CX4240: Introduction to Computational Data Analysis
Table of Contents
Logistics
- Lecture time: Mons and Weds, 4:30pm-5:45pm
- Location: Klaus 1447
- Instructors: M. Mahdi Roozbahani and Chao Zhang
- Teaching Assistant: Hanjun Dai<EMAIL_ADDRESS>and Wendi Ren<EMAIL_ADDRESS>- Office Hours:
- Instructor: Weds 3:30-4:20pm
- TA Office Hour I: Mons 3:30-4:20pm
- TA Office Hour II: Fris 10:30-11:20am
- Piazza: https://piazza.com/class/jqeo3f7s5vc426
- Piazza will be the main place for course discussions and announcements. If you have questions, please ask it on Piazza first because 1) other students may have the same question; 2) you will get help faster compared to sending emails.
- If it's something you do not like to discuss publicly on Piazza, send an email with CX4240 in the subject.
Course Content
This course introduces techniques for computational data analysis, with an emphasis on machine learning algorithms and their applications to real-world data. We will investigate the following question: how to extract useful knowledge from data computationally for decision making and task support? We will focus on on machine learning methods for computational data analysis, which are organized into three parts:
- Basic math for data science and machine learning
- Linear algebra
- Probability and statistics
- Information theory
- Unsupervised machine learning for data exploration
- Clustering analysis
- Dimension reduction
- Kernel density estimation
- Supervised learning for predictive data analysis
- Tree-based models
- Linear classification and regression
- Neural networks
Prerequisites for this course include 1) basic knowledge of probability, statistics, and linear algebra; 2) basic programming experience, preferably in Python.
Schedule
| Date | Topic | Assignment | Due | Readings |
|---|---|---|---|---|
| 1/7/19 | Course Overview | Piazza Signup | GT Honor Code | |
| 1/9/19 | Math Basics: Linear Algebra | Linear Algebra Review by Zico Kolter | ||
| 1/14/19 | Math Basics: Probability and Statistics | Probability Theory Review by Andrew Moore | ||
| 1/16/19 | Math Basics: Information Theory | AS1 Out | Visual Information Theory by Chris Olah | |
| 1/21/19 | No Class (Martin Luther King Day) | |||
| 1/23/19 | Data Analysis Toolbox | NumPy Tutorial; Matplotlib Tutorial | ||
| 1/28/19 | Clustering Analysis and K-Means | AS1 Due | ||
| 1/30/19 | Hierarchical Clustering | |||
| 2/4/19 | Density-Based Clustering | |||
| 2/6/19 | Gaussian Mixture Model | AS2 Out | ||
| 2/11/19 | Evaluation of Clustering Algorithms | |||
| 2/13/19 | Density Estimation | |||
| 2/18/19 | Dimension Reduction | |||
| 2/20/19 | Midterm Review | AS2 Due | ||
| 2/25/19 | Midterm Exam | |||
| 2/27/19 | Linear Regression | |||
| 3/4/19 | Linear Regression | |||
| 3/6/19 | Naïve Bayes and Logistic Regression | |||
| 3/11/19 | Support Vector Machine | AS3 Out | ||
| 3/13/19 | Support Vector Machine | |||
| 3/18/19 | No Class (Spring Break) | |||
| 3/20/19 | No Class (Spring Break) | |||
| 3/25/19 | Neural Networks | |||
| 3/27/19 | Neural Networks | AS3 Due | ||
| 4/1/19 | Decision Tree and Random Forest | |||
| 4/3/19 | Decision Tree and Random Forest | AS4 Out | ||
| 4/8/19 | Model Selection | |||
| 4/10/19 | No Class (Project Preparation) | |||
| 4/15/19 | Project Presentation | |||
| 4/17/19 | Project Presentation | AS4 Due | ||
| 4/22/19 | Course Review | |||
| 4/24/19 | Reading Day | Report Due |
Grading
- Assignments (50%)
- There will be four assignments. Each one is designed for testing your understanding of the taught algorithms. It could be either programming or written analysis.
- You will need to hand in the assignments at the beginning of the class on the due date.
- All assignments follow the "no-late" policy. Assignments received after the due time will receive zero credit.
- All students are expected to follow the Georgia Tech Academic Honor Code.
- Project (20%)
- You are expected to complete a project on computational data analysis with real-life data. Your project needs to be clear about 1) the data you are using; 2) the problem you are attempting to solve; 3) the method you are using; 4) the results and conclusion you attain.
- You will need to turn in a project report and also give an in-class presentation for your project. The project report and the presentation will each count for 10% of your final grade.
- Each project needs to be completed in a team of 2-4 people. Team members need to clearly claim their contributions in the project report.
- Class participation (5%)
- Your class participation score will be graded based on attendance and in-class quizzes.
- Participation in class discussions (including asking relevant questions in class, volunteering to answer questions on Piazza) will be considered when determining your final grade. It will be especially useful when you are right on the edge of two letter grades.
- Midterm Exam (10%)
- The midterm exam will take place on Feb 25th in lieu of the regular class.
- The midterm exam will be a written and open-book exam, but Internet usage will not be allowed.
- There will be no make-up exams. You will get zero credit for your missed midterm exam.
- Final Exam (15%)
- The final exam will be at whatever time is scheduled for this class.
- The final exam will be a written and open-book exam, but no Internet usage will be allowed.
- Again, there will be no make-up exams. You will get zero credit for your missed final exam.
Resources
- Machine learning, by Tom Mitchell
- Pattern recognition and machine learning, by Christopher Bishop
- Data Mining: Concepts and Techniques, by Jiawei Han, Micheline Kamber, and Jian Pei
- The Elements of Statistical Learning, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
- Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Dive into Deep Learning, by Aston Zhang, Zack C. Lipton, Mu Li, and Alex Smola
Other resources, such as machine learning toolboxes and datasets, will be provided throughout the course.


// Record the final score.
score = est.score(X_train, y_train)
logger.info("Final Score: %.6f", score)
model.scores[algo] = score
// Training Log Loss
// Record importances and coefficients, if available.
if hasattr(est, "feature_importances_"):
model.importances[algo] = est.feature_importances_
if hasattr(est, "coef_"):
model.coefs[algo] = est.coef_
// Save the estimator in the model


Enable/Disable Windows features with Deployment Image Servicing and Management (DISM):
*Note* Works well after bypassuac + getsystem (requires system privileges)
*Note2* For Dism.exe to work on x64 systems, the long commands are necessary
To list features which can be enabled/disabled:
%windir%\System32\cmd.exe /c "%SystemRoot%\system32\Dism.exe" /online /get-features
To enable a feature (TFTP client for example):
%windir%\System32\cmd.exe /c "%SystemRoot%\system32\Dism.exe" /online /enable-feature /featurename:TFTP
To disable a feature (again TFTP client):
%windir%\System32\cmd.exe /c "%SystemRoot%\system32\Dism.exe" /online /disable-feature /featurename:TFTP


Logisim 1.0 D/J-K Flip-Flop
| Bibliothek: |
Legacy |
| Eingeführt: |
2.0 Beta 12 |
| Aussehen: |
|
Verhalten
These components exist only for backwards compatibility
with Logisim 1.0X; for new circuits, the
Memory library's flip-flops are
recommended instead.
Each flip-flop stores a single bit of data, which is emitted through
the Q output on the east side. Normally, the value can be
controlled via the inputs to the west side. In particular, the value
changes when the clock input, marked by a triangle on
each flip-flop, rises from 0 to 1; on this rising edge, the value
changes according to the corresponding table below.
| D Flip-Flop |
J-K Flip-Flop |
|---|
|
|
|
Another way of describing the different behavior of the flip-flops
is in English text.
- D Flip-Flop: When the clock rises from 0 to 1, the
value remembered by the flip-flop becomes the value of the D
input (Data) at that instant.
J-K Flip-Flop: When the clock rises from 0 to 1,
the value remembered by the flip-flop
toggles if the J and K inputs are both 1
and the value remains the same if both are 0;
if they are different, then the value becomes 1 if the J
(Jump) input is 1 and 0 if the K (Kill)
input is 1.
Pins
- West edge, marked by triangle (input, bit width 1)
- Clock input: At the instant that this input value switches from 0 to
1 (the rising edge), the value will be updated according to the other
inputs on the west edge. As long as this remains 0 or 1, the other
inputs on the west edge have no effect.
- West edge, other labeled pin(s) (input(s), bit width 1)
- These inputs control how the flip-flop's value changes during the
rising edge of the clock. Their exact behavior depends on the
flip-flop; the above tables summarize their behavior.
- East edge, labeled Q, north end (output, bit width 1)
- Outputs the value currently stored by the flip-flop.
- East edge, south end (output, bit width 1)
- Outputs the complement of the value currently stored by the
flip-flop.
Attribute
Keines.
Verhalten des Schaltwerkzeugs
Keines.
Verhalten des Textwerkzeugs
Keines.
Zurück zur Bibliotheksreferenz


Moderator: Karl G.
Karl G. wrote:
Yes, Wacoo is right.
ven wrote:oh poo lmao sorry.. well he wasn't giving good proof... and he said you were programming this game since you were 6.. must have been a slip up... lol i've been trying to find secret places but havn't succeeded yet... but i havn't checked the update yet because i was out at a campus party last night lol..
Edit: We are saying we can't find the monsters that drop the good gear.
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toString
, equals
and hashCode
// Java
public class Pizza {
private String topping = "";
private int size = 1;
public Pizza(String topping, int size) {
this.setTopping(topping);
this.setSize(size);
}
}
public Boolean getTopping() {
return topping;
}
public void setTopping(String topping) {
this.topping = topping;
}
public Boolean getSize() {
return size;
}
public void setSize(int size) {
this.size = size;
}
// Scala
class Pizza(var topping: String, var size: Integer)
// Scala
class Pizza(var topping: String, private var _size: Integer) {
def size = _size // getter for size
def size_ =(newSize: Integer) { // setter for size
println(s"changing size to $newSize")
_size = newSize
}
}
// variable
var foo = "foo"
foo = "bar" //compiles
// value
val bar = "bar"
bar = "foo" // compilation error
// every value is an object
1.toString
// every operation is a method call
1 + 2 + 3 == (1).+(2).+(3)
// can omit . and ()
"abc" charAt 1 == "abc".charAt(1)
Object Math {
def square(x: Double) = x * x
}
Math.square(5.0)
// 25.0
// Lightweight anonymous functions
val addOne = (x: Int) => x + 1
// Calling the anonymous function
addOne(4) // returns 5
// plusFoo can reference any values/variables in scope
var foo = 1
val plusFoo = (x: Int) => x + foo
plusFoo(5) // returns 6
// changing foo affects the return value of plusFoo
foo = 5
plusFoo(5) // returns 10
val plusOne = (x: Int) => x + 1
val nums = List(1,2,3)
// map takes a function: Int => T
nums.map(plusOne)
// Inline anonymous
nums.map(x => x + 1)
// Short form 😎
nums.map(_ + 1)
// A few more examples for List class
val nums = List(1,2,3,4)
nums.exists(_ == 2) // true
nums.find(_ == 2) // Some(2)
nums.indexWhere
// Inline anonymous
nums.map(x => x + 1)
// Short form 😎
nums.map(_ + 1)
class Pizza(var tomato: Boolean, var cheese: Boolean)
val pizza: Pizza = new Pizza(true, false, "HUGE")
is the same as
val pizza = new Pizza(true, false, "HUGE")
is the same as
val pizza = new Pizza(
tomato = true,
cheese = false,
size = "HUGE"
)
var != val
var secondPizza = new Pizza(true, true, "NORMAL")
secondPizza = new Pizza(false, false, "HUGE") // will compile
val thirdPizza = new Pizza(true, false, "SMALL")
thirdPizza = secondPizza // won't compile
def slice(numPieces: Int): Unit = {
println(s"Pizza is in $numPieces pieces")
}
def
return
keyword is optional
object Oven {
def cook(pizza: )
}
// Java: Check if String contains uppercase character
boolean hasUpperCase = false;
for (int i = 0; i < name.length(); i++) {
if (Character.isUpperCase(name.charAt(i))) {
hasUpperCase = true;
break;
}
}
// Scala: Check if String contains uppercase character
val hasUpperCase = name.exists(_.isUpperCase)
class StringBuilder(s: String) {
def this() = this("")
}
StringBuilder("abc") // same as new StringBuilder("abc")
StringBuilder() // same as new StringBuilder()
new
can be omitted from instance creations.apply
.
package p
type Labelled[T] = (String, T)
val a: Labelled[Int] = ("count", 1)
def b = a._2
def hello(name: String) =
println(s"hello, $name")
enum Color {
case Red, Green, Blue
}
Simplest way to define new types with a finite number of values or constructors
object Planet extends Enumeration {
case class Val(m: Double, rad: Double) extends super.Val {
def surfaceGravity = Planet.G * m / (rad * rad)
}
implicit def toPlanetVal(x: Value): Val = x.asInstanceOf[Val]
val G: Double = 6.67300E-11
val MERCURY = Val(3.303e+23, 2.4397e6)
val VENUS = Val(4.869e+24, 6.0518e6)
val EARTH = Val(5.976e+24, 6.37814e6)
val MARS = Val(6.421e+23, 3.3972e6)
}
enum Planet(m: Double, rad: Double) extends java.lang.Enum {
private final val G = 6.67300E-11
def surfaceGravity = G * m / (rad * rad)
case MERCURY extends Planet(3.303e+23, 2.4397e6)
case VENUS extends Planet(4.869e+24, 6.0518e6)
case EARTH extends Planet(5.976e+24, 6.37814e6)
case MARS extends Planet(6.421e+23, 3.3972e6)
}
enum Option[+T] {
case Some(x: T)
case None
}
sealed abstract class Option[+T] {
object Option {
case class Some[+T](x: T) extends Option[T]
object Some {
def apply[T](x: T): Option[T] = Some(x)
}
val None = new Option[Nothing] { ... }
}
Compiled to hierarchies of case classes and objects.
enum Tree[T] {
case True extends Tree[Boolean]
case False extends Tree[Boolean]
case IsZero(n: Tree[Int]) extends Tree[Boolean]
case Zero extends Tree[Int]
case Succ(n: Tree[Int]) extends Tree[Int]
case If(cond: Tree[Boolean],
thenp: Tree[T],
elsep: Tree[T]) extends Tree[T]
}
case class Username(name: String)
case class Password(hash: Hash)
def help(id: Username | Password) = {
val user = id match {
case Username(name) => lookupName(name)
case Password(name) => lookupPassword(hash)
}
...
}
type Command = "Click" | "Drag" | "KeyPressed"
def handleEvent(kind: Command) = kind match {
case "Click" => mouseClick()
case "Drag" => moveTo()
case "Click" => keyPressed()
}
case class Point(x: Double, y: Double)
implicit class PointOps(p1: Point) {
def +(p2: Point): Point =
Point(p1.x + p2.x, p1.y + p2.y)
}
val p1 = Point(1.0, 2.0)
val p2 = Point(3.0, 4.0)
println(p1 + p2) // Point(3.0,4.0)
case class Point(x: Double, y: Double)
implicit class PointOps(p1: Point) {
def +(p2: Point): Point =
Point(p1.x + p2.x, p1.y + p2.y)
}
val p1 = Point(1.0, 2.0)
val p2 = Point(3.0, 4.0)
println(p1 + p2) // Point(3.0,4.0)
case class Point(x: Double, y: Double)
object PointOps {
def (p1: Point) + (p2: Point): Point =
Point(p1.x + p2.x, p1.y + p2.y)
}
import PointOps._
val p1 = Point(1.0, 2.0)
val p2 = Point(3.0, 4.0)
println(p1 + p2) // Point(3.0,4.0)
trait Ord[T] {
def (x: T) compareTo (y: T): Int
def (x: T) < (y: T) = x.compareTo(y) < 0
}
delegate IntOrd for Ord[Int] {
def (x: Int) compareTo (y: Int) =
if (x < y) -1 else if (x > y) 1 else 0
}
def maximum[T](xs: List[T]) given Ord[T] =
xs.reduce((x,y) => if (x < y) y else x)
implicit
as a modifier[T]
ers:
delegate IntOrd for Ord[Int] { ... }
maps to:
implicit object IntOrd extends Ord[Int] { ... }
[T]
ers:
delegate ListOrd[T] for Ord[List[T]] given (ord: Ord[T]) { ... }
maps to:
class ListOrd[T](implicit ord: Ord[T]) extends Ord[List[T]] {
...
}
final implicit def ListOrd[T](implicit ord: Ord[T]): ListOrd[T] =
new ListOrd[T]
trait SemiGroup[T] {
def (x: T) combine (y: T): T
}
trait Monoid[T] extends SemiGroup[T] {
def unit: T
}
delegate for Monoid[String] {
def (x: String) combine (y: String) = x concat y
def unit= ""
}
def sum[T: Monoid](xs: List[T]): T =
xs.foldLeft( the[Monoid[T]].unit )(_ combine _)
implicit def strToToken(str: String): Token =
new Keyword(str)
implicit
as a modifier will go away
implicit
conversions Conversion
class:
delegate for Conversion[String, Token] {
def apply(str: String): Token = new KeyWord(str)
}
Or using an alias delegate:
delegate for Conversion[String, Token] =
new KeyWord(_)
implicit
s are Scala's most distinguished feature.
object Logarithms {
opaque type Logarithm = Double
object Logarithm {
// These are the ways to lift to the logarithm type
def apply(d: Double): Logarithm = math.log(d)
def safe(d: Double): Option[Logarithm] =
if (d > 0.0) Some(math.log(d)) else None
}
delegate LogarithmOps {
def (x: Logarithm) toDouble: Double = math.exp(x)
def (x: Logarithm) + (y: Logarithm): Logarithm = Logarithm(math.exp(x) + math.exp(y))
def (x: Logarithm) * (y: Logarithm): Logarithm = Logarithm(x + y)
}
}
Logarithm
as a new typeDouble
but different from itLogarithm
is treated as abstract type:
val l = Logarithm(1.0)
val l2 = Logarithm(2.0)
val l3 = l * l2
val l4 = l + l2
val d: Double = l // error
val l2: Logarithm = 1.0 // error
type Elem[X] = X match {
case String => Char
case Array[t] => t
case Iterable[t] => t
}
A match type reduces to one of a number of right hand sides, depending on a scrutinee type.
enum Tuple {
case Empty
case Pair[H, T]
}
import Tuple._
type Concat[+Xs <: Tuple, +Ys <: Tuple] <: Tuple =
Xs match {
case Empty => Ys
case Pair[x, xs] => Pair[x, Concat[xs, Ys]]
}
| Package objects | 👉 | Exports + toplevel defs |
| Implicit defs, vals, objects, conversions | 👉 | Delegates |
| Implicit classes | 👉 | Extension methods |
| Current macros | 👉 | Inline + staging + match types |
| Scala 3 module |
| Scala 2 module |
| Dotty module |
| Scala 2 module |
Slides can be nested inside of each other.
Use the Space key to navigate through all slides.
Nested slides are useful for adding additional detail underneath a high level horizontal slide.
That's it, time to go back up.
Not a coder? Not a problem. There's a fully-featured visual editor for authoring these, try it out at https://slides.com.
Press ESC to enter the slide overview.
Hold down the alt key (ctrl in Linux) and click on any element to zoom towards it using zoom.js. Click again to zoom back out.
(NOTE: Use ctrl + click in Linux.)
Presentations look great on touch devices, like mobile phones and tablets. Simply swipe through your slides.
Hit the next arrow...
... to step through ...
... a fragmented slide.
There's different types of fragments, like:
grow
shrink
fade-out
fade-right, up, down, left
fade-in-then-out
fade-in-then-semi-out
Highlight red blue green
You can select from different transitions, like:
None -
Fade -
Slide -
Convex -
Concave -
Zoom
reveal.js comes with a few themes built in:
Black (default) -
White -
League -
Sky -
Beige -
Simple
Serif -
Blood -
Night -
Moon -
Solarized
Set data-background="#dddddd"
on a slide to change the background color. All CSS color formats are supported.

Different background transitions are available via the backgroundTransition option. This one's called "zoom".
Reveal.configure({ backgroundTransition: 'zoom' })
You can override background transitions per-slide.

Since reveal.js runs on the web, you can easily embed other web content. Try interacting with the page in the background.
import React, { useState } from 'react';
function Example() {
const [count, setCount] = useState(0);
return (

You clicked {count} times

<button onClick={() => setCount(count + 1)}>
Click me
</button>

);
}
Code syntax highlighting courtesy of highlight.js.
| Item | Value | Quantity |
|---|---|---|
| Apples | $1 | 7 |
| Lemonade | $2 | 18 |
| Bread | $3 | 2 |
These guys come in two forms, inline: The nice thing about standards is that there are so many to choose from
and block:
“For years there has been a theory that millions of monkeys typing at random on millions of typewriters would reproduce the entire works of Shakespeare. The Internet has proven this theory to be untrue.”
You can link between slides internally, like this.
There's a speaker view. It includes a timer, preview of the upcoming slide as well as your speaker notes.
Press the S key to try it out.
Presentations can be exported to PDF, here's an example:
Set data-state="something"
on a slide and "something"
will be added as a class to the document element when the slide is open.
Additionally custom events can be triggered on a per slide basis by binding to the data-state
name.
Reveal.addEventListener( 'customevent', function() {
console.log( '"customevent" has fired' );
} );
Press B or . on your keyboard to pause the presentation. This is helpful when you're on stage and want to take distracting slides off the screen.


By Saskia | January 16, 2019
Last updated: May 01, 2019
If you are an R user but don’t know yet what Shiny and Shiny apps are, you definitely should read up on it on https://shiny.rstudio.com.
But in a nutshell, Shiny is an R package that builds interactive web apps straight from R. You just need to write an R script that follows a specific Shiny syntax and once you run the script, the package builds a HTML file as user interface (UI) with interactive input elements (based on Javascript) that trigger certain computations in R. The output of theses computations can be then shown as output in the UI. You can easily extend your Shiny apps with CSS themes, htmlwidgets, and JavaScript actions. Since you need to run R in the background you can either launch a Shiny app directly in your R Studio IDE or host it on a website that is connected to a Shiny server (e.g. R Studio hosts the cloud service www.shinyapps.io, where you can deploy your app).
Shiny apps can be very helpful to make statistical concepts and theories more accessible and I started to use it more and more in my stats courses at the university. I’ve even started to set up my own Shiny Server using DigitalOcean (see my recent post on that: Run shiny server on your own DigitalOcean droplet):
Link: https://saskiaotto.de/shiny
The Shiny website has also a big gallery of apps you can explore. Here are some examples linked to marine science that are worth playing around with. I embedded the websites of the Shiny app (if they are served over a secure HTTPS connection) so you can play around with them straight from here: simply move your cursor inside the app frame and scroll -vertically, but also horizontally- to see more of the app.
ggplot catalogue as Shiny app
This is a nice app that provides a catalog of different ggplots as a complement to the book Creating More Effective Graphs by Naomi Robbins and is maintained by Joanna Zhao and Jennifer Bryan.
You can filter for plot types you are interested in, then select the one you like best and see the R code to reproduce this graph:
3D Network Mapper
With this app you can visualize networks exported from NodeXL in a 3 dimensional view. NodeXL is a free Excel add-in that supports network and content analysis. You could, for instance, built a socio-ecological network with NodeXL and then load your .xlsx file into the app for 3D visualization. If you don’t have any data yet, try out the demo:
USGS Shiny app to inform stakeholder
This Shiny app written by Taylor Stewart from USGS (U.S. Geological Survey) is a good example of how to communicate science to fisheries managers, the fishing industry and the public in a more interactive way. The app serves as tool to explore trawl survey data taken by the Lake Erie Biological Station. Scroll down to see the actual app:
Link: https://gallery.shinyapps.io/lake_erie_fisheries_stock_assessment_app/
But NOTE: The current web location for this tool is temporary and it will be hosted on a USGS server at some point.
Modelling apps by the NIOZ Department of Estuarine & Delta Systems
The Department of Estuarine & Delta Systems, which is part of the Royal Netherlands Institute for Sea Research (NIOZ), has developed various R packages for marine data (checkout their R website: http://www.rforscience.com) and also a few interesting Shiny apps to demonstrate their modelling work. They are worth exploring if you work or study these fields but also to learn what one can do with R and Shiny apps (well, sometimes the code is written in Fortran to be faster…). Unfortunately, the website is not served through HTTPS so if you want to play around with them go on the weblinks below:
OMEXDIA - a biogeochemical model of the sediment
The first model example is a vertically resolved biogeochemical model (one of the first ones), which describes the cycle of oxygen, nitrogen, and carbon in marine sediments. The model comprises of 600 nonlinear equations, so bear in mind that each time you alter the value of a parameter, a quite substantial mathematical problem is solved.
Link: http://www.rforscience.com/modelling/omexdia/
Coded by: Karline Soetaert
Tracer model
This app implements a simplified version of a bacterivory model that describes how a tracer, injected as labeled glucose, propagates through a food web. Such simple models are efficient tools to quantify how much a certain organism relies on a particular food source (here bacteria):
Mussel DEB
The app implements the standard dynamic energy budget (DEB) model simulating the growth of the blue mussel. Specifically, it models how an organism allocates its resources in structural and reserve tissues:
Network topology of deep-sea food webs
Here, you can explore the structure and flow of six deep-sea food webs:
Link: http://www.rforscience.com/modelling/deepseafoodwebs/
Coded by: Karline Soetaert


The goal of the MUSIC, ARTS and CHILD DEVELOPMENT research project is to investigate the impact of arts and music training on children’s cognitive, emotional, social, and brain development.
This research project is a collaborative effort between USC Brain and Creativity Institute, the Los Angeles Philharmonic YOLA program, and Camino Nuevo Charter Academy. Additionally, it is funded by the generous support of the Los Angeles Philharmonic Association and the GROW @ Annenberg.


--- layout: post title: pen and ink date: '2015-01-25T07:30:41+01:00' categories: - Sketches picture_url: https://film-Storyboards.tumblr.com/post/109099434217 ---


Open Badges and Primary Computing
Sep 04, 2014
Rising Stars have been working with Makewav.es to develop a set of digital badges to support assessment for the primary computing curriculum, with each of the programme of study bullet points mapped to its own badge. These are linked to the outcomes from units in Switched on Computing, but as they’re available to all Makewav.es users (free registration required) and are based on the programme of study content I think they’d work very well for schools using their own or other schemes of work too.
As these are based on Mozilla‘s excellent Open Badge infrasturcture, the badges are portable so can be converted into Open Badges when pupils hit the age of thirteen and can create their own personal Open Badge accounts.
I’d see this as a great way to record and reward pupils’ attainment against the statutory attainment target for the new curriculum:
By the end of each key stage, pupils are expected to know, apply and understand the matters, skills and processes specified in the relevant programme of study.
Key Stage 1
Problem solver 1 understand what algorithms are, how they are implemented as programs on digital devices, and that programs execute by following precise and unambiguous instructions
Programmer 1 create and debug simple programs
Logical Thinker 1 use logical reasoning to predict the behaviour of simple programs
Content Creator 1 use technology purposefully to create, organise, store, manipulate and retrieve digital content
Beyond School recognise common uses of information technology beyond school
E-safety 1 use technology safely and respectfully, keeping personal information private; identify where to go for help and support when they have concerns about content or contact on the internet or other online technologies
Key Stage 2
Problem solver 2 design, write and debug programs that accomplish specific goals, including controlling or simulating physical systems; solve problems by decomposing them into smaller parts
Programmer 2 use sequence, selection, and repetition in programs; work with variables and various forms of input and output
Logical thinker 2 use logical reasoning to explain how some simple algorithms work and to detect and correct errors in algorithms and programs
Communicator understand computer networks, including the internet; how they can provide multiple services, such as the World Wide Web, and the opportunities they offer for communication and collaboration
Searcher use search technologies effectively, appreciate how results are selected and ranked, and be discerning in evaluating digital content
Content Creator 2 select, use and combine a variety of software (including internet services) on a range of digital devices to design and create a range of programs, systems and content that accomplish given goals, including collecting, analysing, evaluating and presenting data and information
E-safety 2 use technology safely, respectfully and responsibly; recognise acceptable/unacceptable behaviour; identify a range of ways to report concerns about content and contact
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Lyrics of the Hearthside
by Paul Laurence Dunbar
A Bridal Measure
Additional Information
- Year Published: 1913
- Language: English
- Country of Origin: United States of America
- Source: Dunbar, P.L. (1913). The Complete Poems of Paul Laurence Dunbar. New York: Dodd, Mead, and Company.
-
Readability:
- Flesch–Kincaid Level: 6.5
- Word Count: 179
- Genre: Poetry
- Keywords: happiness, marriage, sing, singing
- ✎ Cite This
Downloads
Come, essay a sprightly measure,
Tuned to some light song of pleasure.
Maidens, let your brows be crowned
As we foot this merry round.
From the ground a voice is singing,
From the sod a soul is springing.
Who shall say ‘t is but a clod
Quick’ning upward toward its God?
Who shall say it? Who may know it,
That the clod is not a poet
Waiting but a gleam to waken
In a spirit music–shaken?
Phyllis, Phyllis, why be waiting?
In the woods the birds are mating.
From the tree beside the wall,
Hear the am’rous robin call.
Listen to yon thrush’s trilling;
Phyllis, Phyllis, are you willing,
When love speaks from cave and tree,
Only we should silent be?
When the year, itself renewing,
All the world with flowers is strewing,
Then through Youth’s Arcadian land,
Love and song go hand in hand.


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The particle filter attempts to represent the probability
distribution directly through sampling. Regions of higher
probability are populated with more particles. Regions of
lower probability are depleted of particles. The particle
filter has several important properties:
- Unlike the Gaussian filters, no assumption is made
on the form of the distribution. A particle filter can often
track multimodal, complex distributions more accurately than
a Gaussian filter if enough particles are used.
- The computational cost and accuracy can be traded off: more
particles will give a more accurate depiction of the true
distribution, but will take more storage and computational power
to track.
The 2.5D robot on a track demo is shown below. Particles are
represented by small semi-transparent dots with lines
indicating the heading. The mean and covariance of the particles
are shown as a covariance ellipse with the mean heading in
dark blue and the one standard deviation depicted in light
blue.
Change the encoder readings and click move to propagate forward.
After each movement step, each particle is propagated forward by
the amount indicated by the encoder readings plus some sampled
that is distributed according to the matrix $Q$.
Click on the plot to sample a measurement at a particular location.
The measurement will resample the particles, weighted by the relative
likelihood derived from Bayes rule.
Below is a comparison between the particle filter (blue) and an Unscented
Kalman Filter (green). Movements and


To use, or to accumulate, that is the question. Edit the fields below to see what the optimal strategy is for maximizing your AA Smartfuel discounts. Strategies are ranked by the highest amount saved (equivalent to the lowest amount spent ) and then by the number of refuelling steps needed for a full tank.
Discount
Discount ammount accumulated so far.
Discount amount each time the minimum spend threshold is met.
Minimum spend needed to be eligible for the discount amount above.
Maximum litres of fuel that can be discounted.
Vehicle Fuel Capacity
According to Wikipedia, the average maximum fuel capacity for a vehicle is between 45 to 60 litres.
Remaining Fuel
Fuel remaining in the tank.
Fuel needed for a full tank.
No discounts used.


Configuring Jabber XMPP server in Debian and Ubuntu, XMPP
Difficulty: 2
Time: 20 Minutes
Jabber is a simple, mature and powerful messaging protocol based on Extensible Messaging and Presence Protocol (XMPP). Jabber was developed to collaborate in real-time between two or more people, based on the text typed. Enterprises use Jabber for internal communication, developers use it for programming skills improvement, and individuals use it for chatting. Hosting providers use it for high availability and stable communication services. Jabber is written in Erlang and has its own web-based management interface. This article explains the installation of Jabber XMPP server on Debian and Ubuntu.
You will need access to sudo
privileges to complete all the steps.
After you have installed the server, you need to change a few configuration settings.
ejabberd.cfg
file that resides in /etc/ejabberd/
.
with this line:
with this line:
ESC
, and then type :wq and press the Enter key.
After following these steps, you will get an overview of the installation and basic configuration of Ejabberd server. To change any other configuration logs, go to the browser, type http://coolexample.com:5280/admin, and then enter your admin credentials to get access to the web-based management panel


{% extends 'people_base.html' %} {% block title %}{{whose}} contacts{% endblock %} {% load nonce avatar vcard format %} {% block people %}
Your contacts can see your updates even if your stream is hidden from the public. Those with a blue background are also your followers.
{% endifequal %} {% if actor_tiles %} {% include 'actor_tiles.html' %} {% else %}No contacts yet, but maybe soon :)
{% endif %}


Starting point templates for your next project.
Boilerplates are conceptual starter project templates that are included with Brutalist Framework. They live in the /bp folder. They are also examples of what could be done with some of the BELL and core components.
Downloadversion 2.1
Released 10/13/2020
Download 2.1
Legacy Versions <IP_ADDRESS> (released 2/20/2020) 1.9 (legacy)
A collection of javascript plugins to further enhance user experience.
Starting point templates
Join Brutal Blast » Subscribe to our e-Newsletter
Roadmap
Concrete5 CMS
BradFogelstrom.com


Chapter 5 Margin of Error
Testing leads to failure, and failure leads to understanding Burt Rutan
5.1 Introduction & motativating example
Suppose we’re comparing two advert designs. At great expense, it has been decided to change the font to Comic Sans. Does this change work? Being a (data) scientist we decide to (humanely8) experiment on people by randomly showing them the advert. From past experience, you know that customers spent 45 seconds (on average) on your site. After switching to comic sans, we recorded the amount of time spent on the site by 20 customers
34 51 30 79 54 31 57 62 59 41 77 55 35 3 69 46 47 66 63 59
Should we consider switching to comic sans?
Clearly time will vary visit–by–visit. On some visits, customers might spent more time (up to 79 seconds), but on others visits, they may only spend a few seconds. To get an overall impression, we could work out the average time for the above sample \[ \bar x = \frac{34 + 51 + 30 + \ldots + 59}{20} = 50.9 \] The new website does seem to be perform slightly better. But we have a very small sample. If we took another twenty visits, we would get a different estimate. We need to account for this sampling variability of the mean \(\bar x\), and the most common way of doing this is to perform a hypothesis test.
5.2 One sample test
The one–sample z–test can be useful when we are interested in how the mean of a set of sample observations compares to some target value. The mean in our sample, as always, is denoted by \(\bar x\). The standard notation in statistics for the population mean is the Greek symbol \(\mu\) (pronounced “mu”). Obviously, \(\bar x\) is our sample estimate of \(\mu\).
In the example above, we’d like to know whether our new font has affected the amount of time people spend on our site. In hypothesis testing, we make this assertion in the null hypothesis, denoted by \(H_0\) and often written down as \[ H_0: \mu = 45 \] We usually test against a general alternative hypothesis \(H_1\) \[ H_1: \mu \ne 45 \] which says “\(\mu\) is not equal to 45”.
When performing the hypothesis test, we assume \(H_0\) to be true. We then ask ourselves the question:
How likely is it that we would observe the data we have, or indeed anything more extreme than this, if the null hypothesis is true?
We can get a handle on this question thanks to the Central Limit Theorem in Statistics. Although we will not go into the details here, this result tells us that the quantity \[ Z = \frac{\bar x - \mu}{s/\sqrt{n}} \] follows a normal distribution (when \(n\) is reasonably large). In this formula
- \(\bar x\) is our sample mean;
- \(\mu\) is the assumed value of the population mean under the null hypothesis \(H_0\);
- \(s\) is the sample standard deviation;
- \(n\) is the sample size.
When \(n\) is small, the central limit theorem tells us that \(Z\) follows a \(t\)-distribution. A \(t\) distribution is similar to the normal, except it has fatter tails (imagine pushing down on the normal distribution and spreading the weight). Provided your sample is large (\(n > 10\)), then the \(z\) and \(t\) tests are equivalent.
Using our example data set, if the null hypothesis is true, then \(\mu = 45\), so we have \[ Z = \frac{\bar x - \mu}{s/\sqrt{n}} = \frac{50.9 - 45}{18.2/\sqrt{20}} = 1.45 \] The obvious question is, how likely is it to have observed this value?
Since the normal distribution is symmetric, \(Z = 1.45\) is just as extreme as \(Z = −1.45\), and so the shaded region in the following diagram illustrates the \(p\)–value - the probability of observing the data we have, or anything more extreme than this, if the null hypothesis is true. In other words, the answer to our earlier question!
The closer the area of the shaded region (the \(p\)–value) is to 0, the less plausible it is that we would observe the data we have if the null hypothesis is true, that is, the more evidence we have to reject \(H_0\). So, we need to work out the area of the shaded region under the curve in the diagram above, which can be done using R
pnorm(1.45, lower.tail = FALSE) * 2
#> [1] 0.147
So the \(p\)-value is 0.15.
Earlier, we said that the smaller this \(p\)–value is, the more evidence we have to reject \(H_0\). The question now, is:
What constitutes a p–value small enough to reject H_0?
The convention (but by no means a hard–and–fast cut–off) is to reject \(H_0\) if the p–value is smaller than 5%. Thus, here we would say:
- Our p–value is greater than 5% (in fact, it’s larger than 10% – a computer can tell us that it’s exactly 14.7%)
- Thus, we do not reject \(H_0\)
- There is insufficient evidence to suggest a real deviation from the previous value
Care should be taken not be too strong in our conclusions. We can only really state that the sample does not suggest that the new design is better.
Absence of evidence is not evidence of absence
To perform the entire procedure in R, we use the t.test()
function
comic = c(34, 51, 30, 79, 54, 31, 57, 62, 59, 41, 77, 55, 35, 3, 69, 46, 47, 66, 63, 59)
t.test(comic, mu = 45)
#>
#> One Sample t-test
#>
#> data: comic
#> t = 1, df = 20, p-value = 0.2
#> alternative hypothesis: true mean is not equal to 45
#> 95 percent confidence interval:
#> 42.4 59.4
#> sample estimates:
#> mean of x
#> 50.9
Technically this is performing a t-test which is why the associated \(p\)-value is slightly larger than our calculation. However, when the sample size is large, the t.test()
function is equivalent to a z-test.
5.2.1 Example: OKCupid
The OKCupid dataset provides heights of their users. An interesting question is, how consistent are the heights given by users with the average height across the USA? First we need to extract the necessary information. R makes subsetting straightforward
## Select Males
height = cupid$height[cupid$sex == "m"]
## Remove missing values
height = height[!is.na(height)]
## Convert to cm
height = height * 2.54
mean(height)
#> [1] 179
From the CDC paper we discover the average height in the USA is 69.3 inches. We can use the \(t\)-test function in R (since the sample size is large, this is equivalent to a \(z\)-test), to obtain a \(p\)-value
t.test(height, mu = 69.3)
#>
#> One Sample t-test
#>
#> data: height
#> t = 3000, df = 40000, p-value <2e-16
#> alternative hypothesis: true mean is not equal to 69.3
#> 95 percent confidence interval:
#> 179 179
#> sample estimates:
#> mean of x
#> 179
Since the \(p\)-value is small, we reject \(H_0\) and conclude that the sample gives evidence that males in San Francisco are different to the rest of the USA (or just lie!).
5.2.2 Errors
A Type I Error occurs when the null hypothesis is true but is wrongly rejected. This often referred to as a “false hit”“, or a false positive (e.g. when a diagnostic test indicates the presence of a disease, when in fact the patient does not have the disease).
The rate of a Type I Error is known as the size of the test and is usually denoted by the Greek symbol \(\alpha\), pronounced “alpha”, and usually equals the significance level of the test - that is, the p–value beyond which we have decided to reject H 0 (e.g. 5% or 0.05 in our earlier examples).
A Type II Error occurs when the null hypothesis is false, but erroneously fails to be rejected. Hence, we fail to assert what is present, and so this is often referred to as a ‘miss’. The rate of the Type II Error is usually denoted by the Greek symbol \(\beta\), pronounced “beta”, and is related to the power of a test (which equals 1 − \(\beta\)).
5.3 Two sample z-test
Suppose we want to test another improvement to our website. We think that adding a blink tag would be a good way of attracting customers. Monitoring the first twenty customers we get
21 32 46 19 29 31 37 28 50 29 34 40 26 20 48 7 39 30 40 34
How do we compare the website that uses the Comic Sans font to the blinking site? We use a two sampled z-test! As with the one–sample test, we must start by setting up our hypotheses. In a two–sample z test, the null hypothesis is always that the population means for the two groups are the same \[
H_0: \mu_1 = \mu_2
\] While the alternative hypothesis is that the two pages differ, i.e. \[
H_1: \mu_1 \ne \mu_2.
\] The corresponding test statistic is \[
Z = \frac{\bar x_1 - \bar x_2}{s \sqrt{1/n_1 + 1/n_2}}.
\] This time we will jump straight into R and use the t.test()
function.
blink = c(21, 32, 46, 19, 29, 31, 37, 28, 50, 29, 34, 40, 26, 20, 48, 7, 39, 30, 40, 34)
t.test(comic, blink, var.equal = TRUE)
#>
#> Two Sample t-test
#>
#> data: comic and blink
#> t = 4, df = 40, p-value = 3e-04
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> 9.37 28.43
#> sample estimates:
#> mean of x mean of y
#> 50.9 32.0
In this example, since the p-value is relatively, we can conclude that the two web-designs do appear to be different.
5.4 Confidence intervals
The idea of a confidence interval is central to statistics. When we get an answer, we don’t just want a point estimate, i.e. a single number, we want a plausible range. In fact whenever you see opinion polls in newspapers, they typically come with a margin of error of \(\pm 3\)%. It’s always amusing to see the fuss that people make when an opinion poll raises by 1% which is likely to be down to random noise.
Confidence intervals provide an alternative to hypothesis tests for assessing questions about the population mean (or population means in two sample problems), although often they are used alongside hypothesis tests. Due to lack of time, we will not cover the general theory supporting the construction of confidence intervals, although this theory is the same as that discussed in Section 4 for hypothesis tests and revolves around the Central Limit Theorem. The aim is to use the information in our sample to obtain an interval within which we might expect the true population mean to lie, with a specified level of confidence.
Recall that the sample mean \(\bar x\) is an estimate of the population mean \(\mu\). The population mean does exist; we’d like to know what it is, but usually this isn’t possible (unless we are able to take a census). The next best thing is to take a sample from the population and find \(\bar x\) - if our sample is representative of the population as a whole, we might feel confident that \(\bar x\) is a good estimator for \(\mu\), and might be close to the true value.
The problem is, if we were to take many samples from the population, and so calculate many \(\bar x\)’s, they are all likely to be different to each other. Which one would we trust the most? In reality, we don’t have the resources to take many samples, and so we’d like a way of capturing the variability of $x $ based on the information in our single sample. This is where statistical theory can help us out!
Central to the idea of margin of error, is the central limit theorem.
5.4.1 Construction
Most confidence intervals are symmetric and two–sided. Thus, to obtain a confidence interval for the population mean we:
- Find the mean in our sample, \(\bar x\)
- Subtract some amount from \(\bar x\) to obtain the lower bound of our confidence interval
- Add the same amount in (2) to our sample mean \(\bar x\) to obtain the upper bound of our confidence interval
The amount we add and subtract from \(\bar x\) is a function of the sample standard deviation \(s\) and the sample size \(n\); it also depends on just how “confident”" we want to be of capturing the population mean \(\mu\) within our interval! To be 100% confident, the lower and upper bounds would need to extend to the smallest and largest values possible for our random variable, and so it makes no sense to find such an interval; the standard is to find a 95% confidence interval.
The formula for a symmetric, two–sided 95% confidence interval for the population mean \(\mu\) is \[ \left(\bar{x}-z \times \frac{s}{\sqrt{n}}, \hspace{0.5cm} \bar{x}+z\times \frac{s}{\sqrt{n}}\right), \] often condensed to just \[ \bar{x} \pm z \times \frac{s}{\sqrt{n}}; \] where \(z\) is a critical value from the standard normal distribution. For the standard interval 95% confidence interval, the \(z\) value is 1.96, often rounded to 2. So the interval becomes \[ \bar{x} \pm \frac{2 s}{\sqrt{n}}. \] If we wanted a 90% interval, we would use \(z = 1.645\). For a 99% interval, we would use \(z = 2.576\)
5.4.2 Example: Comic Sans
Let’s return to our Comics Sans example. The average time spent on the site was \(\bar x = 50.9\) with a standard deviation of \(s = 18.2\). This gives a 95% confidence interval of \[ 50.9 \pm 1.96 \frac{18.2}{\sqrt{20}} = (42.92, 58.88). \] Alternatively, we could use R and extract the confidence interval from
t.test(comic)
#>
#> One Sample t-test
#>
#> data: comic
#> t = 10, df = 20, p-value = 1e-10
#> alternative hypothesis: true mean is not equal to 0
#> 95 percent confidence interval:
#> 42.4 59.4
#> sample estimates:
#> mean of x
#> 50.9
to get the interval \((42.38,59.42)\). Notice this interval is slightly wider, since it’s using the exact \(t\)-distribution.
5.5 The Central Limit Therem (CLT)
One of the reasons the normal distribution is so useful is the central limit theorem. This theorem states that if we average a large number (say 309) of variables10, then the result is approximately normally distributed.
So if we observe data, \(x_1, x_2, \ldots, x_n\), where the mean and variance of \(x_i\) are \(\mu\) and \(\sigma^2\), then \[ S_n = \frac{x_1 + x_2 + \ldots + x_n}{n} \] has a normal distribution with mean \(\mu\) and variance \(\sigma^2/n\).
The standard error of the mean is defined as the standard deviation of the sample mean, i.e. \(\sigma/\sqrt{n}\). Remember that \(\sigma\) is the population standard deviation, so we estimate the standard error using \(s/\sqrt{n}\)
5.5.1 Example: Customer waiting times
Figure 5.1 shows the waiting time (in minutes) of \(40\) customers. The figure shows that the average wait is around 1 to 2 minutes, but some unfortunate customers have a significantly longer wait. The data are clearly not normal, as waiting times must be positive and the distribution isn’t symmetric.
From chapter 2, we can quickly estimate the mean and standard deviation as 1.946 and 0.433, i.e. \(S_n = 1.946\) . The CLT allows us to take this inference one step further. Since we know that \(S_n\) is approximately normal, that implies that with probability 95%, the true mean lies between \(2.19 \pm 2 \times 2.04/20 = (1.28, 3.1).\)
Mathematically the central limit theorem only holds as \(n\) tends to infinity. However in this simple example, the underlying distribution is clearly not normal, so the confidence interval, given our finite sample, isn’t actually 95%, it’s more like 91%. Which is still not too bad.
The central limit theorem is a powerful idea. It allows to get a handle on the uncertainty whenever we estimate means. One word of warning though. If the distribution is particularly odd, then we’ll need a larger sample size for the normality approximation to be accurate.


Player 1 Start
Player 2 Start
Player 3 Start
Player 4 Start
Air block
Solid block
Ice block
Kill zone
Blink block
Map Data


| Elements' Summary |
| formatCode |
The format code, for example, "YYYY-MM-DD". |
| formatName |
A name or description that is displayed in the number formatter
dialog. |
| formatKey |
A unique (within one locale) identifier. |
| formatType |
Type may be one of "short", "medium", "long". |
| formatUsage |
Usage category, for example, "DATE" or "FIXED_NUMBER",
corresponding with KNumberFormatUsage values. |
| formatIndex |
The index used by the number formatter, predefined values
corresponding with NumberFormatIndex values. |
| isDefault |
If a format code is the default code of a formatType
group. |
| Elements' Details |
formatCode
string formatCode;
- Description
- The format code, for example, "YYYY-MM-DD".
|
|
formatName
string formatName;
- Description
- A name or description that is displayed in the number formatter
dialog.
|
|
formatKey
string formatKey;
- Description
- A unique (within one locale) identifier.
|
|
formatType
string formatType;
- Description
- Type may be one of "short", "medium", "long".
|
|
formatUsage
string formatUsage;
- Description
- Usage category, for example, "DATE" or "FIXED_NUMBER",
corresponding with KNumberFormatUsage values.
|
|
formatIndex
short formatIndex;
- Description
- The index used by the number formatter, predefined values
corresponding with NumberFormatIndex values.
|
|
isDefault
boolean isDefault;
- Description
- If a format code is the default code of a formatType
group.
|
|
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


| neardate {survival} | R Documentation |
A common task in medical work is to find the closest lab value to some index date, for each subject.
neardate(id1, id2, y1, y2, best = c("after", "prior"), nomatch = NA_integer_)
id1 |
vector of subject identifiers for the index group |
id2 |
vector of identifiers for the reference group |
y1 |
normally a vector of dates for the index group, but any orderable data type is allowed |
y2 |
reference set of dates |
best |
if |
nomatch |
the value to return for items without a match |
This routine is closely related to match
and to
findInterval
, the first of which finds exact matches and
the second closest matches. This finds the closest matching date
within sets of exactly matching identifiers.
Closest date matching is often needed in clinical studies. For
example data set 1 might contain the subject identifier and the date
of some procedure and data set set 2 has the dates and values for
laboratory tests, and the query is to find the first
test value after the intervention but no closer than 7 days.
The id1
and id2
arguments are similar to match
in
that we are searching for instances of id1
that will be found
in id2
, and the result is the same length as id1
.
However, instead of returning the first match with id2
this
routine returns the one that best matches with respect to y1
.
The y1
and y2
arguments need not be dates, the function
works for any data type such that the expression
c(y1, y2)
gives a sensible, sortable result.
Be careful about matching Date and DateTime values and the impact of
time zones, however, see as.POSIXct
.
If y1
and y2
are not of the same class the user is
on their own.
Since there exist pairs of unmatched data types where the result could
be sensible, the routine will in this case proceed under the assumption
that "the user knows what they are doing". Caveat emptor.
the index of the matching observations in the second data set, or
the nomatch
value for no successful match
Terry Therneau
data1 <- data.frame(id = 1:10, entry.dt = as.Date(paste("2011", 1:10, "5", sep='-'))) temp1 <- c(1,4,5,1,3,6,9, 2,7,8,12,4,6,7,10,12,3) data2 <- data.frame(id = c(1,1,1,2,2,4,4,5,5,5,6,8,8,9,10,10,12), lab.dt = as.Date(paste("2011", temp1, "1", sep='-')), chol = round(runif(17, 130, 280))) #first cholesterol on or after enrollment indx1 <- neardate(data1$id, data2$id, data1$entry.dt, data2$lab.dt) data2[indx1, "chol"] # Closest one, either before or after. # indx2 <- neardate(data1$id, data2$id, data1$entry.dt, data2$lab.dt, best="prior") ifelse(is.na(indx1), indx2, # none after, take before ifelse(is.na(indx2), indx1, #none before ifelse(abs(data2$lab.dt[indx2]- data1$entry.dt) < abs(data2$lab.dt[indx1]- data1$entry.dt), indx2, indx1))) # closest date before or after, but no more than 21 days prior to index indx2 <- ifelse((data1$entry.dt - data2$lab.dt[indx2]) >21, NA, indx2) ifelse(is.na(indx1), indx2, # none after, take before ifelse(is.na(indx2), indx1, #none before ifelse(abs(data2$lab.dt[indx2]- data1$entry.dt) < abs(data2$lab.dt[indx1]- data1$entry.dt), indx2, indx1)))


Rate Limits API¶
Application credentials and User credentials can access a variety of endpoints.
Each of these endpoints can be accessed a limited number of times.
For example a set of user credentials can perform 180 searches in 15 minutes intervals. If you try to do more than 180 searches in the 15 minutes timeframe the REST API will raise an exception.
Tweetinvi offers 3 levels of support to manage RateLimits.
These rate limits handlers let you focus on your application and reduce the need to use the Rate Limits API directly.
Rate Limits Cache¶
Rate limits are cached locally by Tweetinvi.
When using the TrackOnly
or TrackAndAwait
tweetinvi will cache the rate limits allowances to reduce the number of calls performed to the twitter api.
The rate limits operations offer a From
of type RateLimitSource
that let you define if your rate limit operations will be performed from the cache or from the twitter api.
You can decide to clear your cache at any point of time.
await client.RateLimits.ClearRateLimitCacheAsync();
Get Rate Limits¶
RateLimits.GetRateLimitsAsync()
retrieves the state of rßate limit allowances at a specific point of time.
var rateLimits = await client.RateLimits.GetRateLimitsAsync();
var searchTweetsLimits = rateLimits.SearchTweetsLimit;
RateLimits
contain the following information
| Property | Description |
|---|---|
| Limit | Maximum number of requests available |
| Remaining | Number of requests that can be performed before the reset. When this value hits 0, you cannot perform this action until the reset. |
| ResetDateTime | DateTime of when the rate limits allowance will be reset |
| ResetDateTimeInMilliseconds | Number of milliseconds before the rate limits allowance is reset |
| ResetDateTimeInSeconds | Number of seconds before the rate limits allowance is reset |
Get url endpoint allowance¶
RateLimits.GetEndpointRateLimitAsync()
retrieves the rate limits allowances of a specific endpoint
var url = "https://api.twitter.com/1.1/users/show.json?screen_name=tweetinviapi";
// get the endpoint rate limits
var endpointRateLimit = await client.RateLimits.GetEndpointRateLimitAsync(url);
// you can specify which source to use to retrieve the rate limits
var endpointRateLimit = await client.RateLimits.GetEndpointRateLimitAsync(url, RateLimitsSource.CacheOrTwitterApi);
Note that rate limits for Application credentials are different than for User credentials.
As some endpoints are not accessible through application credentials, attempting to access it will return null.
Wait for endpoint allowance¶
RateLimits.WaitForQueryRateLimitAsync()
waits the necessary time for the rate limits allowances to become available.
If the allowance for a specific endpoint is not empty,
WaitForQueryRateLimitAsync
will complete immediately.If the allowance for a specific endpoint is empty,
WaitForQueryRateLimitAsync
will wait for the allowance to reset.
var url = "https://api.twitter.com/1.1/users/show.json?screen_name=tweetinviapi";
// waits if no allowance is available for the get user endpoint
await client.RateLimits.WaitForQueryRateLimitAsync(url);
// you can specify which source to use to retrieve the rate limits
await client.RateLimits.WaitForQueryRateLimitAsync(url, RateLimitsSource.CacheOrTwitterApi);
Rate Limits - Special Endpoints¶
For some endpoint Twitter has other type of rate limits. They usually apply rules to prevent bots.
These rules are not strict and can be dependent on the way an account is used.
For example you are likely to be stopped publishing tweets if you do publish more than 300 tweets within 3 hours.
Other rules apply and I suggest you check Twitter documentation to learn about them.


Company History
In the late 1970's Larry and Shelly Pearson were living in the Monterey Bay area with their one daughter and one son, Stacy and John. Looking for a sustainable way to support their young family, they came across a small cookie store in downtown Carmel and were immediately interested in the concept. Larry came from a strong business background, developed during seven years working under three very successful entrepreneurs, and becoming an entrepreneur himself was appealing to him. Shelly's artistic creativity paired with Larry's entrepreneurial spirit gave the Pearsons an opportunity to create something special. In 1980 after months of perfecting several gourmet recipes, the Salmon Cookie Company retail bakery was opened on the Pike Place Market, in Seattle, Washington. The original cookie flavors were Salmon Chocolate Chip, Chocolate Chip Walnut, Oatmeal Raisin, Peanut Butter and Peanut Butter Chocolate Chip
The Salmon Cookie Company quickly developed a loyal cookie following thanks to the Pearsons' dedication to affordable gourmet cookies made with quality ingredients, impeccable customer service and sparkling clean work and retail spaces. Following the immediate success of the Pike Place marker store, the business expanded by delivering fresh baked cookies in many unique flavor combinations to retailers in San Francisco and Monterey Bay areas. Later, two more retail locations were opened within Alki Beach and at the Capitol Hill.
Today the company has expanded its distribution of frozen products throughout the Western United States. Salmon Cookie still delivers freshly baked cookies daily on the Northern Seattle coast and since 1995 has sold gourmet cookie gifts online and shipped them to cookie lovers across the country. After 34 years, we continue to take pride in serving our superior fresh-baked cookies to our loyal cookie consumers. We use only the finest ingredients available including Guittard chocolate, fresh salmon, pure vanilla extract, pure cane sugar and unbromated-unbleached flour to make cookies that taste like they just popped out of the oven in your mother's kitchen. Now that is special!


The following tests are to ensure that Esprima can be loaded using AMD (Asynchronous Module Definition) loader such as RequireJS.


Implements sparse regression with paired covariates (Rauschenberger et al.).
| Version: | 0.0.2 |
| Depends: | R (≥ 3.0.0) |
| Imports: | glmnet, Matrix, survival, CorShrink |
| Suggests: | knitr, testthat, pROC, edgeR |
| Published: | 2018-10-27 |
| Author: | Armin Rauschenberger [aut, cre] |
| Maintainer: | Armin Rauschenberger <a.rauschenberger at vumc.nl> |
| BugReports: | https://github.com/rauschenberger/palasso/issues |
| License: | GPL-3 |
| URL: | https://github.com/rauschenberger/palasso |
| NeedsCompilation: | no |
| Citation: | palasso citation info |
| Materials: | README NEWS |
| CRAN checks: | palasso results |
| Reference manual: | palasso.pdf |
| Vignettes: |
article vignette |
| Package source: | palasso_0.0.2.tar.gz |
| Windows binaries: | r-devel: palasso_0.0.2.zip, r-release: palasso_0.0.2.zip, r-oldrel: palasso_0.0.2.zip |
| OS X binaries: | r-release: palasso_0.0.2.tgz, r-oldrel: palasso_0.0.2.tgz |
| Old sources: | palasso archive |
Please use the canonical form https://CRAN.R-project.org/package=palasso to link to this page.


Web Developer with a Mechanical Engineering background
Find Out MoreBachelor of Science Mechanical Engineering, University of Vermont, May of 2015.
Full Stack JavaScript Web Development Bootcamp, December of 2018.
Skills & ProjectsBuilt and deployed a full scale application for Revision Military.
Experience with Create React App building small single page applications as well as Express and Gatsby.
Strong HTML, CSS, and JavaScript fundamentals.
Node, Express, Firebase, Strapi, Ruby, and GraphQL, in addition to SQL and noSQL databases.
Developed and deployed an application to facilitate testing, analysis recording of quality control data at Revision Military. The application integrated with tools and equipment in the lab. It eliminated the need for physical forms and increased the amount of product that could be tested in a single day.
"Leight developed a web application for real-time entry of data into a SQL database. Our Lab has benefitted from improved data accuracy by reducing opportunity for transcription errors and reduced data entry time by 1-2 hours per day." - Ed Roberts, QC & Lab Manager
Worked alongside fellow classmates and faculty for a Capstone Design Project, designing a Force-Feedback device for a pair of cross-country ski poles. Strain gauges were mounted to poles and data was sent wirelessly to the athlete using Bluetooth and Arduino in real time. The team used SolidWorks and 3D printing to create lightweight housings to hold all components of the prototype.
A simple app built with only React, Express, and MongoDB demonstrates how a front-end, back-end, and database work together without using any heavy frameworks. The project is fully tested using Cypress and Enzyme.
Built with React, Power Hour is a single page app that integrates with Spotify's API and changes the music at certain intervals based on the user's input. The Spotify Player acts as a remote and will play music through the logged in user's own Spotify Application and device. The app is deployed on Netlify and requires no backend support.


#WalangPasok: Class suspensions, Wednesday, November 20, 2019
Bookmark and refresh this page for a list of areas where classes have been suspended for Wednesday, November 20, 2019
by Rappler.comSubscribe
Email Address
Newsletters
- Glenda Gloria's Take this Tuesday
- Huddle by Miriam Grace Go
- The Newsbreak Agenda by Chay Hofileña
- Sometimes, all you need is video by Lilibeth Frondoso


Digit Recognition
This is a Digit Recognition project. Draw any digit between 0 to 9 to recognize it.
The digit is:
The accuracy is:


In February, Dr. Scott Sampson will present a lecture titled ‘Fossil Threads in the Web of Life’ In the meantime, you can see him in an interview about bridging the human-nature divide in the following YouTube video.


{% load static %}
We believe that political opinions in today's connected world can be swayed by the media and reading what others have to say about politicans. Humans are susceptible to this because of our emotions, but with PolitiStats we wanted to take that aspect out of politics by letting complex algorithms produce sentiment analysis for news and tweets about the politician. This web application was developed using Django and a number of APIs inlcuding the Twitter Developer API, the Google Civic Information API, and the NewsAPI. Our team used the Agile method in the developement process and it proved key to stack on top of the tasks for each sprint. The product allows users to not only understand who represents their state, but what kind of public opinions about them as well. The idea stemmed from the unprecedented 2020 Presidential election and the importance that young voters would play in its outcome. We wanted users to have an experience with that consists of minimal bias and have access to information to help them form their own opinions. All of the data being analyzed is publicly avalible and the algorithms being used are not politically motivated. We also incorporated two features to aid those with visual impairments: a text magnifier, and a light/dark mode toggle switch. In the future, we plan add a map view, search by postal code, tensorflow capabilities, and better integration with mobile devices.


Clone the repository and install all the requirements
`git clone https://github.com/Pranav-Khurana/MPR.git`
`pip install requirements.txt`
Move to the directory of experiment you wish to analyse and study (suppose haxby) and run [experiment].py file.
`cd haxby`
`python haxby.py`
Analyse the output at http://localhost:8000/[experiment]/[experiment]_output.html (suppose for haxby http://localhost:8000/haxby/haxby_output.html).
Read details about the experiment in [experiment].md file.


- doAddLine(int, int, int, int) - Static method in class aldercroft.aldercroftGUImethods
-
This function adds a new line object to table and the lists
- doAddPoint(int, int) - Static method in class aldercroft.aldercroftGUImethods
-
This function adds a new point object to table and the lists
- doAddRow(String) - Static method in class aldercroft.aldercroftGUImethods
-
This function adds an object which was insert in the table to the lists
- doClearPanel() - Static method in class aldercroft.aldercroftGUImethods
-
This function removes all objects from the panel
- doDeleteRow(String) - Static method in class aldercroft.aldercroftGUImethods
-
This function gets the selected rows of the table and deletes the associated objects in the table and the lists
- doEditRow(String) - Static method in class aldercroft.aldercroftGUImethods
-
This function edits an object in the table and updates the lists
- doExit() - Static method in class aldercroft.aldercroftGUImethods
-
- doExport() - Static method in class aldercroft.aldercroftGUImethods
-
This function exports either points or lines to a .csv file
- doExportSelected() - Static method in class aldercroft.aldercroftGUImethods
-
This function exports selected points or lines to a .csv file
- doImport() - Static method in class aldercroft.aldercroftGUImethods
-
This function imports a .csv file and appends the data to the existing tables.
- doLineSelect() - Static method in class aldercroft.aldercroftGUImethods
-
This function sets the Modus to "line_select" to allow the user to select a line
- doMove() - Static method in class aldercroft.aldercroftGUImethods
-
This function sets the Modus to "move" to allow the user to move an object
- doPaint() - Static method in class aldercroft.aldercroftGUImethods
-
This function sets the Modus to "draw" to draw selected objects
- doPointSelect() - Static method in class aldercroft.aldercroftGUImethods
-
This function sets the Modus to "point_select" to allow the user to select a point
- doSaveAll() - Static method in class aldercroft.aldercroftGUImethods
-
This function stores all objects from the local lists in the Database
- doSelect() - Static method in class aldercroft.aldercroftGUImethods
-
This function sets the Modus to "select" to allow the user to select objects
- drawLine() - Static method in class aldercroft.aldercroftGUImethods
-
This function sets the Modus to "line" to allow the user to draw a line
- drawPoint() - Static method in class aldercroft.aldercroftGUImethods
-
This function sets the Modus to "point" to allow the user to draw a point


Full documentation regarding the select is available on bootswatch
If you need a muti-select option simply add multiple="" to your select element.
multiple=""
select


em,rem,vh(viewport height),and vw (viewport width)units+Responsive design Explained
first heading
second heading
third heading


luck of the draw typography workshop lowa state university college of design october 22-23, 2004
bcdbhcbdhdbvjhbvjbbvbv bhvbbvhbvhvbhvbhbvhbvhbvhb dchvcvhchdvcgdvcgvvcvcvgvc vgcvgdvgvdcvduvcdvcdvcvdcv dvcdvcgvududvucvu
bcdbhcbdhdbvjhbvjbbvbv bhvbbvhbvhvbhvbhbvhbvhbvhb dchvcvhchdvcgdvcgvvcvcvgvc vgcvgdvgvdcvduvcdvcdvcvdcv dvcdvcgvududvucvu


Company Profile
Our Fabric Designs allows you create your own custom fabric by the yard! Imagine the possibilities! A quilt created with images of your family and friends. Curtains with a pattern from that necktie you saw in the vintage shop. A sun dress made from a clip art image you discovered online. Custom printed pillows! It's all possible and more, thanks to digital textile printing.
Contact
<EMAIL_ADDRESS>(+44)2007222111
Projects — coming soon...
copyrights© 2019, dnbldn


Expected a { to open the function definition.
foo() {
echo "hello world"
}
foo()
foo() {
echo "hello world"
}
foo
ShellCheck found what appears to be the start of a function definition, but without a function body.
One common cause is that you are trying to call a function by appending parentheses, e.g. foo()
like in C. Bash does not use or allow parentheses after a function name to call it. The function foo
should be called using just foo
like in the example.
If you are declaring a function, make sure it looks like the correct code above, and that it does not try to declare any parameters (parameters are instead accessed with $1
and up).
If you are trying to do something else, look up the syntax for what you are trying to do.
POSIX allows the body of a function to be any compound command, e.g. foo() for i; do :; done
. Since this usage is rare, ShellCheck intentionally requires the body to be { ..; }
(or ( ..; )
):
foo() {
for i; do :; done
}
This additional structure requirement helps improve error messages and suggestions by not parsing down a path that less advanced users wouldn't expect.


Module openid.server.server
OpenID server protocol and logic.
Overview
An OpenID server must perform three tasks:
-
Examine the incoming request to determine its nature and
validity.
-
Make a decision about how to respond to this request.
-
Format the response according to the protocol.
The first and last of these tasks may performed by the decodeRequest
and encodeResponse
methods of the Server
object. Who gets to do the
intermediate task -- deciding how to respond to the request -- will
depend on what type of request it is.
If it's a request to authenticate a user (a checkid_setup
or checkid_immediate
request), you need
to decide if you will assert that this user may claim the identity in
question. Exactly how you do that is a matter of application policy,
but it generally involves making sure the user has an account with your
system and is logged in, checking to see if that identity is hers to
claim, and verifying with the user that she does consent to releasing
that information to the party making the request.
Examine the properties of the CheckIDRequest
object, optionally check
CheckIDRequest.returnToVerified
, and and
when you've come to a decision, form a response by calling CheckIDRequest.answer
.
Other types of requests relate to establishing associations between
client and server and verifying the authenticity of previous
communications. Server
contains all the logic and data
necessary to respond to such requests; just pass the request to Server.handleRequest
.
OpenID Extensions
Do you want to provide other information for your users in addition
to authentication? Version 2.0 of the OpenID protocol allows consumers
to add extensions to their requests. For example, with sites using the
Simple
Registration Extension, a user can agree to have their nickname and
e-mail address sent to a site when they sign up.
Since extensions do not change the way OpenID authentication works,
code to handle extension requests may be completely separate from the
OpenIDRequest
class here. But you'll
likely want data sent back by your extension to be signed. OpenIDResponse
provides methods with
which you can add data to it which can be signed with the other data in
the OpenID signature.
For example:
# when request is a checkid_* request
response = request.answer(True)
# this will a signed 'openid.sreg.timezone' parameter to the response
# as well as a namespace declaration for the openid.sreg namespace
response.fields.setArg('http://openid.net/sreg/1.0', 'timezone', 'America/Los_Angeles')
There are helper modules for a number of extensions, including Attribute
Exchange
, PAPE
, and Simple
Registration
in the openid.extensions
package.
Stores
The OpenID server needs to maintain state between requests in order
to function. Its mechanism for doing this is called a store. The store
interface is defined in openid.store.interface.OpenIDStore
.
Additionally, several concrete store implementations are provided, so
that most sites won't need to implement a custom store. For a store
backed by flat files on disk, see openid.store.filestore.FileOpenIDStore
.
For stores based on MySQL or SQLite, see the openid.store.sqlstore
module.
Upgrading
From 1.0 to 1.1
The keys by which a server looks up associations in its store have
changed in version 1.2 of this library. If your store has entries
created from version 1.0 code, you should empty it.
From 1.1 to 2.0
One of the additions to the OpenID protocol was a specified nonce
format for one-way nonces. As a result, the nonce table in the store
has changed. You'll need to run contrib/upgrade-store-1.1-to-2.0 to
upgrade your store, or you'll encounter errors about the wrong number
of columns in the oid_nonces table.
If you've written your own custom store or code that interacts
directly with it, you'll need to review the change notes in openid.store.interface
.


{% extends "base.html" %} {% import "bootstrap/wtf.html" as wtf %} {% block title%}添加文章{%endblock%} {%block page_content%}


Dual [page:PerspectiveCamera PerspectiveCamera]s used for effects such as [link:https://en.wikipedia.org/wiki/Anaglyph_3D 3D Anaglyph] or [link:https://en.wikipedia.org/wiki/parallax_barrier Parallax Barrier].
[example:webgl_effects_anaglyph effects / anaglyph ]
[example:webgl_effects_parallaxbarrier effects / parallaxbarrier ]
[example:webgl_effects_stereo effects / stereo ]
Default is `1`.
Default is `0.064`.
Left camera. This is added to [page:Layers layer 1] - objects to be rendered by the left camera must also be added to this layer.
Right camera.This is added to [page:Layers layer 2] - objects to be rendered by the right camera must also be added to this layer.
Update the stereo cameras based on the camera passed in.
[link:https://github.com/mrdoob/three.js/blob/master/src/[path].js src/[path].js]


SweetFreeStuff.com
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Free Aquaphor Healing Ointment from Dr Oz (1st 2,500 at 3pm ET 10/29)
Posted: 28 Oct 2014 11:10 AM PDT
http://feedproxy.google.com/&edburner&utm_medium=email
First 2,500 people to sign up at 3pm ET on Wednesday 10/29 will receive
a Free Aquaphor Healing Ointment!
The post Free Aquaphor Healing Ointment from Dr Oz (1st 2,500 at 3pm ET
10/29) appeared first on SweetFreeStuff.com.
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Free Subscription to Maxim Magazine
Posted: 28 Oct 2014 10:52 AM PDT
http://feedproxy.google.com/&edburner&utm_medium=email
The post Free Subscription to Maxim Magazine appeared first on
SweetFreeStuff.com.
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Free Samples from SampleSource
Posted: 28 Oct 2014 04:48 AM PDT
http://feedproxy.google.com/&edburner&utm_medium=email
Free Samples from SampleSource
The post Free Samples from SampleSource appeared first on
SweetFreeStuff.com.
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Free Book Why One Way? (Religious)
Posted: 28 Oct 2014 01:42 AM PDT
http://feedproxy.google.com/&edburner&utm_medium=email
Free Book ‘Why One Way?’
The post Free Book ‘Why One Way?’ (Religious) appeared first on
SweetFreeStuff.com.
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Free Salada Green Tea Sample (Expired)
Posted: 27 Oct 2014 01:10 PM PDT
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Free Salada Green Tea Sample
The post Free Salada Green Tea Sample (Expired) appeared first on
SweetFreeStuff.com.


iPhone Price Comparison
$1099 ~ $1449
$999 ~ $1349
$749 ~ $899
$999 ~ $1149
iPhone XS Max
iPhone XS
iPhone XR
iPhone X


Heroes showcase is a project to showcase your favorite heroes. Here you can find more information about these beings you admire, and even become one of them!! Seriously tough, this is just a test project made with the Angular framework. Enjoy!!


--- title: About ---
<EMAIL_ADDRESS>Hi, I'm Yuesong Wang. My English name is BOB. I am now a MS student in UC Santa barbara major in Computer Science. This is a place for me to post my dairy and blogs related with the technical problems I encountered with. More content will be added.


Hi, I am Chitroopa
I am passionate about learning new technologies, building websites and solving problems
Follow me on:
This project recreats wikipedia home page. CSS divs, spans, classes, floats and Bootstrap pannels, columns, rows, container classes are used to style the page.
Languages Used:
This project list out the animals available to be adopted, along with pictures and descriptions of each animal.CSS inheritance and cascading styles are used to style the page.
Languages Used:
This a website for a private island resort and it has the following webpages: accommodations, activities, travel arrangements, Gallery, contact information.
Languages Used:
Simple pet blog for cat named Bellodonna.
Languages Used:


--- date: 2013-04-18 10:20:02 author: Steve Gill title: "PhoneGap Facebook Plugin" format: html tags: - Plugin - Community ---
The PhoneGap Facebook Plugin has been named to the Facebook Technology Partners Program! Check out the plugin repository on Github.
Follow the readme to get started. It includes instructions for using this plugin with iOS and Android devices. Currently, the plugin supports Facebook SDK 3.0 for Android and Facebook SDK 3.2 for iOS.
The PhoneGap Facebook Plugin is open source and MIT licensed. It is completely free to use, just like PhoneGap! The plugin allows developers to use a modified version of Facebook's web SDK and maps certain calls to the native Facebook iOS and Android SDKs. You can now have your users directed to the installed Facebook app for single sign-on instead of navigating through the device browser.
The plugin comes with three example apps that show Facebook APIs in action. The newest addition to the examples apps folder is Scrumptious. The Scrumptious directory includes its own readme with instructions on how to get started. This app interacts with Facebook's Open Graph and requires setting up a web server to handle the open graph objects.
View the screencast below to get a quick introduction to the PhoneGap Facebook Plugin and the Scrumptious example application.


CUSB faculty recruitment: Check how to apply for 60 posts
by Careers DeskBy: Careers Desk | New Delhi | Published: December 9, 2019 4:43:48 pm
CUSB faculty recruitment: Apply at cusb.ac.in. (Express photo by Praveen Khanna/ Representational image)
CUSB faculty recruitment: The Central University of South Bihar (CUSB) has invited applications for the post of professors, associate professors and assistant professors at its official website, cusb.ac.in. A total of 60 posts are to be filled through this recruitment drive. The last date to submit online applications is December 18 while candidates can download fee receipt till December 23.
Candidates will be selected based on their academic score, research publications and teaching experience with weight of 80 per cent, 10 per cent and 10 per cent each. Candidates will have to submit the online form, payment receipt and required documents by post as well.
CUSB faculty recruitment: Eligibility
Education: For the post of professor, candidates should have a PhD in concerned/allied/relevant discipline with 10 years of teaching experience. For associate professor, an experience of eight years is required and for the post of assistant professor, one needs a NET qualification along with 55 per cent marks in relevant master’s degree.
CUSB faculty recruitment: Fee
Candidates will have to pay Rs 2000 as an application fee for reserved category candidates the same is Rs 1000.
CUSB faculty recruitment: How to apply
Apart from filling applications online, candidates also need to send a printed copy of the online application form along with fee receipt at ‘The Assistant Registrar, Recruitment Cell, Central University of South Bihar, SH – 7, Gaya Panchanpur Road, Village – Karhara,
Post – Fatehpur, PS – Tekari, Gaya – 824 236 (Bihar)’. The last date to receive postal forms is December 23.
For all latest Govt Jobs 2019, Railway Jobs, Bank Jobs and SSC Jobs log on to IndianExpress.com.  Stay Connected.
For all the latest Jobs News, download Indian Express App
© IE Online Media Services Pvt Ltd


AddFont(string family [, string style [, string file [, string dir]]])
FPDF_FONTPATH
constant (if that constant is defined)font
directory located in the same directory as fpdf.php
family
style
B
: boldI
: italicBI
or IB
: bold italicfile
dir
$pdf->AddFont('Comic', 'I');
$pdf->AddFont('Comic', 'I', 'comici.php');


Comra918.github.io
La Nuova Era Aurora Compact Commercial Espresso Coffee. จัดชุดเปิดร้านราคาพิเศษ NEW SHOP SET เครื่องชงกาแฟ,สินค้า. Produttore macchine da caffè espresso. With its Sleek Style and Compact size the La Nuova Era Aurora Commercial Espresso Machine is designed to pour perfect espresso and froth exceptional silky milk in the smallest of areas.  Для оформления заказа воспользуйтесь.
Aurora is a machine conceived for wide spaces, equipped with an important tray for cups in order to ease bartenders. Read more. New. Andromeda professional coffee machines. In this new version of the legendary model Andromeda there is all the experience gained over the years by La Nuova Era. Read more. Arabika professional coffee machines. Arabika pays tribute to the Italian tradition.
Professional coffee machines - La Nuova Era, coffee machines. La Nuova Era Espresso Cafe Machines Australia catalogue. Итальянская кофемашина La Nuova Era - доставка и сервис. Aurora è il futuro di una nuova Milano interconnessa, immersa nel verde, dove la casa è un emozione fatta di tecnologia, finiture di altissimo pregio, ambienti luminosi e personizzazione degli spazi. Contatta un nostro consulente. เครื่องชงกาแฟ la nuova era รุ่น aurora compact s2gแถมฟรีเครื่องบด macap m5- เครื่องชงกาแฟขนาดใหญ่ เหมาะสำหรับร้านที่มีปริมาณการขาย ประมาณ 100-150 แก้ว / ชั่วโมง สอบถาม. Aurora La nuova Milano da vivere. Serviced Areas The Club Café initiative has been created by Club Partners in conjunction with our team of coffee industry specialists to create a program that offers the opportunity to create an exciting coffee experience within your club and a competitive edge in this thriving market.
Hand Made Espresso Machines for an New Era. Hand made in Milan, Italy La Nuova Era Produces High Quality Espresso Machines of all sizes for home and commercial. La Nuova Era and Quamar grinder have teamed up this year to bring you the best of combination for your home, office or cafe. To have a great coffee you need a great machine and also a great grinder. La Nuova Era fa parte del Gruppo Argentini che è composto dalle storiche divisioni Metal Global (www.metalglobal.it) e Quality Form (www.qualityform.it). La nuova era AURORA COMPACT Handbücher.
La Nuova Era Espresso Machines. Traditional Coffee Machines - Queensland Coffee Machine.


The SKY UX tutorials get you up and running with SKY UX. They demonstrate actions such as how to create and serve single-page applications and how to create and edit pages and components within SPAs.
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layout: post
title: the 7th cavalry at fort riley first draft
date: '2016-02-27T18:31:55+01:00'
categories:
- Sketches
- Sketchbooks
- Horses
- Storyboards
picture_url: https://film-Storyboards.tumblr.com/post/140116613186/the-7th-cavalry-at-fort-riley-first-draft
---
The 7th Cavalry at Fort Riley, first draft.


The irony in these results, is that the smaller and local elections have a bigger impact on people’s lives than who the president is.
The presidential election may decide who the leader of our country is, but the president himself doesn’t decide what funding your community gets: you get to decide that. That’s the beauty of democracy and local elections: if you find the issue important and want it funded, you can vote to get it funded. Unfortunately, this is a pitfall of democratic participation. People tend to skip elections that aren’t sensationalized on the news.
Recognizing the importance of these local issues is the first step in centering our voices through democracy. There is no better way to do this than voting in local elections. Levies are an important yet overlooked aspect of elections that fund the issues and programs that we voters care about.
Levies can be complicated to understand if you have never heard of them. A levy is a compulsory tax imposed in a municipality, sometimes based on property value, in order to pay for government services and public expenditures. Put simply, levies are the taxes we pay to receive services from our local government.
This election cycle, in Athens County, there are three levies up to be voted for, all for renewal. A renewal levy allows for the county to continue to collect taxes as scheduled year to year. The other type of levy is a replacement levy, which replaces levies with a revamped plan when their time frame ends.
The first of the three levies up for renewal provides Emergency Medical Services funding. This levy provides $1 million over five years beginning in 2022 with tax payments first due in 2023. This levy is pretty important to the function of the county. EMS is one of the most essential functions for the county and for Athens County citizens.
The next levy is a renewal levy for senior service and facilities. This levy is for $750,000 over five years beginning in 2022 with tax payments first due in 2023. This levy is of course important as well because it supports senior citizens. As they are one of the vulnerable populations, they should certainly be a goal of all voters.
The final levy on the ballot is a renewal levy for a general fund for operation, acquisition, construction and renovation of mental health and recovery services and facilities. This levy is for $1 million over 10 years beginning in 2022 with tax payments first due in 2023. It should also be a simple choice for voters, as mental health services are increasingly important in our lives, and these services should remain and stay accessible.
Voters should vote in support of all of these levies. Levies are crucial to the function of our government. Just because there are no large national elections on the ballot this year doesn’t mean this election cycle isn’t important. Go vote, and make sure to support these levies and support our community.
Mikayla Rochelle is a graduate student studying public administration at Ohio University. Please note that the views and opinions of the columnists do not reflect those of The Post. What are your thoughts? Tell Mikayla by tweeting her at @mikayla_roch.
Correction: A previous version of this article stated the renewal levy for senior service and facilities was for $75 million, when it should've said $750,000. This article has been updated to reflect the most accurate information.