#selector(method name)
Selectors in Swift are similar to selectors in Objective-C. A selector is used to refer to a method or a variable's getter or setter that is available in the Objective-C runtime.
The value of a selector expression is an instance of the Selector type that can be passed to functions expecting a Selector such as perform(<selector>, with:self, afterDelay:0.5).
Consider the following class and its methods:
class Speaker: NSObject {
@objc
func apologize() {
print("I'm sorry")
}
@objc
func shout(message: String) {
print("\(message.uppercased())!")
}
@objc
func scream(_ message: String) {
print("\(message.uppercased().characters.map { "\($0) " }.joined() )!")
}
@objc
func say(date: Date) {
print(DateFormatter.localizedString(from: date, dateStyle: .long, timeStyle: .none))
}
@objc
func say(text string: String) {
print("\(string)")
}
}
In the simplest case, when there's no method overloading happening, referencing a method on a class works by simply naming the class and the method using dot syntax, like so:
Speaker().perform(#selector(Speaker.apologize))
Speaker().perform(#selector(Speaker.shout), with: "Hello")
Speaker().perform(#selector(Speaker.scream), with: "Hello")
When there is only a single method with the same name
(like apologize()
or
shout(message:)
), you
can leave out the parentheses like we've done above.
However, when there is ambiguity caused by method overloading like in
the case of the two variants of say(_:)
,
the signature needs to be more explicit:
Speaker().perform(#selector(Speaker.say(text:)))
Speaker().perform(#selector(Speaker.say(date:)))
Referencing a static or class method works exactly like instance methods. The only difference is that the target object is the class, not an instance of the class, as illustrated below:
class Speaker: NSObject {
@objc
static func apologize() {
print("We are sorry")
}
@objc
class func apologizeHard() {
print("We are very sorry")
}
}
Speaker.perform(#selector(StaticSpeaker.apologize))
Speaker.perform(#selector(StaticSpeaker.apologizeHard))
Referencing a property to either get its value or set its value using a
Selector works through the getter:
and setter:
prefixes in front of the property's name.
Consider this Person class and its name property:
class Person: NSObject {
@objc var name: String = ""
init(_ name: String) {
self.name = name
super.init()
}
}
We can reference the setter method the Objective-C runtime generates for the
name property with
#selector(setter: Person.name)
and
the equivalent getter method with
#selector(getter: Person.name)
, as
illustrated below:
let person = Person("Bob")
print(person.perform(#selector(getter: Person.name)).takeUnretainedValue())
// => "Bob"
person.perform(#selector(setter: Person.name), with: "Alice")
print(person.name)
// => "Alice"
#keyPath
selector instead of a literal keypath string:
Referencing a property to either get its value or set its value using a
Selector works through the getter:
and setter:
prefixes in front of the property's name.
Consider this Person class and its name property:
class Person: NSObject {
@objc var name: String = ""
init(_ name: String) {
self.name = name
super.init()
}
}
We can reference the setter method the Objective-C runtime generates for the
name property with
#selector(setter: Person.name)
and
the equivalent getter method with
#selector(getter: Person.name)
, as
illustrated below:
let person = Person("Bob")
print(person.perform(#selector(getter: Person.name)).takeUnretainedValue())
// => "Bob"
person.perform(#selector(setter: Person.name), with: "Alice")
print(person.name)
// => "Alice"


Official Website of the City of Milwaukee
CALL for Action (414) 286-CITY | Click for Action
Elected Officials and Administration
|
Health, Safety and Services |
Arts and Architecture
Business and Development |
Judicial and Oversight
Jobs and Employees
|


Author: Jan Sorgalla
Version: 0.2.8 (Changelog)
Download: jcarousel.tar.gz or jcarousel.zip
Source Code: http://github.com/jsor/jcarousel
Bugtracker: http://github.com/jsor/jcarousel/issues
Licence: Dual licensed under the MIT
and GPL licenses.
jCarousel is a jQuery plugin for controlling a list of items in horizontal or vertical order. The items, which can be static HTML content or loaded with (or without) AJAX, can be scrolled back and forth (with or without animation).
The following examples illustrate the possibilities of jCarousel:
To use the jCarousel component, include the jQuery
library, the jCarousel source file and a jCarousel skin stylesheet file inside the
tag
of your HTML document:
  <link rel="stylesheet" type="text/css" href="/path/to/skin/skin.css" />
The download package contains some example skin packages. Feel free to build your own skins based on it.
jCarousel expects a very basic HTML markup structure inside your HTML document:

jCarousel automatically wraps the required HTML markup around the list. The class attribute applies the jCarousel skin "name" to the carousel.
To setup jCarousel, add the following code inside the
tag of your HTML document:

jCarousel accepts a lot of configuration options, see chapter "Configuration" for further informations.
After jCarousel has been initialised, the fully created markup in the DOM is:

First item

Second item

As you can see, there are some elements added which have assigned classes (in addition to the classes you may have already assigned manually). Feel free to design your carousel with the classes you can see above.

to the top

element.

's under

are the next/prev buttons of the carousel. The first one illustrates a disabled button, the second an enabled one. The disabled
button has the attribute
disabled
(which actually makes no sense for

elements, but you can also use <button>
elements or
whatever you want) as well as the additional class jcarousel-prev-disabled
(or jcarousel-next-disabled
).

elements of the list have the class
jcarousel-item-n
assigned where n
represents the position in the list.

for a horizontal carousel.By passing the callback function itemLoadCallback
as configuration
option, you are able to dynamically create

items for the content.

itemLoadCallbackFunction
is a JavaScript function that is called
when the carousel requests a set of items to be loaded. Two parameters are
passed: The instance of the requesting carousel and a flag which indicates
the current state of the carousel ('init', 'prev' or 'next').

jCarousel contains a convenience method add()
that can be
passed the index of the item to create and the innerHTML string of the
item to be
created. If the item already exists, it just updates the innerHTML. You can
access the index of the first and last visible element by the public variables carousel.first
and carousel.last
.
The instance of the carousel will be stored with the data()
method of jQuery under the key jcarousel
for a simple access.
If you have created a carousel like:
jQuery(document).ready(function() { jQuery('#mycarousel').jcarousel(); });
You can access the instance with:
var carousel = jQuery('#mycarousel').data('jcarousel');
You can also access methods of the instance directly, for example the add()
method:
var index = 1; var html = 'My content of item no. 1'; jQuery('#mycarousel').jcarousel('add', index, html);
Sometimes people are confused how to define the number of visible items because there is no option for this as they expect (Yes, there is an option visible
, we discuss this later).
You simply define the number of visible items by defining the width (or height) with the class .jcarousel-clip
(or the more distinct .jcarousel-clip-horizontal
and .jcarousel-clip-vertical
classes) in your skin stylesheet.
This offers a lot of flexibility, because you can define the width in pixel for a fixed carousel or in percent for a flexible carousel (This example shows a carousel with a clip width in percent, resize the browser to see it in effect).
So, why there is an option visible
? If you set the option visible
, jCarousel sets the width of the visible items to always make this number of items visible. Open this example and resize your browser window to see it in effect.
jCarousel accepts a list of options to control the appearance and behaviour of the carousel. Here is the list of options you may set:
| Property | Type | Default | Description |
|---|---|---|---|
| vertical | bool | false | Specifies wether the carousel appears in horizontal or vertical orientation. Changes the carousel from a left/right style to a up/down style carousel. |
| rtl | bool | false | Specifies wether the carousel appears in RTL (Right-To-Left) mode. |
| start | integer | 1 | The index of the item to start with. |
| offset | integer | 1 | The index of the first available item at initialisation. |
| size | integer | Number of existing
 elements if size is not passed explicitly | The number of total items. |
| scroll | integer | 3 | The number of items to scroll by. |
| visible | integer | null | If passed, the width/height of the items will be calculated and set depending on the width/height of the clipping, so that exactly that number of items will be visible. |
| animation | mixed | "fast" | The speed of the scroll animation as string in jQuery terms ("slow"
or "fast" ) or milliseconds as integer
(See jQuery Documentation).
If set to 0, animation is turned off. |
| easing | string | null | The name of the easing effect that you want to use (See jQuery Documentation). |
| auto | integer | 0 | Specifies how many seconds to periodically autoscroll the content.
If set to 0 (default) then autoscrolling is turned off.
|
| wrap | string | null | Specifies whether to wrap at the first/last item (or both) and jump
back to the start/end. Options are "first" , "last" ,
"both" or "circular" as string. If set to null ,
wrapping is turned off (default). |
| initCallback | function | null | JavaScript function that is called right after initialisation of the carousel. Two parameters are passed: The instance of the requesting carousel and the state of the carousel initialisation (init, reset or reload). |
| setupCallback | function | null | JavaScript function that is called right after the carousel is completely setup. One parameter is passed: The instance of the requesting carousel. |
| itemLoadCallback | function | null | JavaScript function that is called when the carousel requests a set
of items to be loaded. Two parameters are passed: The instance of the
requesting carousel and the state of the carousel action (prev, next
or init). Alternatively, you can pass a hash of one or two functions
which are triggered before and/or after animation:
itemLoadCallback: { onBeforeAnimation: callback1, onAfterAnimation: callback2 } |
| itemFirstInCallback | function | null | JavaScript function that is called (after the scroll animation) when
an item becomes the first one in the visible range of the carousel.
Four parameters are passed: The instance of the requesting carousel
and the
 object itself, the index which indicates
the position of the item in the list and the state of the carousel action
(prev, next or init). Alternatively, you can pass a hash of one or two
functions which are triggered before and/or after animation:
itemFirstInCallback: { onBeforeAnimation: callback1, onAfterAnimation: callback2 } |
| itemFirstOutCallback | function | null | JavaScript function that is called (after the scroll animation) when
an item isn't longer the first one in the visible range of the carousel.
Four parameters are passed: The instance of the requesting carousel
and the
 object itself, the index which indicates
the position of the item in the list and the state of the carousel action
(prev, next or init). Alternatively, you can pass a hash of one or two
functions which are triggered before and/or after animation:
itemFirstOutCallback: { onBeforeAnimation: callback1, onAfterAnimation: callback2 } |
| itemLastInCallback | function | null | JavaScript function that is called (after the scroll animation) when
an item becomes the last one in the visible range of the carousel. Four
parameters are passed: The instance of the requesting carousel and the

 object itself, the index which indicates the
position of the item in the list and the state of the carousel action
(prev, next or init). Alternatively, you can pass a hash of one or two
functions which are triggered before and/or after animation:
itemLastInCallback: { onBeforeAnimation: callback1, onAfterAnimation: callback2 } |
| itemLastOutCallback | function | null | JavaScript function that is called when an item isn't longer the last
one in the visible range of the carousel. Four parameters are passed:
The instance of the requesting carousel and the

object itself, the index which indicates the position of the item in
the list and the state of the carousel action (prev, next or init).
Alternatively, you can pass a hash of one or two functions which are
triggered before and/or after animation:
itemLastOutCallback: { onBeforeAnimation: callback1, onAfterAnimation: callback2 } |
| itemVisibleInCallback | function | null | JavaScript function that is called (after the scroll animation) when
an item is in the visible range of the carousel. Four parameters are
passed: The instance of the requesting carousel and the

object itself, the index which indicates the position of the item in
the list and the state of the carousel action (prev, next or init).
Alternatively, you can pass a hash of one or two functions which are
triggered before and/or after animation:
itemVisibleInCallback: { onBeforeAnimation: callback1, onAfterAnimation: callback2 } |
| itemVisibleOutCallback | function | null | JavaScript function that is called (after the scroll animation) when
an item isn't longer in the visible range of the carousel. Four parameters
are passed: The instance of the requesting carousel and the

object itself, the index which indicates the position of the item in
the list and the state of the carousel action (prev, next or init).
Alternatively, you can pass a hash of one or two functions which are
triggered before and/or after animation:
itemVisibleOutCallback: { onBeforeAnimation: callback1, onAfterAnimation: callback2 } |
| animationStepCallback | function | null | JavaScript function that is called after each animation step. This
function is directly passed to jQuery's .animate() method
as the step parameter. See the jQuery documentation for the
parameters it will receive. |
| buttonNextCallback | function | null | JavaScript function that is called when the state of the 'next' control is changing. The responsibility of this method is to enable or disable the 'next' control. Three parameters are passed: The instance of the requesting carousel, the control element and a flag indicating whether the button should be enabled or disabled. |
| buttonPrevCallback | function | null | JavaScript function that is called when the state of the 'previous' control is changing. The responsibility of this method is to enable or disable the 'previous' control. Three parameters are passed: The instance of the requesting carousel, the control element and a flag indicating whether the button should be enabled or disabled. |
| buttonNextHTML | string |

 |
The HTML markup for the auto-generated next button. If set to null ,
no next-button is created. |
| buttonPrevHTML | string |

 |
The HTML markup for the auto-generated prev button. If set to null ,
no prev-button is created. |
| buttonNextEvent | string | "click" | Specifies the event which triggers the next scroll. |
| buttonPrevEvent | string | "click" | Specifies the event which triggers the prev scroll. |
| itemFallbackDimension | integer | null | If, for some reason, jCarousel can not detect the width of an item, you can set a fallback dimension (width or height, depending on the orientation) here to ensure correct calculations. |
Thanks to John Resig for his fantastic jQuery
library.
jCarousel is inspired by the Carousel
Component written by Bill Scott.


--- title: World Map featured_image: projects/worldmap.png link: http://worldmap.amitkolambikar.com/ date: 2016-12-15 ---
World Map is built using vanilla Javascript and D3JS. Its a repersentation of the world Map on a map. It supports names of different countries and zoom-in zoom-out. Feel free to play with it.


You need to click on the name or ID number of the person you want to
add a training or qualification record for.
| Name or ID number | Records | Status |
|---|---|---|
|
Long-term absent |
{{ worker.trainingCount + worker.qualificationCount }} |
0" class="govuk-!-margin-bottom-0">
{{ worker.expiredTrainingCount }}
expired
0" class="govuk-!-margin-bottom-0">
{{ worker.missingMandatoryTrainingCount }}
missing
0" class="govuk-!-margin-bottom-0">
OK
|
There are no matching results
Make sure that your spelling is correct.


Firstly thanks in advance to whoever can help me, im pretty new to arduino software and this is my first time trying to connect sensor data to Emoncms.
Im trying to send sensor readings from my CT sensor to the Emoncms web application in order to visualise my energy usage, ive got the correct readings appearing on my serial monitor but for some reason the live feed isnt appearing in the Emoncms application. Im pretty sure this has something to do with my code trying to connect with the application, any ideas?
[code]
#include "EmonLib.h" // Include Emon Library
#include <SPI.h>
#include <Ethernet.h>
// assign a MAC address for the ethernet controller.
// fill in your address here:
byte mac[] = {
0x00, 0xAA, 0xBB, 0xCC, 0xDE, 0x02 };
// fill in an available IP address on your network here,
// for auto configuration:
IPAddress ip(192, 168, 0, 11); //ip address of ethernet sheild
IPAddress subnet(255, 255, 255, 0);
IPAddress DNS(8, 8, 8, 8);
IPAddress gw(192, 168, 0, 1);
//IPAddress gw(192, 168, 0, 254);
// initialize the library instance:
EthernetClient client;
char server[] = "http://emoncms.org/"; //emoncms URL
//IPAddress server(213, 138, 101, 177);
boolean lastConnected = false; // state of the connection last time through the main loop
String apikey = "MYAPIKEY"; //api key
int node = 0; //if 0, not used
const unsigned long postingInterval = 3*1000; // delay between updates, in milliseconds
EnergyMonitor emon1; // Create an instance
void setup()
{
Serial.begin(9600);
// give the ethernet module time to boot up:
delay(1000);
// Display a welcome message
Serial.println("HTTP emoncms client v0.1 starting...");
emon1.voltage(2, 234.26, 1.7); // Voltage: input pin, calibration, phase_shift
emon1.current(1, 111.1); // Current: input pin, calibration.
}
void loop()
{
emon1.calcVI(20,2000); // Calculate all. No.of half wavelengths (crossings), time-out
emon1.serialprint(); // Print out all variables (realpower, apparent power, Vrms, Irms, power factor)
float realPower = emon1.realPower; //extract Real Power into variable
float apparentPower = emon1.apparentPower; //extract Apparent Power into variable
float powerFActor = emon1.powerFactor; //extractPower Factor into Variable
float supplyVoltage = emon1.Vrms; //extract Vrms into Variable
float Irms = emon1.Irms; //extract Irms into Variable
Serial.print("Real Power, ");
Serial.print("Apparent Power, ");
Serial.print("Power Factor, ");
Serial.print("Supply Voltage, ");
Serial.print("Irms = (");
}
// this method makes a HTTP connection to the server:
void sendData(float realPower, float apparentPower, float PowerFActor, float supplyVoltage, float Irms) {
// if there's a successful connection:
if (client.connect(server, 80)) {
Serial.println("Connecting...");
// send the HTTP PUT request:
//client.print("GET /emoncms/input/post.json?apikey=MYAPIKEY&json={power");
client.print("GET /emoncms.org/input/post.json?json={power:200}&apikey=MYAPIKEY");
client.print(":");
client.print(",realPower:");
client.print(realPower);
client.print(",apparentPower:");
client.print(apparentPower);
client.print(",PowerFActor:");
client.print(PowerFActor);
client.print(",supplyVoltage:");
client.print(supplyVoltage);
client.print(",Irms:");
client.print(Irms);
client.println("} HTTP/1.1");
client.println("Host: ip(192, 168, 0, 11)");
client.println("User-Agent: Arduino-ethernet");
client.println("Connection: close");
client.println();
}
else {
// if you couldn't make a connection:
Serial.println("Connection failed");
Serial.println("Disconnecting...");
client.stop();
}
}
[/code]
Re: Sending Arduino Sensor data to Emoncms
You should probably edit your post to hide your APIKey.
one issue is the you have the IP ADDRESS of the server set but in your Client.print string you have emoncms.org.
you can simply test this in a webrowser
try http://<ipaddress of server>/<client.print string>
if you get the response Ok you know it's correct.
Also have a look at the Api help page on the input tab on emoncms.org
Re: Sending Arduino Sensor data to Emoncms
Would changing the line:
client.print("GET /emoncms.org/input/post.json?json={power:200}&apikey=MYAPIKEY");
to:
client.print("GET /input/post.json?json={power:200}&apikey=MYAPIKEY");
do it?
Re: Sending Arduino Sensor data to Emoncms
Thanks for both your responses; Trystan i made the change you recommended but the values from the sensor are still not appearing in emoncms. any other ideas?
Re: Sending Arduino Sensor data to Emoncms
{
client.print(F("GET /input/post.json?node=1&csv="));
client.print(F("User-Agent: Arduino-ethernet"));
client.println(F("Connection: close"));
client.println();
}
So for you, I would try something like:
if(client.connect("emoncms.org",80))
{
client.print(",realPower:");
client.print(realPower);
client.print(",apparentPower:");
client.print(apparentPower);
client.print(",PowerFActor:");
client.print(PowerFActor);
client.print(",supplyVoltage:");
client.print(supplyVoltage);
client.print(",Irms:");
client.print(Irms);
client.print("}&apikey="
client.println(F("Connection: close")); // Although not technically necessary, I found this helpful
client.println();
Re: Sending Arduino Sensor data to Emoncms
I am new in Arduino Programming and I am trying to upload sensor data onto emoncms website using GSM shield , EmonTx V2 shield and Arduino duemilanove. I just want to upload real power readings
ive got the correct readings appearing on my serial monitor but for some reason the live feed isnt appearing in the Emoncms application. I think their is something wrong in my code while connecting to website , please if anybody could help
#include "EmonLib.h" // Include Emon Library
#include <SPI.h>
#include <GSM.h>
#define GPRS_APN "data.lycamobile.co.uk" // replace your GPRS APN
#define GPRS_LOGIN "lmuk" // replace with your GPRS login
#define GPRS_PASSWORD "plus" // replace with your GPRS password
#define PINNUMBER ""
GSMClient client;
GPRS gprs;
GSM gsmAccess;
char server[] = "http://emoncms.org/"; //emoncms URL
boolean lastConnected = false; // state of the connection last time through the main loop
String apikey = "b340460c41753172b26XXXXXXXX9fb0297"; //api key
int node = 0; //if 0, not used
const unsigned long postingInterval = 3*1000; // delay between updates, in milliseconds
EnergyMonitor ct1;
const int LEDpin = 9;// Create an instance
void setup()
{
Serial.begin(9600);
// give the ethernet module time to boot up:
delay(1000);
// Display a welcome message
Serial.println("HTTP emoncms client v0.1 starting...");
ct1.voltage(0, 300.26, 1.7); // Voltage: input pin, calibration, phase_shift
ct1.current(1, 60.606);
pinMode(LEDpin, OUTPUT);
digitalWrite(LEDpin, HIGH);
boolean notConnected = true;
// After starting the modem with GSM.begin()
// attach the shield to the GPRS network with the APN, login and password
while(notConnected)
{
if((gsmAccess.begin(PINNUMBER)==GSM_READY) &
(gprs.attachGPRS(GPRS_APN, GPRS_LOGIN, GPRS_PASSWORD)==GPRS_READY))
notConnected = false;
else
{
Serial.println("Not connected");
delay(1000);
}
}
Serial.println("Connected to GPRS network"); // Current: input pin, calibration.
}
void loop()
{
ct1.calcVI(20,2000); // Calculate all. No.of half wavelengths (crossings), time-out
ct1.serialprint(); // Print out all variables (realpower, apparent power, Vrms, Irms, power factor)
float realPower = ct1.realPower; //extract Real Power into variable
Serial.print("ct1.realpower ");
Serial.print(" ");
}
// this method makes a HTTP connection to the server:
void sendData(float realPower) {
// if there's a successful connection:
if (client.connect(server, 80)) {
Serial.println("Connecting...");
// send the HTTP PUT request:
//client.print(" /emoncms/input/post.json?apikey=MYAPIKEY&json={power");
client.print("GET /emoncms.org/input/post.json?json={power:200}&apikey=b340460c4175317XXXXX6809fb0297");
client.print(realPower);
client.println("} HTTP/1.1");
client.println("Host: emoncms.org");
client.println("User-Agent: GSM shield");
client.println("Connection: close");
client.println();
}
else {
// if you couldn't make a connection:
Serial.println("Connection failed");
Serial.println("Disconnecting...");
client.stop();
}
}


[gecode-users] cloning variable problem
Guido Tack
tack at ps.uni-sb.de
Tue Oct 24 23:12:30 CEST 2006
Hi!
> I've been trying to create an IntVar X1 in a space S1, and create a
> clone of X1 namely X2 in a space S2. i called the update method of
> IntVar to create the clone.
> It all seems to work ok, but when i try to impose some propagators i
> got a Segmentation Fault.
The problem is that you must not call the update method of an IntVar
outside the copy constructor of a space. You basically cannot create a
clone of an IntVar outside of the copy constructor of your space.
For copying to work, the variables need to store the location they were
copied to, so that sharing can be detected and the same variable is not
copied twice. All this is done behind your back in the clone function of
the space. The space takes care that variables are in a clean state after
copying. If you just call update, noone takes care of cleaning up the
variable's state, that's why you get a segmentation fault.
Cheers,
Guido
--
Guido Tack
http://www.ps.uni-sb.de/~tack
More information about the gecode-users
mailing list


Set Up Your Wifi for SUSI
SSID
Password
Hotword
Snowboy
Pocket Sphinx
Wake Button
On
Off
Reboot Smart Speaker
To reset your SUSI smart speaker (..to get back here..), hold the button for 10 seconds.


--- title: examine keywords: fastai sidebar: home_sidebar summary: "API details." description: "API details." nb_path: "nbs\06_examine.ipynb" ---
visualize_layers
visualize_layers(fn, learn, nrows=5, ncols=5)
fn
learn
nrows
5
ncols
Visualize how an image is transformed as it goes through the layers in the model
view_layers
view_layers(image, layer:int)
image
layer
int


I want to make functional, useful things, learning a ton along the way.
I am a front end / full stack web developer, most recently creating, improving and debugging functional frontend apps for clients using combinations of Vue.js, HTML, CSS , JQuery/AJAX, and venturing into the back end (.NET Core) with some C# and SQL when needed. My strengths involve tenacity to stick with a problem, and the ability to both develop independently or lead / work closely with team members as needed. Solving problems solo can be hugely satisfying, though I also enjoy tackling challenges with others. I am driven by the desire to deliver quality results, and working alongside likeminded colleagues.


[gecode-users] Taking an n-ary OR of distinct constraints?
Jay
kruusoomaakari at hotmail.com
Thu May 7 17:52:18 CEST 2015
Hi,
I am building a model incrementally. I know in advance how many
variables there will be, but the constraints will be added to the model
through a graph search. I will have n integer variables, each taking a
value from an integer domain {1,...,k}. During the graph search, we are
given a sets of alldifferent (i.e. distinct) constraints. In a set of
alldiff constraints, at least one has to be satisfied (but not
necessarily all!).
For example, a constraint to the model could be: (alldifferent(x1,x2,x3)
OR alldifferent(x2,x4,x6) OR alldifferent(x1,x2,x8)), where x_i's are
variables. If this 3-ary OR of 3 alldiff constraints is denoted by X,
the ultimate model could be say: (X AND Y AND Z), where Y and Z are
similar sets of alldiffs.
As far as I could tell from the documentation, an n-ary OR is not
supported between constraints. I can do this easily in MiniZinc though
(just use \/), so I wonder if I am mistaken here. If that is the case,
what is a convenient way of modeling such an n-ary OR between
alldifferent constraints?
More information about the users
mailing list


Preparation time
less than 30 mins
Cooking time
no cooking required
Serves
Serves 4
Mackerel is plentiful, cheap and packed with oily-fish goodness. Choose fish with shiny eyes that are stiff with freshness.
To make the chutney, heat the olive oil in a large pan and cook the onion and garlic until soft. Add the mustard seeds, ground ginger, turmeric, cardamom and star anise and stir well.
Add the tomatoes, vinegar, sugar and salt and simmer everything thickens. When thick, add the greengages and cook until soft. Remove from the heat and let cool slightly.
To fry the mackerel, heat the oil in a large, heavy frying pan. Place each mackerel fillet, skin side down, and fry for about five minutes or until cooked through.
Serve the mackerel with spoonfuls of warm chutney.


Rock
Paper
Scissors
Welcome, please enter your name to get started:
Enter
Start the Game
Home
About Us


Assess the tracking results for code performances and quality of the data. This is actually a user-interface class, and output interface to papers and presentations. What is worth mentioning is that compared with TAAssess, this class is specifically for MWDCs around the target, and for DCs made by P.Ma.
More...
#include <TAAssessTa.h>
|
| virtual | ~TAAssessTa () |
| |
| void | SetROOTFile (const string &file) |
| | assess tracking results of MWDC array U and MWDC array D More...
|
| |
| virtual void | EvalDCArr (int runId=0, bool isDCArrD=true) |
| |
| virtual void | PostEval (int round=0, bool isDCArrD=true) |
| | evaluation done after Eval event by event More...
|
| |
|
| static TAAssessTa * | Instance () |
| |
| static void | EvalDCArr (const string &rootfile, DetArr_t *detList, int runid=0, bool isDCArrD=true) |
| |
| static bool | PostEval (const string &rootfile, int round=0, bool isDCArrD=true, bool is3D=true) |
| |
Assess the tracking results for code performances and quality of the data. This is actually a user-interface class, and output interface to papers and presentations. What is worth mentioning is that compared with TAAssess, this class is specifically for MWDCs around the target, and for DCs made by P.Ma.
- Author
- SUN Yazhou, asia..nosp@m.rabb.nosp@m.it@16.nosp<EMAIL_ADDRESS>- Date
- Created: 2018/3/30 Last revised: 2018/5/25, SUN Yazhou.
- Copyright
- 2017-2018, SUN Yazhou.
◆ DetArr_t
◆ ~TAAssessTa()
| TAAssessTa::~TAAssessTa |
( |
| ) |
|
|
virtual |
◆ TAAssessTa()
| TAAssessTa::TAAssessTa |
( |
| ) |
|
|
protected |
◆ EvalDCArr() [1/2]
| void TAAssessTa::EvalDCArr |
( |
const string & |
rootfile, |
|
|
DetArr_t * |
detList, |
|
|
int |
runid = 0 , |
|
|
bool |
isDCArrD = true |
|
) |
| |
|
static |
◆ EvalDCArr() [2/2]
| void TAAssessTa::EvalDCArr |
( |
int |
runId = 0 , |
|
|
bool |
isDCArrD = true |
|
) |
| |
|
virtual |
◆ Instance()
◆ PostEval() [1/2]
| bool TAAssessTa::PostEval |
( |
const string & |
rootfile, |
|
|
int |
round = 0 , |
|
|
bool |
isDCArrD = true , |
|
|
bool |
is3D = true |
|
) |
| |
|
static |
◆ PostEval() [2/2]
| void TAAssessTa::PostEval |
( |
int |
round = 0 , |
|
|
bool |
isDCArrD = true |
|
) |
| |
|
virtual |
evaluation done after Eval event by event
◆ SetROOTFile()
| void TAAssessTa::SetROOTFile |
( |
const string & |
file | ) |
|
|
inline |
assess tracking results of MWDC array U and MWDC array D
◆ fDetList
◆ fInstance
◆ fNullDCArr
| bool TAAssessTa::fNullDCArr = false |
|
staticprotected |
to mark if the DCArr for the class is valid or not
◆ fROOTFile
| string TAAssessTa::fROOTFile |
|
protected |
The documentation for this class was generated from the following files:


Velda Kiara,Founder
Her passion for writing articles lead to the development of The Hold Space organisation.The organisastion is a platform for showcasing our atistic selves through writing and painting.
We aim at providing quality education on various writing courses for example in writing book reviews. We have award winning writers who share their skills and knowledge on this such as Dr.Maya Angelou. We are passionate about teaching people how to write their best stories the only way they know how to. Join us as we tell our story and help you too to write yours.
The following are pictures of posts we have done.


Whether you are looking for a straight forward airport transfer or organising a conference for 200 people then Drivers' Club can provide you with a totally professional and reliable driver.
Drivers' Club has a range of drivers with different experience all at your reach.
We are totally customer focused, so your needs and requirements are the most important thing to us. We go out of our way to give you what you need providing good old style personal service.
In these difficult times value for money is a key component of any service you may require, so we do all we can to give you the very best value we can.
Please have a browse through our website; we are sure you will find what you are looking for, but if you can't, then please
Click on the individual driver for details


--- layout: default title: Home ---
Welcome to Obama-chan's github pages. Right now it's in it's basic stages, however I plan to continue building upon it until it looks awesome and unique. The purpose of my github pages is to host my projects that I finished or currently working on for employers and others to see.


Meeting the underserved needs of the frail and elderly.
Relief RN nursing care and advocacy services allow patients to maintain an independent and dignified lifestyle while remaining safely at home for as long as possible.
My professional nursing oversight will advance wellness initiatives and focus on preventive safety measures. Senior care should be aimed at facilitating the patients' sense of control and assisting them in taking ownership of their health outcomes. Rest assured, I am committed to addressing family members' concerns and delivering any and all healthcare needs as they arise.


---
layout: post
title: Schoolwork since 11/26
date: '2013-12-05T21:55:00.002-07:00'
author: Jacob .
tags:
- homeschool-journal
modified_time: '2013-12-05T21:55:53.346-07:00'
blogger_id: tag:blogger.com,1999:blog-6400085873105072120.post-245643401819852331
blogger_orig_url: http://www.svlark.com/2013/12/schoolwork-since-1126.html
---
Spanish: Started Unit 1 > Lesson 1 > Core Lesson again. More hands-on this time.
Math: Lots of work on counting money
Composition: Week 11, Day two
Science: Reading about skeleton
Grammar: working on Lesson 48 (he, she, him, her, his, hers, it, its)
PE:


Never: Uptime of 30+ days
Expected to be self-healing (auto-restart on failure)
Where's the HTML5?
This is all HTML/CSS/JavaScript.
Many mobile apps are also hybrid native / HTML5.
HTML & JS hosted locally on device
Native plug-ins for many operations
Network operations relegated to background
Web engine built into the app / device (typically WebKit)
More control
Able to customize
Robust & mature test strategy
Targeting 1,000,000+ devices
Failure not an option
Web sites
Mobile apps
The question isn't whether or not you need to harden your site / apps...
Practices - foreign to most web developers
Platforms - maturing but not quite there yet
Libraries - completely clueless
Tools - focused on spot debugging
A robust test discipline is critical.
“Long term stability” testing
“Stress” testing
“Soak” testing
Performance degradation (Inconsistency, Sluggishness)
Abnormal behavior
Unhandled exceptions
Data handling
Lost functionality
Reboots / browser resets
Corrupt state
Ensure clean environment before each test
Reset cookies and local storage
Restart browser and device
Test result database: By device, By build
Statistics matter for performance measurements
7+ samples (min, max, mean)
Comparable data sets
Comparable run-time environments
Comparable server-side environment
Comparable network environment
Purpose: Simulate normal usage over an extended period of time over a large number of instances
# of units: as many as possible (50+ units)
Methodology: Normal usage scenarios run continuously over a period of 1-2 days
Target: No noticeable degradation
Purpose: Simulate a long run-time by accelerating the usage time
# of units: as many as possible (50+)
Methodology: Automated scripts with a key/click rate as fast as possible --> 1 day test can simulate 15-30 days of usage
Target: No noticeable degradation
Purpose: Find latent bugs that only show up over days or weeks
# of units: small(er) number (10-20)
Methodology: Well-defined set of tests run once or twice a day for as many days as possible
Target: 30 days uptime with no degradation in functionality or performance
Test environments
Test automation tools
Know your browser
Brute-force debugging techniques
Racks of physical hardware
Amazon AWS
Multiple browser instances
Proprietary
IR blasters
Help me out - what do you use?
WebKit Web Inspector
Chrome Debugger
GC collector stats
Memory stats
DOM tree stats
Firebug
Console logs
Etc.
Challenge: Figure out how to automate data collection across large # of instances!
Garbage collector memory pressure
Hard upper limit? GC bogs down
No upper limit? Unexpected memory growth
Cache memory pressure
Graphics cache
Fix them (duh) - Brute force
You’ll miss some bugs (duh) - Resilient application architecture(s)
DOM leaks
Timer storms
Dangling callbacks
Exceptions
Beware of your libraries!
DOM leaks - Walk DOM tree, dump # to console
Timer storms - Wrap all timer calls with helpers you can instrument later
Dangling callbacks - Wrapper for all callbacks
Exceptions - window.onerror(), automatic iframe reset
Automatically kills an app that is misbehaving
CPU hog
Memory hog
Automatically restart an app that is mission-critical
App prioritization
Sand-boxing
Process separation
Storage separation (cookies, IndexDB, web storage)
Not always available
No help for problems in big apps (173K lines of JavaScript)
Designed to protect you from others...but why not from yourself?
Okay to restart whole app
Native app framework does not support automatic restart
Small amount of management code in root page
Whole app wrapped in iframe
Delete / re-create iframe when app misbehavior detected
Not okay to restart whole app
Very large apps
High data / network usage on start-up
More resilient user experience
Seamless recovery from bugs with only minor user disruption
More complex
More restrictions
Core management logic in root page
Segment different parts of your app into "micro-apps"
Each micro-app wrapped in iframe
Communication between root page and micro-apps via postMessage()
Most micro-apps started / stopped on-demand
Some micro-apps run longer – restart when misbehavior detected
Duplicate files loaded into each iframe
Extra work to proxy communication across iframe boundaries
With a little work your libraries can be iframe sandbox friendly!


I've been investigating methipropamine as a study drug, so far it seems leagues above ritalin in that it gives no comedown, or other aftereffects. About the same 6 hour duration too. Shame it's completely unresearched, but having used it 6/7 days last week there seem to be no noticeable aftereffects, which is always a good sign.
Have used between 30-70mg/day in the last week, and have settled on 35mg +15mg as great for 5+ hours of solid theory work, followed by 3-4 hours of still increased attentiveness.
35mg typically comes up within 20-30 minutes, providing an ideal level of stimulation until t+2, after which it begins to tail off, the effects are not particularly noticeable after t+4 but effects do seem to remain until around t+6. Taking after a large meal seems to reduce the effect by about 25% but doubled the effective time, removing the need to redose.
Redosing twice (35+20+15 and 25+15+20) seems to cause stomach discomfort resembling trapped air. Neither cannabis or anything else seems to shake it, but it is relatively minor.
Not much appetite suppression at this level, no need to eat but perfectly able to, no need to snack as I work like I normally do to excess. There does seem to be a certain amount of mild time dilation, in that I am not particularly aware of the passing of time and am unable to guess it nearly as well as normal.
All ROAs oral, measured with a decent lab balance.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Analyzing censored variables usually requires the use of optimization algorithms. This package provides an alternative algebraic approach to the task of determining the expected value of a random censored variable with a known censoring point. Likewise this approach allows for the determination of the censoring point if the expected value is known. These results are derived under the assumption that the variable follows an Epanechnikov kernel distribution with known mean and range prior to censoring. Statistical functions related to the uncensored Epanechnikov distribution are also provided by this package.
| Version: | 1.1.1 |
| Depends: | R (≥ 3.0.0) |
| Suggests: | knitr, rmarkdown |
| Published: | 2016-02-04 |
| Author: | Mathias Borritz Milfeldt [aut, cre] |
| Maintainer: | Mathias Borritz Milfeldt <mathias at milfeldt.dk> |
| License: | LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL] |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | epandist results |
| Reference manual: | epandist.pdf |
| Vignettes: |
Introduction to epandist |
| Package source: | epandist_1.1.1.tar.gz |
| Windows binaries: | r-devel: epandist_1.1.1.zip, r-release: epandist_1.1.1.zip, r-oldrel: epandist_1.1.1.zip |
| OS X binaries: | r-release: epandist_1.1.1.tgz, r-oldrel: epandist_1.1.1.tgz |
| Old sources: | epandist archive |
Please use the canonical form https://CRAN.R-project.org/package=epandist to link to this page.


PROscorerTools: Tools to Score Patient-Reported Outcome (PRO) and Other
Psychometric Measures
Provides a reliable and flexible toolbox to score
patient-reported outcome (PRO), Quality of Life (QOL), and other
psychometric measures. The guiding philosophy is that scoring errors can
be eliminated by using a limited number of well-tested, well-behaved
functions to score PRO-like measures. The workhorse of the package is
the 'scoreScale' function, which can be used to score most single-scale
measures. It can reverse code items that need to be reversed before
scoring and pro-rate scores for missing item data. Currently, three
different types of scores can be output: summed item scores, mean item
scores, and scores scaled to range from 0 to 100. The 'PROscorerTools'
functions can be used to write new functions that score more complex
measures. In fact, 'PROscorerTools' functions are the building blocks of
the scoring functions in the 'PROscorer' package (which is a repository
of functions that score specific commonly-used instruments). Users are
encouraged to use 'PROscorerTools' to write scoring functions for their
favorite PRO-like instruments, and to submit these functions for
inclusion in 'PROscorer' (a tutorial vignette will be added soon). The
long-term vision for the 'PROscorerTools' and 'PROscorer' packages is to
provide an easy-to-use system to facilitate the incorporation of PRO
measures into research studies in a scientifically rigorous and
reproducible manner. These packages and their vignettes are intended to
help establish and promote "best practices" for scoring and describing
PRO-like measures in research.
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=PROscorerTools
to link to this page.


Jack and Jill went up one hill and down another.
By any other name, this rose is still a rose.
The superhero towered high above all others.
You will find the culprit over there.
We all know the ways to write good code.
The rain fell mainly upon the green trees of Spain.
Sentences should end with a period, . right?


Popular Areas in nc goast town camping
Ghost Town in the Sky - Maggie Valley, NC - Wild West Mile High ...
Ghost Town in the Sky - Maggie Valley, NC - Wild West Mile High Theme Park. . Any nice RV camping spots and places to hunt gemstones in your area?
http://www.city-data.com/articles/Ghost-Town-in-the-Sky-Maggie-Valley-NC.html
Ghost Town in the Sky | Western NC Attractions
Located at the top of this mountain, in Maggie Valley, is a mile high fun center with an old west setting. Maggie Valley, NC - Located at the top of this mountain, .
http://www.westernncattractions.com/ghost-town-in-the-sky/
Ghost Town in the Sky - Camping in North Carolina
Ghost Town in the Sky is a Wild West-themed amusement park in Maggie Valley, North Carolina, USA. An unusual aspect of this park is that it is located atop a .
http://www.campingnc.com/attractions/ghost_town_in_the_sky.htm
Hillbilly Campground
SHADE: it's been said we's the shadiest and coolest campground in these here . Maggie Valley, N.C. 28751 . We are the closest campground to Ghost Town .
http://www.hillbillycampground.com/
Alamance County, North Carolina - Wikipedia, the free encyclopedia
According to a 1975 study of the history of post offices in North Carolina history by Treasure Index, Alamance County has 27 ghost .
http://en.wikipedia.org/wiki/Alamance_County,_North_Carolina
North Carolina Haunted Places
Oct 19, 2012 . North Carolina Ghost Light Legend Caught on Film! . the town of Caroleen when there came up a horrible thunderstorm. . A group of boy scouts was camping at the location and one scout set his tent up inside the circle.
http://crazyhorsesghost.hubpages.com/hub/North-Carolina-Haunted-Places


WITNESS LIST Business & Commerce Committee February 15, 2005 - 9:00A SB 332 FOR: Grant, Kathy (Texas Cable and Telecommunications Association), Austin, TX Morrow, Bill (Grande Communications), Austin, TX Ratliff, Thomas (Western Wireless), Austin, TX Siegel, Howard (Logix Communications), Austin, TX ON: Erhart, Carl (Verizon Southwest), Austin, TX Gray, Weldon R. (Texas Statewide Telephone Cooperative, Inc. & Eastex Telephone Cooperative, Inc.), Henderson, TX Hudson, Paul (Public Utility Commission), Austin, TX Hunsucker, Michael R. (Sprint Corporation), Austin, TX Newton, Jan (SBC), Austin, TX Whittington, Pam (Public Utility Commission), Austin, TX Registering, but not testifying: For: Flippo, Kristie (Time Warner Telecom), Addison, TX Hicks, Sheri (Texaltel), Austin, TX Krabill, Nancy (Time Warner Telecom), McKinney, TX SJR 7 FOR: Doggett, Robert (Texas Low Income Housing Information Service), Austin, TX Norman, Scott (Texas Association of Reverse Mortgage Lenders), Austin, TX Registering, but not testifying: For: Bhatia, Khelan (AARP), Austin, TX Dennis, Rachel (Texas Bankers Association), Austin, TX Rudd, Jim (Texas Association of Mortgage Brokers), Austin, TX Scurlock, Steve (Independent Bankers Association of Texas), Austin, TX Stegall, Melodie (Credit Union Legislative Coalition), Austin, TX Temple, Larry (Texas Mortgage Bankers Association), Austin, TX On: Pettijohn, Leslie (Consumer Credit Commissioner), Austin, TX


| add_p_ {gtsummary} | R Documentation |
The 'group ='
argument can be passed as a string, rather than with non-standard
evaluation as in add_p. Review the help file for add_p fully documented
options and arguments.
add_p_(x, test = NULL, pvalue_fun = NULL, group = NULL, include = NULL, exclude = NULL)
x |
Object with class |
test |
List of formulas specifying statistical tests to perform,
e.g.
Tests default to |
pvalue_fun |
Function to round and format p-values.
Default is style_pvalue.
The function must have a numeric vector input (the numeric, exact p-value),
and return a string that is the rounded/formatted p-value (e.g.
|
group |
Column name of an ID or grouping variable. The column can be
used calculate p-values with correlated data (e.g. when the test argument
is |
include |
Character vector of variable names to include from output. |
exclude |
Character vector of variable names to exclude from output. |


We have all worked with data at some point of time. Depending on the source, the data may not
be regular, smooth and uniform, and requires refinement before applying for any practical use. This software
lets you do that very easily and efficiently in a graphical way including many others useful data operation.
Interactive Data Editor let's you,
Plot any 2D and gridded 3D data.
Edit/Refine data including many useful data operations.
Fit a function to the data.
Take a look at the Features or Tutorial to learn
about the different features of Interactive Data Editor.
Download the software to get started.
Interactive Data Editor supports several useful features to work with data,
Edit one or multiple points by dragging with mouse and keyboard keys.
Automatically smooth data using spline and linear Kernel smoother.
Fit any function to the data.
Plot any 2D and gridded 3D data
Fully configurable plot window with LaTeX support.
Export images in five format in any resolution.
Simultaneously work with multiple datasets.
Fill missing values.
Remove bad data points.
Repeat or mirror data any required times.
Filter data subject to a given condition.
Swap data between two lines or surfaces.
Supports undo/redo.
Change sign of any selected part of the data.
Spreadsheet editor with all basic excel like features including formula parsing.
And many more... Check out the Tutorial to see different features in
action.
The software is available as pre-built binaries for different platforms. Install the appropriate installer
for your system
The software can also be downloaded directly from snap store. If you are using Linux, do prefer installing
it
from snap for one step installation and smooth auto-update


New shuttle service debuts at Sabino Canyon Recreation Area
TUCSON, Ariz. (AP) - New shuttle service for visitors debuted Saturday at Sabino Canyon Recreation Area on the outskirts of Tucson.
Officials say the emission-free electric shuttles will operate hourly on the Sabino Canyon route between 9 a.m. and 4 p.m. and that the service eventually will have five of the open-air vehicles capable of carrying up to 60 passengers.
Gas-powered transit shuttles will continue to operate on the Bear Canyon route every half hour starting at 9:15 a.m. through 4:15 p.m.
Partners in the new service include the U.S. Forest Service, Tucson Electric Power and Regional Partnering Center, the owner of the shuttles operated by Total Transit Enterprises/Total Ride.
TEP contributed $1.5 million in startup costs and a $1 million interest-free, 10-year loan.
Sign up for Daily Newsletters
Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


this is about retirement planning
different accounts and what the do for you.
the 401 retirement plan/account is employer based, meaning that you contribute to one when you earn a paycheck as an employee. this deffers from being a contract employee. your employer will match your contributions or just give you a set amount to a retirement account. when an employer matches your contribution, they will match you a percentage up to an fixed amount. such as 50% up to 6%. this means if you contribute 6% of your pay to the 401k they will add on top of ALL other pay another 3%. this means that per check you will be putting away a total of 9% per check. if you contribute 4% then your employer will contribute 2% for a total of 6%. some employers will will match dollar for dollar to a set amount, like 3 and 3 up to 3.
if you do not take advantage of the contribution matching at a job you are simply leaving money on the table. when you leave an employer you may not be contributing to it anymore but that does not mean you do not have let it be or forfit it. you have options. you can usually keep it with the broker that is with that company and still manage it. however when you start a new retirement account with a new employer you can roll it over into the new account. this is normal and a smart move.
pre-tax contributions are exacly that taken pre-tax and put in the account so that you can let it grow and tax it when you pull it out in retirement. hopefully you are in a smaller tax bracket when you are retired.
roth contributions are post tax. that means you pay the taxes up front and after retirement you can withdraw with out tax. pretty simple but it is a good idea to combine your contribution types to minimize risk and allow you options in taxes when withdrawing.
rollover funds may be subject to restrictions and or other stuff MORE INFO NEEDED
a rollover is when you take an account you left with a previouse employer or in an IRA and you want to consolidate it ino a single account.
when you participate in a retirement plan you will be narrowed down to certain investment options by the people managing the plan and the contract involved. usually you are restricted to mutual funds and they are pretty safe and narrow in scope. the managers intention is to provide a near full proof investment option set.
usually retirement plans will include...
some retirement plans will allow you to use an external brokerage after you have accumulated a certain amount with mutual fund only restrictions. but that opens you up to the entire mutual fund market.
you should become familiar with your plans features and restrictions.
the accounts i have experience with allow you to move principal or all investment funds from a fund and re alocate it. I have used this to pull money from investments into stable cash when i thought the market was unstable or headed for a crash/dip. you must be careful because some funds will give you time restrictions on moving money in and out of funds.
mutual funds have minimum buy in's. usually 2.5k or 5k. there will be probably minimums for additional investments. this


Sorry for inconvenience
We are upgrading
Please vist our breakdq office near NETWORLD AND MURTHY STUDIO for BAHUBALI TICKETS
For Booking Online, Download our Android App
Dowload Here


Ploror688.github.io
Nightingales: The Extraordinary Upbringing and Curious Life of Miss Florence Nightingale Gillian Gill on Amazon.com. FREE shipping on qualifying offers. Florence. Florence Nightingale is famous for changing the way nurses were perceived during her time, raising help me write an essay about myself standards for nursing KS2 World Cup Competition Robin Hood Primary School.
Board of Management Meeting 22/10/2018 Agreed Report: Agreed Report: The Board of Management ratified a New Homework Policy, Anti-bullying Policy Draft 8 and AUP Policy.
Get an answer for 'Why Is Florence Nightingale Famous?' and find homework help for other History questions at eNotes.
Improve your Child's Attention Span - Boys Forward Institute. Florence nightingale primary homework help - select the service, and our experienced scholars will accomplish your task flawlessly Fast and trustworthy writings. Sa mayamang̃ bayan ng̃ Santa Cruz, Maynilà ay dito nakákita ng̃ unang̃ liwanag, ang̃ isáng̃ sang̃gól na babaing̃ tinawag na Teodora, anák ni Kapitáng̃. Seamless Care is a complete medical equipment and fixtures management firm. We are not only experts in the planning and procurement of world class medical equipment. Florence Nightingale Questions and Answers - eNotes.com. Coventry City Council provides an extensive resource of web links to related web sites and organisations of interest and help to both local citizens and the general.
HOMEWORK HELP: TALAMBUHAY NG MGA BAYANI. Woodlands History homework help brings history alive with easy to read information and photographs on many homework topics including Tudors, Victorians, Romans. Henry Clay and the Missouri Compromise of 1820 - Video.
Fascinating facts, did-you-knows, information, videos, images and timeline all about Florence Nightingale to support primary school history topic. Mere School, Wiltshire, a single form entry primary school and feeder primary for Gillingham School in Dorset.
Florence Nightingale Introduction How well do you know Florence Nightingale? Do you know that she rebelled her mother and sister for her call in nursing? Florence. Florence Nightingale Homework Help - Why Is Florence. Florence nightingale primary homework help - nardonegroup.org. Florence Nightingale Homework Help ― Why Is Florence. Florence Nightingale - Your Nursing Assignments Partner. External links and resources Coventry City Council. Florence Nightingale - Primary Homework
Funny and educational - a great collection of stories and poems for kids based on tales from British History. Who was Emmeline Pankhurst? Watch this story, one of our 'British tales' videos about characters and people from British history Robin Hood Primary School The next instalment of our Olympic fortnight was this year World Cup competition. Florence Nightingale Questions and Answers - Discover the eNotes.com community of teachers, mentors and students just like you that can answer any question. Florence Nightingale was a famous nurse. She is best known for the work she did to care for the wounded soldiers during the Crimean In 1854 the Crimean War broke out between the Russians and the Turks, the French, and the British. Nightingale traveled to Turkey with a group of nurses. Why Is Florence Nightingale Famous? eNotes.
Primary school visits Florence Nightingale Museum London. Florence Nightingale was a truly inspirational nurse. Why is Florence Nightingale Famous? Florence Nightingale is famous for her nursing work during.
Florence Nightingale for children Florence Nightingale. Florence Nightingale Homework Help - Facts About Florence. The Oaks Primary School Home; About Us. Welcome; Contact Details; Vision Values; Meet The Staff; Meet The Governors.
The Victorians Homework Tasks by tsyczynski - Teaching. Florence Nightingale - Kids Britannica Kids Homework. Dear Twitpic Community - thank you for all the wonderful photos you have taken over the years. We have now placed Twitpic in an archived state. In 1819, Missouri applied for statehood, threatening to tip the balance of senatorial power in favor of the slave states. Find out how Henry.
Cookies on the BBC website. British Broadcasting Corporation Home. In nightingale to see this content you need to have both Javascript enabled and Flash installed. 50 ways to improve your child’s attention span and behaviour without drugs Used with permission from Thomas Armstrong. In 1854 the English nurse Florence Nightingale Florence Nightingale was named after the city where she was born—Florence, Improved homework.
Florence Nightingale - Students Britannica. Norwood Primary School . 1st November 2018 - Year R and Pre-School Open Morning at 9.30am 2nd November 2018 - Ukulele Class Assembly Emmeline Pankhurst LearnEnglish Kids British Council. Do you need help with your homework? Are you learning about Florence Nightingale? Do you have a question you would like to ask or need help with your homework. Homework tasks designed to last up to 10 weeks. A selection of creative and research activities. Can be edited to suit your stage or class. Facts About Florence Nightingale - Primary Facts. Nightingales: The Extraordinary Upbringing and Curious.


Big Hit reveals Jungkook had admitted to breaking traffic law + came to agreement with victim
by jennywillBig Hit Entertainmnet confirmed that Jungkook had indeed violated a traffic law.
The label said, "Currently, the police have not finished their proceedings on the matter, so we cannot reveal the details. However, last week, Jungkook made a mistake and did cause an accident with another car. Neither the victim nor Jungkook was injured badly. After the accident, Jungkook admitted that he violated the law, and followed the procedure diligently. He came to an amicable agreement with the victim. We apologize once again to the victim, and also apologize for worrying the fans."
Thank goodness it wasn't anything major!


JSON Web Tokens are an open, industry standard RFC 7519 method for representing claims securely
between two parties.
jwt.io
→ Nothing more than a JSON-Object, which can be trusted to not have been modified since it was created.
Tokens consist of three parts separated by . (dots):
→ xxx.yyy.zzz
Normally consists of two parts:
{
"alg": "HS256",
"typ": "JWT"
}
A payload could look like this:
{
"sub": "1234567890",
"name": "John Doe",
"admin": true
}
→ Builds second part of the token
→ Debugger
For the authentication use case:
Authorization: Bearer <token>


Mom, three young children dead in San Diego shooting
by Sara DornA San Diego mom and three of her four sons were killed in a shooting early Saturday, according to a report.
Cops found six people with gunshot wounds inside the family’s home in Paradise Hills, a suburb of San Diego, before 7 a.m. Saturday, ABC 10 San Diego reported, citing San Diego Police Lt. Matt Dobbs.
Two of the boys, ages 9 and 5, were taken to a nearby hospital, where they were pronounced dead. A 3-year-old boy, the 29-year-old mother and a 31-year-old man were found at the scene with fatal gunshot wounds. It’s unclear how the man was affiliated with the family.
The woman’s 11-year-old son survived the shooting and was undergoing surgery Saturday.
The mom, who had not been named by authorities as of Saturday afternoon, lived with her kids in an apartment on the property, while their relatives lived in the main residence, neighbors told ABC 10.
One neighbor who saw police at the home hours earlier on Friday night said the dispute appeared to be a domestic incident.
Police believe one of the dead adults is the shooter.


class: center, middle, inverse, title-slide .title[ # Introduction to GAM(M)S ] .subtitle[ ## Generalised Additive (Mixed) Models ] .author[ ### Dr Stefano Coretta ] .date[ ### 12-14 December 2022 ] --- class: inverse background-image: url(../img/time-travel.jpg) background-size: contain # Time travel... --- layout: true ## Linear models --- `$$y = 3 + 2x$$` where `\(x = (2, 4, 5, 8, 10, 23, 36)\)` ---  ---  --- * In research contexts, we have `\(x\)` and `\(y\)`. * For example, vowel duration and VOT, speech rate and pitch, etc. ---  --- * The formula: `\(y = \beta_0 + \beta_1x\)` * `\(\beta_0\)` is the **intercept** * `\(\beta_1\)` is the **slope** -- * We know `\(x\)` and `\(y\)`. * We need to estimate `\(\beta_0\)`, `\(\beta_1\)` = `\(\hat{\beta_0}, \hat{\beta_1}\)`. -- * We can add more predictors: * `\(y = \beta_0 + \beta_1x_1 + \beta_2x_2 + ... + \beta_nx_n\)` -- * `lm(y ~ x, data)` ($y$ as a function of `\(x\)`). ---  ---  --- layout: false layout: true ## LM with non-linear data ---  ---  ---  --- How to account for non-linearity in a linear model? -- * Use **higher-degree polynomials** * quadratic: `\(y = \beta_0 + \beta_1x + \beta_2x^2\)` * cubic: `\(y = \beta_0 + \beta_1x + \beta_2x^2 + \beta_3x^3\)` * `\(n\)`th: `\(y = \beta_0 + \beta_1x + \beta_2x^2 + \beta_3x^3 + ... + \beta_nx^n\)` ---  ---  ---  ---  ---  --- layout: false ## Generalised additive models * **G**enrealised **A**dditive **M**odel**s** (GAMs) * `\(y = f(x)\)` * `\(f(x)\)` = some function of `\(x\)` (or *smooth function*) --- ## Smooth terms * LMs have **parametric terms** * `\(\beta_nx_n\)` * `x` in `R` * For linear effects. -- * GAMs add (non-parametric) **smooth terms** (or simply smooths, also smoothers): * `\(f(x)\)` * `s(x)` in `R` * For non-linear effects. -- * `library(mgcv); gam(y ~ s(x), data)`: `\(y\)` as *some* function of `\(x\)` --- ## Smoothing splines, basis, basis functions * Smooths in GAMs are **smoothing splines**. * Splines are defined piecewise with a set of polynomials. -- * The set of polynomials is called a **basis**. * The basis is composed of **basis functions** (the polynomials). * The number of basis functions is called the **dimension of the basis** (`k` in mgcv). -- * A spline is the sum of the products of each basis function and its coefficient. --- ## Basis functions and knots  --- ## Smoothing parameter * Wiggliness is related to **number of basis functions**. * more basis functions, more wiggliness (less smoothing). -- * The **smoothing parameter** penalises wiggliness. * High values = less wiggliness (more smoothing). * This is estimated from the data. --- ## Smoothing splines * There are **several kinds of splines**. * Each has its own basis functions. * Most common splines: * *Thin plate regression splines*. * *Cubic regression splines*. * For more info, run `?smooth.terms`. --- layout: true ## A simple GAM --- ```r simple <- gam( y ~ # Cubic regression spline. # k is the dimension of the basis. s(x, bs = "cr", k = 10), data = sim_nl_a ) ``` --- ```r summary(simple) ``` ``` ## ## Family: gaussian ## Link function: identity ## ## Formula: ## y ~ s(x, bs = "cr", k = 10) ## ## Parametric coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 10.1165 0.1028 98.37 <2e-16 ## ## Approximate significance of smooth terms: ## edf Ref.df F p-value ## s(x) 6.939 8.01 38.69 <2e-16 ## ## R-sq.(adj) = 0.755 Deviance explained = 77.2% ## GCV = 1.1593 Scale est. = 1.0681 n = 101 ``` --- ```r predict_gam(simple, series = "x", length_out = 30) %>% plot() ```  --- ```r simple_5 <- gam(y ~ s(x, bs = "cr", k = 5), data = sim_nl_a) predict_gam(simple_5, series = "x", length_out = 30) %>% plot() ```  --- layout: false layout: true ## Comparing groups ---  ---  --- * `by`-variables with ordered factors ```r compare <- gam( y ~ group + s(x, bs = "cr", k = 5) + s(x, bs = "cr", k = 5, by = group), data = sim_nl ) ``` --- To use `by`-variables with ordered factors: * Change factor to an **ordered factor**. * Change factor contrast to **treatment contrast** (`contr.treatment`). * The default in ordered factors is `contr.poly`, this won't work. * Include the factor as a **parametric term**. * Include a **reference smooth** and a **difference smooth** with the `by`-variable. --- ```r sim_nl <- sim_nl %>% mutate(group_o = ordered(group, levels = c("a", "b"))) contrasts(sim_nl$group_o) <- "contr.treatment" ``` --- ```r compare <- gam( y ~ # parametric term group_o + # reference smooth s(x, bs = "cr", k = 5) + # difference smooth s(x, bs = "cr", k = 5, by = group_o), data = sim_nl ) ``` --- ```r summary(compare) ``` ``` ## ## Family: gaussian ## Link function: identity ## ## Formula: ## y ~ group_o + s(x, bs = "cr", k = 5) + s(x, bs = "cr", k = 5, ## by = group_o) ## ## Parametric coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 10.1165 0.1096 92.34 <2e-16 ## group_ob -2.4947 0.1549 -16.10 <2e-16 ## ## Approximate significance of smooth terms: ## edf Ref.df F p-value ## s(x) 4.000 4.000 64.99 <2e-16 ## s(x):group_ob 3.576 3.896 39.67 <2e-16 ## ## R-sq.(adj) = 0.873 Deviance explained = 87.8% ## GCV = 1.2725 Scale est. = 1.2122 n = 202 ``` --- ```r library(tidygam) predict_gam(compare, series = "x", length_out = 30) %>% plot(comparison = "group_o") ```  --- layout: false class: center middle inverse ## HANDS-ON 1 --- layout: true ## Comparing across groups (interactions) --- * Technically, GAMs **don't allow interactions**. * They are ADDITIVE (interactions require multiplication). -- * We can get interaction-like comparisons by creating **factor interactions** and using them as `by`-variables. --- Let's use the ACCH data frame from the hands-on. ```r acch <- read_delim("data/ACCH.txt") %>% mutate( StimSlide.RT = StimSlide.RT/1000, RT_log = log(StimSlide.RT), ACC = ifelse(StimSlide.ACC == 0, "incorrect", "correct") ) acch ``` ``` ## # A tibble: 4,796 × 13 ## Subject Group Trial Condi…¹ item Remove StimS…² StimS…³ Struc…⁴ Number ## <dbl> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <chr> <chr> ## 1 202 monolingual 1 1 1 no 1 6.21 Subject Match ## 2 202 monolingual 1 1 2 no 1 3.89 Subject Match ## 3 202 monolingual 15 1 3 no 0 4.37 Subject Match ## 4 202 monolingual 26 1 4 no 1 3.70 Subject Match ## 5 202 monolingual 15 1 5 no 1 5.63 Subject Match ## 6 202 monolingual 33 1 6 no 1 3.78 Subject Match ## 7 202 monolingual 23 1 7 no 1 4.68 Subject Match ## 8 202 monolingual 26 1 8 no 1 3.78 Subject Match ## 9 202 monolingual 28 1 9 no 1 4.21 Subject Match ## 10 202 monolingual 24 1 10 no 1 5.42 Subject Match ## # … with 4,786 more rows, 3 more variables: First_NP <chr>, RT_log <dbl>, ## # ACC <chr>, and abbreviated variable names ¹Condition, ²StimSlide.ACC, ## # ³StimSlide.RT, ⁴Structure ``` --- Now, let's create a factor interaction between `Group` and `Structure`. We also need to make it into an ordered factor with treatment contrasts. ```r acch <- acch %>% mutate( Gr_St = as.ordered(interaction(Group, Structure)) ) contrasts(acch$Gr_St) <- "contr.treatment" head(acch$Gr_St) ``` ``` ## [1] monolingual.Subject monolingual.Subject monolingual.Subject ## [4] monolingual.Subject monolingual.Subject monolingual.Subject ## attr(,"contrasts") ## [1] contr.treatment ## 4 Levels: bilingual.Object < monolingual.Object < ... < monolingual.Subject ``` --- ```r rt_gam <- gam( RT_log ~ Gr_St + s(Trial) + s(Trial, by = Gr_St), data = acch ) ``` --- ```r summary(rt_gam) ``` ``` ## ## Family: gaussian ## Link function: identity ## ## Formula: ## RT_log ~ Gr_St + s(Trial) + s(Trial, by = Gr_St) ## ## Parametric coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 1.64593 0.01194 137.842 < 2e-16 ## Gr_Stmonolingual.Object -0.04542 0.01562 -2.908 0.00365 ## Gr_Stbilingual.Subject -0.09169 0.01678 -5.466 4.85e-08 ## Gr_Stmonolingual.Subject -0.10281 0.01551 -6.629 3.76e-11 ## ## Approximate significance of smooth terms: ## edf Ref.df F p-value ## s(Trial) 1.000 1.000 1.110 0.292 ## s(Trial):Gr_Stmonolingual.Object 4.026 4.966 1.115 0.328 ## s(Trial):Gr_Stbilingual.Subject 3.285 4.070 1.830 0.125 ## s(Trial):Gr_Stmonolingual.Subject 2.342 2.918 1.938 0.122 ## ## R-sq.(adj) = 0.0145 Deviance explained = 1.73% ## GCV = 0.13978 Scale est. = 0.13935 n = 4796 ``` --- ```r # library(tidygam) predict_gam(rt_gam, series = "Trial", length_out = 50) %>% plot(comparison = "Gr_St") ```  --- ```r predict_gam(rt_gam, series = "Trial", length_out = 50, separate = list(Gr_St = c("Group", "Structure"))) %>% plot(comparison = "Group") + facet_grid(Structure ~ .) ```  --- ```r get_difference(rt_gam, "Trial", list(Gr_St = c("bilingual.Object", "monolingual.Object"))) %>% plot() ```  --- ```r get_difference(rt_gam, "Trial", list(Gr_St = c("bilingual.Object", "bilingual.Subject"))) %>% plot() ```  --- ```r get_difference(rt_gam, "Trial", list(Gr_St = c("monolingual.Object", "monolingual.Subject"))) %>% plot() ```  --- ### Reporting Generalised additive mixed models were fitted with mgcv v1.8-26 (Wood 2011; Wood 2017). The smooths used thin plate regression splines as basis (Wood 2003). The ordered factor difference smooths method described in Sóskuthy (2017) and Wieling (2018) was used to model the effect of factor terms in GAMs. Logged reaction times were modelled with a GAMM with the following terms: a parametric term for the interaction of Group (monolingual and bilingual) and Structure (subject and object); a reference smooth over Trial; a difference smooth term over Trial with a by-Group/Structure interaction variable. According to the difference smooth plots, there is a significant difference in the effect of Trial on reaction times between bilingual and monolingual subjects in the Object condition. The difference is located along Trials 1 to 20 and about 28 to 38. There is also a significant difference in Trial effect in the bilingual group, between the Object and Subject condition, over trials 1 to about 35. Finally, there is a significant difference in effect of Trial on reaction times in the monolingual group, between Object and Subject, over trial 1 and 30. --- layout: false layout: true ## Tensor product smooths and interactions --- | | LM | GAM | |-----------------|---------|------------------------| | Num × cat | `num:cat` | `s(num, by = cat)` | | Num × cat × cat | `num:cat:cat` | `s(num, by = cat_cat)` | --- | | LM | GAM | |-----------------|---------|------------------------| | Num × cat | `num:cat` | `s(num, by = cat)` | | Num × cat × cat | `num:cat:cat` | `s(num, by = cat_cat)` | | Num × num | `num:num` | `te(num, num)` | | Num × num × cat | `num:num:cat` | `te(num, num, by = cat)` | --- * **Tensor product smooths** are smooths that create interactions between two numeric predictors. * Tensor product smooths are specified with `te()` or `ti()`. * `ti(x) + ti(y) + ti(x, y)`. * `te(x, y)`. -- Note that if the two numeric predictors are on the same scale (isotropic), you can use `s()` instead of `te/ti()`.  * **G**eneralised **A**dditive **M**ixed **M**odel (GAMM) * Two ways of including random effects: * Use the `"re"` basis function for random intercept and slopes. * Include a **random smooth** term with the **factor smooth interaction** as basis (`bs = "fs"`). --- * **Factor smooth interaction**: * `bs = "fs"`. * A smooth is fitted at each level of a factor. * The random effect variable *needs to be a factor*. * `s(Trial, Subject, bs = "fs", m = 1)` --- ```r acch <- acch %>% mutate(Structure_o = as.ordered(Structure)) contrasts(acch$Structure_o) <- "contr.treatment" ``` --- ```r rt_gam_2 <- gam( RT_log ~ Gr_St + s(Trial) + s(Trial, by = Gr_St) + s(Trial, Subject, bs = "fs", m = 1) + s(Trial, Subject, by = Structure_o, bs = "fs", m = 1), data = acch ) ``` --- .medium[ ```r summary(rt_gam_2) ``` ``` ## ## Family: gaussian ## Link function: identity ## ## Formula: ## RT_log ~ Gr_St + s(Trial) + s(Trial, by = Gr_St) + s(Trial, Subject, ## bs = "fs", m = 1) + s(Trial, Subject, by = Structure_o, bs = "fs", ## m = 1) ## ## Parametric coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 1.618266 0.037652 42.979 <2e-16 ## Gr_Stmonolingual.Object 0.001025 0.062932 0.016 0.9870 ## Gr_Stbilingual.Subject -0.023624 0.052270 -0.452 0.6513 ## Gr_Stmonolingual.Subject -0.104227 0.046724 -2.231 0.0257 ## ## Approximate significance of smooth terms: ## edf Ref.df F p-value ## s(Trial) 1.6225 1.8555 0.177 0.821 ## s(Trial):Gr_Stmonolingual.Object 1.0000 1.0000 0.000 0.988 ## s(Trial):Gr_Stbilingual.Subject 1.7496 2.2769 0.884 0.373 ## s(Trial):Gr_Stmonolingual.Subject 0.6667 0.6667 0.041 0.869 ## s(Trial,Subject) 19.8833 24.2136 3.472 <2e-16 ## s(Trial,Subject):Structure_oSubject 1.6667 1.6667 0.929 0.279 ## ## Rank: 96/97 ## R-sq.(adj) = 0.0301 Deviance explained = 3.61% ## GCV = 0.13803 Scale est. = 0.13714 n = 4796 ``` ] --- ```r predict_gam( rt_gam_2, separate = list(Gr_St = c("Group", "Structure")), exclude_terms = c("s(Trial,Subject)", "s(Trial,Subject):Structure_oSubject") ) %>% select(-Structure_o) %>% distinct() %>% plot(series = "Trial", comparison = "Structure") + facet_grid(~ Group) ``` --- .center[ ![:scale 60%](index_files/figure-html/rt-gam-2-plot-1.png) ] --- ```r get_difference( rt_gam_2, "Trial", list(Gr_St = c("bilingual.Object", "bilingual.Subject")) ) %>% plot() ``` Due to a bug in the `get_difference()` function, you get an error when there is no significant difference across the entire smooth (I am hoping to fix this asap). --- ```r get_difference(rt_gam_2, "Trial", list(Gr_St = c("monolingual.Object", "monolingual.Subject"))) %>% plot() ```  --- ```r pdq_20 <- readRDS("data/pdq_20.rds") %>% mutate( subject = as.factor(subject), Co_Ag = as.ordered(interaction(Condition, Age)) ) contrasts(pdq_20$Co_Ag) <- "contr.treatment" pdq_20 ``` ``` ## # A tibble: 88,008 × 8 ## subject trial Condition Age timebins Soundfile pupil.bin…¹ Co_Ag ## <fct> <dbl> <chr> <chr> <dbl> <chr> <dbl> <ord> ## 1 1 1 Sparse YA -500 NAMword_675_Mule.wav 84.5 Spar… ## 2 1 1 Sparse YA -480 NAMword_675_Mule.wav 75.8 Spar… ## 3 1 1 Sparse YA -460 NAMword_675_Mule.wav 65.4 Spar… ## 4 1 1 Sparse YA -440 NAMword_675_Mule.wav 54.3 Spar… ## 5 1 1 Sparse YA -420 NAMword_675_Mule.wav 35.7 Spar… ## 6 1 1 Sparse YA -400 NAMword_675_Mule.wav 20.2 Spar… ## 7 1 1 Sparse YA -380 NAMword_675_Mule.wav 8.72 Spar… ## 8 1 1 Sparse YA -360 NAMword_675_Mule.wav 0.680 Spar… ## 9 1 1 Sparse YA -340 NAMword_675_Mule.wav -11.4 Spar… ## 10 1 1 Sparse YA -320 NAMword_675_Mule.wav -23.3 Spar… ## # … with 87,998 more rows, and abbreviated variable name ¹pupil.binned ``` --- ```r pdq_gam_2 <- bam( pupil.binned ~ Co_Ag + s(trial) + s(trial, by = Co_Ag) + s(trial, subject, bs = "fs", m = 1), data = pdq_20, method = "fREML" ) ``` --- ```r summary(pdq_gam_2) ``` ``` ## ## Family: gaussian ## Link function: identity ## ## Formula: ## pupil.binned ~ Co_Ag + s(trial) + s(trial, by = Co_Ag) + s(trial, ## subject, bs = "fs", m = 1) ## ## Parametric coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 158.45 53.18 2.979 0.00289 ## Co_AgSparse.OA -597.33 66.55 -8.976 < 2e-16 ## Co_AgDense.YA -79.92 66.48 -1.202 0.22934 ## Co_AgSparse.YA -66.90 66.51 -1.006 0.31448 ## ## Approximate significance of smooth terms: ## edf Ref.df F p-value ## s(trial) 8.537 8.577 21.488 < 2e-16 ## s(trial):Co_AgSparse.OA 8.939 8.986 38.045 < 2e-16 ## s(trial):Co_AgDense.YA 7.445 7.554 7.836 2.23e-05 ## s(trial):Co_AgSparse.YA 7.691 7.783 10.760 < 2e-16 ## s(trial,subject) 168.348 178.000 140.639 < 2e-16 ## ## R-sq.(adj) = 0.251 Deviance explained = 25.2% ## fREML = 6.1948e+05 Scale est. = 74604 n = 88008 ``` --- ```r predict_gam( pdq_gam_2, separate = list(Co_Ag = c("Condition", "Age")), exclude_terms = c("s(trial,subject)"), length_out = 50) %>% plot(series = "trial", comparison = "Condition") + facet_grid(~ Age) ``` --- .center[ ![:scale 60%](index_files/figure-html/pdq-gam-2-plot-1.png) ] --- ```r gam.check(pdq_gam_2) ```  ``` ## ## Method: fREML Optimizer: perf newton ## full convergence after 16 iterations. ## Gradient range [-4.080442e-05,6.895211e-05] ## (score 619476.4 & scale 74604.25). ## Hessian positive definite, eigenvalue range [0.2772215,44000.13]. ## Model rank = 220 / 220 ## ## Basis dimension (k) checking results. Low p-value (k-index<1) may ## indicate that k is too low, especially if edf is close to k'. ## ## k' edf k-index p-value ## s(trial) 9.00 8.54 0.96 <2e-16 ## s(trial):Co_AgSparse.OA 9.00 8.94 0.96 0.005 ## s(trial):Co_AgDense.YA 9.00 7.44 0.96 <2e-16 ## s(trial):Co_AgSparse.YA 9.00 7.69 0.96 <2e-16 ## s(trial,subject) 180.00 168.35 0.96 <2e-16 ``` --- ``` Method: fREML Optimizer: perf newton full convergence after 16 iterations. Gradient range [-4.080436e-05,6.895202e-05] (score 619477.1 & scale 74604.25). Hessian positive definite, eigenvalue range [0.2772215,44000.13]. Model rank = 220 / 220 Basis dimension (k) checking results. Low p-value (k-index<1) may indicate that k is too low, especially if edf is close to k'. k' edf k-index p-value s(trial) 9.00 8.54 0.98 0.050 s(trial):Co_AgSparse.OA 9.00 8.94 0.98 0.055 s(trial):Co_AgDense.YA 9.00 7.44 0.98 0.035 s(trial):Co_AgSparse.YA 9.00 7.69 0.98 0.065 s(trial,subject) 180.00 168.35 0.98 0.070 ``` --- ```r pdq_gam_3 <- bam( pupil.binned ~ Co_Ag + s(trial, k = 15) + s(trial, by = Co_Ag, k = 15) + s(trial, subject, bs = "fs", m = 1), data = pdq_20, method = "fREML" ) ``` --- ```r gam.check(pdq_gam_3) ```  ``` ## ## Method: fREML Optimizer: perf newton ## full convergence after 11 iterations. ## Gradient range [-0.001896662,0.0002222863] ## (score 618011.4 & scale 72239.12). ## Hessian positive definite, eigenvalue range [2.199531,44000.13]. ## Model rank = 240 / 240 ## ## Basis dimension (k) checking results. Low p-value (k-index<1) may ## indicate that k is too low, especially if edf is close to k'. ## ## k' edf k-index p-value ## s(trial) 14.0 11.3 0.99 0.14 ## s(trial):Co_AgSparse.OA 14.0 12.6 0.99 0.15 ## s(trial):Co_AgDense.YA 14.0 13.3 0.99 0.17 ## s(trial):Co_AgSparse.YA 14.0 13.2 0.99 0.20 ## s(trial,subject) 180.0 165.2 0.99 0.17 ``` --- ``` Method: fREML Optimizer: perf newton full convergence after 11 iterations. Gradient range [-0.001896662,0.0002222863] (score 618012.1 & scale 72239.12). Hessian positive definite, eigenvalue range [2.199531,44000.13]. Model rank = 240 / 240 Basis dimension (k) checking results. Low p-value (k-index<1) may indicate that k is too low, especially if edf is close to k'. k' edf k-index p-value s(trial) 14.0 11.3 1 0.60 s(trial):Co_AgSparse.OA 14.0 12.6 1 0.53 s(trial):Co_AgDense.YA 14.0 13.3 1 0.53 s(trial):Co_AgSparse.YA 14.0 13.2 1 0.58 s(trial,subject) 180.0 165.2 1 0.59 ``` --- ```r predict_gam( pdq_gam_3, separate = list(Co_Ag = c("Condition", "Age")), exclude_terms = c("s(trial,subject)"), length_out = 50) %>% plot(series = "trial", comparison = "Condition") + facet_grid(~ Age) ``` --- .center[ ![:scale 60%](index_files/figure-html/pdq-gam-3-plot-1.png) ] --- layout: false class: center middle inverse ## HANDS-ON 3 --- layout: false layout: true ## GAMs with binomial/Bernoulli families --- * So far we've been looking at GAM(M)s with a Gaussian family. -- * GAM(M)s can also be fitted to binomial/Bernoulli data! * `family = binomial()` * This is like a logistic regression fitted with `glm(er)`, but it allows for non-linear effects. --- ```r acch %>% select(Subject, Group, Trial, ACC, Structure, Number) ``` ``` ## # A tibble: 4,796 × 6 ## Subject Group Trial ACC Structure Number ## <dbl> <chr> <dbl> <chr> <chr> <chr> ## 1 202 monolingual 1 correct Subject Match ## 2 202 monolingual 1 correct Subject Match ## 3 202 monolingual 15 incorrect Subject Match ## 4 202 monolingual 26 correct Subject Match ## 5 202 monolingual 15 correct Subject Match ## 6 202 monolingual 33 correct Subject Match ## 7 202 monolingual 23 correct Subject Match ## 8 202 monolingual 26 correct Subject Match ## 9 202 monolingual 28 correct Subject Match ## 10 202 monolingual 24 correct Subject Match ## # … with 4,786 more rows ``` --- ```r acc_gam_2 <- bam( StimSlide.ACC ~ Gr_St + s(Trial) + s(Trial, by = Gr_St) + s(Trial, Subject, bs = "fs", m = 1), data = acch, family = binomial() ) ``` --- .medium[ ```r summary(acc_gam_2) ``` ``` ## ## Family: binomial ## Link function: logit ## ## Formula: ## StimSlide.ACC ~ Gr_St + s(Trial) + s(Trial, by = Gr_St) + s(Trial, ## Subject, bs = "fs", m = 1) ## ## Parametric coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) 2.84896 0.64918 4.389 1.14e-05 ## Gr_Stmonolingual.Object -1.87898 1.07995 -1.740 0.0819 ## Gr_Stbilingual.Subject 2.11010 0.24004 8.791 < 2e-16 ## Gr_Stmonolingual.Subject -0.05063 1.09055 -0.046 0.9630 ## ## Approximate significance of smooth terms: ## edf Ref.df Chi.sq p-value ## s(Trial) 1.000 1.000 0.014 0.90639 ## s(Trial):Gr_Stmonolingual.Object 1.000 1.000 0.075 0.78392 ## s(Trial):Gr_Stbilingual.Subject 1.000 1.000 4.996 0.02540 ## s(Trial):Gr_Stmonolingual.Subject 3.694 4.581 13.532 0.02071 ## s(Trial,Subject) 17.476 22.441 48.815 0.00108 ## ## R-sq.(adj) = 0.0681 Deviance explained = 12.2% ## fREML = 6708.4 Scale est. = 1 n = 4796 ``` ] --- ```r predict_gam( acc_gam_2, separate = list(Gr_St = c("Group", "Structure")), tran_fun = plogis, exclude_terms = "s(Trial,Subject)", length_out = 30 ) %>% plot(series = "Trial", comparison = "Structure") + facet_grid(~ Group) ``` --- .center[ ![:scale 60%](index_files/figure-html/acc-gam-2-plot-1.png) ] --- **Shallow Morphological Processing.** - English L1 and L2 speakers (L2 speakers are native speakers of Cantonese). - Lexical decision task (Word vs Non-Word). - Target: unkindness ([[un]-[kind]]-ness). - Primes: prolong (Unrelated), unkind (Constituent), kindness (Non-Constituent). --- ```r shallow <- read_csv("data/shallow.csv") shallow ``` ``` ## # A tibble: 1,950 × 9 ## Group ID List Target RT RT_log Relation_type Branching accuracy ## <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> ## 1 L1 L1_01 A unawareness 603 6.40 Unrelated Left correct ## 2 L1 L1_01 A unholiness 739 6.61 Constituent Left correct ## 3 L1 L1_01 A unhappiness 370 5.91 Unrelated Left correct ## 4 L1 L1_01 A unsharpness 821 6.71 Constituent Left correct ## 5 L1 L1_01 A unripeness 1035 6.94 Unrelated Left incorrect ## 6 L1 L1_01 A undeniable 833 6.72 Unrelated Right correct ## 7 L1 L1_01 A unskillful 740 6.61 Constituent Right correct ## 8 L1 L1_01 A unkindness 498 6.21 NonConstituent Left correct ## 9 L1 L1_01 A unwariness 1133 7.03 NonConstituent Left correct ## 10 L1 L1_01 A unclearness 513 6.24 Constituent Left correct ## # … with 1,940 more rows ``` ---  --- ```r shallow <- shallow %>% mutate( Gr_Rel = as.ordered(interaction(Group, Relation_type)), ID = as.factor(ID), accuracy = factor(accuracy, levels = c("incorrect", "correct")) ) contrasts(shallow$Gr_Rel) <- "contr.treatment" ``` --- ```r sha_gam <- bam( accuracy ~ Gr_Rel + s(RT_log) + s(RT_log, by = Gr_Rel) + s(ID, bs = "re") + s(ID, RT_log, bs = "re"), data = shallow, family = binomial(), method = "fREML" ) ``` --- .medium[ ```r summary(sha_gam) ``` ``` ## ## Family: binomial ## Link function: logit ## ## Formula: ## accuracy ~ Gr_Rel + s(RT_log) + s(RT_log, by = Gr_Rel) + s(ID, ## bs = "re") + s(ID, RT_log, bs = "re") ## ## Parametric coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) 2.1299 0.2301 9.258 < 2e-16 ## Gr_RelL2.Constituent 0.2720 0.3249 0.837 0.40238 ## Gr_RelL1.NonConstituent -0.8558 0.2713 -3.154 0.00161 ## Gr_RelL2.NonConstituent -0.6001 0.3294 -1.822 0.06847 ## Gr_RelL1.Unrelated -0.4892 0.2453 -1.994 0.04616 ## Gr_RelL2.Unrelated -0.4517 0.2940 -1.536 0.12449 ## ## Approximate significance of smooth terms: ## edf Ref.df Chi.sq p-value ## s(RT_log) 1.000e+00 1.000 15.878 6.77e-05 ## s(RT_log):Gr_RelL2.Constituent 1.616e+00 2.018 0.778 0.6628 ## s(RT_log):Gr_RelL1.NonConstituent 1.476e+00 1.810 2.707 0.3116 ## s(RT_log):Gr_RelL2.NonConstituent 1.000e+00 1.000 0.889 0.3457 ## s(RT_log):Gr_RelL1.Unrelated 1.935e+00 2.458 2.953 0.3914 ## s(RT_log):Gr_RelL2.Unrelated 1.000e+00 1.000 5.084 0.0242 ## s(ID) 3.667e+01 63.000 91.863 < 2e-16 ## s(ID,RT_log) 3.737e-04 63.000 0.000 0.4919 ## ## R-sq.(adj) = 0.114 Deviance explained = 14.9% ## fREML = 2638 Scale est. = 1 n = 1920 ``` ] --- ```r predict_gam( sha_gam, exclude_terms = c("s(ID)", "s(ID,RT_log)"), separate = list(Gr_Rel = c("Group", "Relation_type")), tran_fun = plogis, length_out = 50 ) %>% plot(series = "RT_log", comparison = "Relation_type") + facet_grid(~ Group) ``` --- .center[ ![:scale 60%](index_files/figure-html/sha-gam-plot-1.png) ] --- layout: false class: center middle inverse ## HANDS-ON 4


home
menu
locations
• beverages •
Hot Drinks
Cold Drinks
Non Caffeinated Drinks
check out our
wide selection of
bread in person!


{% load mezzanine_tags shop_tags rating_tags i18n bccf_rating_tags comment_tags %} Home {% include "includes/product.html" %} {% include "includes/product.html" %}


Metro UI CSS
Main
Tiles
Tiles 1
Images
Notices
Buttons
Form
Typography
Bonus
Switch Theme
Github
Download
Кнопки
Button (.metro-button)
Button
class='metro-button'
Button
class='metro-button default'
Button
class='metro-button disabled'
Button
tag='button' class='metro-button' with attr
disabled
Command Button (.metro-command-button)
Yes, share and connect
Use this option for home or work
Yes, share and connect
Use this option for home or work
Yes, share and connect
Use this option for home or work
Yes, share and connect
Use this option for home or work
Radio Button
Checked
Unchecked
Disabled
Disabled Checked
Checkbox Button
Checked
Unchecked
Disabled
Disabled Checked
Toggle Switch
On
Off
Disabled Off
Disabled On


|
GT RoboCup SSL
Soccer software, robot firmware
|
Sends and receives data to/from the radio Converts the simplified structures to the rtp packet to send to the radio driver Reverse happens on receive, raw data -> rtp packet -> simplified structures. More...
Public Member Functions | |
| RadioLink () | |
| void | send (const BatteryVoltage &batteryVoltage, const FPGAStatus &FPGAStatus, const KickerInfo &kickerInfo, const RobotID &robotID) |
| Sends a packet of data to the radio. More... | |
| bool | receive (KickerCommand &kickerCommand, MotionCommand &motionCommand) |
| Receive the latest packet from the radio Only fills data portion of structs. More... | |
Sends and receives data to/from the radio Converts the simplified structures to the rtp packet to send to the radio driver Reverse happens on receive, raw data -> rtp packet -> simplified structures.
| RadioLink::RadioLink | ( | ) |
| bool RadioLink::receive | ( | KickerCommand & | kickerCommand, |
| MotionCommand & | motionCommand | ||
| ) |
Receive the latest packet from the radio Only fills data portion of structs.
| void RadioLink::send | ( | const BatteryVoltage & | batteryVoltage, |
| const FPGAStatus & | FPGAStatus, | ||
| const KickerInfo & | kickerInfo, | ||
| const RobotID & | robotID | ||
| ) |
Sends a packet of data to the radio.
Assumes data in structs are valid


${generalCss}
Map Download Manager:
Number of
active
map downloads:
${activeCount}
Number of
waiting
map downloads:
${waitingCount}


#BDP Project
Graphical Analysis
About
Main Work Area
{{message}}
cloud
Logo
search
view_module
refresh
more_vert
EAK Kode:
Beskrivelse:


Pegasus attack: Israeli spyware firm NSO tracked down in Paris | Exclusive
Israel-based cyber tech firm NSO Group, which was found missing from Israel has now been tracked down by India Today TV, in France.
by Loveena Tandon, Ankit KumarOn a day when lawmakers in India kept themselves busy conferring over the right platform to discuss the WhatsApp snooping case, India Today TV travelled all the way to Paris to track down the company at heart of the snooping scandal.
Israel-based cyber tech firm NSO Group, which was found missing from Israel has now been tracked down by India Today TV, in France.
NSO Group, which had gone into some kind of hiding since the snooping scandal broke out, has been finally tracked down. This is the first time that NSO officials were confronted on camera over the questions of snooping on activists in India.
The NSO officials in Paris were asked repeated questions over the snooping issue and Pegasus spyware by India Today TV’s Loveena Tondon.
NSO Officials were seen evading questions asked by India Today crew and kept running away from the cameras. The NSO team which was in Paris to give a demonstration of their latest technology said that they are only allowed to give a demonstration to government officials.
The spokesperson of the Israeli firm tried to dodge the questions by asking us to send our questions on email.
NSO is the company that created Pegasus spyware, used to crack WhatsApp's security walls and snoop on at least 1400 users globally, including 121 users in India.
The Ministry of Electronics and Information Broadcasting on Wednesday informed Parliament that WhatsApp had informed it of the breach in May and assured that it has plugged the vulnerabilities.
Later in October, WhatsApp officially informed the 121 individuals whose mobile phones were targeted by Israeli spyware in the run-up to the Lok Sabha elections this year. It included prominent human rights activists, journalists and lawyers.
In its latest reply, submitted on Wednesday, WhatsApp said that it regretted the breach.


Sharing reports of current events and perspectives that the Mainstream Media simply won’t touch. Better news feeds can create better worlds! Sometimes these feature strong language or an image or two that’s risque. It’s a sign of the times rather than an attempt to be NSFW. I’ll make mention if anything too naughty is coming up.
Rothchild’s Created Israel
https://www.youtube.com/watch?v=Kj5eypBex-U
Police involved in human trafficking?
http://livingresistance.com/2017/03/25/police-involved-dc-missing-girls-factual-analysis/
Thousands protest pedogate.
Russia bans adoption of Russians to US until pedogate is resolved
http://yournewswire.com/putin-bans-u-s-parents-adopting-russian-orphans-due-to-pedo-epidemic/
500 kids have gone missing in DC just in the three months of 2017
Nurses against vaccines
DMT experiences now legal in Washington
https://www.minds.com/blog/view/522381159758311424
Gun Control Video from Brazil- really freaking good
https://www.facebook.com/deputadopeninha/videos/1653753107969951/
60 minutes practically confirms ISIS attack on US soil is a false flag
http://www.cbsnews.com/news/terrorism-in-garland-texas-what-the-fbi-knew-before-the-2015-attack/
US kills 200 Iraqis in air strikes
http://www.latimes.com/world/middleeast/la-fg-mosul-civilians-airstrike-20170324-story.html
Libya Puppets lose control of oil ports
This page is synchronized from the post: ‘Aggroed’’s Antidote 3/27/17’


| Enum | Description |
|---|---|
| Geoblock.StateEnum |
The state of the geo-blocking.
|
| Geoblock.TypeEnum |
The type of geo-blocking to apply.
|
| IndexRecordings.StateEnum |
The state of the recording.
|
| IndexStreamSource.TypeEnum |
An **akamai** stream source is a Wowza Streaming Cloud edge resource that send streams to a Wowza Streaming Cloud transcoder using the RTMP protocol.
|
| IndexStreamTarget.TypeEnum |
WowzaStreamTarget is a Wowza CDN target.
|
| IndexTranscoder.WorkflowEnum |
The method by which the transcoder was created, either transcoder for a transcoder created through the transcoder workflow or live_stream for a transcoder created automatically as part of the live stream workflow.
|
| LiveStream.BillingModeEnum |
The billing mode for the stream.
|
| LiveStream.BroadcastLocationEnum |
The location of your stream.
|
| LiveStream.ClosedCaptionTypeEnum |
The type of closed caption data being passed from the source.
|
| LiveStream.DeliveryMethodEnum |
The type of connection between the video source and the transcoder.
|
| LiveStream.DeliveryTypeEnum |
For streams whose encoder is wowza_streaming_engine.
|
| LiveStream.EncoderEnum |
The video source for the live stream.
|
| LiveStream.PlayerLogoPositionEnum |
The corner of the player in which you want the player logo to appear.
|
| LiveStream.TargetDeliveryProtocolEnum |
The type of stream being delivered from Wowza Streaming Cloud.
|
| LiveStream.TranscoderTypeEnum |
The type of transcoder, either transcoded for streams that are transcoded into adaptive bitrate renditions or passthrough for streams that aren't processed by the transcoder.
|
| LiveStreamActionState.StateEnum |
The state of the live stream.
|
| LiveStreamState.StateEnum |
The state of the live stream.
|
| Output.FramerateReductionEnum |
Reduce the frame rate of the transcoded output rendition.
|
| Output.H264ProfileEnum |
The encoding method.
|
| Output.KeyframesEnum |
The interval used to define the compression applied to a group of frames.
|
| Output.StreamFormatEnum |
The contents of the stream.
|
| PlayerState.StateEnum |
The state of the player.
|
| Property.KeyEnum |
chunkSize defines the duration of the time-based audio and video chunks that Wowza Streaming Cloud delivers to the target.
|
| Property.SectionEnum |
The section of the stream target configuration table that contains the property.
|
| Recording.StateEnum |
The state of the recording.
|
| RecordingState.StateEnum |
The state of the recording.
|
| Schedule.ActionTypeEnum |
The type of action that the schedule should trigger on the transcoder.
|
| Schedule.RecurrenceDataEnum |
The day or days of the week that a recurring schedule should run.
|
| Schedule.RecurrenceTypeEnum |
A schedule can run one time only (once) or repeat (recur) until a specified end_repeat date.
|
| Schedule.StateEnum |
A schedule must be enabled to run.
|
| ScheduleState.StateEnum |
A schedule must be enabled to run.
|
| StreamSource.LocationEnum |
If location_method is region, specify a location as close as possible to the source encoder.
|
| StreamSource.LocationMethodEnum |
The method used to determine the location of the stream source, either by region or based on the source encoder's ip_address.
|
| StreamSource.TypeEnum |
An **akamai** stream source is a Wowza Streaming Cloud edge resource that send streams to a Wowza Streaming Cloud transcoder using the RTMP protocol.
|
| StreamSourceAkamai.LocationEnum |
If location_method is region, specify a location as close as possible to the source encoder.
|
| StreamSourceAkamai.LocationMethodEnum |
The method used to determine the location of the Akamai stream source, either by region or based on the source encoder's ip_address.
|
| StreamTarget.LocationEnum |
Only for targets whose type is WowzaStreamTarget and provider is not akamai_cupertino.
|
| StreamTarget.TypeEnum |
WowzaStreamTarget is a Wowza CDN target.
|
| StreamTargetProperty.KeyEnum |
chunkSize defines the duration of the time-based audio and video chunks that Wowza Streaming Cloud delivers to the target.
|
| StreamTargetProperty.SectionEnum |
The section of the stream target configuration table that contains the property.
|
| StreamTargetsState.StateEnum |
The state of the transcoder's stream targets.
|
| StreamTargetState.StateEnum |
The state of the output stream target.
|
| StreamTargetUll.RegionOverrideEnum |
Only for ultra low latency stream targets whose source_delivery_method is **pull**.
|
| StreamTargetUll.SourceDeliveryMethodEnum |
The type of connection between the stream source and the ultra low latency stream target.
|
| StreamTargetUll.StateEnum |
The state of the ultra low latency stream target.
|
| StreamTargetWowza.LocationEnum |
Only for Wowza stream targets whose provider is not akamai_cupertino.
|
| StreamTargetWowza.ProviderEnum | |
| Transcoder.BillingModeEnum |
The billing mode for the transcoder.
|
| Transcoder.BroadcastLocationEnum |
The location where Wowza Streaming Cloud transcodes your stream.
|
| Transcoder.BufferSizeEnum |
The size, in milliseconds, of the incoming buffer.
|
| Transcoder.ClosedCaptionTypeEnum |
The type of closed caption data being passed from the source.
|
| Transcoder.DeliveryMethodEnum |
The type of connection between the source encoder and the transcoder.
|
| Transcoder.PlayMaximumConnectionsEnum |
The number of users who are allowed to connect directly to the transcoder.
|
| Transcoder.ProtocolEnum |
The transport protocol for the source video.
|
| Transcoder.TranscoderTypeEnum |
The type of transcoder, either transcoded for streams that are transcoded into adaptive bitrate renditions or passthrough for streams that aren't processed by the transcoder.
|
| Transcoder.WatermarkOpacityEnum |
The opacity, or percentage of transparency, of the watermark.
|
| Transcoder.WatermarkPositionEnum |
The corner of the video frame in which you want the watermark to appear.
|
| TranscoderState.StateEnum |
The state of the transcoder.
|
| VersionNumber.StatusEnum |
The status of the version.
|
Copyright © 2019. All rights reserved.


You can add the geographical coordinates of the area or site in longitude/latitude degrees or in the Dutch national coordinate system RD (Rijksdriehoekstelsel), as a rectangular box covering the entire research area (4 coordinates: outer East, West, North and South coordinates). The used reference system needs to be specified in the drop-down menu.
Longitude/latitude coordinates should be entered in decimal degrees. The latitude value should be entered in the ‘X’-box; the longitude in the ‘Y’-box. RD coordinates should be entered in meters as single values, without the use of grouping characters or separators such as dots or commas.
| Coordinate system | North | East | South | West |
|---|---|---|---|---|
| RD (in m.) | 461355 | 106555 | 460150 | 105350 |