Sometimes an algorithm's performance relies on multiple insights being traded together - such as pairs trading and an options straddle. These insights should be grouped. Insight groups signal to the execution models that the insights need to be acted on as a single unit to maximize the alpha created.
When you return the grouped insights from your Alpha Model, simply use the Insight.Group()
helper method to mark the insights as a set.
// Insight helper for grouping insights together return Insight.Group(insight1, insight2, insight3);
# Insight helper for grouping insights together return Insight.Group( [ insight1, insight2, insight3 ] )


The Prince of Egypt
Starring The Voices Of: Val Kilmer and Ralph Fiennes Directed By Brenda Chapman, Steve Hickner and Simon Wells. Running Time: 1 hour 37 minutes
A Dreamworks SKG Picture.
After the average Mouse Hunt, the silly Small Soldiers and the entertaining Antz, Dreamworks try again with the children's film market with this stunning adaptation of the Moses story. As the film warns us at the beginning, it's not the full story, and some liberties have been taken, but generally this the Bible story we all know and love, featuring burning bushes, the plagues and the parting of the red sea. The only thing missing is Charlton Heston. Instead, we get Val Kilmer voicing Moses, an hebrew who is placed in a river by his mother and rescued by the Queen of Egypt (Helen Mirren.) We flash forward several years to see Moses and brother Rameses (Fiennes) all grown up. Moses soon finds out from brother and sister Miriam and Aaron (Sandra Bullock and Jeff Goldblum) that he isn't really the son of the Pharaoh Seti (Patrick Stewart) and that it his destiny to free the Hebrews from the Egyptians tyrannical rule. He soon sets about this, along with his wife Tzipporah (Michelle Pfeiffer.) Rameses and his two wizard friends (voiced by Steve Martin and Martin Short) try to stop Moses completing his task.
While the plot is familiar and somewhat exciting for adults, for children reared on Aladdin and Hercules, this film could be the most godawful thing they've ever seen. Gone is the quick fire humour and standard, easy to follow plot. But, spectacularly, and to Dreamworks credit, nearly every child in the audience was captivated by the story and events happening on screen. Jeffrey Katzenberg, who produced this film, certainly knows how to keep a child interested. He's included some songs in the film, plus some comedy, and a exciting chariot chase, to keep the children interested. And somehow, himself and the directors still manage to get all the drama and intensity of the story across, without ever being boring. It's a remarkable achievement.
Also remarkable is the animation, which puts Hercules, an almost 'slap dash' effort, to shame. Relying on hand drawn and computer animation, the backgrounds and characters are amazingly detailed. The film seems a little caught up with it's amazing visuals by showing a lot of sweeping visuals and 'moving' shots, but they _do_ look incredible. The final 'plague' where God kills every first born child, and the parting of the red sea have to be seen to be believed. Kudos to the animators.
The vocal talent is also very good. Val Kilmer does a fine job as Moses (although he doesn't sing) and Ralph Fiennes is superb as Rameses (and he _does_ sing.) Sandra Bullock and Jeff Goldblum are great, and Steve Martin and Martin Short are very good, but not in the film long enough. Finally, Michelle Pfeiffer does excellent work as Moses wife. Kudos also goes to the voice of God, who instead of booming and bombastic is quiet and subdued. Sadly, he is uncredited, and I couldn't quite work out who the voice was. (But e-mail if you do.)
The songs are good, ranging from the usual love sonnets to the traditional big production number. They don't disturb the movie, and while not particularly memorable, they are nice to listen to (they're well sung aswell.) The musical score is suitably epic, with some beautiful tunes. Coupled with the superb visuals, The Prince Of Egypt is a visual and aural treat, backed up with a great story. Entertaining both for kids and adults, this film is a must see.
RATING=**** OUT OF *****
A David Wilcock Review ©1998
DAVID WILCOCK<EMAIL_ADDRESS>Visit the Wilcock Movie Page! http://wilcockmovie.home.ml.org -OR- http://www.geocities.com/Hollywood/9061 Recieve Wilcock Movie Page Reviews via E-MAIL Send a blank E-MAIL to<EMAIL_ADDRESS>to join the mailing list!
"You know, for kids"- Norville Barnes
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rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
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Related links: index of all rec.arts.movies.reviews reviews


MNIST Digits Classification with numpy
only
Example on Digits Classification with the help of MNIST dataset of handwritten digits and Convolutional Neural Network.
Test online here
Content
Short description of the content. Full codes you can find inside the course by link above:
- MNIST Digits Classification with
numpy
only- Loading MNIST dataset
- Plotting examples of digits from MNIST dataset
- Preprocessing loaded MNIST dataset
- Saving and Loading serialized models
- Functions for dealing with CNN layers
- Naive Forward Pass for Convolutional layer
- Naive Backward Pass for Convolutional layer
- Naive Forward Pass for Max Pooling layer
- Naive Backward Pass for Max Pooling layer
- Forward Pass for Affine layer
- Backward Pass for Affine layer
- Forward Pass for ReLU layer
- Backward Pass for ReLU layer
- Softmax Classification loss
- Creating Classifier - model of CNN
- Initializing new Network
- Evaluating loss for training ConvNet1
- Calculating scores for predicting ConvNet1
- Functions for Optimization
- Creating Solver Class
- _Reset
- _Step
- Checking Accuracy
- Train
- Overfitting Small Data
- Training Results
- Full Codes
MNIST Digits Classification with numpy
only
In this example we’ll test CNN for Digits Classification with the help of MNIST dataset.
Following standard and most common parameters can be used and tested:
| Parameter | Description |
|---|---|
| Weights Initialization | HE Normal |
| Weights Update Policy | Vanilla SGD, Momentum SGD, RMSProp, Adam |
| Activation Functions | ReLU, Sigmoid |
| Regularization | L2, Dropout |
| Pooling | Max, Average |
| Loss Functions | Softmax, SVM |
Contractions:
- Vanilla SGD - Vanilla Stochastic Gradient Descent
- Momentum SGD - Stochastic Gradient Descent with Momentum
- RMSProp - Root Mean Square Propagation
- Adam - Adaptive Moment Estimation
- SVM - Support Vector Machine
For current example following architecture will be used:
Input
–> Conv
–> ReLU
–> Pool
–> Affine
–> ReLU
–> Affine
–> Softmax
For current example following parameters will be used:
| Parameter | Description |
|---|---|
| Weights Initialization | HE Normal |
| Weights Update Policy | Vanilla SGD |
| Activation Functions | ReLU |
| Regularization | L2 |
| Pooling | Max |
| Loss Functions | Softmax |
Loading MNIST dataset
After downloading files from official resource, there has to be following files:
- train-images-idx3-ubyte.gz
- train-labels-idx1-ubyte.gz
- t10k-images-idx3-ubyte.gz
- t10k-labels-idx1-ubyte.gz
Plotting examples of digits from MNIST dataset
After dataset was load, it is possible to show examples of training images.
Result can be seen on the image below.
Preprocessing loaded MNIST dataset
Next, creating function for preprocessing MNIST dataset for further use in classifier.
- Normalizing data by
dividing / 255.0
(!) - up to researcher - Normalizing data by
subtracting mean image
anddividing by standard deviation
(!) - up to researcher - Transposing every dataset to make channels come first
- Returning result as dictionary
As a result there will be following:
x_train: (59000, 1, 28, 28)
y_train: (59000,)
x_validation: (1000, 1, 28, 28)
y_validation: (1000,)
x_test: (1000, 1, 28, 28)
y_test: (1000,)
Saving and Loading serialized models
Saving loaded and preprocessed data into ‘pickle’ file.
Functions for dealing with CNN layers
Creating functions for CNN layers:
- Naive Forward Pass for Convolutional layer
- Naive Backward Pass for Convolutional layer
- Naive Forward Pass for Max Pooling layer
- Naive Backward Pass for Max Pooling layer
- Forward Pass for Affine layer
- Backward Pass for Affine layer
- Forward Pass for ReLU layer
- Backward Pass for ReLU layer
- Softmax Classification loss
Creating Classifier - model of CNN
Creating model of CNN Classifier:
- Creating class for ConvNet1
- Initializing new Network
- Evaluating loss for training ConvNet1
- Calculating scores for predicting ConvNet1
Defining Functions for Optimization
Using different types of optimization rules to update parameters of the Model.
Vanilla SGD updating method
Rule for updating parameters is as following:
Creating Solver Class
Creating Solver class for training classification models and for predicting:
- Creating and Initializing class for Solver
- Creating ‘reset’ function for defining variables for optimization
- Creating function ‘step’ for making single gradient update
- Creating function for checking accuracy of the model on the current provided data
- Creating function for training the model
Overfitting Small Data
Training Results
Training process of Model #1 with 12 000 iterations is shown on the figure below:
Initialized Filters and Trained Filters for ConvNet Layer is shown on the figure below:
Training process for Filters of ConvNet Layer is shown on the figure below:
MIT License
Copyright (c) 2018 Valentyn N Sichkar
github.com/sichkar-valentyn
Reference to:
Valentyn N Sichkar. Neural Networks for computer vision in autonomous vehicles and robotics // GitHub platform. DOI: 10.5281/zenodo.1317904


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Naturalizations Hit 11-Year High as Election Year Approaches 16 Nov 2019, 08:45 The U.S. Citizenship and Immigration Services naturalized 833,000 people — an 11-year high in oaths of citizenship — in fiscal 2019, which ended Sept. 30. This fiscal year, USCIS administered the Oath of Allegiance to 60 of America's newest citizens, from 51 countries


About
Hello! I am a person of many passions. As a lifelong animal-lover, I pursued a degree in equine husbandry and
eventually started working with cats and dogs as a veterinary assistant. After I realized I was devoting much of my
free time towards writing code for video game mods, I took the leap in 2020 and enrolled in UNC's Coding Boot Camp.
As an entry-level full-stack web developer, I am looking forward to blending my new technical knowledge with the
skills I've built through years of working in customer service and with animals. No matter the industry, I feel
attention to detail, flexibility, and compassion are paramount to any project or client interaction.
Work
Contact/Links
- Email<EMAIL_ADDRESS>- Phone:<PHONE_NUMBER>
- GitHub


Before Change
parents_deep.add(p)
// no loops that are typically caused by one to one relationships
if entity_id in self.get_forward_entities(p):
continue
to_add = self.get_forward_entities(p, deep=True)
parents_deep = parents_deep.union(to_add)
parents = parents_deep
return set(parents)
After Change
for relationship in self.get_forward_relationships(entity_id):
parent_eid = relationship.parent_entity.id
direct_path = RelationshipPath([(True, relationship)])
yield parent_eid, direct_path
if deep:
sub_entities = self.get_forward_entities(parent_eid, deep=True)
for sub_eid, path in sub_entities:
yield sub_eid, direct_path + path
def get_backward_entities(self, entity_id, deep=False):
Get entities that are in a backward relationship with entity


Surface Example
The surface example shows how to make a simple 3D surface graph using Q3DSurface and combining the use of widgets for adjusting several adjustable qualities. This example demonstrates the following features:
- How to set up a basic QSurfaceDataProxy and set data for it.
- How to use QHeightMapSurfaceDataProxy for showing 3D height maps.
- Three different selection modes for studying the graph.
- Axis range usage for displaying selected portions of the graph.
- Changing theme.
- How to set a custom surface gradient.
For instructions about how to interact with the graph, see this page.
Running the Example
To run the example from Qt Creator, open the Welcome mode and select the example from Examples. For more information, visit Building and Running an Example.
Creating the Application
First, in main.cpp
, we create a QApplication, instantiate Q3DSurface, and a window container for it:
QApplication app(argc, argv); Q3DSurface *graph = new Q3DSurface(); QWidget *container = QWidget::createWindowContainer(graph);
The call to QWidget::createWindowContainer is required, as all data visualization graph classes (Q3DBars, Q3DScatter, and Q3DSurface) inherit QWindow. Any class inheriting QWindow cannot be used as a widget any other way.
Then we'll create horizontal and vertical layouts. We'll add the graph with the container and the vertical layout into the horizontal one:
QWidget *widget = new QWidget; QHBoxLayout *hLayout = new QHBoxLayout(widget); QVBoxLayout *vLayout = new QVBoxLayout(); hLayout->addWidget(container, 1); hLayout->addLayout(vLayout); vLayout->setAlignment(Qt::AlignTop);
The rest of the code in main.cpp
is creating control widgets for features in Q3DSurface. We have separated code for changing these features into surfacegraph.cpp
and only connect signals from widgets into methods in surfacegraph.cpp
. Next chapter explains more about using Q3DSurface.
Setting up Proxies and Data
First we instantiate a new QSurfaceDataProxy and attach it to a new QSurface3DSeries:
m_sqrtSinProxy = new QSurfaceDataProxy(); m_sqrtSinSeries = new QSurface3DSeries(m_sqrtSinProxy);
Then we fill the proxy with a simple square root and sine wave data. This is done by creating a new QSurfaceDataArray
instance and adding QSurfaceDataRow
elements to it. The created QSurfaceDataArray
is set to be the data array for the QSurfaceDataProxy.
void SurfaceGraph::fillSqrtSinProxy() { float stepX = (sampleMax - sampleMin) / float(sampleCountX - 1); float stepZ = (sampleMax - sampleMin) / float(sampleCountZ - 1); QSurfaceDataArray *dataArray = new QSurfaceDataArray; dataArray->reserve(sampleCountZ); for (int i = 0 ; i < sampleCountZ ; i++) { QSurfaceDataRow *newRow = new QSurfaceDataRow(sampleCountX); // Keep values within range bounds, since just adding step can cause minor drift due // to the rounding errors. float z = qMin(sampleMax, (i * stepZ + sampleMin)); int index = 0; for (int j = 0; j < sampleCountX; j++) { float x = qMin(sampleMax, (j * stepX + sampleMin)); float R = qSqrt(z * z + x * x) + 0.01f; float y = (qSin(R) / R + 0.24f) * 1.61f; (*newRow)[index++].setPosition(QVector3D(x, y, z)); } *dataArray << newRow; } m_sqrtSinProxy->resetArray(dataArray); }
The height map is created by instantiating a QHeightMapSurfaceDataProxy with a QImage containing the height data. The method QHeightMapSurfaceDataProxy::setValueRanges() is used to define the value range of the map. In our example the map is from imaginary position of 34.0° N - 40.0° N and 18.0° E - 24.0° E. These values are used to show and position the map to the axis.
QImage heightMapImage(":/maps/mountain"); m_heightMapProxy = new QHeightMapSurfaceDataProxy(heightMapImage); m_heightMapSeries = new QSurface3DSeries(m_heightMapProxy); m_heightMapSeries->setItemLabelFormat(QStringLiteral("(@xLabel, @zLabel): @yLabel")); m_heightMapProxy->setValueRanges(34.0f, 40.0f, 18.0f, 24.0f);
For demonstrating different proxies this example has two radio buttons which the user can use to switch between the series. When the user selects the Sqrt & Sin
radio button, the selected series is activated with the following code. First we set the decorative issues like enable the grid for the surface and select the flat shading mode. Next lines define the axis label format and value ranges. Automatic label rotation is set to improve label readability at low camera angles. Finally we make sure the correct series is added to the graph:
m_sqrtSinSeries->setDrawMode(QSurface3DSeries::DrawSurfaceAndWireframe); m_sqrtSinSeries->setFlatShadingEnabled(true); m_graph->axisX()->setLabelFormat("%.2f"); m_graph->axisZ()->setLabelFormat("%.2f"); m_graph->axisX()->setRange(sampleMin, sampleMax); m_graph->axisY()->setRange(0.0f, 2.0f); m_graph->axisZ()->setRange(sampleMin, sampleMax); m_graph->axisX()->setLabelAutoRotation(30); m_graph->axisY()->setLabelAutoRotation(90); m_graph->axisZ()->setLabelAutoRotation(30); m_graph->removeSeries(m_heightMapSeries); m_graph->addSeries(m_sqrtSinSeries);
When the Height Map
radio button is activated, the following code sets the correct series active. The axis label format is set to show N and E letters and ranges are set to the imaginary coordinates. Auto adjusting Y-axis range is fine for our height map surface, so we ensure it is set.
m_heightMapSeries->setDrawMode(QSurface3DSeries::DrawSurface); m_heightMapSeries->setFlatShadingEnabled(false); m_graph->axisX()->setLabelFormat("%.1f N"); m_graph->axisZ()->setLabelFormat("%.1f E"); m_graph->axisX()->setRange(34.0f, 40.0f); m_graph->axisY()->setAutoAdjustRange(true); m_graph->axisZ()->setRange(18.0f, 24.0f); m_graph->axisX()->setTitle(QStringLiteral("Latitude")); m_graph->axisY()->setTitle(QStringLiteral("Height")); m_graph->axisZ()->setTitle(QStringLiteral("Longitude")); m_graph->removeSeries(m_sqrtSinSeries); m_graph->addSeries(m_heightMapSeries);
Selection Modes
Q3Dsurface supports three different selection modes. These are demonstrated in the example with radio buttons, which the user can use to activate a suitable selection mode. The following inline methods are connected to radio buttons to activate the selected mode.
void toggleModeNone() { m_graph->setSelectionMode(QAbstract3DGraph::SelectionNone); } void toggleModeItem() { m_graph->setSelectionMode(QAbstract3DGraph::SelectionItem); } void toggleModeSliceRow() { m_graph->setSelectionMode(QAbstract3DGraph::SelectionItemAndRow | QAbstract3DGraph::SelectionSlice); } void toggleModeSliceColumn() { m_graph->setSelectionMode(QAbstract3DGraph::SelectionItemAndColumn | QAbstract3DGraph::SelectionSlice); }
Axis Ranges for Studying the Graph
The example has four slider controls for adjusting the min and max values for X and Z axis. When selecting the proxy these sliders are adjusted so that one step on the slider moves the range by one segment step:
// Reset range sliders for Sqrt&Sin m_rangeMinX = sampleMin; m_rangeMinZ = sampleMin; m_stepX = (sampleMax - sampleMin) / float(sampleCountX - 1); m_stepZ = (sampleMax - sampleMin) / float(sampleCountZ - 1); m_axisMinSliderX->setMaximum(sampleCountX - 2); m_axisMinSliderX->setValue(0); m_axisMaxSliderX->setMaximum(sampleCountX - 1); m_axisMaxSliderX->setValue(sampleCountX - 1); m_axisMinSliderZ->setMaximum(sampleCountZ - 2); m_axisMinSliderZ->setValue(0); m_axisMaxSliderZ->setMaximum(sampleCountZ - 1); m_axisMaxSliderZ->setValue(sampleCountZ - 1);
The ranges are set for the axes like this:
void SurfaceGraph::setAxisXRange(float min, float max) { m_graph->axisX()->setRange(min, max); } void SurfaceGraph::setAxisZRange(float min, float max) { m_graph->axisZ()->setRange(min, max); }
Themes
Q3DSurface supports all the themes Qt Data Visualization has. The example has a pull down menu for selecting the theme. The following method is connected to the menu to activate the selected theme. The theme type is changed to another predefined theme, which overwrites all theme properties to predefined values:
void SurfaceGraph::changeTheme(int theme) { m_graph->activeTheme()->setType(Q3DTheme::Theme(theme)); }
Custom Surface Gradients
The example demonstrates the custom surface gradients with two push buttons. The gradient can be defined with QLinearGradient where the desired colors are set to positions. The following code shows how to create an example gradient and set it to the series. Note that you also need to change the color style to Q3DTheme::ColorStyleRangeGradient to actually use the gradient.
QLinearGradient gr; gr.setColorAt(0.0, Qt::black); gr.setColorAt(0.33, Qt::blue); gr.setColorAt(0.67, Qt::red); gr.setColorAt(1.0, Qt::yellow); m_graph->seriesList().at(0)->setBaseGradient(gr); m_graph->seriesList().at(0)->setColorStyle(Q3DTheme::ColorStyleRangeGradient);
Files:


[[item.key]]
[[_percentage(item.votes)]]
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Type
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in the chat to vote
[[timestamp]]


A Waterfall Chart is a chart that shows a certain category events on the timeline.
To build a simple Waterfall chart follow the instructions in the example source code:
It is possible to build fancy waterfall charts with more complex functionality, like the one below. Each series provides a separate instance of bar chart that is rendered inside grid, which is a separate empty chart positioned using CDK Overlay on top of bar charts.
To build an advanced Waterfall chart below follow the instructions:
Chart
with an instance of
XYGrid
passed in as an argument.
new BarStatusGridConfig()
config instance goes as an argument to XYGrid
with the
showBottomAxis
option set to false
.
XYGrid
Overlay
and corresponding
Portal
to attach the grid to
a correct point of the first series on the page.
HorizontalBarAccessors
. Using the accessor, assign
the color or the bar to the corresponding data type retrieved from the ChartPalette
.
BandScale
method
padding
.


Microsoft Azure Logic App Services provides a mechanism for doing such integrations and business process automations, thereby creating workflows by orchestrating Software as a Service (SaaS) components.
#1 Creating Service Principle to authenticate REST call
Follow this link on how to create to complete this step.
#2 Select a REST API to call and Create a logic app resource from Azure portal
Navigate to https://resources.azure.com and login with your azure account to select API to call
Now navigate to https://portal.azure.com and create a logic app with desired configuration and location
Add a timer module if you want to run it in interval
Add a HTTP REQ-RES with authentication set to Azure AD
"authentication":
{
"audience": "https://management.azure.com",
"clientId": "Service principle ID",
"secret": "Service principle secret",
"tenant": "Azure AD Tenant ID",
"type": "ActiveDirectoryOAuth"
},
Add a parse json action and use the response json value to generate schema
Select the appropriate value that you want to validate.
Final workflow should look like this.
JSON file for entire workflow


401 Unauthorized
Access is denied due to invalid credentials
You do not have permission to view this page using the credentials that you supplied.
@( email: String, postUrl: Call)(implicit request: RequestHeader)
Access is denied due to invalid credentials
You do not have permission to view this page using the credentials that you supplied.


Here’s What Janhvi Kapoor Feels About Brother Arjun Kapoor’s Chemistry With Rakul Preet Singh
The film, not titled yet, is being directed by debutant Kaashvie Nair and produced by Bhushan Kumar, Nikkhil Advani, and John Abraham.
by IANSActors Arjun Kapoor and Rakul Preet Singh have started shooting for their rom-com film, and they are already being praised for looking “cute” together.
Arjun on Saturday took to Instagram and shared a collage of pictures in which he and Rakul are seen goofing around while posing for the images.
“Picture Shuroo… other information bhi upload hogi soon,” he captioned the image.
Reacting to the collage, Janhvi, who is Arjun’s step-sister, wrote: “Looking so cute.”
The film, not titled yet, is being directed by debutant Kaashvie Nair and produced by Bhushan Kumar, Nikkhil Advani, and John Abraham.
Talking about the project, Rakul had said: “The film is in the rom-com space, and it is the story of characters who belong to India and Pakistan, so it’s a cross-border, coming-of-age rom-com.”
Talking about Arjun, apart from the Kaashvie Nair’s yet to be titled rom-com, the actor also has a big project lined up for release in form of filmmaker Ashutosh Gowariker’s period drama Panipat which also stars Sanjay Dutt and Kriti Sanon in lead.
The historical war film will release on 6th December 2019.
Meanwhile, Rakul’s latest release Marjaavaan which has Sidharth Malhotra, Riteish Deshmukh and Tara Sutaria has been gaining a wonderful response from the cine-goers.


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hcash
2.0.0
hcash
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#include <MemoryOutStream.h>
Public Member Functions | |
| MemoryOutStream () | |
| ~MemoryOutStream () | |
| void | Clear () |
| char const * | GetText () const |
|
inline |
|
inline |
| void UnitTest::MemoryOutStream::Clear | ( | ) |
| char const * UnitTest::MemoryOutStream::GetText | ( | ) | const |


During my Ph.D. migration project, I considered the migration of several GUI aspects:
- visual
- behavioral
- business
These elements are the main ones. When perfectly considered, you can migrate the front-end of any application. But, we are missing some other stuff For example, how do you migrate i18N files?
In this post, I’ll present how to build a simple migration tool to migrate I18N files from .properties
(used by Java) to .json
format (used by Angular).
I18N files
First, let’s see our source and target.
As a source, I have several .properties
files, including I18N for a Java project.
Each file has a set of key/value and comments.
For example, the EditerMessages_fr.properties
is as follow:
##########
# Page : Edit
##########
pageTitle=Editer
classerDemande=Demande
classerDiffusion=Diffusion
classerPar=Classer Par
And it’s Arabic version EditerMessages_ar.properties
#########
# Page : Editer
#########
pageTitle=تحرير
classerDemande=طلب
classerDiffusion=بث
classerPar=تصنيف حسب
As a target, I need only one JSON file per language. Thus, the file for the french translation looks like this:
{
"EditerMessages" : {
"classerDemande" : "Demande",
"classerDiffusion" : "Diffusion",
"classerPar" : "Classer Par",
"pageTitle" : "Editer"
}
}
And the Arabic version:
{
"EditerMessages" : {
"classerDemande" : "طلب",
"classerDiffusion" : "بث",
"classerPar" : "تصنيف حسب",
"pageTitle" : "تحرير"
},
}
To perform the transformation from the .properties
file to json
, we will use MDE.
The approach is divided into three main steps:
- Designing a meta-model representing internationalization
- Creating an importer of properties files
- Creating a JSON exporter
Internationalization Meta-model
I18N files are simple. They consist of a set of key/values. Each value is associated with a language. And each file can be associated with a namespace.
For example, in the introduction example, the namespace of all entries is “EditerMessages”.
I designed a meta-model to represent all those concepts:
Once the meta-model is designed, we must create an importer that takes .properties
files as input and produces a model.
Properties Importer
To produce a model, I first look for a .properties
parser without much success.
Thus, I decided to create my own parser.
Given a correctly formatted file, the parser provides me the I18N entries.
Then, by iterating on this collection, I build an I18N model.
I18N parser
To implement the parser, I used the PetitParser2 project. This project aims to ease the creation of new parsers.
First, I downloaded the last version of Moose, and I installed PetitParser using the command provided in the repository Readme:
Metacello new
baseline: 'PetitParser2';
repository: 'github://kursjan/petitparser2';
load.
In my Moose Image, I created a new parser.
To do so, I extended the PP2CompositeNode
class.
PP2CompositeNode << #CS18NPropertiesParser
slots: { };
package: 'Casino-18N-Model-PropertyImporter'
Then, I defined the parsing rules. Using PetitParser2, each rule corresponds to a method.
First, start
is the entry point.
start
^ pairs end
pairs
parses the entries of the .properties
files.
pairs
^ comment optional starLazy, pair , ((newline / comment) star , pair ==> [ :token | token second ]) star , (newline/comment) star ==> [ :token |
((OrderedCollection with: token second)
addAll: token third;
yourself) asArray ]
The first part of this method (before ==>
) corresponds to the rule parsed.
The second part (after ==>
), to the production.
The first part tries to parse one or several comment
.
Then, it parses one pair
followed by a list of comment
, newline
, and pair
.
This parser is clearly not perfect and would require some improvement. Nevertheless, it does work for our context.
The second part produces a collection (i.e. a list) of the pair
.
Building the I18N model
Now that we can parse one file, we can build a I18N model.
To do so, we will first parse every .properties
file.
For each file, we extract the language
and the namespace
based on the file name.
Thus, EditerMessages_fr.properties
is the file for the fr
language and the EditerMessages
namespace.
Then, for each file entry, we instantiate an entry in our model inside the namespace and with the correct language attached.
importString: aString
(parser parse: aString) do: [ :keyValue |
(self model allWithType: CS18NEntry) asOrderedCollection
detect: [ :entry |
"search for existing key in the file"
entry key name = keyValue key ]
ifOne: [ :entry |
"If an entry already exists (in another language for instance)"
entry addValue: ((self createInModel: CS18NValue)
name: keyValue value;
language: currentLanguage;
yourself) ]
ifNone: [
"If no entry exist"
(self createInModel: CS18NEntry)
namespace: currentNamespace;
key: ((self createInModel: CS18NKey)
name: keyValue key;
yourself);
addValue: ((self createInModel: CS18NValue)
name: keyValue value;
language: currentLanguage;
yourself);
yourself ] ]
After performing the import, we get a model with, for each namespace, several entries. Each entry has a key and several values. Each value is attached to the language.
JSON exporter
To perform the JSON export, I used the NeoJSON project. NeoJSON allows one to create a custom encoder.
For the export, we first select a language. Then, we build a dictionary with all the namespaces:
rootDic := Dictionary new.
(model allWithType: CS18NNamespace)
select: [ :namespace | namespace namespace isNil ]
thenDo: [ :namespace | rootDic at: namespace name put: namespace ].
To export a namespace (i.e., a CS18NNamespace
), I define a custom encoder:
writter for: CS18NNamespace customDo: [ :mapper |
mapper encoder: [ :namespace | (self constructNamespace: namespace) asDictionary
]
].
constructNamespace: aNamespace
| dic |
dic := Dictionary new.
aNamespace containables do: [ :containable |
(containable isKindOf: CS18NNamespace)
ifTrue: [ dic at: containable name put: (self constructNamespace: containable) ]
ifFalse: [ "should be an CS18NEntry"
dic at: containable key name put: (containable values detect: [ :value | value language = language ] ifOne: [ :value | value name ] ifNone: [ '' ]) ] ].
^ dic
The custom encoder consists on converting a Namespace
into a dictionary of entries with the entries keys and their values in the selected language.
Perform the migration
Once my importer and exporter are designed, I can perform the migration.
To do so, I use a little script.
It creates a model of I18N, imports several .properties
file entries in the model, and exports the Arabic entries in a JSON file.
"Create a model"
i18nModel := CS18NModel new.
"Create an importer"
importer := CS18NPropertiesImporter new.
importer model: i18nModel.
"Import all entries from the <myProject> folder"
('D:\dev\myProject\' asFileReference allChildrenMatching: '*.properties') do: [ :fileRef |
self record: fileRef absolutePath basename.
importer importFile: fileRef.
].
"export the arabian JSON I18N file"
'D:/myFile-ar.json' asFileReference writeStreamDo: [ :stream |
CS18NPropertiesExporter new
model: importer model;
stream: stream;
language: ((importer model allWithType: CS18NLanguage) detect: [ :lang | lang shortName = 'ar' ]);
export
]
Ressource
The meta-model, importer, and exporter are freely available in GitHub.


This guide will provide the necessary information on how to create a new website using Cardinal.Js framework.
Start by cloning the cardinal-app-template repository.
Next, go ahead and remove the existing .git
folder from your web-site
folder, initialize a new empty git repo and commit the existing content to your own repository.
You've just developed your new website's basic structure, using the Cardinal.Js framework.
base
tag in your cardinal index.html file.
historyType="query"
in your menu.json
file.
To update the instance of Cardinal.Js framework used, you must be within the web-site root folder and to execute the command:
To add a Hello-world page to the website follow the steps:
web-site/pages
folder
hello-world.html
Open hello-world.html
file and add the following code as content:
menu.json
file in order to add a new entry for the hello-world page into the menu configuration.


Spawn (1997) * * 1/2 A movie review by Serdar Yegulalp Copyright 1997 by Serdar Yegulalp
CAPSULE:
SPAWN is the kind of movie they used to pass out scratch 'n sniff cards for -- "to enhance the sensory experience". It's not the greatest film ever made -- not by a mile -- but it's ambitious as all hell, pardon the pun, and is so over the top that it actually works. It's about a man who's been chosen to lead the Devil's armies on earth (and eventually, in heaven), and it slams this comic-book story home with gobbets of gory special effects and eye-searing computer graphics. If this isn't your cup of Diet Pepsi, I say pick another review. If it is, read on.
The man who we come to know as Spawn (Michael Jai White) starts off in life as an assassin for a secret government bureau. He's sick of it and wants out, but his boss (Martin Sheen, looking weirdly like Stephen King with that beard and hair) double-crosses him. Instead of dying, however, our hero is whisked down to hell and drafted into the service of Satan (named Malebolgia in this movie): if he leads Satan's armies, he will get to see his wife and children once more. He's also given a controller, of sorts, a vile bucket of suet and four-letter words with a clown's face (played, incredibly, by John Leguizamo), who turns out to be another stab in the back.
The movie quickly turns into a high-tech special effects ride, with the usual obligatory nods towads a plot and theme. Movies like this succeed or fail largely based on the level of energy and inventiveness they possess, not the originality of their plots, and SPAWN is if nothing else energetic.  The soundtrack's a loud, unending roar of industrial-strength drumbeats and guitars; the acting rapid-fire and goofy. There's always something weird to look at or listen to.
The movie's sole ambition is to be brassily entertaining, and it does that well. The worst thing you can say about SPAWN, I guess -- beyond it being very silly frat-boy level fun -- is that it sets itself up for a sequel.
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


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s2n-doxygen-full
|
Go to the source code of this file.
Macros | |
| #define | UBSECerr(f, r) ERR_UBSEC_error((f),(r),__FILE__,__LINE__) |
| #define | UBSEC_F_UBSEC_CTRL 100 |
| #define | UBSEC_F_UBSEC_DH_COMPUTE_KEY 101 |
| #define | UBSEC_F_UBSEC_DH_GENERATE_KEY 111 |
| #define | UBSEC_F_UBSEC_DSA_DO_SIGN 102 |
| #define | UBSEC_F_UBSEC_DSA_VERIFY 103 |
| #define | UBSEC_F_UBSEC_FINISH 104 |
| #define | UBSEC_F_UBSEC_INIT 105 |
| #define | UBSEC_F_UBSEC_MOD_EXP 106 |
| #define | UBSEC_F_UBSEC_MOD_EXP_CRT 110 |
| #define | UBSEC_F_UBSEC_RAND_BYTES 107 |
| #define | UBSEC_F_UBSEC_RSA_MOD_EXP 108 |
| #define | UBSEC_F_UBSEC_RSA_MOD_EXP_CRT 109 |
| #define | UBSEC_R_ALREADY_LOADED 100 |
| #define | UBSEC_R_BN_EXPAND_FAIL 101 |
| #define | UBSEC_R_CTRL_COMMAND_NOT_IMPLEMENTED 102 |
| #define | UBSEC_R_DSO_FAILURE 103 |
| #define | UBSEC_R_MISSING_KEY_COMPONENTS 104 |
| #define | UBSEC_R_NOT_LOADED 105 |
| #define | UBSEC_R_REQUEST_FAILED 106 |
| #define | UBSEC_R_SIZE_TOO_LARGE_OR_TOO_SMALL 107 |
| #define | UBSEC_R_UNIT_FAILURE 108 |
| #define UBSECerr | ( | f, | |
| r | |||
| ) | ERR_UBSEC_error((f),(r),__FILE__,__LINE__) |
Definition at line 70 of file e_ubsec_err.h.
| #define UBSEC_F_UBSEC_CTRL 100 |
Definition at line 75 of file e_ubsec_err.h.
| #define UBSEC_F_UBSEC_DH_COMPUTE_KEY 101 |
Definition at line 76 of file e_ubsec_err.h.
| #define UBSEC_F_UBSEC_DH_GENERATE_KEY 111 |
Definition at line 77 of file e_ubsec_err.h.
| #define UBSEC_F_UBSEC_DSA_DO_SIGN 102 |
Definition at line 78 of file e_ubsec_err.h.
| #define UBSEC_F_UBSEC_DSA_VERIFY 103 |
Definition at line 79 of file e_ubsec_err.h.
| #define UBSEC_F_UBSEC_FINISH 104 |
Definition at line 80 of file e_ubsec_err.h.
| #define UBSEC_F_UBSEC_INIT 105 |
Definition at line 81 of file e_ubsec_err.h.
| #define UBSEC_F_UBSEC_MOD_EXP 106 |
Definition at line 82 of file e_ubsec_err.h.
| #define UBSEC_F_UBSEC_MOD_EXP_CRT 110 |
Definition at line 83 of file e_ubsec_err.h.
| #define UBSEC_F_UBSEC_RAND_BYTES 107 |
Definition at line 84 of file e_ubsec_err.h.
| #define UBSEC_F_UBSEC_RSA_MOD_EXP 108 |
Definition at line 85 of file e_ubsec_err.h.
| #define UBSEC_F_UBSEC_RSA_MOD_EXP_CRT 109 |
Definition at line 86 of file e_ubsec_err.h.
| #define UBSEC_R_ALREADY_LOADED 100 |
Definition at line 89 of file e_ubsec_err.h.
| #define UBSEC_R_BN_EXPAND_FAIL 101 |
Definition at line 90 of file e_ubsec_err.h.
| #define UBSEC_R_CTRL_COMMAND_NOT_IMPLEMENTED 102 |
Definition at line 91 of file e_ubsec_err.h.
| #define UBSEC_R_DSO_FAILURE 103 |
Definition at line 92 of file e_ubsec_err.h.
| #define UBSEC_R_MISSING_KEY_COMPONENTS 104 |
Definition at line 93 of file e_ubsec_err.h.
| #define UBSEC_R_NOT_LOADED 105 |
Definition at line 94 of file e_ubsec_err.h.
| #define UBSEC_R_REQUEST_FAILED 106 |
Definition at line 95 of file e_ubsec_err.h.
| #define UBSEC_R_SIZE_TOO_LARGE_OR_TOO_SMALL 107 |
Definition at line 96 of file e_ubsec_err.h.
| #define UBSEC_R_UNIT_FAILURE 108 |
Definition at line 97 of file e_ubsec_err.h.


1 Ghz Processor
1 GB of RAM
Graphics Accelerator and Video card capable of 800x600 resolution at 16-bit color
100 MB of hard disk space
Microsoft Windows XP or later Windows Operating System
Mouse (or some other device that fulfills the same function)


I love problem solving which brought me to Product Management. I am currently working at a fast-growing Seattle based startup called Hiya. I work on delivering data products which solves Hiya’s enterprise customers’ needs.
Recently I have worked on delivering A/B test and Sentiment Analysis tools which aim to provide actionable insights to businesses on maximizing their revenue.
I am an MSc graduate from Indian Institute of Technology Kharagpur and a was on PhD track at University of Hawaii-Mānoa. My love for problem solving and delivering practical applications of data science brought me to my current role as Product Manager. And it has been an exciting journey so far!
At Hiya, I am part of its mission to bring trust, identity and intelligence to phone calls. I pioneer data backed decision making and am leading the delivery of several data products including A/B test and Sentiment Analysis tool. I solve problems for Hiya’s premium enterprise customers by helping them maximize revenue potential. I am also leading the diagnostics for FCC regulations on phone number identity fraud using my data science background.
As beautiful it is advertised, Hawaii is also the epicenter of unique Earth Science research projects. Here, I published paper on Earth’s surface and subsurface processes using big data from ship-borne instruments. I got a once-in-a-lifetime opportunity to spend 41 days at sea across the Hawai‘ian islands, where I studied the complete data acquisition to analysis cycle (read a brief summary here and the full paper here). I also got funded for an individual research project aimed at revamping 50 years old gravity repository of Oahu. I have presented my research at several international conferences such as AGU-2018 and REU-2019, to audience of all educational background. In addition, I held the position of Teaching Assistant, wherein I made lecture videos on Earth Science subjects and conducted daily training-feedback sessions.
I started my professional career in this insurance giant. I collaborated with cross-disciplinary teams of claims and risk engineers to identify engineering insurance claim hotspots and causes. This helped the sales team to present data-backed pitch for VAS to the targeted clients; thus increasing client on-boarding and generating revenue. I was also leading development of an online dashboard aimed at increasing client engagement leading to their retention.
CGPA: 3.89/4, East-West Center Alum, Student Athletes Mentor & Tutor
College Basketball & Volleyball player, Techno-Management Fest Organizer
Trained classification models to predict claim outcome on real-time (unbalanced) dataset with AUC >0.85 in Python, thus identified potential claim producing policy holders
Queried large-scale structured insurance claims data, performed analysis and made interactive Tableau dashboards shown to target clients which helped onboarding and increase annual revenue
Used hierarchal linear regression to predict increasingly advancing shoreline of 9 beaches (nested data) in R, which helped policymakers develop an effective coastal development plan


Self-Calm
by Chris DeLeon (2018)
Roman Emperor Marcus Auralias' notes, traditionally titled Meditations, are a foundational text for practical Stoicism. They comprise a multitude of observations and recommendations, grouped into sections such as "Keeping thoughts tranquil", and "Cooperation, collaboration and contribution". They offer advice for living a contented life, with less regret, sorrow, anger and fear.
Several translations exist, themselves dated from through the ages. This book combines them, to synthesize a text which doesn't hew as precisely to the original as a straight translation might, but is instead intended to have the most relevance to modern ears.
Confession: It's taken me months to get through it. Partly this is because I've allowed myself to be distracted by more gaudy and immediate books along the way. Partly it's because I found the best way to read this was slowly. Practically every paragraph is worth reflecting upon, to consider how it applies to my own life, and ponder what practical, specific next steps are implied.
If I had to criticize, it would only be the superficial wrinkle that there's a lot of repetition, such as hundreds of suggestions to not fear death. Apparently the original documents were accumulated over decades of Auralias' life.
Genuinely life changing.
You can get the epub for free from the author's gumroad page.


CorvOS is free Linux distribution focused on K-12 schools. The name derives from the latin word corvus, the scientific name for the Raven and Crow family of a birds.
CorvOS takes the best attributes of a traditional Linux desktop: Speed, Security, Flexibility and merges it with the Simplicity of ChromeOS without the privacy issues and Google vendor lock-in.
As of today, there are over 3000 and counting laptops and desktops all over the world running CorvOS. Check out The Flock page to find out where and how it's being used.
CorvOS is still under active development and will release the first stable, public version in late summer of 2018.
Don't want to wait? Head to our GitHub and help make this release extra awesome!
Want to help make CorvOS extra awesome? Please join our team and head on over to our GitHub!
CorvOS Project GitHubCorvOS is based on Xubuntu 18.04, the phenomenal XFCE powered operating system.
Basing CorvOS on Xubuntu ensures a solid, trusted base that will receive updates for multiple years.
To learn more about Xubuntu, check out their website: Xubuntu.org
CloseCorvOS has a self cleaning student profile that ensures a consistent experience for student users.
Upon logoff, the system automatically cleans the home directory and any web browsing data that was created during that session.
CloseNot a Linux pro? No problem! CorvOS was designed with the average user in mind.
CorvOS has a set of tools that make setting up and administering CorvOS boxes a breeze.
Close


A “Spider” is a computer program. The Spider goes to different websites, explores, finds new things, and makes notes. When finished, it’s notes are organised in to files describing everything it has seen.
The next step is “Word Embedding”. A complicated topic! Word embedding creates a map, with coordinates, and puts each word on the map in it’s own location. Artificial Intelligence research from written language translation websites is used. English words that have a similar meaning, are grouped in to close areas on the map. This is important! Similar ideas are in similar areas in the map.
When you search for a word, your computer does this:
We use a program called Fathom to see how many people use this website, and to find out what website they came from if they clicked a link to come here. When you do a search, we don’t find out what words you typed in to the search box. We might be able to guess what kind of thing you were looking at, by seeing which search results your computer downloaded, but we don’t know exactly what you wrote. We don’t send your information to Google or Facebook at all, but if you come here using one of their apps like Google Chrome or Facebook App, they might be able to track you while you use their app.
Find Sign is open source (mostly with Unlicense software license). You can get the source code at GitHub.
Find Sign indexes copyright data, so the dataset is not open source, but it is open access. You can download an up to date copy of Find Sign’s dataset using a BitTorrent app that supports webseed (most do!) by using the automatically generated datasets.torrent. If you download the GitHub repository, and add the datasets folder from the torrent, and run it from a regular static http server, you should have a fully functioning local copy of Find Sign including a full copy of the search index.
You should be able to easily reconfigure the spider to index other SignBank based sign language dictionary websites, or index instagram and youtube sources. Take a look at the spider code for examples of how to build custom spiders to index other websites.
If you just want to ingest the search index, a reasonable approach is to download the datasets torrent, and read through all the json files in the def folder. These files are regular json files, sharded by some hashing, but easy to index in to another search index. paths to video files are relative to the path to the folder containing the index.bin file.


Life is all about going through upward and all in to pursue our dreams. Don’t let any kinds of obstacle and failure pin you down or push you against your success of life. Fight hard the battle of life and take the challenges with courage, determination and perseverance. Just keep moving forward with hope and this day is not so far when life will put the crown on you head as a successful human being. Here we offering some of the best inspirational messages about life and good thoughts from various reliable source that will be your best supporter to pursue your dreams of life.
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| 8:30 - 8:40 | Opening Remarks |
| 8:40 - 9:20 | Keynote 1: Onur Mutlu (ETH, CMU)
“Accelerating Genome Analysis: A Primer on an Ongoing Journey” (slides) |
| 09:20 - 09:40 | Mohammed Alser+, Hasan Hassan*, Akash Kumar&, Onur Mutlu* and Can Alkan+ (+Bilkent Univ., *ETH Zurich, &TU Dresden)
Exploring Speed/Accuracy Trade-offs in Hardware Accelerated Pre-Alignment in Genome Analysis (slides) |
| 09:20 - 09:40 | Lisa Wu, Frank Nothaft, Brendan Sweeney, David Bruns-Smith, Sagar Karandikar, Johnny Le, Howard Mao, Krste Asanovic, David Patterson and Anthony Joseph (UC Berkeley)
Accelerating Duplicate Marking In The Cloud |
| 10:00 - 10:30 | Coffee break |
| 10:30 - 11:10 | Invited Talk: Bertil Schmidt (JGU Mainz)
“Next-Generation Sequencing: Big Data meets High Performance Computing Architectures” |
| 11:10 - 11:30 | Wenqin Huangfu+, Zhenhua Zhu*, Tianqi Tang+, Xing Hu+, Yu Wang* and Yuan Xie+ (+UCSB, *Tsinghua University)
GAME: GPU Acceleration of Metagenomics Clustering |
| 11:30 - 11:50 | Jose M. Herruzo+, Sonia Gonzalez-Navarro+, Pablo Ibañez*, Victor Viñals*, Jesus Alastruey* and Oscar Plata+ (+Univ. of Malaga, *Univ. of Zaragoza)
Exact Alignment with FM-index on the Intel Xeon Phi Knights Landing Processor |
| 11:50 - 13:30 | Lunch |
| 13:30 - 14:10 | Keynote 2: Srinivas Aluru (Georgia Tech)
“Automata Processor and its Applications in Bioinformatics” |
| 14:10 - 14:30 | Tommy Tracy Ii, Jack Wadden, Kevin Skadron and Mircea Stan (UVA)
Streaming Gap-Aware Seed Alignment on the Cache Automaton |
| 14:30 - 14:50 | Roman Kaplan, Leonid Yavits and Ran Ginosar (Technion)
Processing-in-Storage Architecture for Large-Scale Biological Sequence Alignment |
| 14:50 - 15:10 | Xueqi Li, Guangming Tan, Yuanrong Wang and Ninghui Sun (ICT)
The Genomic Benchmark Suite: Characterization and Architecture Implications |
| 15:10 - 15:30 | Coffee break |
| 15:30 - 16:10 | Invited Talk: Can Alkan (Bilkent University)
"Addressing Computational Burden to Realize Precision Medicine" (slides) |
| 16:10 - 16:30 | Sergiu Mosanu and Mircea Stan (UVA)
Burrows-Wheeler Short Read Aligner on AWS EC2 F1 (slides) |
| 16:30 - 16:50 | Angélica Alejandra Serrano-Rubio, Amilcar Meneses-Viveros, Guillermo B. Morales-Luna and Mireya Paredes-López (CINVESTAV-IPN)
Towards BIMAX: Binary Inclusion-MAXimal parallel implementation for gene expression analysis |
| 16:50 - 17:00 | Short break |
| 17:00 - 17:15 | Meysam Taassori+, Anirban Nag+, Keeton Hodgson+, Ali Shafiee* and Rajeev Balasubramonian+ (+Univ. of Utah, *Samsung Electronics)
Memory: The Dominant Bottleneck in Genomic Workloads (slides) |
| 17:15 - 17:30 | Meysam Roodi and Andreas Moshovos (Univ. of Toronto)
Gene Sequencing: Where Time Goes |
| 17:30 - 17:45 | Calvin Bulla, Lluc Alvarez and Miquel Moreto (BSC)
Are Next-Generation HPC Systems Ready for Population-level Genomics Data Analytics? (slides) |
| 17:45 - 17:50 | Closing remarks |
Social Event |
|
| 18:15 | Bus leaves to social event (Heurigen) |
Talk abstract:
Genome analysis is the foundation of many scientific and medical discoveries as well as a key pillar of personalized medicine.
Any analysis of a genome fundamentally starts with the reconstruction of the genome from its sequenced fragments.
This process is called read mapping.
One key goal of read mapping is to find the variations that are present between the sequenced genome and reference genome(s) and to tolerate the errors introduced by the genome sequencing process.
Read mapping is currently a major bottleneck in the entire genome analysis pipeline because state-of-the-art genome sequencing technologies are able to sequence a genome much faster than the computational techniques that are employed to reconstruct the genome.
New sequencing technologies, like nanopore sequencing, greatly exacerbate this problem while at the same time making genome sequencing much less costly.
This talk describes our ongoing journey in greatly improving the
performance of genome read mapping. We first provide a brief
background on read mappers that can comprehensively find variations
and tolerate sequencing errors. Then, we describe both algorithmic and
hardware-based acceleration approaches. Algorithmic approaches exploit
the structure of the genome as well as the structure of the underlying
hardware. Hardware-based acceleration approaches exploit specialized
microarchitectures or new execution paradigms like processing in
memory. We show that significant improvements are possible with both algorithmic and hardware-based approaches and their combination.
We conclude with a foreshadowing of future challenges brought about by very low cost yet highly error prone new sequencing technologies.
Talk abstract: This talk will introduce the Micron Automata Processor (AP), a novel computing architecture that enables massively parallel execution of numerous non-deterministic finite automata. The processor inspires a new programming paradigm of solving problems using complex pattern matching engines executed over streaming data. The first part of this talk will focus on the processor characteristics, programming and execution environment, and design principles we discovered that are of value in developing applications on the AP. The second part will feature my group's research on developing bioinformatics algorithms for the AP including database search and motif detection.
Talk abstract: The progress of NGS has a major impact on medical and genomic research. This high-throughput technology can now produce billions of short DNA fragments in excess of a few Terabytes of data in a single run. This leads to massive datasets used by a wide range of applications including personalized cancer treatment and precision medicine. In addition to the hugely increased throughput, the cost of using high-throughput technologies has been dramatically decreasing. Low sequencing cost of around US$1K per genome has now rendered large population-scale projects feasible. However, in order to make effective use of the produced data, the design of big data algorithms and their efficient implementation on modern HPC systems is required. In this talk, I will present the design of scalable algorithms for metagenomic read classification and for massively parallel hash maps on multi-GPU nodes.
Talk abstract:
The main computational bottleneck of HTS data analysis is to map the reads to a reference genome, for which clusters are typically used.
However, building clusters large enough to handle hundreds of petabytes of data is infeasible.
Additionally, the reference genome is also periodically updated to fix errors and include newly sequenced insertions, therefore in many large scale genome projects the reads are realigned to the new reference.
Therefore, we need to explore volunteer grid computing technologies to help ameliorate the need for large clusters.
However, since the computational demands of HTS read mapping is substantial, and the turnaround of analysis should be fast, we also need a method to motivate volunteers to dedicate valuable resources.
For this purpose, we propose to merge distributed read mapping techniques with the popular cryptocurrency protocols.
Cyryotocurrencies such as Bitcoin calculate a value (called nonce) to ensure new block (i.e. “money”) creations are limited in the system, however, this calculation serves no other practical purpose.
Our solution (Coinami) replaces nonce with a token signed by an authority that can be acquired by returning the alignment results assigned by the authority.
Authorities have two main tasks in our system: 1) inject new problem sets (i.e. “alignment problems”) into the system, and 2) check for the validity of the results to prevent counterfeit
<EMAIL_ADDRESS>Short bio: Leonid received his MSc and PhD in Electrical Engineering from the Technion. After graduating, he co-founded VisionTech where he co-designed a single chip MPEG2 codec. Following VisionTech’s acquisition by Broadcom, he co-founded Horizon Semiconductors where he co-designed a Set Top Box on chip for cable and satellite TV.
Leonid is a postdoc fellow in Electrical Engineering in the Technion. He co-authored a number of patents and research papers on SoC and ASIC. His research interests include non von Neumann computer architectures and processing in memory
<EMAIL_ADDRESS>Short bio:
Roman received his BSC and MSc from the faculty of Electrical Engineering, Technion, Israel in 2009 and 2015, respectively. He is now a PhD candidate in the same faculty under the supervision of Prof. Ran Ginosar.
Roman's research interests are parallel computer architectures, in-data processing, accelerators for machine learning and big data, and novel computer architectures for bioinformatics applications.


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Help
By: Ryan Thornhill & Andy Yao
1. Extract contents into the webapps directory of your tomcat installation (catalina/webapps/)
2. In the terminal navigate to webapps/cmput391project/WEB-INF/classes/
3. Run make in that directory
4. Start / Restart Tomcat (starttomcat)
5. Open a web browser and navigate to http://ug<#>.cs.ualberta.ca:<first port number>/cmput391project/login.jsp
a. You can obtain your workstation number by typing hostname into the terminal
The login module is the entry point for the site and allows a user to authenticate in order to use all features they are authorized for.
Accessed via {BASE_URL}/Login.jsp
This page is the entry point for the system. A user can enter a username and password then hit the “sign in” button
Accessed via{BASE_URL}/modifyAccount.jsp
This page can be used to alter account information for the currently signed in user. Click on any column in the table to edit its value.
The user management module is accessible only from an administrator account. It can be used to edit and create information about users, persons, and family doctors.
All of the information in the users, persons and family_doctors tables can be edited from the main User Management page. Use the tabs at the top of the page to view the corresponding information. To edit information click on the cell you wish to alter, a popup will allow you to enter a new value. To add a value to the table click on the “add <data item>” button at the bottom of the page.
The creation pages can all be reached by navigating to the tab corresponding to the information you wish to add and selectin the “add” button at the bottom of the page.
Fill in all fields and click submit.
Fill in all fields and click submit. Class must be (a administrator, p patient, d doctor, or r radiologist)
Fill in the id for the doctor to be pared with the id of the patient.
The report generation module can be used to find patients with a given diagnosis during a given time period.
To use this module simply fill in the diagnosis field and select a starting and ending date. Click on the search button and the results will be shown.
The uploading module is used to upload an image file for a specified radiology record
To use this module type in the radiology record id and click submit. Then choose the image you wish to upload by clicking the browse button. Finally, click upload to upload the image for the specified radiology record.
The search module is used to search for radiology records found in the database
To use this module type in a keyword or a list of keywords separated by spaces and/or a start date and/or an end date in the text boxes beside the approriate labels. Additionally a checkbox is supplied in which if checked will sort the results by date and then by search term match rather than just search term match. Finally click search to view the results. The results will onl display if at least 1 of the textboxes are filled.
The data analysis module is used to generate an OLAP report based on the values of patient name, test type, and/or a period of time
To use this module select patient and/or test type if these fields are required. And optionally a time period. Then select whether weekly, monthly, or yearly results are desired. Finally click submit to view the report in the form of a table.


new Cursor(db, items)
Note that the DB is optional - a cursor can refer to a set of deleted objects, in which case the cursor holds the last reference to those objects. Certain types of chaining cannot be done because these objects are not linked to a DB (specifically delete - which requires a DB to delete from)
Cursors can be chained together, such as: cursor = db.find({a: 1}).each(fn, this).sort("a").delete(); At the end of that chain, all objects matching a === 1 will be passed to the function fn (bound to this), then sorted on "a" (asc by default) then deleted. The cursor variable will point to a cursor holding all objects that were deleted. The cursor will *not* be bound to any database.
You can also join any cursor with a database (can be the same DB that cursor is bound to, or any other).
A cursor is generally only created by operations on a database or a cursor, they are not usually created by users.
Parameters:
| Name | Type | Description |
|---|---|---|
db |
Object | Database that actually holds the objects referred to here. Can be null. |
items |
Array | List of items to initially populate the cursor with. Can be null. |
- Source:
Methods
-
call(cb) → {Object}
-
Call the given callback, passing the cursor. This can be used in the middle of a chain of calls.
Parameters:
Name Type Description cb
function Callback function to call - Source:
Returns:
Cursor object is returned.- Type
- Object
-
delete() → {Object}
-
Delete all items in the Cursor from the DB. Note that after this... the cursor is emptied because these items dont exist any more.
- Source:
Returns:
undefined- Type
- Object
-
each(cb, binding) → {Object}
-
Call the given callback function for each item in the cursor. The call back is called as cb(object, index, this)
Parameters:
Name Type Description cb
function Callback function called on each item. binding
Object Optional. Bound to the - Source:
Returns:
Return the current cursor for more chaining- Type
- Object
-
exists(qry) → {Boolean}
-
Returns true if there exists at least ONE item that matches the query
Parameters:
Name Type Description qry
Object Query - Source:
Returns:
True if there exists at least one match.- Type
- Boolean
-
filterWhere(cb, binding)
-
Find all elements using a custom test function. The function should return true on a successful test.
Parameters:
Name Type Description cb
function Callback for testing inclusion binding
Object Context for the callback function - Source:
-
first() → {Object}
-
Return the first element in the cursor, or null if the cursor is empty.
- Source:
Returns:
First element- Type
- Object
-
join(db, db_prop, operator, cursor_prop) → {Object}
-
Join two data sets. A cursor provides the first set, the db provides the second. We create a new cursor where each element is a merged object containing properties from cursor, and properties from db. Properties from cursor have precendence (and the _id value points to the DB belonging to cursor) The join is done by creating a query of the form: db_prop: { operator: cursor_prop } db_prop: { $eq: db_prop } Note that all joins are *inner* (to use the semantics of SQL). If there is no db object that matches a cursor object (and v.v) then nothing is returned for that row. NOTE: the objects are now *NOT* stored in the DB - instead they are copies. Therefore you must take care in deletes and updates. They *do* contain the _id of the cursor object.
Parameters:
Name Type Description db
Object The jsObjDB providing the second set of values db_prop
String The db property on which to merge. operator
String The operator used to compare db_prop to cursor_prop, Defaults to $eq cursor_prop
String The cursor property on which to merge. Defaults to db_prop - Source:
Returns:
Cursor object *not* linked to a DB, with all the merged objects.- Type
- Object
-
last() → {Object}
-
Returns the last element in the cursor, or null if the cursor is empty.
- Source:
Returns:
Last element- Type
- Object
-
nth(n) → {Object}
-
Returns the nth element in the cursor. Note that this will return null if n is outside the range of elements (<0, >=length). You can use [] instead, which will return undefined if the element is outside range.
Parameters:
Name Type Description n
Number index of the item - Source:
Returns:
The item to be returned.- Type
- Object
-
sizeof() → {Number}
-
A cursor is a set of references to some elements in a DB. One reference for each object and one reference to the DB. Each reference is 8 bytes.
- Source:
Returns:
Number of bytes (roughly) taken by this Cursor.- Type
- Number
-
sort(property, asc) → {Object}
-
Sort elements in the cursor. This replaces the array sort function, because it accepts more complex property names and array indexes - e.g. "a[3].b"
Parameters:
Name Type Description property
String Name of the property to sort on asc
Boolean Ascending? Defaults to true - Source:
Returns:
Returns the cursor, sorted- Type
- Object
-
toString() → {String}
-
Return a string like representation of the cursor.
- Source:
Returns:
String like representation of the cursor.- Type
- String
-
uniq(property) → {Object}
-
Return a Cursor with only items that have unique values for the given property. Duplicates will be removed. Note that the property can be of the form: "a[3].b.c"
Parameters:
Name Type Description property
String Property that will be used for uniqueness tests. - Source:
Returns:
Cursor object is returned.- Type
- Object
-
update(changes) → {Object}
-
Execute a set of updates in order. The cursor should be ordered if the order up updates is important.
Parameters:
Name Type Description changes
Object Object containing the changes to be made - Source:
Returns:
Cursor, after changes made- Type
- Object


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Start Here
WARNING! The email preview will render images in a slightly
different position than what they will be once the email is sent. What you see in the browser is what it should look like once the email is sent.
Best practice would be to continue to test what it looks like until you get it just right because of these differences.
Additionally the original size of the images matter, you will need to keep them small to fit inside the table cells.
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WITNESS IMMUNITY
REPEALED. L 1978, c 212, §1.
CHAPTER 621C
Section
621C-1 Self-incrimination claim precluded
621C-2 Procedures
621C-3 Use immunity
621C-4 Transactional immunity
621C-5 Penalty


Jumping off rocks
Another very mellow day. Floated out of bed, hung around for a bit, then a few of us headed off in J's Magic Bus to a fine little spot about 40 minutes away, called Dowd Falls. A little waterfall at the top of a rock pool, with its sides rising to 40ft or so. Perfect for throwing yourself off.
I remember once in Ibiza doing an 80ft cliff jump, but that was on the tailend of a heavy drinking session, when lack of sobriety ineveitably clouds judgement.  But it had to be done. Didn't do any somersaults or backflips, just did some bruising on my feet and some panicky shouting.
But the pain of the bruising was significantly deadend by the BBQ at the hostel in the evening, where there was free wine, and all you can eat and drink jobby. This time it was on land, but it proved just as deadly.
Back to the main page


Search results for : {{search_term}}
Found {{total}} repositories coresponding to the term "{{search_term}} ", displaying the latest created ones :
{% for item in top5 %}
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{% extends 'base.html' %} {% load humanize %} {% block content %}
| Type | Preview | JSON |
|---|---|---|
| Deaths & Cases by county | {"cases": {"cases_cum_probable": [31, 40, 0, 12, 4, 0, 0, 345, 89, 0, 309, 50, 3, 0, 0], "cases_cum_confirmed": [31, 24, 0, 13,... | Get Highcharts data |
| For table w/sparkline excluding national (regional_drilldown) | {"aaData": [{"cases_cum": 63, "new_weekly_deaths": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 6, 4, 2, 4, 15, 1], "total_deaths_all": 34,... | Get table JSON |
| For table w/sparkline including national | Get table JSON | |
| For table w/sparkline only national | Get table JSON |


Example 1: Cauchy matrices
Working over the field of rational numbers, let
x = [ 3, 4, 5 ]
and
y = [ 0, 1, 2 ].
Then, the Cauchy matrix constructed from
x and
y is
/ 1/3 1/2 1 \
| 1/4 1/3 1/2 |
\ 1/5 1/4 1/3 /,
which has inverse
/ 30 -180 180 \
| -36 192 -180 |
\ 9 -36 30 /.
Example 2: Matrix inversion via row reduction
Working over the field of rational numbers, let
/ 0 2 2 \
M = | 3 4 5 |
\ 6 6 7 /.
The initial augmented matrix
A is
/ 0 2 2 | 1 0 0 \
| 3 4 5 | 0 1 0 |
\ 6 6 7 | 0 0 1 /.
We need
A00 to be non-zero, so swap rows
0 and
1:
/ 0 2 2 | 1 0 0 \ / 3 4 5 | 0 1 0 \
| 3 4 5 | 0 1 0 | --> | 0 2 2 | 1 0 0 |
\ 6 6 7 | 0 0 1 / \ 6 6 7 | 0 0 1 /.
We need
A00 to be 1, so divide row
0 by 3:
/ 3 4 5 | 0 1 0 \ / 1 4/3 5/3 | 0 1/3 0 \
| 0 2 2 | 1 0 0 | --> | 0 2 2 | 1 0 0 |
\ 6 6 7 | 0 0 1 / \ 6 6 7 | 0 0 1 /.
We need
A20 to be 0, so subtract row
0 scaled by 6 from row
2:
/ 1 4/3 5/3 | 0 1/3 0 \ / 1 4/3 5/3 | 0 1/3 0 \
| 0 2 2 | 1 0 0 | --> | 0 2 2 | 1 0 0 |
\ 6 6 7 | 0 0 1 / \ 0 -2 -3 | 0 -2 1 /.
We need
A11 to be 1, so divide row
1 by 2:
/ 1 4/3 5/3 | 0 1/3 0 \ / 1 4/3 5/3 | 0 1/3 0 \
| 0 2 2 | 1 0 0 | --> | 0 1 1 | 1/2 0 0 |
\ 0 -2 -3 | 0 -2 1 / \ 0 -2 -3 | 0 -2 1 /.
We need
A21 to be 0, so subtract row
1 scaled by −2 from row
2:
/ 1 4/3 5/3 | 0 1/3 0 \ / 1 4/3 5/3 | 0 1/3 0 \
| 0 1 1 | 1/2 0 0 | --> | 0 1 1 | 1/2 0 0 |
\ 0 -2 -3 | 0 -2 1 / \ 0 0 -1 | 1 -2 1 /.
We need
A22 to be 1, so divide row
2 by −1, which makes the left side of
A a
unit upper triangular matrix:
/ 1 4/3 5/3 | 0 1/3 0 \ / 1 4/3 5/3 | 0 1/3 0 \
| 0 1 1 | 1/2 0 0 | --> | 0 1 1 | 1/2 0 0 |
\ 0 0 -1 | 1 -2 1 / \ 0 0 1 | -1 2 -1 /.
We need
A12 to be 0, so subtract row
2 from row
1:
/ 1 4/3 5/3 | 0 1/3 0 \ / 1 4/3 5/3 | 0 1/3 0 \
| 0 1 1 | 1/2 0 0 | --> | 0 1 0 | 3/2 -2 1 |
\ 0 0 1 | -1 2 -1 / \ 0 0 1 | -1 2 -1 /.
We need
A02 to be 0, so subtract row
2 scaled by 5/3 from row
0:
/ 1 4/3 5/3 | 0 1/3 0 \ / 1 4/3 0 | 5/3 -3 5/3 \
| 0 1 0 | 3/2 -2 1 | --> | 0 1 0 | 3/2 -2 1 |
\ 0 0 1 | -1 2 -1 / \ 0 0 1 | -1 2 -1 /.
We need
A01 to be 0, so subtract row
1 scaled by 4/3 from row
0, which makes the left side of
A the identity matrix:
/ 1 4/3 0 | 5/3 -3 5/3 \ / 1 0 0 | -1/3 -1/3 1/3 \
| 0 1 0 | 3/2 -2 1 | --> | 0 1 0 | 3/2 -2 1 |
\ 0 0 1 | -1 2 -1 / \ 0 0 1 | -1 2 -1 /.
Since the left side of
A is the identity matrix, the right side of
A is
M-1. Therefore,
/ -1/3 -1/3 1/3 \
M^{-1} = | 3/2 -2 1 |
\ -1 2 -1 /.