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Located on the most western edge of the island of Maui, Lahaina is noted as the sunniest spot in Hawaii and is a popular destination for cruisers.
Position 20°52.1'N, 156°41'W.
All boats, including dinghies, must call the Harbourmaster on VHF 16 before entering the harbour.
The harbour is easily identified by locating the tall chimney with a black band at the top. It is about half a mile north of the harbour entrance. The gap through the reef is buoyed.
Yachts prefer to come here after completing formalities at Kahului commercial harbour.
There are no visitors' berths in the marina, although the Harbour master will try and find a berth for yachts needing repairs.
Yachts can anchor off the town but the holding is poor as there is only a thin layer of sand over dead coral.
The best option is to use one of the 7 free Lahaina Yacht Club mooring (white balls and marked LYC). Check the security of the mooring and then check in with the yacht club on Front Street. The anchorage is rolly here so it is best to rig an anti-roll device of some type.
A calmer anchorage can be found a little further north at the far end of the town.
There is a dinghy dock where they can be left for a fee.
Last updated July 2011.


Clustering
Please enter the number of clusters, then choose a CSV file with two columns and click submit.
Number of clusters:


Home
Neighborhood hardware store specializing in key services, Valspar paint, custom screen and glass services and plumbing fixtures and tools. Peterson’s Hardware Store has been part of the community since 1958. We are here to help you with any hardware, plumbing, and household needs. We also repair windows,screens, cut glass, mix paints, and make keys. As a mom and pop store we are grateful for your business and are open 7 days a week for whatever you need during these hard times.
4831 S Western Ave #2325, Los Angeles, CA 90062
Hours: Monday 8AM–6PM Tuesday 8AM–6PM Wednesday 8AM–6PM Thursday 8AM–6PM Friday 8AM–6PM Saturday 8AM–5PM Sunday 9:30AM–2PM
Phone:<PHONE_NUMBER>


Future
public class Future<R> : Cancelable
A Callable
closure gets wrapped inside an instance of Future
which controls it’s execution and handles
it’s result or error.
-
Returns
true
if theFuture
has been canceled.Declaration
Swift
public var isCancelled: Bool { get }
-
The state of the Future.
Declaration
Swift
public var futureState: FutureState { get }
-
Get the results returned from the Future executing the
Callable
. Calling this method will block until theFuture
has been executed and either completed, thrown an error, or been canceled.Throws
Any error thrown by theCallable
orExecutorError.CallableCanceled
if theFuture
was canceled.Declaration
Swift
public func getResult() throws -> R
Return Value
The result from the
Future
‘s execution of theCallable
. -
Blocks the calling thread until the
Future
was completed execution or been canceled.Declaration
Swift
public func join()
-
Cancels the
Future
. If theFuture
has already completed or already been canceled then calling this method does nothing.Declaration
Swift
public func cancel()
-
DO NOT CALL THIS METHOD DIRECTLY!!!!! This method should only be called by the
Executor
to execute the scheduledCallable
.Declaration
Swift
public func execute(cancelable: inout Cancelable)
Parameters
cancelable
The object to monitor for cancelability.


This API includes functions for importing and exporting subsections of the config tree to and from a file.
This API also includes an iterator object that can be used to iterate over the list of trees currently known by the system.
An example of printing the list of trees in a system:
le_cfgAdmin_IteratorRef_t iteratorRef = le_cfgAdmin_CreateTreeIterator();
{
char treeName[MAX_TREE_NAME] = { 0 };
{
printf("Tree: \"%s\"\n", treeName);
}
else
{
printf("Tree: \"%s\", truncated.\n", treeName);
}
}
le_cfgAdmin_ReleaseTreeIterator(iteratorRef);
Copyright (C) Sierra Wireless Inc. Use of this work is subject to license.


MF Firmware related upgrade instructions
In the process of using the MF face recognition module, if there is a firmware bug, a new function firmware release, a mistakenly erased firmware, font resources, and image resources, then the firmware needs to be restored by burning.
MF face recognition module firmware, resource description
MF Face Recognition Module The firmware, font resources, picture resources, descriptions are as follows:
| Type | Burning Address | Length | Description |
|---|---|---|---|
| Firmware | 0x000000 | ||
| Face Model | |||
| Font Resources | |||
| Picture Resources | |||
| Board level configuration | 0x7FF000 | 4 KB | |
| Face Information | 0x800000 | 64 KB | |
After getting the MF module, the general developer does not need to care about the resource distribution
MF firmware description
VIS: Visible light (VIS)
IR: infrared light (IR)
| Firmware classification | Direction | Protocol | Identification method | Number of firmware types | |
|---|---|---|---|---|---|
| MF0 trial version module | horizontal version, vertical version | bin | vis | 2 | |
| MF1 offline module | horizontal and vertical | bin/json | vis+ir | 8 | |
| MF2 WeChat semi-finished product | Horizontal version, vertical version | bin | vis | Support | 2 |
| MF4 WeChat (finished product) | Vertical version | bin | vis | Support | 1 |
| MF5 WeChat (finished product) | Vertical version | bin/json | vis | Support | 2 |
- Font resources
- Picture resources
MF firmware burning (upgrade, restore factory configuration) steps
In the process of using the MF face recognition module, if there is a firmware bug, a new function firmware release, a mistakenly erased firmware, font resources, and image resources, then the firmware needs to be restored by burning.
Preparation:
Before upgrading and restoring the factory configuration MF face recognition module, we need to prepare software and hardware.
Hardware preparation:
MF Face Recognition Module
USB Type-C data cable
Software preparation:
Burning software: kflash_gui (Kflash_gui v1.6.5 version is used here)
Download link:
Github: kflash_gui
Burn resource files (burn according to the situation): MF face recognition module firmware, algorithm model files, font resource files, image resource files
Get the resource files and send a unified email to: Email<EMAIL_ADDRESS>The recommended email format is as follows:
Problem type: [MF firmware resource acquisition]
Use hardware/firmware version: [MF0/MF1/MF2/MF4/MF5]
Content: [Get the latest firmware/Firmware is missing/Model is missing (describe the relevant situation)]
Machine code: [xxxxxxxxxxxxxxxxxxxxx (only required when the model is lost)]
Burn firmware, resource files
Use the USB Type-C data cable to connect the MF face recognition module to the computer
Open Kflash_gui, select the file to be burned, select the version (default is automatic selection
, if the burn fails, select MaixDuino
), select the serial port number (CH522 has two serial ports, try the other one if it fails), configure the wave Special rate (default is 150000
, if programming fails, reduce the baud rate appropriately, such as 115200
)
How to get Key (Machine Code)
During use, if the model of the module is lost or needs to be replaced, you need to send the Key
to<EMAIL_ADDRESS>The recommended email format is as follows:
First download key_gen.bin
After using the kflash_gui
to burn the firmware ken_gen.bin
into the module, open the serial port and the baud rate is 115200,8,N,1
kflash_gui is K210 firmware burning tool, source code: Sipeed/kflash_gui
It is recommended to use XCOM
to view the serial port information
Tap DTR
and release it to reset the module and see the startup information
MF Firmware Version Description
Due to the different imaging directions of the cameras, there are two firmware versions: horizontal and vertical versions;
How to confirm the firmware corresponding to the camera: (identified by the camera silkscreen)
| Horizontal board | Vertical board |
|---|---|


Ethereum
Smart Contracts add conditions that are resolved automatically for transaction to occur.
A Smart Contract is executable code that rappresent some businnes logic.
Smart Contracts are embedded in the block chain and interaction occurs by mean of transactions.
Both Users and Smart Contracts can interact with Smart Contracts.
[ Previous ] ethereum [ Next ]


In our lab, we focus on studying many-body entanglement realized by ultracold atoms, which is a powerful tool in both fundamental researches and physical applications.
Entanglement can be used to characterize topological orders, which are new phases that cannot be described by the symmetry breaking. There are many interesting, important, and counter-intuitive phenomena that were discovered in the past few years, such as quantum Hall effects, topological insulators, and new types of superconductors. And we want to engineer an artificial, universal, and versatile platform in order to synthesize novel quantum matters that have never been found before.
On the other hand, entanglement is also a useful resource in the applications of quantum metrology and quantum computation. By integrating the quantum entanglement with precision measurement, we can design and build sensors that beyond the standard quantum limit, a fundamental limit of current precision measurement. We have created the world record in year 2015 (Nature 519, 439), that 3000 atoms are all entangled together with a negative probability distribution function measured. In the future, we will work on building larger systems with more particles entangled together and use them in precision measurement. Furthermore, we will work on apparatus as nodes of quantum networks and quantum computers, following the first single-photon transistor (Science 341, 768) we have invented a few years ago.
This platform is to realize the ability of manipulating each individual atom at low entropy, which can be achieved by optical tweezers and a new method of laser cooling (Science 358, 1078). By cooling one atom directly in each optical tweezers, we can re-assemble the atomic ensemble back to a condensate that has the absolute zero temperature. The symmetry of atoms array is no longer limited by the standing wave of optical lattices and it could be any type such as hexagonal lattice, five-folds quasi-crystal, and lattices with defects. Combining the techniques of quantum gas microscopes and Feshbach resonance, we can even create any type of materials according to our design with adjustable mutual interactions and correlations.
By integrating the ideas of machine learning (arXiv 1803.01786, in review), we can further recognize novel quantum phases with the assistance of artificial intelligence.
This experiment is to apply the quantum entanglement in metrology and sensing. We will design a new generation of optical resonators (Febry-Pérot cavities) to realize strong interaction between single photon and single atom. By using the cavity as a medium, each individual atom can interact with the other atoms with an infinite interaction-range, while the photons will also interact with each other strongly (Phys. Rev. Lett. 113, 113603). With the assistance of mutual strong interaction, the system will evolve into a deep-entangled state. The uncertainty during the measurement goes beyond the standard quantum limit that is defined by the central limit theorem in math. The entanglement will help us to beat the classical statistics and push the metrology into a new regime — the quantum era.
Meanwhile, by combining the up-to-date techniques such as digit mirror devices, quantum gas microscopes, and quantum devices, we can probe each atom individually or collectively. This will help us to figure out open questions in quantum physics, e.g., how entanglement (or information) is created, spread or annihilated.


{% extends "sentry/partial/client_config/python_base.html" %} {% load i18n %} {% block inner %}
{% blocktrans with 'http://pythonpaste.org/deploy' as link %}A filter factory for PasteDeploy exists to allow easily inserting Raven into a WSGI pipeline:{% endblocktrans %}
[pipeline:main] pipeline = raven tm MyApp [filter:raven] use = egg:raven#raven dsn =<EMAIL_ADDRESS>include_paths = my.package, my.other.package exclude_paths = my.package.crud
{% trans " In the [filter:raven] section, you must specify the entry-point for raven with the use = key. All other raven client parameters can be included in this section as well." %}
{% trans "Add the following lines to your project's .ini file to setup SentryHandler:" %}
[loggers] keys = root, sentry [handlers] keys = console, sentry [formatters] keys = generic [logger_root] level = INFO handlers = console, sentry [logger_sentry] level = WARN handlers = console qualname = sentry.errors propagate = 0 [handler_console] class = StreamHandler args = (sys.stderr,) level = NOTSET formatter = generic [handler_sentry] class = raven.handlers.logging.SentryHandler args = ('http://public:secret@example.com/1',) level = WARNING formatter = generic [formatter_generic] format = %(asctime)s,%(msecs)03d %(levelname)-5.5s [%(name)s] %(message)s datefmt = %H:%M:%S{% endblock %}


Here is a paragraph. It is a beautiful paragraph.
Michael Choi
John Supsupin
Trey Villafane
Click me for crazy things!
Tom Petty
Eric Clapton
Ozzy Osbourne
Michael Manring
Doug Pinnick


Here are the 32 contestants for STEEMFEST MINNOW. These folks have created a post and submitted it in the comments of the previous STEEMFEST MINNOW post. Please read these posts. If you want one of them to be a finalist then please make an individual comment for them with only their steemit handle. So, for example, if I want aggroed to get the all expense paid trip to STEEMFEST then I’d write a comment and only leave “@aggroed” in it. That’s one vote for aggroed. 5 people with the most unique mentions by unique commenters will be guaranteed a finalist contender spot.
I’ll announce finalists on Thursday and work with several judges to collect scores of those 10 accounts and choose 1 winner for the all expense paid trip.The winner as well as 1st and 2nd runner up will be announced on Monday a week from today on the @minnowsupport wall. The weekly resteem will start again then too through the @minnowsupport account.
The contestants
https://steemit.com/steemfest/@erickjongo/why-i-sho-go-to-steemfest-2017620t223329842z
https://steemit.com/steemfest/@abh12345/steemfest-2017-more-than-just-a-dream
https://steemit.com/steemfest/@livingwaters/steemfest-contest-entry
https://steemit.com/steemfest/@siersod/steemfest-minnow-my-entry
https://steemit.com/steemfest/@slidex/steemfest-minnow-hopefully-my-next-trip
https://steemit.com/life/@ghulammujtaba/yes-i-worked-more-than-16-hours-a-day-for-almost-15-years
https://steemit.com/steemfest/@christianytony/steemfest-minnow-what-i-bring-to-steemfest
https://steemit.com/steemit/@blapone420/steemfest-minnow-comp-lisbon-2017
https://steemit.com/steemfest/@flurgx/sign-up-for-steemfest-minnow-all-expense-paid-trip-contest
https://steemit.com/steem/@sanees/steemfest2
https://steemit.com/steamfest/@xpency/fighting-my-way-to-steemfest2-are-you-all-not-supporting
https://steemit.com/steemfest/@varunsangwan/here-is-my-story-and-application-for-steemfest-2
https://steemit.com/steemfest2/@stormriderstudio/contest-entry-send-a-minnow-to-steemfest2
Good Luck
It’s tough competition in there, but best of luck to all participants.  You have approximately 48hrs to drum up some support. People may vote for more than one post. Please just leave additional comments for additional authors you want to support. Comments including more than one author or additional content may not be scored correctly.
This page is synchronized from the post: ‘STEEMFEST MINNOW- People’’s choice chance’


MEET JOE BLACK A film review by Matt Williams
RATING: * * * out of * * * *
Meet Joe Black is your classic boy-meets-girl, boy-likes-girl, boy-gets-killed-in-horrible-accident, supernatural-entity-takes-over-boy's-body, supernatural-entity-falls-in-love-with-girl story. Based on the 1934 film Death Takes a Holiday, Meet Joe Black is a well acted romantic drama which explores the meanings of life and love.
William Parrish (Anthony Hopkins) is a billionaire businessman on the brink of his 65th birthday. He has built a huge media empire, and raised two beautiful daughters, Allison (Marcia Gay Harden) and Susan (Claire Forlani). William has been able to negotiate his way out of many tough spots...but he has suddenly begun fearing his own mortality, the one situation he won't be able to talk his way out of.
And he has good reason to fear, for Death himself is stalking him. However, Death has a deal to make. Death has taken possession of a man who has recently died (Brad Pitt), and wants William to be his guide in a holiday among the living. In return, William gets to live a few extra days. Of course, he agrees.
Death, under the human name of "Joe Black", gets to experience life as a complete innocent. He never before has experienced the simple pleasures of a springy matters or peanut butter. And he's not quite compared for the more complex pleasures when he and Susan fall in love. But, is their affair doomed to an early demise?
At nearly three hours in length, Meet Joe Black is a little bit on the leisurely side, though it never gets boringly lengthy. Instead, the film gets a chance to develop some nicely textured characters and situations. You get to soak in and enjoy the atmosphere, rather than being hurriedly rushed through it.
The romantic angle works well, though, surprisingly, the interactions between Joe Black and William Parrish are more interesting than those with his daughter. This is most likely due to Anthony Hopkins, who once again displays his superb acting skills. William is a desperate man who has had a seemingly ideal life, and doesn't want to let it go. By contrast, Claire Forlani is merely the romantic interest. She gets to show Joe the ropes of love, but is hardly as interesting a character.
As Joe Black, Brad Pitt seems a bit stilted. Granted, he's supposed to be out-of-place, but the overly formal technique used by Pitt begins to distract from the role. Still, he is able to hold his own, and over the course of the three hours, his character begins to grow on you.
The main character who seems out of place here is Drew (Jake Weber), an ambitious executive in Williams' company who is Joe Black's main rival for Susan's affections. Weber plays the character with a hammy relish reminiscent of Snidley Whiplash. This cartoonish foil an aberration in a film filled many more dimensional characters.
The supernatural elements of the film are handled murkily, but never distractingly so. We never learn what is the exact nature of Death, or why he's never done something like this before. It actually gets a bit creepy when Death talks to the dying in their own voices/accents. (His initial conversations with Hopkins sound shockingly like Hannibal Lecter...) And the ultimate resolution to one of the film's supernatural problems seems, well, somewhat superficial.
Still, even with the film's length, it was a pleasant time at the movies, something you wouldn't necessarily expect from a film about Death...but one which Meet Joe Black delivers anyhow.
Copyright 1998 Matt Williams
- Matt Williams<EMAIL_ADDRESS>Reviewer for Cinematter: http://www.cinematter.com Home of over 650 reviews, and information on nearly 700 upcoming releases
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


There once was a man named O'Dell
Who loved to write HTML
But his structure was bad, his semantics were sad
and his markup didn't read very well.
Ron was backed into a corner by the marauding netherbeasts. Scared, but determined to protect his friends, he raised his wand and prepared to do battle, hoping that his distress call had made it through.
Meanwhile, Harry was sitting at home, staring at his royalty statement and pondering when the next spin off series would come out, when an enchanted distress letter flew through his window and landed in his lap. He read it hazily and sighed; "better get back to work then", he mused.


--- layout: default nav: attributes > ic-on-beforeSend ---
ic-on-success
- JQuery AJAX Request life cycle attributeThis attribute is a javascript expression that will be evaluated after a successful AJAX request is completed
on behalf
of Intercooler. It corresponds to the success
property in JQuery's ajax()
method.
This attribute is a valid javascript expression, evaluated within an isolated scope.
This attribute may be placed on parent elements, allowing you to specify behavior across multiple elements.
Available symbols are:
data
- The data to be send to the servertextStatus
- The AJAX textStatusxhr
- The XHR object
Returning false
from this expression will halt Intercooler's normal request processing and
prevent content swapping.
The ic-on-success
can be placed on an parent element, allowing general request processing
for an entire page. Intercooler will find the closest element with this attribute and evaluate it.


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 = 16
"""
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 = 16
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), \
'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
input_image = tf.placeholder(tf.float32,[None,image_width,image_height,image_channels])
tensor_Z = tf.placeholder(tf.float32,[None,z_dim])
learn_rate = tf.placeholder(tf.float32)
return input_image, tensor_Z, learn_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
alpha=0.19
batchSize,height_i,width_i,deepth_i = images.get_shape().as_list()
kernal_size = 5
stridesNum = 2
with tf.variable_scope('discriminator',reuse=reuse):
# Input layer is height_i x width_i x deepth_i
Layer1 = tf.layers.conv2d(images, 64, kernal_size, strides=stridesNum, padding='same',\
kernel_initializer=tf.contrib.layers.xavier_initializer())
relu1 = tf.maximum(alpha * Layer1, Layer1)
relu1 = tf.nn.dropout(relu1,0.8)
# Now height_i-kernal_size x width_i/2 x 64
#print(relu1.shape)
Layer2 = tf.layers.conv2d(relu1, 128, kernal_size, strides=stridesNum, padding='same',\
kernel_initializer=tf.contrib.layers.xavier_initializer())
bn2 = tf.layers.batch_normalization(Layer2, training=True)
relu2 = tf.maximum(alpha * bn2, bn2)
relu2 = tf.nn.dropout(relu2,0.8)
# height_i/4 x width_i/4 x 128
#print(relu2.shape)
Layer3 = tf.layers.conv2d(relu2, 256, kernal_size, strides=stridesNum, padding='same',\
kernel_initializer=tf.contrib.layers.xavier_initializer())
bn3 = tf.layers.batch_normalization(Layer3, training=True)
relu3 = tf.maximum(alpha * bn3, bn3)
relu3 = tf.nn.dropout(relu3,0.8)
# height_i/8 x width_i/8 x 256
#print(relu3.shape)
Layer4 = tf.layers.conv2d(relu3, 512, kernal_size, strides=stridesNum, padding='same',\
kernel_initializer=tf.contrib.layers.xavier_initializer())
bn4 = tf.layers.batch_normalization(Layer4, training=True)
relu4 = tf.maximum(alpha * bn4, bn4)
relu4 = tf.nn.dropout(relu4,0.8)
# height_i/8 x width_i/8 x 256
#print(relu4.shape)
# Flatten it
flat = tf.reshape(relu4, (-1, 4*4*512))
logits = tf.layers.dense(flat, 1)
#print(logits.shape)
outputs = tf.sigmoid(logits)
return outputs, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
with tf.variable_scope('generator',reuse=not is_train):
kernal_size = 5
strideNum = 2
alpha=0.2
# First fully connected layer
gLayer1 = tf.layers.dense(z, 4*4*512)
# Reshape it to start the convolutional stack
gLayer1 = tf.reshape(gLayer1, (-1,4,4, 512))
gLayer1 = tf.layers.batch_normalization(gLayer1, training=is_train)
relu1 = tf.maximum(alpha * gLayer1, gLayer1)
# 5x5x256 now
#print(relu1.shape)
gLayer2 = tf.layers.conv2d_transpose(relu1, 256, 4, strides=1, padding='valid',\
kernel_initializer=tf.contrib.layers.xavier_initializer())
gLayer2 = tf.layers.batch_normalization(gLayer2, training=is_train)
relu2 = tf.maximum(alpha * gLayer2, gLayer2)
relu2 = tf.nn.dropout(relu2,0.8)
#print(relu2.shape)
gLayer3 = tf.layers.conv2d_transpose(relu2, 128, kernal_size, strides=strideNum, padding='same',\
kernel_initializer=tf.contrib.layers.xavier_initializer())
gLayer3 = tf.layers.batch_normalization(gLayer3, training=is_train)
relu3 = tf.maximum(alpha * gLayer3, gLayer3)
relu3 = tf.nn.dropout(relu3,0.8)
# 14x14x128 now
#print(relu3.shape)
gLayer4 = tf.layers.conv2d_transpose(relu3, 64, kernal_size, strides=strideNum, padding='same',\
kernel_initializer=tf.contrib.layers.xavier_initializer())
gLayer4 = tf.layers.batch_normalization(gLayer4, training=is_train)
relu4 = tf.maximum(alpha * gLayer4, gLayer4)
relu4 = tf.nn.dropout(relu4,0.8)
# 14x14x128 now
#print(relu4.shape)
# Output layer
g_logits = tf.layers.conv2d_transpose(relu4, out_channel_dim, kernal_size, strides=1, padding='same',\
kernel_initializer=tf.contrib.layers.xavier_initializer())
# 28x28 x out_channel_dim now
#print(g_logits.shape)
g_outputs = tf.tanh(g_logits)
return g_outputs
"""
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)))
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.
import math
import time
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
step_count = 0
input_real, input_z,l_rate = model_inputs(data_shape[2],data_shape[1],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, learning_rate, beta1)
print_info_batch_cnt = 20
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
lastBatchesTime = time.time()
for epoch_i in range(epoch_count):
batch_cnt = 0
for batch_images in get_batches(batch_size):
batch_images = batch_images*2
step_count += 1
batch_cnt += 1
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# TODO: Train Model
_ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z,l_rate:learning_rate })
_ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images,l_rate:learning_rate })
# Run g_optim twice to make sure that d_loss does not go to zero
_ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images,l_rate:learning_rate })
if step_count % print_info_batch_cnt == 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),
"Batch {}/{}...".format(batch_cnt,math.ceil(data_shape[0]/batch_images.shape[0])),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g),\
"This {} batches takes:{:.1f} sec".format(print_info_batch_cnt,(time.time()-lastBatchesTime)))
lastBatchesTime = time.time()
if step_count % 400 == 0:
show_generator_output(sess,25,input_z,data_shape[3],data_image_mode)
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 = 128
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 4
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 = 128
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
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.


This is your, chapter's information. This information will not be distributed or sold under any circumstances.


Design patterns love story
Richard tilted his head to watch the waves push flotsam against the boat hull below. Up and down, the flotsam moved. Up and down. Richard had an idea.
“Virginia, my dear”, he said to the blond woman beside him. “We’ve been singletons on this ship for a long time”.
“I know, Richard”, she replied. “My mean step-mother, the intercepting filter that she is, denies me time with others.”
Richard paused for a moment, to contemplate strategy. Her father, with his pipes and filters, would return soon, and force them to communicate over his message bus. He glanced aft, and saw no one else around. Richard turned his front controller to face Virginia, and looked her in the eyes. She was close now, and Richard could feel his active record rising.
“Virginia”, he whispered. “There is no observer in sight. Let us run below deck. I want to peel away your façade, and tightly couple.”
“Oh yes, Richard”, she blushed, and leaned towards him. “I want you to give me a dependency injection”.
(Copiata dupa http://odetocode.com/blogs/scott/archive/2005/11/22/design-patterns-a-love-story.aspx )
https://en.wikipedia.org/wiki/Factory_(object-oriented_programming)
a factory is a function or method that returns objects of a varying prototype or class from some method call, which is assumed to be "new"
Simple Factory( constructor )
Factory Method
Abstract Factory
Instead of having contructors, make simple functions that construct object
Example:Convert.ToDouble
FactoryConstructor demo and discussion
Define an interfaceor abstract class for creating an object, but let subclasses decide which class to instantiate. The Factory method lets a class defer instantiation it uses to subclasses. (Gang Of Four)
Example: https://msdn.microsoft.com/en-us/library/system.data.idbcommand.createparameter.aspx
Remarks
When inheriting from IDbCommand, a .NET Framework data provider implements a strongly-typed version of CreateParameter.
Demo Factory Method
The abstract factory pattern provides a way to encapsulate a group of individual factories that have a common theme without specifying their concrete classes
Example: DbProviderFactories.GetFactory , MVC Controller Factory
Demo Abstract Factory
So I go to the hardware store to buy the tools, and I ask the sales clerk where I can find a hammer.
"A hammer?" he asks. "Nobody really buys hammers anymore. They're kind of old fashioned."
[...]
According to our research, what people really needed wasn't a Universal Hammer after all. It's always better to have the right kind of hammer for the job. So, we started selling hammer factories, capable of producing whatever kind of hammers you might be interested in using. All you need to do is staff the hammer factory with workers, activate the machinery, buy the raw materials, pay the utility bills, and PRESTO...you'll have *exactly* the kind of hammer you need in no time flat.
[...]
And this is the way everyone is doing it now? Everyone is using a general-purpose tool-building factory factory factory now, whenever they need a hammer?
Thank you


Rockets are cool!
RSS
Rockets are so dope.

Or you could go to SPACE!


head_ref_restored
by None at 2021-11-04 20:22:20.head_ref_deleted
by None at 2021-11-04 20:26:33.head_ref_restored
by None at 2021-11-04 20:26:44.review_requested
by None at 2021-11-04 22:37:18.review_requested
by None at 2021-11-04 22:37:18.We can try to replicate this on staging.
transferred
by None at 2021-11-04 20:38:36.labeled
by None at 2021-11-04 20:38:44.unassigned
by Trey Pendragon at 2021-11-04 16:06:44.link is going to
/piranesi/catalog?f%5Bexhibit_piranesi_readonly_author_ssim%5D%5B%5D=Vasi%2C+Giuseppe%2C+1710-1782
instead of
piranesi/catalog?f%5Breadonly_author_ssim%5D%5B%5D=Vasi%2C+Giuseppe%2C+1710-1782&q=&search_field=all_fields
The facet prefix seems to be the issue here. It works with or without the search_field param.
assigned
by None at 2021-11-04 14:38:19.🐢 🐢 🐢
Bottom of the hole is the iiif-prezi gem. Our code is calling manifest.label
and it is returning nil. label
is where the value of full_title_tesim
originates.
https://github.com/iiif-prezi/osullivan/blob/development/lib/iiif/presentation/manifest.rb#L5
closed
by Trey Pendragon at 2021-11-04 16:46:37.deployed
by None at 2021-11-04 16:31:07.referenced
by Trey Pendragon at 2021-11-04 16:46:37.merged
by Trey Pendragon at 2021-11-04 16:46:37.closed
by Trey Pendragon at 2021-11-04 16:46:37.head_ref_deleted
by Trey Pendragon at 2021-11-04 16:46:38.closed
by None at 2021-11-04 17:50:15.referenced
by None at 2021-11-04 17:50:15.merged
by None at 2021-11-04 17:50:15.closed
by None at 2021-11-04 17:50:15.head_ref_deleted
by None at 2021-11-04 17:50:16.assigned
by Eliot Jordan at 2021-11-04 19:12:50.@kelea99 I'm assuming we want it to look like it does in the Firefox screenshot in all browsers. Is this correct?
Correct, @sdellis . It looks odd dropping below the thumbnail to me. What are your thoughts?
Yes, that's what I thought. I just wanted to double check!
@kelea99 Actually, I do have one thought... if the title is more than one line to the title, it looks strange, even in Firefox, as the thumbnails seem to float. I suggest we line them up with the top of the title block, like we do in the catalog.
We also may want to think about putting the thumbnails on the left if we know we are going to have thumbnails and they provide enough information to the user to help them decide relevance. See: https://www.nngroup.com/articles/mobile-list-thumbnail/
I think we have items without thumbnails. In the East Asian tables of contents collection: https://dpul.princeton.edu/ealtoc/catalog?utf8=%E2%9C%93&exhibit_id=ealtoc&search_field=all_fields&q=
I would bet these are MVWs that did not have thumbnails selected for them in the parent, @hackartisan . checking EAL in figgy now and doing a test.
yup. Test was an MVW. no thumbnail set on the children or selected in the parent. I set one and now it shows in DPUL: https://dpul.princeton.edu/ealtoc/catalog?utf8=%E2%9C%93&exhibit_id=ealtoc&search_field=all_fields&q=Ce+fu+yuan+gui+%2F+%5BWang+Qinruo+deng+bian%5D.
Right, I think they don't have thumbnails on purpose. We might ask Tom.
Music content doesn't have thumbnails, either.
assigned
by Shaun Ellis at 2021-11-04 14:30:54.mentioned
by Kim Leaman at 2021-11-04 14:32:38.subscribed
by Kim Leaman at 2021-11-04 14:32:38.mentioned
by Shaun Ellis at 2021-11-04 14:34:26.subscribed
by Shaun Ellis at 2021-11-04 14:34:26.mentioned
by Kim Leaman at 2021-11-04 15:35:04.subscribed
by Kim Leaman at 2021-11-04 15:35:05.mentioned
by None at 2021-11-04 18:46:15.subscribed
by None at 2021-11-04 18:46:15.@bess and @kelynch I apologize for the delay in these updates, but the commits which I have introduced should resolve the bugs which were discovered earlier.
mentioned
by Bess Sadler at 2021-11-04 16:56:35.subscribed
by Bess Sadler at 2021-11-04 16:56:35.mentioned
by Kate Lynch at 2021-11-04 16:56:35.subscribed
by Kate Lynch at 2021-11-04 16:56:35.Please see my progress for documenting the progress of this within the branch https://github.com/pulibrary/symplectic-elements-utils/compare/upgrading-deployments-jrgriffiniii?expand=1
closed
by Kate Lynch at 2021-11-04 15:20:46.closed
by Kate Lynch at 2021-11-04 15:20:48.Utilizing the core dspace embargo-lifter command-line tool, I've written a script that executes the command, captures the output, and sends an email to specified parties.
It is in its own repository here: https://github.com/pulibrary/dataspace_embargo_lifter
Happy to pair with someone to test this and get it reviewed.
Contacted user to try again; awaiting feedback.
assigned
by Kate Lynch at 2021-11-04 20:19:36.I added the reporting user to the review pool for this Collection, and also refreshed the item status by claiming these items' tasks and returning them to the pool so that the reporting user can see and claim them. I'm waiting to hear back now to see if this resolves their issue.
demilestoned
by Kate Lynch at 2021-11-04 20:12:03.milestoned
by Kate Lynch at 2021-11-04 20:12:03.assigned
by Kate Lynch at 2021-11-04 20:19:40.We can do this by aborting the current workflow for the item in the DataSpace admin UI, which will send the back to the submitting user with the option to delete, see screenshot of the DataSpace admin UI:
The user can delete a submission by clicking the "Remove" button pictured.
I've emailed the reporting user asking for the titles of the test submissions that need to be removed so I can abort their workflows.
assigned
by Kate Lynch at 2021-11-04 20:19:50.This duplicates https://github.com/pulibrary/dspace-development/issues/523
labeled
by James R. Griffin III at 2021-11-04 20:40:36.labeled
by James R. Griffin III at 2021-11-04 20:40:36.milestoned
by James R. Griffin III at 2021-11-04 20:40:36.assigned
by James R. Griffin III at 2021-11-04 20:40:36.closed
by James R. Griffin III at 2021-11-04 20:44:09.https://princeton.service-now.com/service?id=ticket&table=incident&sys_id=96bee9fe87ebfc109cd197183cbb3508&view=sp tracks this issue in SN@P.
labeled
by James R. Griffin III at 2021-11-04 20:40:18.labeled
by James R. Griffin III at 2021-11-04 20:40:18.milestoned
by James R. Griffin III at 2021-11-04 20:40:18.assigned
by James R. Griffin III at 2021-11-04 20:40:18.https://princeton.service-now.com/service?id=ticket&table=incident&sys_id=96bee9fe87ebfc109cd197183cbb3508&view=sp tracks this issue in SN@P.
transferred
by James R. Griffin III at 2021-11-04 20:36:46.labeled
by James R. Griffin III at 2021-11-04 20:36:57.labeled
by James R. Griffin III at 2021-11-04 20:37:04.assigned
by James R. Griffin III at 2021-11-04 20:37:08.milestoned
by James R. Griffin III at 2021-11-04 20:37:13.labeled
by James R. Griffin III at 2021-11-04 20:59:50.labeled
by James R. Griffin III at 2021-11-04 20:59:50.labeled
by James R. Griffin III at 2021-11-04 20:59:50.milestoned
by James R. Griffin III at 2021-11-04 20:59:50.assigned
by James R. Griffin III at 2021-11-04 20:59:51.unassigned
by Cliff Wulfman at 2021-11-04 15:35:44.These are ingested here: https://figgy.princeton.edu/?f%5Bmember_of_collection_titles_ssim%5D%5B%5D=Board+of+Trustees+Minutes+Volumes+I-XII
closed
by Esmé Cowles at 2021-11-04 16:37:26.labeled
by Kim Leaman at 2021-11-04 17:56:36.labeled
by Eliot Jordan at 2021-11-04 19:18:11.labeled
by Eliot Jordan at 2021-11-04 19:18:11.labeled
by Eliot Jordan at 2021-11-04 19:27:10.labeled
by Eliot Jordan at 2021-11-04 19:27:10.labeled
by Eliot Jordan at 2021-11-04 19:53:06.labeled
by Eliot Jordan at 2021-11-04 19:53:06.closed
by None at 2021-11-04 20:33:34.@hackartisan Thank you! Should be ready for re-review.
review_requested
by Trey Pendragon at 2021-11-04 19:15:48.mentioned
by None at 2021-11-04 19:15:56.subscribed
by None at 2021-11-04 19:15:56.referenced
by None at 2021-11-04 20:33:34.merged
by None at 2021-11-04 20:33:34.closed
by None at 2021-11-04 20:33:34.head_ref_deleted
by None at 2021-11-04 20:33:36.labeled
by Eliot Jordan at 2021-11-04 21:40:14.labeled
by Eliot Jordan at 2021-11-04 21:40:14.renamed
by Eliot Jordan at 2021-11-04 21:41:15.deployed
by None at 2021-11-04 15:49:39.referenced
by None at 2021-11-04 16:05:08.merged
by None at 2021-11-04 16:05:08.closed
by None at 2021-11-04 16:05:08.head_ref_deleted
by None at 2021-11-04 16:05:09.referenced
by None at 2021-11-04 19:18:34.merged
by None at 2021-11-04 19:18:34.closed
by None at 2021-11-04 19:18:34.head_ref_deleted
by None at 2021-11-04 19:18:34.closed
by Christina Chortaria at 2021-11-04 14:04:09.head_ref_force_pushed
by None at 2021-11-04 10:49:38.renamed
by None at 2021-11-04 11:33:44.referenced
by Christina Chortaria at 2021-11-04 11:36:25.merged
by Christina Chortaria at 2021-11-04 11:36:25.closed
by Christina Chortaria at 2021-11-04 11:36:25.head_ref_deleted
by Christina Chortaria at 2021-11-04 11:36:26.review_requested
by None at 2021-11-04 11:32:51.referenced
by None at 2021-11-04 13:00:17.merged
by None at 2021-11-04 13:00:17.closed
by None at 2021-11-04 13:00:17.head_ref_deleted
by None at 2021-11-04 13:00:18.closed
by Francis Kayiwa at 2021-11-04 14:17:19.mentioned
by Francis Kayiwa at 2021-11-04 13:05:04.subscribed
by Francis Kayiwa at 2021-11-04 13:05:04.referenced
by Francis Kayiwa at 2021-11-04 14:23:13.merged
by Francis Kayiwa at 2021-11-04 14:23:13.closed
by Francis Kayiwa at 2021-11-04 14:23:13.head_ref_deleted
by Francis Kayiwa at 2021-11-04 14:23:14.referenced
by Francis Kayiwa at 2021-11-04 18:19:34.merged
by Francis Kayiwa at 2021-11-04 18:19:34.closed
by Francis Kayiwa at 2021-11-04 18:19:35.head_ref_deleted
by Francis Kayiwa at 2021-11-04 18:19:35.deployed
by None at 2021-11-04 14:36:21.referenced
by Christina Chortaria at 2021-11-04 15:52:57.merged
by Christina Chortaria at 2021-11-04 15:52:57.closed
by Christina Chortaria at 2021-11-04 15:52:57.head_ref_deleted
by Christina Chortaria at 2021-11-04 15:52:57.Also, after RH removes all the child accessrestrict components that are the same as their parent, this could become more urgent, as the note inheritance should really be more active at that point.
Yes, we plan to tag this as "sudden priority" once Regine finishes up the data cleanup since many notes will be incorrect, which will significantly impact users and public svs staff.
labeled
by None at 2021-11-04 18:49:07.labeled
by Eliot Jordan at 2021-11-04 21:17:35.labeled
by Eliot Jordan at 2021-11-04 21:21:21.labeled
by Eliot Jordan at 2021-11-04 21:27:48.labeled
by Eliot Jordan at 2021-11-04 21:29:30.labeled
by Eliot Jordan at 2021-11-04 21:35:01.


title('Mobile Web: Credits') ->navbar_image('about') ->header('Credits'); $page->content_begin(); ?>
The MIT Mobile Web is developed and maintained by MIT's Information Services & Technology (IS&T) as a service to the MIT community. The initial design and implementation took place in January-May 2008. The site was officially launched in June 2008.
We hope you find the site useful – and fun! Please send any feedback to mobiweb@mit.edu.


a basic function:
“calling” a basic function:
No arguments, passed in, not returning anything, basic named function declaration syntax:
Generally the way callback functions are used is by constructing a function parameter which is called in the body of the function and then by passing an anonymous inline function:
essentially, this is a different code pattern, but the result is the same thing as if we made a named function declaration like so:
Closure… returning an anonymous functions… “higher order functions”
functional programming in JavaScript
Using the cb:
synchronous vs. asynchronous code…
synchronous code
Higher order functions: functions that return an anonymous function.
Kinda like saying:
Functional design principles stem from closures, e.g. decorators.
Invoking the return:
Closure and scope
MORE EXPLANATION OF Higher Order Function Syntax:
the foo()
part returns the “pre-run” inner function and
the following ();
returns the inner function after being
run, e.g.
this works:
works:
works:
sibling functions cannot see into each other, e.g. bar and baz are on the same level:
Also:
a function can call itself from inside of itself…
factorial 5, or, (5!) is 5 * 4 * 3 * 2 * 1 = 120
debugger;
will pause executionfin


Congratulations, your order has been placed!
{{ orderItem.key }}: {{ orderItem.value }}
{{ sub.key }}: {{ sub.value }}


{% extends "blog/header.html" %} {% block content %}
You can buy premium mebership for $5, and get your blog featured on main page of website.


Abhishek Dutta,
Relja Arandjelovic,
and
Andrew Zisserman
Overview
VGG Image Search Engine (VISE) is an open source project developed at the Visual Geometry Group and released under the BSD-2 clause license. This standalone application can be used to make a large collection of images searchable by using image regions as query.
VISE builds on the C++ codebase developed by Relja Arandjelovic during his DPhil / Postdoc at the Visual Geometry Group, Department of Engineering Science, University of Oxford.
Downloads
- vise-1.0.0.zip : VISE docker image and execution scripts
- https://gitlab.com/vgg/vise/ : source code repository
Installation Instructions
Please see INSTALL document that contain installation instructions for:User Guide
Please see User Guide for more details.
Acknowledgements
This work is supported by EPSRC programme grant Seebibyte: Visual Search for the Era of Big Data (EP/M013774/1).


Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
Expiry: April 18, 2014
Discount: 25%
Applicable to:: Rosemary cake
Website: http://bakery.com


Privacy policy
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Last modified: 16 December, 2011.
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This page provides a quick visualization of the training runs for all our default agents.
See the baselines main page for details on the hyperparameters used.


--- layout: data_model title: ObservableCompositionType this_version: 1.0.1 ---
The ObservablesCompositionType enables the specification of higher-order composite observables composed of logical combinations of other observables.
| Field Name | Type | Description |
|---|---|---|
| @operatorrequired | OperatorTypeEnum |
The operator field enables the specification of complex compositional cyber observables by providing logical operators for defining interrelationships between constituent cyber observables defined utilizing the recursive Observable element. |
| Observable0..n | ObservableType |
The Observable construct represents a description of a single cyber observable. |


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| Services_entry (const char *service_name) |
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| void | show (int indent) |
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| int | proc_service_index (Proxy_info *proc) |
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Service_provider * | proc_service_find (Proxy_info *proc) |
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bool | add_service (O2string our_ip_port, O2node *service, char *properties) |
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O2err | service_remove (const char *srv_name, int index, Proxy_info *proc) |
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O2err | insert_tap (O2string tapper, Proxy_info *proxy, O2tap_send_mode send_mode) |
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O2err | tap_remove (Proxy_info *proc, const char *tapper) |
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void | pick_service_provider () |
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void | remove_if_empty () |
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| O2node (const char *key_, int tag_) |
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virtual const char * | get_proc_name () |
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virtual O2status | status (const char **process) |
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void * | operator new (size_t size) |
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void | operator delete (void *ptr) |
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◆ proc_service_index()
| int Services_entry::proc_service_index |
( |
Proxy_info * |
proc | ) |
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find address of service in services that is offered by proc, if any. Note that if proc is the local process (o2_ctx->proc), the result can be an OSC or BRIDGE node, since these are proxies for the local process.
◆ service_find()
Use initial part of an O2 address to find an o2_service using a hash table lookup.
- Parameters
-
| name | points to the service name (do not include the initial '!' or '/' from the O2 address). |
- Returns
- The pointer to the service, which is a Hash_node, Handler_node, or Proxy_info (Proc_info, OSC_info or Bridge_info)
◆ service_provider_replace()
| O2err Services_entry::service_provider_replace |
( |
const char * |
service_name, |
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O2node ** |
node_ptr, |
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O2node * |
new_service |
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) |
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static |
replace the service named service_name offered by proc with new_service. This happens when we change from all-service handler to per-node handlers or vice versa. Also happens when we delete a service, and when we remove a remote service when it disconnects.
precondition: service_name does not have '/' && new_service != NULL
CASE 1: called from o2_method_new(), installing a global handler for service, maybe replacing an existing one, maybe not. CASE 2: called from o2_method_new(), replacing a global handler with a hash_node where we can install a Hash_node based on the next node in the address. (no more cases because we moved others to o2_service_remove)
◆ show()
| void Services_entry::show |
( |
int |
indent | ) |
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virtual |
The documentation for this class was generated from the following files:
- /Users/rbd/o2/src/services.h
- /Users/rbd/o2/src/services.cpp