The development of biometrics on mobile devices is set to have an outsized impact on mobile wallets and international money transfer. Advances such as fingerprint login, retinal scan, and facial recognition offer a rare opportunity for remittance companies to both combat fraud and improve the user experience.
Mobile wallet transactions alone are expected to reach nearly $1.4 trillion in 2017, growing 32% compared to 2016, and the number of mobile phone users will top 5 billion. Biometrics represents the next major technological advance that will push FinTech past traditional banks.
With the current mobile focus of the remittance industry, it is uniquely positioned to not only ensure money arrives but also to know the identity of every customer – at scale and without a retail footprint. One of the most critical parts of enabling and safeguarding money transfers requires knowing a user’s identity — and biometrics offers a way to do just that.
Entering the next phase of FinTech
The internet was the original game changer, enabling digitally funded remittances for the first time. Then, the advent of smartphones pushed the industry into a new phase by simplifying the experience and empowering the customer to send money from anywhere. Now, the advanced biometrics offered on mobile devices ushers in a new era.
Biometrics improves the user experience by reducing form fields, eliminating the need to upload a picture of a physical ID, and fully automating the know-your-customer (KYC)/anti-fraud process. Moreover, for the first time, digitally funded transfers will offer better KYC and fraud checks than banks or brick-and-mortar competitors.
Today, capturing an image of a photo ID is the ultimate method of KYC. If a company has your ID, they know who you are – or so we are told.
In the future, easy-to-fake physical IDs will not be sufficient, as the standard is pushed to fingerprints, retinal scans or face recognition.
In other words, the mobile connection to users will be more valuable than an in person, retail experience.
Understanding the vulnerabilities
The authentication of identities and information – especially in financial transactions – is far from a new concept. However, traditional methods will not be able to keep up with the rapidly changing technology landscape.
With hacked or compromised credentials, attackers can wreak havoc by posing as legitimate users and moving or stealing unauthorized funds. Not only is there a risk of theft, but fraudsters also exploit peer-to-peer (P2P) money transfer services for money laundering and terrorism financing. Considering the fact that P2P payments are expected to be used by nearly 129 million adults in the US by 2021, the threat isn’t going away anytime soon.
 In addition to KYC, it’s also crucial to leverage technological advancements to stay one step ahead of fraudsters and their accelerating threats.
International impact
Advances in biometrics will have a disproportionately positive impact on people from developing countries where the current ID technology is low and less reliable.
The number of people living outside of the country in which they were born has surpassed 244 million, representing a 41% increase between 2000 and 2015. Many of these people come from places where the identity infrastructure is weak and disconnected from developed systems in the US and Europe.
However, with biometrics, remittance companies will no longer need to accommodate a driver’s license from India or voting card from Mexico. Instead, they can use biometrics to safely and securely identify customers across the world.
New challenges in fraud in money laundering
Of course, for every technological advance in combating fraud and money laundering, there is a response from resourceful criminals across the world. Even with biometrics, money launderers will attempt synthetic biometrics or general hacking.
Still, with legacy players lagging behind and focused on traditional methods of fraud prevention, advances by FinTechs will make them the less desirable adversary and push much of the challenges to the less equipped competitors.
Do customers actually want this?
Technological advances are great, but only valuable if customers buy in. People are growing accustomed to mobile biometrics – from unlocking their devices with the touch of a finger to using facial or voice recognition to make purchases.
Not only are users comfortable with this type of authentication, they prefer it. About 93% of consumers would rather use biometrics than passwords. This represents a mindset shift to methods that are based on who a person is, not what information they can remember.
Implementing biometrics as a security measure also helps to build trust with users, especially since more than three-quarters (78%) of customers believe a mobile app with biometrics is more secure compared to apps without it. With that trust, comes the opportunity to drive increased brand loyalty and better user experience.
There are many innovations on the road ahead, but few stand to impact remittances – and FinTech as a whole – like biometrics. With more users, the continued proliferation of mobile devices and the relentless threat of cybercrime, FinTechs are poised to push past banks as the preferred and most trusted custodians of consumer funds.


#math #computer_science #startup
#speedcubing #powerlifting #cinema
August 29, 2017
August 15, 2017
December 07, 2016


Highlights! Former UFC fighter Ross Pearson KO’d by stunning rolling thunder kick
by Dan HiergesellFormer UFC fighter Ross Pearson can’t seem to catch a break these days, even in his debut for Probellum MMA.
Pearson, 35, entered the cage earlier tonight (Sat., Nov. 16, 2019) at Probellum 1 in London, England, in search of his first professional win since defeating Mizuto Hirota via decision nearly two years ago. Not only did “Real Deal” leave empty handed but he suffered the most brutal knockout defeat of his 15-year MMA career.
Pearson’s Probellum debut came against French veteran Davy Gallon, who took the former UFC fighter into the third round. While the two fighters circled one another on the feet Gallon launched a ridiculous rolling thunder kick. Fight fans are used to seeing this type of move used at the end of a round with very limited success, but the attack connected flush with Pearson and immediately cut the lights off.
Check the finish out below:
Pretty intense, huh?
While this is easily one of the best knockouts of 2019 it’s an extremely unfortunate beginning to Pearson’s Probellum MMA career. The accomplished English fighter had compiled 26 fights in UFC prior to this bout with Gallon, who has been fighting on the open circuit his entire career, but it didn’t matter.
Pearson got caught and Gallon walked away with one of the best finishes you will ever see.


HE MAY BE DEAD BUT HE'S STILL ELVIS A film review by Thomas E. Billings Copyright 1989 Thomas E. Billings
"He May Be Dead, But He's Still Elvis"
Directed by: John Myrhe Distributed by: Offensive Films Length: 10-15 minutes (approx); color
Reviewed by: Thomas E. Billings; Dept. of Statistics; University of California, Berkeley Reviewer contact<EMAIL_ADDRESS>This gem of a short film is a biting satire of the music business, and the exploitation of dead rock stars. The film begins with a commercial for the new album recorded by the dead Elvis Presley, a double album that is silent [no music; remember - he's dead!]. It's titled "Presley Comes Alive," and we are exhorted to buy, buy, buy now, now, now! There follows a press conference where it is announced that, to counteract the drop in sales of Elvis memorabilia, the promotion company, Elvis Exploitation, Inc., has dug up his corpse. The decomposing (blue, zombie-like) corpse of Elvis Presley is then unceremoniously dumped on a table at the press conference. The corpse is then sent around the country on various money-making schemes. All this happens in the first 3 minutes of this film!
There follows more humor in the same vein. We see scenes from a new movie starring the dead Elvis, "Necrophilia, Hawaiian-Style." There's more to the film than just the above points, and the ending is grotesque, but extremely funny. I will not spoil the film by revealing any more details.
The basic premise of this film is really sick. It is a low budget film, and it looks low budget. The humor is sophomoric and sick at times. Now forget what I just said and go see it! This is an outrageously funny satire; one of the funniest short films that I have ever seen!
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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 = 10
"""
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
real_inputs = tf.placeholder(tf.float32, shape=(None, image_width, image_height, image_channels))
z_inputs = tf.placeholder(tf.float32, shape=(None, z_dim))
learning_rate = tf.placeholder(tf.float32)
return real_inputs, z_inputs, 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 variabes 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 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):
# image: 28x28x[1|3]
x = tf.layers.conv2d(images, 128, (5,5), (2,2), padding='same', activation=None)
x = tf.maximum(0.2 * x, x)
# 14x14x64 now
x = tf.layers.conv2d(x, 256, (5,5), (2,2), padding='same', activation=None)
x = tf.layers.batch_normalization(x, training=True)
x = tf.maximum(0.2 * x, x)
# 7x7x128 now
x = tf.layers.conv2d(x, 512, (5,5), (2,2), padding='valid', activation=None)
x = tf.layers.batch_normalization(x, training=True)
x = tf.maximum(0.2 * x, x)
# 2x2x512 now
# print(x.shape)
flat = tf.reshape(x, [-1, 4*4*512])
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
#print([var.name for var in tf.trainable_variables()])
#tmp = discriminator(tf.zeros([1, 28, 28, 3], tf.float32), reuse=True)
"""
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 variabes 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):
x = tf.layers.dense(z, 2*2*512, activation=None)
x = tf.reshape(x, shape=[-1,2,2,512])
x = tf.layers.batch_normalization(x, training=is_train)
x = tf.maximum(alpha * x, x)
# 4x4x256 now
#print("x: ", x.shape)
conv1 = tf.layers.conv2d_transpose(
x, 256, (5,5), (2,2), padding='valid', activation=None)
conv1 = tf.layers.batch_normalization(conv1, training=is_train)
conv1 = tf.maximum(alpha * conv1, conv1)
# 7x7x128 now
#print("conv1: ", conv1.shape)
conv2 = tf.layers.conv2d_transpose(
conv1, 128, (5,5), (2,2), padding='same', activation=None)
conv2 = tf.layers.batch_normalization(conv2, training=is_train)
conv2 = tf.maximum(alpha * conv2, conv2)
# 14x14x64 now
#print("conv2: ", conv2.shape)
# Output layer, 28x28x3
logits = tf.layers.conv2d_transpose(
conv2, out_channel_dim, (5,5), (2,2), padding='same', activation=None)
# 28x28x3 now
#print("logits: ", logits.shape)
out = tf.tanh(logits)
return out
#tf.reset_default_graph()
#print("================")
#tmp = generator(tf.zeros([1, 100], tf.float32), 3)
#print("================")
"""
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)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_real, labels=tf.ones_like(d_model_real)*0.9))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_fake, labels=tf.ones_like(d_model_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
#tf.reset_default_graph()
print(data_shape)
input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
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):
batch_images = batch_images*2
steps += 1
# TODO: Train Model
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})
if steps % 100 == 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, epoch_count),
"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 % 300 == 0:
show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


Jeremiah 51:18
They are vanity, the work of errors: in the time of their visitation they shall perish.


FD
Section: Linux Programmer's Manual (4)
Updated: 2014-05-10
Index
Return to Main Contents
NAME
fd - floppy disk device
CONFIGURATION
Floppy drives are block devices with major number 2.
Typically they
are owned by
root.floppy
(i.e., user root, group floppy) and have
either mode 0660 (access checking via group membership) or mode 0666
(everybody has access).
The minor
numbers encode the device type, drive number, and controller number.
For each device type (that is, combination of density and track count)
there is a base minor number.
To this base number, add the drive's
number on its controller and 128 if the drive is on the secondary
controller.
In the following device tables, n represents the
drive number.
Warning: If you use formats with more tracks
than supported by your drive, you may cause it mechanical damage.
Trying once if more tracks than the usual 40/80 are supported should not
damage it, but no warranty is given for that.
If you are not sure, don't create device
entries for those formats, so as to prevent their usage.
Drive-independent device files which automatically detect the media
format and capacity:
5.25 inch double-density device files:
| Name | Capac. | Cyl. | Sect. | Heads | Base minor #
|
|
| fdnd360 | 360K | 40 | 9 | 2 | 4
|
5.25 inch high-density device files:
| Name | Capac. | Cyl. | Sect. | Heads | Base minor #
|
|
| fdnh360 | 360K | 40 | 9 | 2 | 20
|
| fdnh410 | 410K | 41 | 10 | 2 | 48
|
| fdnh420 | 420K | 42 | 10 | 2 | 64
|
| fdnh720 | 720K | 80 | 9 | 2 | 24
|
| fdnh880 | 880K | 80 | 11 | 2 | 80
|
| fdnh1200 | 1200K | 80 | 15 | 2 | 8
|
| fdnh1440 | 1440K | 80 | 18 | 2 | 40
|
| fdnh1476 | 1476K | 82 | 18 | 2 | 56
|
| fdnh1494 | 1494K | 83 | 18 | 2 | 72
|
| fdnh1600 | 1600K | 80 | 20 | 2 | 92
|
3.5 inch double-density device files:
| Name | Capac. | Cyl. | Sect. | Heads | Base minor #
|
|
| fdnu360 | 360K | 80 | 9 | 1 | 12
|
| fdnu720 | 720K | 80 | 9 | 2 | 16
|
| fdnu800 | 800K | 80 | 10 | 2 | 120
|
| fdnu1040 | 1040K | 80 | 13 | 2 | 84
|
| fdnu1120 | 1120K | 80 | 14 | 2 | 88
|
3.5 inch high-density device files:
| Name | Capac. | Cyl. | Sect. | Heads | Base minor #
|
|
| fdnu360 | 360K | 40 | 9 | 2 | 12
|
| fdnu720 | 720K | 80 | 9 | 2 | 16
|
| fdnu820 | 820K | 82 | 10 | 2 | 52
|
| fdnu830 | 830K | 83 | 10 | 2 | 68
|
| fdnu1440 | 1440K | 80 | 18 | 2 | 28
|
| fdnu1600 | 1600K | 80 | 20 | 2 | 124
|
| fdnu1680 | 1680K | 80 | 21 | 2 | 44
|
| fdnu1722 | 1722K | 82 | 21 | 2 | 60
|
| fdnu1743 | 1743K | 83 | 21 | 2 | 76
|
| fdnu1760 | 1760K | 80 | 22 | 2 | 96
|
| fdnu1840 | 1840K | 80 | 23 | 2 | 116
|
| fdnu1920 | 1920K | 80 | 24 | 2 | 100
|
3.5 inch extra-density device files:
| Name | Capac. | Cyl. | Sect. | Heads | Base minor #
|
|
| fdnu2880 | 2880K | 80 | 36 | 2 | 32
|
| fdnCompaQ | 2880K | 80 | 36 | 2 | 36
|
| fdnu3200 | 3200K | 80 | 40 | 2 | 104
|
| fdnu3520 | 3520K | 80 | 44 | 2 | 108
|
| fdnu3840 | 3840K | 80 | 48 | 2 | 112
|
DESCRIPTION
fd special files access the floppy disk drives in raw mode.
The following
ioctl(2)
calls are supported by fd devices:
- FDCLRPRM
-
clears the media information of a drive (geometry of disk in drive).
- FDSETPRM
-
sets the media information of a drive.
The media information will be
lost when the media is changed.
- FDDEFPRM
-
sets the media information of a drive (geometry of disk in drive).
The media information will not be lost when the media is changed.
This will disable autodetection.
In order to reenable autodetection, you
have to issue an FDCLRPRM.
- FDGETDRVTYP
-
returns the type of a drive (name parameter).
For formats which work
in several drive types, FDGETDRVTYP returns a name which is
appropriate for the oldest drive type which supports this format.
- FDFLUSH
-
invalidates the buffer cache for the given drive.
- FDSETMAXERRS
-
sets the error thresholds for reporting errors, aborting the operation,
recalibrating, resetting, and reading sector by sector.
- FDSETMAXERRS
-
gets the current error thresholds.
- FDGETDRVTYP
-
gets the internal name of the drive.
- FDWERRORCLR
-
clears the write error statistics.
- FDWERRORGET
-
reads the write error statistics.
These include the total number of
write errors, the location and disk of the first write error, and the
location and disk of the last write error.
Disks are identified by a
generation number which is incremented at (almost) each disk change.
- FDTWADDLE
-
Switch the drive motor off for a few microseconds.
This might be
needed in order to access a disk whose sectors are too close together.
- FDSETDRVPRM
-
sets various drive parameters.
- FDGETDRVPRM
-
reads these parameters back.
- FDGETDRVSTAT
-
gets the cached drive state (disk changed, write protected et al.)
- FDPOLLDRVSTAT
-
polls the drive and return its state.
- FDGETFDCSTAT
-
gets the floppy controller state.
- FDRESET
-
resets the floppy controller under certain conditions.
- FDRAWCMD
-
sends a raw command to the floppy controller.
For more precise information, consult also the <linux/fd.h> and
<linux/fdreg.h> include files, as well as the
floppycontrol(1)
manual page.
FILES
/dev/fd*
NOTES
The various formats permit reading and writing many types of disks.
However, if a floppy is formatted with an inter-sector gap that is too small,
performance may drop,
to the point of needing a few seconds to access an entire track.
To prevent this, use interleaved formats.
It is not possible to
read floppies which are formatted using GCR (group code recording),
which is used by Apple II and Macintosh computers (800k disks).

This used to be common with older 8-inch floppies.
SEE ALSO
chown(1),
floppycontrol(1),
getfdprm(1),
mknod(1),
superformat(1),
mount(8),
setfdprm(8)
Index
- NAME
-
- CONFIGURATION
-
- DESCRIPTION
-
- FILES
-
- NOTES
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 02:55:19 GMT, September 18, 2014


HEMORRHOIDAL- mineral oil, petrolatum, phenylephrine hydrochloride and shark liver oil ointment
Meijer Distribution Inc
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
Active ingredient
Mineral oil 14%
Petrolatum 71.9%
Phenylephrine HCl 0.25%
Shark liver oil 3.0%
Purpose
Protectant
Vasoconstrictor
Uses
- helps relieve the local itching and discomfort associated with hemorrhoids
- temporarily shrinks hemorrhoidal tissue and relieves burning
- temporarily provides a coating for relief of anorectal discomforts
- temporarily protects the inflamed, irritated anorectal surface to help make bowel movements less painful
Warnings
For external and/or intrarectal use only
Ask a doctor before use if you have
- heart disease
- high blood pressure
- thyroid disease
- diabetes
- trouble urinating due to an enlarged prostate gland
Ask a doctor or pharmacist before use if you are
presently taking a prescription drug for high blood pressure or depression.
When using this product
do not exceed the recommended daily dosage unless directed by a doctor.
Stop use and ask a doctor if
- bleeding occurs
- condition worsens or does not improve within 7 days
- introduction of the applicator into the rectum causes additional pain
If pregnant or breast-feeding,
ask a health professional before use.
Keep out of reach of children.
If swallowed, get medical help or contact a Poison Control Center right away. (1-800-222-1222)
Directions
- adults: when practical, cleanse the affected area by patting or blotting with an appropriate cleansing pad
- gently dry by patting or blotting with toilet tissue or a soft cloth before applying ointment
- when first opening the tube, puncture foil seal with top end of cap
- apply to the affected area up to 4 times daily, especially at night, in the morning or after each bowel movement
- for intrarectal use:
- remove cover from applicator and attach applicator to tube
- lubricate applicator well, then gently insert applicator into the rectum
- thoroughly clean applicator after each use and replace cover
- also apply ointment to external area
- children under 12 years of age: ask a doctor
Other information
store at 68°-77°F (20°-25°C)
Inactive ingredients
beeswax, benzoic acid, BHA, corn oil, glycerin, lanolin, lanolin alcohol, methylparaben, paraffin, propylparaben, purified water, thyme oil, tocopherol
Questions or comments?
1-800-719-9260
Principal Display Panel
Prevents Further Irritation
Prompt Soothing Relief from Painful Burning, Itching and Discomfort
Shrinks Swollen Hemorrhoidal Tissue
Protects Irritated Tissue
Relieves Internal and External Discomfort
Compare to Preparation H® active ingredients
Hemorrhoidal Ointment
Prevents Further Irritation


 They might be interested in coding, and it's never too early or too late to start! The computer science industry is rapidly growing, and learning to code at a young age gives your child an edge in an increasingly competitive workforce. Coding also fosters incredible creativity and builds up critical thinking ability, both important life skills to have. We know parents always want what's best for their kids, so here are some resources to start your child off on their coding adventure. These are all great sites designed to make learning a blast instead of a bore!
Code.org has material for all ages, including those as young as 4, to those in middle or even high school. There is a gallery of projects made by other users that can be "remixed" by anyone, giving them the chance to learn the code for themselves. It is all layed out in a simple manner that is easy for a child to follow!
Visit this site!This site, for ages 5-17, allows users to learn Python or Javacript through an RPG-roleplaying game. This fun and innovative take on coding guides kids through different worlds as they progress, making learning programming fun!
Visit this site!Bringing a competitive game aspect into coding, this free site uses the Japanese martial arts practice of kata as an incentive to learn how to code in the language of your choice-they have a lot to choose from!. Geared towards older kids, you have the ability to look at other users' code, allowing a deeper understanding of the language and process.
Visit this site!


Step indicators are used to represent an ordered, sequential process. They can be used as navigation, or just as a visual indicator for where a user is within a process.
Horizontal
Applicant information
Mark information
Goods and services
Vertical
The veritcal style is useful for navigation or for “scroll spy” behavior.


@Sync @CallingThread fun getSavedNetworks(): List<WifiConfiguration>?
Overrides SavedNetworkApi.getSavedNetworks
To retrieve a list of saved networks on a user's device.
Return
List of WifiConfiguration|null - List of saved networks on a users device
See Also
WiseFyPrechecks.getSavedNetworksChecks
WiseFySearch.findSavedNetworksMatchingRegex
Author
Patches
Since
3.0
@Async @WiseFyThread fun getSavedNetworks(callbacks: GetSavedNetworksCallbacks?): Unit
Overrides SavedNetworkApi.getSavedNetworks
To retrieve a list of saved networks on a user's device.
callbacks
- The listener to return results to
See Also
WiseFyPrechecks.getSavedNetworksChecks
WiseFySearch.findSavedNetworksMatchingRegex
Author
Patches
Since
3.0


--- title: "Medicinal Garden - Past Themes" layout: "default" ---
Nestled in the middle of Red Butte Garden’s three Terrace Gardens, between the Fragrance and Herb Gardens, this space is home to plants known or believed to have medicinal properties. The Medicinal Garden was originally built and planted in 1996 around a planetree (Platanus), under which, according to legend, Hippocrates taught his pupils the art of medicine on the Greek island of Kos.
In order to make this garden area more relevant and interesting, the Red Butte Garden horticulture staff will highlight a different medicinal plant theme every year. This is also in line with the Garden's mission to connect people with plants and the beauty of living landscapes.


Mr. James Nicholas Calabar, Dear Prospective Business Partner, I am e-mailing to you this business proposition having gone through your remarkable profile on the internet. Though, this medium (Internet) has been greatly abused, I choose to reach you through it because it still remains the fastest, surest and most secured means for me to communicate with you at this time. However, this correspondence is unofficial and private, and it should be treated as such. Official telephone calls and fax messages to me WILL NOT be entertained for security reasons and for the success of this project. For further briefing, you can reach me only via my private email address as stated below or through my lawyer whose contact shall be provided to you in due cause. My name is Mr. James Nicholas. I am the chief financial controller to Mr. Charles Taylor, the ex-president of Liberia who is presently on exile in Nigeria. As his private financial controller, I have access to his financial records more than any of his family members. Mr Charles Taylor has over $1 billion dollars outside Liberia and his family members or people around him are not aware of this. With his present notion of arrest and following his restricted movement and the recent proclamation by Zurich authority, the U.N war crimes court and the America Government, has agreed that all Bank account belonging to Charles Taylor should be reported and freezed. Therefore I want to use this opportunity to make good money for myself. I have worked for him for over 10 years and I have nothing to show for it. I am only leaving on his empty promises and hopes. Presently, I have complete information to authorize with documents for collecting /receiving $200 million from one of his financial sources in South Africa. And I need your assistance in receiving the funds in Europe on my behalf. Charles Taylor has prepared himself legally and secretly arranged with Top most Government agents to transfer the funds upon instructions. In light of the above, I am seeking your cooperation to act as the authorized beneficiary of the said funds to enable us claim the funds for our mutual benefit. As you probably already aware, Mr. Charles Taylor maybe handed over by his host country to the new Liberia Government to face trial soon hence the need for urgent attention to this matter. Your prompt responce to my proposal will be highly appreciated. As soon as I receive your geniue intention to cooperate with me, I will have my lawyer provide you with the necessary details as required to enable us conclude this transaction successfully. I thank you and look forward to hearing from you shortly. Regards James Nicholas please reply via my private e-mail below. e-mail<EMAIL_ADDRESS>.......................................... Because of charles Taylors notion of arrest, and with the present development the security alert-ness is higher, so I will introduced my lawyer to you upon your responce to my proposal.


Syntax:
logbitp index integer => generalized-boolean
Arguments and Values:
index---a non-negative integer.
integer---an integer.
generalized-boolean---a generalized boolean.
Description:
logbitp is used to test the value of a particular bit in integer, that is treated as if it were binary. The value of logbitp is true if the bit in integer whose index is index (that is, its weight is 2^index) is a one-bit; otherwise it is false.
Negative integers are treated as if they were in two's-complement notation.
Examples:
(logbitp 1 1) => false (logbitp 0 1) => true (logbitp 3 10) => true (logbitp 1000000 -1) => true (logbitp 2 6) => true (logbitp 0 6) => false
Side Effects: None.
Affected By: None.
Exceptional Situations:
Should signal an error of type type-error if index is not a non-negative integer. Should signal an error of type type-error if integer is not an integer.
See Also: None.
Notes:
(logbitp k n) == (ldb-test (byte 1 k) n)


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J602 Features
Following is from the J602 Release Highlights and Overview
If your are familiar with the previous release (J601) see J602 Features for an overview of what's new.
If your are familiar with J50x, see J601 Features for an overview of those changes before reading J602 features.
J has a console interface called Jconsole and a GUI interface called Jwd. Use Jwd for an easier introduction to both standard and new features. In Windows start Jwd with the blue J icon in the J program group. In Linux start Jwd by running jwd in the J bin folder (for example, ~/j602/bin/jwd). In Mac start Jwd with the red J icon in the J folder.
The system includes 3 online books that are introductions: J Primer by Eric Iverson, J for C Programmers by Henry Rich, and Learning J by Roger Stokes. They vary in approach and you can probably benefit from reading all three. Hopefully you will find one which matches your requirements and outlook.
Take the time to read the User Manual chapter J System Overview.
Whether you are new to J or an old hand, it is a good idea to start by exploring the Jwd menu. Browse the Help menu to get an idea of what is there and how to navigate. Try Studio|Demos to get an idea of what J can do. And do some of the labs in Studio|Labs.
www.jsoftware.com, www.jsoftware.com/jwiki, and the J forums are J resources. Take some time and explore J on the web.
J602 features
J Engine
See Release Notes (Help|Release Notes) for a summary of J Engine changes.
M. memoization will be of interest to some users.
_. (IEEE floating point NaN (Not A Number)) treatment has changed. See Release Notes and Indeterminate for details.
IDE
Readonly is default for ijs (script edit) window. Edit|Read Only (Ctrl+T) toggles readonly. Edit|Configure|Session sets file categories to open readonly.
Menu File|Open creates and opens a non-existent file name.
Prompts to delete or save temporary scripts (n.ijs for integer n) are removed. They are saved as required and are not deleted unless empty.
Session close dialog is shown if CONFIRMCLOSE_j_ is set to 1. The default value is 1 and can be changed in Edit|Configure|Session.
Installation
Installation has been changed to be simpler for each platform.
A standard install puts the J install and user folder in your home folder. For 32 bit systems the install folder is j602 and the user folder is j602-user. For 64 bit they are j64-602 and j64-602-user. Vista home is similar to Unix. XP home is "\Documents and Settings\username".
A standard install in home rather than a system folder such as "Program Files" or /usr... requires no admin privilege for either the install or for JAL updates.
See Install for details.
Package Manager (pacman)
Package Manager is the extensibility platform for J programming systems, consisting of the core system library and addons contributed by J users. It features online package updating from portal www.jsoftware.com with addons documentation, source versioning, build and distribution systems. Pacman is sometimes referred to as J Application Library (JAL).
Pacman is a critical step that opens J development to shared contributions from users. We urge you to take a look at what is already available and to take the time and effort to provide your own contributions back to the J community.
Pacman beta versions that ran on J601 required sometimes problematic install steps and configuration. J602 JAL should run 'out of the box' on all platforms without any extra steps or configs. In part this was done by providing the new tools folder that includes the zip and wget tools required in Windows.
Treeview Control
Treeview control (built on isigraph as are grid and plot controls) is now available. See treeview demo.
Splitter Control
Splitter control (built on isigraph) is now available. This control was developed by Oleg Kobchenko. See splitter demo.
cd (shared library call) enhanced - PCALL
The cd enhancements suggested and modeled by Oleg Kobchenko with his PCALL facility have been implemented. The changes allow cd to call a procedure directly by its address (rather than indirectly through module and procedure names). It also allows calls of objects with a THIS pointer and a vtable.See Calling Procedures for details.
Application Distribution - Installer
New lab "Application Distribution - Installer" presents a way to packaging simple J applications for distribution. The core idea is to use the same installer as J (Windows exe, Linux sh, or Mac dmg) with a zip of all the system and application files in the same standard folder structure as in the development system.
J Engine Protocol - Decomitted
The major user of the J Engine Protocol in J50x was the Unix Jwdp front end that used the J Engine in a separate task with sockets. This architecture had serious performance limitations and was replaced in J60x with the direct use by the Java Jwdp front end of the J Engine as a shared library.This new architecture has proven so successful that continuing support for the J Engine Protocol isn't justified and it is decommitted.
Applications that used JEP should replace it with either direct use of the J Engine as a shared library or with a much simpler application specific socket protocol between the client and server. For example, they could be reworked to use the facilities shown in Lab Client/Server (Simple Socket).
J Front End - Example Source Code
A simple and complete source example of a console J Front End is available in folder system/examples/jfe. This source demonstrates all aspects of the J Engine shared library API. In particular it shows output and wd callbacks to the front end.
bin Folder
Files for J Front Ends and the J Engine are now in the J bin folder.
tools Folder
The tools folder has utilities (binaries and related files) used by J. The jpcre shared library for regex has been moved to the tools/regex folder. The J Windows folders tools/zip and tools/ftp contain the files that JAL requires that are available standard in Unix.
BINPATH, PROFILE, 1!:45
J Front End sets BINPATH_z_ as the fullpath of the folder that contains the JFE. This is the J bin folder with the the JFE, J Engine, profile.ijs, etc. The J folder is back one level and contains the addons, help, system, and tools folders in addition to the bin folder.
INSTALLFOLDER_z_ and PROFILE_z_ are no longer set.
Foreign 1!:45 (full path of profile.ijs) is decommitted.
 See
Install for details.
wd editm control
The Windows limit that prevented typing in an editm control with more than 30000 characters has been removed.
wd setlocale command
setlocale is a new wd command that sets a locale name for a control so that events run wdhandler in the control locale instead of the form locale.
wd'setlocale id locale'
This allows defining control event handlers in the control locale (usually a numbered control object locale) rather than in the parent locale (usually a numbered form object locale). Previously you had to define handlers in the form object locale that passed events on to the control object handlers.
setlocale works for all controls but is probably most useful with control objects based on an isigraph.
syslocalec is a new entry in the wdq result that is the child locale.
syslocalep is a new entry in the wdq result that is the parent locale. The old entry syslocale is no longer set. Replacing syslocale with syslocalep is compatible with other naming conventions.
A handler in a control object locale can use syslocalep to access the forms object for services and data. For example, to call a routine in the parent to inform it of changes in the control (perhaps to update another control).
JEXEServer and JDLLServer
J OLE automation server changes are covered in J OLE Automation Server. The main change is in the steps necessary to load profile.
New J Icons
The new J icons (J folder bin\icons) in various formats, sizes, and colors were created for Jsoftware by Ric Sherlock. Thanks!
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--- layout: post title: Code Avaiable tags: - Would u prefer... status: publish type: post published: true meta: _edit_last: "1" --- For anyone who's intreseted in how woulduprefer.com works the source code is freely avaiable on my git hub.


VFP (the Czech Exchange Format for Land Consolidation) is a light-weight XML data format for the interchange of Land Consolidation data between applications in the Czech Republic.
OGR has support for VFP reading if GDAL is build with expat library support.


Classes
The abstract bitbucket api class.
The addon api class.
The abstract addon api class.
The linkers api class.
The abstract linkers api class.
The values api class.
The bitbucket api interface.
The current user api class.
The hook events api class.
The repositories api class.
The abstract repository api class.
The users api class.
The abstract users api class.
The branch restrictions api class.
The commit api class.
The abstract commit api class.
The approval api class.
The comments api class.
The statuses api class.
The abstract statuses api class.
The build api class.
The branch restrictions api class.
The components api class.
The default reviewers api class.
The diffs api class.
The downloads api class.
The file history api class.
The forks api class.
The hooks api class.
The issues api class.
The abstract issues api class.
The attachments api class.
The changes api class.
The comments api class.
The voting class.
The watching class.
The milestones api class.
The patches api class.
The pipelines api class.
The pipelines config api class.
The abstract pipelines config api class.
The build number api class.
The schedules api class.
The abstract schedules api class.
The executions api class.
The ssh api class.
The abstract ssh api class.
The key pair api class.
The known hosts api class.
The variables api class.
The abstract pipelines api class.
The steps api class.
The properties api class.
The pull requests api class.
The abstract pull requests api class.
The approval api class.
The comments api class.
The commits api class.
The diff api class.
The patch api class.
The statuses api class.
The refs api class.
The abstract refs api class.
The branches api class.
The tags api class.
The src api class.
The versions api class.
The watchers api class.
The snippets api class.
The abstract snippets api class.
The users api class.
The abstract users api class.
The comments api class.
The commits api class.
The diffs api class.
The files api class.
The patches api class.
The watchers api class.
The watching api class.
The teams api class.
The abstract team api class.
The followers api class.
The following api class.
The hooks api class.
The members api class.
The permissions api class.
The pipelines config api class.
The abstract pipelines config api class.
The variables api class.
The projects api class.
The repositories api class.
The users api class.
The abstract user api class.
The followers api class.
The following api class.
The hooks api class.
The pipelines config api class.
The abstract pipelines config api class.
The variables api class.
The repositories api class.
The ssh keys api class.
The Bitbucket API 2.0 client.
This is the api limit exceeded exception class.
This is the bad request exception class.
This is the client error exception class.
This is the decoding failed exception class.
This is the bitbucket exception interface.
This is the invalid argument exception class.
This is the runtime exception class.
This is the server error exception class.
This is the validation failed exception class.
The Bitbucket HTTP client builder class.
This is the file resource class.
This is the response mediator class.
A plugin to add authentication to the request.
A plugin to throw bitbucket exceptions.
A plugin to remember the last response.
This is the result pager class.
This is the result pager interface.


This tool calculates Euclidean distance, direction, and allocation from a single source or set of sources.
Example applications include answering questions such as the following:
If Use current map extent is checked, only those features in the input layer that are visible within the current map extent will be analyzed. If unchecked, all features in the input layer will be analyzed, even if they are outside the current map display extent.
The layer that defines the sources to which the distance will be calculated. The layer can be an image service or a feature service.
For an image service, the input type can be integer or floating point.
For a feature service, the input can be point, line, or polygon.
The maximum distance to be calculated. For example, if you provide a value of 10,000 meters, the Euclidian distance will be calculated from the source locations out to that value (distance).
If no value is provided, the distance will be calculated out to the defined extent.
The available units are Kilometers, Meters, Miles, and Feet.
The cell size and unit for the output rasters.
The units can be kilometers, meters, miles, or feet.
The name of the layer that will be created in My Content and added to the map. The default name is based on the tool name and the input layer name. If the layer already exists, you will be prompted to provide another name.
You can specify the name of a folder in My Content where the result will be saved using the Save result in drop-down box.
The name of the layer that will be created in My Content. You must provide a name that does not currently exist in My Content.
You can specify the name of a folder in My Content where the result will be saved using the Save result in drop-down box.
The name of the layer that will be created in My Content. You must provide a name that does not currently exist in My Content.
You can specify the name of a folder in My Content where the result will be saved using the Save result in drop-down box.
A field on the input source layer that holds the values that define each source. It can be an integer or a string field of the source dataset.
The default is Value. If using a feature service and no Value field is available, then the first available integer or string field will be used. If no valid fields exist, the ObjectID field (for example, OID or FID, depending on the type of feature input) will be used.


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Teacher's name - Class name
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This library can be used for custom integration projects where the standard BoxCast embedded player may not suffice.
We do strongly encourage you to use the standard embedded player instead of this library where possible, because it is the most robust and well-tested playback option.
See Related:
<script src="https://js.boxcast.com/libs/boxcast-sdk-js-2.0.2/browser.js"></script>
<script>
const { analytics, api } = new BoxCastSDK();
</script>
or, install via NPM
/* Important: If you are running this on node, use this: */
import BoxCastSDK from "boxcast-sdk-js/node";
/* otherwise, if you are running this in a browser, use this: */
import BoxCastSDK from "boxcast-sdk-js/browser";
const { analytics, api } = new BoxCastSDK();
You will need to know your BoxCast account ID and corresponding channel IDs in order to properly utilize this library. Contact BoxCast developer support if you need assistance.


WITNESS LIST Judiciary Committee May 16, 2005-2:00P SB 103 Registering, but not testifying: For: Robinson, Jim (Texas Assn. of Appraisal Dists.) Winn, Gerald "Buddy" (Self and Texas Assoc. of Appraisal Districts) SB 729 On: Reynolds, Carl (Office of Court Administration- Director) SB 978 For: Lippincott, Chris (Texas Association Against Sexual Assault) Against: Pitchinson, MIchael (Texas Conference of Urban Counties) Registering, but not testifying: For: Sarate, Sebastian (Self and People Against Violent Crime (PAVC), Verna Lee Carr People Against Violent Crime (PAVC) & Janice Sa) Williams, Julie (American Collectors Association of Texas) On: Reynolds, Carl (Office of Court Administration- Directior) SB 1122 For: Nugent, Jerry (Texas Board of Law Examiners) Strickland, Jack (Self and Tx Board of Law Examiners- Chairman) Registering, but not testifying: On: Shaheen, Rodney A. (Self and Texas Board of Law Examiners) Vaughan, Julia (Texas Board of Law Examiners) SB 1147 On: Reynolds, Carl (Office of Court Administration- Director) SB 1459 For: Patterson, Jan (Self) Against: Law, Ken (Court of Appeals, 3rd District) SB 1578 Against: Beavers, Walker (Self) On: Wendlandt, Cavitt (Office of the Attorney General) Registering, but not testifying: For: Lenz, Carole (Harris County Precinct 3- Commissioner Steve Radack) On: Ward, Randy (Texas Department of Transporation) SB 1597 For: Powell, Leilah (Bexar County Commissioners Court) Registering, but not testifying: For: Wolff, Nelson M. (Bexar County Commissioners Court) SB 1832 For: Lenz, Carole (Harris County & Harris County Pct. 3- Commissioner Steve Radack)


Image de-skew
Detect & fix image skew.
code
Github
cloud_download
npm
save
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Samples
Try these built in samples:
Skewed Image
Check
Normal Image
Check
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Get Started Now


--- ### 2005 Grand Marquis Factory Aero Concept #### Original: ![Alt text](img/dark-beige-before.jpg) #### Aero added using Pixelmator: ![Alt text](img/dark-beige-after.jpg) --- Copyright © wheelsandbytes 2015-2017. All Rights Reserved