|
Molssi Driver Interface Library
|
The MolSSI Driver Interface (MDI) library enables codes to interoperate via the MDI.
The source code of the MDI library is available at GitHub at https://github.com/MolSSI/molssi_driver_interface
<NAME
command for more information. The following is a list of commands that are officially part of the MDI standard.
All physical quantities communicated through MDI must be expressed in atomic units.
The engine performs a time propagation step for the atomic coordinates. The engine updates its energy, which can be queried with the <ENERGY
command.
The driver sends a set of cell vectors to the engine, which resizes its simulation cell to the dimensions specified by the cell vectors.
The engine sends a set of cell vectors to the driver, in the same format as specified for the >CELL
command.
The driver sends a set of atomic charges to the engine, which replaces its atomic charges with those sent by the driver.
<NATOMS
The engine sends a set of atomic charges to the driver, in the same format as specified for the >CHARGES
command.
<NATOMS
The driver sends a set of atomic coordinates to the engine, which replaces its atomic coordinates with those sent by the driver.
3 * <NATOMS
The engine sends a set of atomic coordinates to the driver.
3 * <NATOMS
The engine sends its most recently calculated energy to the driver. The MD_INIT
, SCF
, and ATOM_STEP
commands can be used to cause the engine to calculate a new energy.
The engine terminates and can no longer be sent commands.
The driver sends a set of atomic forces to the engine, which replaces its internal forces with the forces sent by the driver.
3 * <NATOMS
ATOM_STEP
command, after any normal calculation of forces and immediately prior to propagation of the atomic positionsThe driver sends a set of atomic forces to the engine, which adds the forces sent by the driver to its internal forces.
3 * <NATOMS
ATOM_STEP
command, after any normal calculation of forces and immediately prior to propagation of the atomic positionsThe engine calculates and sends a set of atomic forces to the driver. These forces include all force contributions, including the force contributions associated with any constraint algorithm (e.g. SHAKE, RATTLE, etc.).
3 * <NATOMS
The engine sends the driver the mass of each of the atom types.
<NTYPES
<TYPES
command)The engine performs any initialization operations that are necessary before an MD simulation can be time propagated through the use of the ATOM_STEP
command. This engine calculates the energy of the system, which can be queried by the <ENERGY
command.
The engine sends the driver a string that corresponds to the argument of -name
in the MDI initialization options. This argument allows a driver to identify the purpose of connected engine codes within the simulation. For example, a particular QM/MM driver might require a connection with a single MM code and a single QM code, with the expected name of the MM code being "MM" and the expected name of the QM code being "QM". After initializing MDI and accepting communicators to the engines, the driver can use this command to identify which of the engines is the MM code and which is the QM code.
MDI_NAME_LENGTH
The engine sends the driver the number of atoms in the engine's system.
The engine sends the driver the number of different types of atoms (e.g. "H", "He", "C", "O", etc.) in the engine's system.
The driver sends a set of atomic forces to the engine, which replaces its internal forces with the forces sent by the driver.
3 * <NATOMS
ATOM_STEP
command, after calculation of all forces except those related to constraint algorithms (e.g. SHAKE, RATTLE, etc.) and prior to application of any constraint algorithmsThe driver sends a set of atomic forces to the engine, which adds the forces sent by the driver to its internal forces.
3 * <NATOMS
ATOM_STEP
command, after calculation of all forces except those related to constraint algorithms (e.g. SHAKE, RATTLE, etc.) and prior to application of any constraint algorithmsThe engine calculates and sends a set of atomic forces to the driver. These forces include all force contributions except those associated with any constraint algorithm (e.g. SHAKE, RATTLE, etc.).
3 * <NATOMS
The engine performs a full self-consistent field calculation in order to relax the electronic density distribution. The engine updates its energy, which can be queried with the <ENERGY
command.


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I’ve been here for about three months now - just long enough (I’d say) to claim that the transition has been made from this feeling like a short trip to this feeling like a full move. And with that, the realisation that I most likely won’t return to the UK for just under a year has made me miss it.
Bristol is a wonderful city with a strong pulse. I don’t miss the University per se, but the city itself, and the people I met during University who still live there, either at Uni or in the working life. I was ready to graduate and move away from academia, having done my part and received my grade. But I’ve found it increasingly hard moving from such an active musical lifestyle to having nothing. I keep up playing music as a hobby, yes, but there isn’t that community, that outlet to play it publicly, and enjoy it with others, friends, who are into the same thing as you. At least, I haven’t found it yet.
Things are different in America. It’s not better than the UK, and it’s not worse. It’s just different. It’s interesting noting cultural differences - there’s a different conversational style, a different sense of humour, different social expectations. British conversation staples such as the weather, laced with a touch of self-deprecation are lost on the Americans, whereas several American nuances of a more direct approach are lost on me. We share (or rather, they’ve inherited) a language, but conversation isn’t usually as fluid as with my British counterparts - naturally because I’ve been a part of one culture for 22 years, and the other for a mere three months. There’s a lot to pick up on.
Previously in my life the run up to Christmas has always followed a sort of loose routine, but here the traditions and even the environment (it’s still basically Summer, for a start) are rather set apart from what I’m used to. Ordinarily it’s a time to spend with family, whereas I’m not able to do that this year. In short, I’m a little out of my comfort zone.
But despite the difficulties, that’s okay. One should rattle things up in their early twenties. It’s Thanksgiving today, and despite never acknowledging it as a holiday before I’m getting into the feel of it.
It’s a joy to live in SF. I really like being involved and present in a place where the next big thing is just waiting to emerge, along with countless other ideas bubbling under the surface. Bristol is vibrant and creative too, but SF has much more cultural diversity (it’s a lot bigger). That said, SF also feels much more anonymous. More turbocharged. One of the main things I love about Bristol vs. cities like London is how naturally laid back it is. There’s a lot going on, but it’s all happening at a pace that never feels uncomfortable or overly urgent. It feels more harmonious than SF, which suffers from a bit of a gulf between groups of individuals within notably variant pay brackets, to put it one way. Maybe, for cultural differences outlined above, a Californian native would feel the same friction living in the UK.
People have said to me that SF feels like a small city, despite its breadth and size. I can agree with that, it feels a lot smaller now than the daunting metropolis it resembled when I first arrived - and I was still encountering new places in Bristol at the same rate four years on as when I arrived as a fresher.
I have a life in two places, and a whopping great stretch of water does its part to ensure that they don’t overlap. While I’m progressing headlong through one, having a great time doing it, I’m still holding onto the other. God Save The Queen.


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| Uses of ParseException in com.parse |
|---|
| Methods in com.parse with parameters of type ParseException | |
|---|---|
abstract void |
GetDataCallback.done(byte[] data,
ParseException e)
|
abstract void |
CountCallback.done(int count,
ParseException e)
Override this function with the code you want to run after the count is complete. |
abstract void |
FindCallback.done(List<T> objects,
ParseException e)
Override this function with the code you want to run after the fetch is complete. |
abstract void |
SignUpCallback.done(ParseException e)
Override this function with the code you want to run after the signUp is complete. |
abstract void |
SendCallback.done(ParseException e)
Override this function with the code you want to run after the send is complete. |
abstract void |
SaveCallback.done(ParseException e)
Override this function with the code you want to run after the save is complete. |
abstract void |
RequestPasswordResetCallback.done(ParseException e)
Override this function with the code you want to run after the request is complete. |
abstract void |
DeleteCallback.done(ParseException e)
Override this function with the code you want to run after the delete is complete. |
abstract void |
LocationCallback.done(ParseGeoPoint geoPoint,
ParseException e)
Override this function with the code you want to run after the location fetch is complete. |
abstract void |
RefreshCallback.done(ParseObject object,
ParseException e)
Override this function with the code you want to run after the save is complete. |
abstract void |
LogInCallback.done(ParseUser user,
ParseException e)
Override this function with the code you want to run after the save is complete. |
abstract void |
GetCallback.done(T object,
ParseException e)
Override this function with the code you want to run after the fetch is complete. |
abstract void |
FunctionCallback.done(T object,
ParseException e)
Override this function with the code you want to run after the cloud function is complete. |
| Methods in com.parse that throw ParseException | ||
|---|---|---|
static ParseUser |
ParseUser.become(String sessionToken)
Authorize a user with a session token. |
|
static
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ParseCloud.callFunction(String name,
Map<String,?> params)
Calls a cloud function. |
|
int |
ParseQuery.count()
Counts the number of objects that match this query. |
|
void |
ParseObject.delete()
Deletes this object on the server. |
|
static void |
ParseObject.deleteAll(List<ParseObject> objects)
Deletes each object in the provided list. |
|
ParseUser |
ParseUser.fetch()
|
|
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ParseObject.fetch()
Fetches this object with the data from the server. |
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static List<ParseObject> |
ParseObject.fetchAll(List<ParseObject> objects)
Fetches all the objects in the provided list. |
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static
|
ParseObject.fetchAllIfNeeded(List<T> objects)
Fetches all the objects that don't have data in the provided list. |
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void |
ParseObject.fetchFromLocalDatastore()
Loads data from the local datastore into this object, if it has not been fetched from the server already. |
|
ParseUser |
ParseUser.fetchIfNeeded()
|
|
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ParseObject.fetchIfNeeded()
If this ParseObject has not been fetched (i.e. |
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List<T> |
ParseQuery.find()
Retrieves a list of ParseObjects that satisfy this query. |
|
T |
ParseQuery.get(String objectId)
Constructs a ParseObject whose id is already known by fetching data from the server. |
|
byte[] |
ParseFile.getData()
Synchronously gets the data for this object. |
|
T |
ParseQuery.getFirst()
Retrieves at most one ParseObject that satisfies this query. |
|
static ParseUser |
ParseUser.logIn(String username,
String password)
Logs in a user with a username and password. |
|
void |
ParseObject.pin()
Stores the object and every object it points to in the local datastore, recursively. |
|
void |
ParseObject.pin(String name)
Stores the object and every object it points to in the local datastore, recursively. |
|
static void |
ParseObject.pinAll(List<ParseObject> objects)
Stores the objects and every object they point to in the local datastore, recursively. |
|
static void |
ParseObject.pinAll(String name,
List<ParseObject> objects)
Stores the objects and every object they point to in the local datastore, recursively. |
|
void |
ParseObject.refresh()
Refreshes this object with the data from the server. |
|
static void |
ParseUser.requestPasswordReset(String email)
Requests a password reset email to be sent to the specified email address associated with the user account. |
|
void |
ParseObject.save()
Saves this object to the server. |
|
void |
ParseFile.save()
Saves the file to the Parse cloud synchronously. |
|
static void |
ParseObject.saveAll(List<ParseObject> objects)
Saves each object in the provided list. |
|
void |
ParsePush.send()
Sends this push notification while blocking this thread until the push notification has successfully reached the Parse servers. |
|
void |
ParseUser.signUp()
Signs up a new user. |
|
static void |
ParseTwitterUtils.unlink(ParseUser user)
Unlinks a user from a Twitter account. |
|
static void |
ParseFacebookUtils.unlink(ParseUser user)
Unlinks a user from a Facebook account. |
|
void |
ParseObject.unpin()
Removes the object and every object it points to in the local datastore, recursively. |
|
void |
ParseObject.unpin(String name)
Removes the object and every object it points to in the local datastore, recursively. |
|
static void |
ParseObject.unpinAll()
Removes the objects and every object they point to in the local datastore, recursively. |
|
static void |
ParseObject.unpinAll(List<ParseObject> objects)
Removes the objects and every object they point to in the local datastore, recursively. |
|
static void |
ParseObject.unpinAll(String name)
Removes the objects and every object they point to in the local datastore, recursively. |
|
static void |
ParseObject.unpinAll(String name,
List<ParseObject> objects)
Removes the objects and every object they point to in the local datastore, recursively. |
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What is CSI CODEGOLF?
CSI-VIT presents Code Golf, our beginner friendly variant of the wildly popular coding competition. Use nearly every language in your arsenal to write the shortest code you can. The smaller the code, the more points you get! Care to play a game?
Join us at graVITas'20


Services
Package 1: Single page site
- Custom designed site just for your organization
- Quickly conveys critical information about your organization
- Looks great on mobile and desktop devices
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- All the benefits of Package 1
- PLUS a simple animation to make your site memorable
Package 3: Multipage site
- Include additional pages to give provide even more infomation about your organization.
- Simple animation plans are also available for multisite designs
Other fun stuff I've been doing
About Leigh D'Amico
I'm a former industrial engineer, a veteran relocation expert
(aka military spouse) and a current home educator who delights in making order out
of chaos.
With the help of SkillCrush, Front End Masters, Google, and a library card, I'm now a
front-end web designer and developer helping small businesses and nonprofit organizations grow by establishing their initial presence on the Web with a custom design site.
Contact Leigh D'Amico
Want your potential customers and community
to be able to find you on the Web?
Let's talk.


as3flobile on the iPhone
2010 September 13th by todd anderson
as3flobile is a set of ActionScript 3 components targeting the Flash Player on Mobile Devices, whether it be embedded in the browser or in an Adobe AIR application. You find out more about the project at http://github.com/bustardcelly/as3flobile.
What with the latest breaking news on rollback of restrictions for iOS application submissions, i decided to set some time to drop the as3flobile components into an AIR app and publish with the pfi. They work great and actually are quite a bit more responsive (go figure!) in regards to both animation and touch response (due to hardware i suppose).
The as3flobile project actually started out as a way to play around with Flash on the iPhone, but shifted to a suite a components targeting mobile devices that supported the Flash Player in browser (i forget why the focused changed ). In any event, i just wanted to write a quick post that, yes, you can use as3flobile components in an AIR application on Android and now iOS… (well, i guess it was always possible, just never a strong possibility that anyone would be able to see it unless you added them to your provision ).
If you are interested, the as3flobile project is available on github, so check it out.
Here’s also a great refresher post on the PFI by Christian Cantrell.
Posted in AIR, AS3, Android, Flash, as3flobile.


Change member access from (typed) dot syntax to (untyped) square brackets with string literal syntax.


--- layout: post title: Peplum sketches date: '2014-08-19T07:30:00+02:00' categories: - Sketches - Sketchbooks - Storyboards picture_url: https://film-Storyboards.tumblr.com/post/95185777215 ---


Explore NCTQ data and analysis of state laws, rules, and regulations that shape the teaching profession- from teacher preparation, licensing, and evaluation to compensation, professional development, and dismissal policies.
Use the links below to learn more about the national landscape, dig into individual state policies and recommendations, or create a custom report comparing state data.
State Analyses provide an in-depth look into each state's teacher policy findings and recommendations. Select a state for an overview and to explore by policy area.


With BitClaims Patients and Providers can crowdsource and capitalize their own healthcare using blockchain and smart contract technology.
To empower communities to take charge of healthcare costs. We provide a platform that is powered by smart contracts and blockchain technology so that patients and providers can connect directly in a trustless, secure and compliant way.
Architecting a new environment for healthcare using immutable ledger technology and smart contracts.
What is a BitClaim?
The BitClaims core business functions that enables donators to become members of a participant group which holds BitClaims that are tied to the direct cash flow of the BitClaims platform. To find out more on what BitClaims are and how to participate, please contact us.
Blockchain Developer


{% extends "base.html" %} {% block title %}
Medieval catalogues > BENEDICTINES: THE SHORTER CATALOGUES > Norwich > Bale, c. 1550
{% endif %}12 identified entries found.
All data was derived from the List of Identifications by Professor Richard Sharpe.
{% if not printing %}
A key to codes used in the List is available (opens in new tab).{% endif %}


Cayla Zabel
Objective Statement
Aspiring software dev
Education
Bellevue College 2012-2014
University of Hawaii at Manoa 2014-2016
Code Fellows Nov 2016-Current
Things I love to do
- I love to be active, whether it be in the gym or outdoors.
- Spend time with the people I love.
- Play soccer
- Learn
My Top Ten Favorite Places to Be
- Seattle
- Honolulu
- New York City
- The Ocean
- San Antonio
- A Soccer Field
- Los Angeles
- In the Mountains
- Miami
- Denver


{% extends "design-system-page-layout-guide.html" %} {% set section_id = "styles" %} {% set page_id = "colour" %} {% set page_title = "Colour" %} {% block sectionOneTop %}{% endblock %} {% block sectionLeftHandColumn %} {% include "includes/design-system/design-system-side-nav.html" %} {% endblock %} {% block sectionOneRightContent %}
The LEEDS.GOV.UK colour palette.
The colour contrast ratio for text and interactive elements should be at least 4.5:1 as recommended by the W3C.


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Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems.
| Version: | 5.2.1 |
| Depends: | R (≥ 2.10), AlgDesign, scales, scatterplot3d, emoa, grDevices |
| Suggests: | R.rsp, EMMREML, quadprog, UsingR, glmnet, leaps, Matrix |
| Published: | 2018-11-24 |
| Author: | Deniz Akdemir |
| Maintainer: | Deniz Akdemir <deniz.akdemir.work at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | STPGA results |
| Reference manual: | STPGA.pdf |
| Package source: | STPGA_5.2.1.tar.gz |
| Windows binaries: | r-devel: STPGA_5.2.1.zip, r-release: STPGA_5.2.1.zip, r-oldrel: STPGA_5.2.1.zip |
| OS X binaries: | r-release: STPGA_5.2.1.tgz, r-oldrel: STPGA_5.2.1.tgz |
| Old sources: | STPGA archive |
Please use the canonical form https://CRAN.R-project.org/package=STPGA to link to this page.


4. Generative Learning Algorithms
4.1. Discriminative & Generative
discriminative：try to learn \(p(y|x)\) directly, such as logistic regression
or try to learn mappings from the space of inputs to the labels \(\left \{ 0, 1\right \}\) directly, such as perceptron
generative：algorithms that try to model \(p(x|y)\) and \(p(y)\)(called class prior), such as guassian discriminant analysis and naive bayes
when predicting, use bayes rule：
\[p(y|x)=\frac{p(x|y)p(y)}{p(x)}\]
then：
\[\hat{y}=\underset{y}{argmax}\ \frac{p(x|y)p(y)}{p(x)}= \underset{y}{argmax}\ {p(x|y)p(y)}\]
4.2. Guassian Discriminant Analysis(GDA)
multivariant normal distribution
guassian distribution is parameterized by a mean vector \(\mu \in \mathbb{R}^{d}\) and a covariance matrix \(\Sigma \in \mathbb{R}^{d \times d}\), where \(\Sigma >= 0\) is symmetric and positive semi-definite. also written \(\mathcal{N}(\mu,\Sigma)\), it’s density is given by：
\[p(x;\mu,\Sigma)=\frac{1}{(2\pi)^{d/2}\left | \Sigma \right |^{1/2} }exp\left ( -\frac{1}{2}(x-\mu)^{T}{\Sigma}^{-1}(x-\mu)\right )\]
unsurprisingly, for random variable \(X \sim \mathcal{N}(\mu,\Sigma)\):
\[E[X] = \int_{x}xp(x;\mu, \Sigma)dx = \mu\]
\[Cov(X) = E[(X - E(X))(X - E(X))^{T}] = \Sigma\]
GDA:
when we have a classification problem in which the input features x are continous, we can use GDA, which model \(p(x|y)\) using a multivariant normal distribution:
\[y\sim Bernoulli(\phi)\]
\[x | y=0 \sim \mathcal{N}(\mu_{0},\Sigma) \]
\[x | y=1 \sim \mathcal{N}(\mu_{1},\Sigma) \]
writing out the distribution:
\[p(y) = \phi^{y}(1 - \phi)^{1 - y}\]
\[p(x| y=0)=\frac{1}{(2\pi)^{d/2}\left | \Sigma \right |^{1/2} }exp\left (-\frac{1}{2}(x-\mu_{0})^{T}{\Sigma}^{-1}(x-\mu_{0})\right )\]
\[p(x| y=1)=\frac{1}{(2\pi)^{d/2}\left | \Sigma \right |^{1/2} }exp\left (-\frac{1}{2}(x-\mu_{1})^{T}{\Sigma}^{-1}(x-\mu_{1})\right )\]
the log-likelihood of the data is given by：
\[\begin{split}
\begin{equation}
\begin{split}
l(\phi,\mu_{0},\mu_{1},\Sigma) &= log\prod_{i=1}^{n}p(x^{(i)},y^{(i)};\phi,\mu_{0},\mu_{1},\Sigma) \\
&= log\prod_{i=1}^{n}p(x^{(i)}|y^{(i)};\mu_{0},\mu_{1},\Sigma)p(y^{(i)};\phi)
\end{split}
\end{equation}
\end{split}\]
we find the maximum likelihood estimate of the parameters are：
\[\phi = \frac{1}{n}\sum_{i=1}^{n}1\left \{ y^{(i)}=1 \right \}\]
\[\mu_{0} = \frac{\sum_{i=1}^{n}1\left \{ y^{(i)}=0 \right \}x^{(i)} }{\sum_{i=1}^{n}1\left \{ y^{(i)}=0 \right \}} \]
\[\mu_{1} = \frac{\sum_{i=1}^{n}1\left \{ y^{(i)}=1 \right \}x^{(i)} }{\sum_{i=1}^{n}1\left \{ y^{(i)}=1 \right \}} \]
\[\Sigma=\frac{1}{n}\sum_{i=1}^{n}(x^{(i)} - \mu_{y^{(i)}})(x^{(i)} - \mu_{y^{(i)}})^{T}\]
as we wanted
4.3. GDA and logistic regression
the GDA model has an interesting relationship to logistic regression.
if we view the quantity \(p(y=1|x; \phi,\Sigma,\mu_{0},\mu_{1})\) as a function of \(x\), we’ll find that it can be expressed in the form:
\[
p(y=1|x; \phi,\Sigma,\mu_{0},\mu_{1}) = \frac{1}{1 + exp(-\theta^{T}x)}
\]
where \(\theta\) is some appropriate function of \(\phi,\Sigma,\mu_{0},\mu_{1}\).
proof:
\[\begin{split}
\begin{equation}
\begin{split}
p(y=1|x; \phi,\Sigma,\mu_{0},\mu_{1}) &= \frac{p(y=1, x)}{p(x)} \\
&=\frac{p(x|y=1)p(y=1)}{p(x|y=1)p(y=1) + p(x|y=1)p(y=1)} \\
&=\frac{\phi\cdot exp\left (-\frac{1}{2}(x-\mu_{1})^{T}{\Sigma}^{-1}(x-\mu_{1})\right )}{\phi\cdot exp\left (-\frac{1}{2}(x-\mu_{1})^{T}{\Sigma}^{-1}(x-\mu_{1})\right ) + (1 - \phi)\cdot exp\left (-\frac{1}{2}(x-\mu_{0})^{T}{\Sigma}^{-1}(x-\mu_{0})\right )} \\
&=\frac{1}{1 + \frac{1 - \phi}{\phi}exp\left(-\frac{1}{2}\left((x-\mu_{0})^{T}{\Sigma}^{-1}(x-\mu_{0}) - (x-\mu_{1})^{T}{\Sigma}^{-1}(x-\mu_{1})\right)\right)} \\
&=\frac{1}{1 + \frac{1 - \phi}{\phi}exp\left(-\frac{1}{2}\left((\mu_{1}^T -\mu_{0}^T)\Sigma^{-1}x + x^{T}\Sigma^{-1}(\mu_{1}-\mu_{0}) + (\mu_{0}^{T}\Sigma^{-1}\mu_{0} - \mu_{1}^{T}\Sigma^{-1}\mu_{1})\right)\right)}
\end{split}
\end{equation}
\end{split}\]
because of \(x^{T}a=a^{T}x\), we can express the above as:
\[\frac{1}{1 + exp(-\theta^{T}x)}\]
so the separating surface of GDA is \(\theta^{T}x=0\).
as logistic regression, GDA can be interpreted by logistic model, but with different optimization strategy.
4.4. Naive Bayes
in GDA，x is continous, when x is discrete, we can use naive bayes.
suppose \(x=(x_{1}, x_{2},..., x_{d})\), for simplicity, we assume \(x_{j}\) binary here, we of course have：
\[\begin{split}
\begin{equation}
\begin{split}
p(x|y) &= p(x_{1},x_{2},...,x_{d}|y) \\
&= p(x_{1}|y)p(x_{2}|y,x_{1})...p(x_{d}|y,x_{1},...,x_{d-1})
\end{split}
\end{equation}
\end{split}\]
we will assume that \(x_{j}\)’s are conditionally independent given \(y\). this assumption is called the naive bayes assumption, and the resulting algorithm is called the naive bayes classifier：
\[\begin{split}
\begin{equation}
\begin{split}
p(x|y) &= p(x_{1}|y)p(x_{2}|y,x_{1})...p(x_{d}|y,x_{1},...,x_{d-1}) \\
&= p(x_{1}|y)p(x_{2}|y)...p(x_{d}|y)
\end{split}
\end{equation}
\end{split}\]
our model is parameterized by:
\[y\sim Bernoulli(\phi)\]
\[x_{j}|y=1 \sim Bernoulli(\phi_{j|y=1})\]
\[x_{j}|y=0 \sim Bernoulli(\phi_{j|y=0})\]
note:
\[k = \sum_{i=1}^{n}1\left\{y^{(i)}=1\right\}\]
\[s_{j} = \sum_{i=1}^{n}1\left\{x_{j}^{(i)}=1 \wedge y^{(i)}=1\right\}\]
\[t_{j} = \sum_{i=1}^{n}1\left\{x_{j}^{(i)}=1 \wedge y^{(i)}=0\right\}\]
we can write down the joint log likelihood of the data:
\[\begin{split}
\begin{equation}
\begin{split}
\mathcal{L}(\phi, \phi_{j|y=1}, \phi_{j|y=0}) &= log\prod_{i=1}^{n}p(x^{(i)},y^{(i)})\\
&=\sum_{i=1}^{n}log(p(x^{(i)}, y^{(i)})) \\
&=\sum_{i=1}^{n}log(p(y^{(i)})\prod_{j=1}^{d}p(x_{j}^{(i)}|y^{(i)})) \\
&=\sum_{y^{(i)}=1}(log(\phi) + \sum_{j=1}^{d}log(p(x_{j}^{(i)}|y=1))) + \sum_{y^{(i)}=0}(log(1 - \phi) + \sum_{j=1}^{d}log(p(x_{j}^{(i)}|y=0))) \\
&=k\ log(\phi) + (n-k)log(1 - \phi) + \sum_{j=1}^{d}(s_{j}\ log(\phi_{j|y=1}) + (k-s_{j})log(1 - \phi_{j|y=1}) + t_{j}\ log(\phi_{j|y=0}) + (n -k - t_{j})log(1 - \phi_{j|y=0})
\end{split}
\end{equation}
\end{split}\]
maximizing this equal to maximize each part, we derive:
\[\phi=\frac{k}{n}\]
\[\phi_{j|y=1} = \frac{s_{j}}{k}\]
\[\phi_{j|y=0} = \frac{t_{j}}{n-k}\]
as expected
4.5. Laplace Smoothing
there is problem with the naive bayes in its current form
if \(x_{j}=1\) never occur in the training set, then \(p(x_{j}=1|y=1)=0,p(x_{j}=1|y=0)=0\).
hence when predicting a sample \(x\) with \(x_{j}=1\), then:
\[\begin{split}
\begin{equation}
\begin{split}
p(y=1|x) &= \frac{\prod_{k=1}^{d}p(x_{k}|y=1)p(y=1)}{\prod_{k=1}^{d}p(x_{k}|y=1)p(y=1) + \prod_{k=1}^{d}p(x_{k}|y=0)p(y=0)} \\
&= \frac{0}{0}
\end{split}
\end{equation}
\end{split}\]
does’t know how to predict.
to fix this problem, instead of:
\[\phi_{j|y=1}=\frac{\sum_{i=1}^{n}1\left\{x_{j}^{(i)}=1 \wedge y^{(i)}=1\right\}}{\sum_{i=1}^{n}1\left\{y^{(i)}=1\right\}}\]
\[\phi_{j|y=0}=\frac{\sum_{i=1}^{n}1\left\{x_{j}^{(i)}=1 \wedge y^{(i)}=0\right\}}{\sum_{i=1}^{n}1\left\{y^{(i)}=0\right\}}\]
we add 1 to the numerator, add 2 to the denominator to:
avoid 0 in the parameter
\(\phi_{j|y=1}\) and \(\phi_{j|y=0}\) is still a probability distribution.
i.e:
\[\phi_{j|y=1}=\frac{1 + \sum_{i=1}^{n}1\left\{x_{j}^{(i)}=1 \wedge y^{(i)}=1\right\}}{2 + \sum_{i=1}^{n}1\left\{y^{(i)}=1\right\}}\]
\[\phi_{j|y=0}=\frac{1 + \sum_{i=1}^{n}1\left\{x_{j}^{(i)}=1 \wedge y^{(i)}=0\right\}}{2 + \sum_{i=1}^{n}1\left\{y^{(i)}=0\right\}}\]
4.6. Text Classification
bernoulli event model:
first randomly determined whether a spammer or non-spammer
then runs through the dictionary deciding whether to include each word j.
multinomial event model:
first randomly determined whether a spammer or non-spammer
then each word in the email is generating from some same multinomial distribution independently.
using multinomial event model, if we have training set \(\left \{ (x^{(1)},y^{(1)}),...,(x^{(n)},y^{(n)}) \right \}\) where \(x^{(i)}=(x_{1}^{(i)},...,x_{d_{i}}^{(i)})\ \)(here \(d_{i}\) is the number of words in the i-th training example)
using maximum likelihood estimates of parameters like the above:
\[\phi = \frac{1}{n}\sum_{i=1}^{n}1\left \{ y^{(i)}=1 \right \}\]
\[\phi_{k|y=1}=\frac{\sum_{i=1}^{n}\sum_{j=1}^{d_{i}}1\left \{x_{j}^{(i)}=k\wedge y^{(i)}=1 \right \}}{\sum_{i=1}^{n}1\left \{ y^{(i)}=1 \right \}d_{i}}\]
\[\phi_{k|y=0}=\frac{\sum_{i=1}^{n}\sum_{j=1}^{d_{i}}1\left \{x_{j}^{(i)}=k\wedge y^{(i)}=0 \right \}}{\sum_{i=1}^{n}1\left \{ y^{(i)}=0 \right \}d_{i}}\]
add laplace smoothing with respect to multinomial event model:
\[\phi_{k|y=1}=\frac{1 + \sum_{i=1}^{n}\sum_{j=1}^{d_{i}}1\left \{x_{j}^{(i)}=k\wedge y^{(i)}=1 \right \}}{\left | V \right | + \sum_{i=1}^{n}1\left \{ y^{(i)}=1 \right \}d_{i}}\]
\[\phi_{k|y=0}=\frac{1 + \sum_{i=1}^{n}\sum_{j=1}^{d_{i}}1\left \{x_{j}^{(i)}=k\wedge y^{(i)}=0 \right \}}{\left | V \right | + \sum_{i=1}^{n}1\left \{ y^{(i)}=0 \right \}d_{i}}\]
\(\left | V \right |\) is the size of the vocabulary
in short:
\[\phi_{k|y=1}=\frac{1 + number\ of\ words\ k\ occur\ in\ spammer}{\left | V \right | + number\ of\ words\ in\ spammer}\]


In these problematic times when Covid-19 is doing serious damage to people in India, the nationwide vaccination program started by the government is a ray of hope for people. However the massive population of India overshadows the limited number of vaccination centres. People are unable to book a slot for vaccination. The moment, they become aware of available slots, its already booked.
This is where CowinWatch comes to play. All you need to do is register on it with your age and district and it will start providing you hourly notifications regarding slots availability.
Sounds easy enough. Try it.
Note: Its a Discord Bot and will work on that platform only.
| Command | Use |
|---|---|
| *help | To view all commands. Registration not required. |
| *start | To begin the registration process and private message between bot and user.
One time use command. Not required after registration. |
| *register <age> <district> | To register a user with age and district
For eg. *register 20 ernakulam This registers a user in group of 18+ and in district Ernakulam. |
| *my_det | To get details of registered user. REGISTRATION REQUIRED |
| *upd <age> | To update a user's age (REGISTRATION REQUIRED) For eg. *upd 45 :-This changes the age of user to 45 |
| *upd <district> | To update a user's district (REGISTRATION REQUIRED). For eg. *upd Thrissur This changes the district of user to Thrissur |
| *slots | To manually get the slots of the selected district (REGISTRATION REQUIRED)
User can give this command in the DM to get slots availability. |
| *on | To subscribe to hourly updates of available slots (REGISTRATION REQUIRED) |
| *off | To unsubscribe from hourly updates of available slots (REGISTRATION REQUIRED) |


Aggu Media is a flat laid Creative Agency rooted into traditional stronghold of art, that is blend in fine mixture with Digital Age Tools to help visulaise anyone from romanitcally involved BFs to MNC Conglomorates who comes with an Idea (The one you call Brand / Product / Deisgn / Art / Ad / Keemo Samosa), into a reviting form be it in Digital or Print or Physical Product. To simply put we do anything virtually you can imagine of digitally and to a larger extent in ground. We are leading the global stage in use of 2D Vector in Animatons and Illustration with our in-house developed Bi-Linear Vectorisation Methods. We see quality as qunitessential and Our Work is Our Passion. We always love to cherish long term commitment with our client. Our results are nothing less than the best


Data Scientist - Grid Analytics - Burlingame, CA 94010
Description:
Sentient Energy is looking for a Data Scientist, a full-time position on the Data Science and Analytics team. This team is responsible for characterizing, classifying and predicting the behavior of its customers’ distribution grids. Our efforts help our customers streamline operations, save energy, and increase safety.
This position offers a unique opportunity to work with emerging technologies in the rapidly developing and highly visibility Smart Grid space within the Green Energy sector. The innovation and creativity with which we develop and implement utility solutions are among the distinctions that make Sentient Energy the leader in Distribution Network sensing and monitoring.
Position Location: Sentient Energy office in Burlingame, CA
Job Opportunity
Using historical distribution grid event and waveform data to develop proofs-of-concept for new customer features
Applying statistical and machine learning techniques to predict grid performance based on captured disturbance and outage data
Developing tools and visualizations to cross-validate feature behavior across tens of thousands of sensors.
Requirements:
Education and Experience
Degree in computer science, applied math, statistics, engineering, or related field.
Bachelor of Science with 4+ years experience or Masters with 2+ years experience
Systems and Software
Strong statistics and machine learning background.
Experience writing programs to support exploratory data analysis (EDA) and visualizations.
Experience working with and creating reports from large data sets.
Nice to Have
Database experience.
Strong programming and scripting skills (Python a plus).
Masters or continuing education in Data Science/Data Engineering.
Experience with Python libraries for signal processing, machine learning and visualization (e.g., scipy, scikit-learn, matplotlib).
Experience working with signals (e.g., digital signal processing, circuit analysis)
Attributes
Excellent written and verbal communications skills.
Persistent, self-motivated, flexible.
Strong sense of ownership and accountability.
Excellent organizational and time-management skills.
Demonstrated self-starter with an ability to work effectively in a matrix organization.
Compliance to Company Policies, Procedures and Core Values.
Compensation
Competitive salary and incentive bonuses, generous options grants, paid time off (vacation, floating holidays), paid holidays, medical, dental, vision and life insurance, 401(k) Plan.


If you want to play around with the styles you saw in class:
- Open
index.html
in your browser or open a Go Live server in VSCode.
- Open any of
css
files and make some changes!
- Make sure to uncomment the line in the
.wrapper
selector where it says display: block;
.
- Make some changes. When you save the CSS, the browser will update. ___ # LOs ## Box Model and Positioning
- Describe how:
- padding and margins work in the box model
- the browser positions a fixed positioned element
- the browser positions a relatively positioned element
- the browser positions absolutely positioned elements with and without a relatively positioned parent element
- the browser positions a static positioned element
- Identify elements rendered with specific padding and margin settings
- Apply padding and margins to HTML elements to achieve a desired layout
- Apply positioning settings to elements (fixed, relative, and absolute) to HTML elements to achieve a desired layout
- Identify which HTML elements have a default "inline" display value
- Identify which HTML elements have a default "block" display value
- Describe and use z-index positioning of elements
- Identify the different types of media that a media query can target
- Explain how the media features (and prefixed subfeatures) of "aspect ratio", "height", "orientation", and "width"
are applied
- Use media queries to change the styles of content in an HTML page to achieve a desired effect
Flexbox
- Explain how flexible box layout lays out elements
- Use the flex property to specify grow, shrink, and basis values.
- Use the flex-direction property to direct the layout of the content
- Use the flex-wrap property to affect the wrap of content layout within an element using flexible box layout
- Use align-self, justify-content, and align-items to change the way that children elements are laid out in a flexible box layout
- Use the order property to change the order in which elements will appear in a flexible box layout
Grid
- Explain how grid layout lays out elements
- Use the grid-template-columns, grid-template-rows, and grid-template properties to specify the layout of the grid using relative and absolute measures
- Use grid-template-areas to label areas of a grid and grid-area to assign an element to the area
- Use grid-column-gap, grid-row-gap, and grid-gap to set the "gutter" areas between elements in a grid layout
- Use grid-column-start/grid-column-end and grid-row-start/grid-row-end to create spans across multiple columns and rows with positive integers, negative integers, and in conjunction with the "span" operator
- Explain and use the shorthand versions of grid-column and grid-row to define how an element will span a grid layout
- Use the order property to change the default order in which items are laid out
- Explain and use the "fr" unit of measure
- Use justify-items, align-items, justify-content and align-content to layout items in each grid area
External Resources:


Dhaka: The Applegate Division of Supreme Court on Sunday fixed July 2 to pass the order on a leave-to-appeal against the High Court order that granted bail to BNP Chairperson Khaleda Zia in a murder case filed over the arson attack in Cumillaon Feb 3, 2015. A four-member bench of the Appellate Division headed by Chief Justice Syed Mahmud Hossain fixed the date after completing hearing of the leave-to-appeal. Besides, the SC fixed Monday for hearing another leave-to-appeal filed by the state in another case filed against Khaleda under Special Power Act over the same arson attack. Attorney General Mahbubey Alam stood for the state while Khandaker Mahbub Hossain, AJ Mohammad Ali and Joynul Abedin for Khaleda. On May 28, the High Court granted bail for six months to BNP Chairperson Khaleda Zia in two cases filed in Cumilla. Later, the government filed a petition with the Appellate Division seeking a stay on the bail order. Eight people were killed and at least 20 others injured when miscreants hurled a petrol bomb at a bus at Jogmohanpur in Chouddagram of Cumilla district during the BNP-led alliance’s movement on February 3, 2015. Two cases were filed against Khaleda in this connection. On February 8, Khaleda Zia was sent to jail after a special court sentenced her to five years’ rigorous imprisonment in the Zia Orphanage Trust graft case.
Chairman & Managing Director: Alhaj Mohammad Mosaddak Ali BSEC Building (Level-4), 102 Kazi Nazrul Islam Avenue, Karwan Bazar, Dhaka-1215 Telephone: +88 02 9143381-5, Fax: +88 02 9143366-7


Manage your waitlist below or view public waitlist.
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Section 4 Methodology and results
4.1 Extreme Clustering
The first analysis consisted of a modification of a clustering technique - partition around medoids - used in Bracken et al. (2015).
3,000 case studies were randomly sampled from the selected data to alleviate the simulation. The structural condition time series and the lat, long position of the sample was the only variables provided. A range from 2 to 20 clusters was analyzed. Figures 4.2 and 4.3 show the optimal and third-best performance according to the average silhouette method reproduced in figure 4.1.
The similarity between the pattern showed by the 6-cluster simulation and the North American Climate zones/types made us reflect about a potential connection. That is why new variables associated with monthly extreme temperatures and annual precipitation were added as covariates.
4.1.1 Adding climate variables to bridge location
Monthly climate-division data from the National Centers for Environmental Information (Vose et al. 2014) was collected for the period 1991-2017. For each of the 344 datapoints scatterd throughout the Continental US, the anual monthly maximum and minimum temperature and annual precipitation was aggregated.
To translate this information into bridge-located data, a local polynomial regression with a low alpha (0.05) and linear degree was used for each climate variable. Fig
4.2 Principal Component Analysis
A second analysis was carried out using PCA. In this case all variables with exception of the deficiency condition of the bridge were considered in the simulation. As a first approach, the climate variables were not included (Fig. 4.5). Repeating the process with tmax, tmin, and prec lead to a greater explanation of the variance in firsts four PC (see fig. 4.6).
With the second analysis (climate - considered), the influence of each eigenvector on the attributes of our dataset was plotted and analyzed (fig. 4.7)
4.2.1 Multinomial regression
The first 4 PC were used to fit a multinomial regression on the structural condition of the bridge, as they represent almost 60% of the variance of the model. The best model using step AIC criteria was the one including all four PC as covariates.
Two statistics were calculated to assess the accuracy of the regression. First, the ranked probability score for the model without climate variables was of 7.5 %. That is, the increase in the accuracy by predicting through the model instead of the count based probabilities was of 7.5 %. The introduction of the climate variables (tmax, tmin, prec) increased the ranked probability score skill from 7.5 % to 8.6 %.
Additionally, a confusion matrix caret::confusionMatrix
was calculated to evaluate the “false positives” and “false negatives” the model predicted. The output shown below evidences how hard it is for the model to be accurate, as the number of “false 1 identified as such” (structurally deficient) is similar to non identified “true 1”. Only a small fraction of “true 1” are identified correctly.
## Reference
## Prediction 1 2 3
## 1 78 151 613
## 2<PHONE_NUMBER>
## 3 633 1236 8563
4.3 Self Organizing Maps
As an alternative to the previous machine learning techniques, a 3 by 3 node SOM clustering has been carried out. Figure 4.8 the weight of each attribute on each of the 9 nodes. The resulting distribution aggregates statistically closer bridges in the same nodes, with a potential for better accuracy in the regression of each separately.
To better explain this approach, two properties from the ensemble, the design load knowledge and the reconstruction effect, will be plotted. Figure 4.9 shows the strong signal of the design load variable on the red node. Similarly, the comparative relevance of bridges on an specific node related to the binary variable “reconstruction” is plotted in figure 4.10.
Alternatively, a hierarchical clustering could be carried out on the map to obtain fewer number of nodes and reduce regression models. As an example, a k-means with 4 clusters on the nodes is showed in figure 4.11.
References
Bracken, C., B. Rajagopalan, M. Alexander, and S. Gangopadhyay. 2015. “Spatial Variability of Seasonal Extreme Precipitation in the Western United States.” Journal of Geophysical Research: Atmospheres 120 (10): 4522–33. https://doi.org/10.1002/2015JD023205.
Vose, Russell S., Scott Applequist, Mike Squires, Imke Durre, Matthew J. Menne, Claude N. Williams, Chris Fenimore, Karin Gleason, and Derek Arndt. 2014. “Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions.” J. Appl. Meteor. Climatol. 53 (5): 1232–51. https://doi.org/10.1175/JAMC-D-13-0248.1.


kansascity: 1 posts
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newjersey: 2 posts
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Sep 24 Perl Developer - (Nashville, TN )
Sep 22 Perl Programmer - (Daytona)
Oct 31 Web developer with knowledge of CGI programming in C -
Oct 30 PERL/PHP Developer - (Denver (or remote))
Oct 29 perl scraping script - (dallas)
Oct 25 Java/Perl - Social Media Engineer - (Greater New York City Area)
Oct 17 ruby on rails, perl programmer - (orange/rockland ny)
Oct 17 Ruby on Rails, Perl Programmer - (orange/rocklandNY NJ)
Oct 15 Freelance Perl developer needed - (KC)
Oct 12 Looking for Perl programmer - (Wilmington)
Oct 09 Looking for Perl programmer - (Wilmington)
Nov 05 Looking for PERL and/or CGI programmer (Linux) - (South Miami)
Nov 05 Looking for PERL and/or CGI programmer (Linux) - (South Miami)
Nov 05 Looking for PERL and/or CGI programmer (Linux) - (South Miami)
Nov 01 PERL Web developer - (63105)


You just created an event named {{ instance.title }}. That's awesome!
Here are the details we have for it:
You want {{ instance.max_attendees|default_if_none:"any number of" }} people, max. And you're cool with each person bringing {{ instance.max_guests|default_if_none:"any number of" }} guests each. {% if instance.cost %}You've also said the cost is "{{ instance.cost }}" per person.{% endif %}
If you need to edit your event, please use this URL: http://{{ domain }}{% url 'events:edit' instance.shortid %}.
Thanks for hosting your event with us!
The Headcount.io Team


--- layout: post title: Ora-12514 date: 2008-07-12 15:46:38.000000000 +09:30 type: post published: true status: publish categories: - Oracle tags: - ora meta: posturl_add_url: 'yes' _edit_last: '1' views: '861' ---
一般出这个问题都是/opt/oracle/product/10.2/db_1/network/admin/listener.ora文件惹的祸
# listener.ora Network Configuration File: /opt/oracle/product/10.2/db_1/network/admin/listener.ora
# Generated by Oracle configuration tools.
SID_LIST_LISTENER =
(SID_LIST =
(SID_DESC =
(SID_NAME = PLSExtProc)
(ORACLE_HOME = /opt/oracle/product/10.2/db_1)
(PROGRAM = extproc)
)
(SID_DESC =
(GLOBAL_DBNAME=ZYF)
(ORACLE_HOME=/opt/oracle/product/10.2/db_1)
(SID_NAME=ZYF)
)
)
LISTENER =
(DESCRIPTION_LIST =
(DESCRIPTION =
(ADDRESS = (PROTOCOL = TCP)(HOST = hbjob.site)(PORT = 1521))
)


| Struts 2 > Home > View Developers Guide > Tags |
The framework provides a tag library decoupled from the view technology. In this section, we describe each tag in general terms, such as the attributes it supports, what the behaviors are, and so forth. Most tags are supported in all template languages (see JSP Tags, Velocity Tags, and FreeMarker Tags), but some are currently only specific to one language. Whenever a tag doesn't have complete support for every language, it is noted on the tag's reference page.
The types of tags can be broken in to two types: general and HTML. Besides function and responsibility, the biggest difference between the two is that the HTML tags support templates and themes. In addition to the general tag reference, we also provide examples for using these generic tags in each of the support languages.
Be sure to read the Tag Syntax document to learn how tag attribute syntax works.
General tags are used for controlling the execution flow when the pages render. These tags also allow for data extraction from places other than your action or the value stack, such as Internationalization, JavaBeans, and including additional URLs or action executions.
Unlike general tags, HTML tags do not provide much control structure or logic. Rather, they are focussed on using data, either from your action/value stack or from the Data Tags, and displaying data in rich and reusable HTML. All HTML tags are driven by templates and themes. While general tags simply output some content directly from the tag (if there is any content to output), the HTML tags defer to a template, often grouped together as a theme, to do the actual rendering.
Template support allows HTML tags to build a rich set of reusable UI components that can be customized to fit exact requirements. For details, see Themes and Templates.
| Themes and Templates | A must-read explanation of how themes and templates are uses when rendering HTML tags. |
|---|---|
| Form Tags | provide all form-related HTML output, such as form, textfield, and select. |
| Non Form Tags | provide all non-form-related HTML output, such as a, div, and tabbedPanel. |
The framework strives to support multiple development environments. The framework does not impose a single template language. Almost any common language can be used, and there are hooks for new languages. By default, almost every single tag is supported in JSP, Velocity, and FreeMarker. In each of these sections, you'll find examples and techniques for applying the generic tag reference toward your specific language or template choice.
|
Please make sure you have read the Tag Syntax document and understand how tag attribute syntax works. |
Within the form tags, there are two classes of tags: the form tag itself, and all other tags, which make up the individual form elements. The behavior of the form tag is different than the elements enclosed within it.
As explained in Themes and Templates, the HTML Tags (which includes Form Tags) are all driven by templates. Templates are grouped together to create themes. The framework bundles three themes in the distribution.
| simple | Sometimes too simple | |
|---|---|---|
| xhtml | Extends simple | (default) |
| ajax | Extends xhtml |
The predefined themes can be used "as is" or customized.
| xhtml layout The xhtml theme renders out a two-column table. If a different layout is needed, do not write your own HTML. Create a new theme or utilize the simple theme. |
The downside of using the simple theme is that it doesn't support as many of the attributes that the other themes do. For example, the label attribute does nothing in the simple theme, and the automatic display of error messages is not supported.
All the form tags extend the UIBean class. This base class provides a set of common attributes, that can be grouped in to three categories: templated-related, javascript-related, and general attributes. The individual attributes are documented on each tag's reference page.
In addition to the common attributes, a special attribute exists for all form element tags: form (${parameters.form}). The form property represents the attributes used to render the form tag, such as the form's id. In a template, the form's ID can be found by calling ${parameters.form.id}.
|
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| When some attributes don't apply Some tag attributes may not be utilized by all, or any, of the templates. For example, the form tag supports the tabindex attribute, but none of the themes render the tabindex. |
In many of the tags (except for the form tag) there is a unique relationship between the name and value attributes. The name attribute provides the name for the tag, which in turn is used as the control attribute when the form is submitted. The value submitted is bound to the name. In most cases, the name maps to a simple JavaBean property, such as "postalCode". On a submit, the value would be set to the property by calling the setPostalCode mutator.
Likewise, a form control could be populated by calling a JavaBean accessor, like getPostalCode. In the expression language, we can refer to the JavaBean property by name. An expression like "%{postalCode}" would in turn call getPostalCode.
<EMAIL_ADDRESS>action="updateAddress"><EMAIL_ADDRESS>label="Postal Code" name="postalCode" value="%{postalCode}"/><EMAIL_ADDRESS>However, since the tags imply a relationship between the name and value, the value attribute is optional. If a value is not specified, by default, the JavaBean accessor is used instead.
<EMAIL_ADDRESS>action="updateAddress"><EMAIL_ADDRESS>label="Postal Code" name="postalCode"/><EMAIL_ADDRESS>While most attributes are exposed to the underlying templates as the same key as the attribute (${parameters.label}), the value attribute is not. Instead, it can be accessed via the nameValue key (${parameters.nameValue}). The nameValue key indicates that the value may have been generated from the name attribute rather than explicitly defined in the value attribute.
All form tags automatically assign an ID to the control, but the ID can be overridden if needed.
| Forms | The default ID is the action name. For example, "updateAddress". |
|---|---|
| Controls | The default ID is the form's name concatenated with the tag name. For example, "updateAddress_postalCode". |
The required attribute on many UI tags defaults to true only if you have client-side validation enabled, and a validator is associated with that particular field.
|
Every Form UI component (in xhtml / css_xhtml or any others that extends of them) could have tooltip assigned to a them. The Form component's tooltip related attribute once defined will be applicable to all form UI component that is created under it unless explicitly overriden by having the Form UI component itself defined that tooltip attribute.
In Example 1, the textfield will inherit the tooltipDelay adn tooltipIcon attribte from its containing form. In other words, although it doesn't defined a tooltipAboveMousePointer attribute, it will have that attributes inherited from its containing form.
In Example 2, the the textfield will inherite both the tooltipDelay and tooltipIcon attribute from its containing form but tooltipDelay attribute is overriden at the textfield itself. Hence, the textfield actually will have tooltipIcon defined as /myImages/myIcon.gif, inherited from its containing form and tooltipDelay defined as 5000, due to overriden at the textfield itself.
Example 3, 4 and 5 shows different way of setting the tooltipConfig attribute. |
<!-- Example 1: --> <s:form tooltipConfig="#{'tooltipDelay':'500', 'tooltipIcon='/myImages/myIcon.gif'}" .... > .... <s:textfield label="Customer Name" tooltip="Enter the customer name" .... /> .... </s:form> <!-- Example 2: --> <s:form tooltipConfig="#{'tooltipDelay':'500', 'tooltipIcon':'/myImages/myIcon.gif'}" ... > .... <s:textfield label="Address" tooltip="Enter your address" tooltipConfig="#{'tooltipDelay':'5000'}" /> .... </s:form> <-- Example 3: --> <s:textfield label="Customer Name" tooltip="One of our customer Details'"> <s:param name="tooltipConfig"> tooltipDelay = 500 | tooltipIcon = /myImages/myIcon.gif </s:param> </s:textfield> <-- Example 4: --> <s:textfield label="Customer Address" tooltip="Enter The Customer Address" > <s:param name="tooltipConfig" value="#{'tooltipDelay':'500', 'tooltipIcon':'/myImages/myIcon.gif'}" /> </s:textfield> <-- Example 5: --> <s:textfield label="Customer Telephone Number" tooltip="Enter customer Telephone Number" tooltipConfig="#{'tooltipDelay':'500', 'tooltipIcon':'/myImages/myIcon.gif'}" /> |
For detailed descriptions of each tag, including usage examples, see the Tag Reference.


Concepts
Even though this says that $\mu(G)=\int_{\mathbb{R}^2}\chi d(x, y)=0$, we can't actually directly calculate the integral, so we take a different method for proving it.
Hypothesis
Coming soon
Results
Coming soon
Proof
We use the fact that the graph is uniformly continuous, and the if and only if condition for zero content. That is, we use the fact that $G$ is uniformly continuous to show that for all $\epsilon>0$, there exists a finite set of boxes $\{I_i\subseteq\mathbb{R}^n:1\leq i\leq m\}$ such that
$$\begin{align*}
G\subseteq\bigcup_{i=1}^n I_i
\end{align*}$$ and $$\begin{align*}
\sum_{i=1}^n \mu(I_i)< \epsilon.
\end{align*}$$
Since $f$ is continuous and bounded on the closed domain $[a, b]$, then $f$ is uniformly continuous on $[a, b]$. That means for all $\epsilon'>0$, where $\epsilon'>2(b-a)\epsilon$, there exists some $\delta>0$ such that $|f(x)-f(y)|<\epsilon'$ for all $x, y$ that satisfies $|x-y|<\delta$.
We partition the interval $[a, b]$ into segments of length $\frac{n-a}{N}$ such that $\delta>\frac{b-a}{N}$ (ie. $N>\frac{b-a}{N}$).
Let $x_i=a+\frac{b-a}{N}$, and $I_j=x_i\times 2\epsilon'$. Being uniformly continuous, the graph is contained in $I_j$, and the volume of $I_j$ is $\frac{b-a}{N}2\epsilon'$ meaning $\mu(I_j)=2\frac{b-a}{N}\epsilon'$. Then $\sum_{j=1}^n\mu(I_j)=2(b-a)\epsilon'<\epsilon$. Hence by the condition for content zero, $G$ has content zero.
Related
Reminds me of fractuals and dimensions.


Contents
function demoRevolutionSurface
%DEMOREVOLUTIONSURFACE One-line description here, please. % output = demoRevolutionSurface(input) % % Example % demoRevolutionSurface % % See also % % ------ % Author: David Legland % e-mail<EMAIL_ADDRESS>% Created: 2007-04-20 % Copyright 2007 INRA - BIA PV Nantes - MIAJ Jouy-en-Josas.
Draw a torus with vertical axis as revolution axis
circle = circleToPolygon([10 0 3], 50);
[x, y, t] = revolutionSurface(circle, linspace(0, 4*pi/3, 50));
figure;
surf(x, y, t);
axis equal;
Draw a torus with horizontal axis as revolution axis
circle = circleToPolygon([0 10 3], 50);
[x, y, t] = revolutionSurface(circle, [0 0 1 0], linspace(0, pi/3, 50));
figure;
surf(x, y, t);
axis equal;
drawnow;
end
Inner function to avoid call to geom2d module
function varargout = circleToPolygon(circle, varargin) %CIRCLETOPOLYGON Convert a circle into a series of points % % P = circleToPolygon(CIRC, N); % Converts the circle CIRC into an array of (N+1)-by-2 of double, % containing x and y positions of vertices. % CIRC is given as [x0 y0 r], where x0 and y0 are coordinate of center, % and r is the radius. % The resulting polygon is closed (first and last vertices are the same). % % P = circleToPolygon(CIRCLE); % uses a default value of N=64 vertices. % % Example % circle = circleToPolygon([10 0 5], 16); % figure; % drawPolygon(circle); % % See also: % circles2d, polygons2d, circleArcToPolyline, ellipseToPolygon % % % --------- % author : David Legland % created the 06/04/2005. % Copyright 2010 INRA - Cepia Software Platform. % % HISTORY % 2007-04-20 return a closed polygon with N+1 vertices, use default N=64 % 2011-12-09 rename to 'circleToPolygon' % determines number of points N = 64; if ~isempty(varargin) N = varargin{1}; end % create circle t = linspace(0, 2*pi, N+1)'; x = circle(1) + circle(3) * cos(t); y = circle(2) + circle(3) * sin(t); if nargout == 1 varargout{1} = [x y]; elseif nargout == 2 varargout{1} = x; varargout{2} = y; end end


- David LeeThese sound exactly like computers did in 1950's SF movies !!! Finally the future is here !
Short Version
The implementation was first written as a command-line application in Java. The Web service is the same application with a simple embedded (Jetty) HTTP server.
Encode
- text input is converted into ASCII (Punycode with a checksum)
- each character is mapped to a high note, low note and duration (notes taken from a pentatonic scale)
- pure tones are generated and shaped according to the mapping
four pairs of tone bursts : checksum and the three characters
Decode
- incoming tone is trimmed
- tone is split into chunks, each corresponding to one character
- each chunk is analysed to find tones present and durations
- tones mapped to ASCII characters
- text reassembled and converted back
Long Version : Introduction
This write-up is rough & ready, mostly I wanted notes for myself, offboard memory. But however incoherent, hopefully it contains enough information to explain how this thing works.
There is some digital signal processing jargon, but DSP can just be treated as black-box stuff, there's really no need to go into the detailed maths to build things. (It's been many years since I last worked on stuff like this and my maths is totally rusted, the revision I did for this was mostly skimming Wikipedia - see Reference links).
Facets of Beep
It took me about a month part-time to put the service together, but making another implementation would take much less time. My early attempts used a fairly suboptimal system configuration and most of the time was spent trying to optimize it. Now a usable configuration has been arrived at, the optimization overhead is no longer necessary. But space exploration can be fun...
- Beeps and the Web : see ChirpChirp [and PS below]
- DSP Optimization using a Genetic Algorithm : the first version of the system was sensitive to noise and distortion so I added some filters etc. for pre- and post-processing. There were a lot of parameters to tweak so I hooked up a genetic algorithm to optimize the thing automatically.
- DSP Representation on the Web of Data : to be able to change configuration and parameters easily, a systematic representation was needed. I hacked up the current internal representation as I went along, but it's crying out to be done in a standard fashion. RDF is the obvious choice, so I've started a DSP vocabulary/ontology. I know exactly how to proceed with this, see Future Directions.
Design Considerations
The original idea was to encode HTTP URLs as little tunes, with the characters in URLs somehow mapping to musical notes. It's essentially the same idea as used in touch-tone phone signals, but tweaked to make the beeps easier on the ear.
Text Processing
International coverage (i.e. IRIs) is fairly essential, which basically means supporting Unicode. Because of this it was decided not to include any optimisations or other special features for URLs, and simply support arbitrary text. Unicode contains a lot of characters, and mapping from these to musical pitches directly is almost certainly unfeasible. However Punycode is an encoding syntax by which a string of Unicode characters can be transformed uniquely and reversibly into a much smaller character set. Basically it's a mapping to ASCII. Roughly speaking, characters that can be expressed in ASCII are expressed that way, but multiple ASCII characters are used to represent other individual Unicode characters. In general, the cost of reducing the size of the character set is an increase in the resulting string length.
Phonic Constraints
To achieve a reasonable compromise between length of (beep) messages, musicality and implementation complexity, it was decided that a maximum of two concurrent tones (one bass, one treble) was probably most suitable. By restricting these to a pentatonic scale, arbitrary combinations of notes could be used without major dissonance. Another consideration was that generic hardware should be able to transmit and receive the beeps, so a range of pitches was chosen that should be suitable for common electroacoustic transducers, i.e. nothing too low or too high (approx. 250-1500Hz, which is around female speech range and a bit higher, or an extended soprano singer). The restriction to the notes of the pentatonic scale (i.e. 5 per octave) meant a little more space for data was desirable, so the treble notes may appear as half- or whole-length.
This may give the impression a lot of planning went into this aspect : not really, most decisions were made during experimentation, based on what sounded about right.
Encoding
Generating Web Beeps is relatively straightforward, details of the requirements can be found in the specification, there's an example of how this may be implemented in Java in the source code.
This is how it's done:
DSP Blocks
To play around with processing the audio signals both as individual character chunks and the combined signal, the following pipeline was set up:
sorry messy diagram - will tidy later
This is the generalized form of the setup - in practice not really needed, experimentation showed that virtually no pre- or post-processing was required.
The Chars to Chunks block is the most interesting bit. Each chunk, corresponding to a single ASCII character, has a high frequency and a low frequency component (high and low bits of the char) of duration 1 or 2 units.
Lookup tables are used to determine appropriate frequencies for the low tone and high tone, then the corresponding sine waves are generated and summed.
Decoding
Getting the text back out from the beeps is rather more convoluted (hee hee), though is based on standard digital signal processing (DSP) techniques.
Again, a generalized pipeline was set up. Here pre- and post-processing was more of a consideration.
Split is done simplify by locating the start of the tone and then chopping it into equal-sized lengths correcponding to the duration of individual tones.
Chunks to Chars is the interesting bit here. The decoder has to find what tone (if any) occurs in each half of the chunk. Some kind of a pitch detector is needed.
Implementation
The reference implementation has been written in Java.
Generating signals is pretty easy, but manipulating them can get hard. So I assumed I'd need to use a piece of kit like Octave (very like Matlab, only open source). But as I got into it, it turned out to be straightforward, given the material available on the Web.
For an internal representation of signals I started with a
List<Double>
, expecting to have to turn everything over to
arrays at some point for performance (arrays give me a headache, always
out-by-one). That point didn't arrive, although I've had to translate to/from
arrays here and there for interfacing with libs. Long live
Collections!
Visualization
I started by playing with generated tones in Audacity, passing them to and from code as .wav files. Very clunky - it quickly became obvious there was something I needed above anything else: a simple way of visualizing signals.
Scales and offsets are always a pain, but it didn't take that long to get something together that was usable:
Plotter.java - a little bit of Swing which takes a List<Double> and jams it in the window.
FFT Detection
The decoder was first developed using FFT for pitch detection - a suitable FFT class was easily found online (a direct port of a C version). A peak detector (to identify the peaks in the power spectrum delivered by the FFT) was also needed. After several unsatisfying experiments I found a public domain peak detector. The FFT+peak detector setup basically worked but proved very sensitive to noise and distortion. The code for this is still in the repository, it's handy having FFT around for spectrum analysis.
Pre- and Post-Processing
Because of the robustness problem, I hooked up a series of processing blocks at various points in the pipeline to help reduce harmonic distorion artifacts and out-of-band noise. Basically filters plus simple envelope shapers and normalizers.
Cross-correlation Blind Alley
So I tried a second approach, experimenting with cross-correlation. My intuition said there must be some way of correlating individual reference tone sine waves with the Beep signals. Rather than run through every frequency, as in FFT, just pick the ones of interest, the ones known to have been used in generating the signal.
 This was a problem, especially since at least some of them would interact with each other.
So I got coding again :
- refactored the code to allow a systematic way of passing parameters to each of the modules
- set up a simulation of a noisy line between encoder and decoder
- decided on a fitness measure based on accuracy, performance etc.
- set up a genetic algorithm to make lots of little simulations and breed them
Some Parameters
This is the current setup. Note that many of the parameters have the value .on = false. It turned out that given a better pitch detection algorithm, most of these were redundant.
Codec : class org.hyperdata.beeps.DefaultEncoder
Empty Pipeline
Encoder.pre
Component : Encoder.pre.chunkEnv = class org.hyperdata.beeps.processors.EnvelopeShaper
Encoder.pre.chunkEnv.on = false
Encoder.pre.chunkEnv.attackProportion = 0.06
Encoder.pre.chunkEnv.decayProportion = 0.13
Component : Encoder.pre.chunkNorm = class org.hyperdata.beeps.processors.Normalise
Encoder.pre.chunkNorm.on = false
Empty Pipeline
class org.hyperdata.beeps.DefaultDecoderCodec : class org.hyperdata.beeps.DefaultDecoderDecoder.core
Component : Decoder.core.cropper = class org.hyperdata.beeps.processors.Cropper
Decoder.core.cropper.silenceThreshold = 0.39328963702325803
Decoder.core.cropper.on = true
Component : Decoder.core.normalise = class org.hyperdata.beeps.processors.Normalise
Decoder.core.normalise.on = false
Component : Decoder.core.chunker = class org.hyperdata.beeps.processors.Chunker
Decoder.core.chunker.cropProportion = 0.6337724901356069
Decoder.core.chunker.on = true
Component : Decoder.core.pitchFinder = class org.hyperdata.beeps.pitchfinders.GoertzelPitchFinder
Decoder.core.pitchFinder.gThreshold = 4194.939772585178
Decoder.pre
Component : Decoder.pre.compressor = class org.hyperdata.beeps.processors.Compressor
Decoder.pre.compressor.on = false
Decoder.pre.compressor.windowLength = 464
Decoder.pre.compressor.lowThreshold = 0.6343658962166174
Decoder.pre.compressor.highThreshold = 0.760342929650305
Component : Decoder.pre.HP_FIR = class org.hyperdata.beeps.processors.FIRProcessor
Decoder.pre.HP_FIR.shape = HP
Decoder.pre.HP_FIR.on = false
Decoder.pre.HP_FIR.window = Hamming
Decoder.pre.HP_FIR.cutoff =<PHONE_NUMBER>7466493
Decoder.pre.HP_FIR.npoints = 1228
Component : Decoder.core.normalise = class org.hyperdata.beeps.processors.Normalise
Decoder.core.normalise.on = false
Component : Decoder.pre.LP_FIR1 = class org.hyperdata.beeps.processors.FIRProcessor
Decoder.pre.LP_FIR1.shape = LP
Decoder.pre.LP_FIR1.on = false
Decoder.pre.LP_FIR1.window = Hanning
Decoder.pre.LP_FIR1.cutoff = 10285.082818002804
Decoder.pre.LP_FIR1.npoints = 3795
Component : Decoder.core.normalise = class org.hyperdata.beeps.processors.Normalise
Decoder.core.normalise.on = false
Component : Decoder.pre.LP_FIR2 = class org.hyperdata.beeps.processors.FIRProcessor
Decoder.pre.LP_FIR2.shape = LP
Decoder.pre.LP_FIR2.on = false
Decoder.pre.LP_FIR2.window = Hanning
Decoder.pre.LP_FIR2.cutoff = 1125.173350363232
Decoder.pre.LP_FIR2.npoints = 89
Component : Decoder.core.normalise = class org.hyperdata.beeps.processors.Normalise
Decoder.core.normalise.on = false
Decoder.post
Component : Decoder.post.chunkNorm = class org.hyperdata.beeps.processors.Normalise
Decoder.post.chunkNorm.on = false
Component : Decoder.post.chunkEnv = class org.hyperdata.beeps.processors.EnvelopeShaper
Decoder.post.chunkEnv.on = true
Decoder.post.chunkEnv.attackProportion = 0.14
Decoder.post.chunkEnv.decayProportion = 0.091
Goertzel to the Rescue
After spending loads of time on the FFT then Danny's Own Correlation versions, I remembered the close similarity between the encoding here and the encoding of DTFM signals. How's that usually done? Turns out that there's an algorithm that in effect does the kind of known-freq correlation thing I was looking for. It goes by the name of Goertzel. What's more it's remarkable simple - from Wikipedia:
s_prev = 0
s_prev2 = 0
normalized_frequency = target_frequency / sample_rate;
coeff = 2*cos(2*PI*normalized_frequency);
for each sample, x[n],
s = x[n] + coeff*s_prev - s_prev2;
s_prev2 = s_prev;
s_prev = s;
end
power = s_prev2*s_prev2 + s_prev*s_prev - coeff*s_prev*s_prev2;
In Web Beep, the frequencies known to have been used in the creation of Beeps (in the Character Mapping tables) are passed to the Goertzel algorithm in turn, to test each chunk in turn.
The values obtained are used for a reverse-lookup of the characters from the frequency/duration combinations.
Notes/ Future Directions
An in-browser Javascript implementation seems like a good step.
Applications for Web Beep
Dunno really, but see ChirpChirp [and PS below]
As a kind of QR Code for the ear, Web Beeps should be useful in a lot of places. Suggestions please...
Ring Tone - use your email, Home Page or personal URI.
Application for the DSP bits
Take it from me, pluggable, modular, parameterized,
declaratively-configurable DSP systems should have plenty of applications.
Making such things Web-friendly would almost certainly be very advantageous. So
I've started a DSP Processing
vocab/ontology.
The Java representation contains the important structural information needed
for described DSP systems, so my plan is to add a variant on the
toString()
method in the classes of the code
(describe()
) to deliver RDF/Turtle and use a bit of reflection to
get the code to describe itself.
To enhance this a bit there's David Huyn's RDFizer Javadoc thingy, which I plan to tweak to use HTTP URLs (rather than its current URNs).
Now if someone will please suggest applications for Web-friendly pluggable, modular, parameterized, declaratively-configurable DSP systems...
Patent Pending?
No. Software patents hurt innovation.
In any case, I'm sure many variations of this have been done before, though it's hard to find out what there is. Searches I've tried mostly led to something related to mobile/cellphone ringtones (I wanted the name "Web Tone" but it's already taken).
The basic idea is fairly trivial: what if you had something like DTFM tones only not so hard on the ear? Given that, implementations are pretty obvious given minimal background knowledge of character encoding, music and DSP. Simple matter of guesswork, experimentation & banging out the code. The implementation described here is pretty arbitrary, mostly based on what seemed like a good idea at the time.
PS. I already held Danbri responsible for the idea as he'd been talking about something vaguely similar recently. What I'd completely forgotten was that he'd suggested exactly this idea - and even done a write-up: ChirpChirp.
OMG, I have become Danbri's personal LazyWeb...


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Rewards
What would be the point of killing monsters, if you did not get any reward
for it. RER adds new trophies with the sole purpose of being sold so that
you can earn a few crowns from the monsters you killed. Because trophies
may not sell much with the economy altering mods you use, it also includes
an option for direct crown drops from creatures if you need it.
Crowns
In the crown settings, you control the base values for each encounter type.
And then for each creature you control how much of the base value they drop in %.
This value has some randomness you cannot edit, it varies by between 80% and 120%
of what you set. So if you set 50% to the Sirens it will in reality vary
between 40% and 60%
This allows you to tell RER that Sirens should drop few crowns and also control
the amount of crowns you get based on the encounter type. Some people do not
want crowns outside of contracts so they set the From ___ sliders to 0
except for the From contracts slider.
Then you can tweak the amount dropped on a per creature basis.
Trophies
You can control from which encounters you get RER trophies with the first toggles.
Creatures from an encounter with the trophies disabled won't drop trophies.
You can also change the price of the trophies you get, but note it is not
retroactive and won't change the price of the trophies you already picked.
And finally, you can edit the chance for the creature to drop trophies.
100 means it has 100% chance to drop a trophy, 50
means 50% and 0 means no trophies drop this creature.
Containers refill
RER has always been about revitalizing old content, or places. This feature
will refill the containers of regions where you kill monsters.
Containers are: boxes, bags, chests, barrels, ...
Obvsiously we all have a different view of balance, this is why this feature
comes with a few (a lot of) sliders.
But first, let's talk about the presets: the defaults preset
is applied automatically the first time you play with the mod; and second:
the OFF preset to disable the feature.
Now the main sliders:
- chance to trigger allows you to define the chance for
it to happen every time you kill an encounter from RER. Note that it only works
for RER encounters.
- Only empty containers by default the feature only works
on containers that are empty to prevent filling the containers with tons
of stuff.
-
Radius, it controls how far around the player RER will
look for containers.
-
Number of containers tells the maximum number of containers that will
be refilled when this system triggers. It is not guaranteed to
reach this number if there are not enough containers around you.
Each slider that starts with the little yellow dash is a loot table. Every
time this system triggers it will randomly pick one loot table for each
container (if you allowed more than one container per trigger) and then
fill the container with the objects from the loot table.
The values you set to these sliders control the chance for the loot table
to be picked. The value is like a ratio/coefficient, 15 doesn't
mean this loot table will have 15% chance to be picked. Instead it means
that it has half the chance to be picked compared to a 30.


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CommentCommentaryController
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CreateShortController
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LoginController
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ManagerStartController
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ManagerUserController
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RegisterPropertyController
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WindowApp
This constructor builds up the JFram next to all the JPanels and Controllers
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Ping-Pong
Let's Play
Rules:
Ping Pong will count up to your number from 1, with the following exceptions:
Numbers divisible by 3 become: "ping"
Numbers divisible by 5 become: "pong"
Numbers divisible by 3
and
5 become: "ping-pong"
Submit
About the Ping-Pong App
Created by:
Andrew Niekamp
GitHub Username/URL:
aniekamp


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Egypt says it's unearthed large animal mummy, likely a lion
by EndPlayCAIRO (AP) - Egypt's Ministry of Antiquities says local archeologists have unearthed the mummy of an unusually large animal, most likely a lion or lioness.
The ministry said on Monday the mummy was excavated in Saqqara, a town south of Cairo that was a vast necropolis in antiquity and is home to the famed Step Pyramid.
Archeologists frequently find mummified cats but the recovery of a lion is rare. In 2004, the first lion skeleton was found, revealing the sacred status of the animal during ancient times.
The ministry says it will expand on the discovery at a press conference after running radar scans.
Egypt has stepped up promotion of its archeological treasures in hopes of reviving a tourism sector slow to recover from the 2011 uprising that toppled longtime autocrat Hosni Mubarak.


| filehandle/stream = a valid variable set with the OpenFile, ReadFile command, or OpenTCPStream (v1.52+) The value returned is a floating point number. |
|
Once you've opened a disk file (or stream) for reading, use this command to read a single floating point number from the file. Note, each value written uses 4 bytes of space. Reading beyond the end of file does not result in an error, but each value read will be zero.
Streams can only be used in Blitz Basic v1.52 or greater. |
|
; Reading and writing a file using ReadFloat and WriteFloat functions
; Initialise some variables for the example Num1# = 10.5 ; store 10.5 Num2# = 365.25 ; store 365.25 Num3# = 32767.123 ; 32767.123 is the largest positive Short Integer Value in BlitzBasic ) Num4# = -32768.123 ; -32768.123 the largest negative Short Integer Value in BlitzBasic ) ; Open a file to write to fileout = WriteFile("mydata.dat") ; Write the information to the file WriteFloat( fileout, Num1 ) WriteFloat( fileout, Num2 ) WriteFloat( fileout, Num3 ) WriteFloat( fileout, Num4 ) ; Close the file CloseFile( fileout ) ; Open the file to Read filein = ReadFile("mydata.dat") Read1# = ReadFloat( filein ) Read2# = ReadFloat( filein ) Read3# = ReadFloat( filein ) Read4# = ReadFloat( filein ) ; Close the file once reading is finished CloseFile( filein ) Print "Floating Point Data Read From File - mydata.dat " Print Read1 Print Read2 Print Read3 Print Read4 WaitKey() |


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Abstract
In this paper, I looked into the distributions of news consumption and election interest in the 2015 Canadian Election Study. Using R, I was able to graph these results and examine their distributive properties. I hypothesized that the distributions of news consumption and interest in the federal election will both be right skewed. As a result of examining the graphs, I concluded that the distribution of federal election interest was right skewed, whereas, the distribution of news consumption followed a normal distribution.
Introduction
The 2015 Canadian federal election was held October 19, 2015. Liberal party leader, Justin Trudeau, ended the nine-year reign of Stephen Harper to become the 23rd Canadian Prime Minister (Zurcher, 2015). The Canadian Election Study (CES) is a survey conducted to examine Canadians’ opinions regarding political parties, election campaigns and party leaders (Northrup, 2016). This study has been an integral part of Canadian democracy research since 1965 (Fournier, 2015).
As per reports, voter turnout increased 7.2 percent from 2011 to 2015, marking the largest rise in turnout since 1900 (Voter Turnout). I am interested in exploring whether surveys of Canadians’ political interest correlate with this increase in voter turnout in the 2015 election. Additionally, I would like to follow up by exploring whether news consumption could be a factor for the distribution of interest in the 2015 Canadian federal election.
In order to investigate these properties, I will graph two survey results from the 2015 CES. The first is the distribution of interest in the 2015 federal election. The second graphed result is the distribution of news consumption in the 2015 CES. I will be comparing their distributive properties to see if there is a correlation between news consumption and interest in the federal election.
I hypothesize that:
- The distribution of interest in the 2015 federal election will represent a right-skewed distribution. The null hypothesis is that the distribution of interest in the 2015 federal election will not represent a right-skewed distribution.
- The distribution of news consumption in the 2015 CES will represent a right-skewed distribution. The null hypothesis is that the distribution of news consumption in the 2015 CES will not represent a right-skewed distribution.
Graphs
install.packages("tidyverse")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
install.packages("devtools")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
devtools::install_github("hodgettsp/cesR")
## Skipping install of 'cesR' from a github remote, the SHA1 (7c780beb) has not changed since last install.
## Use `force = TRUE` to force installation
library(tidyverse)
## ── Attaching packages ───────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ── Conflicts ──────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(cesR)
library(labelled)
get_ces("ces2015_web")
## TO CITE THIS SURVEY FILE: Fournier, Patrick, Fred Cutler, Stuart Soroka and Dietlind Stolle. 2015. The 2015 Canadian Election Study. [dataset]
##
## LINK: https://ces-eec.arts.ubc.ca/english-section/surveys/
ces2015_web <- to_factor(ces2015_web)
p <- ces2015_web %>%
ggplot(aes(x = p_mediatim)) +
geom_bar()
d <- p + ggtitle("Distribution of News Consumption in 2015 CES") + xlab("Dialy Time Spent") + ylab("Individuals")
d <- d + theme_grey(base_size = 8)
plot(d)
In this graph, the distribution of news consumption in the 2015 CES follows a normal distribution. The largest portion of individuals spend 30-60 minutes watching, reading and listening to the news. In addition, over 3000 individuals chose not to answer this survey question.
a <- ces2015_web %>%
ggplot(aes(x = interest)) +
geom_bar()
b <- a + ggtitle("Distribution of Federal Election Interest in 2015 CES") + xlab("Level of Interest") + ylab("Individuals")
b <- b + theme_grey(base_size = 8)
plot(b)
In this graph, we find that the distribution of federal election interest in the 2015 CES follows a right-skewed distribution. Approximately 3500 individuals reported interest levels between 7-10 out of 10. In addition, 500 individuals chose not to answer this survey question.
Discussion
Through the graphical evidence, we reject the null hypothesis that the distribution of interest in the 2015 federal election will not represent a right-skewed distribution. As Elections Canada has found, voting in the election and political interest are, in fact, related (Explaining the Turnout). This is further exemplified in the graph of the distribution of federal election interest. The right-skewed nature of this graph shows that the majority of individuals reported between 7-10 level of interest in the upcoming election. This high level of interest could be a reason as to why there was a reported 7.4 percent increase in voter turnout from 2011 to 2015. According to Elections Canada, 16.1 percent of individuals who did not vote in the election cited a lack of interest as the catalyst for their decision (Explaining the Turnout). Therefore, interest is a large contributing factor to voter turnout.
We fail to reject the null hypothesis that the distribution of news consumption in the 2015 CES will not represent a right-skewed distribution. I had initially believed that the high levels of interest surrounding this 2015 election could be contributed to a high volume of daily news consumption. However, as seen in the two graphs above, the distributive properties of these two data sets do not line up. While news consumption follows a normal distribution, the interest levels in the election follow a right-skewed distribution. This may suggest that higher number of hours spent watching, reading or listening to the news is not correlated with higher levels of interest in the election. Curtis et al. had previously concluded that there is a positive correlation between age and political interest for Canadians (Curtis, 1976). However, thus far, there is not much research in the subject of election interest.
Weaknesses
The main limitations of this work include the following:
- Considerable amount of “Not Answered” and “Refuse to Answer” results in both the Interest and News Consumption charts. Individuals seem unwilling to answer certain questions.
- There could be confounding factors that contribute noise to the data in the CES. For instance, the CES study included attention-based questions that told the subjects what answer to respond with. However, these questions were not answered with 100 percent coherence. Thus, it can be extrapolated that not all subjects were answering with complete attention and consistency to the study.
- There could be human error in writing the code in R to create the graphs used in this report.
Next Steps
The future steps would be the following:
- Reducing the number of potential response answers (Reference as to why this is beneficial). Some individuals might lose their patience reading through a thorough survey, such as the CES, and thus resort to simple “NA” answers. To mitigate for this, researchers can limit their potential answers to individual questions to under five possible answers. In addition, reducing the overall question count may increase the response rate to certain questions.
- It may be insightful to perform a follow-up multi-variable study looking into the various factors that led to the increase in voter turnout from 2011 to 2015. As a result, it will be possible to see how much federal election interest has affected this increase in voters during this period.
- Researchers should examine what factors persuade an individual to be more interested in a federal election. Unlike I initially hypothesize, news consumption does not seem to be an indicator of interest levels for Canadians.
- It would be particularly interesting to compare the distributions of interest levels in the 2015 Canadian federal election to previous years to see what sort of trend has been occurring over the years. Likewise, it would be interesting to break down the different forms of news consumption (ie. paper, electronic, etc.) and see how their consumption has been trending over the past couple decades as technology has advanced.
References
- Zurcher, Anthony. “Trudeau Brings Liberals Back on Top.” BBC News, BBC, 20 Oct. 2015, www.bbc.com/news/world-us-canada-34566369.
- Northrup, David. “The 2015 Canadian Election Study.” Toronto, Ontario, Canada: The Institute for Social Research, York University (2016).
- Fournier, Patrick, Fred Cutler, Stuart Soroka and Dietlind Stolle. 2015. The 2015 Canadian Election Study. [dataset]
- Explaining the Turnout Decline in Canadian Federal Elections: A New Survey of Non-voters. Elections Canada. Accessed September 26, 2020. https://www.elections.ca/content.aspx?section=res&dir=rec/part/tud&document=interest&lang=e
- Voter Turnout at Federal Elections and Referendums. Elections Canada. Accessed September 26, 2020. https://www.elections.ca/content.aspx?section=ele&dir=turn&document=index&lang=e
- Curtis, James E., and Ronald D. Lambert. “Voting, Election Interest, and Age: National Findings for English and French Canadians.” Canadian Journal of Political Science, vol. 9, no. 2, 1976, pp. 293–307., doi:10.1017/s0008423900043730.
- Paul A. Hodgetts and Rohan Alexander (2020). cesR: Access the CES Datasets a Little Easier.. R package version 0.1.0.
- Hadley Wickham, Jim Hester and Winston Chang (2020). devtools: Tools to Make Developing R Packages Easier. https://devtools.r-lib.org/,https://github.com/r-lib/devtools.
- R Core Team (2020). R: A language and environment for statistical computing. R, Foundation for Statistical Computing, Vienna, Austria. URL, https://www.R-project.org/.
- JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone (2020). rmarkdown: Dynamic Documents for R. R package version 2.3. URL https://rmarkdown.rstudio.com.


Smokepurpp & Lil Skies Link Up For The "Dirty Dirty"
An ode to life's small pleasures.
by Mitch FindlayDirty Dirty
Smokepurpp Feat. Lil Skies
Editor Rating: HOTTTTT
User Rating: n/a - 0% (0)
There's a conundrum present on Smokepurpp and Lil Skies' new duet "Dirty Dirty." On one hand, the artwork's visual aesthetic is cool, the Apex Martin & Diego Ave-produced beat enjoyable, and both rappers turn in solid performances. Yet between Smokepurpp and Lil Skies, the ability to string together a cohesive segue is nowhere to be seen. Prone to non-sequitur, both parties skate from topic to topic with seemingly little rhyme or reason; they're liable to switch from diamond check listing to sexcapade chronicling to gun talking within a four-bar frame.
And yet they're clearly having a grand old time, and we can hardly fault them for their formula. Not when it's this effective. For Purpp, the vibe and aesthetic have always been positioned at the forefront of his artistry, and "Dirty Dirty" succeeds in that regard. It can be thrown on and enjoyed in a mindless state and sometimes that's all we need. Would you like to see more innovation from the young Purpple One moving forward?


Siege on Ba Sing Se Comes to an End!
Earth KingdomAfter 600 long days of siege the Fire Nation has begun withdrawing troops. Life has been difficult for citizens of Ba Sing Se while cut off from trade as many goods have been difficult to acquire. Repairs of the outer wall have already begun and are expected to be finished early next week. Read More
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Nominations open for Plymouth Armed Forces Awards
There are nine categories in the inaugural Plymouth Armed Forces Awards
by Jak BallPlymouth has a rich heritage when it comes to the military, which is clear to see all over the city.
To help recognise that history and what people or businesses are doing to help those in and around the Armed Forces today, an awards night is to take place.
Nominations for the Plymouth Armed Forces Awards 2020 have opened and will provide a much needed platform for showcasing the achievements of the local Armed Forces community, veterans, reservists and cadets.
The inaugural event will be hosted at the Plymouth Guildhall on June 24, 2020 and will see nine categories open to nominations that celebrate what Plymouth and the Armed Forces community have achieved.
Those categories are:
-Forces Initiative Of The Year
-Role Model Of the Year
-Partner Of The Year
-Inspiration Of The Year
-Advocate Of The Year
-Veteran Business Of The Year
-Employer Of The Year
-Reservist Of The Year
-Young Person Of The Year
To be considered for an award, nominees must have a direct link to Plymouth or have contributed to the Armed Forces community or families based in the city.
A team of judges will then decide the shortlists and winners of each category based on the information that has been provided in the nomination form.
To nominate someone for an award, visit www.plymoutharmedforcesawards.co.uk