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

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
input_image = tf.placeholder(shape=[None, image_width, image_height, image_channels],
dtype=tf.float32,
name='input_image')
input_z = tf.placeholder(shape=[None, z_dim],
dtype=tf.float32,
name='input_z')
learning_rate = tf.placeholder(
dtype=tf.float32,
name='learning_rate')
return input_image, input_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)
"""
alpha = 0.1
with tf.variable_scope('discriminator', reuse=reuse):
# Layer 1
conv_l1 = tf.layers.conv2d(images, 64, 5, 2, padding='SAME')
conv_l1 = tf.maximum(alpha * conv_l1, conv_l1)
# Layer 2
conv_l2 = tf.layers.conv2d(conv_l1, 128, 5, 2, padding='SAME')
conv_l2 = tf.layers.batch_normalization(conv_l2, training=True)
conv_l2 = tf.maximum(alpha * conv_l2, conv_l2)
# Layer 3
conv_l3 = tf.layers.conv2d(conv_l2, 256, 5, 2, padding='SAME')
conv_l3 = tf.layers.batch_normalization(conv_l3, training=True)
conv_l3 = tf.maximum(alpha * conv_l3, conv_l3)
# Flatten
flat = tf.reshape(conv_l3, (-1, 4 * 4 * 256))
# Dense layer
logits = tf.layers.dense(flat, 1)
logits = tf.layers.dropout(logits, rate=0.5)
# Output
output = tf.sigmoid(logits)
return output, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
alpha = 0.1
with tf.variable_scope('generator', reuse=not(is_train)):
# FC 1
fc1 = tf.layers.dense(inputs=z, units=7 * 7 * 128)
fc1 = tf.reshape(fc1, (-1, 7, 7, 128))
fc1 = tf.layers.batch_normalization(fc1, training=is_train)
fc1 = tf.maximum(alpha * fc1, fc1)
# Layer 2
conv_l2 = tf.layers.conv2d_transpose(fc1, 64, 5, 2, padding='SAME')
conv_l2 = tf.layers.batch_normalization(conv_l2, training=is_train)
conv_l2 = tf.maximum(alpha * conv_l2, conv_l2)
# Layer 3
conv_l3 = tf.layers.conv2d_transpose(conv_l2, out_channel_dim, 5, 2, padding='SAME')
# Output
output = tf.tanh(conv_l3)
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)
"""
import numpy as np
generative_model = generator(input_z, out_channel_dim)
disc_model_real_images, disc_logits_real_images = discriminator(input_real)
disc_model_prod_images, disc_logits_prod_images = discriminator(generative_model, True)
aux_real = tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_logits_real_images,
labels=tf.ones_like(disc_logits_real_images)*np.random.uniform(0.7, 1.2))
aux_prod = tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_logits_prod_images,
labels=tf.zeros_like(disc_logits_prod_images)*np.random.uniform(0.0, 0.3))
disc_loss_real_images = tf.reduce_mean(aux_real)
disc_loss_prod_images = tf.reduce_mean(aux_prod)
discriminative_loss = disc_loss_real_images + disc_loss_prod_images
aux_generative = tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_logits_prod_images, labels=tf.ones_like(disc_model_prod_images))
generative_loss = tf.reduce_mean(aux_generative)
return discriminative_loss, generative_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)
"""
trainable_var = tf.trainable_variables()
# print(trainable_var[0].name)
generative_var = [v for v in trainable_var if v.name.startswith('generator')]
discriminative_var = [v for v in trainable_var if v.name.startswith('discriminator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=beta1)
genTraining_operation = optimizer.minimize(g_loss, var_list=generative_var)
discTraining_operation = optimizer.minimize(d_loss, var_list=discriminative_var)
return discTraining_operation, genTraining_operation
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
input_image, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
disc_loss, gen_loss = model_loss(input_image, input_z, data_shape[3])
disc_opt, gen_opt = model_opt(disc_loss, gen_loss, lr, beta1)
steps = 0
print_every = 10
show_every = 100
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
steps += 1
batch_images = batch_images*2
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
sess.run(gen_opt, feed_dict={input_image: batch_images, input_z: batch_z, lr: learning_rate})
sess.run(disc_opt, feed_dict={input_image: batch_images, input_z: batch_z, lr: learning_rate})
if steps % print_every == 0:
train_disc_loss = disc_loss.eval(feed_dict={input_image: batch_images, input_z: batch_z})
train_gen_loss = gen_loss.eval(feed_dict={input_z: batch_z})
print("Epoch {}/{} ---".format(epoch_i + 1, epoch_count),
"Generator Loss: {:.4f} ---".format(train_gen_loss),
"Discriminator Loss: {:.4f}".format(train_disc_loss))
if steps % show_every == 0:
show_generator_output(sess, batch_size, input_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 = 32
z_dim = 100
learning_rate = 1e-4
beta1 = 0.1
"""
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 = 32
z_dim = 100
learning_rate = 1e-4
beta1 = 0.1
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


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Inherited from |
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x.__init__(...) initializes x; see help(type(x)) for signature
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Connection will establish to the remote host using ssh. It will take user_name ,ip_address and password as arguments<br> and will return the handle.
|
Creates a number,specified by 'number,' of subinterfaces on eth0. Ip addresses start at 'net'.'start'.1.1 with a 24 bit netmask. Addresses increment sequentially in the third quad, therefore all interfaces are in different subnets on the same machine. When the third quad reaches 255, it is reset to 1 and the second quad is incremented. Every single ip address is placed in a file in /tmp titled 'ip_table{net}.txt'. The file is used by 'pingall_interfaces()' as a fping argument This method returns true if all interfaces are created without a hitch, and false if a single interface has issues |
Copies the /tmp/ip_table{ net }.txt file from the machine you wish to ping, then runs fping with a source address of { netsrc }.{ netstrt }.1.1 on the copied file. Check every single response for reachable or unreachable. If all are reachable, function returns true. If a SINGLE host is unreachable, then the function stops and returns false. If fping is not installed, this function will install fping then run the same command |
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| Generated by Epydoc 3.0.1 on Mon Aug 10 11:12:35 2015 | http://epydoc.sourceforge.net |


This web page allows to convert an amount of australian dollars into euros by the actual exchange rate
Exchange rate: 0.6602389
Australian Dollars (AUD): AUD = Euros:
Euros: = Australian Dollars: AUD
Australian dollars....................................Euros


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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object org.itemscript.core.foundries.ItemscriptFoundry<T>
T
- The supertype that this foundry will create.public class ItemscriptFoundry<T>
The implementation class for JsonFoundry
. You can either instantiate a typed instance
of this class, or subclass it.
| Constructor Summary | |
|---|---|
ItemscriptFoundry(JsonSystem system,
java.lang.String location,
java.lang.String nameKey)
Subclasses must call this constructor. |
| Method Summary | |
|---|---|
T |
create(JsonValue params)
Create a new object using the supplied JsonValue as a parameter. |
T |
create(java.lang.String name)
Create an instance of an object using the supplied name to find the factory. |
T |
create(java.lang.String name,
JsonObject params)
Create an instance of an object using the supplied name to locate the factory. |
JsonObject |
createFromArray(JsonArray array)
Override this method if your foundry knows how to create an object from a JsonArray. |
T |
createFromFactories(JsonObject factories,
JsonObject params)
|
T |
createFromFactories(JsonObject factories,
java.lang.String name,
JsonObject params)
|
java.lang.String |
findMissingName(JsonObject params)
|
void |
put(FactoryName<T> factoryName)
Put a FactoryName into this foundry. |
void |
put(java.lang.String name,
JsonFactory<T> factory)
Put a JsonFactory into this foundry. |
void |
put(java.lang.String name,
JsonValue params)
Put a JsonObject as a factory. |
JsonSystem |
system()
Get the JsonSystem object associated with this object. |
java.lang.String |
toString()
|
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
|---|
public ItemscriptFoundry(JsonSystem system, java.lang.String location, java.lang.String nameKey)
system
- The associated JsonSystem.location
- The mem:/ location that factories in this foundry will be stored under.nameKey
- The key in the objects supplied to create(JsonValue)
that contains the name of the factory.| Method Detail |
|---|
public final T create(JsonValue params)
JsonFoundry
create
in interface JsonFoundry<T>
params
- The parameters for creating an object.
public final T create(java.lang.String name)
JsonFoundry
create
in interface JsonFoundry<T>
name
- The name used to find the right factory.
public final T create(java.lang.String name, JsonObject params)
JsonFoundry
create
in interface JsonFoundry<T>
name
- The name used to find the right factory.params
- The params specifying the object to create.
public JsonObject createFromArray(JsonArray array)
array
- The array supplied to create(JsonValue)
create(JsonObject)
.public final T createFromFactories(JsonObject factories, JsonObject params)
public final T createFromFactories(JsonObject factories, java.lang.String name, JsonObject params)
public java.lang.String findMissingName(JsonObject params)
public final void put(FactoryName<T> factoryName)
JsonFoundry
FactoryName
into this foundry.
put
in interface JsonFoundry<T>
factoryName
- The factory to be put in this foundry.public final void put(java.lang.String name, JsonFactory<T> factory)
JsonFoundry
JsonFactory
into this foundry.
put
in interface JsonFoundry<T>
name
- The name of the factory.factory
- The factory to be put in this foundry.public void put(java.lang.String name, JsonValue params)
JsonFoundry
JsonObject
as a factory.
put
in interface JsonFoundry<T>
name
- The name of the factory.params
- The JsonObject
describing the factory.public JsonSystem system()
HasSystem
system
in interface HasSystem
public final java.lang.String toString()
toString
in class java.lang.Object
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My current tasks
{{ todos.length }}
Any new task added will be saved to a remote database.
{{ todo.text }}
Add


Consider the set S(r) of points (x,y) with integer coordinates satisfying |x| + |y|
r.
Let O be the point (0,0) and C the point (r/4,r/4).
Let N(r) be the number of points B in S(r), so that the triangle OBC has an obtuse angle, i.e. the largest angle α satisfies 90°
So, for example, N(4)=24 and N(8)=100.
What is N(1,000,000,000)?


|
Fig 1. Ali Hanks (left) talking to our Pinewood helpers: Jennifer, Helena, and Stephanie (from right to left). |
|
Fig 2. Brian Plimley (right) talking to our Pinewood helpers: Jennifer, Helena, and Stephanie (from right to left). |
|
Fig 3. (Left) Stephanie and Helena working with the Pinewood device. (Right) Stephanie helping Ali to reinstall the Pinewood device. |
|
Fig 4. Similar work done at Campolindo by Alexander! |


projectX merupakan sebuah platform kelas online yang membantu kamu memperoleh Ilmu dengan mudah dan fleksibel.
Gabung ke Kelas SekarangFitur 1
Save time with a multitude of styled components designed to showcase your content
Fitur 2
Construct mockups or production-ready pages in-browser with Variant Page Builder
Fitur 3
Take comfort in 6 months included support with a dedicated support forum
Kelas Terbaru
Membuat Aplikasi Android dengan React Native dan Expo
Following BEM conventions for readability
Beli SekarangFrequently Asked Questions
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Open a refund request with us and we can work with you to resolve it ASAP.
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Yes, we accept all major credit cards, including AMEX, so rack up those points!
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| 838 | (A) | he prayes the prynce in haste and alle the pepiłł aftire |
| 842 | (C) | ¶ He prayde prynces fyrste . & ałł þe pepułł aftur |
| 829 | (D) | ¶ vaspasyan prayed þe princes & þe peple aftir |
| 805 | (E) | ¶ waspasyan preyde his his lordes al abowte |
| (Ex) | ---- no text ---- | |
| 859 | (L) | prayeþ princes on ernest & alle þe peple after |
| 781 | (P) | He prayes princes firste & ałł þe pepiłł after |
| 814 | (U) | Vespasian praies the princes / and al the puple after |
| (V) | ---- no text ---- |


{% extends "base_driver.html" %} {% block content %}
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Factsheet:
Game title:
GunHero
Developer:
Olli-Samuli Lehmus
Based in Vantaa, Finland
Release Date:
18.04.2017
Description:
Fight your way through multiple environments filled with traps and obstacles, each more dangerous and deadly than the last. Shoot bullets and throw grenades at your enemies while releasing prisoners from their cages.
GunHero is a 2D-action-platformer-shooter filled with handcrafted levels set in various environments, where each one contains its own unique dangers.
- Shoot your way through enemy troops, pick up weapons from your dead foes, yield destruction with your grenades and become victorious with the help of your shield! But don't forget about the prisoners and hostages that desperately need your help!
- Watch out for the deadly traps and obstacles!  Well, maybe a spinning laser beam, or a missile launcher. You can travel on rotating and moving platforms, climb ladders or fly using the large propellers. Just don't get trapped on the roof of an elevator as death by getting crushed between an elevator and a wall is not a pleasant death.
- Having trouble? Could the world maybe use a few heroes more? Just invite a few of your favorite friends over for some good old-fashioned couch co-op! Enemies won't stand a chance against you.
Features:
- Over 100 handcrafted levels set in five different environments
- Multiple weapons to choose from
- Grenades and explosions
- Loot, health and ammo boxes to help you
- Fast, satisfying combat with permanent blood decals
- Multiple enemy types, including generals, bazooka men and grenadiers
- Laser beams, elevators and missile launchers
- Awesome and intense boss fights
- Double jumping and ladder climbing
- Powerup shield to block incoming damage
- Propellers, blade spinners and tanks
- Ferris wheels, lava and spike balls
- Flame traps, trampolines and fireball cannons
- Cool world map to help track your progress
- Achievements and cloud saves
- Difficulty settings to match your skill level
- Couch co-op with friends
- Keyboard and mouse support
- Game controller support
- Fully remappable controls
Trailer:
Screenshots:
Logos:
Download All Images as .zipPress Copy:
Write me an email and I'll get back to you:
About Olli-Samuli Lehmus:
Olli-Samuli Lehmus is a Finnish hobbyist game-developer born in 1998. GunHero is Olli-Samuli's first commercial game project.
Contact:
Twitter: twitter.com/tyyppi_77
Website: gunherogame.com
IndieDB: indiedb.com/games/gunhero


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Lactuca spp.
Contrary to another experience report here, I do not believe the active components (lactucin, lactucopicrin, and possibly lactcic acid) of L. spec. are not effectively extracted by acid-base extraction used for most alkaloids. All of these active components are at least slightly acidic containing one or more carboxyl groups, so they are more solubable in water as a basic salt than in their acid state, thus they are in a way the opposite of alkaloid which are alkaline. All are also soluable in water in their acid form, so base-acid extraction also doesn't work.
Several sources claim lactucerin to be the primary inactive ingredient in lactucarium(the dried form of the milky liquid that flows from the stem). Liturature, most of which dates from the turn of the century, states that lactucerin is insoluable in water, lactucin, lactucopicrin, lactucic acid are soluable in water and alcohol to some degree, and that lactucin can be isolated by extraction with cold alcohol.
Procedure:
1. about 50g of fresh L. spec. were placed in blender with 5g of baking soda and 2 cups of water and blended until of even consistency with no peices much larger than a grain of rice. This should yeild a mix of sodium salts of active components.
2. The mixture was thouroughly filtered through cotton and coffee filters, the solids were returned to blender and extracted with 1 cup of water.
3. Water portions were combined and extracted with mixed petroleum distilates which were discarded.
4. Water was evaporated on low heat over 36 hours, yeilding tan, crubmly solid.
5. Substance was smoked off aluminum foil with a toilet paper tube, produced signifagant sedation but little euphoria or other thought alteration compared to opium or heroin, smoked.
Procedure 2:
1. 12g of fress L. spec. was placed in freezer with .5 cup anhydrous alcohol, made using MgSO4, this was allowed to sit for 2 days with occasional shaking, yeilded almost clear liquid.
2. this liquid filterd and allowed to evaporate at near room temperature until dry, yeilding off-white clear crust.
3. this was partially redisolved in 1/8 cup anhydrous alcohol at near 0C which was evaporated at room temperature to yeild pure white crystals
4. these crystals were disolved in 5 mL hot water and injected over 5 min. as 3 shots(the water had to be kept hot for it to remain in solution, it became cloudy immediately after removing from heat). Produced sedation and a good warm fuzzy fealing, fell asleep soon afterwards, but with sufficient willpower could have remained awake.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Shelton Davis
-
Play
Happiness Designer - user experience, interaction, industrial, package, marketing, paper ninjas |
Lorem ipsum about this page, what will be on it, etc.
Title of project
Title of project
Title of project
Title of project
Title of project


Recall that the contravariant form of the magnetic ﬁeld in (ψ,𝜃,ϕ) coordinates is given by Eq. (157), i.e.,
|
| (259) |
Next, let us derive the corresponding form in (ψ,𝜃,ζ) coordinates. Using the deﬁnition of ζ, equation (259) is written as
The expression of the magnetic ﬁeld in Eq. (261) can be rewritten in terms of the ﬂux function Ψp and Ψt discussed in Sec. 9.2. Equation (261) is
|
| (262) |
which, by using Eq. (236), i.e., ∇Ψ = ∇Ψp∕(2π), is rewritten as
|
| (263) |
which, by using Eq. (243), i.e., q = dΨt∕dΨp, is further written as
|
| (264) |


K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K. The algorithm works iteratively to assign each data point to one of K groups based on the features that are provided. Data points are clustered based on feature similarity.
This utility lets you upload csv file(dataset), optimize the tuning paramaters for kmeans and visualize the clusters as recognized by the algorithm.


AMRITAPURI
Amritapuri is the headquarters of Amma's worldwide mission and the spiritual home for Amma's monastic disciples and hundreds of householder devotees.
Amritapuri is the headquarters of Amma's worldwide mission and the spiritual home for Amma's monastic disciples and hundreds of householder devotees.
Sasthamcotta Lake is the largest freshwater lake in Kerala and said to have derived its name from an ancient Sastha Temple located near the lake. The lake is the biggest source of drinking water for the residents of kollam town.
Voted as the first 'Beach Wedding Destination' in Kerala, the Kollam Beach requires no introduction. This scenic and breathtaking natural wonder is also known as the Mahatma Gandhi Beach and boasts of pristine blue waters that seem to unite with the bright sky at an infinite distance, while tourists marvel at the sheer magnificence of the majestic scene.


Project {{ project.name }}
Delete project
{{ project.description }}
{% if request.user|is_super_user %}
Administration details
Project uuid: {{ project.uuid }}
Project URN: {{ project.urn }}
Members
| Username | Roles | Actions |
|---|---|---|
| {{ member.object.username }} |
{% for role in member.projectrole_set.all %}
{% if role.project == project %}
{{ role.name }}
{{ role.description }} {% if not forloop.last %},{% endif %}
{% endif %}
{% endfor %}
|
update, remove |
Aggregates
Below you can manage, add and remove aggregates in your project.
You can only create slices on aggregates that are in the project.
When you add an aggregate to the project, you might be prompted
for some additional information that the aggregate needs.
{% with project.aggregates.all as aggregate_list %}
{% include "project/project_simple_agg_list.html" %}
{% if not aggregate_list.count %}
This project has no aggregates added to it. To be able to
create slices that can reserve resources on aggregates, you will
need to add aggregates to the project.
{% endif %}
{% endwith %}
Slices
| Name | Description | Size | Owner | Reserved? | Actions |
|---|---|---|---|---|---|
| {{ slice.name }} | {{ slice.description }} | {{ slice.resource_set.all.count }} |
{% with slice.owner as user %}
{{ user.username }}
{% include "users/tooltipdesc.html" %}
{% endwith %}
|
{% if slice.started and not slice.modified %}{% else %}{% endif %} | view, delete |
Role Requests
When users try to do something in the project that they do not have
permission for, they will get to a page that allows them to request
additional permissions be requesting a particular role from a user
who can give it to them.
Below is a list of roles users are currently requesting from you. You can approve or deny these requests. You can also delegate a role to allow a user to be able to give the role out to others.
Below is a list of roles users are currently requesting from you. You can approve or deny these requests. You can also delegate a role to allow a user to be able to give the role out to others.
| Requester | Requested Role | Message | Actions |
|---|---|---|---|
|
{% with req.requester as user %}
{{ user.username }}
{% include "users/tooltipdesc.html" %}
{% endwith %}
|
{{ role.name }}
{{ role.description }}
|
{{ req.message|truncatewords:10 }}
{{ req.message }}
|
Allow, Delegate, Deny |
Roles
| Name | Description | Permissions | Actions | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| {{ role.name }} |
{{ role.description|truncatewords:12 }}
{{ role.description }}
|
{{ role.obj_permissions.all.count }}
{% for obj_perm in role.obj_permissions.all %}
{{ obj_perm.permission.name }}
|
update{% if role.name != "owner" and role.name != "researcher" %}, delete{% endif %} |


This SRFI describes an interface for calling C
functions from Scheme, calling Scheme functions from C, and allocating
storage in the Scheme heap. Scheme manages stub functions in C that
negotiate between the calling conventions of Scheme and C and the memory
allocation policies of both worlds.
The following facilities are available for
interfacing between Scheme and C:
- Scheme code can call C functions.
- The external interface provides full
introspection for all Scheme objects. External code may inspect,
modify, and allocate Scheme objects arbitrarily.
- External code may signal errors to the
Scheme system.
- External code may call back into Scheme.
Scheme correctly unrolls the process stack on non-local exits.
- External modules may register bindings of
names to values with a central registry accessible from Scheme.
Conversely, Scheme code can register shared bindings for access by C
code.
The interface is closely based on that of
Scheme 48 and scsh.


You will be given a random number at the start of the game
There are four crystals below. Clicking each crystal will add a specific number of points to your total score
Win the game by matching your total score to the random number.
Wins: 0
Losses: 0
Score: 0


panelet_category.Rd
Plot discrete categorical values belonging to class labels. Each category can have a unique color and a color for missing if exists. User has the option to perform an association test and output p-value and a contingency table summarizing count and proportion.
panelet_category(pp,pp.col,gr, var.n="var.n",NA.flag=FALSE, NA.col="grey", get.pval=FALSE, border=FALSE, border.col="black", legend=FALSE,...)
| pp | required. vector. A vector of categorical values of interest |
|---|---|
| pp.col | required. vector. A vector of colors of the same length as the unique values in the categorical variable of interest |
| gr | required. vector. Class labels as passed to |
| var.n | character. A character specifying the name of the variable. Set to |
| NA.flag | logical, default is |
| NA.col | default is "grey". see |
| get.pval | logical, default is |
| border | logical, default is |
| border.col | default is |
| legend | logical. default is |
| ... | pass optional arguments here |
...
can pass other parameters. Use it for controlling names and colors of panelets via cex, col
. border line type and thickness can be controlled via lty
and lwd
respectively.
A colored panelet
of categorical values is plotted. Make sure the variable is ordered according to the group labels in panelet_group
.
A colored panelet
of categorical values is plotted.
if legend = TRUE
, returns a key mapping the group labels to specific color
if get.pval =TRUE
, returns a table summarizing the counts and proportions along with a p-value from Fisher's Exact Test
makepanel
#adjust margins and number of panelet values par(mfrow=c(5,1),mar=c(0,8,0,14)) #sort the entire data frame according to the group solution you are interested in mtcars.sort = mtcars[order(mtcars$cyl),] #plot group panelet_group(gr=mtcars.sort$cyl, gr.col=c("red","blue","green"), gr.name="cyl",cex=2, border=TRUE, legend=FALSE)#> $key #> [,1] [,2] [,3] #> "4" "6" "8" #> col "red" "blue" "green" #>


A more spacious and simplified design inspired by physical wallets. Your funds should be easily accesible with all the important details summarized in a glance.
What changed?
Swipe to Send/Receive
Quickly send a payment to show your receive address by swiping right to send a payment or swipe left to see view your wallet address.


Convolutional Neural Network is one of the best method to recognize images.
Tag is just as same as "TAG" on your clothes.
Maybe, simple is the best. And many of easy(or simple) things in HTML is important.
Simple way to see web page's source code is "right click -> view sources(or ctrl+u)".
W3C
This is heading 1
This is heading 2
This is heading 3
This is heading 4
This is heading 5
This is heading 6
Statistics are important, if you are trying
to study sth(like HTML or CSS) you should see
which tags were used before.
ref: advancedwebranking.com
If you want to learn sth new, you should believe 'statistics'.
Celuv in HTML
br
br tag is "break line" tag. It's literally break lines. aka new line or 'enter' in word.b tag is used for use many new lines.
p
p tag is 'paragraph' tag. It can make a paragraph.
Web browser prefers heading rather than span like words.If you want to use image in web browser.
You should dive into Unsplash.com
Everything in tag is called 'Attribute', and it doesn't depend on it's orders.
How to make Web in the internet?
What is NAIVE WEB?
What is difference between web and internet?
Web is a part of Internet.
Web is born in Swiss.Link
Web's problem.Link


In charge of setting group goals, motivating, and communicating with team members.
Responsible for the research and design of different components and their materials
In charge of thrust, drag, weight, and propulsion of rocket
Design of the fully functional payload and electronic subsystems of the rocket
Ensures the rocket's deployment system allow for a successful recovery
Responsible for the visual design of the rocket and team gear


Sample post
Random link
Hey, I'm Benjamin.
I'm a software engineer at Google developing security and privacy-related technologies.


Donald Trump honors dog injured in raid that killed Isis leader
Donald Trump honored a hero dog injured in a raid that killed an infamous Isis leader with an invitation to the White House.
The president surprised reporters at the White House rose garden on Monday by trotting out Conan, a Delta Force dog wounded during the operation that killed Abu Bakr al-Baghdadi in Syria last month.
Trump said: ‘We actually just gave Conan a medal and a plaque. I actually think Conan knew exactly what was going on. But a dog that is very, very special.’
The president went on to call Conan a ‘tough cookie,’ a ‘hero’ and ‘the ultimate fighter, the ultimate everything.’
Conan became a nameless celebrity shortly after the raid when Trump told the press: ‘Our K-9, as they call – I call it a dog, a beautiful dog, a talented dog – was injured and brought back.’ At that time, Conan’s name was still classified.
Trump later declassified a picture of Conan, a Belgian Malinois, in a tweet, which was retweeted more than 130,000, becoming one of the president’s most popular tweets. Trump did not name Conan in the tweet, but later revealed his name.
Advertisement Advertisement
The president seemed jovial at Monday’s unscheduled appearance and even suggested Conan would turn on reporters who asked questions.
‘It’s trained that if you open your mouths, you will be attacked. You want to be very, very careful,’ he said.
Trump had previously said Conan was injured while chasing Baghdadi down a dead-end tunnel after it was feared the Isis leader was wearing a suicide vest, which he detonated, killing himself and three children.
Conan was reportedly injured from an electrocution. He is now recovering.
Trump was also set to sign a bipartisan bill, Preventing Animal Cruelty and Torture (PACT) Act, on Monday, which will officially make animal cruelty a felony charge.
The bill was passed in the Senate on November 5 as an expansion on the 2010 Animal Crush Video Prohibition Act, which made the creating and distribution of ‘animal crushing’ videos illegal, which often fetishize women in high heels stepping on small animals and crushing them, according to Care Animal Rights.


The Chief Inspector of Ofsted today put forward a series of radical and far-reaching recommendations to make a lasting difference to the prospects of thousands of ‘unseen children’ from low income backgrounds who are being let down by the education system.
The recommendations aimed at closing the attainment gap between England’s poorest children and those from better off backgrounds were contained in a lecture delivered by Sir Michael Wilshaw in central London.
The speech at Church House in Westminster and the accompanying report, entitled
‘Unseen children’
, marked 20 years since Ofsted first published a report into the achievements of the poorest children in the education system and 10 years since a follow-up study in 2003.
Sir Michael set out the main conclusions of the report, which followed widespread deliberation by an expert panel of head teachers, academics and educational leaders. They are:
-
the distribution of underachievement has shifted. 20 or 30 years ago, the problems were in the big cities. Inner London schools were the best funded and worst achieving in the country. Now, schools in inner and outer London are the best performing, and performance in parts of Birmingham, Greater Manchester, Liverpool and Leicester has also improved
-
the areas where the most disadvantaged children are being let down by the education system in 2013 are no longer deprived inner city areas, instead the focus has shifted to deprived coastal towns and rural, less populous regions of the country, particularly down the East and South-East of England. These are places that have felt little impact from national initiatives designed to drive up standards for the poorest children
-
a significant number of poorer children are also being failed by schools in areas of relative affluence, such as Kettering, Wokingham, Norwich and Newbury. It is in these areas, in coasting or sometimes sinking schools, that unseen disadvantaged children remain unsupported and unchallenged
Alongside the evidence report published by Ofsted, is a league table ranking local authorities based on how effectively schools in these areas are serving their most disadvantaged pupils.
Strikingly, this shows that the areas where poor children are doing worst in terms of educational opportunity are relatively prosperous parts of South East England, like West Berkshire. As a local authority area, West Berkshire:
-
has the worst attainment in the whole country at primary school
-
has the second worst attainment at secondary school
-
is in the bottom three local authorities for qualifications at 19
In his speech, Sir Michael set out 8 recommendations aimed at making a lasting difference in closing the attainment gap for the poorest children. These are:
-
Ofsted to be tougher in future with schools that are letting down their poor children. Schools previously judged outstanding, which are not doing well by their poorest children, will be re-inspected
-
the development and roll-out of sub-regional challenges aimed particularly at raising the achievement of disadvantaged children.
-
a more strategic approach should be taken to the appointment of National Leaders of Education to ensure that they are matched with schools in need of support
-
government should ensure that teachers on funded schemes are directed to underperforming schools in less fashionable or more remote or challenging places. The concept of a ‘National Service Teacher’ should be an urgent consideration for government.
-
government should review assessment in reception and key stage 1, with a view to publishing progress measures from the start of school to end of key stage 1
-
government should be prepared to dismantle inadequate colleges that have grown too large to assure quality across their different activities. Smaller specialist units, particularly University Technology Colleges, should be created with stronger links to business, commerce and industry
-
all recommendations in the Richard Review of apprenticeships should be implemented in full
-
all post-16 providers should report on the rate of progress and outcomes for all young people who had previously been eligible for free school meals
Sir Michael said:
The quality of education is the most important issue facing Britain today. In the long term, our success as a nation – our prosperity, our security, our society – depends on how well we raise and educate our young people across the social spectrum.
In the last 20 or 30 years, the performance of schools in inner London, Birmingham, Greater Manchester, Liverpool and Leicester has been transformed. The distribution in educational underachievement has shifted.
Today, many of the disadvantaged children performing least well in school can be found in leafy suburbs, market towns or seaside resorts. Often they are spread thinly, as an ‘invisible minority’ across areas that are relatively affluent. We need new policies and approaches to deal with underachievement in these areas.
Poor, unseen children can be found in mediocre schools the length and breadth of our country. They are labelled, buried in lower sets, consigned as often as not to indifferent teaching. They coast through education until – at the earliest opportunity – they sever their ties with it.
There are stark consequences for our nation if we do not act with sufficient urgency. We will continue to lose our place as a competitive nation and bear great economic costs of failure. By the time the next Access and Achievement report is published, I hope I can say that most, if not all, of the recommendations I have made today have been implemented and that our poorest children have continued to improve their educational performance.
Notes to editors
-
The Access and Achievement Expert Panel was made up of:
-
Dame Yasmin Bevan, Executive Principal and Headteacher, Challney High School for Boys and Denbigh High School
-
Dr Kevan Collins, Chief Executive, Education Endowment Foundation
-
Dr Hilary Emery, Chief Executive, National Children’s Bureau
-
Professor Chris Husbands, Director, Institute of Education, University of London
-
Debbie Jones, Immediate Past President, Association of Directors of Children’s Services; and Executive Director, Children and Young Person’s Services, Lambeth
-
Sir Daniel Moynihan, Chief Executive Officer, Harris Foundation
-
Carol Norman, Headteacher, Welbeck Primary School, Nottingham and National Leader of Education
-
Mike Raleigh, Education Adviser
-
Dame Ruth Silver, Chair, Learning and Skills Improvement Service
-
Professor Robert Slavin, Professor, Institute for Effective Education, University of York and Director, Center for Research and Reform in Education, John Hopkins University
-
Professor David Woods CBE, Former Adviser, London Schools and The London Challenge; and Principal National Challenge Adviser for England
-
The Office for Standards in Education, Children’s Services and Skills (Ofsted) regulates and inspects to achieve excellence in the care of children and young people, and in education and skills for learners of all ages. It regulates and inspects childcare and children’s social care, and inspects the Children and Family Court Advisory Support Service (Cafcass), schools, colleges, initial teacher training, work-based learning and skills training, adult and community learning, and education and training in prisons and other secure establishments. It assesses council children’s services, and inspects services for looked after children, safeguarding and child protection.


GYM HERO
{% if not g.user %}
HOME
EXERCISES
WORKOUTS
TIMERS
{% else %}
HOME
EXERCISES
WORKOUTS
TIMERS
TEAMS
ATHLETES
MY PROFILE
{% endif %}
{% if g.user %}
LOGOUT
{% else %}
REGISTER
LOGIN
{% endif %}
GYM HERO
HIIT Timer
Tabata Timer
Round Timer
Dark
Light
TIMERS
TIMERS APP


WELCOME BACK TO PARALLAXO!
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This document provides a gentle introduction to implementing Metropolis-Hastings algorithms. It is intended to give a basic understanding on fundamental principles. The theoretical derivation, involved examples and applications can be found in Robert & Casella (2004): Monte Carlo Statistical Methods.
We aim to obtain a sample from \(f\) without directly drawing from it. The Metropolis-Hastings algorithm generates such a sample by drawing from the proposal density \(g\) instead. The generated sample will be accepted with probability rho
. The proposal density can be chosen freely and its best if it is easy to simulate from \(g\). The algorithm is given by:
f <- function(x) ... #target density
q <- function(y,x) ... #proposal density, easy to sample from
rho <- function(x, y) f(y) / f(x) * q(y,x) / q(y,x) #acceptance probability
n <- 1e5 #length of chain
X <- c(1) #initial value of Markov chain
for (i in 1:n) {
Y <- ... #draw from g( . , X[i])
if(runif(1) < rho(X[i], Y)) X[i+1] <- Y #accept with rho
else X[i+1] <- X[i] #reject with 1-rho
}
We start by simulating from \(Gamma(4.3, 6.2)\), using a \(Gamma(5, 6)\) proposal. As the proposal does not depend on the current state of the Markov chain, this version of the algorithm is coined independent Metropolis-Hastings.
alpha0 <- 4.3 #shape
beta0 <- 6.2 #rate
alpha <- 5 #proposal shape
beta <- 6 #proposal rate
f <- function(x) dgamma(x, alpha0, beta0) #target density
q <- function(x) dgamma(x, alpha, beta) #proposal density
rho <- function(x, y) f(y) / f(x) * q(x) / q(y) #acceptance probability
n <- 1e5 #length of chain
Y <- rgamma(n, alpha, beta) #draw from proposal
X <- c(1) #initial value of Markov chain
for (i in 1:n) {
if (runif(1) < rho(X[i], Y[i])) X[i + 1] <- Y[i] #accept with rho
else X[i + 1] <- X[i] #reject with 1-rho
}
# show empirical and theoretical means
data.frame(empirical = mean(X), theoretical = alpha0/beta0)
## empirical theoretical
## 1 0.6932973 0.6935484
# visualize
require('ggplot2')
x <- seq(0, 3, l = 1e3)
ggplot() + theme_classic() + labs(y = '', x = '') +
geom_histogram(aes(x = X, y = ..density..), bins = 50, alpha = 0.3) +
geom_line(aes(x, f(x)), col = 'red') +
geom_line(aes(x, q(x)), col = 'blue')
Light gray is the histogram of the generated sample, red the underlying target density, and blue the proposal density. The sample captures the characteristics of \(f\) well.
However, nothing less can be expected when using a proposal that is so closely related to the target density. It does not make too much sense to use a Gamma proposal to generate a sample from the Gamma distribution, so lets proceed with another proposal.
We now consider the density of \(Exp(1)\) as proposal. We replace:
q <- function(x) dexp(x, 1) # proposal density
Y <- rexp(n, 1) # draw from proposal
Also, we highlight the independence of the initial value of the Markov chain by setting
X <- c(234) # absurd initial value
Running the code gives:
## empirical theoretical
## 1 0.693696 0.6935484
Although both the initial value lies at the tail of the target density \(f\) (red) and the proposal \(g\) (blue) differs from \(f\), the generated sample captures the characteristics of the target well.
Naturally, using the \(Exp(1)\) as proposal leads to much more generated samples being rejected than before. The shape of the proposal is crucial in the efficiency of the algorithm.
Now consider the case where the proposal density \(g\) does not have enough mass where the target density does \(f\). We replace
q <- function(x) dexp(x, 10) # proposal density
Y <- rexp(n, 10) # draw from proposal
This gives:
## empirical theoretical
## 1 0.6538496 0.6935484
The proposal \(Exp(10)\)-density \(g\) (blue) exhibits very little mass in regions where the target density \(f\) (red) does. Very few samples can be generated in those regions. Although the left tail of \(f\) is well approximated, the right tail is not. While enough samples are produced asymptotically, the Markov chain converges prohibitively slow.
We now turn to generating samples from a posterior. Consider the hierarchical model \[\begin{align*} X|\lambda &\sim Poi(\lambda)\\ \theta &\sim Gamma(\alpha, \beta); \end{align*}\] from which we can obtain the posterior as \[\begin{equation*} \theta|X=x \sim Gamma\left(\alpha + \sum_{i=1}^n x_i, 1 + \beta\right). \end{equation*}\]
Using the prior as proposal, the acceptance probability is given as \[\begin{equation*} \rho(\theta,\vartheta) = \min\left\{ 1, \frac{\pi(\vartheta|x)\pi(\theta)}{\pi(\theta|x)\pi(\vartheta)} \right\} = \min\left\{ 1, \frac{f(x|\vartheta)}{f(x|\theta)} \right\}. \end{equation*}\]
Thus:
set.seed(123)
alpha0 <- 5.3 #shape
beta0 <- 8.2 #rate
m <- 3 # sample size (if m large, the proposal is bad!)
sample <- rpois(m, rgamma(1, alpha0, beta0)) #draw sample
rho <- function(x, y) dpois(sum(sample), y) / dpois(sum(sample), x) # acc prob
n <- 1e4 #length of chain
Y <- rgamma(n, alpha0, beta0) #draw from prior
X <- c(1) #initial value of Markov chain
for (i in 1:n) {
if ( runif(1) < rho(X[i],Y[i] )) X[i + 1] <- Y[i] #accept with rho
else X[i + 1] <- X[i] #reject with 1-rho
}
# sampling with knowledge of the posterior
f <- function(x) dgamma(x, alpha0 + sum(sample), beta0 + 1) #posterior
q <- function(x) dgamma(x, alpha0, beta0) #prior
# visualize
require('ggplot2')
x <- seq(0, 3, l = 1e3)
ggplot() + theme_classic() + labs(y = '', x = '') + ylim(c(0,1.5)) +
geom_histogram(aes(x = X, y = ..density..), bins = 50, alpha = 0.3) +
geom_line(aes(x, f(x)), col = 'red') +
geom_line(aes(x, q(x)), col = 'blue')
As in the example above, the right tail of the proposal does not have enough mass to estimate the right tail of \(f\) well enough. This can be mitigated by choosing a more suitable proposal. In fact, there is no reason to believe that the prior serves as a good proposal, apart from mathematical simplicity.
In our case, if the sample size m
is large, the prior serves as a bad proposal. Instead, other proposals should be considered.
If the proposal is generated by \(Y \sim q(|y-x|)\), the algorithm is referred to as Random Walk Metropolis Hastings. We generate from a standard normal density using \(Y \sim X + \mathcal{U}([-\delta, \delta])\), for three step sizes delta <- c(0.1, 1, 100)
.
set.seed(123)
delta <- c(0.1, 1, 100) #use three different step sizes for the random walk
rho <- function(x, y) exp((x ** 2 - y ** 2) / 2) #acceptance probability
n <- 1e4 #length of chain
X1 <- X2 <- X3 <- c(0) #starting values
for (i in 1:n) {
#generate proposal
Y1 <- X1[i] + runif(1,-delta[1], delta[1])
Y2 <- X2[i] + runif(1,-delta[2], delta[2])
Y3 <- X3[i] + runif(1,-delta[3], delta[3])
#generate next state of Markov chain
if (runif(1) < rho(X1[i], Y1)) X1[i + 1] <- Y1 #accept with rho
else X1[i + 1] <- X1[i] #reject with 1-rho
if (runif(1) < rho(X2[i], Y2)) X2[i + 1] <- Y2 #accept with rho
else X2[i + 1] <- X2[i] #reject with 1-rho
if (runif(1) < rho(X3[i], Y3)) X3[i + 1] <- Y3 #accept with rho
else X3[i + 1] <- X3[i] #reject with 1-rho
}
#plot results
require('reshape')
require('ggplot2')
df <- melt(data.frame(X1, X2, X3))
ggplot(df) + theme_classic() +
labs(y = '', x = '') +
geom_histogram(aes(x = value, y = ..density..), bins = 30, alpha = 0.3) +
stat_function( fun = dnorm, color = 'red') +
facet_wrap(~variable, labeller = as_labeller(c(X1 = 'g(x) = x + U[-0.1, 0.1]',
X2 = 'g(x) = x + U[-1, 1]',
X3 = 'g(x) = x + U[-100, 100]'))
)
The histogram of the generated sample is given in light gray, the target density of the standard normal distribution in red. If the step size is too small to explore the support of \(f\) at reasonable speed, the corresponding Markov chain converges slowly (left).
If the step size is too large, many proposals will be rejected and the chain stays too at the same value (right). If the step size is adequate for the target density however, \(f\) can be approximated well. Of course, the right choice of delta
requires knowledge of \(f\), which might not be available.
In conclusion, the Metropolis Hastings algorithm can be applied on virtually any density \(f\). However, in order to ensure reasonable convergence speed knowledge about \(f\) is required so that a suitable proposal \(g\) can be chosen.


Class Channel
Inheritance
Inherited Members
Namespace: System.Dynamic.ExpandoObject
Syntax
public class Channel
Properties
Aggregations
An optional list of aggregators. These aggregations are added by default on each aggregation requests.
Declaration
public ICollection<AggregatorBase> Aggregations { get; set; }
Property Value
|
System.Collections.Generic.ICollection<AggregatorBase>
|
Audit
ExtendedSimpleSearchFields
An Optional list of fields. These fields extend the list of simple search fields outside the bounds of any schema field configuration.
Declaration
public ICollection<string> ExtendedSimpleSearchFields { get; set; }
Property Value
|
System.Collections.Generic.ICollection<System.String>
|
Filter
An optional search filter. Limits the content document result set on each search and aggregation request.
Declaration
public FilterBase Filter { get; set; }
Property Value
|
FilterBase
|
GrantedUserRoleIds
User roles granted access to the channel.
Declaration
public ICollection<string> GrantedUserRoleIds { get; set; }
Property Value
|
System.Collections.Generic.ICollection<System.String>
|
Id
ID of channel.
Declaration
public string Id { get; set; }
Property Value
|
System.String
|
MissingResultsDisplayPatterns
Display pattern to use for rendering details when 0 results are returned
Declaration
public TranslatedStringDictionary MissingResultsDisplayPatterns { get; set; }
Property Value
|
TranslatedStringDictionary
|
Names
Language specific names.
Declaration
public TranslatedStringDictionary Names { get; set; }
Property Value
|
TranslatedStringDictionary
|
SearchIndexId
The search index ID where the channel requests the content from. Only RootContentSearchIndex is supported.
Declaration
public string SearchIndexId { get; set; }
Property Value
|
System.String
|
Sort
Default sort order specified for the channel to sort the results of a content search.
Declaration
public ICollection<SortInfo> Sort { get; set; }
Property Value
|
System.Collections.Generic.ICollection<SortInfo>
|
SortFields
Fields to be used for sorting in content browser when displaying the channel. The information is only set and consumed by the client, not by the server.
Declaration
public ICollection<SortField> SortFields { get; set; }
Property Value
|
System.Collections.Generic.ICollection<SortField>
|
SortOrder
Numeric sort order of the channel. Channels are returned ordered by this field.
Declaration
public int SortOrder { get; set; }
Property Value
|
System.Int32
|
ViewForAll
Grants rights to all the users to view the channel.
Declaration
public bool ViewForAll { get; set; }
Property Value
|
System.Boolean
|
Methods
FromJson(String)
Declaration
public static Channel FromJson(string data)
Parameters
|
System.String
data
|
Returns
|
Channel
|
ToJson()
Declaration
public string ToJson()
Returns
|
System.String
|


{% load helper_tags %}
{% if not rec_job.enabled %}
The "{{ rec_job|link_or_label:profile }}" Recurring Job is disabled. Please enable it or ask your Administrator
to do so.
{% endif %}
Scheduled Snapshots are implemented by the "{{ rec_job|link_or_label:profile }}" Recurring Job. In order for a snapshot to be
taken on the schedule designated here, that Recurring Job must be enabled and set to run every hour on the hour.
{% portal_label %} will use its own server time to judge whether it is the right time to take a scheduled snapshot,
so make sure you know what the time is on the {% portal_label %} instance and that the timezone ({% cb_timezone_info %}) is correct.
Schedule:
{% if server.scheduled_snapshot_schedule %}
{{ server.scheduled_snapshot_schedule }}
{% else %}
No schedule is set, so no snapshots will be automatically taken for this Server.
Use the Edit icon to configure a snapshot schedule.
{% endif %}
{% if next_snapshot %}
Next Expected Snapshot:
{{ next_snapshot }}
{% endif %}
Maximum # of Snapshots: {% infotip max_tip %}
{# Setting it to 0 would be an odd choice, but we should respect it and not treat it like being unset #}
{% if server.scheduled_snapshot_maximum is not None %}
{{ server.scheduled_snapshot_maximum }}
{% else %}
No maximum number of snapshots is configured for this Server, so the default on the
Recurring Job will be used.
{% endif %}


To quickly complete a method call of a static method located anywhere in your project, libraries or JDK, enter a prefix
and press &shortcut:CodeCompletion; twice. You'll see all the matching methods. If you want the method you chose to be imported statically,
press &shortcut:ShowIntentionActions; and see what happens:


View
Consumers
Producers
Queues
Topics
Broker Groups
Profiles
Brokers
Slave Brokers
Containers
Configuration


--- layout: paper title: Publications --- Latent Relation Representations for Universal Schemas, , International Conference on Learning Representations 2013
@inproceedings{riedel2013latent, month={May}, year={2013}, author={Riedel, Sebastian and Yao, Limin and McCallum, Andrew}, booktitle={International Conference on Learning Representations}, title={Latent Relation Representations for Universal Schemas}, }


Gender :
App Development Web Development Data Analytics Database Management
C/C++ HTML CSS JavaScript


{% include breadcrumbs.html %} Written by Jason Swingen, {% include page-meta.html %} {{ content }} {% include sidebar2.html %}


Our conference is dedicated to providing a harassment-free conference experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), or technology choices.
We do not tolerate harassment of conference participants in any form. Sexual language and imagery is not appropriate for any conference venue, including talks, workshops, parties, Twitter and other online media.
Conference participants violating these rules may be sanctioned or expelled from the conference without a refund at the discretion of the conference organisers.
http://confcodeofconduct.com(subject to change)
Time to get yourself into the venue, get a coffee, and maybe a snack.
Opening remarks from the team.
One of the main challenges for widespread adoption of blockchain technology is scalability. In this session, Lio will share insights of a decentralized exchange project Enuma is working on, which recently received an Ethereum Foundation grant for tackling scalability issue.
The current system of centralized exchanges and decentralized platforms has inherent weaknesses which limit its potential to service the rapidly developing cryptocurrency market. These weaknesses are also a hindrance to widespread acceptance of cryptocurrency as an asset class.
Lio will discuss payment channels, in particular Sprites, and how it addresses the inherent scalability shortcomings and helps improving the liquidity of the market and increasing efficiency, thereby making cryptocurrency exchange more transparent, orderly and secure.
Sprites was originally developed by researchers at the University of Illinois at Urbana-Champaign to overcome shortfalls in existing payments channels that limit transaction volumes. Enuma is excited to build one of its first real-world applications where it will help increase the speed of transactions whilst reducing collateral cost.
Golang is a relatively young programming language from Google.
It has gained much popularity and is now ranked highly in several programming language indexes.
This talk will share some of useful Golang tips gained from real world experience.
Kotlin has proven itself to be the "better Java" and is supported on a wide range of platforms, including Android, JVM and native. It also cross-compiles to JavaScript.
This talk will take us through the process of developing a full stack web application using Kotlin on both the front end and back end.
We all like to tinker, it keeps us curious. Perhaps being given the chance to build an ATM though was a bridge too far - cashboxes, printers, scanners, Raspberry Pi's, oh my! Plenty of integration challenges.
In this talk, I'll describe my journey in building the software components for integrating a collection of hardware, to finally creating a workable crypto ATM production model.
Challenges working with remote teams, managed-unmanaged code interop, cross-compilation targeting embedded devices, the craziness of Qt, we’ve got it covered!
Lunch is on us. Grab some food and a drink, and chat with your fellow coders.
Accessible websites are websites that are accessible to everyone, not just you or the people you think use your website.

Even if you don’t know design, don’t know HTML or CSS, or don’t know how to code at all, there are some things you can keep in mind to improve accessibility on your website.
Creating company culture and trust through an entirely remote team can be difficult. It’s why the majority of companies have a small or tried-and-failed approach to their remote team members.
As an entirely remote team working across at least four time-zones, Pixelcabin has only ever known the remote way of doing things. As more software development teams grow through remote team members, how can the team stay cohesive and most importantly, deliver great code and products?
Gregor Vand will talk through the processes Pixelcabin have in place for building and maintaining happy remote teams, with a healthy dose of examples of what worked, and more importantly, what failed!
Redux has been used heavily in mid to large size React applications. However, developers struggle with maintainability and scalability as the state of Redux store and the number of reducers are growing.
Using Redux with TypeScript allow developers to maintain their React Redux project with the benefits of both strong and weak type programming.
WebAssembly is here, and in this talk we explore Blazor, a new experimental frontend framework.
It allows .NET developers to build web browser Single Page Apps (SPAs) written in C#!
Even if this is not your language of choice, we'll explore why this advancement is exciting, as other traditional programming languages may adopt the same technique, and whether this is truly the rumoured death of JavaScript.
Grab some tea, coffee, and snacks, and chat.
Is it possible to create life in a computer? In trying to do so, what can we learn?
In this talk, I will share my journey from writing Pascal programs of Conway's Game of Life in the 1990's, to the recent discovery of new life forms called Lenia.
Along the way, we will discuss topics like artificial intelligence, evolutionary algorithms, interactive UI design, and artificial life.
Software engineers generally don't like the notion that powerful, effective solutions can come into existence without an intelligent designer actively thinking things through. And yet no-one at this conference would question the validity of the theory of evolution.
A strange irony, and one I want to explore further in my talk as I present a protocol which allows software to evolve instead of being designed.
Product design can go wrong and affect users negatively. How do your users feel using your product? What’s the impact that you cause? Can we build better products if we follow certain principles?
This talk will explain how to truly care about our users and improve their experience by taking a more ethical approach. Most importantly, this talk will hopefully be an inspiration, question the status quo and help us build for a better future web.
When I started coding, everything was easy. I went to our scrum board, I took a card, It had acceptance criteria, maybe I had to ask a few questions, but more or less I was left to code in peace.
Everything was simple.
I could be the 10x engineer everyone talks about.
By why was it so simple? Why have I never been so productive (and so messy) in my professional career ever since?
The answer is People.
Bringing the day to a close. A last word from our Organisers.
Our after-party. Details will be published nearer the time.


◆ LoadFromFile()
| static std::unique_ptr<VmdMotion> vmd::VmdMotion::LoadFromFile |
( |
char const * |
filename | ) |
|
|
inlinestatic |
◆ LoadFromStream()
| static std::unique_ptr<VmdMotion> vmd::VmdMotion::LoadFromStream |
( |
std::ifstream * |
stream | ) |
|
|
inlinestatic |
◆ SaveToFile()
| bool vmd::VmdMotion::SaveToFile |
( |
const std::u16string & |
| ) |
|
|
inline |
◆ SaveToStream()
| bool vmd::VmdMotion::SaveToStream |
( |
std::ofstream * |
stream | ) |
|
|
inline |
◆ bone_frames
◆ camera_frames
◆ face_frames
◆ ik_frames
◆ light_frames
◆ model_name
| std::string vmd::VmdMotion::model_name |
◆ version
| int vmd::VmdMotion::version |
The documentation for this class was generated from the following file:


Class Metric
Package Metric
Project Metric
click to zoom, for detailed metrics, press ctrl or ⌘ while hovering


|
Fig. 1
|
Substantial enhanced radial growth after extreme wetness. En ... |
|
Fig. 2
|
Spatial distribution of tree radial growth change after extr ... |
|
Fig. 3
|
Spatial distribution of NDVI change after extreme climate ev ... |
|
Fig. 4
|
Response to extreme wetness in woody biomass from six CMIP5 ... |


def select_file(self, n):
if n < len(self.recent_paths):
recent = self.recent_paths[n]
del self.recent_paths[n]
self.recent_paths.insert(0, recent)
elif n:
path = self.file_combo.currentText()
if os.path.exists(path):
def select_file(self, n):
if n < len(self.recent_paths):
super().select_file(n)
// TODO: This is weird. Has it remained here from "Browse data sets"
// or from when this combo was editable? A `n` this large can come from
// `reload`, but ... how?!


Thank you!
We have sent an email with more instructions to {{ email }}.
Don't have a mobile identity wallet yet? Follow these instructions to get one.
{% load static %}
We have sent an email with more instructions to {{ email }}.
Don't have a mobile identity wallet yet? Follow these instructions to get one.


{% extends 'base.html'%} {% import 'macros.html' as macro%} {% import 'bootstrap/wtf.html' as wtf %} {% block content %}
Pitcher
Add New Pitch mate
Posted Pitches :
{{macro.displayPitches(pitch)}}


openvidu-mvc-node
A secure OpenVidu sample app with a Node backend and a traditional MVC frontend. It makes use of openvidu-node-client to connect to OpenVidu Server. With regard to the use of OpenVidu, it is identical to openvidu-js-node. This tutorial is intended for developers who feel more comfortable with MVC web architectures for their frontends. Embedded JavaScript is the template engine of choice for this tutorial.
Understanding this tutorial
OpenVidu is composed by the modules displayed on the image above.
- openvidu-browser: JavaScript library for the browser. It allows you to manage your video-calls straight away from your clients
- openvidu-node-client: server SDK for Node. Quick alternative to REST API
- openvidu-server: application to control Kurento Media Server
- Kurento Media Server: handles low level operations of media flow transmissions
Running this tutorial
1) Clone the repo:
git clone https://github.com/OpenVidu/openvidu-tutorials.git
1) You will need node to execute the app. You can install them with:
sudo curl -sL https://deb.nodesource.com/setup_12.x | sudo bash -
sudo apt-get install -y nodejs
3) Run the tutorial with the following commands. They will install the NPM dependencies and will execute server.js
server passing two arguments: "localhost:4443" as the URL where openvidu-server will be listening and "MY_SECRET" as the secret share with it:
cd openvidu-tutorials/openvidu-mvc-node
npm install
node server.js https://localhost:4443 MY_SECRET
4) openvidu-server and Kurento Media Server must be up and running in your development machine. The easiest way is running this Docker container which wraps both of them (you will need Docker CE):
docker run -p 4443:4443 --rm -e openvidu.secret=MY_SECRET openvidu/openvidu-server-kms:2.12.0
5) Go to https://localhost:5000
to test the app once the server is running. The first time you use the docker container, an alert message will suggest you accept the self-signed certificate of openvidu-server when you first try to join a video-call. To test two users in the same computer, use a standard window and an incognito window.
If you are using Windows, read this FAQ to properly run the tutorial
To learn some tips to develop with OpenVidu, check this FAQ
Understanding the code
This is a very basic web application with a pretty simple JS/HTML/CSS frontend and a straightforward Node backend built with express that serves HTML files with a MVC approach, building the templates with the help of Embedded JavaScript. OpenVidu assumes you can identify your users so you can tell which users can connect to which video-calls, and what role (and therefore what permissions) each one of them will have in the calls. You can do this as you prefer. Here our backend will manage the users and their sessions with the easy-to-use and non-intrusive express-session API.
-
Backend: node server
server.js
: single file which handles all operations of server. It returns HTML templates as response to HTTP requests.
-
Frontend templates: Plain JS/HTML/CSS files served by the backend, with
.ejs
extension to support Embedded JavaScript (/views
folder)index.ejs
: template with the login formdashboard.ejs
: template with the form to join a video-callsession.ejs
: template of the video-call itself
-
Frontend static files (
/public
folder)openvidu-browser-VERSION.js
: openvidu-browser library. You don't have to manipulate this filestyle.css
: some CSS classes to style the templates
Let's describe the code following this scenario: a user logs in to the app and connects to the video-call "TUTORIAL", where he publishes his webcam. A second user will connect to the same video-call just after that and publish its own webcam. Both of them will leave the call after a while.
1) User logs in
At path /
a login form will be displayed:
The form will execute a POST operation to path /dashboard
whenever "Log in" button is clicked, passing the username and the password:
<form class="form-group jumbotron" action="/dashboard" method="post">

User
<input class="form-control" type="text" name="user" required="true"></input>

Pass
<input class="form-control" type="password" name="pass" required="true"></input>

<button class="btn btn-lg btn-info" type="submit">Log in</button>

</form>
server.js
at /dashboard
first checks if the user is already logged (maybe he has just refreshed /dashboard
page), and if so it just redirects to the dashboard itself. If the user is actually logging in, the method checks that the params are correct and if so sets a new express-session for the newly logged user (adding a loggedUser property with its username in the req.session object). Finally it returns dashboard.ejs
template:
app.post('/dashboard', dashboardController);
app.get('/dashboard', dashboardController);
function dashboardController(req, res) {
// Check if the user is already logged in
if (isLogged(req.session)) {
// User is already logged. Immediately return dashboard
user = req.session.loggedUser;
res.render('dashboard.ejs', {
user: user
});
} else {
// User wasn't logged and wants to
// Retrieve params from POST body
var user = req.body.user;
var pass = req.body.pass;
if (login(user, pass)) { // Correct user-pass
// Validate session and return OK
// Value stored in req.session allows us to identify the user in future requests
req.session.loggedUser = user;
res.render('dashboard.ejs', {
user: user
});
} else { // Wrong user-pass
// Invalidate session and return index template
req.session.destroy();
res.redirect('/');
}
}
}
2) User connects to "TUTORIAL" video-call
dashboard.ejs
template will display a form asking for the video-call to connect and the nickname the user wants to have in it. So our 'publisher1' user would write TUTORIAL in "Session" field:
The form will execute a POST operation to path /session
whenever "Join!" button is clicked, passing the nickname and the session name:
<form class="form-group" action="/session" method="post">

Participant
<input class="form-control" type="text" name="data" required="true"></input>

Session
<input class="form-control" type="text" name="sessionname" required="true"></input>

<button class="btn btn-lg btn-success" type="submit">Join!</button>

</form>
When server.js
receives a request at /session
path is when things get interesting.
First of all there are some important attributes in this class we must mention:
// Environment variable: URL where our OpenVidu server is listening
var OPENVIDU_URL = process.argv[2];
// Environment variable: secret shared with our OpenVidu server
var OPENVIDU_SECRET = process.argv[3];
// Entrypoint to OpenVidu Node Client SDK
var OV = new OpenVidu(OPENVIDU_URL, OPENVIDU_SECRET);
// Collection to pair session names with OpenVidu Session objects
var mapSessions = {};
// Collection to pair session names with tokens
var mapSessionNamesTokens = {};
Rest controller method receives both params sent by the client (whatever nickname the user has chosen and "TUTORIAL" as the sessionName). First it prepares a param we will need a little further on: tokenOptions
.
app.post('/session', (req, res) => {
// Check the user is logged ...
// The nickname sent by the client
var clientData = req.body.data;
// The video-call to connect
var sessionName = req.body.sessionname;
// Role associated to this user
var role = users.find(u => (u.user === req.session.loggedUser)).role;
// Optional data to be passed to other users when this user connects to the video-call
// In this case, a JSON with the value we stored in the req.session object on login
var serverData = JSON.stringify({ serverData: req.session.loggedUser });
// Build tokenOptions object with the serverData and the role
var tokenOptions = {
data: serverData,
role: role
};
Just after that an if-else statement comes into play: does the session "TUTORIAL" already exist?
if (mapSessions[sessionName]) { ...
In this case it doesn't because 'publisher1' is the first user connecting to it. So we focus on the else branch:
else {
// Create a new OpenVidu Session asynchronously
OV.createSession()
.then(session => {
// Store the new Session in the collection of Sessions
mapSessions[sessionName] = session;
// Store a new empty array in the collection of tokens
mapSessionNamesTokens[sessionName] = [];
// Generate a new token asynchronously with the recently created tokenOptions
session.generateToken(tokenOptions)
.then(token => {
// Store the new token in the collection of tokens
mapSessionNamesTokens[sessionName].push(token);
// Return session template with all the needed attributes
res.render('session.ejs', {
sessionName: sessionName,
token: token,
nickName: clientData,
userName: req.session.loggedUser,
});
})
.catch(error => {
console.error(error);
});
})
.catch(error => {
console.error(error);
});
}
We are almost there! Now in session.ejs
JavaScript code (preceded by a tag


About
My name is Braxton Lee, and I am a sophmore Conputer Science major at Rose-Hulman Institute of Technology in Terre Haute, IN. My hometown is Honolulu, HI, where I went to `Iolani School. The goal of this website is to teach you a little more about myself, my projects, and my hobbies.
When I'm not doing schoolwork, by farovite things to do are to either get outside and enjoy nature through hiking or spending time at the ocean, or by making my own small programming projects. Like most other students my age, I also enjoy video games, usually of the racing variety.


ipwhois is a Python package focused on retrieving and parsing whois datafor IPv4 and IPv6 addresses.
Official site
Opensource


Star Wars: The Rise of Skywalker Production Wrapped Over the Weekend
by Tim AdamsWhile counting down the number of days until the release of Star Wars: The Rise of Skywalker, director J.J. Abrams revealed the film recently completed production on Sunday.
Abrams joined Good Morning America Monday morning, with his segment kicking off with a blockbuster ad for The Rise of Skywalker that aired during the American Music Awards Sunday night. When George Stephanopoulos asked Abrams if the movie is done, the director replied, "Just yesterday! So now people have to see it."
Reshoots for The Rise of Skywalker ran into the month of October, with Abrams explaining the thought process that goes into working on a blockbuster film like the highly-awaited Star Wars sequel. The first clip from The Rise of Skywalker was also unveiled, featuring Rey, Finn, Poe Dameron, Chewbacca, BB-8 and C-3Po attempting to escape a team of Jet Troopers.
Click the button below to start this article in quick view.
Directed and co-written by J.J. Abrams, Star Wars: The Rise of Skywalkerstars Daisy Ridley, Adam Driver, John Boyega, Oscar Isaac, Lupita Nyong’o, Domhnall Gleeson, Kelly Marie Tran, Joonas Suotamo, Billie Lourd, Keri Russell, Anthony Daniels, Mark Hamill, Billy Dee Williams, and Carrie Fisher, with Naomi Ackie and Richard E. Grant. The film arrives on Dec. 20.
https://schema.orgBreadcrumbListListItem1Homehttps://www.cbr.com/ListItem2Movieshttps://www.cbr.com/category/movies/ListItem3Movie Newshttps://www.cbr.com/category/movies/news-movies/ListItem4Star Wars: The Rise of Skywalker Production Wrapped Over the Weekendhttps://www.cbr.com/star-wars-the-rise-of-skywalker-wraps-production/


JavaScript it’s the brains that works behind the scenes on web pages. HTML gives the content along with some description to the content type, CSS takes the HTML and styles is based on the type descriptions. JavaScript works in the background to create the functionality of page.
Control loops let a program repeat a task for a set amount of cycles or until a condition is met. For example putting on socks this would involve putting a sock on two times, we know we need to do this twice so we could use a for loop. Another example would be cleaning a plate we want to keep scrubbing until it is clean, we don’t know at the start how dirty the plate is so we could use a while loop.
Arrays and object literals are quite similar they are both used to store multiple values nested within a parent component. Arrays are stored with a numeric index, so to access a specific value you must know the exact location of the value. Object literals store values with a string description index, this means that the value can be accessed with just a description.
Functions are a way of storing a process to be used over and over again. Functions can be passed and return variables.
Functions are useful because they reduce the amount of repetition needed, a set of instructions can be captured in a function and used by calling that function. They also make maintenance easier not only because it makes reading the code easier, it also means that rather than editing parts of the code in multiple places only the function need to be edited.


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Are Attributes and Property the same?
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In how many ways a Javascript code can be involved in an HTML file?
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What are the new ways to define a variable in Javascript?
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one of this is a Typed Language.
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Are Javascript and JScript the same.
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Are Typescript and Javascript the same?


Accessibility is an important indicator of both the livability and sustainability of a city. For this project we have developed an approach for quantifying urban accessibility (to healthcare, green spaces, schools, supermarkets and other points of interest). Our approach measures proximity at the parcel level and does not depend on any subjectively-defined "access thresholds". By utilizing readily-available public data and open-source routing algorithms, our approach is also readily scalable.
For more information, read our short article on the Adapting Cities website and see our published paper here.
This project involves the development of an agent-based model (ABM) to explore issues of rural poverty and development in Ethiopia. The ABM will model the decision-making process of individual households, allowing us to explore household-level food security and wealth under a variety of scenarios.


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layout: archive
title: Lean Prover Zulip Chat Archive
permalink: 113489newmembers/37535nmninsetsimpliesminsets.html
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The archive URL format has changed. Please find this page at:
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--- layout: page title: About header-img: "img/about-bg.png" ---
I’ve always been a builder and a dreamer. I remember putting together beautiful city architectures out of playbacks in my kindergarten class, while my classmates was busy sucking boogers out of his nose through a bendy-straw. That’s when I knew I was different. I want to know it all, but I have to conquer one thing at a time. I like cars, too. I took up coding as a hobby but I decided to make it my career.


Recipe 10.4. Calculating Apache Hits per IP AddressCredit: Mark Nenadov, Ivo Woltring ProblemYou need to examine a log file from Apache to count the number of hits recorded from each individual IP address that accessed it. SolutionMany of the chores of administering a web server have to do with analyzing Apache logs, which Python makes easy: def calculateApacheIpHits(logfile_pathname):
''' return a dict mapping IP addresses to hit counts '''
ipHitListing = { }
contents = open(logfile_pathname, "r")
# go through each line of the logfile
for line in contents:
# split the string to isolate the IP address
ip = line.split(" ", 1)[0]
# Ensure length of the IP address is proper (see discussion)
if 6 < len(ip) <= 15:
# Increase by 1 if IP exists; else set hit count = 1
ipHitListing[ip] = ipHitListing.get(ip, 0) + 1
return ipHitListing DiscussionThis recipe supplies a function that returns a dictionary containing the hit counts for each individual IP address that has accessed your Apache web server, as recorded in an Apache log file. For example, a typical use would be: HitsDictionary = calculateApacheIpHits( "/usr/local/nusphere/apache/logs/access_log") This function has many quite useful applications. For example, I often use it in my code to determine the number of hits that are actually originating from locations other than my local host. This function is also used to chart which IP addresses are most actively viewing the pages that are served by a particular installation of Apache. This function performs a modest validation of each IP address, which is really just a length check: an IP address cannot be longer than 15 characters (4 sets of triplets and 3 periods) nor shorter than 7 (4 sets of single digits and 3 periods). This validation is not stringent, but it does reduce, at tiny runtime cost, the probability of placing into the dictionary some data that is obviously garbage. As a general technique, low-cost, highly approximate sanity checks for data that is expected to be OK (but one never knows for sure) are worth considering. However, if you want to be stricter, regular expressions can help. Change the loop in this recipe's function's body to: import re # an IP is: 4 strings, each of 1-3 digits, joined by periods ip_specs = r'\.'.join([r'\d{1,3}']*4) re_ip = re.compile(ip_specs) for line in contents: match = re_ip.match(line) if match: # Increase by 1 if IP exists; else set hit count = 1 ip = match.group( ) ipHitListing[ip] = ipHitListing.get(ip, 0) + 1 In this variant, we use a regular expression to extract and validate the IP at the same time. This approach enables us to avoid the split operation as well as the length check, and thus amortizes most of the runtime cost of matching the regular expression. This variant is only a few percentage points slower than the recipe's solution. Of course, the pattern given here as ip_specs is not entirely precise either, since it accepts, as components of an IP quad, arbitrary strings of one to three digits, while the components should be more constrained. But to ward off garbage lines, this level of sanity check is sufficient. Another alternative is to convert and check the address: extract string ip just as we do in this recipe's Solution, then: # Ensure the IP address is proper try: quad = map(int, ip.split('.')) except ValueError: pass else: if len(quad)==4 and min(quad)>=0 and max(quad)<=255: # Increase by 1 if IP exists; else set hit count = 1 ipHitListing[ip] = ipHitListing.get(ip, 0) + 1 This approach is more work, but it does guarantee that only IP addresses that are formally valid get counted at all. See AlsoThe Apache web server is available and documented at http://httpd.apache.org; regular expressions are covered in the docs of the re module in the Library Reference and Python in a Nutshell. |


SNOMED International's SNOMED CT Browser provides ways to browse and search SNOMED CT. The browser has been implemented as part of development within the SNOMED International Open Tooling Framework, by SNOMED International and its development partners
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If you would like to get involved in the development, this code is available under an Apache v2 open source license. You can also find more information on the current backlog of feedback that is up for possible development here - SNOMED International Browser service page
Releases will allow the selection of SNOMED CT content from the International Edition or other extensions, with specific dates.
Perspectives are pre-defined browsing layouts for specific purposes. Use the responsive perspective for best results in tablets and phones.
SNOMED International Privacy Policy Cookie Settings
This is a browser for the SNOMED CT Daily Build.
This browser contains a test build of the terminology for evaluation purposes only. There can be no expectation of continuity of identifiers, descriptions text or changes in components (e.g. relationships).
No new concepts or identifiers may be used in clinical systems.
Releases will allow the selection of SNOMED CT content from the International Edition or other extensions, with specific dates.
Perspectives are pre-defined browsing layouts for specific purposes. Use the responsive perspective for best results in tablets and phones.
SNOMED International Privacy Policy
© SNOMED International vproject_version
For more information on the Community Content platform and the content provided within this browser visit - snomed.org/community-content
This browser contains the first, draft view into different content projects being created collaboratively by the SNOMED community in the Community Content area. This content has not been verified by SNOMED International but has been quality assured by those working on each project.
At this stage, it is strongly recommended to not use any new concepts or identifiers in production clinical data.
Future iterations of this browser will provide ways for you to provide feedback on any new content that you see in a project.
Releases will allow the selection of SNOMED CT content from the International Edition or other extensions, with specific dates.
Perspectives are pre-defined browsing layouts for specific purposes. Use the responsive perspective for best results in tablets and phones.
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This Concrete Domains (CD) Technical Preview (TP) has been published in line with the SNOMED CT January 2021 International Edition, with drug concept strengths and counts expressed as concrete values in the new Relationships file.
It is a full RF2 International Edition package (as opposed to a standalone Delta file containing only Concrete Domains), containing all the January 2021 International content together with the new CD Relationships file. The release files offer a preview of the final release package contents and format once the CD transition is complete.
The CD TP Browser presents the TP content through the familiar SNOMED CT Browser user interface for ease of viewing and consideration of the impact and scope of the changes. Whilst browsing the content you can use the Feedback button in the header bar to send your thoughts and questions to SNOMED International via a pop-up form.
The CD TP Browser is optimized for the Google Chrome web browser, but may be usable in other modern web browsers, such as Safari, Firefox and Microsoft Edge. Internet Explorer is not supported since it is now deprecated by Microsoft in favour of Microsoft Edge.
Releases will allow the selection of SNOMED CT content from the International Edition or other extensions, with specific dates.
Perspectives are pre-defined browsing layouts for specific purposes. Use the responsive perspective for best results in tablets and phones.
SNOMED International Privacy Policy
© SNOMED International vproject_version


isEventVariable variable module
This function tests if a variable is a event variable of the module. If yes, 1 is returned. Otherwise, 0 is returned.


--- scripts: /js/submit.js ---
This is the home of a BF Joust hill running on the #esoteric
IRC channel in freenode.
For more details on the game, see the BF Joust and BF Joust strategies pages at the esolangs wiki.
The score table and result matrix, as well as a breakdown report and a game viewer, are available — if your browser is compatible enough with the client-side fanciness. If not (or if you prefer a more austere view), the old-fashioned report.txt and breakdown.txt files are also an option.
For serious use, consider joining the #esoteric
IRC channel (on freenode).
The command to use is !zjoust name_of_your_program your_code_goes_here
.
If your program is larger than what comfortably fits in an IRC message, write an URL instead.
The zemhill
bot should respond and tell you how things went.
If you want to tune your program before submitting, the !ztest name code
command is available.
It is otherwise identical to !zjoust
, except does not really submit your program.
You can do this by private message to zemhill
if you want.
You can also try the game out using the form below (if it works), but then you won't get your name on the leaderboards — all submissions via the form are considered to be owned by web
.


Check your details
| Are you or your partner currently in hospital? |
No |
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| Are you or your partner living permanently in a care home? |
No |
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| Where do you and your partner live? |
In a property we own |
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| Do you or your partner have to pay Council Tax? |
Yes |
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| Council tax per month |
£100 |
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| Do you or your partner have a mortgage or loan secured on your home? |
Yes |
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| Mortgage or loan payment per month |
£300 |
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| Do you or your partner pay ground rent? |
No |
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| Do you or your partner have to pay any service charges? |
No |
Change this |
| Do you or your partner have a loan to adapt your home for the special needs of a disabled person? |
No |
Change this |
| Does anyone else live in your home with you and your partner? |
No |
Change this |
Save - continue to my low income scheme home


To start, click the "+" button and add an amount of crypto that you own.
Default prices are in USD, use the Options page to change the currency used.


UI Library - Buttons
Back
UI Library
Button
Default
Small button
Default button
Large button
Default Status
Normal
Hover
Press
Focus
Disabled
Primary
Small button
Default button
Large button
Primary Happy
Normal
Hover
Press
Focus
Disabled
Primary Positive
Normal
Hover
Press
Focus
Disabled
Primary Negative
Normal
Hover
Press
Focus
Disabled


-
Channel 1
-
Channel 2
-
Channel 3
-
Channel 4
Incompatible resolution
Sense resistor value in Ohms
Sample rate in samples per second
Refresh rate in milliseconds
Enable 8x HW Averaging
This will reset everything to default and overwrite your saved settings!
Are you sure?
PACView
GUI for the PAC1934 4-Channel DC Power Monitor Evaluation Board


Check Bazel version
Overview
We recommend forcing all users to update to at least the same version of Bazel as the continuous integration, so they don't trip over incompatibilities with rules used in the project.
check_bazel_version
check_bazel_version(minimum_bazel_version, message)
Verify the users Bazel version is at least the given one.
This should be called from the WORKSPACE
file so that the build fails as
early as possible. For example:
# in WORKSPACE:
load("@build_bazel_rules_nodejs//:defs.bzl", "check_bazel_version")
check_bazel_version("0.11.0")
Attributes
minimum_bazel_version |
a string indicating the minimum version |
message |
optional string to print to your users, could be used to help them update |