Though this is frequently Truth in Television, it isn't advised. Animal welfare organizations and professionals agree that pets are a long-term commitment that should be agreed upon by the entire household. Pets should also be planned and prepared for before buying.
Examples:
open/close all folders
Comic Books
- In Supreme Power, little Mark Milton is given a puppy for his first birthday. Unfortunately, the puppy smells that he's not human and barks at him, causing little Mark to panic, and, well... that was the day they realized that he had heat vision.
"Huh... well... I guess Spot was kind of the right name after all."
Comic Strip
- One strip of The Far Side has a boy receiving cockroaches for a Christmas or birthday present and a voice offscreen says, "Maybe next year you can have that puppy."
Fanfiction
- In this fanfction of The Addams Family with the same title as the series, it's revealed that Kitty Cat the lion was given to Wednesday for her first birthday when he was a cub.
Film — Animation
- Finding Nemo: Dr. Sherman gives Nemo to his niece Darla as a birthday present. This is rather unfortunate as Darla keeps killing her fish upon receiving them after shaking their plastic bag too hard.
- Lady from Lady and the Tramp was brought as a puppy by Jim for his wife's birthday.
- At the end of Teen Titans: The Judas Contract, Raven gives a great dane puppy to Damian, showing how they've grown closer to one another.
- Toy Story: At the end of the first movie, Andy is given a puppy for Christmas. The puppy's name is revealed in the second movie as "Buster".
Film — Live-Action
- In John Wick, the titular character gets a beagle puppy named Daisy as a last gift from his deceased wife as something to remember her by. But when Daisy is cruelly and pointlessly killed by Iosef Tarasov, John takes up arms again in search of revenge.
- Homeward Bound II: Lost in San Francisco: Riley, leader of the gang of strays, was given as a Christmas gift for a young boy when he was a puppy. The boy showed no interest in him whatsoever, so the parents abandoned Riley in the street, giving him a severe distrust in humans.
Literature
- Deconstructed in A Dog's Purpose. A man surprises his girlfriend Wendi with a puppy when their relationship is on the rocks. Wendi's lease doesn't allow for pets, so she has to keep Bear a secret. She's also clueless about how to raise a puppy. Wendi ended up giving Bear to her parents when he grew too big for her. Her parents abused and neglected Bear, ultimately releasing him onto the streets when they didn't want to deal with him anymore.
- In Frankensteins Cat, a little boy is given a dog for Christmas... an undead one sewn together like Frankenstein's monster.
- In Harry Potter: Harry gets his pet owl Hedwig from Hagrid as a birthday present in the first book. Hermione gets a cat for hers from her parents in the third book.
- In A Song of Ice and Fire, Jon Snow gifts to his half-brothers and himself direwolves.
- Stephen King's short story "L. T.'s Theory of Pets" fully shows the downsides of this trope. It focuses on the crumbling marriage between narrator L.T. and his wife, whose marital problems are only made worse by the pets they give each other as presents (his wife gets LT a dog, which dislikes him instantly and sides with his wife, while LT gets his wife a cat, which immediately takes to L.T. instead). In a foreword for the story, Stephen King recalls how he got the idea from a Dear Abby column that criticized the trope:
Stephen King, quoting Dear Abby: A pet is just about the worst sort of present one can give anyone. It makes the assumption that the pet and the recipient will hit it off, for one thing; it assumes that feeding and animal twice a day and cleaning up its messes was the very thing you had been pining to do.
- Deconstructed to a horrible extent in Trainspotting; Tommy gets a kitten for his ex-girlfriend to try to get back together with him. She rejects them, and the kitten ends up causing Tommy's premature death by toxoplasmosis (he was HIV positive).
- Defied in the Eva Ibbotson book Journey to the River Sea. The heroine wants to gift her foster family with a pet before meeting them, but her governess tells her to wait until they know the household and what pets they already have. Which is for the best, because the family is made of prissy English xenophobes who don't want to acknowledge that they live in Brazil.
- At the end of the first Fudge book, Tales of a Fourth-Grade Nothing, Fudge comes back from the hospital after he swallowed Peter's turtle Dribble, and their parents shower him with presents to celebrate his recovery, making Peter feel as if they don't care that he, Pete, has lost his pet. But then their dad brings in a present for Peter too: a dog. In memory of Dribble, Peter names the dog Turtle.
Live Action TV
- In The Flash, Season 3, episode "Borrowing Problems from the Future" , Barry and Iris move into a loft together, and H.R. gifts them a pet turtle named McSnurtle as a "housewarming gift". The turtle quickly vanishes again afterwards.
- Friends: In "The One With A Chick And A Duck", Joey buys a chicken from an animal sanctuary as a gift for Chandler after seeing (and misunderstanding) a news report about people who buy chicks for Easter, and then find they cannot properly care for them. Chandler tries to return the chicken, but changes his mind when he finds out the chicken is most likely going to be euthanized, and instead buys a duck. The two birds continue to make appearances in the rest of the series until after season 6.
- Mr. Roper buys his wife a parakeet for their anniversary in Three's Company, although it's really only a pretext for him to buy himself a partner for the pet bird he already owns. He gives the bird to his neighbors for safekeeping so he can surprise Helen, but Jack accidentally sits on the box containing the live animal.
- Drake & Josh: Backfires in "Merry Christmas Drake and Josh". The Freudian Excuse for their Christmas-hating Parole Officer is that, when he was a kid, his mother got him a caged monkey as a gift. The monkey freaked out and attacked him before escaping, traumatizing him for life.
- Victorious: After the two break up, Jade buys Beck a dog so that he'll take her back. This ends up backfiring when the dog ends up mauling Beck's dad.
Music
- In Marshmello's music video "Happier", a girl portrayed by Miranda Cosgrove receives a puppy golden retriever on her birthday, which becomes her best friend. A few years later, the dog dies of old age, which devastates her. At the end of the video, the protagonist is now grown up and her daughter, who celebrates her birthday, receives a similar puppy, which makes her mother shed a tear.
- Swedish musician and comedian Povel Ramel mentions in his memoirs how he was once forced to do this when he went last minute present shopping on Christmas Eve, and the only store he found that was still open was a pet store. Among other things, his then-girlfriend got a monkey for Christmas...
Video Games
- Emperor: Rise of the Middle Kingdom: The Palace can have a menagerie of exotic and local animals. Local wildlife can be caught by some gods and exchanged with other cities, who usually gift one from their climate in return (e.g. forest climates can supply pandas and tigers, desert climates can supply vultures and antilopes, etc.). There's no real effect on gameplay (except when the mission requires a certain amount of animals), but if the Palace is destroyed the animals will escape, and the predators will wreak havoc on your walkers.
Western Animation
- Courage the Cowardly Dog: In "Freaky Fred," we learn that Muriel's nephew, the eponymous demented barber Fred, was given a pet hamster when he was a lad who Courage's fur reminds him of. Unfortunately, as with the hamster, this inspires Fred to be... naughty.
- Doug: "Doug's Christmas Story" reveals that Doug received Porkchop as a Christmas present.
- Futurama: Fry attempts to get Leela a gift on Christmas Eve, but all of the stores are closed early in preparation for Santa's arrival, except the pet store. He almost gets her a swarm of lizards, but ends up going with a parrot instead.
Real Life
- According to a legend, if the King of Siam didn't like someone, he would give them a white elephant as a present (or make it known he was to do so). In this culture white elephants were sacred, so the recipient couldn't put it to work but still had to feed it, creating a drain on his resources. Turning down the present would be a grave insult, so the only way to avoid it was to absent yourself from court—which was the intention. This led to the term "white elephant" (meaning a troublesome or unwanted gift).


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_real = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], name='input_real')
input_z = tf.placeholder(tf.float32, [None, z_dim], name='z_dim')
learning_rate = tf.placeholder(tf.float32, name='learning_rate')
return input_real, 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 activate_fn(input):
alpha = 0.2
# swish
return input * tf.nn.sigmoid(input)
# return tf.maximum(alpha * input, input)
Xavier = tf.contrib.layers.xavier_initializer()
keep_prob = 0.2
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
with tf.variable_scope('discriminator', reuse=reuse):
# 28x28x3 -> 14x14x64
L1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', kernel_initializer=Xavier)
L1 = tf.layers.dropout(L1, keep_prob, training=True)
L1 = activate_fn(L1)
# 7x7x128
L2 = tf.layers.conv2d(L1, 128, 5, strides=2, padding='same', kernel_initializer=Xavier)
L2 = tf.layers.dropout(L2, keep_prob, training=True)
L2 = tf.layers.batch_normalization(L2, training=True)
L2 = activate_fn(L2)
# 4x4x256
L3 = tf.layers.conv2d(L2, 256, 5, strides=2, padding='same', kernel_initializer=Xavier)
L3 = tf.layers.dropout(L3, keep_prob, training=True)
L3 = tf.layers.batch_normalization(L3, training=True)
L3 = activate_fn(L3)
# print('DL3', L3.shape)
# Flatten
flat = tf.reshape(L3, [-1, 4*4*256])
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
with tf.variable_scope('generator', reuse=not is_train):
# Fully connect
L1 = tf.layers.dense(z, 4*4*256)
# 4x4x256
L2 = tf.reshape(L1, (-1, 4, 4, 256))
L2 = tf.layers.dropout(L2, keep_prob, training=is_train)
L2 = tf.layers.batch_normalization(L2, training=is_train)
L2 = activate_fn(L2)
# print('GL2', L2.shape)
# 7x7x128 因为7x7经过4x4的filter,1x1的strides和Valid之后会变成4x4，所以反过来就是7x7
L3 = tf.layers.conv2d_transpose(L2, 128, 4, strides=1, padding='valid', kernel_initializer=Xavier)
L3 = tf.layers.dropout(L3, keep_prob, training=is_train)
L3 = tf.layers.batch_normalization(L3, training=is_train)
L3 = activate_fn(L3)
# print('GL3', L3.shape)
# 14x14x64
L4 = tf.layers.conv2d_transpose(L3, 64, 5, strides=2, padding='same', kernel_initializer=Xavier)
L4 = tf.layers.dropout(L4, keep_prob, training=is_train)
L4 = tf.layers.batch_normalization(L4, training=is_train)
L4 = activate_fn(L4)
# print('GL4', L4.shape)
# Output 28x28xout_channel_dim
logits = tf.layers.conv2d_transpose(L4, out_channel_dim, 5, strides=2, padding='same', kernel_initializer=Xavier)
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
g_model = generator(input_z, out_channel_dim, True)
d_model_real, d_logits_real = discriminator(input_real, False)
d_model_fake, d_logits_fake = discriminator(g_model, True)
smooth = 0.1
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*(1-smooth)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
out_channel_dim = 3 if data_image_mode == 'RGB' else 1
d_loss, g_loss = model_loss(input_real, input_z, out_channel_dim)
d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
steps = 0
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 *= 2
# Input for Generator
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
_ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
_ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
if steps % 100 == 0:
train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{} - Steps {}...".format(epoch_i+1, epoch_count, steps),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
show_generator_output(sess, 16, input_z, out_channel_dim, 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 = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


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| Package | Description |
|---|---|
| edu.biu.protocols.yao.primitives | |
| edu.biu.scapi.circuits.encryption | |
| edu.biu.scapi.circuits.fastGarbledCircuit | |
| edu.biu.scapi.circuits.garbledCircuit |
| Modifier and Type | Method and Description |
|---|---|
MultiKeyEncryptionScheme |
CryptoPrimitives.getMultiKeyEncryptionScheme()
Returns the default MultiKeyEncryptionScheme.
|
| Modifier and Type | Method and Description |
|---|---|
CryptoPrimitives.Builder |
CryptoPrimitives.Builder.mes(MultiKeyEncryptionScheme mes)
Sets the given MultiKeyEncryptionScheme.
|
byte[] |
KProbeResistantMatrix.transformKeys(byte[] originalKeys,
MultiKeyEncryptionScheme mes)
Gets a original keys and transform them into keys that corresponds to the matrix.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AES128MultiKeyEncryption
This is a semi-classical double encryption scheme in which we use a PRF (here, we use AES) twice on each value with different keys
to double encrypt.
|
class |
AESFixedKeyMultiKeyEncryption
This encryption scheme uses AES with a fixed key and thus has the benefit of not needing to repeatedly perform the costly setKey
procedure for AES.
|
class |
HashingMultiKeyEncryption
We implement this double encryption scheme using Hashing.
|
| Constructor and Description |
|---|
FastGarbledBooleanCircuitExtendedImp(FastGarbledBooleanCircuit gbc,
MultiKeyEncryptionScheme mes)
This constructor should be used in case the garbling is done using a MultiKeyEncryptionScheme.
|
FastGarbledBooleanCircuitExtendedImp(FastGarbledBooleanCircuit gbc,
MultiKeyEncryptionScheme mes,
PseudorandomGenerator prg)
This constructor should be used in case the garbling is done using a PRG and seed.
|
| Constructor and Description |
|---|
FreeXORGarblingParameters(BooleanCircuit ungarbledCircuit,
MultiKeyEncryptionScheme mes,
boolean isRowReduction)
This constructor creates a garbling parameters' object for a regular representation of FreeXORGarbledBooleanCircuit.
|
GarbledBooleanCircuitExtendedImp(GarbledBooleanCircuit gbc,
MultiKeyEncryptionScheme mes)
This constructor should be used in case the garbling is done using a MultiKeyEncryptionScheme.
|
GarbledBooleanCircuitExtendedImp(GarbledBooleanCircuit gbc,
MultiKeyEncryptionScheme mes,
PseudorandomGenerator prg)
This constructor should be used in case the garbling is done using a PRG and seed.
|
StandardGarblingParameters(BooleanCircuit ungarbledCircuit,
MultiKeyEncryptionScheme mes,
java.security.SecureRandom random,
boolean isRowReduction)
This constructor creates a garbling parameters' object for a regular representation of StandardGarbledBooleanCircuit.
|


Sum of divisors
问题描述 :
mmm is learning division, she’s so proud of herself that she can figure out the sum of all the divisors of numbers no larger than 100 within one day!
But her teacher said "What if I ask you to give not only the sum but the square-sums of all the divisors of numbers within hexadecimal number 100?" mmm get stuck and she’s asking for your help.
Attention, because mmm has misunderstood teacher’s words, you have to solve a problem that is a little bit different.
Here’s the problem, given n, you are to calculate the square sums of the digits of all the divisors of n, under the base m.
But her teacher said "What if I ask you to give not only the sum but the square-sums of all the divisors of numbers within hexadecimal number 100?" mmm get stuck and she’s asking for your help.
Attention, because mmm has misunderstood teacher’s words, you have to solve a problem that is a little bit different.
Here’s the problem, given n, you are to calculate the square sums of the digits of all the divisors of n, under the base m.
输入:
Multiple test cases, each test cases is one line with two integers.
n and m.(n, m would be given in 10-based)
1≤n≤109
2≤m≤16
There are less then 10 test cases.
n and m.(n, m would be given in 10-based)
1≤n≤109
2≤m≤16
There are less then 10 test cases.
输出:
Multiple test cases, each test cases is one line with two integers.
n and m.(n, m would be given in 10-based)
1≤n≤109
2≤m≤16
There are less then 10 test cases.
n and m.(n, m would be given in 10-based)
1≤n≤109
2≤m≤16
There are less then 10 test cases.
样例输入:
10 2 30 5
样例输出:
110 112HintUse A, B, C...... for 10, 11, 12...... Test case 1: divisors are 1, 2, 5, 10 which means 1, 10, 101, 1010 under base 2, the square sum of digits is 1^2+ (1^2 + 0^2) + (1^2 + 0^2 + 1^2) + .... = 6 = 110 under base 2.
2012 Asia Tianjin Regional Contest-B
题目大意就是找出n的约数，然后把约数在m进制下展开，各个数位的每一位平方求和，然后按m进制输出。
直接模拟即可。
#include <iostream> #include <cstdio> #include <cstdlib> #include <cstring> #include <cmath> using namespace std; const int MAXP = 40000; int prime[MAXP] = {0},num_prime = 0; int isNotPrime[MAXP] = {1, 1}; int digit[MAXP]; int n,m; /*void GetPrime() { for(int i = 2 ; i < MAXP ; i ++){ if(! isNotPrime[i]) prime[num_prime ++]=i; for(int j = 0 ; j < num_prime && i * prime[j] < MAXP ; j ++) { isNotPrime[i * prime[j]] = 1; if( !(i % prime[j])) break; } } }*/ int sqr(int x){ return x*x;} int GetSqrValue(int i,int m){ int sum=0; while (i){ sum+=sqr(i%m); i/=m; } return sum; } void PrintBaseM(int ans,int m){ int nPs=0; while (ans){ digit[nPs++]=ans%m; ans/=m; } for (int i=nPs-1;i>=0;--i) if (digit[i]<10)cout<<digit[i]; else cout<<char(digit[i]+55); cout<<endl; } int main() { //GetPrime(); while (cin>>n>>m){ int ans=0; int N= int(sqrt(n*1.0)); for (int i=1;i<=N;++i) if (!(n%i)) { ans+=GetSqrValue(i,m); if (n/i != i) ans+=GetSqrValue(n/i,m); } PrintBaseM(ans,m); } return 0; }
版权声明：本文为博主原创文章，未经博主允许不得转载。
参考：http://blog.csdn.net/kqzxcmh/article/details/8117989
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A customizable IgxHierarchicalGrid with predefined features You can read more about configuring the igx-hierarchical- grid component in the README or the official documentation.


The <md-list-item>
directive is a container intended for row items in a <md-list>
container.
The md-2-line
and md-3-line
classes can be added to a <md-list-item>
to increase the height with 22px and 40px respectively.
.md-avatar
- class for image avatars
.md-avatar-icon
- class for icon avatars
.md-offset
- on content without an avatar
Note: We automatically apply special styling when the inner contents are wrapped inside
of a <md-button>
tag. This styling is automatically ignored for class="md-secondary"
buttons
and you can include a class of class="md-exclude"
if you need to use a non-secondary button
that is inside the list, but does not wrap the contents.


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Source code for illumina_fastq.utils
# -*- coding: utf-8 -*-
###
# © 2018 The Board of Trustees of the Leland Stanford Junior University
# Nathaniel Watson
#<EMAIL_ADDRESS>#<EMAIL_ADDRESS>###
# Created Jan, 2017
import sys
import os
import datetime
import gzip
#from memory_profiler import profile
# @profile #used for memory_profiler
[docs]def parseIlluminaFastqAttLine(attLine):
"""
Given the title line of a FASTQ record, tonizes the line and stores the tokens in a dict.
The Illumina FASTQ Att line format (as of CASAVA 1.8 at least) is:
@<instrument-name>:<run ID>:<flowcell ID>:<lane>:<tile>:<x-pos>:<y-pos> <read number>:<is filtered>:<control number>:<barcode sequence>
Args:
attLine: `str`. The title line of a FASTQ record, minus any trailing whitespace.
Returns:
`dict`. The keys are:
1. instrument,
2. runId,
3. flowcellId,
4. lane,
5. tile,
6. xpos,
7. ypos,
8. readNumber,
9. isFiltered,
10. control,
11. barcode
"""
uid = attLine.strip()
header = uid.lstrip("@").split(":")
dico = {}
dico["instrument"] = header[0]
dico["runId"] = header[1]
dico["flowcellId"] = header[2]
dico["lane"] = header[3]
dico["tile"] = header[4]
dico["xpos"] = header[5]
ypos, readNumber = header[6].split()
dico["ypos"] = ypos
dico["readNumber"] = readNumber
dico["isFiltered"] = header[7]
dico["control"] = header[8]
dico["barcode"] = header[9]
return dico
[docs]def getPairedendReadId(read_id):
"""
Given either a forward read or reverse read identifier, returns the corresponding paired-end
read identifier.
Args:
read_id: `str`. The forward read or reverse read identifier. This should be the entire
title line of a FASTQ record, minus any trailing whitespace.
Returns:
`str`: The pairend-end read identifier (title line).
Example:
Setting read_id to "@COOPER:74:HFTH3BBXX:3:1101:29894:1033 1:N:0:NATGAATC+NGATCTCG" will return
@COOPER:74:HFTH3BBXX:3:1101:29894:1033 2:N:0:NATGAATC+NGATCTCG
"""
part1, part2 = read_id.strip().split()
if part2.startswith("1"):
part2 = part2.replace("1", "2", 1)
elif part2.startswith("2"):
part2 = part2.replace("2", "1", 1)
else:
raise Exception("Unknown read number in {title}".format(title=read_id))
return part1 + " " + part2
[docs]def isForwardRead(seqid):
"""Indicates whether the passed-in read identifier is a forward or reverse read identifier.
Args:
seqid: `str`. A read identifier of a FASTQ record.
Returns:
`bool`: True if a forward read identifier, False otherwise.
"""
if seqid.split()[1].startswith("1"):
return True
return False
[docs]def yieldRecs(fastqFile, log=sys.stdout, barcodes=[]):
"""
A generator function that reads a FASTQ file and yields records, one at a time.
The records to yield can be restricted to the specified set of barcodes.
Args:
fastqFile: `str`. Path to the FASTQ file to parse.
log: A file handle to write log messages to. Defaults to STDOUT.
Yields:
A list containing one element per line of a FASTQ record. Each element is whitespace stripped.
"""
log.write("Parsing " + fastqFile + "\n")
log.flush()
fastqFileExt = os.path.splitext(fastqFile)[-1]
if fastqFileExt == ".gz":
fh = gzip.open(fastqFile)
else:
fh = open(fastqFile)
data = []
count = 0
lineCount = 0
for line in fh:
lineCount += 1
count += 1
data.append(line.strip())
if count == 4:
barcode = data[0].rsplit(":", 1)[-1]
if barcode in barcodes or not barcodes:
yield data
count = 0
data = []
if lineCount % 1000000 == 0:
# every million lines
log.write(str(datetime.datetime.now()) + ": " + str(lineCount) + "\n")
log.flush()
fh.close()
log.write("Finished parsing " + fastqFile + "\n")
log.flush()


done_all
Two-Factor Authentication
replay
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The data contains {{result.unknown_count}} column heading(s) that are not recognized by this database. Please contact us or file a ticket if you didn't expect to see this message.


echoframework
/
org.echo.mobile.framework.support
/
Api
/
NETWORK_BROADCAST
NETWORK_BROADCAST
NETWORK_BROADCAST


- N - the base is an N in the reference genome GRCh37
- L - depth of coverage is much lower than average
- H - depth of coverage is much higher than average
- Z - too many reads with zero mapping quality overlap this position
- Q - the average mapping quality at the position is too low
- P - the base passed all filters
- 0 - an overlapping base was never observed in aligned reads
Not all regions of the genome are equally accessible to short-read sequencing technologies and thus some called bases in resequenced genomes are more prone to false positives than others. The 1000 Genomes Project (The 1000 Genomes Project Consortium 2010, 2015) provide an assessment of the regions of the genome that are accessible to next-generation sequencing by creating genome masks, in which each base in the genome is coded as follows:
Regions marked as N, L, H, Z, or Q are less accessible to short reads and more prone to false positives.
PopHuman incorporates the Pilot-style mask that was produced using the same definition as used in the 1000 Genomes Project Pilot Paper (The 1000 Genomes Project Consortium 2010) and that excludes the portion of the genome where depth of coverage (summed across all samples) is higher or lower than the average depth by a factor of 2-fold. It also excludes sites where >20% of overlapping reads had mapping quality of zero. Overall, this Pilot-style mask in the autosomal chromosomes results in about 6.8% of bases marked as N, 1.1% marked L, 0.2% marked H, and 2.4% marked Z. The remaining 89.4% of passed are marked passed (P) and correspond to 95.9% of non-N bases (The 1000 Genomes Project Consortium 2015).


Nonparametric Tests to identify the differential enrichment region for two conditions or time-course ChIP-seq data. It includes: data preprocessing function, estimation of a small constant used in hypothesis testing, a kernel-based two sample nonparametric test, two assumption-free two sample nonparametric test.
| Version: | 1.0 |
| Published: | 2016-07-20 |
| Author: | Vicky Qian Wu ; Kyoung-Jae Won ; Hongzhe Li |
| Maintainer: | Vicky Qian Wu <wuqian7 at gmail.com> |
| License: | GPL (≥ 2.15.1) |
| NeedsCompilation: | yes |
| CRAN checks: | ChIPtest results |
| Reference manual: | ChIPtest.pdf |
| Package source: | ChIPtest_1.0.tar.gz |
| Windows binaries: | r-devel: ChIPtest_1.0.zip, r-release: ChIPtest_1.0.zip, r-oldrel: ChIPtest_1.0.zip |
| OS X binaries: | r-release: ChIPtest_1.0.tgz, r-oldrel: ChIPtest_1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=ChIPtest to link to this page.


{% extends "../base.html" %}
{% block content %}
Are you sure you want to display the current layer on the main
Projector/LCD unit of the printer?
{% if slicing %}
This plate is still being processed, the image of some
layers is still missing.
{% endif %}
{% if faulty||risky %}
{% if faulty %}
Faulty layers:
{% for fault in faulties %}
{{fault}}
{% endfor %}
{% endif %}
{% if risky %}
{% if faulty %} {% endif %}
Overhang areas on layers:
{% for hang in overhangs %}
{{hang}}
{% endfor %}
{% endif %}


The Compression Ratio (CR) is the ratio of size of the summarized text document to
the total size of the original text documents.
CR = |d|/|D|
Where |d| represents the size of the summarized text is document and |D| is the
total size of the original text collection.
Rouge Score: It is used for evaluating the summarization.
ROUGE
ROUGE stands for Recall-Oriented Understudy for Gisting Evaluation, it includes
several measures to quantitatively compare system-generated to human-generated summaries,
counting the number of overlapping n-grams of various lengths, word pairs
and word sequences between the summaries.
In this
work the average precision, recall and F-measure scores generated by ROUGE-1,
ROUGE-2, and ROUGE-L are used to measure the performance of the summaries
The performance parameters of proposed summarizers i.e. compression ratio, ROUGE are evaluated for three different scenarios:
The
summarizers are evaluated for the following three cases:
Case 1: Summarization without performing clustering and semantic similarity.
Case 2: Summarization with clustering but without considering semantic similarity.
Case 3: Summarization by considering both clustering and semantic similarity.
Results of Compression Ratio
It is apparent from the bubble graph below that considering the semantic similarity
(Case 3) will definitely give better results for generating effective and meaningful summary
of text document collections. These results clearly indicates that semantic similarity along
with the clustering gives better summarization results as compared to the summarization
without semantic similarity and clustering.
Results of ROUGE Score
As expected from the results, ROUGE scores are found higher for
the case III than the other two cases. Case III consider both the textual similarity (using
clustering) and semantic similarity which makes sure that best summarization content
units participate in the summary generation. Case II gives better results than the Case I
results, in other words summarization using clustering gives better summarization results
as compared to the summarization performed without performing clustering. It
indicates that summarization performed on the clustered text documents is more accurate
since similar text information is grouped within the same clusters.


Welcome to Git and GitHub introduction project
Feel free to edit this project and send pull request.
If you don't know how, visit this
link
.


WITNESS LIST
Finance
February 20, 2015 9:00 AM
FOR:
Kramer, Dr. Ken Water Resources Chair (also providing written testimony) (Sierra Club,
Lone Star Chapter), Austin, TX
Lee, Donald Executive Director (Texas Conference of Urban Counties), Austin, TX
Lyon, Lynn alternative fuel advocate (North Texas Natural Gas Vehicle Consortium)
Reed, Cyrus Conservation Director (also providing written testimony) (Sierra Club, Lone
Star Chapter), Austin, TX
Swigert, Scott Director of Parks and Recreation (Texas Recreation and Parks Society
(TRAPS)), Deer Park, TX
Teschner, Richard Professor Emeritus, University of Texas - El Paso (Myself, as chief
campaigner for the state park new headquarters/visitors center)
ON:
Beck, Tina LBB Staff (also providing written testimony) (LBB)
Brymer, David Director, Air Quality (also providing written testimony) (TCEQ - Texas
Commission on Environmental Quality), Austin, TX
Buckles, Scott Farm & Ranch Vice Chairman - TSSWCB (also providing written
testimony) (Texas State Soil and Water Conservation Board), Temple, TX
Bush, George P. Land Commissioner (also providing written testimony) (General Land
Office)
Cole, Celia CEO (also providing written testimony) (Feeding Texas), Austin
Craddick, Christi Chairman, Railroad Commission of Texas (also providing written
testimony) (Railroad Commission of Texas), Austin, TX
Davis, Jamie Assistant Professor (also providing written testimony) (University of Texas
at Austin), Austin, TX
Dimitry, Lauren Health & Fitness Policy Associate (also providing written testimony)
(Partnership for a Healthy Texas), Austin, TX
Duggins, Ralph attorney (also providing written testimony) (Parks and Wildlife
Commission), FW
Ellis, Dee Executive Director/State Vet (also providing written testimony) (Texas Animal
Health Commission)
Foster, John Programs Officer (also providing written testimony) (Texas State Soil and
Water Conservation Board), Temple, TX
Green, Kathy Director of Advocacy (Capital Area Food Bank of Texas), Austin
Henry, Jennifer Asst. Deputy Director, Budget & Planning (also providing written
testimony) (General Land Office)
Hyde, Richard Executive Director (also providing written testimony) (Texas Commission
on Environmental Quality), Austin, TX
Isom, Rex Executive Director (also providing written testimony) (Texas State Soil and
Conservation Board), Temple, TX
Jones, Grahame Chief of Special Operations, TPWD (also providing written testimony)
(Texas Parks and Wildlife Dept.)
Laine, Larry Chief Clerk (also providing written testimony) (Tx General Land Office)
Lambert, Tom LBB Staff (also providing written testimony) (LBB)
McGeady, John LBB Staff (also providing written testimony) (LBB)
WITNESS LIST
Finance
February 20, 2015 9:00 AM
Merz, Evelyn L. Real Estate Mgmt (also providing written testimony) (Lone Star Chapter
Sierra Club), Houston, TX
Miller, Sid Commissioner of the Texas Dept of Agriculture (also providing written
testimony) (Texas Dept. of Agriculture)
Morales, Ernie Feedlot & Ranching (also providing written testimony) (Texas Animal
Health Commission), Devine, TX
Patterson, Kevin Executive Administrator (TWDB)
Riggs, Brendon LBB Staff (also providing written testimony) (LBB)
Rister, Milton Executive Director (also providing written testimony) (Railroad
Commission), Austin, TX
Romo, Joel Campaign Director (American Hearth Association), Austin, TX
Rubinstein, Carlos Chairman, TWDB (also providing written testimony) (TWDB)
Salsman, John Safety Director (also providing written testimony) (Texas Low Level
Radioactive Waste Disposal Compact Commission)
Sharma, Shreela Professor at the UT School of Public Health (also providing written
testimony) (UT School of Public Health/Brighter Bites), Houston
Shaw, Bryan Chairman, TCEQ (also providing written testimony) (Texas Commission on
Environmental Quality), Austin, TX
Sifuentez, Elizabeth Director, Budget & Planning (also providing written testimony)
(Texas Commission on Environmental Quality), Austin, TX
Smith, Carter Parks & Wildlife Dept Executive Director (also providing written testimony)
(TPWD)
Smith, Zelma LBB Staff (also providing written testimony) (LBB)
Wales, Michael LBB Staff (also providing written testimony) (LBB)
Wiles, Mark LBB Staff (also providing written testimony) (LBB)
Zajicek, Kenny Fiscal Officer (also providing written testimony) (Texas State Soil and
Water Conservation Board), Temple, TX
Registering, but not testifying:
ON:
Bacon, April Administrator (Tx Dept Ag)
Baker, Toby Commissioner, TCEQ (Texas Commission on Environmental Quality),
Austin, TX
Carlton, Neal Coordinator of State/Federal Relations (Tx Dept. of Agriculture), A, TX
Cover, Zak Commissioner (Texas Commission on Environmental Quality), Austin, TX
Day, Liz CFO (Texas Commission on Environmental Quality)
Dudley, Patrick Policy Specialist (Texas Department of Agriculture), Austin, TX
Edmiston, DVM, William Veterinarian (TAHC (Commissioner))
Fearneyhough, Jason Deputy Commissioner of Agriculture (Tx Dept. of Agriculture),
Austin, TX
Garcia, Ramiro Deputy Director, Office of Compliance & Enforcement (Texas
Commission on Environmental Quality (TCEQ)), Austin, TX
Gleeson, Thomas Budget Director (Public Utility Commission), Austin, TX
Heikkila, Dawn Deputy Executive Director, Policy & Administration (Tx Parks and
Wildlife Department), Austin, TX
WITNESS LIST
Finance
February 20, 2015 9:00 AM
Hunter, Dan Assistant Commissioner (Texas Department of Agriculture), Austin, TX
Ing, Leigh Engineering - regulatory (Texas Low Level Radioactive Waste Disposal
Compact Commission)
Keel, Terry Assistant Commissioner, TDA (the Texas Department of Agriculture), Austin,
TX
Landry, Amanda CFO, TWDB (TWDB)
Lloyd, Brian Executive Director (Public Utility Commission), Austin, TX
Luedcker, Mary Government-Industry Relations & Assistant General Counsel (Texas
Animal Health Commission), Austin, TX
Luna, Steven Director of Finance (Texas Animal Health Commission), Austin, TX
Roberts, Walt Asst. Commissioner Texas Dept of Ag. (Texas Dept of Agriculture)
Sachnowitz, Danny Deputy Director (Texas Treasury Safekeeping Trust Company)
Sanchez, Rebecca Administrator for Budget & Acctg (TX Dept of Agriculture), Austin
Simmons Jr., Ralph Poultry/Cattle Rancher (Tx. Animal Health Comm.), Center, TX
Smith, Kellie Housewright Assistant Commissioner TDA Rural Health Care (TDA)
Stepney, L'Oreal Deputy Director, Office of Water (Texas Commission on Environmental
Quality (TCEQ)), Austin, TX
Wang, Wei Chief Financial Officer (Railroad Commission of Texas)
Warner, Diana TDA Chief Financial Officer (Tx Dept of Agriculture)
Providing written testimony:
FOR:
Choale, Elizabeth Director of Government Relations/General Counsel (Texas Veterinary
Medical Association (TVMA))
Gentsch, Bryan J. Executive Vice President (Texas Seed Trade Association), Austin, TX
Presley, Steve pharmacist (Texas State Railroad Authority), Palestine, TX
ON:
Craft, Elena Senior Health Scientist (Environmental Defense Fund)
Huffman, Laura The Nature Conservancy, State Director (The Nature Conservancy),
Austin, TX
Lozano, Suzanne V. Quality Improvement Consultant (American Heart Association), San
Antonio, TX
Pruett, Dan Non Profit Executive (Meals on Wheels)
Sabo, Jason Consultant (Children at Risk), Austin, TX


Home
Experimental physics today relies on huge international collaborations, built by thousands of scientists over multiple decades. These massive engineering projects are pushing the limits of modern technology to probe the universe with unimaginable precision. As a couple of passionate graduate students, we decided to build this website to present an accessible calendar of major upcoming milestones for some of humanity's biggest and most awe-inspiring projects.
We're still under construction, so not everything is up here yet!
Schedule
| Project | Description | Next Big Event | Date | |
|---|---|---|---|---|
| EHT | The Event Horizon Telescope (EHT) is an international collaboration aiming to capture the first image of a black hole by creating a virtual Earth-sized telescope | first results | soon! | |
| LIGO | The Laser Interferometer Gravitational-wave Observatory (LIGO) detected gravitational waves directly for the first time in history on September 14, 2015. In 2017 it made history again by detecting a binary neutron star merger for the first time. LIGO is currently being upgraded to Advanced Ligo, which will be about a factor of two more sensitive. We can't wait to see what it will find next! | Run 3 | early 2019 | |
| LHC | The Large Hadron Collider (ATLAS, CMS, etc) | what's next? | who knows | |
| KAGRA | The Kamioka Gravitational wave detector (KAGRA) is Japan's contribution to the exciting new field of gravitational wave astronomy. It will join the LIGO-Virgo collaboration around 2020. | First Run | 2020 | |
| JWST | The James Webb Space Telescope (JWST) is the premier next-gen space observatory, with a 6.5m infrared telescope. | Launch | 2021 | |
| SPACEX | Elon Musk is a man on a mission. Will he succeed in his ultimate quest to make humankind a multi-planetary species? We'll have to find out! Before a manned mission, they will send at least one unmanned probe to test the technology and deliver a payload to the Martian surface. | Red Dragon: unmanned Mars probe | early 2020's | |
| LSST | The Large Synoptic Survey Telescope (LSST) in Chile will compile the largest public data set in the world. Probing the nature of dark matter, dark enery, and more. | Full science operations | 2023 | |
| ITER | The world's largest nuclear fusion reactor, ITER, is currently being built in southern France. It will use a tokamak powered by superconducting magnets to fuse a plasma of Deutrerium and Tritium into Helium. The ITER team hopes to create the first net-positive energy fusion reactor, and unlock a new form of energy production for humanity. | First plasma | 2025 | |
| DUNE | The Deep Underground Neutrino Experiment (DUNE) will consist of two neutrino detectors placed in the world’s most intense neutrino beam. One near the source of the beam at FermiLab, and a second, much larger detector more than a kilometer underground at the Sanford Underground Research Laboratory in South Dakota — 1,300 kilometers downstream of the source. These detectors will enable scientists to search for new subatomic phenomena and potentially transform our understanding of neutrinos and their role in the universe. | Neutrino beam operational | 2026 | |
| LISA | The Laser Interferometer Space Antenna (LISA) is a space-based observatory composed of three spacecraft in an equilateral triangle 1 million miles on each side. It will be able to detect gravitational waves of longer wavelength than LIGO, probing super-massive black hole mergers and extreme mass ratio inspirals. | Launch | approx 2034 |
Last updated: 2018.07.06


Use the arrow keys to move the lines to the middle (no win state yet). Redder colors move them and their neighbors more. Refresh the page for a new game. It takes only three moves to win. In chrome, hold ctrl+shift+i to see the solution from its initial state. Perform the moves backwards reading from the end.


Mason Rudolph's Agent Considering Legal Action Against Myles Garrett
The agent for Steelers quarterback Mason Rudolph says legal action may be taken against Cleveland Browns lineman Myles Garrett for attacking Rudolph with a helmet during Thursday night’s game.
by Warner Todd Huston“I am gathering all information and no options have been removed from the table,” sports agent Tim Younger told ESPN on Friday morning.
The agent’s warning comes on the heels of Thursday’s fight between Rudolph and Garrett, an altercation which saw Garrett use a helmet like a weapon to club Rudolph over the head.
Younger took to his Twitter account to define Garrett’s actions as indefensible:
After the game, Rudolph called the attack “cowardly” and “bush league.”
On Friday morning, Garrett was indefinitely suspended for the fight. The league also fined both teams $250,000 for the incident:
For his part, Garrett later claimed his actions were “foolish” and “out of character” for him.
“I made a mistake,” he told the media. “I lost my cool, and I regret it. It’s going to come back to hurt our team.”
“He understands what he did,” Browns coach Freddie Kitchens added. “He understands that’s totally unacceptable, and we’ve got to get through it.”
Follow Warner Todd Huston on facebook.com/Warner.Todd.Huston.


--- title: Catering ---
Let Shorty Goldstein’s bring hand-made Jewish Deli food to your next event. We are available for catering of all sizes. For parties up to 100 people, please allow 72 hours lead time. For larger events and certain items more notice may be necessary as everything is made by hand, in house.
Delivery is available in the Bay Area. Delivery fees are not included in catering menu pricing.
Compostable plates, napkins and utensils available upon request.
For a sample catering menu click here.
From hors d’oeurves and aperitifs, to custom plated dinners with beer and wine pairings, please consider Shorty Goldstein’s for your next event. Contact us at<EMAIL_ADDRESS>for sample menus and more details.


Consider the ﬁeld-line-following coordinates (ψ,𝜃,α), where α is the generalized toroidal angle deﬁned by α = ϕ −δ with δ = ∫ 0𝜃 d𝜃 and = B ⋅∇ϕ∕(B ⋅∇𝜃), which is the local safety factor. The time evolution of (ψ,𝜃,α) of a guiding-center is then written as
|
| (14) |
and similarly
|
| (15) |
|
| (16) |
Using the expression of dX∕dt given by Eq. (10), Eqs. (14)-(16) can be written as (presently dropping the E × B drift, which will be discussed later):
|
| (20) |
For a general tokamak magnetic conﬁguration speciﬁed numerically, all the above 2D equilibrium quantities are computed by interpolating pre-computed numerical tables. We deﬁne the following numerical tables:
|
| (21) |
|
| (22) |
|
| (23) |
|
| (24) |
|
| (25) |
|
| (26) |
|
| (27) |
|
| (28) |
|
| (29) |
|
| (30) |
Next, let us discuss the E × B drift:
|
| (31) |
|
| (32) |
|
| (33) |
Using δE = δE∥b + δEx∇x + δEy∇y, where x = ψ and y = α, the above drifts are written as
Note that 𝜃(r) and ϕ(r) are multi-valued functions whereas ∇𝜃(r) and ∇ϕ(r) happen to be single-valued functions. However ∇α(r) and ∇δ(r) are still multi-valued functions. [It is ready to see this by examining the special case that 𝜃 is a straight-ﬁeld line poloidal angle, in which δ = ∫ 𝜃ref𝜃 d𝜃 is simpliﬁed to q𝜃. Then ∇δ is written as
|
| (37) |
where the ﬁrst term 𝜃∇q is a multi-valued function since 𝜃 is multi-valued.]
For multi-valued functions, if a single branch is chosen, then there will be discontinuity at the the branch cut. In numerically constructing the coordinates (ψ,𝜃,α), the principal value of 𝜃 is chosen in the range [−π : π) and the branch cut for 𝜃 is chosen on the high-ﬁeld-side midplane. The toroidal shift δ = ∫ 𝜃ref𝜃 d𝜃 can be considered as a derived angle based on 𝜃 and thus its principal value and branch cut are determined by those of 𝜃.
The (ψ,𝜃,α) coordinates of a particle change continuously when we evolve them by integrating Eqs. (14)-(16), during which 𝜃 can move beyond [−π,π). When a particle’s 𝜃 moves beyond the range [−π : π), one or multiple ±2π shifts are imposed on 𝜃 until 𝜃 are within [−π : π). Note that a corresponding shift in α is needed to keep the particle at the same spatial location when doing the 𝜃 shift. This is because, although (ψ,𝜃,ϕ) and (ψ,𝜃 − 2mπ,ϕ) correspond to the same spatial location, points (ψ,𝜃,α) and (ψ,𝜃 − 2mπ,α) do not, where m is an integer. Speciﬁcally, the usual toroidal angle ϕ of point (ψ,𝜃,α) is ϕ1 = α + ∫ 𝜃ref𝜃 d𝜃 while ϕ of point (ψ,𝜃 − 2mπ,α) is ϕ2 = α + ∫ 𝜃ref𝜃−2mπ d𝜃. The diﬀerence between ϕ1 and ϕ2 is ϕ2 −ϕ1 = −2mπq. This indicates that, to keep the point at the same spatial location when shifting 𝜃 by −2mπ, α should be shifted by +2mπq, i.e., the new coordinates of the point should be (ψ,𝜃 − 2mπ,α + 2mπq). This process is illustrated in Fig. 1. A typical evolution of (𝜃,α) involving shifting is shown in Fig. 2.
When α exceeds the range [0 : 2π], one or multiple ±2π shifts are imposed on α until α are within [0 : 2π]. Since, for ﬁxed ψ and 𝜃, the generalized toroidal angle α is equivalent to the usual toroidal angle ϕ. No complication like the case of 𝜃 arises when doing the α shift.
One way of avoiding the subtle (𝜃,α) shift problem is to evolve particles’ ϕ, instead of α. In this case, we have
To verify code implementation, two methods are used to compute the guiding-center orbits. The ﬁrst method uses the cylindrical coordinates and then interpolate the orbits into magnetic coordinates using pre-computed mapping table between the cylindrical and magnetic coordinates. The second method directly uses the magnetic coordinates in pushing the orbits. The following ﬁgures compare the results obtained by these two methods, which indicates they agree with each other. This provides conﬁdence on the correctness of the numerical implementation.


Recently I was offered a job that paid significantly more than the one I had most previously. At this point I had been unemployed for a bit over two months.
Everything was going peachy-keen until I showed up to officially receive the offer. My prospective employer apologetically informed me that they had forgotten to tell me that I was required to undergo a 'drug test' prior to actually starting employment, as required by the company that had recently bought them out. Having been a regular daily cannabis user for the past seven years or so, I figured I was screwed.
After several days of despair and depression, I began researching ways to beat a drug test. I had been provided with a Chain of Custody form, which told me that I was getting a 5-drug urinalysis panel (for cannabis, cocaine, opiates, amphetamines, and PCP -- the 'NIDA 5', the most common urinalyses). The Web site for the lab where I was to be tested bragged about their adulterant detection skills (I highly recommend researching the tests and the lab prior to any screening!), so I knew that route was out. They even test for glutaraldehyde.
After more research, I formulated a plan using only methods that had been proven to be reliable, with a low margin of error. Being extremely averse to risk with so much cash on the line, I invested $30 in Concentrated Urine (CU) from a fine online merchant, and a few bucks at GNC for B-vitamins and creatine, and CVS for condoms.
The plan was this: Just prior to the test, stop at a restaurant to use the restroom. Mix one vial of CU with piss-warm tap water (3 oz per vial in an unlubricated condom. Tie off the top. Strap to upper inner thigh, wedged against scrotum, with masking tape, to bring the sample to and maintain it at body temperature. This way I could prick the condom and 'naturally' donate 'my sample'. However, in case this substitution was risky (due to observation), I had Plan B.
Plan B was the usual dilution trick -- drink loads of water. Take a B-vitamin to make the watery pee nice and yellow. Eat some creatine so the lab won't suspect dilution and summarily flunk you (they test for it -- even if you aren't diluting or on drugs, a vegetarian should always eat some creatine before the test). Actually peeing carried a much higher risk, but there were no other options.
I arrived at the lab and did all the paperwork stuff. They looked under my hat to see if I was carrying a substitute sample. (Ha!) I was led to a restroom and told not to flush or turn on the sink until I had donated and handed it over. I was alone and unobserved in the stall. (No Plan B needed!)
I unzipped and pricked the condom, and 'pissed' into the little cup (with a temperature strip on the side of it), making sure to drench all inner and outer surfaces of the cup with piss (take that!).
I said 'I'm done' and the lady came in and took the cup, scrutinized the temperature, and found it to be normal. A week later I started my job (and have held it a while) so apparently the CU passed the test (for creatine and all). I was alarmed when I mixed it because it doesn't stink -- but it's just really clean pee. I was almost surprised that it worked, figuring that CU might be a scam like those 'detox' products are. It Isn't. This Shit Works.
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.


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If one were to take the premise behind Fire Blast Tauros and slap dual STAB on it, they'd get Rose Red/Iris Blue's Arcanine. Arcanine's sparse, normal-infested movepool becomes its blessing as it gains very powerful normal STAB off of its very high 110 attack.  It doesn't have the wide coverage of other normal types, but it certainly has the raw power to be a solid wallbreaker and potential end-game win condition.
- Fire Blast
- Body Slam
- Hyper Beam
- Agility
STAB attacks and a utility move. Arcanine doesn't have a whole lot else it can use, but those first 3 attacks hit most of the tier like a truck. Agility can be used to ignore paralysis and sweep in the end-game if the Arcanine checks are weakened enough.
Reflect can help take on opposing normal types even better. Ember can guaranteed burn foes if you want to spread burn more quickly. Toxic can support Dream Eater users if Arcanine is on a Dream Eater team.
Rock types: Golem, Rhydon, Onix are examples of Pokemon that can resist Arcanine's dual STAB and threaten their own STAB back.
Bulky water types: Starmie, Omastar, and other bulky water types can diffuse Arcanine's power. Body Slam paralysis can be a problem for speed-reliant water types, however.
Earthquake users: Faster earthquake users like Tauros and Dugtrio can threaten Arcanine out. They struggle to switch in safely though.
Author: Kurai
Publish Date: 24th July 2020


|
Exports |
{extend} = require "underscore"
module.exports = |
Create a record of these
|
create: (address, attributes, callback=@defaultCallback) ->
attributes["created_at"] = "now"
attributes["updated_at"] = "now"
@setTime attributes
@request "POST",
path: address
body: attributes
done: (error, output, response, request) =>
if output
@read "#{address}/#{output._id}", callback
else
callback error, undefined, response, request |
Read one record, or many records, from an
|
read: (address, criteria, callback=@defaultCallback) ->
if criteria instanceof Function
callback = criteria
criteria = undefined
if address.match @RECORD_ADDRESS_PATTERN
@request "GET",
path: address
done: (error, output, response, request) ->
record = if output
extend {id: output._id}, output._source
callback error, record, response, request
else
@request "POST",
path: "#{address}/_search"
body: criteria
done: (error, output, response, request) ->
records = if output
extend {id: _id}, _source for {_id, _source} in output.hits.hits
callback error, records, response, request |
Update the record at
|
update: (address, attributes, callback=@defaultCallback) ->
attributes["updated_at"] = "now"
@setTime attributes
@request "PUT",
path: address
body: attributes
done: (error, output, response, request) =>
if error is undefined
@read address, callback
else
callback error, undefined, response, request |
Delete the record located at this
|
delete: (address, callback=@defaultCallback) ->
callback = arguments[2] or callback
if address.trim() is ""
callback new Error """
ElasticStorage::delete received a blank address. Refused to send a
DELETE request because it could erase all your indices.
"""
else
@request "DELETE",
path: address
done: (error, output, response, request) ->
callback error, undefined, response, request |
|
|
|
|
|
|
|
Matches |
RECORD_ADDRESS_PATTERN: ///
^
[a-zA-Z0-9_-]+ # index
/
[a-zA-Z0-9_-]+ # collection
/
[a-zA-Z0-9_-]+ # id
$
/// |
|
Attribute names that match this pattern are time attributes. |
TIME_ATTRIBUTE_PATTERN: ///
^
[a-z_]+_at
$
/// |
|
Set time attributes with a value of |
setTime: (attributes) ->
pattern = @TIME_ATTRIBUTE_PATTERN
timestamp = (new Date).toJSON()
for name, value of attributes when name.match(pattern) and value is "now"
attributes[name] = timestamp |


Before Change
with open(os.path.join(os.path.dirname(__file__), "MANIFEST.in"), "w") as f:
f.write(MANIFEST_IN.strip("\n "))
LONG_DESCRIPTION =
// [Porcupine](https://github.com/Picovoice/porcupine).
After Change
f.write("include pvporcupinedemo/porcupine_demo_file.py\n")
f.write("include pvporcupinedemo/porcupine_demo_mic.py\n")
with open(os.path.join(os.path.dirname(__file__), "README.md"), "r") as f:
long_description = f.read()
setuptools.setup(


AMQP Directory is a collection of AMQP 1.0-compatible messaging components
Libraries for writing AMQP client and server applications. Messaging APIs provide efficient, high-level interprocess communication.
Servers for building AMQP messaging networks. Messaging servers offer enhanced reliability in distributed systems.
AMQP messaging in the cloud. Cloud services simplify the deployment and operation of messaging networks.
AMQP adapters and plugins for other software components
Tools for exploring, testing, and managing AMQP messaging
More AMQP resources


| Editing comments |
Some archivers supports comments, for example, the zip archiver supports zipfile comments. To edit the comments of the archive, click Actions –> Comments.
You will see a Comments window which has an editable textarea with the archive's comments. Here you can modify the comments. Press "Save" button to save the modified comment to the archive. Pressing "Cancel" simply discards any change you made.
To remove comments from the archive, simply delete the existing comment and press "Save".
By default, if an archive with comments are opened they are immediately shown in the Comments window. You can turn off this behaviour of displaying comments from the Preferences: Miscellaneous.
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--- products: new: limit: {{theme_settings.homepage_new_products_count}} featured: limit: {{theme_settings.homepage_featured_products_count}} top_sellers: limit: {{theme_settings.homepage_top_products_count}} carousel: {{theme_settings.homepage_show_carousel}} blog: recent_posts: limit: {{theme_settings.homepage_blog_posts_count}} --- {{#partial "hero"}} {{#if carousel}} {{/if}} {{{snippet 'home_content'}}} {{/partial}} {{#partial "page"}} {{#each shipping_messages}} {{> components/common/alert-info message}} {{/each}}


Copying and pasting
You can copy and paste (multiple) nodes between
mindmaps, as you might expect.
You can copy a single node without its children by
Copy Single
Pasting external data into FreeMind
In addition, you can paste normal text or HTML from
other applications.
Normal text
-
Multiple lines are pasted as multiple nodes.
-
URLs are recognized automatically and links are
created.
-
Lines with different depth are pasted in different
depth.
e.g.
-
Source
-
.. is pasted as
HTML
-
HTML is pasted as normal text, but also links
contained in HTML are separately pasted, e.g.:
where the red arrows indicate Web links.
File list
-
Paste a set of files selected in Explorer on
Windows
Pasting branches from FreeMind into
other applications
As text
The tree structure is displayed by indent
is pasted as
A node
child one
child two
Links are pasted, too, in < > brackets:
Source
~
Google
... is
pasted as
Google <http://www.google.com/>
As RTF
-
You can paste the branch contents into ...
-
Word
-
an Outlook message
-
Wordpad
-
Text formatting -- color and font -- is preserved
in RTF applications:
A node
child one
child two
-
Links are pasted in a similar fashion as in pasting
text


CHEM 122 - Quantum Mechanics and Spectroscopy (3 Units)
(Taken from the UC Berkeley Course Guide)
Course Overview
Summary
Postulates and methods of quantum mechanics and group theory applied to molecular structure and spectra.
Prerequisites
CHEM 120A
Topics Covered
- Introduction to spectroscopy: Light and electromagnetic radiation, classical description of radiation field, light-matter interaction.
- Optical transitions: Einstein model for absorption in a two-level system, Einstein coefficients, time-dependent perturbation theory, transition dipoles, relation to absorption cross section and extinction coefficient.
- Born-Oppenheimer approximation: Electronic and nuclear Schrödinger equations, translations, rotations, vibrations, and electronic levels. Separation of energy scales.
- Rotational absorption spectrum: The rigid rotor for linear molecules, wavefunctions and eigenenergies. Selection rules, populations, and transitions. Isotope effects. Coupling of molecular rotations and nuclear spin. Bose-Einstein and Fermi-Dirac statistics. The non-rigid rotor. Symmetric and asymmetric tops, selection rules.
- Vibrational absorption spectrum: The harmonic oscillator, lowering and raising operators, selection rules and overtones. Anharmonic corrections. Dissociation energy. Isotope effects.
- Vibrational-rotational absorption spectrum: Rotation-vibration coupling and selection rules. R, P and Q branches. Zero gap. Role of temperature. Anharmonic and non-rigid corrections. Band head.
- Vibrations of polyatomic molecules and molecular symmetry: Normal modes. Molecular symmetry and symmetry groups, representation of normal coordinates. Symmetry considerations in prediction of selection rules.
- Raman spectrum: Light scattering, microscopic description of scattering and the polarizability tensor, spectrum of scattered light, classical and quantum descriptions of Raman scattering, intensities and selection rules, transitions.
- Electronic spectrum: Molecular orbital model, electronic terms and selection rules, vibronic structure and the Franck-Condon principle. Birge-Sponer extrapolation to determine the dissociation energy.
Workload
Coursework
- List of stuff required from students
Time Commitment
3 hours of lecture per week.
Choosing the Course
When To Take
This course is primarily for upper division students/graduate students. Ideally, you should take this course after Chem 120A, as the material in this course delves into the practical aspects of quantum mechanics. This course can also be taken in conjunction with CHEM 125.
What Next?
Written by: Angela Lee
Last edited: Spring 2019


a structured display of topics from FCC comments
Mouseover to explore, click to zoom in, click the center to zoom out.


Over 500 people displaced by rainstorm in Kakumiro
by Ruth NasejjeOver 500 residents in Kibijo Sub County have been left homeless and in dare need for support after a rain storm ravaged several villages leaving property and crops destroyed.
.
Over 500 residents in Kibijo Sub County have been left homeless and in dare need for support after a rain storm ravaged several villages leaving property and crops destroyed.
.


Javascript Fetch Tutorial
Make sure to run the spring boot javascript project
Sign up user using POST
Sign Up
Get user using GET
Get User
Update user using PUT
Update User
Upload File
file input
Upload File
Upload Files
files input
Upload Files


| DOSE: T+ 0:00 |
20 mg |
oral |
2C-I |
(capsule) |
| T+ 0:00 |
2 joints/cigs |
smoked |
Cannabis |
|
| T+ 3:15 |
0.5 lines |
insufflated |
Cocaine |
(powder / crystals) |
I picked up my 5 gel-caps filled with 2-ci on a friday night. They were supposedly filled with 20 mg of 2-ci each.  One of them, named S, took 20 mg as well, the other, his name will be M, took 10 mg. Although S reported a very intense body high and slight visuals between T+0:50 and T+1:30 then slowly fading until T+3:00 or so, and M had some closed eye visuals during about the same time period, I will not talk about them because they did not have effects as intense as mine. We also smoked a couple joints around this time. About .25 grams in each.
11:30 pm - T+1:15
I start noticing a devloping body high and some visuals forming in my peripheral vision and when I stare at patterns. I spend a few minutes laying on a couch and watching the pattern in a wood door.  Then, while waiting for my friends to get ready for a night out, I lay on the couch, the effects slowly building.
12:00 pm - T+1:45
I am having very real open eye visuals. Patterns moving, shadows breathing, blurred vision. I wasn't expecting this ammount of visuals from it. I can only compare it at this point to Psilocybin. I decide to stand up and put on my Chemical Brothers CD. I also inform my friends that I'll be staying in for a little longer while they go out and that I'd meet back up with them later.
Now, the first few tracks of the CD were pretty terrifying and I was in a bad place, mentally. While my eyes were closed I would see horribly dark images of patterns and dripping blood and stuff like that. At one point I felt like I was standing in the middle of the highway with cars coming at me from behind. I decide to sit up again and change the song to something happier and more relaxing. I then felt waves of warmth, tingles, chills, and feelings I had never felt before rushing over my body in waves every minute or so. I was very relaxed for most of the time, enjoying this part of the visuals.
1:00 am - T+2:45
I start feeling much better, and attempt going to the bathroom to pee. My coordination isn't so bad anymore and I'm not having as many hallucinations. I go outside for a cigarette and begin having waves of hallucinations. I'd be fine at one point and then the next minute I'd feel like I needed to sit down because of the visuals. I went back inside and I decided to go with a friend to meet up with my other friends that left earlier, S and M. We started walking and time seemed to pass very slowly. We passed by a gate with bright lights near it and the shadows danced wildly. We walked down a dark alley in order to get to the apartment and I suddenly felt scared that my friend was going to murder. This feeling quickly subsided, but it was very weird, and worth mentioning.
1:30 am - T+3:15
For some reason, beyond me, I decide to do a half line of cocaine. I'm still slightly tripping and shadows are still moving, all though I'm more coherent and I'm not hallucinating as much. Within minutes of taking the small line, I started hallucinating more again. Patterns moved again, shadows danced more, people seemed more awkward, and I felt more vulnerable. We went outside for a cigarette and I put an altoid in my mouth to fight the nasal drip. I'm about half way through my cigarette when I finally gag, cough, and puke. I felt much better after puking, but it came on very suddenly.
2:30 am - T+4:15
I puke again on the way home. Once again it was very short and surprising, luckily I was outside again while this happened. We get back to my friend's place and I sit on his couch and turn on the heat. I notice that if I concentrate on something for 3 seconds or longer, I start seeing things again. So I start looking at everything I can to see it's, sort of, other side. The part of something we don't see in our every day lives. This is my favorite part of the trip because I was very relaxed and only hallucinated when I wanted to.
3:15 am - T+5:00
I the effects have mostly worn off, still a little body high. Sleep is somewhat difficult, but I eventually fall asleep and sleep soundly, for the most part. I liked this, FOR THE MOST PART. At first it was surprising and scary, but then it was magical and amazing, although somewhat nausieting. If I were to do it again, and I might, I'd so 15 mg instead. Until I've become used to the effects.
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.


This implementation shows how to share data between parent component and dialog component.
This dialog loads its content from a component and shows custom content inside the header, scrollable content and footer. Additionally it makes use of the configuration options to set a fixed width, height and top offset.
Dialog with content loaded from a TemplateRef.


#include <network.hpp>
|
| | Network (const Configuration &config, const string &name) |
| |
| virtual | ~Network () |
| |
| virtual void | WriteFlit (Flit *f, int source) |
| |
| virtual Flit * | ReadFlit (int dest) |
| |
| virtual void | WriteCredit (Credit *c, int dest) |
| |
| virtual Credit * | ReadCredit (int source) |
| |
| int | NumNodes () const |
| |
| virtual void | InsertRandomFaults (const Configuration &config) |
| |
| void | OutChannelFault (int r, int c, bool fault=true) |
| |
| virtual double | Capacity () const |
| |
| virtual void | ReadInputs () |
| |
| virtual void | Evaluate () |
| |
| virtual void | WriteOutputs () |
| |
| void | Display (ostream &os=cout) const |
| |
| void | DumpChannelMap (ostream &os=cout, string const &prefix="") const |
| |
| void | DumpNodeMap (ostream &os=cout, string const &prefix="") const |
| |
| int | NumChannels () const |
| |
| const vector< FlitChannel * > & | GetInject () |
| |
| FlitChannel * | GetInject (int index) |
| |
| const vector< CreditChannel * > & | GetInjectCred () |
| |
| CreditChannel * | GetInjectCred (int index) |
| |
| const vector< FlitChannel * > & | GetEject () |
| |
| FlitChannel * | GetEject (int index) |
| |
| const vector< CreditChannel * > & | GetEjectCred () |
| |
| CreditChannel * | GetEjectCred (int index) |
| |
| const vector< FlitChannel * > & | GetChannels () |
| |
| const vector< CreditChannel * > & | GetChannelsCred () |
| |
| const vector< Router * > & | GetRouters () |
| |
| Router * | GetRouter (int index) |
| |
| int | NumRouters () const |
| |
| | TimedModule (Module *parent, string const &name) |
| |
| virtual | ~TimedModule () |
| |
| | Module (Module *parent, const string &name) |
| |
| virtual | ~Module () |
| |
| const string & | Name () const |
| |
| const string & | FullName () const |
| |
| void | DisplayHierarchy (int level=0, ostream &os=cout) const |
| |
| void | Error (const string &msg) const |
| |
| void | Debug (const string &msg) const |
| |
Definition at line 48 of file network.hpp.
◆ Network()
| Network::Network |
( |
const Configuration & |
config, |
|
|
const string & |
name |
|
) |
| |
◆ ~Network()
◆ _Alloc()
◆ _BuildNet()
◆ _ComputeSize()
| virtual void Network::_ComputeSize |
( |
const Configuration & |
config | ) |
|
|
protectedpure virtual |
◆ Capacity()
| double Network::Capacity |
( |
| ) |
const |
|
virtual |
◆ Display()
| void Network::Display |
( |
ostream & |
os = cout | ) |
const |
|
virtual |
◆ DumpChannelMap()
| void Network::DumpChannelMap |
( |
ostream & |
os = cout , |
|
|
string const & |
prefix = "" |
|
) |
| const |
◆ DumpNodeMap()
| void Network::DumpNodeMap |
( |
ostream & |
os = cout , |
|
|
string const & |
prefix = "" |
|
) |
| const |
◆ Evaluate()
| void Network::Evaluate |
( |
| ) |
|
|
virtual |
◆ GetChannels()
◆ GetChannelsCred()
◆ GetEject() [1/2]
◆ GetEject() [2/2]
◆ GetEjectCred() [1/2]
◆ GetEjectCred() [2/2]
◆ GetInject() [1/2]
◆ GetInject() [2/2]
◆ GetInjectCred() [1/2]
◆ GetInjectCred() [2/2]
◆ GetRouter()
◆ GetRouters()
| const vector<Router *>& Network::GetRouters |
( |
| ) |
|
|
inline |
◆ InsertRandomFaults()
◆ New()
◆ NumChannels()
| int Network::NumChannels |
( |
| ) |
const |
|
inline |
◆ NumNodes()
| int Network::NumNodes |
( |
| ) |
const |
|
inline |
◆ NumRouters()
| int Network::NumRouters |
( |
| ) |
const |
|
inline |
◆ OutChannelFault()
| void Network::OutChannelFault |
( |
int |
r, |
|
|
int |
c, |
|
|
bool |
fault = true |
|
) |
| |
◆ ReadCredit()
◆ ReadFlit()
| Flit * Network::ReadFlit |
( |
int |
dest | ) |
|
|
virtual |
◆ ReadInputs()
| void Network::ReadInputs |
( |
| ) |
|
|
virtual |
◆ WriteCredit()
| void Network::WriteCredit |
( |
Credit * |
c, |
|
|
int |
dest |
|
) |
| |
|
virtual |
◆ WriteFlit()
| void Network::WriteFlit |
( |
Flit * |
f, |
|
|
int |
source |
|
) |
| |
|
virtual |
◆ WriteOutputs()
| void Network::WriteOutputs |
( |
| ) |
|
|
virtual |
◆ _chan
◆ _chan_cred
◆ _channels
◆ _classes
◆ _eject
◆ _eject_cred
◆ _inject
◆ _inject_cred
◆ _nodes
◆ _routers
| vector<Router *> Network::_routers |
|
protected |
◆ _size
◆ _timed_modules
The documentation for this class was generated from the following files:
- gpgpu-sim_distribution/src/intersim2/networks/network.hpp
- gpgpu-sim_distribution/src/intersim2/networks/network.cpp


About
Alex Jones
Background
My name is Alex Jones, I am a senior at Shawnee State university and I’ve spent the majority of my life in southern Ohio. I have been interested in game design / programing since the age of 14. My passion was ignited by games such as Minecraft and Halo reach in which I spent hundreds of hours building levels, from there I moved onto working in Unity3D and building small games mostly with a focus in level design and doing a small amount of programming to add features such as teleporters and player movement. In my senior year of high school, I went through a python tutorial on codecademy.com which was the first time I really programmed anything. I realized that I loved it and my passion slowly moved more towards programming and less towards level design.
Education
I am a senior at Shawnee State University, finishing up my last semester to complete my Bachelor of Science in Digital Simulation and Gaming Engineering Technology. In my studies I have learned how to use multiple programming languages such as C, C++, Java, JavaScript, Python, C#, and Lua. I have also branched out to work with HTML and CSS to set up simple websites. During my final two years of school I have spent time working in multiple game engines such as Unreal, Unity, and GameMaker. My senior project is a yearlong VR project being made in Unity using C# and the SteamVR API. My studies have covered topics such as optimization, operating system design, algorithms, computer graphics, and I am currently in classes covering artificial intelligence and complier design. While my degree is targeted at game design/programming the subject matter applies in many other fields of work.
Skills
Programming Languages
C ++ = 90 :
C# = 80 :
Python = 80 :
Java = 60
Game Engines
Unity = 95 :
Unreal = 70


Fair Trade Coffee supply chain demo
Prove the authenticity of coffee using the Ethereum blockchain.
Product Overview
SKU
UPC
Current Owner ID
Fetch Data 1
Fetch Data 2
Role management
Add roles
Farmer ID
Distributor ID
Retailer ID
Consumer ID
Add Roles
Check if user has a role
User id
Role (farmer, distributor, retailer, consumer)
Check Role
Farm Details
Farm Name
Farm Information
Farm Latitude
Farm Longitude
Product Notes
Harvest
Process
Pack
Price
Sell Item
Distributor's zone
Payment
ETH
Buy
Ship
Retailer's zone
Receive
Consumer's zone
Purchase
Transaction History


"x" "1" "JAPAN CHEMICAL INDUSTRY ECOLOGY- TOXICOLOGY AND INFORMATION CENTER,JAPAN, MUTAGENICITY TEST DATA OF EXISTING CHEMICAL SUBSTANCES BASED ON THETOXICITY INVESTIGATION OF THE INDUSTRIAL SAFETY AND HEAL __ TH LAW, (SUPPL),1997"


 Focusing on obtaining information where others fail, we combine cyber and human intelligence creating our unique methodology, CyberHumint.
{% assign servicebox_extra_class = "alpha" %}
{% assign servicebox_title = "Organizational Intelligence Audit" %}
{% assign servicebox_icon = "icon-attention" %}
{% assign servicebox_text = "" %}
{% assign servicebox_link = "services.html#audit" %}
{% include custom/servicebox.html %}
{% assign servicebox_extra_class = "" %}
{% assign servicebox_title = "Due Diligence" %}
{% assign servicebox_icon = "icon-zoom-in" %}
{% assign servicebox_text = "" %}
{% assign servicebox_link = "services.html#due-diligence" %}
{% include custom/servicebox.html %}
{% assign servicebox_extra_class = "" %}
{% assign servicebox_title = "Competitive Intelligence" %}
{% assign servicebox_icon = "icon-chart-line" %}
{% assign servicebox_text = "" %}
{% assign servicebox_link = "services.html#competitive-intelligence" %}
{% include custom/servicebox.html %}
{% assign servicebox_extra_class = "omega" %}
{% assign servicebox_title = "Cyber Intelligence" %}
{% assign servicebox_icon = "icon-signal" %}
{% assign servicebox_text = "" %}
{% assign servicebox_link = "services.html#cyber-intelligence" %}
{% include custom/servicebox.html %}
Sectors
{% assign sector_link = "sectors.html#banking" %}
{% assign sector_title = "Banking & Finance" %}
{% assign sector_image = "images/Sectors/Finance and banking sector.jpg" %}
{% include custom/sectorbox.html %}
{% assign sector_link = "sectors.html#insurance" %}
{% assign sector_title = "Insurance" %}
{% assign sector_image = "images/Sectors/Insurence sector.jpg" %}
{% include custom/sectorbox.html %}
{% assign sector_link = "sectors.html#energy" %}
{% assign sector_title = "Energy" %}
{% assign sector_image = "images/Sectors/Energy sector.jpg" %}
{% include custom/sectorbox.html %}
{% assign sector_link = "sectors.html#law-firms" %}
{% assign sector_title = "Law Firms" %}
{% assign sector_image = "images/Sectors/Law firm sector.jpg" %}
{% include custom/sectorbox.html %}
{% assign sector_link = "sectors.html#multinational-corporations" %}
{% assign sector_title = "Multinational Corporations" %}
{% assign sector_image = "images/Sectors/Multinational corp. sector.jpg" %}
{% include custom/sectorbox.html %}
{% assign sector_link = "sectors.html#property-investors" %}
{% assign sector_title = "Property Investors" %}
{% assign sector_image = "images/Sectors/Properties investment sector.jpg" %}
{% include custom/sectorbox.html %}


Page not found here. The link you followed may either be broken or no longer exists. Please try again, or take a look at our homepage at: Home Page


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{% block content %}
{% endblock %}


|
tesseract
4.00.00dev
|
Go to the source code of this file.
Classes | |
| struct | MATCH_RESULT |
Macros | |
| #define | MAX_NUM_CLASSES MAX_INT16 |
| #define | MAX_CLASS_ID (MAX_NUM_CLASSES - 1) |
| #define | NO_CLASS (0) |
| #define | NO_PROTO (-1) |
| #define | NO_FEATURE 255 |
| #define | NOISE_FEATURE 254 |
| #define | MISSING_PROTO 254 |
| #define | MAX_NUM_FEAT 40 |
| #define | MAX_FEATURE_ID 250 |
| #define | IsValidFeature(Fid) ((Fid) < MAX_FEATURE_ID) |
| #define | IsValidProto(Pid) ((Pid) >= 0) |
| #define | _ARGS(s) () |
Typedefs | |
| typedef UNICHAR_ID | CLASS_ID |
| typedef inT16 | PROTO_ID |
| typedef uinT8 | FEATURE_ID |
| typedef FLOAT32 | RATING |
| typedef FLOAT32 | CERTAINTY |
| typedef MATCH_RESULT | SORTED_CLASSES[MAX_CLASS_ID+1] |
Functions | |
| int CompareMatchResults | _ARGS ((MATCH_RESULT *Result1, MATCH_RESULT *Result2)) |
| void PrintMatchResult | _ARGS ((FILE *File, MATCH_RESULT *MatchResult)) |
| void PrintMatchResults | _ARGS ((FILE *File, int N, MATCH_RESULT MatchResults[])) |
| #define _ARGS | ( | s | ) | () |
Definition at line 96 of file matchdefs.h.
| #define IsValidFeature | ( | Fid | ) | ((Fid) < MAX_FEATURE_ID) |
all feature matchers that are to be used with the high level classifier must support the following interface. The names will, of course, be unique for each different matcher. Note also that FEATURE_STRUCT is a data structure that is defined specifically for each feature extractor/matcher pair.
Definition at line 90 of file matchdefs.h.
| #define IsValidProto | ( | Pid | ) | ((Pid) >= 0) |
Definition at line 91 of file matchdefs.h.
| #define MAX_CLASS_ID (MAX_NUM_CLASSES - 1) |
Definition at line 32 of file matchdefs.h.
| #define MAX_FEATURE_ID 250 |
Definition at line 52 of file matchdefs.h.
| #define MAX_NUM_CLASSES MAX_INT16 |
| #define MAX_NUM_FEAT 40 |
Definition at line 51 of file matchdefs.h.
| #define MISSING_PROTO 254 |
Definition at line 50 of file matchdefs.h.
| #define NO_CLASS (0) |
Definition at line 36 of file matchdefs.h.
| #define NO_FEATURE 255 |
Definition at line 48 of file matchdefs.h.
| #define NO_PROTO (-1) |
Definition at line 42 of file matchdefs.h.
| #define NOISE_FEATURE 254 |
Definition at line 49 of file matchdefs.h.
a CERTAINTY is an indication of the degree of confidence of the classifier. Higher is better. 0 means the match is as good as the mean of the matches seen in training. -1 means the match was one standard deviation worse than the training matches, etc.
Definition at line 62 of file matchdefs.h.
| typedef UNICHAR_ID CLASS_ID |
a CLASS_ID is the ascii character to be associated with a class
Definition at line 35 of file matchdefs.h.
| typedef uinT8 FEATURE_ID |
FEATURE_ID is the index of a feature within a character description The feature id ranges from 0 to N-1 where N is the number of features in a character description.
Definition at line 47 of file matchdefs.h.
a PROTO_ID is the index of a prototype within it's class. Valid proto id's are 0 to N-1 where N is the number of prototypes that make up the class.
Definition at line 41 of file matchdefs.h.
a RATING is the match rating returned by a classifier. Higher is better.
Definition at line 56 of file matchdefs.h.
| typedef MATCH_RESULT SORTED_CLASSES[MAX_CLASS_ID+1] |
define a data structure for holding an array of match results
Definition at line 76 of file matchdefs.h.
| int CompareMatchResults _ARGS | ( | (MATCH_RESULT *Result1, MATCH_RESULT *Result2) | ) |
| void PrintMatchResult _ARGS | ( | (FILE *File, MATCH_RESULT *MatchResult) | ) |
| void PrintMatchResults _ARGS | ( | (FILE *File, int N, MATCH_RESULT MatchResults[]) | ) |


A visualization of relative crash data, based on day in D.C.
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday


THE USUAL SUSPECTS A film review by Ian Rooney Copyright 1995 Ian Rooney
THE USUAL SUSPECTS is a bad film that somehow got rave reviews. >From the monotonous opening credits I knew I had been taken. The film opens opens with about a five-minute nighttime shot of shimmering black water, nothing else. Nothing but the opening credits, light shimmering on black water and ominous, melodramatic music. I recall thinking of how inexpensive it would be to go with five minutes of black water and generic music that was probably recorded in the 1960's. Having just paid $8 and change to enter, and then paid out for popcorn that costed the equivalent of a full-course dinner at Swiss Chalet, I grew a little impatient waiting for something to happen. Unfortunately, something did happen. A grimy little low-budget film with lots of vulgarity and violence is what happened. Not even Pulp- Fiction level vulgarity and violence, either. Boring cliche's, macho stereotypes, 1970's-TV-Cop-Show familiarity with a lot of vulgar language and grimy homo-eroticism thrown in to spice it up. Cheap! Cheap! It screams cheap!  It is almost like they just dusted off an old film from the seventies and released it. What an appalling. PULP FICTION was gross and vulgar, yes, but at least whoever made it had enough class to get away with it. Whatever low-life made THE USUAL SUSPECTS has no business playing with vulgarity and violence because those are extra luxuries that a film like this simply cannot afford.
The low-lifes in the media who both enjoyed this film and gave it a good review should have their heads checked so that they qualify for disability when they are fired. A lot of people will be disappointed and ripped off by THE USUAL SUSPECTS.
-- Ian Rooney
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
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Related links: index of all rec.arts.movies.reviews reviews


--- layout: post title: EasyEngine or How I Gained 1s in Page Load Time date: 2016-07-17 18:33:17.000000000 +07:00 categories: - How To - Review - WordPress ---
My blog has been hosted on various machines from various providers. It is only recently that I started to aim for performance, though.
For the last couple of years, it has been hosted with EvxOnline. I then switched to a DigitalOcean (affiliate link, direct link available in article footer) droplet with ServerPilot.
While performance was definitely better with DigitalOcean + ServerPilot compared to EvxOnline, I wanted something even faster.
I have had my eyes on EasyEngine for a little while but didn't have the chance to really try it yet. So the day I realized I made a typo in my ServerPilot's app name, fussy me didn't need another excuse to get started with a new stack.
First of all, I need to say that I really like ServerPilot. I've used it for many client and personal projects. It is extremely easy to setup, the stack performs really well, and it keeps your server up to date. If you're looking for good performance without getting your hands too dirty, go for ServerPilot.
If you want to push things a little further however, EasyEngine is, in my opinion, the way to go.
In both cases, I used a $5, SFO1 droplet (512 Mo RAM, 20 Go storage, located in San Fransisco). I also use CloudFlare, even though it doesn't seem to make any difference with my new setup.
The theme I use is exactly the same, as well as the plugins installed.
ServerPilot uses a modern and proven stack: LAMP/LEMP with Nginx as the public facing web server proxying the request through to Apache.
The blog was then loading in 1.7 seconds in average (I didn't do any stress tests, just page load time).
EasyEngine is a bit more complex to setup (actually, it is especially the case when transferring a site, installing from 0 seems pretty straightforward), but it is partly because of the number of options available. It is purely LEMP (no Apache here), although you have the option to install HHVM.
I installed WordPress and got rid of WP Super Cache that is basically replaced by FastCGI. I ran the tests and surprise, the page load time is down to 0.8 seconds! (I know it is not exactly 1 second, but my first tests were showing 0.7s and 1s sounds pretty good :)
Just like I wrote earlier, if you want performance without getting too much in the console, go for ServerPilot. You basically need to copy/paste one command and that's it. ServerPilot will do the rest.
If you want to get high performance and are ready to spend more time in the console, EasyEngine is awesome. With very little customization it performs extremely well. Their site says:
Automatically tweaks server configuration as per available hardware resources
I don't know what this implies exactly, but it seems to be working damn well.
There is one thing I'm wondering about though: how come the performance improvement is that big?  I'd love to hear more from the EE team :)
Some of the links in this article contain affiliate codes. If you wish to visit those links without my affiliate code, here is it.