metadata
All relations in Mongoid contain metadata that holds information about the relation in question, and is a valuable tool for third party developers to use to extend Mongoid.
You can access the metadata of the relation in a few different ways.
# Get the metadata for a named relation from the class or document. Model.reflect_on_association(:relation_name) model.reflect_on_association(:relation_name) # Get the metadata that the current object has in its relation. model.metadata # Get the metadata with a specific relation itself on a specific # document. person.addresses.metadata
the metadata object
The metadata object itself contains more information than one might know what to do with.
- Metadata#class_name
- Metadata#embedded?
- Metadata#extension
- Metadata#foreign_key
- Metadata#foreign_key_default
- Metadata#foreign_key_setter
- Metadata#indexed?
- Metadata#inverse
- Metadata#inverse_foreign_key
- Metadata#inverse_klass
- Metadata#inverse_setter
- Metadata#inverse_type
- Metadata#inverse_type_setter
- Metadata#key
- Metadata#klass
- Metadata#macro
- Metadata#polymorphic?
- Metadata#setter
- Metadata#stores_foreign_key?
- Metadata#validate?


issuer |
string
|
|
|
name of the issuer of the token that should
match the iss claim in the id_token
|
audience |
string
|
|
|
identifies the recipients that the JWT is
intended for and should match the
aud claim
|
jwksCache |
Object
|
<optional>
|
|
cache for JSON Web Token Keys. By default it has
no cache
|
jwksURI |
string
|
<optional>
|
|
A valid, direct URI to fetch the JSON Web Key
Set (JWKS).
|
expectedAlg |
string
|
<optional>
|
'RS256'
|
algorithm in which the id_token was signed and
will be used to validate
|
leeway |
number
|
<optional>
|
60
|
number of seconds that the clock can be out of
sync
|
maxAge |
number
|
<optional>
|
|
max age while validating expiration of the
id_token
|


Charts can be styled using CSS, such as this example overriding the default styles for a pie chart. This can be done without the use of Javascript, allowing designers and developers to better collaborate on chart design.


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Simulate data from a Gaussian graphical model or a Gaussian Bayesian network in two conditions. Given a covariance matrix of a reference condition simulate plausible dysregulations.
| Version: | 0.2 |
| Depends: | R (≥ 3.0) |
| Imports: | mvtnorm, gRbase, graph, igraph, ggm, qpgraph, R.utils, htmlwidgets, shiny, shinydashboard , grDevices, graphics, stats |
| Suggests: | knitr, rmarkdown, clipper, topologyGSA |
| Published: | 2016-09-21 |
| Author: | Elisa Salviato [aut, cre], Vera Djordjilovic [aut], Chiara Romualdi [aut], Monica Chiogna [aut] |
| Maintainer: | Elisa Salviato <elisa.salviato.88 at gmail.com> |
| License: | AGPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | simPATHy results |
| Reference manual: | simPATHy.pdf |
| Vignettes: |
simPATHy |
| Package source: | simPATHy_0.2.tar.gz |
| Windows binaries: | r-devel: simPATHy_0.2.zip, r-release: simPATHy_0.2.zip, r-oldrel: simPATHy_0.2.zip |
| OS X binaries: | r-release: not available, r-oldrel: simPATHy_0.2.tgz |
| Old sources: | simPATHy archive |
Please use the canonical form https://CRAN.R-project.org/package=simPATHy to link to this page.


Transformers have become the most promising models in machine learning, particularly for solving natural language processing tasks. The fact that Transformer based models do so good raises the question of whether the more traditional neural network architectures for processing sequences, recurrent neural networks (RNNs), are obsolete now?
While Transformers do extremely well on many tasks given enough training data and computation (Devlin et al., 2019; Keskar et al., 2019; Radford et al., 2019), several studies have shown that LSTMs, the most popular variants of RNNs, can perform better than Transformers on tasks requiring sensitivity to hierarchical (linguistic) structure, especially when the data is limited (Tran et al., 2018; Dehghani et al., 2019). Theoretically, both RNNs and Transformers can deal with finite hierarchical structures. But, they have different preference inductive biases and the superior performance of LSTMs over Transformers in these cases is attributed to their recurrent inductive bias. The recurrent inductive bias of LSTMs seems to have an important role in enabling them to model the hierarchical structure of the inputs. The question we try to answer in this post is that what is the recurrent inductive bias?
What is Inductive Bias?
Inductive bias is generally defined as any kind of bias in learning algorithms that does not come from the training data. Inductive biases of the learning algorithms determine their generalisation behaviour and the type of solutions they converge to. There are different sources for inductive biases in learning algorithms, for instance, the architectural choices, the objective function, the curriculum strategy, or the optimization regime.
Let’s assume, we are given a task and two models, A and B, with similar expressive power, i.e. the desired solution for the task is realizable for both models. Also assume that model B has a stronger inductive bias toward the solution compared to model A. While model A can eventually learn the solution if we provide it with enough data and computation, model B can achieve this goal with much less data and computation. Hence, designing learning algorithms with proper inductive biases is essential especially when data and compute is limited.
Moreover, in the absence of strong inductive biases, a model can be equally attracted to several local minima on the loss surface; and the converged solution can be arbitrarily affected by random variations, for instance, the initial state or the order of training examples (Sutskever et al., 2013; McCoy et al., 2020; Dodge et al., 2020).
What is Recurrent Inductive Bias?
The inductive bias of RNNs is often referred to as the recurrent inductive bias. Even though this term is used frequently in the literature, I have not been able to find a clear definition for it. Generally, the term refers to any bias that origins from the recurrent architecture. We can distinguish between three main sources of this bias in RNNs:
- The sequential processing of the input: There is an inherent notion of order in the architecture that forces the model to access next tokens in the input one by one.
- No direct access to the past tokens: The model has to compress all the information from past tokens in a hidden state/memory, which is accessible when processing the next token.
- Recursion: The model recursively applies the same function on the varying input at every time step.
In contrast to RNNs, Transformers, process the input in parallel. Although a weak notion of order is encoded by positional embeddings, no explicit assumption is made in the connectivity structure of the architecture. Moreover, they have a global receptive field and can access all tokens through self-attention. Finally, standard Transformers are not recursive, they apply the same set of weights on all input tokens, but they don’t do it recursively.
Recurrent Inductive Bias in Practice
I have done a small experiment to examine the effect of recurrent inductive bias of RNNs in practice. Let’s take a look into it!
The task of subject-verb agreement is proposed by (Linzen et al., 2016) as a proxy for assessing the ability of models to capture hierarchical structure in natural language. In this task, the goal is to predict number-agreement between subjects and verbs in English sentences. Succeeding at this task is a strong indicator that a model can learn syntactic structures. It is shown by (Tran et al., 2018) that the recurrent inductive bias of RNNs helps them to achieve better performance on this task compared to standard Transformers.
To empirically examine the benefits of each of the three sources of the recurrent inductive bias mentioned earlier, we can modify the standard Transformer to have an architecture with specifications that are similar to RNNs. Then we measure how the performance of the models change as we include more aspects of the recurrent inductive bias. These are the three different variants of Transformers we use:
- Transformer: Standard Transformer encoder with a class token (CLS) for classification (BERT style),
- Sequential Transformer: Transformer encoder with future masking where the classification is done using the representation of the last token\footnote{Note that future tokens are masked out by default when using a transformer in the decoder mode, e.g., in a language modelling setup.},
- Sequential Universal Transformer: Universal Transformer (Dehghani et al., 2019) encoder, where we have a recurrence in depth by sharing parameters among all the layers, also with future masking. % Among these variants of Transformer, Sequential Transformer implements sequential access to tokens, and Sequential Universal Transformer has both sequential access to tokens and a form of recursion. Here is a schematic view of the architecture of RNN and the variants of Transformer we discussed.
These models are trained to predict the number of the masked verb in a given sentence (binary classification objective). In the plot below, we can see the mean and standard deviation of the accuracy over multiple trials. As we can see, LSTM achieves the best performance and has the least variance.
Interestingly, comparing all four models, we find that the performance steadily increases as more aspects of the recurrent inductive bias are included.
As another indicator of the quality of the solutions that different models converged to, we look into their confidence calibration[^af15b0ee]. [^af15b0ee]: Confidence calibration captures how well likelihood (confidence) of the prediction of the model predicts its accuracy. For a well-calibrated model, if we bin the confidence scores and compute the accuracy for each bin, the accuracies are perfectly correlated with the confidence values. The Expected Calibration Error (ECE) is computed as the distance between the calibration curve of the model and the perfect calibration curve. In the figure below, we plot the Expected Calibration Error (ECE) of the models. In line with the trends in the performances of these models, the expected calibration error decreases as we move from standard Transformer toward LSTM.
Additionally, as shown in both above figures, we find a decreasing trend in the variance of the models, i.e., adding more inductive biases to the models decreases their variance. This is a piece of empirical evidence that supports the relation between variance of the solutions a model converges to and its inductive biases.
Read more about this in our paper:
@article{abnar2020transferring,
title={Transferring Inductive Biases through Knowledge Distillation},
author={Samira Abnar and Mostafa Dehghani and Willem Zuidema},
year={2020},
eprint={2006.00555},
archivePrefix={arXiv},
}


{% extends "splash.html" %} {% block content %}
Check Back soon to see how we are building a web marvel
Gary Napier on June 28, 2020 at 9:48 pm


virus {border-style px;} #ffffff;} COVID- SARS-CoV- pandemic pathogen []. need operating room current chain infection particles patient patients [], changes coronavirus px; {font-family care effective font-size known potential spread Conflict Infect LINK SARS Statement air available caused double; elective health immunity medical people regarding surfaces surgical used Elsevier Engl Syndrome aware border-color border-width infected procedures solid; systems transfer transfer. use {font-style {font-weight COVID-. Patients SARS-CoV-, Surgical Thus, [[], able aerosolized arthroplasty bold capable cases centre come community contact courier; determine devices disease disrupt does em; ideally isolation long material needs novel pathogens. patients. place positive prevention pt;} remains resource return room. safely strategies surgeons surgery swabs test tests viruses years


This is Sy's Psychic Game!!!
Guess what number I'm thinking of?
Guess, Thumbs Up or Down:
Up
Down
You guessed:
Wins:
Losses:
ThumbsUp chosen:
ThumbsDown chosen:


Split Argument to Variables Utility
This utility makes it easy to split one argument into multiple variables. By specifying the delimiter, you can quickly turn one string into a set of variables for your workflow to use.
Using the Split Argument to Variables Utility
To use this object, specify the delimiter you want to use (comma by default), and what you want the variables to be called ("split" by default).
The output will then contain the list of variables you've just created.
Workflow Example Using the Split Arg to Vars Utility
In this workflow example, you can enter the name of a few (unwilling) participants with a comma between each name, and hit Return to find out who's doing the dishes tonight!
The workflow uses four objects, including the Split Arg to Vars object:
- Keyword Input: Set to the keyword "dishes" with an argument required
- Split Arg to Vars Utility: It splits your string of names into individual variables
- Random Utility: Randomly picks an item from your list of variables
- Large Type Output displays in large font who's the "lucky" winner!
Download the "Who's Doing the Dishes?" Workflow and modify it to your own uses!


New Gran Turismo x Lamborghini Collab Looks Like a Batmobile From 2076
by Luke PlunkettThis is Lamborghini’s Vision GT, a new car unveiled in the flesh last night at the Gran Turismo Championship World Finals in Monaco. It looks like something Bruce Wayne would take to an open track day, but despite this physical unveiling, the car is actually only going to be made digitally inside Gran Turismo Sport.
The Vision GT is envisaged as a single-seat racer with a hybrid V12, which drivers climb into like a fighter jet and which displays its information not on screens or panels, but by projecting it into the air around the driver’s head.
It’s coming to the game in Spring 2020.
This if course isn’t the first time GT has done something like this. Previous GT Vision cars from just 2019 include this Jag:
While in 2017 McLaren designed this:


TSSSF Card App
Beta By @Ripp_
Feel free to check out and fork the source on github
Please report any issues to the issue tracker
Symbols and Powers Guide:
- ♂
- \male
- ♀
- \female
- ⚤
- \malefemale
- ❤
- \ship
-
- \earth
-
- \unicorn
-
- \pegasus
-
- \alicorn
-
- \time
- (Replace)
- \replace
- (Swap)
- \swap
- (Draw)
- \draw
- (New Goal)
- \newgoal
- (Search)
- \search
- (Copy)
- \copy
- Changeling
- \changeling


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Email<EMAIL_ADDRESS>Education
University of Portsmouth: Masters of Research MRes ( Distinction ):
(Feb 2018 – Feb 2019)
I achieved an average of over 80% for my masters in neuroscience research. For this I undertook an investigation of the factors controlling proliferation of central nervous system cells, and their impact on the pathogenesis of Multiple Sclerosis.
Achievements:
- Utilised my skills in Python to manipulate and analyse the data I collected.
- Automated the analysis of hundreds of fluorescence microscopy images using the open source Cell-Profiler project.
- Demonstrated precision and diligence, safely following lab protocols.
- Made well documented incremental improvements to develop an optimised immunohistochemistry protocol.
- Independently planned my project and updated my supervisor on key milestones.
- Gave a clear and confident delivery of my findings for both oral and written presentations.
University of Portsmouth: BSc (Hons) Pharmacology ( 2:1 ):
(Sep 2013 – Jun 2016)
A comprehensive degree covering topics in biology and chemistry relating to the discovery, development, formulation and application of therapeutic drugs.
Achievements:
- Produced a first-class thesis for my final year project, investigating GABA mediated ion channels in CNS cell membranes.
- Mastered the use of Excel and GraphPad Prism for statistical analysis and data visualizations.
- Showed effective teamwork in group projects and extracurricular clubs.
- Successfully organised my academic life around deadlines and other commitments such as coaching the underwater hockey team.
Work Experience
Ecommerce Trading Assistant, Evans Cycles:
(Jun 2019 – Present)
Analytical and administrative tasks supporting trading on the Evans Cycles website. Played key roles in major projects for the business, including a full website redesign and migration to a new platform.
Skills & Experience:
- Collecting and analysing data from multiple sources including Power BI, Google Analytics and SQL database.
- Using Excel (VLOOKUP, pivot tables, macros) to match product data with user metrics.
- Building and sharing reports using Google Data Studio.
- Synthesising data insights into actionable commercial strategies for merchandising the website.
- Updating website content using HTML, CSS and JS.
- Communicating with different departments in the office and via video conference, email and IM whilst working remotely.
Clinical Administrator, Saxonbrook Medical:
(Aug 2016 – Feb 2018)
Office based administrator in a GP surgery, responsible for reading clinical letters, processing prescription requests and updating patient records.
Skills & Experience:
- Confident and clear communication with doctors and patients.
- Use of clinical systems for managing electronic patient records.
- Auditing work for data accuracy.
- Writing detailed SOPs and training new staff.
Store Assistant (Part Time), Homebase:
(Sep 2011 – Sep 2013)
A range of responsibilities around the store, including customer service and stock management.
Interests
- Passion for learning about new technology, especially programming languages and computer hardware. This Included building my own PC and running a Linux file server from my Raspberry PI.
- I enjoy creating personal coding projects and completing online courses. What started out as a hobby turned into a highly valuable skill for my scientific research and my work in Ecommerce.
- Consuming non-fiction media and surrounding new and exciting topics in science and technology.
- I enjoy keeping fit and exploring the outdoors by cycling, running and swimming.
- At university I was captain of the underwater-hockey team. Which involved training new members and arranging tournaments.


{% extends "base_noside.html" %} {% block title %}Invoicy - Error occurred{% endblock %} {% block content %}
There seems to be some issue at our end. Please try again later.


Bitmap
's.Camera.setDisplayOrientation(int)
for normal camera
preview image scaling.MessageDigest
does.LogUtil.LOG_TAG
and class name,
so you could filter messages from a certain class.File
, URL
and
URI
.BmpUtil.ScaleType.resolveScale(int, int, int, int)
, witch
also checks bounds for BmpUtil.MAX_TEXTURE_SIZE
.Matrix
.Bitmap.createScaledBitmap(android.graphics.Bitmap, int, int, boolean)
.MessageDigest
usage for hashing
(ALGORITHMS
).ALGORITHMS
and chooses supported and
the strongest one.BmpUtil.MAX_TEXTURE_SIZE
), scales image down and converts
it back into byte array.


Devlin Shand is a portrait photographer based in San Francisco, California. The visual drama and artistry of his photographs are born of a keen eye for the many moods of Nature and a life-long passion for the wilderness.
His style is unmistakable. His talent for rare captures of amazing light and fleeting atmosphere imbue his portfolio with a sense of the epic, majestic and the bold.
A full-time professional from 2005 onward, Devlin has been frequently mentioned and recognized as an original artist, an innovator and a trendsetter in the modern age of portrait photography.
Portraits are an important part of any photographer's arsenal. We've got them covered.
This template features the 'Lato' font, part of the Google Web Font library, as well as icons from Font Awesome.


Before Change
self.n_train_ = X.shape[0]
self.decision_scores_ = np.zeros([self.n_train_, 1])
self.classes_ = 2 // default as binary classification
if y is not None:
check_classification_targets(y)
self.classes_ = len(np.unique(y))
warnings.warn(
"y should not be presented in unsupervised learning.")
if self.method == "fast":
self._fit_fast()
elif self.method == "default":
self._fit_default()
After Change
self.method = method
self.n_neighbors = n_neighbors
def fit(self, X, y=None):
// Validate inputs X and y (optional)
X = check_array(X)
self._set_n_classes(y)
self.X_train_ = X
self.n_train_ = X.shape[0]
self.decision_scores_ = np.zeros([self.n_train_, 1])


React two point range selector
React integration sample
React plugin sample
State
NSW
Vic
Act
waiting for react


Highslide JS
Highslide JS home page
Documentation
Full API reference
More samples at www.roadrash.no
Barebones samples
Go to one of these examples and modify the code to suit your needs:
Highslide JS core
White 10px border and drop shadow
Drop shadow and no border, semi transparent close button
White outline with rounded corners
Dark design with outer glow
No graphic outline
Image map with heading from the title tag
With Gallery
Gallery example with white design
Gallery example with dark design
Mini gallery hidden under one thumbnail, dimming page background
Two mini galleries on the same page
Controls in the heading
No border and a floating caption
Images within a fixed size restraining box
Vertical thumbstrip to the left, no border
Horizontal thumbstrip at the bottom, white rounded style
Thumbstrip above the image, subsequent thumbnails hidden
Floating thumbstrip to the right
Gallery in the parent page without Highslide popping up at all.
(Note: This example can not be set up in the Highslide Editor)
With HTML
Inline content with wrapper
Inline content with wrapper and headline
AJAX content
Iframed content
Flash content
YouTube content
Full inline content without self rendering wrapper


Alab Pilipinas obliterated in ABL season debut
Two of Alab Pilipinas' 3 new imports struggle in a 35-point horror loss to Thailand's Mono Vampire
by Delfin DioquinoSubscribe
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Our services
Construction & Building Html Template.
Construction & Building Html Template.
We are committed a sustainable future fostering a collaborative spirit.
Read MoreWe are committed a sustainable future fostering a collaborative spirit.
Read MoreWe are committed a sustainable future fostering a collaborative spirit.
Read More"Thank you for guiding us through the construction process, understanding, and always ready to accommodate our needs. We love our new space and know that it was built by the very best!"
"Thank you for guiding us through the construction process, understanding, and always ready to accommodate our needs. We love our new space and know that it was built by the very best!"
"Thank you for guiding us through the construction process, understanding, and always ready to accommodate our needs. We love our new space and know that it was built by the very best!"


QRazyBox Help
Introduction
Getting Started
About QR Code
Interface Overview
Create, Save, and Load Project
Drawing and Decoding QR Code
Editor & Painter
Painter Basic Usage
Format Info Pattern
Undo/Redo using History
Extension Tools
Extract QR Information
Reed-Solomon Decoder
Brute-force Format Info Pattern
Data Masking
Padding Bits Recovery
Examples
Basic Example
Advanced Example
Miscellaneous
QR Code samples
Shortcut Keys
Third-Party & License
Search result for "" :


The next Sea of Thieves update adds a ghostly new Tall Tale and flaming cannonballs
by Christopher LivingstonThe next free monthly update for Sea of Thieves is near, arriving on November 20. It's called The Seabound Soul, and will contain a ghostly new Tall Tale, plus a new tool for bombarding your fellow pirates: fireballs.
The new Tall Tale will invite players to join pirate Captain Pendragon and chase down a spooky ghost ship, the Ashen Dragon. And it looks like you'll need to use your trusty bucket for more than just bailing out your sinking ships, because flaming cannonballs are being added. They don't just explode, but set ships on fire. The trailer above shows several ships completely engulfed in flames, and also shows a player tossing a flaming grenade. Things are heating up, matey.


White-space text nodes between inline boxes are incorrectly suppressed when line breaks within the inline boxes are suppressed, and contain a text node and adjacent space character (U+0020) - which is first or last in the line box
Test passes if box below contains two line boxes
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test


Baby McCabe
03 - 11 - 22
000
Days
00
Hours
00
Minutes
00
Seconds
Invalid User or Code! Please try again.
Baby McCabe1
Invalid User or Code! Please try again.
Days
Hours
Minutes
Seconds
Invalid User or Code! Please try again.


A sparse graph is a graph with relatively few edges .


Evolife Jean-Louis
Dessalles
Telecom ParisTech
www.dessalles.fr
2. How to execute Evolife (+ download)
7. Create your
own Evolife scenario
8. Use Evolife’s
components separately (graphics, ecology, genetic algorithm)
The package
contains:
- Python
sources (*.py)
-
Configuration files (*.evo). These files simply
contain parameter-value pairs. The best way to edit them is to open them from
the configuration editor that you get by executing Starter.bat (or starter.sh). Each configuration file corresponds to a scenario.
- A few
scenarios (Scenarii/S_*.py) that implement specific problems.
|
Alliances.py |
Defines social links between individuals.
Useful for certain scenarios. |
|
Curves.py |
Stores points to be displayed on curves |
|
Default_Scenario.py |
Implements a default scenario from which
actual scenarios are derived. |
|
DNA.py |
Where the hereditary binary string is
defined, and crossover and mutation are implemented. |
|
Evolife_Batch.py |
A silent (no graphic) version of Evolife for batch mode |
|
Evolife_Graphic.py |
A few displays specific to Evolife |
|
Evolife_Main.py |
Evolife main
file. |
|
Evolife_Window.py |
Window system for Evolife. The
2012 version is based on QT (previous versions used Tk). |
|
Genetic_map.py |
Defines the location of genes on the DNA
string |
|
Genome.py |
Defines the genome of individuals as a set of
genes |
|
Group.py |
Defines groups, where reproduction and
interactions take place. |
|
Individual.py |
Defines the individual’s properties, which
bring together a genome, a phenome and social
relations |
|
MyScenario.py |
This file is used to instantiate
the scenario and make it accessible to all other modules. |
|
Observer.py |
Stores simulation results and performs
statistics. |
|
Parameters.py |
Interface with configuration file Evolife.evo |
|
Phenotype.py |
Defines a set of non-inheritable
characteristics called ‘phenes’ |
|
Plot_Area.py |
Defines a zone to display graphics. Can be
used independently from Evolife |
|
Population.py |
Defines the whole population as a set of
groups |
|
S_*.py |
Specific scenarios. |
|
Simulation_Thread.py |
Interface with Evolife’s window system |
|
Tools.py |
A few useful functions |
|
TxtWin.py |
Defines a zone to display texts. Can be used
independently from Evolife |
|
Void_Scenario.py |
A scenario that can be
customized. Its execution launches Evolife’s window system
directly, thus bypassing Evolife_Main.py. |
|
labyr.gif |
Image of the labyrinth (used in one basic
scenario) |
|
*.csv |
Files created by Evolife. They
contain evolution curve coordinates (as they are displayed). These files can
be opened with MS-Excel |
|
*.res |
Files created by Evolife. They
contain coordinates for each curve, averaged over time. Such files may be
concatenated to average over several experiments (useful in batch mode). |
|
EvoStart.gen |
This file contains (in text format) the
genome of an entire population to start from (if that option is chosen). |
|
Readme.html |
Evolife documentation. |
|
Readme.txt |
A text version of this file |
|
Evolife.html |
Homepage for the Evolife package. |
|
Ants.py |
Foraging ant colony |
|
CellularAutomaton.py |
Illustrates one-dimensional cellular automata |
|
Cocktail.py |
Illustrates the ‘cocktail party’ effect |
|
Segregationism.py |
Illustrates Thomas Schelling’s famous experiment |
|
Swallows.py |
Illustrates collective decition |
2. How to execute Evolife (+ download)
7. Create your
own Evolife scenario
8. Use Evolife’s
components separately (graphics, ecology, genetic algorithm)
Evolife
Jean-Louis Dessalles
Telecom ParisTech
www.dessalles.fr


To reserve any of the following books for parents/carers and children dealing with a new baby in the family to your local library, click on the links to reserve them from our online catalogue, visit any of our libraries, or contact us on 01473 351249 or<EMAIL_ADDRESS>You can sign up for a library card online for free. You can sign your child up for a library card at any age.
See also: Books for children dealing with sibling rivalry
Mog and the Baby, by Judith Kerr
“Mog’s peaceful day is shattered when a baby comes to visit. ‘Mog loves babies’, says Mrs Thomas, but Mog isn’t so sure!”
A Baby Sister for Frances, by Russell & Lilian Hoban
“With a new baby in the family, no one pays much attention to Frances any more, at least that is what she thinks. So Frances decides to run away.
There’s Going to be a Baby, by John Burningham & Helen Oxenbury
“When is the baby coming? What will we call it? And what will the baby do? As the arrival of a new sibling draws nearer, the questions that stream through a young child’s mind are followed with sensitivity and humour in this guide to welcoming a little brother or sister.”
The Bump, by Mij Kelly & Nicholas Allan
“Tender and witty, this is a wonderful celebration of a baby’s arrival into the world. It is a funny and poignant book exploring a mother’s joy at being pregnant that will help children understand how much they are loved.”
King Baby, by Kate Beaton
“The perfect gift for any family expecting a new baby, this is a laugh-out-loud take on the joy (and exhaustion!) of having a new lovable little tyrant. All hail King Baby!
“King Baby is gracious - bestowing smiles on his public, allowing tickles and hugs, and posing for photo after adorable photo. But this teeny tiny royal ruler also has many demands, and when his faithful subjects can’t quite keep up, King Baby must take matters into his own hands.”
His Royal Tinyness: a terrible true story, by Sally Lloyd-Jones & David Roberts
“Marianna, the most beautiful, ever so kindest princess, lives happily with her mum, dad and gerbil. Happy, that is, until the new baby comes along.
“His Royal Highness King Baby is so smelly. He’s so noisy. And all the talk in the Land is about him - non-stop, All the time! Has there ever been such a time of wicked rule?
“Told from the over dramatic, reluctant big sister’s perspective, Sally Lloyd-Jones’ narrative voice is brought to life by award-winning illustrator David Roberts.”
The New Baby, by Anne Civardi & Stephen Cartwright
“This book is designed to introduce young children to unfamiliar situations in an amusing and friendly way. It features Stephen Cartwright’s delightful illustrations, providing lots to look at and talk about. It provides an ideal starting point for young children and adults to discuss first experiences.”
Sophie and the New Baby, by Catherine & Laurence Anholt
“From the authors of Going to Playgroup and The New Puppy comes this warm, reassuring picture book about the birth of a new baby and the effect this has on a young sibling.”
My Sister is an Alien, by Rachel Bright
“Alfie is crazy about space. He doesn’t think much about anything else, until his new baby sister comes along. Then he notices that she looks a bit funny, she talks funny and sometimes smells funny - she must be an alien! Alfie knows he must take her back to the moon where she belongs.”
Changes, by Anthony Browne
“Joseph’s father had said when he’d left that morning that everything was going to change. But what had he meant?
“This picture book explores the boy’s state of mind as he prepares for the return of his parents with his new baby sister.”
The New Baby, by Jean & Gareth Adamson & Belinda Worsley
“Topsy and Tim are always finding fun adventures in the real world, and this story is assuring for young children having first experiences of their own. Follow Topsy and Tim as their friend Tony has a new baby brother called Jack. At first Tony is a bit jealous of the new baby, but he soon learns how to be a helpful big brother.”
15 Things Not to Do with a Baby, by Margaret McAllister & Holly Sterling
“Congratulations! You now have a baby in your family. Follow our top tips and you’ll all be very happy!
 Do cuddle your baby, read to your baby, sing to your baby and give your baby lots and lots of love.”
We’re Having a Super Baby, by Abie Longstaff & Jane Massey
“A little boy excitedly awaits the birth of a new sibling. When his little sister arrives she doesn’t do much at first. But he knows that she’s going to be special - he just has to wait. Sure enough, one day he discovers that she isn’t just special - she’s SUPER!”
Ruby’s Baby Brother, by Kathryn White & Miriam Latimer
“Ruby’s mum is having a baby, but Ruby is not very happy about it.  When baby Leon comes along, will she change her mind about having a baby brother?”
Brand New Baby, by Bob Graham
“The Arnolds are a family awaiting the arrival of a new baby. Starting with the final days of Mum’s pregnancy Bob Graham’s wry and delightfully observed story chronicles the arrival of baby Walter and his effect upon the rest of the family.”
Baby on Board, by Kes Gray & Sarah Nayler
“There is a baby growing inside Mum’s tum. At one month, he is just a blob - at two months, he is half a piece of chewing gum long - and at three months he is as big as Mum’s thumb. Watch him grow as the pages get bigger.”
We’re Having a Baby, by Marion Cocklico
“A practical and fun first experiences book for toddlers who are expecting a baby brother or sister - with parent and carer tips.”
Lulu and the Noisy Baby: a busy book about becoming a big sister, by Camila Reid & Ailie Busby
“Lulu is the perfect character to introduce preschoolers to new experiences and here she is welcoming her new baby brother. With an accessible, warm and reassuring text and interactive flaps to lift on every page, Lulu and the Noisy Baby offers a gentle introduction to the arrival of a new baby through the eyes of the older sibling.”


DataDistributionManager library. More...
#include "DataDistributionManagerEnums.h"
Go to the source code of this file.
Typedefs | |
| typedef void * | GENERIC_HANDLE |
| Generic handle definition, used for thread, lock, mutex. More... | |
| typedef long | OPERATION_RESULT |
| OPERATION_RESULT type. More... | |
DataDistributionManager library.
DataDistributionManagerTypes.h contains the public types for DataDistributionManager. The API is documented in this file as comments prefixing the class, function, type, enum, define, etc. The C++ interface is STD C++ '03 compliant and adheres to the Google C++ Style Guide.
| #define CHANNEL_HANDLE IDataDistributionChannel* |
Channel handle definition.
| #define CHANNEL_HANDLE_PARAMETER CHANNEL_HANDLE channelHandle |
Channel handle parameter definition.
| #define DDM_COMMIT_FAILED DDM_UNMAPPED_ERROR_CONDITION + 6 |
Commit failed
| #define DDM_DATA_AVAILABLE 0x20000000 |
Data are available
| #define DDM_ELAPSED_MESSAGE_ACKNOWLEDGMENT_TIMEOUT DDM_DATA_AVAILABLE + 5 |
Timeout on acknowledgment
| #define DDM_ELAPSED_MESSAGE_RECEIVE_TIMEOUT_BEGIN DDM_DATA_AVAILABLE + 3 |
Timeout elapsed waiting for messages from the channel
| #define DDM_ELAPSED_MESSAGE_RECEIVE_TIMEOUT_END DDM_DATA_AVAILABLE + 4 |
End timeout condition waiting for messages from the channel
| #define DDM_END_OF_STREAM DDM_DATA_AVAILABLE + 1 |
Reached end of stream
| #define DDM_FATAL_ERROR DDM_UNMAPPED_ERROR_CONDITION + 1 |
Fatal
| #define DDM_INVALID_DATA DDM_UNMAPPED_ERROR_CONDITION + 3 |
Invalid data
| #define DDM_NO_DATA_RETURNED DDM_DATA_AVAILABLE + 2 |
No data returned
| #define DDM_NO_ERROR_CONDITION 0 |
No error
| #define DDM_NO_TIMESTAMP -1 |
Definition to avoid timestamp on write.
| #define DDM_NOT_IMPLEMENTED DDM_UNMAPPED_ERROR_CONDITION + 8 |
Function not implemented
| #define DDM_PARAMETER_ERROR DDM_UNMAPPED_ERROR_CONDITION + 9 |
Errors in parameters
| #define DDM_POINTER_NOT_SET DDM_UNMAPPED_ERROR_CONDITION + 7 |
Pointer not set within subsystem
| #define DDM_SUBSYSTEM_NOT_STARTED DDM_UNMAPPED_ERROR_CONDITION + 4 |
Subsystem not started
| #define DDM_TIMEOUT DDM_UNMAPPED_ERROR_CONDITION + 2 |
Timeout occurred
| #define DDM_UNMAPPED_ERROR_CONDITION 0xF0000000 |
Unmapped error
| #define DDM_WRITE_FAILED DDM_UNMAPPED_ERROR_CONDITION + 5 |
Failed on write
| #define OPERATION_FAILED | ( | or | ) | (((OPERATION_RESULT)(or)) < 0) |
Check if operation failed.
| #define OPERATION_SUCCEEDED | ( | or | ) | (((OPERATION_RESULT)(or)) >= 0) |
Check if operation succeeded.
| typedef void* GENERIC_HANDLE |
Generic handle definition, used for thread, lock, mutex.
| typedef long OPERATION_RESULT |
OPERATION_RESULT type.
Errors or conditions from underlying layer


Curve:
Field:
n:
P:
Q = n · P:
Scalar multiplication over the elliptic curve in 𝔽.
The curve has points (including the point at infinity).
The subgroup generated by P has points.
Warning: this curve is singular.
Warning: p is not a prime.
Vishwakarma Institute of Technology, Pune.
Curve:
Field:
n:
P:
Q = n · P:
Scalar multiplication over the elliptic curve in 𝔽.
The curve has points (including the point at infinity).
The subgroup generated by P has points.
Warning: this curve is singular.
Warning: p is not a prime.


{% extends "base.html" %} {% block page-title %}Speleo| Contact{% end %} {% block content %}
What follows is what we know about you
| # | Owner | Created | Title | Query |
|---|---|---|---|---|
| {{ count }} | {{ query.user.username }} | 12/12/12 12:12:12 | {{ query.title }} | {{ query.display }} |


What we want most for our wedding is to have our friends and family there to celebrate the occasion with us. So more than anything we’re simply grateful for your presence. If you would like to get us something, we’d love that too...you can find our registry below.


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

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='inputs_real')
inputs_z = tf.placeholder(tf.float32, (None, z_dim), name = 'inputs_z')
learning_rate = tf.placeholder(tf.float32, name='learning_rate')
return (inputs_real, inputs_z, learning_rate)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the 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 generator, tensor logits of the generator).
def leaky_relu(x, alpha=0.2, name='leaky_relu'):
tf.maximum(x, alpha * x, name=name)
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)
"""
alpha = 0.2
# TODO: Implement Function
with tf.variable_scope('discriminator', reuse=reuse):
# Hidden layer
# Input layer is 28x28x3
x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
#x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
relu1 = tf.maximum(alpha * x1, x1)
#relu1 = leaky_relu(x1, alpha=0.01)
# 14x14x64
x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
bn2 = tf.layers.batch_normalization(x2, training=True)
relu2 = tf.maximum(alpha * bn2, bn2)
#relu2 = leaky_relu(bn2, alpha=0.01)
# 7x7x128
x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
bn3 = tf.layers.batch_normalization(x3, training=True)
relu3 = tf.maximum(alpha * bn3, bn3)
#relu3 = leaky_relu(bn3, alpha=0.01)
# 4x4x256
# Flatten it
flat = tf.reshape(relu3, (-1, 4*4*256))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the 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
with tf.variable_scope('generator', reuse=not is_train):
alpha = 0.2
h1 = tf.layers.dense(z, out_channel_dim, activation=None)
# First fully connected layer
x1 = tf.layers.dense(z, 4*4*256)
# Reshape it to start the convolutional stack
x1 = tf.reshape(x1, (-1, 4, 4, 256))
x1 = tf.layers.batch_normalization(x1, training=is_train)
x1 = tf.maximum(alpha * x1, x1)
#x1 = leaky_relu(x1, alpha=0.01)
# 4x4x256 now
x2 = tf.layers.conv2d_transpose(x1, 128, 4, strides=1, padding='valid')
x2 = tf.layers.batch_normalization(x2, training=is_train)
x2 = tf.maximum(alpha*x2, x2)
# x2 = leaky_relu(x2, alpha = 0.01)
# 7x7x128 now
x3 = tf.layers.conv2d_transpose(x2, 64, 5, strides=2, padding='same')
x3 = tf.layers.batch_normalization(x3, training=is_train)
#x3 = leaky_relu(x3, alpha = 0.01)
x3 = tf.maximum(alpha*x3, x3)
# 14x14x64 now
# Output layer
logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
# 28x28x3 now
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
# Generator network here
g_model = generator(input_z, out_channel_dim, is_train=True)
# g_model is the generator output
# Disriminator network here
d_model_real, d_logits_real = discriminator(input_real, reuse=False)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
# Calculate losses
smooth = 0.1
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
labels=tf.ones_like(d_logits_real)*(1-smooth)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.zeros_like(d_logits_real)))
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_logits_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()
g_vars = [var for var in t_vars if var.name.startswith('generator')]
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1).minimize(d_loss, var_list=d_vars)
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
g_train_opt = tf.train.AdamOptimizer(learning_rate, 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")
"""
#model_inputs(image_width, image_height, image_channels, z_dim):
# TODO: Build Model
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_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr, beta1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
i = 0
for batch_images in get_batches(batch_size):
i += 1
# TODO: Train Model
batch_images = batch_images * 2.0
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr:learning_rate})
_ = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr:learning_rate})
if i % 100 == 0:
show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
if i % 10 == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{} - Batch {}/{}: ".format(epoch_i+1, epoch_count, i, data_shape[0]//batch_size),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
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.
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
print(mnist_dataset.shape)
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
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 = 0.4
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


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Five Largest Protests in U.S. History
The tradition of protests that demand progressive change in the United Stated continue.
Here are the country's five of the top protests in terms of attendance.
- George Floyd protests nationwide in 2020 with an estimated 26 million protesters.
- Women's March in 2017 drew about 5.6 million people nationwide.
- March for Our Lives, a student led protest in 2018 against gun violence, drew about 2 million people.
- Women's March in 2018 drew about 1.5 million protesters.
- March on Washington for Lesbian, Gay and Bi Equal Rights and Liberation drew about 1 million people in 1993.
Compiled from wikipedia


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Various Monte Carlo equilibrium sampling algorithms, which simulate only one Markov chain.
Here is how to sample from a PDF using the HMCSampler class. In the following snippet we draw 5000 samples from a 1D normal distribution and plot them:
>>> import numpy >>> from csb.io.plots import Chart >>> from csb.statistics.pdf import Normal >>> from csb.statistics.samplers import State >>> from csb.statistics.samplers.mc.singlechain import HMCSampler
>>> initial_state = State(numpy.array([1.])) >>> grad = lambda q, t: q >>> timestep = 1.5 >>> nsteps = 30 >>> nsamples = 5000
>>> sampler = HMCSampler(Normal(), initial_state, grad, timestep, nsteps)
>>> states = [] >>> for i in range(nsamples): sampler.sample() states.append(sampler.state)
>>> print('acceptance rate:', sampler.acceptance_rate) 0.8
>>> states = [state.position[0]for state in states] >>> chart = Chart() >>> chart.plot.hist([numpy.random.normal(size=5000), states], bins=20, normed=True) >>> chart.plot.legend(['numpy.random.normal', 'HMC']) >>> chart.show()
First, several things which are being needed are imported. As every sampler in this module implements a Markov Chain, an initial state has to be chosen. In the following lines, several parameters are set:
The empty list states is initialized. It will serve to store the
samples drawn. In the loop, sampler.sample()
is repeatedly
called. After each call of sampler.sample()
, the current
state of the Markov Chain is stored in sampler.state and this state is
appended to the sample storage list.
Then the acceptance rate is printed, the numeric values are being extracted from the State objects in states, a histogram is created and finally plotted.
| Classes | |
|
AbstractNCMCSampler Implementation of the NCMC sampling algorithm (Nilmeier et al., "Nonequilibrium candidate Monte Carlo is an efficient tool for equilibrium simulation", PNAS 2011) for sampling from one ensemble only. |
|
|
AbstractSingleChainMC Abstract class for Monte Carlo sampling algorithms simulating only one ensemble. |
|
|
HMCSampler Hamilton Monte Carlo (HMC, also called Hybrid Monte Carlo by the inventors, Duane, Kennedy, Pendleton, Duncan 1987). |
|
|
NCMCProposalCommunicator Holds all information (that is, the trajectory with heat, work, Hamiltonian difference and jacbian) needed to calculate the acceptance probability in the AbstractNCMCSampler class. |
|
|
RWMCSampler Random Walk Metropolis Monte Carlo implementation (Metropolis, Rosenbluth, Teller, Teller 1953; Hastings, 1970). |
|
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SimpleProposalCommunicator This holds all the information needed to calculate the acceptance probability in both the RWMCSampler and HMCSampler classes, that is, only the proposal state. |
| Variables | |
__package__ =
|
| Home | Trees | Indices | Help |
|
|---|
| Generated by Epydoc 3.0.1 on Tue Jul 4 20:19:04 2017 | http://epydoc.sourceforge.net |


Trent Boult likely to miss second Test due to pain in ribs
by ESPNcricinfo staffTrent Boult is likely to miss the second Test of New Zealand's series against England after leaving the field due to a pain in the right-hand side of his ribs on the fifth day of the first Test.
Boult, who bowled one over on the final day before going off, will undergo an MRI scan tomorrow to determine the extent of the injury.
The second Test at Hamilton starts on Friday, and with New Zealand starting a three-match series in Australia soon after that fixture, it seems unlikely he will be risked. Lockie Ferguson and Matt Henry are the other seam-bowling options in the 15-man squad.
Boult has missed only three Tests since 2016, and just six of New Zealand's 70 since his debut.


CPS530
LAB MANUAL
THE LABS
Lab#1
Lab#2
Lab#3
Lab#4
Lab#5
Lab#6
Lab#7
Lab#8
All labs to be done individually
HOW TO SUMBIT
use my.ryerson.ca (D2L/Brightspace)


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DR.SANOU BELLO Dear, I am the head of Accounts and Audit Department of Bank of Africa, Ouagadougou . I decided to contact you after a careful thought that you may be capable of handling this business transaction which i explained below; In my department we discovered an abandoned sum of $10.5m US dollars (Ten million, five hundred thousand US dollars). In an account that belongs to one of our foreign customer who died along with his entire family in 2000 in a plane crash. Since we got information about his death, we have been expecting his next of kin to come over and claim his money because we cannot release it unless somebody applies for it as next of kin or relation to the deceased as indicated in our banking guidelines but unfortunately we learnt that his supposed next of kin(his son and wife) died alongside with him at the plane crash leaving nobody behind for the claim . It is therefore upon this discovery that I and other officials in my department now decided to make this business proposal to you and release the money to you as the next of kin (We want to present you as his busines asociate )to the deceased for safety and subsequent disbursement since nobody is coming for it and we don't want this money to go into the Bank treasury as unclaimed Bill. The Banking law and guideline here stipulates that if such money remained Unclaimed after seven years, the money will be transferred into the Bank treasury as unclaimed fund. The request of foreigner as next of kin in this business is occasioned by the fact that the customer was a foreigner and a Burkinabe cannot stand as next of kin to a foreigner . We agree that 30% of this money will be for you as foreign partner, in respect to the provision of a foreign account, 10 % will be set aside for expenses incurred during the business and 60% would be for me and my colleagues. There after I and my colleagues will visit your country for disbursement according to the percentages indicated. Therefore to enable the immediate transfer of this fund to your account as arranged,you must apply first to the bank as next of kin of the deceased customer. Upon receipt of your reply, I will send to you by fax or email the text of the application. I will not fail to bring to your notice that this transaction is hitch free and that you should not entertain any atom of fear as all required arrangements have been made for the transfer. I expect that you contact me immediately as soon as you receive this letter. Hoping to hear from you immediately. Yours faithfully, DR .SANOU BELLO Accounts & Audit Department , Bank of Africa Nb.also give me your telephone and fax numbers for easy comunication with you. _________________________________________________________________ Express yourself instantly with MSN Messenger! Download today it's FREE! http://messenger.msn.click-url.com/go/onm00200471ave/direct/01/


[add-registry-section]
reg-root, [subkey], [value-entry-name], [flags], [value]
An HKLM specification in such an add-registry section designates the HKey Local Machine registry entry.
The special meaning of this section (along with other DevUpgrd and W2KDriver sections) is linked to OS upgrades from Win95/98 to Win2000, when devices that are not supported by default in Win2000 are already installed on Win95/98 to be upgraded.
The Upgrade Wizard cannot accomplish this transition; the DevUpgrade Migration DLL is a tool that covers this aspect.
The system inf files referenced in Include or Needs sections of the current inf file (of the device to be migrated) might have the same name in Win95/98 and in Win2000. Thus, at a later point during the upgrade process, the system might prompt the user for Win95/98 media, leading to an incorrectly installed driver.
The DevUpgrade Migration DLL is registered on the Win95/98 system to be used at a later time by the OS upgrade process. The devices needed to be migrated have to be registered with the DevUpgrade Migration DLL by adding to the registry values specifying the PnP ID of the device and the path to the Win2000 INF file.
The inf file of the device has to be modified accordingly, as exemplified by AC97SMPL.INF.
For more information about the device migration, see the Microsoft DDK or the MSDN collection.
Processing this section will add into the registry under HKLM the values :
Called from :


If you’re coming to Nottingham from the north, south or west, use the M1 motorway. From here, there are three ways to reach the city centre:
NTU does not provide parking. Nottingham has a variety of different municipal car parks as well as park and ride facilities. If you’re intending to park in the city centre, the closest (and largest) facilities to our City Campus are:
Parking is very limited in Nottingham city centre, so we suggest you use one of the many park and ride services or the tram network.
All trams will pass the NTU stop. Full details can be found on the Nottingham Tram website, but your options will include:
The tram costs £ 3.50 for an adult day ticket. You can buy your ticket from the machines at the tram stop. Change is given, and parking is free. The tram runs every 10-15 minutes and the NTU stop on Goldsmith Street is in the middle of the City Campus.
If you are travelling from the east we suggest you follow the A52 to Nottingham and take the park and ride from Nottingham Racecourse. The bus will drop you off on Lower Parliament Street, which is just a short walk from NTU. Buses run every 15 minutes.
Nottingham train station is close to the city centre and is connected to Nottingham's bus and tram networks. You can find timetable information and ticket prices through the National Rail website.
If you book a few weeks in advance you may find cheaper tickets through East Midlands Trains, The Trainline, Virgin Trains and Northern Rail
Getting to City Campus from the train station
NTU's City Campus is just a fifteen minute walk from Nottingham train station.
Alternatively, you can take a northbound tramfrom the train station. You can buy return tickets for £ 3.50 from the machines at the tram stop. Change is given. NTU has its very own stop for the City Campus located on Goldsmith Street.
East Midlands Airport is approximately 15 miles from Nottingham city centre, and it takes about 30 minutes to travel into the city by car or bus. We do however advise travelling by air the day before your graduation ceremony, to minimise the chance of any disruption which may prevent you attending your ceremony.
The airport is served by the Skylink bus service that runs up to every 20 minutes during the day and hourly at night, 24 hours a day, seven days a week. The Skylink bus stops in Nottingham city centre, just a short walk away from our City Campus. Tickets cost £ 5 for a single or £ 8 for a return.
Alternatively, a taxi from East Midlands Airport to the city centre will cost around £ 25.
Other airports that you can fly into include Heathrow, Birmingham, Manchester, Gatwick, Luton and Stansted. If you fly into any of these you will need to plan your journey from those cities to Nottingham.
Nottingham's Broadmarsh bus station is currently closed for redevelopment and services have been relocated. National Express services are now arriving and departing from Station Street, outside Nottingham train station.
Getting to City Campus from Station Street
From Station Street NTU's City Campus is less than a 15 minute walk away. From the entrance of the train station, turn right and walk towards the Broadmarsh shopping centre. Walk through the Broadmarsh shopping centre and take the ground floor exit by Boots. Continue to walk up Lister Gate and then Wheeler Gate until you reach the Market Square.
Cross over the square and walk up Market Street where you will see the Theatre Royal. Behind the theatre, you will find the concert hall and the University's Newton building.


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Sleeping next to a snorer could be bad for your health
Having a fractured sleeping pattern increases the risk of anxiety and depression
by Graeme Brown, Christopher HarperWe all know someone who snores.
Whether it's your partner or your mum or dad, we all know that it can be an absolute pain to deal with.
And now a new report has revealed that it might even be damaging to your health.
Having a fractured sleeping pattern increases the risk of anxiety and depression, as well as our chances of developing obesity or suffering a stroke, so you've got science on your side next time you want to give a not-so-gentle prod to the person next to you, reports The Mirror .

And it should give cause for concern to the snorer too, as the problem is associated with constant irritability, which is likely to have an affect on your relationship.
Your partner's snoring often interferes with your sleep and waking them up to make it stop will often upset them too, chipping away at your relationship little by little.
A negative atmosphere at home can result in stress, inflammation, changes in appetite and even a weakened immune system causes by the constant arguing.
Queen's University in Canada carried out a study to find the affects of snoring on hearing, using four couples in which one person in the relationship had severe sleep apnea.
It found the sound didn't affect snorers much, but 100% of their partners suffered hearing damage equivalent to sleeping next to an industrial machine, especially in the ear closest to the snores.
Loud noises can also raise blood pressure, as found in a study by the Imperial College of Science in London.
It found the greater the level of noise, the higher the risk of hypertension which can lead to kidney problems, dementia and heart disease.
Graeme Brown


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Zoolander (2001) 2 1/2 stars out of 4. Starring Ben Stiller, Owen Wilson, Will Ferrell, Christine Taylor, Milla Jovovich, Jerry Stiller and Jon Voight. Story by Drake Sather and Ben Stiller. Screenplay by Drake Sather & Ben Stiller and John Hamburg. Directed by Ben Stiller. Approx. 100 minutes. Rated PG-13.
The movie, co-produced, co-written, directed by and starring Ben Stiller, is nothing more than a satirical jab at the fashion industry and the perception of male models as dim-witted, self-obsessed dolts who are pliable and easily manipulated. It almost seems like a redundancy.
It doesn't sound like much, and nor does it seem like it takes much effort to skewer male models. After all, Fabio does it all the time just by being himself.
Zoolander's plot, unfortunately, is in bad taste, considering today's political climate. It revolves around a cartel of designers wanting the new prime minister of Malaysia assassinated because he is vowing to raise the wages of his countrymen who slave in the sweatshops run by said same designers.
It would have been less objectionable if a real nation had not been used and the assassination plot had been altered to perhaps merely overthrowing the leader.
  Recent events speak for themselves as to why this scene should have been deleted.
And, yes, in fairness, people at the preview screening howled, thinking it was hilarious. But there may be some in the audience - myself included - whose sensitivities may preclude them from seeing the humor in such a situation.
But we cannot toss out the entire barrel because a couple of the apples are rotten.
In truth, Zoolander is pure silliness, a no-brainer, just-sit-back-and-laugh feature.
The plot is simple. Since male models are easy to control one must be brainwashed - or in this case given a light rinse - to carry out the diabolical plot. And who is the most vacuous model around, why three-time model of the year, Derek Zoolander master of the "blue-steel" look.
Stiller captures the walk and talk - as well as the look - of the male model. He snaps his head, raises an eyebrow, furrows his brow in incomprehension with comical ease.
His Zoolander is a man-child, as dense as an Adirondack forest. When his reporter girlfriend, Matilda (woodenly played by Stiller's wife, Christine Taylor) tells Zoolander and his main rival, Hansel (Owen Wilson), that she was once bulimic, Zoolander looks at her in awe and says, "You can read minds?"
That's about the level of the movie's humor.
A "walk-off" between Zoolander and Hansel, judged by David Bowie, is one of the film's more inventive sequences, as is a scene in which former X-Files star David Duchovny shows up as a conspiracy theorist who explains to Zoolander and Matilda how all the political assassinations of the past 200 years have been carried out by male models.
Wilson walks away with the film as the laid-back Hansel, who shifts from Zoolander's main rival to ally in his attempt to halt the assassination plot. With his long blonde locks and outlandish outfits, he makes a charming blockhead.
The movie's main villain, Mugatu, is portrayed by Saturday Night Live's Will Ferrell who has yet to find a film role that truly exploits his talents. Here, he is reduced to merely yelling and mugging, while wearing ridiculous outfits with a crop of white hair that looks like it was borrowed from the Three Stooges' Larry Fine.
At least Stiller made the film a family affair, giving his mom, Anne Meara, the small role of a protester, while his dad, Jerry Stiller, plays Zoolander's agent.
Zoolander has its shortcomings. It is definitely not sophisticated humor, but it does provide several laughs. And you will never be able to look at a model again without wondering how akin he is to Derek Zoolander.
Bob Bloom is the film critic at the Journal and Courier in Lafayette, IN. He can be reached by e-mail at<EMAIL_ADDRESS>or at<EMAIL_ADDRESS>Other reviews by Bloom can be found by going to www.jconline.com and clicking on golafayette. Bloom's reviews also can be found on the Web at the Internet Movie Database: http://www.imdb.com/M/reviews_by?Bob+Bloom
========== X-RAMR-ID: 29624 X-Language: en X-RT-ReviewID: 252167 X-RT-TitleID: 1110145 X-RT-SourceID: 872 X-RT-AuthorID: 1363 X-RT-RatingText: 2.5/4
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


Grant Access:
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§580-4 Cross-complaint. In any action for annulment, divorce, or separation, a cross-complaint for annulment, divorce, or separation may be filed and affirmative relief granted thereon as fully and effectually as on an original complaint. The cross-complaint shall be signed and sworn to by the cross-complainant and shall be served in the same manner as an original complaint. [L 1913, c 121, §1; RL 1925, §2969; RL 1935, §4464; RL 1945, §12214; RL 1955, §324-24; am L 1966, c 22, §4; HRS §580-4]
Case Notes
Statute valid; "divorce" means every kind of divorce. 22 H. 425; 25 H. 638; 29 H. 464.
Error to disregard. 30 H. 473.
Cited: 26 H. 128.


Introduction
The digi.me private sharing platform empowers developers to make use of user data from thousands of sources in a way that fully respects a user’s privacy, and whilst conforming to GDPR. Our consent driven solution allows you to define exactly what terms you want data by, and the user to see these completely transparently, allowing them to make an informed choice as to whether to grant consent or not.
Requirements
Development
- Android Studio 3.0 or newer.
- Gradle 5.0 or newer.
- Kotlin 1.30 or newer. *
* The SDK is written entirely in Kotlin, but is fully compatible with Java projects.
Deployment
- Android 5.1 or newer (API Level 21).
Installation
Gradle/Maven
Ensure you have the JCenter repository in your root
build.gradle
file.
(This is automatically added to new Android Studio projects.)Include the digi.me SDK as a dependency in your app
build.gradle
file:implementation "me.digi:sdk:2.1.0"
Manual
- Download the source code for the SDK.
- In Android Studio, import the SDK as a module.
In your app
build.gradle
, include the module as a dependency:implementation project(":sdk")
Getting Started - 5 Simple Steps!
We have taken the most common use case for the digi.me Private Sharing SDK and compiled a quick start guide, which you can find below. Nonetheless, we implore you to explore the documentation further.
This example will show you how to configure the SDK, and get you up and running with retrieving user data.
1. Obtaining your Contract ID, Application ID & Private Key:
To access the digi.me platform, you need to obtain an AppID
for your application. You can get yours by filling out the registration form here.
In a production environment, you will also be required to obtain your own Contract ID
and Private Key
from digi.me support. However, for sandbox purposes, we provide the following example values:
Example Contract ID: fJI8P5Z4cIhP3HawlXVvxWBrbyj5QkTF
Example Private Key:
Download: P12 Key Store
Password: monkey periscope
You should include the P12 file in your project assets folder.
2. Configuring Callback Forwarding:
Because the digi.me Private Sharing SDK communicates with the digi.me app, you are required to forward invocations of onActivityResult
through to the SDK so that it may process responses. In any activity that will be resposible for invoking methods on the SDK, override onActivityResult
as below:
override fun onActivityResult(requestCode: Int, resultCode: Int, data: Intent?) {
super.onActivityResult(requestCode, responseCode, data)
DMEAppCommunicator.getSharedInstance().onActivityResult(requestCode, responseCode, data)
}
3. Configuring the DMEPullClient
object:
DMEPullClient
is the object you will primarily interface with to use the SDK. It is instantiated with a context, and a DMEPullConfiguration
object. The provided context should always be the main application context.
The DMEPullConfiguration
object is instantiated with your AppID
, Contract ID
and Private Key
in hex format. We provide a convenience method to extract the private key. The below code snippet shows you how to combine all this to get a configured DMEPullClient
:
val privateKeyHex = DMECryptoUtilities(applicationContext).privateKeyHexFrom("p12-filename", "p12-password")
val configuration = DMEPullConfiguration("app-id", "contract-id", privateKeyHex)
val pullClient = DMEPullClient(applicationContext, configuration)
4. Requesting Consent:
Before you can access a user’s data, you must obtain their consent. This is achieved by calling authorize
on your client object:
pullClient.authorize(this) { session, error ->
}
NB: this
represents the activity which is setup to forward onActivityResult
, as above.
If a user grants consent, a session will be created and returned; this is used by subsequent calls to get data. If the user denies consent, an error stating this is returned. See Handling Errors.
5. Fetching Data:
Once you have a session, you can request data. We strive to make this as simple as possible, so expose a single method to do so:
pullClient.getSessionData({ file, error ->
// Handle each downloaded file here.
}) { fileList, error ->
// Any errors interupting the flow of data will be directed here, or null once all files are retrieved.
// The file list here will represent the complete list of files that were downloaded.
}
For each file, the first ‘file handler’ block will be called. If the download was successful, you will receive a DMEFile
object. If the download fails, an error.
Once all files are downloaded, the second block will be invoked to inform you of this. In the case that the data stream is interrupted, or if the session obtained above isn’t valid (it may have expired, for example), you will receive an error in the second block. See Handling Errors.
DMEFile
exposes the method fileContentAsJSON
which attempts to decode the binary file into a JSON map, so that you can easily extract the values you need to power your app. Not all files can be represented as JSON, see Raw Data for details.
Contributions
digi.me prides itself in offering our SDKs completely open source, under the Apache 2.0 Licence; we welcome contributions from all developers.
We ask that when contributing, you ensure your changes meet our Contribution Guidelines before submitting a pull request.
Further Reading
The topics discussed under Quick Start are just a small part of the power digi.me Private Sharing gives to data consumers such as yourself. We highly encourage you to explore the Documentation for more in-depth examples and guides, as well as troubleshooting advice and showcases of the plethora of capabilities on offer.
Additionally, there are a number of example apps built on digi.me in the examples folder. Feel free to have a look at those to get an insight into the power of Private Sharing.


Here's how to fast travel with a Flying Taxi in Pokémon Sword and Shield
Source: iMore
As much as I think that Pokémon Sword and Shield's Galar region is an absolute feast for the eyes, it's super convenient to fast traveling around the map instead of backtracking trails the long way. One of the best things about Pokémon Sword and Shield is that you can fly to places you've already been to from pretty early on. In previous games, you usually only acquired this ability once you'd made it through most of the map. The funny thing is, while the game lets you know that Flying Taxis are available, it doesn't really teach you how to use this feature. Here's how to use the Flying Taxi in Pokémon Sword and Shield.
When can I start using the Flying Taxi?
Shortly after you've reached the Motostoke Stadium you'll be able to use the Flying Taxi, which is basically a Corviknight that flies you around the map. Depending on how much time you spend investigating the Wild Area and catching Pokémon, it could take you anywhere from 30 minutes to an hour to unlock the Flying Taxi after you've started a new game.
How to use a Flying Taxi
- While in the game press X on your right Joy-Con.
- When the menu pops up, select Town Map. Source: iMore
- When the map appears, using the joystick or the buttons on your left Joy-Con, move the cursor over the location you want to fast travel to. Note that you can only fast travel to loctions you've been to already.
- Press A to initiate the Flying Taxi. Source: iMore
- The game will ask you if this is really where you want to go. Select "Yes".
In the blink of an eye, you'll be at the city, route, or location you selected and you can continue on with your game.
Source: iMore
There you have it. Now you know how to fast travel around the Galar region map using the Corviknight Flying Taxi. It's kind of funny that the game doesn't walk you through this process, given how tutorial-focused it is at first. At any rate, it's nice that you can fly around the map so easily from the get-go. Enjoy your travels in the Galar region and good luck catching all those new Pokémon!
Additional Equipment
I love these accessories and have found that they greatly improve my Switch gaming sessions. See if anything catches your eye.
Pro Controller ($62 at Amazon)
Whether you've got the larger Nintendo Switch or the Switch Lite, you can benefit from using a Pro Controller. It features motion controls, amiibo functionality, and a better ergonomic design to enhance your gaming experience. It's also wireless, so you won't have to worry about tripping over cables.
Hori Compact Playstand ($13 at Amazon)
Whether you're using a Switch Lite or the larger Switch having a stand can be incredibly helpful. I typically use mine on the go when I either need to charge my Switch or when I simply want to bring the screen closer to my face.
Gimme some space! ($26 at Amazon)
Regardless of the version of Switch you're using, you can always benefit from having more memory on the system. This microSD card has a cute Mario mushroom on it and gives you 128GB. That's plenty of space for screenshots, game data, and downloads.
Updated September 24, 2019: Added section on setting network DNS back to Automatic.
Get More Switch
Nintendo Switch
- Nintendo Switch Review
- Best microSD Cards for your Nintendo Switch
- Best Travel Cases for Nintendo Switch
- Best Nintendo Switch Accessories
- Nintendo Switch Jailbreak: Everything you need to know!
We may earn a commission for purchases using our links. Learn more.


What to Do
When performing sensitive operations, include unique tokens in HTTP requests to prevent cross-site request forgery (CSRF).
Why
CSRF may occur when an attacker can form a URL which performs an action on behalf of an authenticated user. Forming such URLs becomes much more difficult if unique tokens are included in HTTP requests. Including difficult-to-predict tokens in HTTP requests is an effective defense against CSRF attacks.
How
To include unique tokens in HTTP requests:
- Identify sensitive operations. Review application design and code to identify all operations that require authorization.
- Identify code that performs sensitive operations. Identify all pages that are involved in performing sensitive operations. This includes both the pages that link to sensitive operations and the code that actually carries out the sensitive operations.
- Choose a method for generating unique tokens. There are different ways to generate unique tokens.
- Add the unique token to the session. On each page that links to sensitive operations, add code that generates unique tokens and stores them in session variables.
- Add unique tokens to HTTP requests. On each page that links to sensitive operations, add code that sends the generated unique tokens in HTTP requests. One of the simplest ways to do this is to include the tokens in hidden fields in forms.
- Add token validation code. On each page that carries out sensitive operations, add code that compares the tokens sent in HTTP requests to the tokens stored in session variables. This ensures that the tokens are generated by the server as a part of normal application workflow and therefore the requested action is being performed by a legitimate user.
Code Examples
Vulnerabilities


This can be useful when you want to try out features that are only available in a specific browser
Open JavaScript console to see results


|
Elektra
0.9.0
|
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if we should drop the plugin (see below in Rationale)Names for flag:
DEPENDENCIES
-> might be used for add_plugin
ASSEMBLE_DEPS
FIND_DEPS
-> not only finding happensPROCESS_DEPS
HANDLE_DEPS
DEPS_MODE
-> too generic, new terminologyCHECK_DEPS
-> not only checking happensDO_DEPS
-> sounds funnyFIND_PACKAGES
-> not only packages are subject to this phaseCOLLECT
-> needs NOTFIND_PHASE
-> needs NOTDEPENDENCY
-> ambiguousIntroduce a cmake process where all plugins are processed three times. Following cmake variables are used for the phases:
COLLECTION_PHASE
.. collect all add_plugins
DEPENDENCY_PHASE
.. resolve all dependencies, do add_plugins
againADDTESTING_PHASE
.. (reserve for potential 3rd phase)COLLECTION_PHASE
is ON
), add_plugin internally builds up:ADDED_PLUGINS
REMOVED_PLUGINS
ADDED_DIRECTORIES
DEPENDENCY_PHASE
is ON
, only considering ADDED_DIRECTORIES
), with:find_libraries
, actually search for libraries on the system (only relevant libraries of plugins that are considered for inclusion)add_plugin
, with actually adding the pluginsADDTESTING_PHASE
is ON
), eitherADD_TEST
in add_plugin
, oradd_plugintest
(for unittests that have dependencies to bindings)Solves all the issues without adding too much complexity for actually adding plugins.
Maintaining additional mappings is very time-consuming. Simply adding all plugins directories is problematic. It would:
ADDED_DIRECTORIES
of variants will be kept


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The Anchoring mechanism in OtterUI allows the UI Designer to anticipate and support multiple UI resolutions. For example, an interface may be built entirely for a 1024x768 resolution, but must also render properly on 1280x720. Simply stretching of the interface at rendertime may cause the UI to be warped, causing visual artifacts and other anomalies. Anchors address this issue by appropriately manipulating controls when the resolution changes. An anchored control effectively anchors one (or all) of its bounding box's edges against its parent's bounding box edges. The anchor can either be an absolute or relative anchor:
To demonstrate how these two differ, consider a simple sprite on a 500x500 resolution UI:
If the resolution changes, say to 800x400, the sprite will remain at the exact same location as before:
But in the 500x500 resolution the object was in the middle, whereas it is now off the side. Suppose we want the sprite to always be a fixed width from the right side of the view. To do this, select the control, and in the properties window click on the "Anchors" property. It will bring up the following control:
The blue box represents the control, and the darker blue outline represents its bounding box. The gray lines represent the constraints against the control's parent. To create an Absolute Anchor on the right bounding box edge, click the gray lines next to the control's right edge, to turn it black:
This means there is now an Absolute Anchor on the right edge of the control to the right edge of the control's parent. As the parent control's width changes, OtterUI will ensure that the distance between the control's right edge and the parent's right edge remains constant.
Changing the resolution from 500x500 to 800x400 yields the following result:
Relative Anchors work similarly, except that they are created by clicking twice on the gray line, changing it from gray to black to black with a '%' in the middle:
(note, repeatedly clicking on the anchor will repeatedly cycle the anchor between its three types: none, absolute, and relative, in that order)
The Relative Anchor simply means that the OtterUI will maintain the same ratio of a particular edge to its corresponding parent edge among different resolutions. In the above example, changing the anchor type from Absolute to Relative and then switch resolutions from 500x500 to 800x400 yields the following result:
Notice that the distance between the sprite's right edge and the view's right edge is not the same as before? That is because the ratio of the distance between the sprite's right edge and view's right edge against the new resolution is the same as before. In other words, if in the 500x500 resolution the distance was 50px, the ratio would have been:
50px/500px = 0.1
In the new resolution, we need to maintain the same ratio, so the new distance between the two edges is: 800px * 0.1 = 80
As a result, the right edge is placed 80px away from the right edge of the view.
Notes and Caveats:
Multiple Anchors:
Since the anchoring system works on edges, anchors can be placed on any combination of edges with any combination of anchor types (one type per edge). If an edge is not anchored, OtterUI assumes that the edge can be "moved" and will attempt to adhere to the size and placement of the control. If multiple edges are anchored, OtterUI will resize / reposition the control as necessary to maintain the anchors.
Rotation:
At runtime, anchoring is performed pre-rotation, which means that rotated controls may not behave as expected. This is a known issue.
Working in a resolution:
When a control is modified (ex, moved, rotated, etc) in a particular resolution, OtterUI assumes that that was the designer's intention and that the control must appear exactly like that in the particular resolution. What this means in practical terms is that you must remember to switch back to your working resolution whenever you test your work in other resolutions. If, for example, you switch to a test resolution, move a control up 50px, create a keyframe, and switch back to the working resolution the control may be moved in ways you were not expecting. That is because OtterUI was told that the control must appear in a particular way in the resolution that you made changes within. Switching back to the rest resolution will place the control back where it was expected.
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