 |
The derelict space station Skylab finally returns to Earth, ignominiously breaking into 500 separate fragments which are swallowed by the Indian Ocean. That is, except for the ones which crash into Woorlba Sheep Station, near Balladonia in Western Australia. Shortly thereafter, President Jimmy Carter telephones the prime minister of that country to apologize for scattering NASA litter on his nation. |
| 17 Aug 1980 |
Dingos snatch baby Azaria from a campground near Ayers Rock, Australia. Her mother, Lindy Chamberlain, is later convicted of murder and spends three years in prison, but the conviction is ultimately overturned. Apparently there have been a number of baby/dingo incidents over the
years, the dingo not differentiating animals from humans. (The fact that if you rearrange the letters in LINDY CHAMBERLAIN you get CHILDREN BY ANIMAL is just a coincidence.)
|
| 12 Nov 1992 |
Film Romper Stomper premieres. |
| 10 Aug 1994 |
Film Priscilla, Queen of the Desert premieres. |
| 19 Feb 1995 |
The Simpsons episode 2F13 airs, Bart vs. Australia. |
| 28 Apr 1996 |
After openly lamenting the dearth of Japanese tourists, Martin Bryant opens fire with an AR-15 (serial number SP128807) at the Broad Arrow Cafe in Port Arthur, Tasmania, killing 35 and wounding 18. |
| 21 Nov 1997 |
Lead singer of INXS Michael Hutchence found hanged in his hotel room at the Ritz-Carlton in Sydney Australia. Police have denied that his death was due to Autoerotic Asphyxiation. Hutchence is survived by his daughter, Heavenly Hirani Tiger Lily. |


Datalyze
Panes
Pro
Help
Who needs help with a tool like this?


The purpose of this "mini-project" assignment is to give you experience using Android to develop a complete Weather Service app using the Retrofit library, Android's Content Providers, and a SQLite database. Retrofit is used to obtain weather information from a Weather Service web service, and this data is then stored in a timeout cache that uses a database-backed Content Provider for storage. The user will provide a location as a String (such as "Nashville, TN"), and a WeatherData object will be returned to the WeatherActivity that can be used to display various current weather-related information for that location.
Retrofit is covered in the following videos:
Content Providers are covered in the following videos:The remaining material necessary to complete assignment 3 is available in videos from Weeks 3 and 4 of the MOOC.
Skeleton code for this assignment is available [*Update with correct location*]. Pull the appropriate skeleton code into your repository, read it carefully, and complete the "TODO" markers, which reside in the following Java source files: [*Update with correct files*].
Note that there are no automated unit tests for this assignment. Instead, you'll know the program is working when you can successfully access and store the weather data from the server. However, make sure your program works for both valid and invalid input.
This assignment is designed to deepen your understanding with the Retrofit library and Android's Content Providers. It's highly recommended to watch the videos listed above and look at the Acronym example [*Update location*] to gain a better understanding of how to structure your solution. Start early and come to Virtual Office Hours if you have questions or run into problems.


Manage application.conf in several environments
When you work in a team, different developers will use different configuration keys in their application.conf
. For example, the log level or some database configuration. This generally leads to recurrent conflicts when you commit the file using your VCS.
Furthermore, different deployment environments – e.g. dev, test, staging and production – need different configurations.
O ID do framework
To resolve this problem, Play allows you to give an ID to each framework installation. Use the play tool’s id
command to define this ID:
play id
You can then prefix your configuration keys with the framework ID for which the configuration option is intended:
application.name=Cool app
application.mode=dev
application.log=INFO
# Configuration for gbo
%gbo.application.log=DEBUG
%gbo.db=mem
# Configuration for src
%scr.http.port=9500
# Production configuration
%production.http.port=80
%production.application.log=INFO
%production.application.mode=prod
Configurando o ID do framework da linha de comando
You can specify the framework ID to use for a particular command directly from the command line. For example to run an application in production mode you can use:
play run --%production
with these lines defined in the application.conf
file:
application.mode=dev
%production.application.mode=prod
It should be compatible with all commands using the framework ID information. The default ID is still defined using the play id
command.
By the way, play test
is then equivalent to:
play run --%test
Continuing the discussion
Now we shall move on to Deploy em produção.


SMBProtocolFactory Class¶
For those who want to utilize pysmb in Twisted framework, pysmb has a smb.SMBProtocol.SMBProtocol implementation. In most cases, you do not need to touch or import the SMBProtocol directly. All the SMB functionalities are exposed in the SMBProtocolFactory.
- In your project,
- Create a new class and subclass SMBProtocolFactory.
- Override the SMBProtocolFactory.onAuthOK and SMBProtocolFactory.onAuthFailed instance methods to provide your own post-authenthentication handling. Once SMBProtocolFactory.onAuthOK has been called by pymsb internals, your application is ready to communicate with the remote SMB/CIFS service through the SMBProtocolFactory public methods such as SMBProtocolFactory.storeFile, SMBProtocolFactory.retrieveFile, etc.
- When you want to disconnect from the remote SMB/CIFS server, just call SMBProtocolFactory.closeConnection method.
All the SMBProtocolFactory public methods that provide file functionlities will return a twisted.internet.defer.Deferred instance. A NotReadyError exception is raised when the underlying SMB is not authenticated. If the underlying SMB connection has been terminated, a NotConnectedError exception is raised.
All the file operation methods in SMBProtocolFactory class accept a timeout parameter. This parameter specifies the time limit where pysmb will wait for the entire file operation (except storeFile and retrieveFile methods) to complete. If the file operation fails to complete within the timeout period, the returned Deferred instance’s errback method will be called with a SMBTimeout exception.
If you are interested in learning the results of the operation or to know when the operation has completed, you should add a handling method to the returned Deferred instance via Deferred.addCallback. If the file operation fails, the Deferred.errback function will be called with an OperationFailure; on timeout, it will be called with a SMBTimeout.
Example¶
The following illustrates a simple file retrieving implementation.:
import tempfile
from twisted.internet import reactor
from smb.SMBProtocol import SMBProtocolFactory
class RetrieveFileFactory(SMBProtocolFactory):
def __init__(self, *args, **kwargs):
SMBProtocolFactory.__init__(self, *args, **kwargs)
def fileRetrieved(self, write_result):
file_obj, file_attributes, file_size = write_result
# Retrieved file contents are inside file_obj
# Do what you need with the file_obj and then close it
# Note that the file obj is positioned at the end-of-file,
# so you might need to perform a file_obj.seek() to if you
# need to read from the beginning
file_obj.close()
self.transport.loseConnection()
def onAuthOK(self):
d = self.retrieveFile(self.service, self.path, tempfile.NamedTemporaryFile())
d.addCallback(self.fileRetrieved)
d.addErrback(self.d.errback)
def onAuthFailed(self):
print 'Auth failed'
# There will be some mechanism to capture userID, password, client_machine_name, server_name and server_ip
# client_machine_name can be an arbitary ASCII string
# server_name should match the remote machine name, or else the connection will be rejected
factory = RetrieveFileFactory(userID, password, client_machine_name, server_name, use_ntlm_v2 = True)
factory.service = 'smbtest'
factory.path = '/rfc1001.txt'
reactor.connectTCP(server_ip, 139, factory)
SMB2 Support¶
Starting from pysmb 1.1.0, pysmb will utilize SMB2 protocol for communication if the remote SMB/CIFS service supports SMB2. Otherwise, it will fallback automatically back to using SMB1 protocol.
To disable SMB2 protocol in pysmb, set the SUPPORT_SMB2 flag in the smb_structs module to False before creating the SMBProtocolFactory instance.:
from smb import smb_structs
smb_structs.SUPPORT_SMB2 = False
Caveats¶
- A new factory instance must be created for each SMB connection to the remote SMB/CIFS service. Avoid reusing the same factory instance for more than one SMB connection.
- The remote SMB/CIFS server usually imposes a limit of the number of concurrent file operations for each client. For example, to transfer a thousand files, you may need to setup a queue in your application and call storeFile or retrieveFile in batches.
- The timeout parameter in the file operation methods are not precise; it is accurate to within 1 second interval, i.e. with a timeout of 0.5 sec, pysmb might raise SMBTimeout exception after 1.5 sec.
-
class
smb.SMBProtocol.
SMBProtocolFactory
(username, password, my_name, remote_name, domain='', use_ntlm_v2=True, sign_options=2, is_direct_tcp=False)[source]¶ -
__init__
(username, password, my_name, remote_name, domain='', use_ntlm_v2=True, sign_options=2, is_direct_tcp=False)[source]¶ Create a new SMBProtocolFactory instance. You will pass this instance to reactor.connectTCP() which will then instantiate the TCP connection to the remote SMB/CIFS server. Note that the default TCP port for most SMB/CIFS servers using NetBIOS over TCP/IP is 139. Some newer server installations might also support Direct hosting of SMB over TCP/IP; for these servers, the default TCP port is 445.
username and password are the user credentials required to authenticate the underlying SMB connection with the remote server. File operations can only be proceeded after the connection has been authenticated successfully.
Parameters: - my_name (string) – The local NetBIOS machine name that will identify where this connection is originating from.
You can freely choose a name as long as it contains a maximum of 15 alphanumeric characters and does not contain spaces and any of
\/:*?";|+
. - remote_name (string) – The NetBIOS machine name of the remote server. On windows, you can find out the machine name by right-clicking on the “My Computer” and selecting “Properties”. This parameter must be the same as what has been configured on the remote server, or else the connection will be rejected.
- domain (string) – The network domain. On windows, it is known as the workgroup. Usually, it is safe to leave this parameter as an empty string.
- use_ntlm_v2 (boolean) – Indicates whether pysmb should be NTLMv1 or NTLMv2 authentication algorithm for authentication. The choice of NTLMv1 and NTLMv2 is configured on the remote server, and there is no mechanism to auto-detect which algorithm has been configured. Hence, we can only “guess” or try both algorithms. On Sambda, Windows Vista and Windows 7, NTLMv2 is enabled by default. On Windows XP, we can use NTLMv1 before NTLMv2.
- sign_options (int) – Determines whether SMB messages will be signed. Default is SIGN_WHEN_REQUIRED. If SIGN_WHEN_REQUIRED (value=2), SMB messages will only be signed when remote server requires signing. If SIGN_WHEN_SUPPORTED (value=1), SMB messages will be signed when remote server supports signing but not requires signing. If SIGN_NEVER (value=0), SMB messages will never be signed regardless of remote server’s configurations; access errors will occur if the remote server requires signing.
- is_direct_tcp (boolean) – Controls whether the NetBIOS over TCP/IP (is_direct_tcp=False) or the newer Direct hosting of SMB over TCP/IP (is_direct_tcp=True) will be used for the communication. The default parameter is False which will use NetBIOS over TCP/IP for wider compatibility (TCP port: 139).
- my_name (string) – The local NetBIOS machine name that will identify where this connection is originating from.
You can freely choose a name as long as it contains a maximum of 15 alphanumeric characters and does not contain spaces and any of
-
closeConnection
()[source]¶ Disconnect from the remote SMB/CIFS server. The TCP connection will be closed at the earliest opportunity after this method returns.
Returns: None
-
createDirectory
(service_name, path)[source]¶ Creates a new directory path on the service_name.
Parameters: - service_name (string/unicode) – Contains the name of the shared folder.
- path (string/unicode) – The path of the new folder (relative to) the shared folder. If the path contains non-English characters, an unicode string must be used to pass in the path.
- timeout (integer/float) – Number of seconds that pysmb will wait before raising SMBTimeout via the returned Deferred instance’s errback method.
Returns: A twisted.internet.defer.Deferred instance. The callback function will be called with the path parameter.
-
deleteDirectory
(service_name, path)[source]¶ Delete the empty folder at path on service_name
Parameters: - service_name (string/unicode) – Contains the name of the shared folder.
- path (string/unicode) – The path of the to-be-deleted folder (relative to) the shared folder. If the path contains non-English characters, an unicode string must be used to pass in the path.
- timeout (integer/float) – Number of seconds that pysmb will wait before raising SMBTimeout via the returned Deferred instance’s errback method.
Returns: A twisted.internet.defer.Deferred instance. The callback function will be called with the path parameter.
-
deleteFiles
(service_name, path_file_pattern, timeout=30)[source]¶ Delete one or more regular files. It supports the use of wildcards in file names, allowing for deletion of multiple files in a single request.
Parameters: - service_name (string/unicode) – Contains the name of the shared folder.
- path_file_pattern (string/unicode) – The pathname of the file(s) to be deleted, relative to the service_name. Wildcards may be used in th filename component of the path. If your path/filename contains non-English characters, you must pass in an unicode string.
- timeout (integer/float) – Number of seconds that pysmb will wait before raising SMBTimeout via the returned Deferred instance’s errback method.
Returns: A twisted.internet.defer.Deferred instance. The callback function will be called with the path_file_pattern parameter.
-
echo
(data, timeout=10)[source]¶ Send an echo command containing data to the remote SMB/CIFS server. The remote SMB/CIFS will reply with the same data.
Parameters: - data (string) – Data to send to the remote server.
- timeout (integer/float) – Number of seconds that pysmb will wait before raising SMBTimeout via the returned Deferred instance’s errback method.
Returns: A twisted.internet.defer.Deferred instance. The callback function will be called with the data parameter.
-
getAttributes
(service_name, path, timeout=30)[source]¶ Retrieve information about the file at path on the service_name.
Parameters: - service_name (string/unicode) – the name of the shared folder for the path
- path (string/unicode) – Path of the file on the remote server. If the file cannot be opened for reading, an OperationFailure will be raised.
Returns: A twisted.internet.defer.Deferred instance. The callback function will be called with a smb.base.SharedFile instance containing the attributes of the file.
-
listPath
(service_name, path, search=55, pattern='*', timeout=30)[source]¶ Retrieve a directory listing of files/folders at path
Parameters: - service_name (string/unicode) – the name of the shared folder for the path
- path (string/unicode) – path relative to the service_name where we are interested to learn about its files/sub-folders.
- search (integer) – integer value made up from a bitwise-OR of SMB_FILE_ATTRIBUTE_xxx bits (see smb_constants.py). The default search value will query for all read-only, hidden, system, archive files and directories.
- pattern (string/unicode) – the filter to apply to the results before returning to the client.
- timeout (integer/float) – Number of seconds that pysmb will wait before raising SMBTimeout via the returned Deferred instance’s errback method.
Returns: A twisted.internet.defer.Deferred instance. The callback function will be called with a list of smb.base.SharedFile instances.
Retrieve a list of shared resources on remote server.
Parameters: timeout (integer/float) – Number of seconds that pysmb will wait before raising SMBTimeout via the returned Deferred instance’s errback method. Returns: A twisted.internet.defer.Deferred instance. The callback function will be called with a list of smb.base.SharedDevice instances.
-
listSnapshots
(service_name, path, timeout=30)[source]¶ Retrieve a list of available snapshots (a.k.a. shadow copies) for path.
Note that snapshot features are only supported on Windows Vista Business, Enterprise and Ultimate, and on all Windows 7 editions.
Parameters: - service_name (string/unicode) – the name of the shared folder for the path
- path (string/unicode) – path relative to the service_name where we are interested in the list of available snapshots
Returns: A twisted.internet.defer.Deferred instance. The callback function will be called with a list of python datetime.DateTime instances in GMT/UTC time zone
-
onAuthFailed
()[source]¶ Override this method in your SMBProtocolFactory subclass to add in post-authentication handling. This method will be called when the server has replied that the SMB connection has been successfully authenticated.
- If you want to retry authenticating from this method,
- Disconnect the underlying SMB connection (call
self.instance.transport.loseConnection()
) - Create a new SMBProtocolFactory subclass instance with different user credientials or different NTLM algorithm flag.
- Call
reactor.connectTCP
with the new instance to re-establish the SMB connection
- Disconnect the underlying SMB connection (call
-
onAuthOK
()[source]¶ Override this method in your SMBProtocolFactory subclass to add in post-authentication handling. This method will be called when the server has replied that the SMB connection has been successfully authenticated. File operations can proceed when this method has been called.
-
rename
(service_name, old_path, new_path)[source]¶ Rename a file or folder at old_path to new_path shared at service_name. Note that this method cannot be used to rename file/folder across different shared folders
old_path and new_path are string/unicode referring to the old and new path of the renamed resources (relative to) the shared folder. If the path contains non-English characters, an unicode string must be used to pass in the path.
Parameters: - service_name (string/unicode) – Contains the name of the shared folder.
- timeout (integer/float) – Number of seconds that pysmb will wait before raising SMBTimeout via the returned Deferred instance’s errback method.
Returns: A twisted.internet.defer.Deferred instance. The callback function will be called with a 2-element tuple of ( old_path, new_path ).
-
retrieveFile
(service_name, path, file_obj, timeout=30)[source]¶ Retrieve the contents of the file at path on the service_name and write these contents to the provided file_obj.
Use retrieveFileFromOffset() method if you need to specify the offset to read from the remote path and/or the maximum number of bytes to write to the file_obj.
The meaning of the timeout parameter will be different from other file operation methods. As the downloaded file usually exceeeds the maximum size of each SMB/CIFS data message, it will be packetized into a series of request messages (each message will request about about 60kBytes). The timeout parameter is an integer/float value that specifies the timeout interval for these individual SMB/CIFS message to be transmitted and downloaded from the remote SMB/CIFS server.
Parameters: - service_name (string/unicode) – the name of the shared folder for the path
- path (string/unicode) – Path of the file on the remote server. If the file cannot be opened for reading, an OperationFailure will be called in the returned Deferred errback.
- file_obj – A file-like object that has a write method. Data will be written continuously to file_obj until EOF is received from the remote service.
Returns: A twisted.internet.defer.Deferred instance. The callback function will be called with a 3-element tuple of ( file_obj, file attributes of the file on server, number of bytes written to file_obj ). The file attributes is an integer value made up from a bitwise-OR of SMB_FILE_ATTRIBUTE_xxx bits (see smb_constants.py)
-
retrieveFileFromOffset
(service_name, path, file_obj, offset=0L, max_length=-1L, timeout=30)[source]¶ Retrieve the contents of the file at path on the service_name and write these contents to the provided file_obj.
The meaning of the timeout parameter will be different from other file operation methods. As the downloaded file usually exceeeds the maximum size of each SMB/CIFS data message, it will be packetized into a series of request messages (each message will request about about 60kBytes). The timeout parameter is an integer/float value that specifies the timeout interval for these individual SMB/CIFS message to be transmitted and downloaded from the remote SMB/CIFS server.
Parameters: - service_name (string/unicode) – the name of the shared folder for the path
- path (string/unicode) – Path of the file on the remote server. If the file cannot be opened for reading, an OperationFailure will be called in the returned Deferred errback.
- file_obj – A file-like object that has a write method. Data will be written continuously to file_obj until EOF is received from the remote service.
- offset (integer/long) – the offset in the remote path where the first byte will be read and written to file_obj. Must be either zero or a positive integer/long value.
- max_length (integer/long) – maximum number of bytes to read from the remote path and write to the file_obj. Specify a negative value to read from offset to the EOF. If zero, the Deferred callback is invoked immediately after the file is opened successfully for reading.
Returns: A twisted.internet.defer.Deferred instance. The callback function will be called with a 3-element tuple of ( file_obj, file attributes of the file on server, number of bytes written to file_obj ). The file attributes is an integer value made up from a bitwise-OR of SMB_FILE_ATTRIBUTE_xxx bits (see smb_constants.py)
-
storeFile
(service_name, path, file_obj, timeout=30)[source]¶ Store the contents of the file_obj at path on the service_name.
The meaning of the timeout parameter will be different from other file operation methods. As the uploaded file usually exceeeds the maximum size of each SMB/CIFS data message, it will be packetized into a series of messages (usually about 60kBytes). The timeout parameter is an integer/float value that specifies the timeout interval for these individual SMB/CIFS message to be transmitted and acknowledged by the remote SMB/CIFS server.
Parameters: - service_name (string/unicode) – the name of the shared folder for the path
- path (string/unicode) – Path of the file on the remote server. If the file at path does not exist, it will be created. Otherwise, it will be overwritten. If the path refers to a folder or the file cannot be opened for writing, an OperationFailure will be called in the returned Deferred errback.
- file_obj – A file-like object that has a read method. Data will read continuously from file_obj until EOF.
Returns: A twisted.internet.defer.Deferred instance. The callback function will be called with a 2-element tuple of ( file_obj, number of bytes uploaded ).
-
SIGN_NEVER
= 0¶ SMB messages will never be signed regardless of remote server’s configurations; access errors will occur if the remote server requires signing.
-
SIGN_WHEN_REQUIRED
= 2¶ SMB messages will only be signed when remote server requires signing.
-
SIGN_WHEN_SUPPORTED
= 1¶ SMB messages will be signed when remote server supports signing but not requires signing.
-
instance
= None¶ The single SMBProtocol instance for each SMBProtocolFactory instance. Usually, you should not need to touch this attribute directly.
-
isReady
¶ A convenient property to return True if the underlying SMB connection has connected to remote server, has successfully authenticated itself and is ready for file operations.
-
isUsingSMB2
¶ A convenient property to return True if the underlying SMB connection is using SMB2 protocol.
-


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A mode for Winamp AVS to use with CodeMirror, the versatile text editor implemented in JavaScript for the browser.
Let's take a look at this SuperScope from NemoOrange's Jaxx preset. Feel free to edit the code in order to test the highlighter and report an issue if you come across something fishy.
Frame
Beat
Point


A First Look at ATAC-seq & SRM in Erythropoiesis Differentiation
Marjorie Brand's lab produced ATAC-seq open chromatin assays, and Jeff Ranish's lab SRM copy numbers
across these shared time points and equal number of (biological?) replicates.
- day 4 (2 reps)
- day 8 (1 rep)
- day 10 (2 reps)
- day 11 (2 reps)
- day 12 (2 reps)
The ATAC-seq data has just one replicate at day 8, so I have not included the second rep at day 8 from the
SRM data.
This webapp is for the exploration of the following measures and derived quantities:
- KLF1.srm: copy number counts for transcription factor KLF1, bulk SRM
- KLF1.total.hits: number of FIMO matches (pvalue <= 1e-4) of
Hsapiens-SwissRegulon-KLF1.SwissRegulon motif in all areas of open chromatin
- KLF1.top.hits: number of FIMO matches in upper quartile by pvalue, on the possibility
that especially strong motif matches may exhibit a different distribution (they do not appear to)
- KLF1.total.density: FIMO matches (total hits)/ number of open chromatin regions
- KLF1.top.density: same, but using top hits
The same quantities were calculated for FLT1:
- FLI1.srm: copy number counts for transcription factor FLI1, bulk SRM
- FLI1.total.hits: using Hsapiens-jaspar2018-FLI1-MA0475.1
- FLI1.top.hits: as above
- FLI1.total.density: as above
- FLI1.top.density : as above
Jeff has suggested, but I have not yet calculated, the total number of matches of the KLF1 and FLT1
motifs on the whole genome, to provide a background against which to assess the open chromatin
matches.
Other normalizations and transformations will occur to you!


title: Scalable SQL Database with the NoSQL Goodies description: Crate is the scalable database. Crate allows to query and compute data with SQL in real time by providing a distributed aggregation engine, native search and super simple scalability. {% extends "base.html" %} {% block title %}Crate.io: Scalable SQL Database with the NoSQL Goodies{% endblock %} {% block header %}
Crate allows to query and compute data with SQL in real time by providing a distributed aggregation engine, native search and super simple scalability.
Use familiar SQL syntax for real time distributed queries across a cluster. Crate is based on the Lucene / Elasticsearch ecosystem with built-in support for binary objects (BLOBs).
Read more about Crate's features or learn how to install Crate anywhere!
Easy to setup distributed database for apps and services at scale. The perfect fit for ephemeral distributed environments like Docker. Ready-to use for SQL and ORM users.
Migrate with best practice tips.
Learn how to install Crate in the cloud.
JOINs have been one of our most requested features and we would welcome your help testing them. Sign up to our testers list and we'll be in touch with details. {% include "mailchimp-joins.html" %}
| {{ post.title }} |
| {{ event.title }} |
Crate makes scaling a breeze, while still using standard SQL syntax. It offers auto-sharding and replication, super-fast multi index queries, distributed aggregations and sort, lightning-fast full-text search, and super simple cluster management. Cut the number of nodes in half while gaining a 10-20x performance increase.
Crate vs Cassandra
Cassandra is great for writes but not so much for reads and requires more constraining schema definitions. Crate offers outstanding full-text search, SQL interface, dynamic cluster resizing, auto-rebalancing, powerful concurrent reads and writes, and the flexibility to grow by allowing aggregates and sorting fields to be changed at anytime.
Crate vs MongoDB
No need for a team of specialists to run and scale your cluster. A Crate cluster is extremely simply to scale and manage. Faster aggregations, powerful full-text search, binary object store (BLOB) support, a permissive license, the ability to combine schemas with JSON (unstructured) in one database, as well as the ability to query with SQL standard syntax make Crate a better choice.


{{/* edit-mp-form.html contains the form used for editing meal plans. Dot is expected to be of type *mpdata.MealPlan. */}}
Return to meal plan browser
Edit Delete
{{.Notes}}


B&M slashes prices a WEEK AHEAD of Black Friday
Up to 60 per cent off products
by Paul HironsB&M Home Store has launched a huge pre-Black Friday sale a week before the big day.
Bargain hunters can expect discounts of up to 60 per cent on many household names and products.
Read more: Glow in the dark Christmas bedding is available to buy for less than £13
The Black Friday deals will be available for 10 days from today (22.11.19) until December 2, or as long as stocks last.
And shoppers can expect bargains on products like a 50-inch Samsung Smart 4K Ultra HD TV (down to £350 from £450), a Black & Decker 2-In-1 Cordless Vacuum Cleaner (down to £40 instead of £99.99) and a Nescafe Dolce Gusto Piccolo Coffee Machine (down to £25 from £39).
One word of warning though: B&M doesn't feature an online shopping resource so you'll have to to go to your nearest store to pick up your bargain.
This year's Black Friday is due to take place on Friday November 29, with Cyber Monday following on Monday December 2.
It's not just B&M who is launching their pre-Christmas sakes a week early.
The Sun reports that Superdrug, Very.co.uk and Halfords are just some of the other big-name brands that have jumped on the sales bandwagon early this year.
eBay, Currys PC World and AO.com have also begun to discount their products a week early, too.
Read more: Prosecco bath bombs are now available to buy from Firebox for £7.99
A spokesperson for LatestDeals.co.uk told the newspaper: "You shouldn't think of Black Friday as a single day. Really, it's a two-week-long event.
"Last year, we actually found the Friday to be one of the worst days for deals.
"The best deals were really at the beginning of the week when brands all compete against each other to get your money."
Head to our Facebook page @EntertainmentDailyFix and let us know what you think of our story.


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We're thrilled you've chosen one of our premium themes to showcase your products on Shopify. Built by a team of top-notch designers and developers, your new theme is mobile-friendly, SEO-ready, and all set to shine.
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--- layout: default js: - vendor/jquery.adaptive-backgrounds.min - home ---
Lorem Ipsum has been the industry’s standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book.
Read moreContrary to popular belief, Lorem Ipsum is not simply random text.
Read moreThere are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration in some form, by injected humour, or randomised words which don’t look even slightly believable.
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JavaScript Numbers
Floating point arithmetic is not always 100% accurate:
But it helps to multiply and divide:


DOM-based templates use traditional HTML markup and add additional attributes to describe the data binding and events.
There is typically less code involved with this approach, and these frameworks often do more work for you.


PNE in Numbers: Preston ace Brad Potts is to the go-to man off the bench
by Dave SeddonBrad Potts has been the man who Preston boss Alex Neil has turned to the most to come off the bench this season.
In Championship games, the midfielder has joined the action as a substitute seven times.
Only three times has last January’s buy from Barnsley started in the league this campaign.
That is in contrast to the second-half of last season when Potts started 10 games in a row and never finished on the losing side. Knee surgery was to bring his season to an premature end in March.
When his knee needed more rest during pre-season, that appeared to have a knock-on effect in terms of the first three months of this campaign.
Potts had not quite found his rhythm, hence others getting in ahead of him in the midfield.
Last season, Potts and Alan Browne were used an attacking midfielders in a 4-1-4-1 system.
With Neil favouring more of a 4-2-3-1, there has only been one No.10 role up for grabs, with Daniel Johnson and Browne filling that between them.
It is Browne who has the joint second highest number of substitute appearances in the league, coming off the bench six times – he shares that statistic with Jayden Stockley.
The last time Browne was a sub was at Reading a month ago, with him having started the last four Preston games.
Like Browne, Stockley has also forced his way back into the side of late, starting the last two matches after that game-changing appearance from the bench against Blackburn.
Paul Gallagher and Andre Green have come off the bench four times and David Nugent three times.


{% extends "base.html" %} {#Head container#} {% load staticfiles %} {% load bootstrap %} {% load i18n widget_tweaks %} {% block content %} {# Mostly done - need to: Not show the highchart if there's nothing there, and change size of chart based on number of blast hits#}
This graph shows the percentage of sequences in which each query gene was found.
| Gene | % sequences where gene was found |
|---|---|
| {{ key }} | {{ value }} |
This table shows the percent identity for each sequence for each query gene.
| SeqID | {% for key, value in geneseekr_request.geneseekr_results.items %}{{ key }} | {% endfor %}||
|---|---|---|---|
| {{ geneseekr_detail.seqid }} | {% for gene, percent_id in geneseekr_detail.geneseekr_results.items %} {% if percent_id == 0.0 %}{{ percent_id }} | {% elif percent_id == 100.0 %}{{ percent_id }} | {% else %}{{ percent_id }} | {% endif %} {% endfor %}
| Contig | Query Coverage | Percent Identity | Subject Start Position | Subject End Position | E Value |
|---|---|---|---|---|---|
| {{ top_blast_hit.contig_name }} | {{ top_blast_hit.query_coverage|floatformat:"-2" }} | {{ top_blast_hit.percent_identity }} | {{ top_blast_hit.start_position }} | {{ top_blast_hit.end_position }} | {{ top_blast_hit.e_value }} |


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