API Examples¶
Four Python sample use cases were coded using the prototype SWIG API to act as a guide to addressing the changes that are needed for the production system. These cases ranged from a trivial time system conversion use case to a full propagation use case. These cases, shown in Comparison of the SWIG Prototype with the Production API, were then reworked into the API syntax documented here. The following section previews the changes coming to the GMAT API by presenting each of these cases as planned for the API. The examples presented here are in Python. Java examples are presented in API Examples in Java.
Case 1: Time System Conversion¶
GMAT supports five time systems: A.1 Atomic Time (A1), International Atomic Time (TAI), Coordinated Universal Time (UTC), Barycentric Dynamical Time (TDB), and Terrestrial Time (TT). Times in GMAT are stored internally in a modified Julian format, referenced to January 5, 1941 at noon. Conversions between these time systems are performed using a time system converter, coded in the TimeSystemConverter singleton class. The time system converter also provides routines to convert between modified Julian representations and Gregorian representations.
The simplest usage of the time system converter using GMAT’s API consists of two lines of code; lines 4 and 8 shown here:
1 2 3 4 5 6 7 8 | import gmatpy as gmat
# Get the converter
timeConverter = gmat.theTimeSystemConverter
# Convert an epoch
UTCEpoch = 21738.22145
TAIepoch = timeConverter.Convert(UTCepoch, UTC, TAI)
|
This code shows two features of the API. The first line shows how a Python user loads the GMAT system and initializes it for use. As part of the initialization process, several components are created in the GMAT module that are single instance objects, following a singleton design pattern. These singletons are accessed from the API using names prefixed by the character string “the”. Line 4 is an example of this usage. The time system converter singleton is accessed using the object name theTimeSystemConverter. In the python code, the object “timeConverter” is connected to the singleton, and then used to convert a UTC epoch to the TAI time system on line 8.
At this point, the singleton is ready for the user to ineract with it directly. Using the API, the conversion is immediately available:
>>> timeConverter.ConvertMjdToGregorian(UTCEpoch)
'12 Jul 2000 17:18:53.280'
>>> TAIEpoch = timeConverter.Convert(UTCEpoch,2,1)
>>> timeConverter.ConvertMjdToGregorian(TAIEpoch)
'12 Jul 2000 17:19:25.280'
The interactive call shows the correct time system difference arising from the number of leap seconds needed to convert from UTC time to TAI time. Users can access the leap second count at a specified epoch directly as well:
>>> timeConverter.NumberOfLeapSecondsFrom(TAIEpoch)
32.0
Case 2: Coordinate System Conversion¶
The time system converter is a stand alone component in GMAT. It does not require external components to perform conversions. Coordinate systems are more complex. They require connections to other objects in order to compute data. Table 1 shows the settings, required and optional, to define a GMAT coordinate system.
Field |
Type |
Required? |
Description |
|---|---|---|---|
AxisType |
AxisSystem object |
Yes |
Defines the orientation of the coordinate axes |
Origin |
SpacePoint object |
Yes |
Defines the coordinate system origin |
Primary |
SpacePoint object |
No |
Reference body used in coordinate systems that need a primary body |
Secondary |
SpacePoint object |
No |
Reference body used in coordinate systems that need a secondary body |
J2000 Body |
Internal reference object |
Yes |
Reference origin in GMAT (always set to Earth) |
Solar System |
Solar System object |
Yes |
The solar system used in the run |
Coordinate systems are defined in GMAT as a collection of objects, using a core composite component that collect together the axis system defining the directions for the coordinate system axes, the bodies used to set the coordinate system origin and the axis references, and core GMAT settings used to tie the coordinate system into the rest of the executing GMAT code. Many of the user objects in GMAT have a structure like this: a core object that uses other objects to form a composite component consistent with the rest of the running GMAT system.
GMAT performs conversions between coordinate systems using a coordinate system converter, coded in the CoordinateConverter class. The CoordinateConverter class maintains state information about the most recent conversion performed. This state data would cause issues using a state converter in a single instance context, because the state data from one conversion could be accessed in code requesting the state data from a second conversion. For that reason, the CoordinateConverter class does not provide a singleton instance, and a separate object must be created for each use.
A basic use case for the coordinate system converter takes a state in Earth-centered Mean-of-J2000 Equatorial coordinates and converts the state into Earth-centered Earth-fixed coordinates. The Python code demonstrating this conversion using the GMAT API is
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | import gmatpy as gmat
# Setup the GMAT data structures for the conversion
mjd = gmat.A1Mjd(22326.977184)
rvIn = gmat.Rvector6(6988.427, 1073.884, 2247.333, 0.019982, 7.226988, -1.554962)
rvOut = gmat.Rvector6(0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
# Create the converter
csConverter = gmat.CoordinateConverter()
# Create the input and output coordinate systems
eci = gmat.Create("CoordinateSystem",
"ECI", "Earth", "MJ2000Eq")
ecef = gmat.Create("CoordinateSystem",
"ECEF", "Earth", "BodyFixed")
csConverter.Convert(UTCepoch, rvIn, eci, rvOut, ecef)
|
The key features shown in this example for the API are:
Default objects (like the solar system and Earth objects) are set automatically.
GMAT objects are built using the Create() command.
Cases 3 and 4: Force Modeling and Propagation¶
Force models in GMAT – or, more properly, dynamics models – are built by creating an object from the force container class, ODEModel, and adding the constituent forces to that container. The ODEModel object is responsible for accumulating the dynamics into a derivative vector. The dynamics are computed when the GetDerivatives() method is called on the object. The resulting computation is stored in a class member, accessible using the GetDerivativeArray() method.
The ODEModel class is one component of a more complicated propagation subsystem in GMAT. That subsystem is designed with spacecraft propagation in mind. Force modeling requires an associated spacecraft object. During initialization, an instance of the GMAT helper class, PropagationStateManager (PSM), is used to collect data and assemble the state vector used to evaluate the dynamics. The PSM determines the size of the state vector by checking to see if the 6 element Cartesian state, mass flow from spacecraft tanks, and the state transition matrix or state Jacobian (A-matrix) are needed during the propagation. Once the size of the propagation state vector is determined, the complete vector is assembled and initialized, and only then can the dynamics be evaluated.
The steps required for this initialization are largely implemented behind the scenes in the GMAT API. Users that want to manage this setup by hand are referred to the sample code in Comparison of the SWIG Prototype with the Production API. The third example in that chapter shows force model configuration. API users can access GMAT’s dynamics models by configuring the forces piece by piece and assigning them to an ODEModel container. Here is an example of this process for an Earth point mass force model:
1 2 3 4 5 6 7 8 9 10 11 12 13 | import gmatpy as gmat
dynamics = gmat.Create("ODEModel", "EPointMassDynamics")
epm = gmat.PointMassForce("EarthPointMass")
dynamics.AddForce(epm)
# Evaluating only the 6 element Cartesian state
pstate = [7000.0, 0.0, 1000.0, 0.0, 8.0, -0.25]
gmat.Initialize()
pderiv = dynamics.GetDerivativeArray()
dynamics.GetDerivatives(pstate, 0.0)
|
This basic example uses a hidden Spacecraft object for part of the configuration. When this type of automatically generated object is used, the API notifies the user that the object was created, and supplies the user with the object’s name. In general, API users will want to create and assign a spacecraft to the force model, so that spacecraft properties that affect the forces will be under the control of API user. A more complete example of this procedure, adding a solar radiation pressure force and a spacecraft used for the corresponding ballistic data, might look like this:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | import gmatpy as gmat
# Set up the spacecraft
sat = gmat.Create("Spacecraft", "Sat")
sat.SetField("Cr", 1.4)
sat.SetField("SRPArea", 6.5)
sat.SetField("DryMass", 225)
# Make a force container
dynamics = gmat.Create("ODEModel", "EPM_SRP")
dynamics.SetObject(sat)
# Set the forces
epm = gmat.PointMassForce("EarthPointMass")
srp = gmat.SolarRadiationPressure("SRP")
dynamics.AddForce(epm)
dynamics.AddForce(srp)
# Evaluating only the 6 element Cartesian state
pstate = [7000.0, 0.0, 1000.0, 0.0, 8.0, -0.25]
gmat.Initialize()
pderiv = dynamics.GetDerivativeArray()
dynamics.GetDerivatives(pstate, 0.0)
|
The setup for dynamics modeling above extends with little additional configuration to numerical integration. The integration piece of the configuration adds the lines
1 2 3 4 5 6 7 | prop = gmat.Create("PrinceDormand78", "MyIntegrator")
prop.SetPhysicalModel(dynamics)
gmat.Initialize()
for i in range(10):
prop.Step(60.0)
|
To summarize: The goal of the GMAT API is to make API based configuration as simple as possible, while maintaining full access to the capabilities of GMAT. Towards that end, the design of the API can be illustrated through a representative example implementation of a propagation problem. A reference propagation in the GMAT API looks like this:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | import gmatpy as gmat
# Set up the spacecraft
sat = gmat.Create("Spacecraft", "Sat")
sat.SetField("Cr", 1.4)
sat.SetField("SRPArea", 6.5)
sat.SetField("DryMass", 225)
# Make a force container and set its spacecraft
dynamics = gmat.Create("ODEModel", "EPM_SRP")
dynamics.SetObject(sat)
# Set the forces
epm = gmat.PointMassForce("EarthPointMass")
srp = gmat.SolarRadiationPressure("SRP")
dynamics.AddForce(epm)
dynamics.AddForce(srp)
# Propagator configuration
prop = gmat.Create("PrinceDormand78", "MyIntegrator")
prop.SetPhysicalModel(dynamics)
gmat.Initialize()
# Number of steps to take
count = 60
for i in range(count):
prop.Step(60.0)
|
Cases 5: Working with a GMAT Script¶
The script examples above show how an API user interacts with GMAT components directly. This final example shows how a user can work with an existing GMAT script that needs to change settings on one of the scripted objects. For this example, the GMAT sample mission that demonstrates finite burns is used. The full script is the Ex_FiniteBurn.script file in the GMAT samples folder. Part of that script includes the definition of a chemical thruster, shown through the thrust vector portion here:
1 2 3 4 5 6 7 8 | Create ChemicalThruster engine1;
GMAT engine1.CoordinateSystem = Local;
GMAT engine1.Origin = Earth;
GMAT engine1.Axes = VNB;
GMAT engine1.ThrustDirection1 = 1;
GMAT engine1.ThrustDirection2 = 0;
GMAT engine1.ThrustDirection3 = 0;
...
|
An API user might want to change the thrust direction before running the script. The following API code loads the script, adds an orbit normal component to the thrust direction and then runs the script.
1 2 3 4 5 6 7 8 | import gmatpy as gmat
gmat.InterpretScript("../samples/Ex_FiniteBurn.script")
Thruster = gmat.GetConfiguredObject("engine1")
Thruster.SetField("ThrustDirection2", 1.0)
gmat.RunScript()
|


--- layout: post authors: ["Aleksandra Pawlik"] title: From a Helper to an Instructor date: 2013-06-02 time: "09:00:00" tags: ["Community"] ---
This blog post is actually a bit overdue as the first time I instructed at a Software Carpentry bootcamp was over a month ago in Manchester. However, as now I had an opportunity to teach twice (recently in Krakow), this gives me more credit to share my experience of taking a step up from being a helper to being an instructor.
I had helped at bootcamps in the UK so I knew how the materials were taught and what they covered. Nevertheless, it still took me a lot of time to go through the modules I was supposed to teach making sure that I could seamlessly not only follow all topics, examples, and exercises but also present them in a clear manner. As a helper most of what I had to do was to solve ad-hoc problems reported by the bootcamps attendees. As an instructor I had to make sure that everything I was saying was consistent with what I said before and logically led to the next step.
I also learnt first hand that addressing a diversified audience is always challenging. In both Manchester and Krakow I experienced the difficulties of teaching a group who (according to the pre-questionnaire) varied from those who, for example, had never used Python at all to those who "strongly agreed" that they can write a Python program. This meant that I was going to either lose half of the group or make the other half terribly bored. I thought that the former was worse. Software Carpentry is supposed to be improving researchers' computing skills and not discouraging them even more from the world of programming, testing, and using version control. I figured that those who were already quite familiar with the material would catch up with their Facebook in the worst case, and refresh their memory and maybe learn a few details they didn't know in the best case. Pitching at the right level is difficult.
About a year ago I witnessed a situation (not at a SWC bootcamp) when an experienced developer was trying to explain to a complete novice the mysteries of Git. To cut a long story short, it didn't go well—at the end the novice was still clueless, but what's worse, the developer also seamed somewhat lost in his explanations. As I was supposed to teach Git, this memory became quite fresh in my mind. Fortunately, I was able to observe as a helper how Mike Jackson taught Git at previous Software Carpentry bootcamps and I knew that teaching distributed version control can be done effectively without confusing the students and yourself. I used practically the same materials which Mike presented introducing only small tweaks and followed Mike's teaching approach.
On more practical note, I learnt it the hard way that teaching a two-hour block standing rather than sitting affects my typing skills pretty badly. After about 15 minutes my back and wrists were aching, I had to look at my laptop monitor from a very weird angle and the number of typos which I made was growing exponentially. It all could have been avoided if I got myself a chair to sit on.
Instructing at Software Carpentry bootcamps is probably a different experience for everyone. Although, as I had a chance to talk to two other people who recently taught for the first time, there was one thing which we agreed on: allocate sufficient time to prepare for the bootcamp. Don't assume that it'll be enough just to flick through the materials and copy them over to your bootcamp branch. If you are an experienced instructor, this may work. But if you've only been a helper until now, you'll need to put more effort. But the payoff when you see the learners going "Ah, now I get it", is well worth it.


--- layout: post title: How to Set Up Flash Hashes and Error Messages in Rails date: 2015-05-20 23:06:17.000000000 -05:00 type: post published: true status: publish categories: - Ruby on Rails tags: [] meta: is_reblog: '1' post_id: '26' blog_id: '91461367' reblog_snapshot: "O:8:\"stdClass\":7:{s:5:\"title\";s:54:\"How to Set Up Flash Hashes and Error Messages in Rails\";s:4:\"type\";s:4:\"post\";s:9:\"mime_type\";s:0:\"\";s:6:\"format\";b:0;s:12:\"modified_gmt\";s:19:\"2015-05-15 04:15:23\";s:9:\"permalink\";s:108:\"https://christopherrdoddcode.wordpress.com/2015/05/15/how-to-set-up-flash-hashes-and-error-messages-in-rails\";s:7:\"content\";s:2170:\"
flash[:notice] = \"The subject was created successfully\"\r\nWhere should you put it?\r\n\r\nA good place to put it is in the controller after the object successfully saves or is destroyed. For example:\r\n
def create\r\n @subject = Subject.new(subject_params)\r\n\r\n \ if @subject.save\r\n flash[:notice] = \"Subject<EMAIL_ADDRESS>created Successfully\"\r\n redirect_to root_path\r\n\r\n else\r\n render('new')\r\n \ end\r\nend\r\n2. Displaying the notice\r\n\r\nIf using Bootstrap and HAML, you can put this in the view where the user gets redirected to\r\n
-if !flash[:notice].blank?\r\n \ .row\r\n .notice.alert.alert-success\r\n =flash[:notice]\r\nIf you're not using Bootstrap or HAML, try this:\r\n
<% if !flash[:notice].blank? %>\r\n
<%= flash[:notice] %>
\r\n<% end %>\r\nErrors\r\n\r\n1. Write your validations\r\n\r\nFor there to be errors, you need validations. If you haven't built validations, I suggest you do this now.\r\n\r\n2. Generate a partial for displaying your errors\r\n
#app/views/application/_error_messages.html.haml\r\n-if object && object.errors.size > 0\r\n #error-explanation\r\n %h3\r\n \ = pluralize(object.errors.size, 'error')\r\n prohibited this record from being saved\r\n %p problems with the following fields:\r\n %ul\r\n \ - object.errors.full_messages.each do |msg|\r\n %li= msg\r\n3. Create a custom helper method\r\n
#app/helpers/application_helper.rb\r\ndef error_messages_for(object)\r\n render(:partial => 'application/error_messages', :locals => {:object => object})\r\nend\r\n3. Call the helper on the view with the form\r\n
= error_messages_for(@subject)\n
So simple but so many do not use this. We have a legacy app that has NO error messages. Try to login with wrong email, it just does not work but no message to tell you why. Seems like this should be a standard thing.


Classes
The following classes are available globally.
-
This is the base implementation of the ITCB protocol contract. The base doesn’t need to be bound to a protocol, as the specializations will be.
It is a Swift class, and this is the “base” class, meant to be specialized for Central and Peripheral variants.
This class needs to derive from NSObject, as its subclasses are Core Bluetooth delegate classes.
See moreDeclaration
Swift
public class ITCB_SDK : NSObject, ITCB_SDK_Protocol
-
This is the Central specialization of the main SDK interface.
See moreDeclaration
Swift
public class ITCB_SDK_Central : ITCB_SDK, ITCB_SDK_Central_Protocol
-
This is the Peripheral specialization of the main SDK interface.
IMPORTANT NOTE: Peripheral Mode is not supported for WatchOS or TVOS. This class is only included in the iOS and MacOS framework targets.
See moreDeclaration
Swift
public class ITCB_SDK_Peripheral : ITCB_SDK, ITCB_SDK_Peripheral_Protocol


Taylor Swift smashes all-time American Music Awards record
by Entertainment DeskTaylor Swift took the high road as she won six American Music Awards (AMAs) on Sunday to surpass Michael Jackson’s all-time record, avoiding any direct mention of a bitter dispute with her old record company.
Swift won the top award, Artist of the Year, and four others. She was also given an honorary Artist of the Decade award, taking her all-time total to 29 AMAs, organisers said. That easily outstripped the 24 awards picked up by Jackson, reported Reuters.
“All that matters to me is the memories that I have had with you guys, with you the fans, over the years,” Swift, 29, told the audience at the ceremony in Los Angeles after performing a medley of her old hits.
Swift had been expected to speak out about a long feud with record industry executive Scooter Braun, who owns the master recordings to her first six albums after she signed with a new label in 2018.
The singer accused Braun of refusing permission for her to sing songs from her back catalogue at the awards show. The public spat culminated last week with Braun saying that his family had received numerous death threats after the pop singer urged her 122 million Instagram fans to let Braun “know how you feel about this.”
But on Sunday, the Fearless hit maker made no mention of the dispute, although she opened her performance wearing a white shirt printed with the titles of her old albums. “This year, for me, has been a lot. It’s been a lot of good, it’s been a lot of really complicated,” she said, without elaborating.
Swift beat Drake, Ariana Grande, Halsey and Post Malone as Artist of the Year. Newcomer Billie Eilish, 17, took home two statuettes for Best New Artist and Best Alternative Rock Artist after a breakout year that saw her top the US charts with her single Bad Girl.
“You made it all happen,” said Eilish, thanking her fans. She showed her support for environmental causes by wearing a T-shirt with the slogan “No Music on a Dead Planet” as she gave her first awards show performance with All the Good Girls Go to Hell.
Selena Gomez kicked off the ceremony in her first live TV performance in two years, after undergoing a kidney transplant in 2017 and battling anxiety and depression.
Body positive R&B singer Lizzo performed her new single Jerome and Cuban-born Camila Cabello and boyfriend Shawn Mendes sang their hit pop single Señorita, which won the award for Collaboration of the Year.
British rocker Ozzy Osbourne, 70, who has been sidelined by serious health problems for a year, was mostly seated as he took to the stage with rappers Post Malone, Travis Scott and rocker WATT for their single Take What You Want.
Punk rockers Green Day got the audience to its feet as they marked the 25th anniversary of their 1994 breakout album Dookie. Other performers included the Jonas Brothers, Kesha, Halsey, Christina Aguilera and Shania Twain.
Box: Here’s a complete list of the night’s biggest winners
Artist of the Year
Taylor Swift
New Artist of the Year
Billie Eilish
Collaboration of the Year
Shawn Mendes and Camila Cabello for Señorita
Favourite Music Video
Taylor Swift for You Need to Calm Down
Favourite Male Artist – Pop/Rock
Khalid
Favourite Female Artist – Pop/Rock
Taylor Swift
Favourite Duo or Group – Pop/Rock
BTS
Favourite Album – Pop/Rock
Taylor Swift for Lover
Favourite Artist – Rap/Hip-Hop
Cardi B
Favourite Album – Rap/Hip-Hop
Post Malone for Hollywood’s Bleeding
Favourite Male Artist – Soul/R&B
Bruno Mars
Favourite Female Artist – Soul/R&B
Beyoncé
Favourite Album – Soul/R&B
Khalid for Free Spirit
Favorite Artist – Alternative Rock
Billie Eilish
Favourite Artist – Adult Contemporary
Taylor Swift
Favourite Artist – Electronic Dance Music (EDM)
Marshmello
Favourite Soundtrack
Bohemian Rhapsody by Queen


Launching Spark on YARN
Experimental support for running over a YARN (Hadoop
NextGen)
cluster was added to Spark in version 0.6.0. Because YARN depends on version
2.0 of the Hadoop libraries, this currently requires checking out a separate
branch of Spark, called yarn
, which you can do as follows:
git clone git://github.com/mesos/spark
cd spark
git checkout -b yarn --track origin/yarn
Preparations
- In order to distribute Spark within the cluster, it must be packaged into a single JAR file. This can be done by running
sbt/sbt assembly
- Your application code must be packaged into a separate JAR file.
If you want to test out the YARN deployment mode, you can use the current Spark examples. A spark-examples_2.9.2-0.6.0
file can be generated by running sbt/sbt package
. NOTE: since the documentation you’re reading is for Spark version 0.6.0, we are assuming here that you have downloaded Spark 0.6.0 or checked it out of source control. If you are using a different version of Spark, the version numbers in the jar generated by the sbt package command will obviously be different.
 The client will exit once your application has finished running.
Important Notes
- When your application instantiates a Spark context it must use a special “standalone” master url. This starts the scheduler without forcing it to connect to a cluster. A good way to handle this is to pass “standalone” as an argument to your program, as shown in the example above.
- YARN does not support requesting container resources based on the number of cores. Thus the numbers of cores given via command line arguments cannot be guaranteed.


Scatter plots are drawn to explore the correlation between two variables. One variable (independent variable) is considered an explanatory variable, and the other is considered a dependent variable. For example, in the "Neighborhood welfare survey", respondents were asked about their number of household members and their monthly grocery expenses. If someone explores the possible impact of the number of household members on monthly grocery expenses, then the number of household members would be considered an independent variable, and monthly grocery expenses would be a dependent variable (depends on the number of family members). To draw a scatter plot,
in-context


Rocket Lab CEO lacks gratitude toward taxpayers
by New Zealand Taxpayers' Union
16 NOVEMBER 2019
FOR IMMEDIATE RELEASE
Responding to Rocket Lab CEO Peter Beck’s appearance on Morning Report today, New Zealand Taxpayers’ Union spokesman Louis Houlbrooke says:
“We almost spat out our coffee this morning as Peter Beck claimed that his industry receives no taxpayer-funded subsidies.”
“We can all agree Rocket Lab’s achievements are impressive, but Mr Beck does the space industry no favours by denying the contribution of hard-working taxpayers.”
“The Government has multiple funding and subsidy programmes for the space industry. It beggars belief that Mr Beck would have forgotten this, considering his own company received $25 million from Callaghan Innovation in 2013.”
“It’s hard enough already for taxpayers to keep track of how our money is spent. The last thing we need is for the companies that take our money to start denying it.”
ENDS


Provides a means of binding instances of Executor/ExecutorService using
Guice's @Named
, and the ability to customize how
they are initialized via Settings.
The simplest usage, accepting all the defaults, is to, in your Guice
module, simply install an instance of
ThreadModule
and call its
builder()
method, customize things how you want and call
bind()
, e.g.
install(new ThreadModule().builder("workers").bind());
That will get you a thread pool with Runtime.getRuntime().availableProcessors()
threads. Code that wants to use it can then do, e.g.
@Inject public MyService (@Named("workers") Executor threadPool) { ... }
Executor
and ExecuctorService
are bound to the created
executor. If you set the type to SCHEDULED
you also get a binding to ScheduledExecutorService
.
Additionally, an instance of ThreadCount
is bound, which lets you retrieve the actual thread count used, for things (such as
Netty's EventExecutors) which need to know that explicitly.
ThreadGroup
and ThreadFactory
are also bound using the
same binding name.
If you do not pass an explicit UncaughtExceptionHandler
to the
ExecutorServiceBuilder
,
you will need to bind UncaughtExceptionHandler
in your own code
(to log exceptions, or shut something down, whatever you like).
If you get an exception at startup saying that none was bound, that's your problem.
The point here is for some parameters to be customizable via Settings, so configuration files and command-line switches can override defaults. So this library will look in Settings, using the passed binding name, for the following things:
$BINDING_NAME
- an integer value for the thread count$BINDING_NAME.priority
- the thread priority to use$BINDING_NAME.type
- the type - the name of a constant on
ThreadPoolType
- this is useful
for cases where the environment dictates a different choice than the default - for
example, ForkJoinPool is preferable to use with Netty, but if you want
to run your application in, say, a 7MB heap, conventional pools are less
memory-intensive.
ScheduledExecutorService
into something that
is not one. Don't do that, or there will be no ScheduledExecutorService
under the
right name to inject.$BINDING_NAME.stackSize
- Change the stack size for created threads (don't use unless you really know
what you're doing!) rather than use the JVM's default
The stack size is not settable for fork-join and work stealing pools (a work stealing pool is just
a ForkJoinPool
with different initialization parameters); also, the thread group
is not settable. These are limitations of the API exposed by the JDK.
All created ExecutorService
s are attached to Giulius's
ShutdownHookRegistry
, which by default uses a JVM shutdown hook - but
which the giulius-tests
library uses to cleanly shutdown and unload
after each unit test.
By design, ShutdownHookRegistry
runs shutdown hooks in reverse order
that they were installed. However, if you are injecting Guice Providers, it is
possible to wind up with code trying to use an ExecutorService
after
it has been stopped. This usually shows up in the form of a bunch of
ExecutionExcptions
logged at the end of a unit test, which are
harmless but can obscure the source of a real test failure. For example, the
following code can create that situation:
public class MyService implements Runnable { private final Provider<ExecutorService> threadPool; @Inject MyService(@Named("workers") Provider<ExecutorService> threadPool, ShutdownHookRegistry reg) { this.threadPool = threadPool; reg.add(this); // Thread pool is not started yet, but MyService will be stopped after } public void start() { threadPool.get().submit(new SomeWorker()); // thread pool gets started on call to get() } }
To avoid that situation, simply call eager()
on your
ExecutorServiceBuilder
to
ensure it is created before anything that uses it is.


PEMbuilder
StartSSL Certificate
Custom Certificate
Domain name:
Generate certificate
Create certificate without intermediate and root
Name of the output .pem file:
Certificate:
Paste from file
Intermediate:
Paste from file
Root:
Paste from file
Private key:
Paste from file
Password:
My key has no password
Create full certificate
Download full pem file
Show/hide full pem file
{{PEM}}
Show log in console


"SOCHO "a fence; hedge, (1 Chronicles 4:18; R.V., Soco)=So’choh (1 Kings 4:10; R.V., Socoh), Sho’choh (1 Samuel 17:1; R.V., Socoh), Sho’co(2 Chronicles 11:7; R.V., Soco), Sho’cho (2 Chronicles 28:18; R.V., Soco), a city in the plain or lowland of Judah, where the Philistines encamped when they invaded Judah after their defeat at Michmash. It lay on the northern side of the valley of Elah (Wady es-Sunt). It has been identified with the modern Khurbet Shuweikeh, about 14 miles south-west of Jerusalem. In this campaign Goliath was slain, and the Philistines were completely routed.
"SOCHO "a fence; hedge, (1 Chronicles 4:18; R.V., Soco)=So’choh (1 Kings 4:10; R.V., Socoh), Sho’choh (1 Samuel 17:1; R.V., Socoh), Sho’co(2 Chronicles 11:7; R.V., Soco), Sho’cho (2 Chronicles 28:18; R.V., Soco), a city in the plain or lowland of Judah, where the Philistines encamped when they invaded Judah after their defeat at Michmash. It lay on the northern side of the valley of Elah (Wady es-Sunt). It has been identified with the modern Khurbet Shuweikeh, about 14 miles south-west of Jerusalem. In this campaign Goliath was slain, and the Philistines were completely routed.


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The most common 5DOF 3D->2D calibration. More...
The most common 5DOF 3D->2D calibration.
A 2D stereo point, v will be same for rectified images.
A stereo camera class, parameterize by left camera pose and stereo calibration.
A 3D rotation represented as a rotation matrix if the preprocessor symbol GTSAM_USE_QUATERNIONS is not defined, or as a quaternion if it is defined.
Rotation matrix NOTE: the angle theta is in radians unless explicitly stated.
A 3D pose (R,t) : (Rot3,Point3)
A 3D point is just a Vector3 with some additional methods.
A 2D point Complies with the Testable Concept Functional, so no set functions: once created, a point is constant.
A pinhole camera class that has a Pose3 and a fixed Calibration.
A pinhole camera class that has a Pose3 and a Calibration.
A Calibrated camera class [R|-R't], calibration K=I.
A pinhole camera class that has a Pose3, functions as base class for all pinhole cameras.
Calibration of a omni-directional camera with mirror + lens radial distortion.
Calibration of a camera with radial distortion.
Calibration of a camera with radial distortion that also supports Lie-group behaviors for optimization.
Calibration used by Bundler.
The most common 5DOF 3D->2D calibration, stereo version.
Uses same distortionmodel as OpenCV, with http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html but using only k1,k2,p1, and p2 coefficients. K = [ fx s u0 ; 0 fy v0 ; 0 0 1 ] rr = Pn.x^2 + Pn.y^2 \hat{Pn} = (1 + k1*rr + k2*rr^2 ) Pn + [ 2*p1 Pn.x Pn.y + p2 (rr + 2 Pn.x^2) ; p1 (rr + 2 Pn.y^2) + 2*p2 Pn.x Pn.y ] pi = K*Pn
Similar to Cal3DS2, does distortion but has additional mirror parameter xi K = [ fx s u0 ; 0 fy v0 ; 0 0 1 ] Pn = [ P.x / (1 + xi * \sqrt{P.x^2 + P.y^2 + 1}), P.y / (1 + xi * \sqrt{P.x^2 + P.y^2 + 1})] rr = Pn.x^2 + Pn.y^2 \hat{pn} = (1 + k1*rr + k2*rr^2 ) pn + [ 2*k3 pn.x pn.y + k4 (rr + 2 Pn.x^2) ; k3 (rr + 2 Pn.y^2) + 2*k4 pn.x pn.y ] pi = K*pn
If calibration is known, it is more computationally efficient to calibrate the measurements rather than try to predict in pixels.
Use PinholePose if you will not be optimizing for Calibration
Instead of using this class, one might consider calibrating the measurements and using CalibratedCamera, which would then be faster.


We describe here some fundamental laws of boolean algebra, called “monotone laws”. We then tackle some laws specific to Boolean algebra.
module Logic.laws where
open import Lang.dataStructures using (Bool; true; false)
open import Types.product using (_×_; _,_)
open import Logic.logicBasics using (¬; not; xor; ⟂; ⊤; singleton)
open import Logic.equality using (Equiv; _≡_; Rel; Equivalence)
Monotone laws are ones in which the inputs and outputs change monotonically, or the output either remains unchanged on changing inputs or changes in the same way as the input.
We first need to define operations in an abstract way before we proceed with laws on these operations. We used types to define families (types) of operations:
A unary operation can be defined as:
: Set → Set
∘ = A → A ∘ A
A binary operation ★ A
can be defined as:
_ : Set → Set
★= A → A → A ★ A
Here *_
constructs a type family of operations that operate on given type A
.
For proving these laws we need an instance of the equivalence relation ==
.
module BooleanLaws {A : Set} (eq : Equivalence A) where
module Eq₁ = Equivalence eq
open Eq₁
For an operation ★
,
\[ x ★ (y ★ z) ≡ (x ★ y) ★ z \]
: ★ A → Set
associativity _★_ = (x y z : A)
associativity → (x ★ (y ★ z))
((x ★ y) ★ z) ==
\[ x ★ y ≡ y ★ x \]
: ★ A → Set
commutativity _★_ = (x y : A)
commutativity → (x ★ y)
(y ★ x) ==
\[ ( x ★ y ) ★ z ≡ x ★ y × y ★ z \]
_distributesOverˡ_ : ★ A → ★ A → Set _
_*_ distributesOverˡ _+_ =
∀ x y z → (x * (y + z)) == ((x * y) + (x * z))
_distributesOverʳ_ : ★ A → ★ A → Set _
_*_ distributesOverʳ _+_ =
∀ x y z → ((y + z) * x) == ((y * x) + (z * x))
_distributesOver_ : ★ A → ★ A → Set _
= (* distributesOverˡ +) × (* distributesOverʳ +) * distributesOver +
\[ x ★ id_{★} ≡ x \]
These form the major laws of boolean algebra. There exists a bunch of others that we’ll also see here. Note that for non-commutative systems of algebra, identity can exist in two forms: right and left, similarly for directional operations like distributivity, inverses, etc.
: A → ★ A → Set
identityₗ _★_ = (y : A)
identityₗ x → (x ★ y)
== x
: A → ★ A → Set
identityᵣ _★_ = (y : A)
identityᵣ x → (y ★ x)
== x
: A → ★ A → Set
identity = (identityₗ x ★) × (identityᵣ x ★) identity x ★
\[ x ★ id ≡ x \]
: A → ★ A → Set _
leftZero _★_ = ∀ x → (z ★ x) == z
leftZero z
: A → ★ A → Set _
rightZero _★_ = ∀ x → (x ★ z) == z
rightZero z
: A → ★ A → Set _
zero = leftZero z ★ × rightZero z ★ zero z ★
Idempotence is a more specific law of boolean algebra:
\[ x ∧ x ≡ x \]
_idempotentOn_ : ★ A → A → Set _
_★_ idempotentOn x = (x ★ x) == x
: ★ A → Set _
idempotent = ∀ x → ★ idempotentOn x idempotent ★
Another pair of reductive laws that apply only in boolean algebra:
\[ x ∧ (x ∨ y) ≡ x x ∨ (x ∧ y) ≡ x \]
_absorbs_ : ★ A → ★ A → Set _
_∙_ absorbs _◌_ = ∀ x y → (x ∙ (x ◌ y)) == x
: ★ A → ★ A → Set _
absorptive = (∙ absorbs ◌) × (◌ absorbs ∙) absorptive ∙ ◌
The logical AND
and OR
operators satisfy the above laws:
open import Logic.logicBasics
open import Types.operations _==_
∨-assoc : associativity ∨ ∨-assoc true y z = refl ∨-assoc false y z = refl
∨-comm : Commutative ∨ ∨-comm true true = refl ∨-comm true false = refl ∨-comm false true = refl ∨-comm false false = refl
∨-identityˡ : LeftIdentity false ∨ ∨-identityˡ _ = refl
∨-identityʳ : RightIdentity false ∨ ∨-identityʳ false = refl ∨-identityʳ true = refl
∨-identity : Identity false ∨ ∨-identity = ∨-identityˡ , ∨-identityʳ
∨-zeroˡ : LeftZero true ∨ ∨-zeroˡ _ = refl
∨-zeroʳ : RightZero true ∨ ∨-zeroʳ false = refl ∨-zeroʳ true = refl
∨-zero : Zero true ∨ ∨-zero = ∨-zeroˡ , ∨-zeroʳ
∨-inverseˡ : LeftInverse true not ∨ ∨-inverseˡ false = refl ∨-inverseˡ true = refl
∨-inverseʳ : RightInverse true not ∨ ∨-inverseʳ x = ∨-comm x (not x) ⟨ trans ⟩ ∨-inverseˡ x
∨-inverse : Inverse true not ∨ ∨-inverse = ∨-inverseˡ , ∨-inverseʳ


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Documentation generated on Thu, 17 Dec 2009 16:13:03 -0500 by phpDocumentor 1.4.0a2


Becker, Safin and Muster named ATP Cup team captains
(Reuters) - Six-times Grand Slam champion Boris Becker will captain Germany at next year’s inaugural ATP Cup, while fellow former world number ones Marat Safin and Thomas Muster will lead Russia and Austria at the nation-based tournament.
Becker, who won his first major title at Wimbledon in 1985, was chosen by world No. 7 Alexander Zverev to captain Germany, who are drawn in Group F alongside Greece, Canada and hosts Australia and play their matches in Brisbane.
The 24-nation ATP Cup will take place from Jan. 3-12 in Brisbane, Perth and Sydney.
“We have a strong chance in an extremely competitive section of the draw,” Becker said in an ATP Cup media release.
“Playing against the very best and representing your country sets the tone, sets the sport. I think we’re going to see amazing tennis.”
Safin, who won the U.S. Open in 2000 and the centenary Australian Open five years later, will lead a Russian team that includes world No. 4 Daniil Medvedev and No. 17 Karen Khachanov.
“We have a real chance of winning the first Cup with the caliber of players we have in the Russian side and I’ll be doing everything I can to make that happen from the bench,” he said.
Russia face Italy, the United States and Norway in Group D and will play their group games in Perth.
Muster, who claimed the world’s top ranking in 1996 after winning the French Open the previous year, was chosen as captain by world No. 5 Dominic Thiem and welcomed the opportunity to lock horns with his former adversaries.
“The ATP Cup brings some old rivals together as captains,” he said. “The cream of tennis is competing across Australia and the player field is incredibly strong, I feel very excited to be part of it all.”
The winners of the six groups and the two best second-placed finishers reach the ATP Cup Finals in Sydney. Each tie comprises two singles and one doubles match.


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Deshaun Watson Gets Ankle Turned Badly Under Defender On Sack, Stays In Game (VIDEO)
by Darrelle LincolnDeshaun Watson just might be a machine.
The Houston Texans QB isn’t having the best 1st half against the Baltimore Ravens, but it could’ve been way worse as he got sacked in the second quarter and stayed on the ground.
Replay shows his ankle being landed on behind him as a Ravens defender sacked him.
He somehow stayed in the game after that.


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Cheryl Diamond 5k Schlep
Event Date: Sunday, January 12, 2020 - 10:00
The first annual Cheryl Diamond Santa Barbara 5K Schlep will take place on January 12, 2020 at Leadbetter Beach in Santa Barbara at 10AM.
Partially benefiting the Breast Cancer Resource Center, this event will bring together hundreds of participants from the county, ranging in age from 5 - 90 years old, bound together in solidarity to raise the hopes of those battling breast, ovarian and prostate cancer.
Current sponsors include The Jewish Federation of Greater Santa Barbara, American Self Storage, Edhat, El Al Airlines, Congregation B’nai B'rith, Power Of Your Om, Blenders in the Grass, UCSB Hillel, California Property Group, BCRC, the ADL, Fitness 805, Sol Wave Water, Patient Power, Bare Republic and more.
To register, please visit www.5kschlep.org
Event Category: FUNDRAISER


Published on November 21, 2017 and updated on October 19, 2021.
Traffic volume on a website or app isn’t enough to tell how many people will perform any valuable action. This metric is usually paired with others in a funnel that represent the steps from a journey that end on a sale, register, meeting or any other valuable action for a business. This model presumes that traffic must run across each of the funnel steps to reach the desirable outcome. The proportion of the traffic that cross each step is called conversion rate, a metric that is at the center of a CRO project. CRO means Conversion Rate Optimization, and define a set of techniques, tools and methodologies that are systematically used to optimize conversions. The term was coined in 2007 by the Conversion Rate Experts.
The main premise which guide the work of CRO professionals is that there is always something to improve. A CRO project (or program, as some agencies may call) have an end, but leaves for legacy a culture of constant improvement. Optimize conversions may be seen as a work without end, since there is always something to improve. Each CRO project must have its own definition of end, so the final result can be compared with the one obtained before the start of the project in order to evaluate its overall success.
Apart from that premise, other aspects are equally important for a successful CRO project:
- Systematic procedures are the rule: the end result must be verifiable, comparable and replicable, which is only possible with a process that is well defined and followed;
- Each business is a business: the same procedure will produce results that are aligned with each business’s reality, which are different by nature;
- Question the numbers: quantitative data show only what happened, not the reason. Use other tools to gather qualitative data that can improve the results of the experiments.
Experiment management in CRO
Concepts like MVP, rapid experimentation and lean are all based on the ideas of learning fast and following a process with well defined steps. These processes take on the idea of cientific experiments applied on business settings. CRO projects also drink from that source. The end result is a backlog of experiments to be tested against hypothesis, not unlike what is seen on other areas, like Growth Hacking.
There are many tools that can help manage CRO projects. Some of them are:
- Pipefy: Provide a framework for experiment management that can be used inside their app. The steps are: brainstorm, prioritize, prototype, test, learn;
- Moz: Provide a similar framework. The steps are: data collection, hypothesis generation, prototype, implement and test, analyze;
- Conversion XL: They built a framework designed for CRO projects, focused on data quality. The steps are: heuristic analysis, technical analysis, analytics, mouse tracking, qualitative research, user testing.
No single framework is the definitive, and it is possible to combine steps from each of those frameworks to support different business needs.
Tools for CRO projects
There are many options from tools, ranging from cheaper to expensive and from friendly to technical. This opens up the possibility of testing different tools in search of the stack that will better suit each project’s needs. Bad project executions can generate noise in the data collected, which can invalidate any findings. There is a lot of content online to help setup a good “laboratory” for studies. The challenge then is to know which tool to use for each scenario.
The CRO stack
There is a great post on Medium that list 32 tools for CRO, placing each one within a stack, with 4 sets: quantitative tools, qualitative tools, testing tools and workflow tools. A CRO project will need at least one tool from each of the sets. I’ll list some of the tools which I’ve worked with, but I recommend reading through the Medium post to find out many more tools.
Quantitative tools
The quantitative tools stack is comprised of tools that collect and store behavioral data for a product. They are used to understand behavioral patterns. Some of these tools are:
Qualitative tools
Quantitative tools show what people are doing with a product. Qualitative tools, on the other hand, try to explain why those behaviours occur. Some of these tools are:
Testing tools
A running experiment is a test to challenge one hypothesis. Testing tools are the necessary components to run hypothesis tests. Some of these tools are:
Workflow tools
These tools help track the CRO project, as well as the status of each of the experiments. Some of these tools are:
CRO on organizations of different sizes
CRO projects rely on information about the business and its target customers. This data, along with knowledge passed from the business, serve as basis to formulate hypotheses to be verified. Even small businesses have some data to work with in order to generate some hypotheses. Different conversion optimization strategies will be adopted according to the size of the business. Entrepreneur Brian Balfour wrote a blog post in its website explaining how different testing and learning strategies evolve based on the size of the business.
He suggests that tests, as a whole, should be big, specially at companies that are at the beginning. Examples are tests with user flows, different customer segments and value propositions. Once the company knows its business model, as well as its customer base, tests can be more segmented, based on a more narrowed growth strategy.
CRO on apps
CRO projects on apps aren’t that common, although nowadays have some popularity. This happens for at least four reasons:
- Code inside apps is compiled, making its internal structure hard to access using testing tools designed for web applications.
- It is necessary to be approved by all main app stores and follow their app publishing policies, making the process more bureaucratic.
- User need to download / update the app to see the changes.

Even with those drawbacks, there are tools to work specifically with mobile tests, including some of the tools that were listed before. Two of these tools are:
The optimization part in CRO
Optimize in CRO goes beyond the metric that defines conversion. Optimization also happens, on my point of view, when the CRO project leave as a legacy the need and discipline to always learn more the right way, that is, producing knowledge that can be shared, verified, trusted, replicated and useful. The key metric that represents conversion is a deliverable, but if the organization is not capable of sustaining the optimization strategy — or change it when needed — the effort spent on a CRO project will not suffice, and it wont be better than doing tests without well defined procedures at all.
Additional resources
- ConversionXL have a very resourceful blog about CRO;
- MOZ is another company that provides an extensive list of resources on the subject;
- Qualroo created a CRO course;
- Rock Content published an e-book with CRO fundamentals.
Let’s optimize!


Caffe Installation on Ubuntu 14.04 (CPU) with PYTHON support
In this tutorial, I will detail the steps for installing Caffe on a non-GPU(CPU) machine with Ubuntu 14.04 OS. Once we are done with the installation, we’ll also test the GOOGLE INCEPTIONISM (deepdream
) code which uses Caffe. This is a follow up post to my last post – Generating images with Google’s “INCEPTIONISM” – deepdream where I had shared some awesome images generated using deepdream
code. So, let’s get our hands dirty straight away .
Firstly, let’s make a directory named deep-learning
in the $HOME
folder where we will download all the packages.
cd ~
mkdir deep-learning
cd deep-learning
1. Installing the dependencies
To successfully compile Caffe we need to install a few packages first. Execute the below command in the terminal. (These commands will require root access)
NOTE -> Since we will not be using GPUs, we do not require CUDA installation.
# General Dependencies
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install --no-install-recommends libboost-all-dev
# BLAS -- for better CPU performance
sudo apt-get install libatlas-base-dev
# Python -- It comes preinstalled on Ubuntu 14.04
# Required if you want to use Python wrappers for Caffe
sudo apt-get install the python-dev
# Remaining dependencies
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
2. Compile and Install Caffe
The Caffe repository is hosted on GitHub. To clone the repository enter the below command in the terminal.
cd ~/deep-learning
git clone http://github.com/BVLC/caffe.git
cd caffe
The Makefile.config.example
contains the template for configuration file. We’ll use it to write the Makefile.config
configuration file.
cp Makefile.config.example Makefile.config
In the Makefile.config
file we need to set options if we want to use a CPU or a GPU. Open your favorite editor and uncomment the line CPU_ONLY := 1
(most probably, line number 8) as shown below.
8 CPU_ONLY := 1
After setting the configuration, the next step is to make Caffe or brew Caffe .
make all
make test
make runtest
NOTE
If you get an OpenCV undefined reference error
in make all
as shown below follow the instructions below else move to the section 3.
.build_release/lib/libcaffe.so: undefined reference to `cv::imread(cv::String const&, int)'
.build_release/lib/libcaffe.so: undefined reference to `cv::imencode(cv::String const&, cv::_InputArray const&, std::vector<unsigned char, std::allocator<unsigned char> >&, std::vector<int, std::allocator<int> > const&)'
.build_release/lib/libcaffe.so: undefined reference to `cv::imdecode(cv::_InputArray const&, int)'
collect2: error: ld returned 1 exit status
make: *** [.build_release/tools/caffe.bin] Error 1
(Check this issue) for more details. In order to fix the errror, append opencv_imgcodecs
on line 176(most probably) as shown below.
174 LIBRARIES += glog gflags protobuf leveldb snappy \
175 mdb boost_system hdf5_hl hdf5 m \
176 opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs
Now clear of the contents of build
. This is important else the error will persist.
rm -rf ./build/*
Now rerun the previous commands.
make all
make test
make runtest
3. Adding PYTHON support
To add PYTHON support, run the below commands in the terminal. We need Python support to run the deepdream
code.
cd ~/deep-learning/caffe
make pycaffe
echo export PYTHONPATH=~/deep-learning/caffe/python:$PYTHONPATH >> ~/.bashrc
4. Running the INCEPTIONISM code
Read the INCEPTIONISM post by Google Research here
To run the INCEPTIONISM code follow the below instructions.
4.1. Download the GoogLeNet model and clone the deepdream GitHub repository.
cd ~/deep-learning
wget http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel
mv bvlc_googlenet.caffemodel caffe/models/bvlc_googlenet/
git clone https://github.com/google/deepdream.git
4.2. Install the dependencies
sudo pip install "ipython[all]"
sudo pip install numpy
sudo pip install scipy
sudo pip install protobuf
sudo pip install skimage
4.3. Run the IPython Notebook
cd ~/deep=learning/deepdream
ipython notebook
Now the IPython notebook dashboard will open up in your default browser as shown below.
In the list of files, click on dream.ipynb
and you will get an output as shown in the figure below.
So, this is it ! Use this IPython Notebook to play with the deepdream
code to generate some awesome images. I hope you liked the post and THANK YOU for reading.


Enabling Energy Efficiency
Our electric consumption is a major elemement of our
overall national energy usage profile. Efficient usage
is critical to our sustainable future!
Green Button Electricity
- Standard XML Formats
- Subscription and Bulk Access
- Individual Portfolio Management
- Third Party Services and Applications


Tutorials
with a video component
Get jupyter notebooks for the following youtube tutorials here
or run them directly on JuliaBox.
- Intro to Julia (version 1.0) , by Jane Herriman
- Intro to Julia for data science, by Huda Nassar
- Intro to the Queryverse, a Julia data science stack,
by David Anthoff
- Intro to Julia Data Frames,
by Bogumił Kamiński
- Intro to dynamical systems in Julia,
by George Datseris
- Introducción a Julia en español,
by Miguel Raz Guzmán
- Intro to JuliaDB, a package for working with large persistent data sets,
by Josh Day and Shashi Gowda
- Intro to solving differential equations in Julia,
by Chris Rackauckas
- Intro to Julia (version 0.6),
by Jane Herriman
written
}
Books
- Julia: High Performance Programming by Ivo Balbaert, Avik Sengupta, Malcolm Sherrington
(697 pages; published: November 2016; ISBN:<PHONE_NUMBER>704)
In this learning path, you will learn to use an interesting and dynamic programming language—Julia! This book is a combination and curation of the three separate books by the three authors.
- Julia Programming for Operations Research: A Primer on Computing by Changhyun Kwon (246 pages; published: 2016-05; ISBN:<PHONE_NUMBER>793)
This book aims to teach how one can solve various optimization problems arising in operations research and management science.
- Julia High Performance by Avik Sengupta (120 pages; published: 2016-05; ISBN:<PHONE_NUMBER>919)
This is a book about performance optimisation of Julia programs, showing how to design and write Julia code that fully realises the potential speed of the language and its libraries.
- Mastering Julia by Malcolm Sherrington - published by Packt Publishing (410 pages; published: 2015-07; ISBN:<PHONE_NUMBER>310)
- Getting Started with Julia Programming by Ivo Balbaert - published by Packt Publishing (214 pages; published: 2015-02-28; ISBN:<PHONE_NUMBER>795)
- Seven More Languages in Seven Weeks by Bruce Tate, Fred Daoud, Jack Moffit and Ian Dees - published by The Pragmatic Programmers (350 pages; published: 2014-11-15; ISBN: 978-1-94122-215-7)
This book contains a Julia tutorial chapter (written by Jack Moffitt and Bruce Tate) for programmers new to Julia, which is very good, with nice examples and exercises.
- Julia for Data Science by Anshul Joshi (348 pages; published: 2016-09; ISBN:<PHONE_NUMBER>699)
Explore the world of data science from scratch with Julia by your side
- Julia for Data Science by Zacharias Voulgaris PhD (415 pages; published: 2016-09-01; ISBN:<PHONE_NUMBER>304). Master the essentials of data science through the Julia programming ecosystem (no prior knowledge of the language is required), accompanied by a variety of interesting examples and exercises.
- Julia Cookbook by Jalem Raj Rohit - published by Packt Publishing (172 pages; published: 2016-09; ISBN:<PHONE_NUMBER>012)
- Julia Solutions by Jalem Raj Rohit - A comprehensive guide to learn data science for a Julia programmer - Produced by Packt Publishing (2 hours and 52 minutes long; published: January 31, 2017; ISBN:<PHONE_NUMBER>299)
- Getting Started with Julia by Erik Engheim - Learn the new language Julia for high performance technical computing - Produced by Packt Publishing (9 hours and 50 minutes long; published: March 31, 2017; ISBN:<PHONE_NUMBER>978)
- Learning Julia by Anshul Joshi, Rahul Lakhanpal (316 pages; published: November 2017; ISBN:<PHONE_NUMBER>279). This book shows you how to write effective functions, reduce code redundancies, and improve code reuse. It will be helpful for new programmers who are starting out with Julia to explore its wide and ever-growing package ecosystem and also for experienced developers/statisticians/data scientists who want to add Julia to their skill-set.
- Hands-On Computer Vision with Julia by Dmitrijs Cudihins (202 pages; published: June 2018; ISBN:<PHONE_NUMBER>796). Explore the various packages in Julia that support image processing and build neural networks for video processing and object tracking.
- Iterative Solution of Symmetric Quasi-Definite Linear Systems by Dominique Orban and Mario Arioli. This book is intended for researchers and advanced graduate students in computational optimization, computational fluid dynamics, computational linear algebra, data assimilation, and virtually any computational field in which saddle-point systems occur. Krylov.jl is the Julia package that accompanies the book. Ebook for SIAM subscribers.
- Julia 1.0 Programming Cookbook by B. Kamiński, P. Szufel (460 pages; published: November 2018; ISBN:<PHONE_NUMBER>369)
Resources
Julia in the classroom
Julia is ready for the classroom. We encourage instructors to participate in the Julia community resources for questions about Julia or specific packages. This page puts together various resources that instructors may find useful. Tutorials and other learning materials are in the learning section of the website.
MOOCs teaching Julia
- Coursera, University of Cape Town
- edX MITx
- 15.053x, Optimization Methods in Business Analytics MOOC (massive online open course), (Prof. James Orlin)
Classes using Julia for teaching
Julia is now being used in several universities and online courses. If you know of other classes using Julia for teaching, please consider updating this list.


The price of one share of {{stock['name']}} is : {{stock['price']}}.
{% extends "layout.html" %} {% block title %} Quoted {% endblock %} {% block main %}
{{stock['name']}}
{{stock['price']}}
The price of one share of {{stock['name']}} is : {{stock['price']}}.


Here's some text and some things that we want to express.
...And this our final triumphant paragraph. All the text in it should be boldy, triumphantly green.
Here's some text and some things that we want to express.
...And this our final triumphant paragraph. All the text in it should be boldy, triumphantly green.


Alexander Bor
I am a postdoc at the CEU Democracy Institute’s Research Group on De- and Re-Democratization and a Visiting Professor at CEU Vienna. I received an MA in Political Science at the CEU in 2012, and a PhD in Political Science at Aarhus University in 2018, where I also worked as a postdoc until 2022. My research focuses on how the human mind navigates social and political challenges like political polarization, online political hostility, the COVID-19 pandemic, or selecting and evaluating political leaders. My work integrates insights from political behavior, social and evolutionary psychology, and public health. I employs diverse quantitative analytical tools like surveys, experiments and machine learning. My work has been published in leading journals (e.g. Nature, PNAS, APSR, and Psych Science) and covered by international media outlets (e.g. New York Times, Washington Post, The Atlantic).
I am an R enthusiast fascinated with data vizualization and Bayes. I do my best to promote and live up to the standards of open science. I am a proud father of two, a juvenile juggler, a curious cook, and a sourdough samurai.
You can find me on Twitter, on Google Scholar, or on OSF. Feel free to drop me an e-mail!


|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface MLetMBean
Exposes the remote management interface of the MLet MBean.
| Method Summary | |
|---|---|
void |
addURL(String url)
Appends the specified URL to the list of URLs to search for classes and resources. |
void |
addURL(URL url)
Appends the specified URL to the list of URLs to search for classes and resources. |
String |
getLibraryDirectory()
Gets the current directory used by the library loader for storing native libraries before they are loaded into memory. |
Set |
getMBeansFromURL(String url)
Loads a text file containing MLET tags that define the MBeans to be added to the agent. |
Set |
getMBeansFromURL(URL url)
Loads a text file containing MLET tags that define the MBeans to be added to the agent. |
URL |
getResource(String name)
Finds the resource with the given name. |
InputStream |
getResourceAsStream(String name)
Returns an input stream for reading the specified resource. |
Enumeration |
getResources(String name)
Finds all the resources with the given name. |
URL[] |
getURLs()
Returns the search path of URLs for loading classes and resources. |
void |
setLibraryDirectory(String libdir)
Sets the directory used by the library loader for storing native libraries before they are loaded into memory. |
| Method Detail |
|---|
Set getMBeansFromURL(String url) throws ServiceNotFoundException
url
- The URL of the text file to be loaded as String object.
ServiceNotFoundException
- One of the following errors
has occurred: The m-let text file does not contain an MLET tag,
the m-let text file is not found, a mandatory attribute of the
MLET tag is not specified, the value of url is malformed.Set getMBeansFromURL(URL url) throws ServiceNotFoundException
url
- The URL of the text file to be loaded as URL object.
ServiceNotFoundException
- One of the following errors
has occurred: The m-let text file does not contain an MLET tag,
the m-let text file is not found, a mandatory attribute of the
MLET tag is not specified, the value of url is null.void addURL(URL url)
url
- the URL to add.void addURL(String url) throws ServiceNotFoundException
url
- the URL to add.
ServiceNotFoundException
- The specified URL is malformed.URL[] getURLs()
URL getResource(String name)
name
- The resource name
InputStream getResourceAsStream(String name)
name
- The resource name
Enumeration getResources(String name) throws IOException
name
- The resource name.
IOException
- if an I/O exception occurs when
searching for resources.String getLibraryDirectory()
setLibraryDirectory(java.lang.String)
void setLibraryDirectory(String libdir)
libdir
- The directory used by the library loader.getLibraryDirectory()
|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


If you receive the Error message "Unable to find completed Image" after clicking the "Open Folder" button, chances are the Finalized ISO or the Primary WIM is not where it was. It may have been moved or deleted, or Slips7ream may not have access to the drive or path. It may also indicate that there was an undetected problem with the Slipstream Process.
If you receive the Error message "Unable to open folder" after clicking the "Open Folder" button, you can see the exact error by clicking on the Details button in the dialog. Generally, this error means that Windows Explorer failed to open the folder or Slips7ream failed to open Windows Explorer.


--- layout: page title: "An Extensible Markup Language (XML) Patch Operations Framework Utilizing XML Path Language (XPath) Selectors" description: "Extensible Markup Language (XML) documents are widely used as containers for the exchange and storage of arbitrary data in today's systems. In order to send changes to an XML document, an entire copy of the new version must be sent, unless there is a means of indicating only the portions that have changed. This document describes an XML patch framework utilizing XML Path language (XPath) selectors. These selector values and updated new data content constitute the basis of patch operations described in this document. In addition to them, with basic <add>, <replace>, and <remove> directives a set of patches can then be applied to update an existing XML document." ---
| Document Name: | RFC 5261 |
|---|---|
| Document URI: |
urn:ietf:rfc:5261
|
| Online Version: |
https://datatracker.ietf.org/doc/html/rfc5261
|
| Organization: | Internet Engineering Task Force (IETF) |
| Series: | Request for Comments (RFC) |
| Abstract: | Extensible Markup Language (XML) documents are widely used as containers for the exchange and storage of arbitrary data in today's systems. In order to send changes to an XML document, an entire copy of the new version must be sent, unless there is a means of indicating only the portions that have changed. This document describes an XML patch framework utilizing XML Path language (XPath) selectors. These selector values and updated new data content constitute the basis of patch operations described in this document. In addition to them, with basic <add>, <replace>, and <remove> directives a set of patches can then be applied to update an existing XML document. |
application/patch-ops-error+xml
Return to ( Series | Organization | all Specifications )


this test file just exists to reproduce a bug with footnotes in headlines - that only happens in very specific circumstances.
The TLDR is:
HTMLWriter.footnotes should be a pointer field. I didn't notice my error as go translated my pointer-method calls on
non-pointer values rather than complaining - i.e. footnotes.add() transparently gets translated to (&footnotes).add() (docs).
Headlines have to be htmlified twice - once for the outline and once for the headline itself. To do so we have to copy the writer
Copying the writer copies footnotes - which contains a map and a slice. Changes to the map will always be reflected in the original map.
Changes to the slice will only be reflected if the slice doesn't grow.
We can thus end up with a footnote being in the mapping but not the slice - and get an index out of range error.


Development tests for HLSI Interactive Exercises
These are links to test pages for testing HLSI components.
Go to index.html to see the current developing
exercises (not much there yet). Go to ../index.html to see the current "linked"
exercises. See the current
live exercises.
Comparing old and new exercises
- 1 - Bandwidth:
new
old
- 2 - Frequency:
new
old
- 3 - Gain:
new
old
Note: the noise is higher in the new one.
- 4 - Frequency,
Bandwidth, and Gain:
new
old
- 5 - Information Capacity:
new
old
- 6 - Interference and Information Capacity
new
old
- 7 - Information Capacity
new
old
Note: If it ain't broke don't fix it.
- 8 - Information Capacity
new
old
- 9 - Information Capacity
new
old
- 10 - Information Capacity
new
old
This one was not changed. There was no need to
mobilize the code given there is no code replicated
from this exercise.
- 11 - Path Loss
new
old
Note: If it ain't broke don't fix it.
- 12 - Path Loss with Interference
new
old
Note: If it ain't broke don't fix it.
- 13 - Manual Interferer Avoidance
new
old
- 14 - SAS (Spectrum Access System)
- 15 - Rule Based Interferer Avoidance
new
old
Note: The sensing rate is no longer relevant or needed;
because the new version keeps separate timers for each code
that interacts with the signals. There is no longer one
big update function that mashes all the pieces together, all
the different interactions are different modules. There are
pros and cons to modular coding, but we will not get into it
here in this paragraph.
- 16 - Programmable Interferer Avoidance
new
old
- 17 - Machine
Learning Interferer Avoidance
- 18 - machine_learning_time_based_avoidance
Development tests


| deleteAllVisibleCookies |
|
|
| setTimeout |
10000000 |
|
| open |
/vivo/ |
|
| assertTitle |
VIVO |
|
| clickAndWait |
link=Log in |
|
| assertTitle |
Log in to VIVO |
|
| type |
loginName |
<EMAIL_ADDRESS>|
| type |
loginPassword |
Password |
| clickAndWait |
name=loginForm |
|
| assertTitle |
VIVO |
|
| clickAndWait |
link=Index |
|
| assertTitle |
Index of Contents |
|
| clickAndWait |
link=Faculty Member |
|
| assertTitle |
Faculty Member |
|
| verifyTextPresent |
Faculty Member |
|
| verifyTextPresent |
Baker, Able Professor |
|
| verifyElementPresent |
link=Baker, Able |
|
| verifyTextPresent |
Dog, Charlie Member of Department A |
|
| verifyElementPresent |
link=Dog, Charlie |
|
| verifyElementPresent |
link=Foxtrot, Echo |
|
| verifyTextPresent |
Foxtrot, Echo Member of Department B |
|
| verifyElementPresent |
link=Henry, George |
|
| verifyTextPresent |
Henry, George |
|
| verifyTextNotPresent |
Jenkins, Iris |
|
| verifyElementNotPresent |
link=Jenkins, Iris |
|
| clickAndWait |
link=Index |
|
| assertTitle |
Index of Contents |
|
| clickAndWait |
link=Person |
|
| assertTitle |
Person |
|
| verifyTextPresent |
Person |
|
| verifyTextPresent |
Baker, Able Professor |
|
| verifyElementPresent |
link=Baker, Able |
|
| verifyTextPresent |
Dog, Charlie Faculty Member Member of Department A |
|
| verifyElementPresent |
link=Dog, Charlie |
|
| verifyTextPresent |
Foxtrot, Echo Faculty Member Member of Department B |
|
| verifyElementPresent |
link=Foxtrot, Echo |
|
| verifyTextPresent |
Henry, George Faculty Member |
|
| verifyElementPresent |
link=Henry, George |
|
| verifyTextPresent |
Jenkins, Iris |
|
| verifyElementPresent |
link=Jenkins, Iris |
|
| clickAndWait |
link=People |
|
| assertTitle |
People |
|
| click |
link=Faculty Member (4) |
|
| pause |
5000 |
|
| verifyElementPresent |
link=Baker, Able |
|
| verifyTextPresent |
Professor |
|
| verifyElementPresent |
link=Dog, Charlie |
|
| verifyTextPresent |
Member of Department A |
|
| verifyElementPresent |
link=Foxtrot, Echo |
|
| verifyTextPresent |
Member of Department B |
|
| verifyElementPresent |
link=Henry, George |
|
| verifyTextPresent |
Henry, George |
|
| verifyElementNotPresent |
link=Jenkins, Iris |
|
| verifyTextNotPresent |
Jenkins, Iris |
|
| click |
link=Person (5) |
|
| pause |
5000 |
|
| verifyElementPresent |
link=Baker, Able |
|
| verifyTextPresent |
Professor |
|
| verifyElementPresent |
link=Dog, Charlie |
|
| verifyTextPresent |
Faculty Member |
|
| verifyTextPresent |
Member of Department A |
|
| verifyElementPresent |
link=Foxtrot, Echo |
|
| verifyTextPresent |
Faculty Member |
|
| verifyTextPresent |
Member of Department B |
|
| verifyElementPresent |
link=Henry, George |
|
| verifyTextPresent |
Faculty Member |
|
| verifyElementPresent |
link=Jenkins, Iris |
|
| verifyTextPresent |
Jenkins, Iris |
|
| type |
name=querytext |
person |
| clickAndWait |
css=input.search |
|
| assertTitle |
person - VIVO Search Results |
|
| verifyTextPresent |
Search results for 'person' |
|
| verifyTextPresent |
Dog, Charlie Faculty Member Member of Department A |
|
| verifyElementPresent |
link=Dog, Charlie |
|
| verifyTextPresent |
Baker, Able Professor |
|
| verifyElementPresent |
link=Baker, Able |
|
| verifyTextPresent |
Foxtrot, Echo Faculty Member Member of Department B |
|
| verifyElementPresent |
link=Foxtrot, Echo |
|
| verifyTextPresent |
Jenkins, Iris Person |
|
| verifyElementPresent |
link=Jenkins, Iris |
|
| verifyTextPresent |
Henry, George Faculty Member |
|
| verifyElementPresent |
link=Henry, George |
|
| clickAndWait |
link=Index |
|
| assertTitle |
Index of Contents |
|
| clickAndWait |
link=Academic Department |
|
| assertTitle |
Academic Department |
|
| verifyTextPresent |
Academic Department |
|
| verifyTextPresent |
Department A Located in Building A |
|
| verifyElementPresent |
link=Department A |
|
| verifyTextPresent |
Department B Headed by Foxtrot, Echo |
|
| verifyElementPresent |
link=Department B |
|
| clickAndWait |
link=Index |
|
| assertTitle |
Index of Contents |
|
| clickAndWait |
link=Organization |
|
| assertTitle |
Organization |
|
| verifyTextPresent |
Organization |
|
| verifyTextPresent |
Department A Academic Department Located in Building A |
|
| verifyElementPresent |
link=Department A |
|
| verifyTextPresent |
Department B Academic Department Headed by Foxtrot, Echo |
|
| verifyElementPresent |
link=Department B |
|
| clickAndWait |
link=Home |
|
| clickAndWait |
link=Log out |
|
| assertTitle |
VIVO |
|


The paper introduces a new, procedurally generated environment called as CoinRun that is designed to benchmark the generalization capabilities of RL algorithms.
The paper reports that deep convolutional architectures and techniques like L2 regularization, batch norm, etc (which were proposed in the context of generalization in supervised learning) are also useful for RL.
CoinRun is made of multiple levels.
In each level, the agent spawns on the far left side and needs to collect a single coin that lies on the far right side.
There are many obstacles in between and colliding with an obstacle leads to agent’s death.
Each episode extends for a maximum for 1000 steps.
CoinRun is designed such that given sufficient training time and levels, a near-optimal policy can be learned for all the levels.
Generalization can be measure by training an agent on a given set of training tasks and evaluating on an unseen set of test tasks.
9 agents are trained to play CoinRun, on different training sets (each with a different number of levels).
The first 8 agents are trained on sets of size 100 to 16000 levels while the last agent is trained on an unbounded set of levels.
Training a model on an unbounded set of levels provides a good proxy for the train-to-test generalization performance.
Two convolutional architectures (of different sizes) are compared:
Nature-CNN: The CNN architecture used in the Deep Q Network. This is the smaller network among the two models.
IMPALA-CNN: The CNN architecture used in the Imapla architecture.
IMPALA-CNN agent always outperforms the Nature-CNN agent indicating that larger architecture has more capacity for generalization. But increasing the network size beyond a limit gives diminishing returns.
While both L2 regularization and Dropout helps to improve generalization, L2 regularization is more impactful.
A domain randomization/data augmentation approach is tested where rectangular regions of different sizes are masked and assigned a random color. This approach seems to improve performance.
Batch Normalization helps to improve performance as well.
Environment stochasticity is introduced by using sticky actions while policy stochasticity is introduced by controlling the entropy bonus. Both these forms of stochasticity boost performance.
While combining different regularization methods help, the gains are only marginally better than using just 1 regularization approach. This suggests that these different approaches induce similar generalization properties.
Two additional environments are also considered to verify the high degree of overfitting observed in the CoinRun environment:
CoinRun-Platforms:
Unlike CoinRun, each episode can have multiple coins and the time limit is 0increased to 1000 steps.
Levels are larger as well so the agent might need to backtrack their steps.
RandomMazes:
Partially observed environment with square mazes of dimensions 3x3 to 25x25.
Timelimit of 500 steps
Overfitting is observed for both these environments as well.


Javascript Labs
Nested For Loops - Loop from i = 1 to 10 and j = 1 to 10
Click here to run a nested for loop


About Me
I'm 27 years old and recently married.
I like to watch sports and play video games.
Visit My Github
Hi There
Ipsum dispsum flipsum kdkalf aokfaofkoakt
Current Projects
Zippity do dah zippity day
My Favorite Websites
https://codepen.io/
https://www.codecademy.com/
https://www.freecodecamp.org/learn/


--- layout: post title: Store and index IP Address in MySQL table - Faster! date: '2009-01-19T04:37:00+01:00' tags: - oldblogposts - sql tumblr_url: http://hussfelt.tumblr.com/post/28547267418/store-and-index-ip-address-in-mysql-table-faster ---
Normally one would store an IP Address in something like a VARCHAR(15). But when you have a table with a large amount of data and you need to index this field there are a better and faster option!
As many other nice built in things in MySQL there are two functions, INET_ATON() and INET_NTOA(). These two functions are based on the two functions with the same names that are in any tcp capable systems C library.
INET_ATON() will convert an IP Address to an unsigned 32-bit integer, and INET_NTOA() does the opposite thing.
Example;
mysql> SELECT INET_ATON('<IP_ADDRESS>') AS ipn; +------------+ | ipn | +------------+ |<PHONE_NUMBER> | +------------+ mysql> SELECT INET_NTOA(3232235530) AS ipa; +--------------+ | ipa | +--------------+ | <IP_ADDRESS> | +--------------+
Thanks to Arjen’s Journal for the tip!


Diglin\Swisspost\NotificationService
Synopsis
class NotificationService
{
- // constants
- const ORDER_CONFIRMATION = 1;
- const DELIVERY_INFORMATION = 2;
- const COLLECTION_INFORMATION = 4;
- const REMINDER_RECIPIENT = 32;
- const HANDOVER_STATUS = 64;
- const EXCHANGE_RETURN = 128;
- const SATURDAY_DELIVERY = 256;
- const EVENING_DELIVERY_FOLLOWING_DAYS = 257;
- const EVENING_DELIVERY_SAME_DAY = 258;
Constants
| Name | Value |
|---|---|
| ORDER_CONFIRMATION | 1 |
| DELIVERY_INFORMATION | 2 |
| COLLECTION_INFORMATION | 4 |
| REMINDER_RECIPIENT | 32 |
| HANDOVER_STATUS | 64 |
| EXCHANGE_RETURN | 128 |
| SATURDAY_DELIVERY | 256 |
| EVENING_DELIVERY_FOLLOWING_DAYS | 257 |
| EVENING_DELIVERY_SAME_DAY | 258 |


Show All
Enabled
Disabled
close
Installed-Dev
-
Installed - Normal
-
Uninstalled-Dev
-
Uninstalled-Normal
-


Report: Israeli singer trying to drop American citizenship
Why is pop star Omer Adam, who was born in the US to Israeli parents, trying to renounce his US citizenship?
by Arutz Sheva StaffIsraeli pop singer Omer Adam is trying to renounce his American citizenship, Yediot Ahronot reported.
The 26-year-old signer was born in the US to Israeli parents who were working in the US as emissaries. At the age of three, Adam and his family returned to Israel, settling in Mishmar Hashiva in central Israel.
As the United States currently practices a policy of jus soli, or birthright citizenship, Adam is considered a natural born citizen of the US, despite both parents being non-citizens.
According to the report, Adam is looking to renounce his US citizenship over tax considerations.
Unlike most countries, the US requires citizens living abroad to file tax returns with the IRS. While tax deals with many countries shield some from paying taxes to both countries, as a high-earner, Adam may be liable for significant tax payments.
A spokesperson for the singer had “no comment” when asked about the report.


IT Portfolio View
IT Investment View
IT Acquisition View
IT Cost Savings & Avoidance View
IT Systems & Major Applications View
Service Spend Alignment: Hierarchy
Service Spend Alignment: Trend
Service Spend Alignment: Flow


--- title: "School Bully Gets Hit By A Flying Stomp While Attacking Kid Half His Size" featimg: "http://www.viralthread.com/wp-content/uploads/2015/12/Screen-Shot-2015-12-04-at-10.56.20.png" description: "" categories: article_url: "http://www.viralthread.com/school-bully-gets-hit-by-a-flying-stomp-while-attacking-kid-half-his-size/" social_media_msg: "Bullying again!" ---
Thank you world for the proliferation of video-capturing gadgets, so you bullies out there can be recognised for what you really are. When the entire world watched and cheered as Casey Heynes body slammed his aggressor against the concrete, he became a poster boy for anti-bullying worldwide and many victims have since followed in his footsteps.
Not every child crawls up into a defenceless ball, crying and pleading for their antagonist to leave them alone. No, no, no, on the contrary, a few brave souls stand up and defend themselves.
Enter this kid.
 His attacker doesn’t even give him a chance to take off his rucksack before repeatedly punching him and dragging him to the ground. But the boy in red wasn’t about to get his arse whooped without putting up a fight.
Although he tries to protect himself as much as he can, the bully gets on top (literally) and starts to give him a proper pummelling. That is until a red dressed vigilante decides to take a stand.
Despite the fact that there were numerous onlookers checking out the fight, no one bothered to step in. Until the boy’s saviour entered the scene and hit the bully with a double footed flying stomp in the back.
That’s got to hurt.


# Modified by Microsoft Corporation.
# Licensed under the MIT license.
from abc import ABC, abstractmethod
from convlab.lib import logger
logger = logger.get_logger(__name__)
[docs]class Memory(ABC):
'''Abstract Memory class to define the API methods'''
def __init__(self, memory_spec, body):
'''
@param {*} body is the unit that stores its experience in this memory. Each body has a distinct memory.
'''
self.memory_spec = memory_spec
self.body = body
# declare what data keys to store
self.data_keys = ['states', 'actions', 'rewards', 'next_states', 'dones', 'priorities']
[docs] @abstractmethod
def reset(self):
'''Method to fully reset the memory storage and related variables'''
raise NotImplementedError
[docs] @abstractmethod
def update(self, state, action, reward, next_state, done):
'''Implement memory update given the full info from the latest timestep. NOTE: guard for np.nan reward and done when individual env resets.'''
raise NotImplementedError
[docs] @abstractmethod
def sample(self):
'''Implement memory sampling mechanism'''
raise NotImplementedError


Pointer to the last complete population generated by the algorithm, which
is used to generate (via genetic operators) the next generation of
individuals
In addition, keep a copy of the target which is scaled to be in the range
[0, 1] This is from Vladislavleva, to "allow the GP to focus on finding
the structure of the solution, instead of the scale"


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Oct 30, 2009 3:29 PM
At this seminar representatives from Teachers Network will join us to discuss their experience offering materials and resources to support new and veteran teachers in the online environment. Representatives from Teachers Network, a major national and international non-profit organization that has been supporting public school teachers for nearly 30 years (i.e., spanning from new teachers through veteran teachers; K-12), will discuss their approach to online learning for teachers. Specifically, they will be talking about the myriad resources available for teachers on their award-winning website (www.teachersnetwork.org), and in particular, their self-guided and/or facilitated, interactive online courses, including companion multi-media materials (e.g., video, print publications)--all designed "by teachers, for teachers" that are offered nationwide and beyond--primarily sold in bulk to colleges, universities, and school districts (www.teachersnetwork.org/tnoc, www.teachersnetwork.org/videos, etc.). Following this introductory presentation, Teachers Network staff will also available for further discussion and/or to answer any questions attendees might have.


China imposes curfew on children playing games online
BEIJING: China has imposed a curfew to limit the time spent by children playing games online, in the latest part of a government crackdown on youth gaming addiction.
The regulations will mean those under 18 cannot play games online between 10pm and 8am, and for only ninety minutes at a time during the daytime.
In addition, the guidelines will reduce the amount of money minors can spend online playing games to 200 yuan (US$28) per month, rising to 400 yuan for those between 16 and 18 years old.
The new rules will also require all gamers to use a real-name registration and details such a WeChat account, phone number or ID number to sign up.
In the statement published Tuesday (Nov 5), the government also calls for games producers to "modify the game content, functions or rules" to avoid causing addiction in young players.
China is the world's biggest video game market, but the government has been tightening up on the industry amid concerns about health, worsening near-sightedness in children and online addiction.
READ: Game over? China to rein in online games in latest industry setback
READ: Tencent cuts marketing budget for games amid China crackdown - sources
A hashtag about the new rules was one of the most discussed on China's social media platform Weibo on Thursday with 210 million views.
"This statement just means that teenagers can't play the games, because for most teenagers in China, they need to go to school from 6.30am and end at 10pm," one user complained.
"It's a bit of a one-size-fits-all thing," wrote another, saying a graded rating system of games made more sense.
Another claimed that it was easy to obtain fake ID numbers online and that gamers would just use these to create their accounts.
Last year Beijing announced new controls on the number of games that can be played online, limited new releases, and imposed rules on underage players to reduce their screen time.
Chinese giant Tencent - the planet's biggest game company - announced in March it was implementing new curbs on underage video-game playing.
The gaming group had already has launched a real-name identification system for "Honour of Kings" to enforce playtime restrictions on youths.


-
The Future of Web Development - React, Falcor, and ES6
The future of web application development looks a bit different than what we are all used to. I'll show you how to build a simple full-stack JavaScript app using Node.js on the backend, React on the frontend, Webpack for client-side module support, and Netflix's Falcor as an efficient and intuitive...
• Ray Nicholus -
Tomcat Hates Encoded Slashes
After sending a GET request containing escaped slashes to our Tomcat-hosted endpoint, we expect to receive a 2xx response. But instead, our server responds with a 404. What happened?
• Ray Nicholus -
Runtime Configuration for AWS' Lambda
A while ago Amazon released its Lambda service. Lambdas are simple, stateless functions that get executed as callbacks to arbitrary AWS events.
• Mark Feltner


{{ block title }} [BEHAVIOURAL AVOIDANCE RESULTS] {{ endblock }} {{ block content }}
Behavioral Avoidance Results
| Extraversion | {{ player.extraversion }} / 5 |
| Agreeableness | {{ player.agreeableness }} / 5 |
| Conscientiousness | {{ player.conscientiousness }} / 5 |
| Neuroticism | {{ player.neuroticism }} / 5 |
| Openness | {{ player.openness }} / 5 |


Equivalent to clicking the Picture command on the Insert menu. This function is available for Microsoft Excel for Windows only .
Syntax
INSERT.PICTURE(file_name, filter_number)
INSERT.PICTURE?(file_name, filter_number)
File_name is the name, as text, of the file containing the picture that you want to insert into your workbook.
Filter_number is a number specifying which converter Microsoft Excel will use to open the file.
| Convert_type | Converter and filename extension |
| 1 | Windows Bitmaps (bmp) |
| 2 | Windows Metafile (wmf) |
| 3 | DrawPerfect (wpg) |
| 4 | Micrografix Designer/Draw (drw) |
| 5 | AutoCAD Format 2-D (dxf) |
| 6 | HP Graphics Language (hgl) |
| 7 | Computer Graphics Metafile (cgm) |
| 8 | Encapsulated Postscript (eps) |
| 9 | Tagged Image Format (tif) |
| 10 | PC PaintBrush (pcx) |
| 11 | Lotus 1-2-3 Graphic (pic) |
| 12 | AutoCAD Plot Files (plt) |
| 13 | Macintosh PICT (pct) |
Return to index


WITNESS LIST Public Education Committee March 22, 1999-2:00P HB 217 For: Newman, Anne (Texas Family Research Center) HB 268 For: White, Amy (Association of Texas Professional Educators) HB 363 For: Baker, Allan (Lesbian/Gay Rights Lobby) Cohen, Janet (Self) Delomy, Andy (Self) Goodman, Gail (Out Youth Austin) Hobbs, Richard (GLSEN) Hoffman, Donna (National Organization for Women) Johnston, Lon (National Association of Social Workers of Texas) Miles, Carolyn (Tarrant County Lesbian/ Gay Alliance) Parvin, Jennifer (GLSEN) Richardson, Velma (Self) Sampson, Misty (Self) Sinclair, Donald (PFLAF: Houston) Sinclair, Katherine (Self) Todd, Jerry (Self) Whitfill, Jill (Self) Against: Cecil, Stephanie (Self) Gerstenschlager, MerryLynn (Texas Eagle Forum) Hensley, Dianne (CWA) Newman, Anne (Texas Family Research Center) HB 364 For: Baker, Allan (Lesbian/Gay Rights Lobby) Cohen, Janet (Self) Delomy, Andy (Self) Goodman, Gail (Out Youth Austin) Greulich, Rebecca (Self) Hoffman, Donna (National Organization for Women) Johnston, Lon (National Association of Social Workers of Texas) Kinsler, Christi (Stonewall Democrats) Manson, Patrick (Self) Mitchell, Judith (Austin Human Right Commission) Richardson, Velma (Self) Sampson, Misty (Self) Sinclair, Donald (PFLAG: Houston) Sinclair, Katherine (Self) Todd, Jerry (Self) Whitfill, Jill (Self) Wilson, Fred (Self) Against: Cecil, Stephanie (Self) Gerstenschlager, MerryLynn (Texas Eagle Forum) Hensley, Dianne (CWA) Newman, Anne (Texas Family Research) HB 364Committee Substitute (Hochberg) For: Maxwell, Susan (Texas Planning Council for Developmental Disabilities) HB 907 For: Baker, Keith (Self) King, Mary (Texas Association of Secondary Principals) Paschal, Ed (North East ISD) On: Anderson, David (Texas Education Agency) HB 1120 For: Lara, Rene (Texas Federation of Teachers) HB 1120 Committee Substitute (Hochberg) Wall, Marjorie (Texas State Teachers Association) HB 1134 For: White, Amy (Association of Texas Professional Educators) HB 1849 For: Boatwright, Peggy (Self) Brewer, Shellie (Self) Karkosh, Robin (Self) Lara, Rene (Texas Federation of Teachers) Lys, Sandra (Self) White, Amy (Association of Texas Professional Educators) Against: Dankworth, Jube (Self) Hensley, Dianne (CWA) Lambert, Tim (Texas Home School Coalition) Newman, Anne (Texas Family Research Center) Wheatley, Melinda (Texas Public Policy Foundation) HB 2645 Against: Hartman, Eric (Texas Federation of Teachers) HB 2742 For: Jones, JoAnn (Texas Probation Association) Against: Thompson, David (TASA, TASSP, TASB) On: Hartman, Eric (Texas Federation of Teachers)


Hello there welcome to Blog Board!


Dgame.System.Clock
- pure nothrow float asSeconds(uint n);
- To convert the Clock milliseconds to seconds
- pure nothrow float asMinutes(uint n);
- To convert the Clock milliseconds to minutes
- pure nothrow ushort asHours(uint n);
- To convert the Clock milliseconds to hours
- struct Time;
- The Time struct converts ticks to msecs, seconds, minutes and hours.
- uint msecs;
- Milliseconds = Ticks
- float seconds;
- Seconds = Milliseconds / 1000
- float minutes;
- Minutes = Seconds / 60
- ushort hours;
- Hours = Minutes / 60
- pure nothrow this(uint msecs);
- CTor
- static pure nothrow Time remain(Time time);
- Calculate the remaining time.
- class Clock;
- This class handles timer functions and
the window class use these class to calculate the current fps.
Author:
rschuett
- this();
- CTor
- void reset();
- Reset the clock time
- const Time getElapsedTime();
- Returns the elapsed Time since the last reset or the CTor was called.
- const uint getElapsedTicks();
- Returns only the milliseconds since the last reset.
- uint getCurrentFps();
- Returns the current framerate per seconds.
- const pure nothrow float getFpsTime();
- Returns the Time since the last Frame.
- static uint getTicks();
- Returns the milliseconds since the application was started.
- static Time getTime();
- Returns the Time since the application was started.
- static void wait(uint msecs);
- Wait for msecs milliseconds, which means that the application freeze for this time.
- static ulong getPerformanceCounter();
- Use this function to get the
current value of the high resolution counter.
- static ulong getPerformanceFrequency();
- Use this function to get the
count per second of the high resolution counter.
Page generated by Ddoc.


{% extends 'base.html' %} {% load static %} {% block content %}
Let's compare skills that you currently list on LinkedIn profile to the skillset that is most commonly found on Data Scientists profiles in 5 biggest IT Companies. Let's find out what should be kept and what you may consider adding.


--- layout: page permalink: /get-started/ title: Get started ---
PicoTorrent is easy to use and has a tiny user interface giving users a clear overview of their transfers.
Most torrent operations are executed from the context menu. Right click any torrent to view the actions.