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The students in the lab can upload papers to the website.
Use keyword to query database.
Source code is available on my GitHub page. Here
The students in the lab can upload papers to the website.
Use keyword to query database.
Source code is available on my GitHub page. Here


Automate your quality inspection workflow, including request for inspection, streamline the checklist, and issue a COS
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We tailor made the RFI, checklist and COSto meet the Singapore Authority requirements for construction sites.
Pre-set format of RFI, checklist and COS are tailor made to meet BCA, MOM, LTA, PUB and SCDF default formats.
Client, Contractor, Consultant (Design), Consultant (Supervision) are connected and follow the work flow via hand phone exactly like what they are doing right now in traditional way.
Including as many as standard workflows: Request for COS of MOM works, RFI for LTA works, Daily report, ERSS Annex C, D & E, GBW, road opening, and so on…
Activities and route are recorded for all inspection. Every issued COS with Bar Code to verify. Email and notification to remind the user in time.
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Our have in-house construction consultation team and IT development team know the industrial need very well. Except E-Spector, our team provide customisation solution for construction industrial, e.g. worker management system, vehicle management system, safety management system and so on. Tell us what you need, we could customise for you!
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In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# 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)
# !sudo pip3 install -U matplotlib==2.0.2
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
image_placeholder = tf.placeholder(
shape=(None, image_width, image_height, image_channels), dtype=tf.float32)
z_placholder = tf.placeholder(shape=(None, z_dim), dtype=tf.float32)
learning_rate = tf.placeholder(dtype=tf.float32)
return image_placeholder, z_placholder, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that
discriminates on images
. This function should be able to reuse the variables
in the neural network. Use
tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The
function should return a tuple of (tensor output of the discriminator, tensor
logits of the discriminator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
alpha = 0.2
with tf.variable_scope('discriminator', reuse=reuse):
# Input layer is 28x28x3
x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
relu1 = tf.maximum(alpha * x1, x1)
# 14x14x64
x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
bn2 = tf.layers.batch_normalization(x2, training=True)
relu2 = tf.maximum(alpha * bn2, bn2)
# 7x7x128
x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
bn3 = tf.layers.batch_normalization(x3, training=True)
relu3 = tf.maximum(alpha * bn3, bn3)
# 4x4x256
x3 = tf.layers.conv2d(relu3, 512, 3, strides=2, padding='same')
bn3 = tf.layers.batch_normalization(x3, training=True)
relu3 = tf.maximum(alpha * bn3, bn3)
x3 = tf.layers.conv2d(relu3, 1024, 2, strides=1, padding='same')
bn3 = tf.layers.batch_normalization(x3, training=True)
relu3 = tf.maximum(alpha * bn3, bn3)
# Flatten it
flat = tf.keras.layers.Flatten()(relu3)
hidden = tf.layers.dense(flat, 128)
hidden = tf.maximum(alpha*hidden, hidden)
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
alpha = 0.2
with tf.variable_scope('generator', reuse=not is_train):
# First fully connected layer
x1 = tf.layers.dense(z, 4*4*512)
# Reshape it to start the convolutional stack
x1 = tf.reshape(x1, (-1, 4, 4, 512))
x1 = tf.layers.batch_normalization(x1, training=is_train)
x1 = tf.maximum(alpha * x1, x1)
# 4x4x512 now
x2 = tf.layers.conv2d_transpose(x1, 512, kernel_size=4, strides=1)
x2 = tf.layers.batch_normalization(x2, training=is_train)
x2 = tf.maximum(alpha * x2, x2)
# 7x7x256 now
x3 = tf.layers.conv2d_transpose(x2, 256, 5, strides=2, padding='same')
x3 = tf.layers.batch_normalization(x3, training=is_train)
x3 = tf.maximum(alpha * x3, x3)
# 14x14x128 now
x3 = tf.layers.conv2d_transpose(x3, 128, 5, strides=1, padding='same')
x3 = tf.layers.batch_normalization(x3, training=is_train)
x3 = tf.maximum(alpha * x3, x3)
x3 = tf.layers.conv2d_transpose(x3, 64, 5, strides=1, padding='same')
x3 = tf.layers.batch_normalization(x3, training=is_train)
x3 = tf.maximum(alpha * x3, x3)
# Output layer
logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
# 28x28x3 now
out = tf.tanh(logits)
return out
return None
"""
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, output_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, output_dim)
d_model_real, d_logits_real = discriminator(2*input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# 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')]
# Optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
model_input_args = data_shape[1:] + (z_dim, )
input_real, input_z, input_learning_rate = model_inputs(*model_input_args)
output_chanel_dim = 3 if data_image_mode == 'RGB' else 1
d_loss, g_loss = model_loss(input_real, input_z, output_chanel_dim)
d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
saver = tf.train.Saver()
sample_z = np.random.uniform(-1, 1, size=(72, z_dim))
samples, losses = [], []
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
# Sample random noise for G
# batch_images = 2*batch_images
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})
# _ = sess.run(d_opt, feed_dict={input_z: batch_z, input_real: batch_images})
_ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images})
if steps % 40 == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epochs),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
# Save losses to view after training
losses.append((train_loss_d, train_loss_g))
if steps % 200 == 0:
show_generator_output(sess, 24, input_z, output_chanel_dim,
data_image_mode)
saver.save(sess, './checkpoints/generator.ckpt')
show_generator_output(sess, 24, input_z, output_chanel_dim, data_image_mode)
# with open('samples.pkl', 'wb') as f:
# pkl.dump(samples, f)
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
tf.keras.backend.set_learning_phase(1)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 32
z_dim = 100
learning_rate = 0.0001
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.


Specifies a transfer function for the green component of an input graphic; typically used as a child element of an SVGFEComponentTransferElement element.
Syntax
<feFuncR type="gamma" amplitude="2" exponent="5" offset="0"/> <feFuncG type="linear" slope=".5" intercept="0"/> <feFuncB type="table" tableValues="0 1 1 0"/> <feFuncA type="identity"/>
DOM Information
Inheritance Hierarchy
Members
The SVGFEFuncGElement object has these types of members:
Properties
The SVGFEFuncGElement object has these properties.
| Property | Description |
|---|---|
|
Indicates the amplitude of the gamma function. | |
|
Indicates the exponent of the gamma function. | |
|
Indicates the intercept of the linear function. | |
|
Indicates the slope of the linear function. | |
|
Define the lookup table. | |
|
The type of component transfer function. The function type determines the applicability of the other attributes. |
Standards information
- Scalable Vector Graphics: Filter Effects, Section 15.25.8
Remarks
This topic describes the feFuncR
, feFuncG
, feFuncB
, and feFuncA
elements. These four elements are typically children of feComponentTransferelement and specify the transfer functions for the four channels, as follows:
feFuncR
— transfer function for the red component of the input graphicfeFuncG
— transfer function for the green component of the input graphicfeFuncB
— transfer function for the blue component of the input graphicfeFuncA
— transfer function for the alpha component of the input graphic
The following rules apply to the processing of the feComponentTransferelement element:
- If more than one transfer function element of the same kind is specified, the last occurrence is used.
- If any of the transfer function elements are unspecified, the feComponentTransferelement must be processed as if those transfer function elements were specified with their
type
attributes set toidentity
(see below).
In addition to core attributes (id
, xml:base
, xml:lang
, and xml:space
), the following seven attributes are applicable to the feFuncR
, feFuncG
, feFuncB
and feFuncA
elements:
-
type
The
type
attribute can be one of five values:identity
,table
,discrete
,linear
, orgamma
(see below). These five values indicate the type of component transfer function. The value oftype
determines the applicability of the other attributes (as discussed below).In the following, C is the initial component (such as
feFuncR
), C' is the re-mapped component; both in the closed interval [0, 1]:-
For
identity
:C' = C
-
For
table
, the function is defined by linear interpolation between values given in the attributetableValues
(see below). The table has n+1 values (that is, v0 to vn) specifying the start and end values for n evenly sized interpolation regions. Interpolations use the following formula:For a value C < 1, find k such that k/n <= C < (k+1)/n
The result C' is given by C' = vk + (C - k/n)*n * (vk+1 - vk)
If C = 1 then C' = Vn
-
For
discrete
, the function is defined by the step function given in the attributetableValues
(see below), which provides a list of n values (that is, v0 to vn-1) in order to identify a step function consisting of n steps. The step function is defined by the following formula:For a value C < 1 find k such that k/n <= C < (k+1)/n
The result C' is given by C' = vk
If C = 1 then C' = vn-1
-
For
linear
, the function is defined by the following linear equation:C' =
slope
* C +intercept
(see below forslope
andintercept
) -
For
gamma
, the function is defined by the following exponential function:C' =
amplitude
* pow(C,exponent
) +offset
(see below foramplitude
,exponent
, andoffset
)
-
-
tableValues
When
type="table"
,tableValue
is a list of numbers v0, v1, ..., vn separated by white space and/or a comma, which define the lookup table. An empty list results in an identity transfer function. If the attribute is not specified, then the effect is as if an empty list were provided. -
slope
When
type="linear"
,slope
indicates the slope of the linear function. If the attribute is not specified, then the effect is as if a value of 1 were specified. -
intercept
When
type="linear"
,intercept
indicates the intercept of the linear function. If the attribute is not specified, then the effect is as if a value of 0 were specified. -
amplitude
When
type="gamma"
,amplitude
indicates the amplitude of the gamma function. If the attribute is not specified, then the effect is as if a value of 1 were specified. -
exponent
When
type="gamma"
,exponent
indicates the exponent of the gamma function. If the attribute is not specified, then the effect is as if a value of 1 were specified. -
offset
When
type="gamma"
,offset
indicates the offset of the gamma function. If the attribute is not specified, then the effect is as if a value of 0 were specified
See also
Build date: 1/13/2014


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change font size
change font color
Changing the style of an HTML element, is a variant of changing an HTML attribute:


A place for your secrets
This project was built on Node.js with EJS serving the views. For login it uses Passport and oauth 2.0. User passwords are salted and hashed and stored in MongoDB. This project is still being built so it is not currently hosted.


This is a live calculation of the time on Mars including mission times for MSL Curiosity and MER-B Opportunity.
Also see my much more recent (and gentle) Orbits Tutorial.
Mouse over a number on the left to get an explanation below
(in progress)
This is the number of milliseconds since
1 January 1970 00:00:00 UTC.
We get this straight from your browser.
This is the number of days (rather than milliseconds) since a much older epoch than Unix time.
Rather than an elaborate conversion from the Gregorian date to the Julian date, we just divide millis by 86,400,000 to get the number of days since the Unix epoch and add that number to 2,440,587.5, the Julian Date at the Unix epoch.
JDUT = 2,440,587.5 + (millis / 8.64 × 107 ms/day)
We actually need the Terrestrial Time (TT) Julian Date rather than the UTC-based one. This means we basically just add the leap seconds which, since ? are ? + 32.184.
JDTT = JDUT + (? + 32.184) / 86,400
This is the number we're going to use as the input to many of our Mars calculations.
It's the number of (fractional) days since
12:00 on 1 January 2000
in Terrestrial Time.
We know what JDTT was at the J2000 epoch (2,451,545.0) so it's trivial to convert.
ΔtJ2000 = JDTT - 2,451,545.0
The equivalent of the Julian Date for Mars is the Mars Sol Date.
At midnight on the 6th January 2000 (ΔtJ2000 = 4.5) it was midnight at the Martian prime meridian, so our starting point for Mars Sol Date is ΔtJ2000 − 4.5.
The length of a Martian day and Earth (Julian) day differ by a ratio of 1.027491252 so we divide by that.
By convention, to keep the MSD positive going back to midday December 29th 1873, we add 44,796.
There is a slight adjustment as the midnights weren't perfectly aligned. Allison, M., and M. McEwen 2000 has −0.00072 but the Mars24 site gives a more up-to-date −0.00096.
MSD = ([(ΔtJ2000 − 4.5) / 1.027491252] + 44,796.0 − 0.00096)
Coordinated Mars Time (or MTC) is like UTC but for Mars. Because it is just a mean time, it can be calculated directly from the Mars Sol Date as follows:
MTC = (24 h × MSD) mod 24
The mean anomaly is a measure of where an orbiting body is in its orbit. More precisely, it's a measure of how far into the full orbit the body is since its last periapsis (the point in the ellipse closest to the focus).
The mean anomaly is the ratio (time-wise) into the full orbit, multiplied by 2π (radians) or 360° although the value doesn't truly correspond to any angle. The mean anomaly is proportional to time (and hence area swept) rather that the actual angle of the body from the focus (which would be the true anomaly).
So the mean anomaly can be calculated from ΔtJ2000 if we know the mean anomaly at the J2000 epoch (19.3870°) and the mean daily motion (360° / length of anomalistic orbit in days).
This gives us:
M = 19.3870° + 0.52402075°ΔtJ2000
for Mars.
Mars goes around the Sun, but viewed from Mars's point of view, the Sun goes around Mars. I'm not talking about the daily motion of the Sun caused by Mars's rotation, but the year-long motion of the Sun viewed from Mars.
Because the orbit is an ellipse, the Sun will go faster some times than others. Imagine a fictitious Sun, though, that took the same Martian year to go around Mars but which orbited at a constant angular velocity (the mean of the real Sun). This is the fictitious mean Sun and it's easier to calculate its angle first because, like the mean anomaly, it is proportional to time.
Based on observations, Allison and McEwen give the angle at J2000 and the daily change (based on tropical orbit period) as 270.3863° and 0.52403840° / day respectively.
This gives us:
αFMS = 270.3863° + 0.52403840°ΔtJ2000
The eccentricity is the deviation of the orbit's ellipse from being a perfect circle. It varies ever so slightly over time and for Mars is given by e = 0.09340 + 2.477 × 10-9 / day ΔtJ2000 = .
The difference between the actual position of the Sun and the fictitious mean Sun is the same as the difference between the true anomaly and mean anomaly. This is called the Equation of Center.
For a two-body Kepler orbit, this difference can be approximated using a Fourier-Bessel series given the mean anomaly M and eccentricity e. This results in:
(10.691° + 3° × 10-7 ΔtJ2000) sin M
+ 0.623° sin 2M
+ 0.050° sin 3M
+ 0.005° sin 4M
+ 0.0005° sin 5M
We're not quite done yet as the above assumes a two-body Kepler motion and we need to include the perturbations caused by other planets previously calculated.
Once they have been added, we have our equation of center.
By adding this to our mean anomaly, M, we also get our true anomaly ν = °
We can now calculate the actual position of the Sun as follows:
LS = αFMS + (ν − M)
Remember, this is not the daily motion of the Sun caused by Mars's rotation, but the year-long motion of the Sun viewed from Mars. Think of it as where Mars is in its orbit around the Sun, flipped around to be from Mars's perspective (hence "areocentric").


Below you will find the top 10 bacteria samples taken from belly buttons. You can use the Sample Selector on the left to view sample source data.


Writing unit tests for undo/redo commands is easy.
The main idea to simulate a scene, execute actions and perform undo and redo.
Following steps are required.
Each of the listed steps will now be described in detail.
Create a new file in path test/unit/editor/TestDoSomethingCommand.js
.
Navigate to the editor test suite test/unit/unittests_editor.html
and open it.
Within the file, go to the <!-- command object classes -->
and include the new command:
// <!-- command object classes -->
//...
<script src="../../editor/js/commands/AddScriptCommand.js"></script>
<script src="../../editor/js/commands/DoSomethingCommand.js"></script> // add this line
<script src="../../editor/js/commands/MoveObjectCommand.js"></script>
//...
It is recommended to keep the script inclusions in alphabetical order, if possible.
Next, in the same file, go to <!-- Undo-Redo tests -->
and include the test file for the new command:
// <!-- Undo-Redo tests -->
//...
<script src="editor/TestAddScriptCommand.js"></script>
<script src="editor/TestDoSomethingCommand.js"></script> // add this line
<script src="editor/TestMoveObjectCommand.js"></script>
//...
Again, keeping the alphabetical order is recommended.
Open the unit test file test/unit/editor/TestDoSomethingCommand.js
and paste following code:
module( "DoSomethingCommand" );
test("Test DoSomethingCommand (Undo and Redo)", function() {
var editor = new Editor();
var box = aBox( 'Name your box' );
// other available objects from "CommonUtilities.js"
// var sphere = aSphere( 'Name your sphere' );
// var pointLight = aPointLight( 'Name your pointLight' );
// var perspectiveCamera = aPerspectiveCamera( 'Name your perspectiveCamera' );
// in most cases you'll need to add the object to work with
editor.execute( new AddObjectCommand( editor, box ) );
// your test begins here...
} );
The predefined code is just meant to ease the development, you do not have to stick with it.
However, the test should cover at least one editor.execute()
, one editor.undo()
and one editor.redo()
call.
Best practice is to call editor.execute( new DoSomethingCommand( {custom parameters} ) )
twice. Since you’ll have to do one undo (go one step back), it is recommended to have a custom state for comparison. Try to avoid assertions ok()
against default values.
After performing editor.execute()
twice, you can do your first assertion to check whether the executes are done correctly.
Next, you perform editor.undo()
and check if the last action was undone.
Finally, perform editor.redo()
and verify if the values are as expected.
Open the editor’s unit test suite test/unit/unittests_editor.html
in your browser and check the results from the test framework.


If any of the below requirements suits your profile you can contact us at<EMAIL_ADDRESS>Title: Php Develpoer
- Location: Bangalore (Remote)
- Employment Type: Internship/Permanent Job
- Education: Under Graduate/Graduate
- Experience: 0-2 years
- Skills: HTML, CSS, CorePHP, Javascripts, JQuery, MYSQL
- Job Description:
0-2 years of developing experience in PHP.
Good knowledge of working in Core PHP, Mysql, HTML, CSS, Jquery.
Complete understanding of SDLC.
Title: Android Develpoer
- Location: Bangalore (Remote)
- Employment Type: Internship/Permanent Job
- Education: Under Graduate/Graduate
- Experience: 0-2 years
- Skills: Android, HTML, CSS, Javascripts, JQuery, MYSQL, SQLite
- Job Description:
0-2 years of developing experience in Android.
Good knowledge of working in Android, Mysql, SQLite, HTML, CSS, Jquery.
Complete understanding of SDLC.


|
Halide
12.0.0
|
#include <mini_d3d12.h>
Public Member Functions | |
| BEGIN_INTERFACE (IDXGIFactory1 *This, REFIID riid, _COM_Outptr_ void **ppvObject) | |
| IDXGIFactory1 _In_ REFGUID UINT | _In_reads_bytes_ (DataSize) const void *pData) |
Definition at line 6452 of file mini_d3d12.h.
| IDXGIFactory1Vtbl::BEGIN_INTERFACE | ( | IDXGIFactory1 * | This, |
| REFIID | riid, | ||
| _COM_Outptr_ void ** | ppvObject | ||
| ) |
| IDXGIFactory1 _In_ REFGUID UINT IDXGIFactory1Vtbl::_In_reads_bytes_ | ( | DataSize | ) | const |
| IDXGIFactory1 * IDXGIFactory1Vtbl::This |
Definition at line 6464 of file mini_d3d12.h.
| IDXGIFactory1 _In_ REFGUID IDXGIFactory1Vtbl::Name |
Definition at line 6474 of file mini_d3d12.h.
| IDXGIFactory1 _In_ REFGUID UINT IDXGIFactory1Vtbl::DataSize |
Definition at line 6475 of file mini_d3d12.h.
| IDXGIFactory1 _In_ REFGUID _In_opt_ const IUnknown* IDXGIFactory1Vtbl::pUnknown |
Definition at line 6485 of file mini_d3d12.h.
| IDXGIFactory1 _In_ REFGUID _Inout_ UINT* IDXGIFactory1Vtbl::pDataSize |
Definition at line 6493 of file mini_d3d12.h.
| IDXGIFactory1 _In_ REFGUID _Inout_ UINT _Out_writes_bytes_ pDataSize void* IDXGIFactory1Vtbl::pData |
Definition at line 6495 of file mini_d3d12.h.
| IDXGIFactory1 _In_ REFIID IDXGIFactory1Vtbl::riid |
Definition at line 6501 of file mini_d3d12.h.
| IDXGIFactory1 _In_ REFIID _COM_Outptr_ void** IDXGIFactory1Vtbl::ppParent |
Definition at line 6503 of file mini_d3d12.h.
| IDXGIFactory1 UINT IDXGIFactory1Vtbl::Adapter |
Definition at line 6508 of file mini_d3d12.h.
| IDXGIFactory1 UINT _COM_Outptr_ IDXGIAdapter** IDXGIFactory1Vtbl::ppAdapter |
Definition at line 6510 of file mini_d3d12.h.
| IDXGIFactory1 HWND IDXGIFactory1Vtbl::WindowHandle |
Definition at line 6515 of file mini_d3d12.h.
| IDXGIFactory1 HWND UINT IDXGIFactory1Vtbl::Flags |
Definition at line 6516 of file mini_d3d12.h.
| IDXGIFactory1 _Out_ HWND* IDXGIFactory1Vtbl::pWindowHandle |
Definition at line 6522 of file mini_d3d12.h.
| IDXGIFactory1 _In_ IUnknown* IDXGIFactory1Vtbl::pDevice |
Definition at line 6528 of file mini_d3d12.h.
| IDXGIFactory1 _In_ IUnknown _In_ DXGI_SWAP_CHAIN_DESC* IDXGIFactory1Vtbl::pDesc |
Definition at line 6530 of file mini_d3d12.h.
| IDXGIFactory1 _In_ IUnknown _In_ DXGI_SWAP_CHAIN_DESC _COM_Outptr_ IDXGISwapChain** IDXGIFactory1Vtbl::ppSwapChain |
Definition at line 6532 of file mini_d3d12.h.
| IDXGIFactory1 HMODULE IDXGIFactory1Vtbl::Module |
Definition at line 6537 of file mini_d3d12.h.
| IDXGIFactory1 HMODULE _COM_Outptr_ IDXGIAdapter** IDXGIFactory1Vtbl::ppAdapter |
Definition at line 6539 of file mini_d3d12.h.
| IDXGIFactory1 UINT _COM_Outptr_ IDXGIAdapter1** IDXGIFactory1Vtbl::ppAdapter |
Definition at line 6546 of file mini_d3d12.h.


o != arg0
is the same as !(o == (arg0))
.
o != arg0
is the same as !(o == (arg0))
.
the object to compare against this object for dis-equality.
false
if the receiver object is equivalent to the argument; true
otherwise.
MongoDB DB collection.
MongoDB DB collection.save method
object to save to the collection
MongoDB DBCollection.
MongoDB DBCollection.remove method
object to remove from the collection
o == arg0
is the same as if (o eq null) arg0 eq null else o.equals(arg0)
.
o == arg0
is the same as if (o eq null) arg0 eq null else o.equals(arg0)
.
the object to compare against this object for equality.
true
if the receiver object is equivalent to the argument; false
otherwise.
o == arg0
is the same as o.equals(arg0)
.
o == arg0
is the same as o.equals(arg0)
.
the object to compare against this object for equality.
true
if the receiver object is equivalent to the argument; false
otherwise.
Manipulate Network Options
Manipulate Network Options
com.mongodb.Bytes
com.mongodb.Mongo
Adds the "private" fields _id to an object.
Adds the "private" fields _id to an object.
object to which to add fields
whether to add an _id
field or not
the modified object o
Adds the "private" fields _id to an object.
Adds the "private" fields _id to an object.
DBObject
to which to add fields
the modified parameter object
This method is used to cast the receiver object to be of type T0
.
This method is used to cast the receiver object to be of type T0
.
Note that the success of a cast at runtime is modulo Scala's erasure semantics. Therefore the expression1.asInstanceOf[String]
will throw a ClassCastException
at runtime, while the expressionList(1).asInstanceOf[List[String]]
will not. In the latter example, because the type argument is erased as
part of compilation it is not possible to check whether the contents of the list are of the requested typed.
the receiver object.
Emulates a SQL AVG() call ever so gently
Emulates a SQL AVG() call ever so gently
This method creates and returns a copy of the receiver object.
This method creates and returns a copy of the receiver object.
The default implementation of the clone
method is platform dependent.
a copy of the receiver object.
Forces creation of an index on a set of fields, if one does not already exist.
Forces creation of an index on a set of fields, if one does not already exist.
an object with a key set of the fields desired for the index
Returns the database this collection is a member of.
Returns the database this collection is a member of.
this collection's database
find distinct values for a key
find distinct values for a key
query to apply on collection
find distinct values for a key
find distinct values for a key
Drops (deletes) this collection
Drops (deletes) this collection
Drops (deletes) this collection
Drops (deletes) this collection
Drops all indices from this collection
Drops all indices from this collection
Ensures an optionally unique index on this collection.
Ensures an optionally unique index on this collection.
fields to use for index
an identifier for the index
if the index should be unique
Ensures an index on this collection (that is, the index will be created if it does not exist).
Ensures an index on this collection (that is, the index will be created if it does not exist). ensureIndex is optimized and is inexpensive if the index already exists.
fields to use for index
an identifier for the index
Creates an index on a set of fields, if one does not already exist.
Creates an index on a set of fields, if one does not already exist.
an object with a key set of the fields desired for the index
This method is used to test whether the argument (arg0
) is a reference to the
receiver object (this
).
This method is used to test whether the argument (arg0
) is a reference to the
receiver object (this
).
The eq
method implements an [http://en.wikipedia.org/wiki/Equivalence_relation equivalence relation] on
non-null instances of AnyRef
:
* It is reflexive: for any non-null instance x
of type AnyRef
, x.eq(x)
returns true
.
* It is symmetric: for any non-null instances x
and y
of type AnyRef
, x.eq(y)
returns true
if and
only if y.eq(x)
returns true
.
* It is transitive: for any non-null instances x
, y
, and z
of type AnyRef
if x.eq(y)
returns true
and y.eq(z)
returns true
, then x.eq(z)
returns true
.
Additionally, the eq
method has three other properties.
* It is consistent: for any non-null instances x
and y
of type AnyRef
, multiple invocations of
x.eq(y)
consistently returns true
or consistently returns false
.
* For any non-null instance x
of type AnyRef
, x.eq(null)
and null.eq(x)
returns false
.
* null.eq(null)
returns true
.
When overriding the equals
or hashCode
methods, it is important to ensure that their behavior is
consistent with reference equality. Therefore, if two objects are references to each other (o1 eq o2
), they
should be equal to each other (o1 == o2
) and they should hash to the same value (o1.hashCode == o2.hashCode
).
the object to compare against this object for reference equality.
true
if the argument is a reference to the receiver object; false
otherwise.
Checks if this collection is equal to another object.
Checks if this collection is equal to another object.
if the two collections are the same object
This method is called by the garbage collector on the receiver object when garbage collection determines that there are no more references to the object.
This method is called by the garbage collector on the receiver object when garbage collection determines that there are no more references to the object.
The details of when and if the finalize
method are invoked, as well as the interaction between finalize
and non-local returns and exceptions, are all platform dependent.
Returns the full name of this collection, with the database name as a prefix.
Returns the full name of this collection, with the database name as a prefix.
the name of this collection
Returns a representation that corresponds to the dynamic class of the receiver object.
Returns a representation that corresponds to the dynamic class of the receiver object.
The nature of the representation is platform dependent.
a representation that corresponds to the dynamic class of the receiver object.
Find a collection that is prefixed with this collection's name.
Find a collection that is prefixed with this collection's name. A typical use of this might be
Which is equilalent toDBCollection users = mongo.getCollection( "wiki" ).getCollection( "users" );
DBCollection users = mongo.getCollection( "wiki.users" );
the name of the collection to find
the matching collection
Returns the number of documents in the collection that match the specified query
Returns the number of documents in the collection that match the specified query
query to select documents to count
fields to return
Max # of fields
# of fields to skip
number of documents that match query and fields
Returns the number of documents in the collection that match the specified query
Returns the number of documents in the collection that match the specified query
query to select documents to count
fields to return
number of documents that match query and fields
Returns the number of documents in the collection that match the specified query
Returns the number of documents in the collection that match the specified query
query to select documents to count
number of documents that match query
Returns the number of documents in the collection
Returns the number of documents in the collection
number of documents that match query
Returns the database this collection is a member of.
Returns the database this collection is a member of.
this collection's database
Returns the full name of this collection, with the database name as a prefix.
Returns the full name of this collection, with the database name as a prefix.
the name of this collection
Return a list of the indexes for this collection.
Return a list of the indexes for this collection. Each object in the list is the "info document" from MongoDB
list of index documents
Gets the default class for objects in the collection
Gets the default class for objects in the collection
the class
Manipulate Network Options
Manipulate Network Options
com.mognodb.Bytes
com.mongodb.Mongo
get the write concern for this database, which is used for writes to any collection in this database.
get the write concern for this database,
which is used for writes to any collection in this database.
See the documentation for WriteConcern
for more info.
http://www.thebuzzmedia.com/mongodb-single-server-data-durability-guide/
WriteConcern
Enables you to call group with the finalize parameter (a function that runs on each row of the output for calculations before sending a return) which the Mongo Java driver does not yet support, by sending a direct DBObject command.
Enables you to call group with the finalize parameter (a function that runs on each row of the output for calculations before sending a return) which the Mongo Java driver does not yet support, by sending a direct DBObject command. Messy, but it works.
Perform an absurdly simple grouping with no initial object or reduce function.
Perform an absurdly simple grouping with no initial object or reduce function.
Returns a hash code value for the object.
Returns a hash code value for the object.
The default hashing algorithm is platform dependent.
Note that it is allowed for two objects to have identical hash codes (o1.hashCode.equals(o2.hashCode)
) yet
not be equal (o1.equals(o2)
returns false
). A degenerate implementation could always return 0
.
However, it is required that if two objects are equal (o1.equals(o2)
returns true
) that they have
identical hash codes (o1.hashCode.equals(o2.hashCode)
). Therefore, when overriding this method, be sure
to verify that the behavior is consistent with the equals
method.
the hash code value for the object.
Return a list of the indexes for this collection.
Return a list of the indexes for this collection. Each object in the list is the "info document" from MongoDB
list of index documents
Saves document(s) to the database.
Saves document(s) to the database. if doc doesn't have an _id, one will be added you can get the _id that was added from doc after the insert
Inserts a document into the database.
Inserts a document into the database. if doc doesn't have an _id, one will be added you can get the _id that was added from doc after the insert
Saves document(s) to the database.
Saves document(s) to the database. if doc doesn't have an _id, one will be added you can get the _id that was added from doc after the insert
Saves document(s) to the database.
Saves document(s) to the database. if doc doesn't have an _id, one will be added you can get the _id that was added from doc after the insert
This method is used to test whether the dynamic type of the receiver object is T0
.
This method is used to test whether the dynamic type of the receiver object is T0
.
Note that the test result of the test is modulo Scala's erasure semantics. Therefore the expression1.isInstanceOf[String]
will return false
, while the expression List(1).isInstanceOf[List[String]]
will
return true
. In the latter example, because the type argument is erased as part of compilation it is not
possible to check whether the contents of the list are of the requested typed.
true
if the receiver object is an instance of erasure of type T0
; false
otherwise.
MapReduceResult a wrapped result object. This contains the returns success, counts etc, but implements iterator and can be iterated directly
MapReduceResult a wrapped result object. This contains the returns success, counts etc, but implements iterator and can be iterated directly
Due to poor original design on my part, you should probably use the explicitly parameterized fversion of this on collection * @param command An instance of MapReduceCommand representing the required MapReduce
Emulates a SQL MAX() call ever so gently
Emulates a SQL MAX() call ever so gently
Emulates a SQL MIN() call ever so gently
Emulates a SQL MIN() call ever so gently
o.ne(arg0)
is the same as !(o.eq(arg0))
.
o.ne(arg0)
is the same as !(o.eq(arg0))
.
the object to compare against this object for reference dis-equality.
false
if the argument is not a reference to the receiver object; true
otherwise.
Wakes up a single thread that is waiting on the receiver object's monitor.
Wakes up a single thread that is waiting on the receiver object's monitor.
Wakes up all threads that are waiting on the receiver object's monitor.
Wakes up all threads that are waiting on the receiver object's monitor.
Gets the default class for objects in the collection
Gets the default class for objects in the collection
the class
Manipulate Network Options
Manipulate Network Options
com.mognodb.Bytes
com.mongodb.Mongo
Removes objects from the database collection.
Removes objects from the database collection.
the object that documents to be removed must match
Removes objects from the database collection.
Removes objects from the database collection.
the object that documents to be removed must match
does a rename of this collection to newName
does a rename of this collection to newName
new collection name (not a full namespace)
the new collection
Clears all indices that have not yet been applied to this collection.
Clears all indices that have not yet been applied to this collection.
Manipulate Network Options
Manipulate Network Options
com.mongodb.Bytes
com.mongodb.Mongo
Saves an object to this collection.
Saves an object to this collection.
the DBObject
to save
will add _id
field to jo if needed
Saves an object to this collection.
Saves an object to this collection.
the DBObject
to save
will add _id
field to jo if needed
Set hint fields for this collection.
Set hint fields for this collection.
Set the write concern for this database.
Set the write concern for this database.
Will be used for writes to any collection in this database.
See the documentation for WriteConcern
for more info.
(WriteConcern) The write concern to use
http://www.thebuzzmedia.com/mongodb-single-server-data-durability-guide/
WriteConcern
Sets queries to be OK to run on slave nodes.
Sets queries to be OK to run on slave nodes.
Returns a string representation of the object.
Returns a string representation of the object.
The default representation is platform dependent.
a string representation of the object.
The underlying Java Mongo Driver Collection object we proxy.
The underlying Java Mongo Driver Collection object we proxy.
Performs an update operation.
Performs an update operation.
search query for old object to update
object with which to update q
if the database should create the element if it does not exist
if the update should be applied to all objects matching (db version 1.1.3 and above) See http://www.mongodb.org/display/DOCS/Atomic+Operations
WriteConcern for this operation
Performs an update operation.
Performs an update operation.
search query for old object to update
object with which to update q
if the database should create the element if it does not exist
if the update should be applied to all objects matching (db version 1.1.3 and above) See http://www.mongodb.org/display/DOCS/Atomic+Operations
get the write concern for this database, which is used for writes to any collection in this database.
get the write concern for this database,
which is used for writes to any collection in this database.
See the documentation for WriteConcern
for more info.
http://www.thebuzzmedia.com/mongodb-single-server-data-durability-guide/
WriteConcern
Set the write concern for this database.
Set the write concern for this database.
Will be used for writes to any collection in this database.
See the documentation for WriteConcern
for more info.
(WriteConcern) The write concern to use
http://www.thebuzzmedia.com/mongodb-single-server-data-durability-guide/
WriteConcern
Base trait for all MongoCollection wrapper objects. Provides any non-parameterized methods and the basic structure. Requires an underlying object of a DBCollection.


Every decision when creating your business is important. The wrong decision can take years to undo. Having a well-crafted website that not only caters to your customers but to how your business works can set you up for success.
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--- layout: default title: CCA'17 - Conference Program meta: The conference program can be found here keyword: cloud services, iaas, paas, saas, cloud infrastructures, cloud computing, storage, data architectures, distributed networking, computing, cloud management, cloud operations, security and privary, performance, scalability, reliability, virtualization, cloud data analytics, big data management, big data analysis, cloud software, hardware, cloud applications, social clouds systems, mobile clouds systems, cloud economics ---
The Conference Program for the International Conference on Cloud Computing and Applications (CCA'17) is now available! Please follow the link below to access the program:


{% extends "layouts/_block_content.html" %} {% set page_title = "Vulnerability Disclosure Policy" %} {% block main %}
This disclosure policy applies only to vulnerabilities in the ONS products and services under the following conditions:
If you have discovered something you believe to be an in-scope security vulnerability, first you should check the above details for more information about scope, then submit a report on this page. In your submission, include details of:
After you have submitted your report, we will respond to your report within 5 working days and aim to triage your report within 10 working days. We’ll also keep you informed about our progress throughout the process via HackerOne if you have registered for an account.
Priority for bug fixes or mitigations are assessed by looking at the impact severity and exploit complexity. Vulnerability reports might take some time to triage or address. You are welcome to enquire onthe status of the process but should avoid doing so more than once every 14 days. The reason is to allow our teams to focus on the reports as much as possible.
When the reported vulnerability is resolved, or remediation work is scheduled, the Vulnerability Disclosure Team will notify you, and invite you to confirm that the solution covers the vulnerability adequately.
This policy is designed to be compatible with common vulnerability disclosure good practice. It does not give you permission to act in any manner that is inconsistent with the law, or which might cause the ONS to be in breach of any of its legal obligations, including but not limited to:
You must NOT:


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{% block subject %}{{ subject }}{% endblock %} {% block plain %} Hi there, The memory threshold of {{ threshold }} has been surpassed. Details: {{ details }} {% endblock %} {% block html %}
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SALMON COOKIES SALES
Insert new location
Location Name :
Minimum Customers:
Maximum Customers:
Average Cookies :
LOCATION
TOTAL


APCS Projects
Projects from my high school AP Computer Science class
SpinningCircle
Ripple
Minesweeper
Asteroids
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Drops of rain could be heard hitting the pane, which made him feel quite sad. How about if I sleep a little bit longer and forget all this nonsense
He threw himself onto his right and rolled back to where he was.
Doing business like this takes much more effort than doing your own business at home, and on top of that there’s the curse of travelling
He worries about making train connections and irregular food, contact with different people all the time so that you or anyone become friendly.
It can all go to Hell. He felt a slight itch up on his belly, pushed himself up on his back towards the headboard so that he lift his head better
Donec rutrum congue leo eget malesuada
Donec rutrum congue leo eget malesuada


My Sheets
| Title | Owned By | Created On | Shared With |
|---|---|---|---|
| sheet Title | owner | date | groupName |
| sheet Title | owner | date | groupName |
| sheet Title | owner | date | groupName |
My Groups
| Group Name | Created By | Members | Sheets Shared |
|---|---|---|---|
| name of group | owner | list of userNames | list of sheet titles |
| name of group | owner | list of userNames | list of sheet titles |
| name of group | owner | list of userNames | list of sheet titles |
| name of group | owner | list of userNames | list of sheet titles |


This page has UI for listing and registering superheroes on the same page.
That's pretty cluttered. Use the ngRoute module's $routeProvider to split the registration form and the list into two separate HTML templates: register.html and heroes.html
Each "page" should be reachable by clicking the links in the menu above.


View My Portfolio! Below you can click on links to view my Github projects: My First Website About Me Repository Play My Guessing Game!


{include file="base/head" title="$companyInfo.f_company_name"/}
{include file="base/header" /}
{include file="base/article_nav" /} {include file="base/contact_nav" /}
{include file="base/article_breadcrumb" /}
{include file="base/footer" /}


Ahmed Aziz
github
linkedin
Progress with Open Source Society University's
Computer Science curriculum


Build and initialize the workspace client¶
This documentation describes how to build and initialize the workspace clients.
It assumes the documentation build occurs on Ubuntu 12.04LTS,
but things should work similarly on other distributions. It assumes that the
workspace_deluxe
and jars
repos have been cloned (see Getting the code)
but does not assume that the KBase runtime or dev_container
are
installed.
Python client¶
Currently the Python client only supports Python 2.7. The Python client checked
into libs/biokbase/workspace/client.py
does not
require a build, but does require the requests
(v 2+) 3rd party library, which,
depending on the Python version, can be
tricky to install securely.
The following incantation worked for the author:
sudo apt-get install python-dev libffi-dev libssl-dev
curl https://bootstrap.pypa.io/get-pip.py > get-pip.py
sudo python get-pip.py
sudo pip install --upgrade requests
sudo pip install --upgrade requests[security]
For python 2.7.9+ sudo pip install --upgrade requests
should
work.
Change the working directory to the lib directory:
bareubuntu@bu:~/ws$ cd workspace_deluxe/lib/
bareubuntu@bu:~/ws/workspace_deluxe/lib$
Alternatively, add this directory to the PYTHONPATH
. If deploying with
the dev_container
, the client will be copied to
/kb/deployment/lib/biokbase/workspace/client.py
and the user-env
script
will set up the PYTHONPATH
.
Here we use the iPython interpreter to demonstrate initializing the client, but the standard python interpreter will also work:
bareubuntu@bu:~/ws/workspace_deluxe/lib$ ipython
In [1]: from biokbase.workspace.client import Workspace
In [2]: ws = Workspace('https://kbase.us/services/ws', user_id='kbasetest', password=[redacted])
In [3]: ws.ver()
Out[3]: u'0.3.5'
Java client¶
The Java client build requires:
Java JDK 6+ (install instructions)
sudo apt-get install ant
Build the client:
bareubuntu@bu:~/ws/workspace_deluxe$ make compile-java-client
ant compile_client
Buildfile: /home/bareubuntu/ws/workspace_deluxe/build.xml
compile_client:
[mkdir] Created dir: /home/bareubuntu/ws/workspace_deluxe/client_classes
[javac] Compiling 48 source files to /home/bareubuntu/ws/workspace_deluxe/client_classes
[jar] Building jar: /home/bareubuntu/ws/workspace_deluxe/dist/client/WorkspaceClient.jar
[delete] Deleting directory /home/bareubuntu/ws/workspace_deluxe/client_classes
BUILD SUCCESSFUL
Total time: 3 seconds
The client jar is created in dist/client/WorkspaceClient.jar
.
For simplicity, copy the required jars into a single directory:
bareubuntu@bu:~/ws$ mkdir tryjavaclient
bareubuntu@bu:~/ws$ cd tryjavaclient/
bareubuntu@bu:~/ws/tryjavaclient$ cp ../workspace_deluxe/dist/client/WorkspaceClient.jar .
bareubuntu@bu:~/ws/tryjavaclient$ cp ../jars/lib/jars/jackson/jackson-*-2.2.3.jar .
bareubuntu@bu:~/ws/tryjavaclient$ cp ../jars/lib/jars/kbase/auth/kbase-auth-0.3.1.jar .
bareubuntu@bu:~/ws/tryjavaclient$ cp ../jars/lib/jars/kbase/common/kbase-common-0.0.10.jar .
bareubuntu@bu:~/ws/tryjavaclient$ ls
jackson-annotations-2.2.3.jar kbase-auth-0.3.1.jar
jackson-core-2.2.3.jar kbase-common-0.0.10.jar
jackson-databind-2.2.3.jar WorkspaceClient.jar
When creating an application using the WSS it’s advisable to use a build tool
like ant
, maven
, or gradle
to organize the required jars.
This simple program initializes and calls a method on the WSS client:
bareubuntu@bu:~/ws/tryjavaclient$ cat TryWorkspaceClient.java
import java.net.URL;
import us.kbase.workspace.WorkspaceClient;
public class TryWorkspaceClient {
public static void main(String[] args) throws Exception {
WorkspaceClient client = new WorkspaceClient(
new URL("https://kbase.us/services/ws"),
"kbasetest", [redacted]);
System.out.println(client.ver());
}
}
Compile and run:
bareubuntu@bu:~/ws/tryjavaclient$ javac -cp "./*" TryWorkspaceClient.java
bareubuntu@bu:~/ws/tryjavaclient$ java -cp "./:./*" TryWorkspaceClient
0.3.5
For more client initialization and configuration options, see Workspace API documentation.
Perl client¶
Todo
Build and initialization instructions for the Perl client. If this can be done without the KBase runtime & dev_container that’d be ideal.
Javascript client¶
Todo
Build (probably not needed) and initialization instructions for the Javascript client.


Before Change
can be aligned to a brightfield image by this method since the relevant
features are bright in one modality where they are dim in the other.

""")
self.wants_cropping = cps.Binary("Crop output images to retain just the aligned regions?",
True, doc="""
If cropping is chosen, all output images are cropped to retain
just those regions that exist in all channels after alignment.
If cropping is not chosen, the unaligned portions of each
image are padded (with zeroes) and appear as black space.""")
def add_image(self, can_remove = True):
"""Add an image + associated questions and buttons"""
group = cps.SettingsGroup()
After Change

""")
self.crop_mode = cps.Choice(
"Crop mode", [C_CROP, C_PAD, C_SAME_SIZE],
doc = The crop mode determines how the output images are cropped
or padded after alignment. The alignment phase calculates the
areas in each image that are found to be overlapping. In almost
all cases, there will be portions of some or all of the images
that don"t overlap with every other aligned image. These portions
have no counterpart in some image and analysis of these regions
will not be able to use the information from all images. There
are three choices for cropping:

%(C_CROP)s - crop every image to the region of that
image that overlaps with every other image. This makes downstream
analysis more accurate and simpler because all of the output images
have valid pixel data at all positions in the image but it discards
parts of images. Also, the output images may not be the same size
as the input images which may cause problems if downstream modules
use aligned and unaligned images in combination.

%(C_PAD)s - align every image and pad with masked black
pixels to make each image the same size. This results in larger
images, but preserves all information in each of the images. This
may be the best choice if images undergo an operation such as
smoothing that could use the information that would otherwise be
cropped.

%(C_SAME_SIZE)s - maintain the sizes of the images but
align them, masking the unaligned portions. Align finds the
global alignment that preserves the most pixels among all the images
and then repositions each image within an image of similar size
to the input image. This is a reasonable option for alignments
with small displacements since it maintains a consistent image
size which may be useful if output images from different image sets
will be compared against each other after processing.
</ul % globals())
def add_image(self, can_remove = True):
"""Add an image + associated questions and buttons"""


As discussed in Class 5, there was a problem on Quiz 2 that both the professors got wrong probably more than once.
Although it was meant to be a fairly straightforward multiple choice question, it illustrates how tricky it is to think about the subtleties of simple definitions on binary relations and the importance of precise definitions.
The question, as asked on Quiz 2 is below:
Question 2: Notions of Relations
Which of the following are true statements for all sets \(X\) and \(Y\)? Choose all that are correct:
- If there is a bijection between sets \(X,Y\), then there is an injective \((\le 1\) in) function (\(\le 1 \) out) from \(X\) to \(Y\).
- If there is a partial function from \(X\) to \(Y\), then there is surjective mapping \((\ge 1\) in) from \(Y\) to \(X\).
- There is a total (\( \ge 1\) out) function from \(Y\) to \(X\) if and only if there is a surjective mapping from \(X\) to \(Y\).
- If there is a surjective total function from \(X\) to \(Y\), then there is an injective mapping from \(X\) to \(Y\).
In class, we decided that choice 1 was definitely correct, but there was some doubt on the other three choices and confusion about the meaning of mapping which we didn’t define (the TCS book uses the term mapping frequently, but does not formally define it; the MCS (cs2102) book, uses “Mapping Rules” to talk about different kinds of binary relations; according to wikipedia, mapping usually means “total function”, and “partial map” is used ot mean “partial function”).
Assuming “mapping” means “total function”, we believe statements 2, 3, and 4 are all false.
If “mapping” means “binary relation” (which does not add any constraints), then statements 2 and 4 are true, and statement 3 is false.
We’ll leave it as a challenge problem for students to come up with some other non-crazy interpretation of “mapping” that would make any other combination of these statements true. (By “non-crazy”, we mean it has to be a definition that makes at least some sense in a general context, not being specifically contrived to have different meanings based on the question specifics.)
The way challenge problems work is the first student to provide a solution the professors this is interesting and valuable enough in response to the challenge will receive bonus points (in an amount that it totally at our discretion, but might be as much as a full problem set or more); once a solution has been accepted, the challenge isn’t necessarily closed, but for a subsequent solution to also receive a bonus it would have to offer substantial value beyond the first accepted solution.


Tumblr Savior Behavior
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About
Tumblr Savior is a free browser extension created and maintained by Bjorn Stromberg to improve his experience on Tumblr. He thought you might also find it useful and decided to share it with you.
This extension is open source and all source code can be found at https://github.com/bjornstar/Tumblr-Savior.
If you'd like to show your appreciation, send a little love his way at paypal.me/bjornstar
Or become a sponsor on Github for as little as $1.00 per month.
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Game Instructions
Welcome to YachtZoe!, the original dice game. This game is in no way affiliated with its popular knockoff "Yahtzee". To win, you must roll five dice in various combinations to get the highest score possible.
Each player may roll the dice up to 3 times per turn to get the highest scoring combination. The player must select zero or one of the 13 combination categories to complete their turn.
Additionally, players may choose to hold certain dice during their turn and roll the remaining until their turn is complete.
The game ends when all players have completed their score table.
| Combination Name | Description |
|---|---|
| Ones | Total of 1's. Up to 5 points possible. |
| Twos | Total of 2's. Up to 10 points possible. |
| Threes | Total of 3's. Up to 15 points possible. |
| Fours | Total of 4's. Up to 20 points possible. |
| Fives | Total of 5's. Up to 25 points possible. |
| Sixes | Total of 6's. Up to 30 points possible. |
| 3 of a Kind | Three of the same number. The points scored are the total for that turn (all five dice). Up to 30 points possible. |
| Four of a Kind | Four of the same number. The points scored are the total for that turn (all five dice). Up to 30 points possible. |
| Full House | Three of a kind and a pair. 25 points. |
| Small Straight | Four sequential dice. 30 points. |
| Large Straight | Five sequential dice. 40 points. |
| YachtZoe! | Five of a kind. 50 points. |
| Chance | Can be anything. Score is the sum of all dice in that turn. Up to 30 points possible. |


 This will be one of the most difficult years in high school. Your workload will increase, you will begin taking standardized tests, and you will begin your college search early. The key is to not procrastine, get to know your guidance counselors, and not get to overwhelmed.
“Worry is like a rocking chair; it gives you something to do, but never gets you anywhere. ”
-Erma Bombeck
“Enjoy high school. You are going to blink twice, and youll be in your cap and gown staring at your class wondering where time went.”
-Unknown
“Start where you are; use what you have; do what you can. ”
-Arthur Ashe


All of the Christmas films to watch on Netflix
Snuggle up on the sofa and watch a heartwarming movie
by Lea Nakache'Tis the season for hot chocolates, log fires and lazy afternoon's spent curling up on the sofa and watching a festive film.
As always, Netflix has released a whole host of original movies to keep you and the family entertained throughout November and December.
Here's a round-up of what you can watch in the lead of up to the big day.
Holiday in the Wild
If the cold weather isn't your cup of hot cocoa, this movie under the African sun is for you.
Newly-divorced Kate decides to go solo on her second honeymoon, where she meets handsome Derek, who will show her the delight of the continent, giving Kate a new hope.
In summary, it’s all about festive fun with Rob Lowe and elephants.
Let It Snow
Let It Snow takes you back to jolly high school days, filled with prom, friendship, and first loves.
While a snowstorm hits a small US town on Christmas Eve, a group of students decides to stick together, letting their feelings for one another unfurl in the midst of snow.
Klaus
Magician toy-maker Klaus teams up with a postman to bring joy and wonder back to ‘the unhappiest place on Earth’.
With a beautiful graphic style, original story and a little bit of 2D traditional magic, it’s the perfect recipe to awaken the child in all of us.
The Knight Before Christmas
Vanessa Hudgens stars in her second Netflix Christmas movie.
The former High School Musical superstar falls in love with a medieval knight that has been transported to the 21st century.
If this isn't some Christmas-worthy cheesiness, I don’t know what is.
Nailed It! Holiday!
The funny baking show is back with a holiday-special season for a second year, where amateurs have to recreate incredibly intricate cake masterpieces.
Holiday Rush
After being fired from her job, a radio DJ has to find a way to buy his spoiled children all the toys on their Christmas lists, while being forced to live back with his aunt.
Merry Happy Whatever
Ashley Tisdale is the main character of this family story, where she will have to put up with her strong-willed dad who has trouble meeting her new boyfriend.
Sugar Rush Christmas
Release date: November 29
Professional bakers will have to compete to create the perfect holiday cake.
A Christmas Prince -The Royal Baby
Release date: December 5
Behold the Lord of Holiday Cheesiness.December will begin with THE Christmas romcom everybody is waiting for.
After a record-breaking first opus where a journalist falls in love with a prince of a small realm, a second movie about their wedding day, this next film is the perfect closure of this cheesy holiday trinity, as the new Queen and King will welcome their first child.
Home for Christmas
This Norwegian first series is about a struggle everybody can relate: the family’s disappointment about the single love status of their adult children.
To spare herself the trouble, Johanne starts a 24-day hunt for a partner to bring home for the holiday to avoid spending Christmas alone with her family.
Dias de Navidad (Three days of Christmas)
Release date: December 6
This Spanish mini-series follows a family of four sisters celebrating Christmas at three different stages of their lives, from childhood to old age. Year after year, family secrets unfold and relationship change.
Operation Christmas Drop
Release date: December 31
To close the holiday season, Netflix revisits the story of the oldest US military training: a Christmastime airlift humanitarian mission from Andersen Air Force Base in Guam.
Vikings star Alexander Ludwig will be playing one of the heroes of the show.
What movies are you the most excited for? Let us know in the comments!