UICircularRing
@IBDesignable
open class UICircularRing : UIView
UICircularRing
This is the base class of UICircularProgressRing
and UICircularTimerRing
.
You should not instantiate this class, instead use one of the concrete classes provided
or subclass and make your own.
This is the UIView subclass that creates and handles everything to do with the circular ring.
This class has a custom CAShapeLayer (UICircularRingLayer
) which
handels the drawing and animating of the view
Author
Luis Padron
-
Whether or not the progress ring should be a full circle.
What this means is that the outer ring will always go from 0 - 360 degrees and the inner ring will be calculated accordingly depending on current value.
Important
Default = true
When this property is true any value set for
endAngle
will be ignored.Author
Luis Padron
Declaration
Swift
@IBInspectable open var fullCircle: Bool { get set }
-
The style of the progress ring.
Type:
UICircularRingStyle
The five styles include
inside
,ontop
,dashed
,dotted
, andgradient
Important
Default = UICircularRingStyle.inside
Author
Luis Padron
Declaration
Swift
open var style: UICircularRingStyle { get set }
-
The options for a gradient ring.
If this is non-
nil
then a gradient style will be applied.## Important ## Default =
nil
Declaration
Swift
open var gradientOptions: UICircularRingGradientOptions? { get set }
-
A toggle for showing or hiding the value label. If false the current value will not be shown.
Important
Default = true
Author
Luis Padron
Declaration
Swift
@IBInspectable public var shouldShowValueText: Bool { get set }
-
Style for the value knob, default is
nil
.Important
If this is
nil
, no value knob is shown.Declaration
Swift
open var valueKnobStyle: UICircularRingValueKnobStyle? { get set }
-
The start angle for the entire progress ring view.
Please note that Cocoa Touch uses a clockwise rotating unit circle. I.e: 90 degrees is at the bottom and 270 degrees is at the top
Important
Default = 0 (degrees)
Values should be in degrees (they’re converted to radians internally)
Author
Luis Padron
Declaration
Swift
@IBInspectable open var startAngle: CGFloat { get set }
-
The end angle for the entire progress ring
Please note that Cocoa Touch uses a clockwise rotating unit circle. I.e: 90 degrees is at the bottom and 270 degrees is at the top
Important
Default = 360 (degrees)
Values should be in degrees (they’re converted to radians internally)
Author
Luis Padron
Declaration
Swift
@IBInspectable open var endAngle: CGFloat { get set }
-
The width of the outer ring for the progres bar
Important
Default = 10.0
Author
Luis Padron
Declaration
Swift
@IBInspectable open var outerRingWidth: CGFloat { get set }
-
The color for the outer ring
Important
Default = UIColor.gray
Author
Luis Padron
Declaration
Swift
@IBInspectable open var outerRingColor: UIColor { get set }
-
The style for the tip/cap of the outer ring
Type:
CGLineCap
Important
Default = CGLineCap.butt
This is only noticible when ring is not a full circle.
Author
Luis Padron
Declaration
Swift
open var outerCapStyle: CGLineCap { get set }
-
The width of the inner ring for the progres bar
Important
Default = 5.0
Author
Luis Padron
Declaration
Swift
@IBInspectable open var innerRingWidth: CGFloat { get set }
-
The color of the inner ring for the progres bar
Important
Default = UIColor.blue
Author
Luis Padron
Declaration
Swift
@IBInspectable open var innerRingColor: UIColor { get set }
-
The spacing between the outer ring and inner ring
Important
This only applies when using progressRingStyle = 1
Default = 1
Author
Luis Padron
Declaration
Swift
@IBInspectable open var innerRingSpacing: CGFloat { get set }
-
The style for the tip/cap of the inner ring
Type:
CGLineCap
Important
Default = CGLineCap.round
Author
Luis Padron
Declaration
Swift
open var innerCapStyle: CGLineCap { get set }
-
The text color for the value label field
Important
Default = UIColor.black
Author
Luis Padron
Declaration
Swift
@IBInspectable open var fontColor: UIColor { get set }
-
The font to be used for the progress indicator. All font attributes are specified here except for font color, which is done using
fontColor
.Important
Default = UIFont.systemFont(ofSize: 18)
Author
Luis Padron
Declaration
Swift
@IBInspectable open var font: UIFont { get set }
-
This returns whether or not the ring is currently animating
Important
Get only property
Author
Luis Padron
Declaration
Swift
open var isAnimating: Bool { get }
-
The direction the circle is drawn in Example: true -> clockwise
Important
Default = true (draw the circle clockwise)
Author
Pete Walker
Declaration
Swift
@IBInspectable open var isClockwise: Bool { get set }
-
Typealias for animateProperties(duration:animations:completion:) fucntion completion
Declaration
Swift
public typealias PropertyAnimationCompletion = (() -> Void)
-
Overrides the default layer with the custom UICircularRingLayer class
Declaration
Swift
override open class var layerClass: AnyClass { get }
-
Overriden public init to initialize the layer and view
Declaration
Swift
override public init(frame: CGRect)
-
Overriden public init to initialize the layer and view
Declaration
Swift
required public init?(coder aDecoder: NSCoder)
-
Overriden because of custom layer drawing in UICircularRingLayer
Declaration
Swift
open override func draw(_ rect: CGRect)
-
This function allows animation of the animatable properties of the
UICircularRing
. These properties includeinnerRingColor, innerRingWidth, outerRingColor, outerRingWidth, innerRingSpacing, fontColor
.Simply call this function and inside of the animation block change the animatable properties as you would in any
UView
animation block.The completion block is called when all animations finish.
Declaration
Swift
open func animateProperties(duration: TimeInterval, animations: () -> Void)
-
This function allows animation of the animatable properties of the
UICircularRing
. These properties includeinnerRingColor, innerRingWidth, outerRingColor, outerRingWidth, innerRingSpacing, fontColor
.Simply call this function and inside of the animation block change the animatable properties as you would in any
UView
animation block.The completion block is called when all animations finish.
Declaration
Swift
open func animateProperties(duration: TimeInterval, animations: () -> Void, completion: PropertyAnimationCompletion? = nil)


?THE
NEW
YORK
BOTANICAL
GARDEN
EXPLORATION
OF
THE
BAHAMAS
/
.
/i
/
New
Providence
´fr.
r
i
Áp
V.
L.
J.
K.
Brace
f
Collectors
-/?1904
1
b
0
4
b
b
Id
1604666
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


jōhō ga tashikanara konomachi ni サラマンダー ga iruhazuda yo 。
If the information is correct, Salamander should be in this town. Let's go.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 2
I use lemon sole for my poisson meunière, but why not try Pacific halibut or even trout? The lemon and brown butter sauce makes a delicious accompaniment to the delicate fish flavours.
Check the fish for small bones and use tweezers to pull out any that you find.
Mix the flour with the salt and pepper and spread out over a large plate. Pat the fish fillets in the flour so they are evenly coated, and shake off any excess.
Heat the oil in a large frying pan over a high heat. When the oil is smoking hot, place the fish fillets in the pan and lower the heat to medium. Cook for 1-2 minutes on one side until golden-brown, then turn the fillets over and cook for a further 1-2 minutes until the second side is golden-brown. (Flatfish fillets need only 1-2 minutes cooking on each side. If you’re cooking thicker slices or fillets from a fish like trout (2-3cm/1in thick), then 3-4 minutes on each side should be fine.)
When cooked remove the fish from the pan and wrap the fish in aluminium foil to keep warm.
Wipe the pan with paper towels and return to a medium heat. Add the cubes of butter and heat until they melt and become light brown, then turn off the heat and add the lemon juice (stand back a little as it will splutter).
Add the parsley and capers (if using), and swirl the contents of the pan around. Return the fish to the pan, spoon over the juices and serve immediately.


icsharpcode/ILSpy .NET Decompiler with support for PDB generation, ReadyToRun, Metadata (&more) - cross-platform! - icsharpcode/ILSpy
Walking Down the Memory Lane with Bing – Part 2 Get started with .net https://dotnet.microsoft.com?WT.mc_id=ondotnet-c9-cxa Memory Management and garbage collection in .NET https://docs.microsoft.com/en-us/dotnet/standard/garbage-collection/memory-management-and-gc?WT.mc_id=ondotnet-c9-cxa In this e...
jeffkl/KluGet Provides a replacement NuGet experience for command-line based restores. - jeffkl/KluGet
LEOPOLD NASA FOIA 2019 OK Asteroid Source document contributed to DocumentCloud by Jason Leopold (Jason Leopold Investigations).
It's .NET ROCKS! Talking about Blazor with ASP.NET Web Forms developers Jeff talks to Carl and Richard about Server-Side Blazor for long-time Web Forms developers and the audio is presented as .NET Rocks episode 1652 and available at: https://dotnetrocks.com/?show=1652 Stay tuned for a little extra discussion with Carl an...
Demystifying Tail Call Optimization - DEV Community 👩💻👨💻 Exploring what tail call optimization is, how it works and why you should use it. Tagged with tailrecursion, tailcalloptimization, compiler, c.
unoplatform/workshops workshops, study guides and learning materials for the Uno Platform - unoplatform/workshops
UnoConf Livestream - Presented by Uno Platform Watch the UnoConf live stream for presentations from Miguel de Icaza, Jerome Laban (CTO Uno Platform), Rocky Lhotka, and Billy Hollis. Lightning sessions presented by Matt Lacey, Mathieu Fillion, Andres Pineda and Sam Basu. Schedule: • 7:03 Welcome to Un...
BeagleBoard.org® Launches BeagleBone® AI, Offering a Fast Track to Getting Started with Artificial Intelligence at the Edge /PRNewswire/ -- The BeagleBoard.org® Foundation today announces general availability of the newest, fastest, most powerful BeagleBoard.org® BeagleBone® low...
Interview with Scott Hunter on .NET Core 3.0 Chris Woodruff talks to director of program management for the .NET platform, Scott Hunter, about what developers can expect from .NET Core 3.
Out-of-memory exception in a managed application that's running on the 64-bit .NET Framework Describes an issue that causes a managed application that targets the .NET Framework 4.6.1 to throw an out-of-memory exception from the CLR. Workarounds are provided.
[WIP] Pinned Object Heap by VSadov · Pull Request #26111 · dotnet/coreclr to build/test stuff. not ready yet


--- title: "Applications" updated: 2016-10-15 13:00:00 layout: default group: attending menu: sub ordered: 8 status: published ---
Two applications have been prepared for attendees of ISWC2016.
The volunteer application form is closed. Thanks for your submissions!
Recently we are investigating the possibility for new research field of semantic technology for dynamically updating information from sensor measurement on IoT devices.
In the ISWC 2016, we will try to have an experiment as a demonstration of the possibility. Since it is expected to make a collaborative activity with the host city of ISWC, Kobe, we decided to gather user’s trajectories during ISWC as an analytic demonstration for tourist around Kobe. Volunteers are provided a small GPS device. We will try to investigate Points of Interest (PoI) and the relational map among PoI in RDF. We will provide the RDF data to show the possibility and never provide any specific information about individual trajectories, which are just only used as a source of statistical analysis.
We call for the volunteers who allow us to gather your trajectories during ISWC.
We never collect and use any specific personal information for the experiment.
Experimental Condition:
The number of volunteers is limited due to the limitation of the number of GPS devices. Participants are selected on the first-come basis if there will be more applications than the number of GPS devices.
We wish the experiment may contribute to making outcome on semantic technology.
Organizer:


§438-13 Penalties. Any person who practices barbering, operates a barber shop, or acts in any capacity wherein a license, apprentice permit, or temporary permit is required, without a license, apprentice permit, or temporary permit as provided in this chapter shall be fined not more than $100, or imprisoned not more than six months, or both. Each day of violation shall be a separate offense. [L 1947, c 194, §15; RL 1955, §58-13; am L 1961, c 184, §6(b); HRS §438-13; am L 1987, c 246, §10; am L 1993, c 264, §10]
Cross References
Classification of offense and authorized punishment, see §§701-107, 706-640, and 706-663.


As I was venturing through Bodhi Linux, already quite familiar with my surroundings, I decided to take a step into the more obscure regions of Linux. One of the reasons why I wasn’t as hesitant to switch from Windows to Linux was the tempting offer of running favorite Windows games on Linux. Not directly, mind you, but rather through a very robust app called WINE (Wine Is Not an Emulator!). When I installed Wine (or WINE), however, I quickly noticed that not everything was right. Some OpenGL libraries were missing and I could not locate them online properly. Apparently, my situation was common and Wine required me to install nVidia drivers for my GeForce GT 520MX, which in turn would provide the necessary OpenGL libraries. While this procedure is somewhat painless on Windows, it can be at times problematic on Linux. The root of all evil is NVidia itself, which decided not to support Linux properly. Thereby, the options for installing nVidia drivers for Linux are as follows:
Use the well-established app called Additional Drivers (via jockey-gtk) and let the system handle the whole installation
process. The only manual aspect is running nvidia-xconfig
once the installation has completed (though this is
not always mentioned). Supposedly works under Ubuntu 14.04 LTS and its derivatives (Mint, Lubuntu, Peppermint, etc.).
Supposedly was present earlier and then briefly discontinued, but as I joined the Linux family just this year I am unsure.
Download the proprietary drivers from nVidia's website and install them manually. This requires linux headers for a
specific kernel (often pre-installed with the kernel itself, but not always; available from official distribution
repositories), basic Linux knowledge (using the terminal with the X desktop disabled) and running
nvidia-xconfig
, followed by a system reboot.
Enable nVidia drivers through your package manager of choice. Ubuntu has Synaptic, ArchLinux/Manjaro Pacman and
Gentoo/Sabayon – Portage. After all of the packages and their dependencies are installed/compiled, one again has to run
nvidia-xconfig
from the terminal and reboot.
In theory, all 3 options are more or less equivalent to each other, though the recommended solution for Linux users is to rely
on jockey-gtk
or package managers for most installations. When I was fervently fighting with my computer to get
the drivers to work properly, I had that strong feeling that I’m doing something inherently wrong all the time. Turns out, I
was partially correct. After a bit of reading about nVidia drivers for Linux I reasoned that the installation procedure is very
simple... assuming the PC is equipped with a single graphics card, the said nVidia.
Laptops usually come in two flavors – with an integrated graphics card and with an integrated graphics card + a discrete graphics card (ATI/nVidia), meaning hybrid graphics. Laptops without a discrete card are safe from driver issues, but also not well suited for heavy 3D rendering and gaming (although the new Intel HD 4000 is quite decent). I do both and probably that’s why I have to suffer (sigh...). Surprisingly, the discrete card is not directly connected to the screen and merely serves as a more powerful subunit of the integrated card (I'm not entirely familiar with the technical details, apologies). Because of that, it cannot function on its own. A means of switching between the two graphics cards was devised by NVidia in the form of Optimus Technology.
Windows users have it easy. The NVidia Settings platform allows switching between the integrated and discrete card or even setting up profiles for specific graphics-demanding software to improve battery life. Linux users have it hard, to the point that even Linus Torvalds had to express his concern using highly expressive gestures... Optimus for Linux has been introduced only last year as nVidia PRIME with the nVidia 319+ driver line. However, not by NVidia! The company decided to completely disregard all Linux users with switchable graphics and Linus’ middle finger was not merely an act of arrogance, but rather a response to NVidia’s stand on the matter. Now, I'm no F/OSS warrior, but the fact that Linux users are second-class citizens in respect to laptop usage is sad.
Unfortunately, making Optimus work on Bodhi Linux was way beyond my capabilities and I had to abandon the tranquil forests of Bodhi, only to resume my searches...


PART I. GENERAL PROVISIONS AND DEFINITIONS
Note
Chapter repealed January 1, 2019. L 2017, c 181, §§2, 47.
This part, referenced as repealed in the main volume, was reenacted by L 2007, c 244, pt of §2. Source notes for each section in this part begin with L 2007, c 244.
§514A-1 Title. This chapter shall be known as the Condominium Property Act. [L 2007, c 244, pt of §2]


{% extends "base.html" %} {% block title %}EmployeeWare: Open Source HR Software: Org Chart Viewer{% endblock %} {% block headmeta %} {% endblock %} {% block content %}
|
Legend: ↑ : Up one level (...) = Dotted Reporting (p) = Part-Time Employee (c) = Contractor / Consultant |


Its hard to imagine a life without it now. Stay connected around the office.
The office has complimentary Wi-Fi available to all staff that can be accessed with a username and password via a one-time registration. You will be able to retrieve your generated username and password from Circle once you have started in the office. After you have registered the first time, you should be able to log-in without having to input any further details.
Wi-fi is also offered to visitors on site and this can be accessed with a username and password from reception.
There is an acceptable use policy for wi-fi which you can find on Circle when you arrive in the office. It is strongly advised that you follow this policy when accessing the internet on your personal device.


Hyundai Santa Cruz Small Pickup Will Start Production in 2021
by Joey Capparella- Hyundai announced that it will build a compact pickup truck called the Santa Cruz.
- Previewed as a concept model way back in 2015, the Santa Cruz will be assembled at Hyundai's factory in Alabama.
- The Santa Cruz will begin production in 2021, meaning it's likely to go on sale in the U.S. that year.
Ever since Hyundai showed the Santa Cruz pickup concept at the 2015 Detroit auto show, the company has strongly hinted that a production model is on its way. Now we have the real, official confirmation that the Santa Cruz will be built for real, as Hyundai announced that production of this new model will begin in Alabama in 2021.
The fact that the Hyundai Santa Fe is also built at this plant suggests that the production Santa Cruz may share some components with that mid-size crossover. We think it will be a similar type of vehicle as the Honda Ridgeline, which itself is mechanically similar to the Honda Pilot crossover. The Santa Cruz concept (pictured) was a car-based pickup with a small bed and a compact cab with rear-hinged half-doors; we suspect the real thing may be a crew cab with four forward-hinged doors.
Hyundai says it is investing $410 million in the plant to build the Santa Cruz. The planned expansion includes more stamping and welding space, and 200 additional employees will be added. Hyundai also builds the Sonata and Elantra sedans at this facility in Alabama.
Further details on the production Santa Cruz should come late in 2020 as Hyundai prepares to launch its first-ever pickup truck for the U.S. market.
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#include <array_ops.h>
SpaceToDepth for tensors of type T.
Rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of the input tensor where values from the height
and width
dimensions are moved to the depth
dimension. The attr block_size
indicates the input block size.
block_size x block size
are rearranged into depth at each location.block_size * block_size * input_depth
.The data_format
attr specifies the layout of the input and output tensors with the following options: "NHWC": [ batch, height, width, channels ]
"NCHW": [ batch, channels, height, width ]
"NCHW_VECT_C": qint8 [ batch, channels / 4, height, width, 4 ]
It is useful to consider the operation as transforming a 6-D Tensor. e.g. for data_format = NHWC, Each element in the input tensor can be specified via 6 coordinates, ordered by decreasing memory layout significance as: n,oY,bY,oX,bX,iC (where n=batch index, oX, oY means X or Y coordinates within the output image, bX, bY means coordinates within the input block, iC means input channels). The output would be a transpose to the following layout: n,oY,oX,bY,bX,iC
This operation is useful for resizing the activations between convolutions (but keeping all data), e.g. instead of pooling. It is also useful for training purely convolutional models.
For example, given an input of shape [1, 2, 2, 1]
, data_format = "NHWC" and block_size = 2:
x = [[[[1], [2]], [[3], [4]]]]
This operation will output a tensor of shape [1, 1, 1, 4]
:
[[[[1, 2, 3, 4]]]]
Here, the input has a batch of 1 and each batch element has shape [2, 2, 1]
, the corresponding output will have a single element (i.e. width and height are both 1) and will have a depth of 4 channels (1 * block_size * block_size). The output element shape is [1, 1, 4]
.
For an input tensor with larger depth, here of shape [1, 2, 2, 3]
, e.g.
x = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]]
This operation, for block_size of 2, will return the following tensor of shape [1, 1, 1, 12]
[[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
Similarly, for the following input of shape [1 4 4 1]
, and a block size of 2:
x = [[[[1], [2], [5], [6]], [[3], [4], [7], [8]], [[9], [10], [13], [14]], [[11], [12], [15], [16]]]]
the operator will return the following tensor of shape [1 2 2 4]
:
x = [[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], [13, 14, 15, 16]]]]
Arguments:
Returns:
Output
: The output tensor.
Constructors and Destructors |
|
|---|---|
SpaceToDepth(const ::tensorflow::Scope & scope, ::tensorflow::Input input, int64 block_size)
|
|
SpaceToDepth(const ::tensorflow::Scope & scope, ::tensorflow::Input input, int64 block_size, const SpaceToDepth::Attrs & attrs)
|
Public attributes |
|
|---|---|
operation
|
|
output
|
Public functions |
|
|---|---|
node() const
|
::tensorflow::Node *
|
operator::tensorflow::Input() const
|
|
operator::tensorflow::Output() const
|
|
Public static functions |
|
|---|---|
DataFormat(StringPiece x)
|
Structs |
|
|---|---|
|
tensorflow:: |
Optional attribute setters for SpaceToDepth. |
Operation operation
::tensorflow::Output output
SpaceToDepth( const ::tensorflow::Scope & scope, ::tensorflow::Input input, int64 block_size )
SpaceToDepth( const ::tensorflow::Scope & scope, ::tensorflow::Input input, int64 block_size, const SpaceToDepth::Attrs & attrs )
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Output() const
Attrs DataFormat( StringPiece x )


Drupal Gin Toolbar has not been found.
This Open Source extension was developed by Emircan ERKUL under MIT Licence for the Drupal ecosystem.


Rev Obofour buys Bro Sammy’s first album copy for 1 billion cedis
by Lizbeth BrownThe founder and leader of Anointed Palace Chapel, Rev Obofour was at the album launch of popular gospel musician, Bro Sammy.
Rev Obofour popularly known as Iron Boy was the chairman at the launching and also bought the first copy of the album for 1 billion cedis.
This was announced by one of Rev Obofour’s junior pastors before he arrived and added that the money was ready for pick up.
The launching of the album dubbed “Aduro biaa nkadem” took place on 24th November 2019 at Dichemso Gospel Light Assemblies of God in the Ashanti Region.
The programme was graced by several celebrities like Emelia Brobbey, Tracey Boakye, Kwaku Manu, Wayoosi, Nayas among others.
Watch the video below;
Also, during Bro Sammy’s performance, Rev Obofour proved that indeed he is wealthy, sprayed huge sums of money on the gospel musician.
This is Bro Sammy’s fourth album launch and obviously it was a star-studded programme.


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{# Put all special fields here, these fields will not be included in the list. #} {% set special_fields = ['name', 'id', 'description'] %} {# Put all field synonyms here, these can be used as human-readable versions of field names. #} {% set field_synonyms = {'internal_id':'Internal ID'} %}


Joey Carter
PhD Student in Physics
University of Toronto
{{ .Content }}
{{ partial "header" . }}
PhD Student in Physics
Joey Carter
University of Toronto
{{ .Content }}


Spec_triangVSU specification of the Triang module
triang2.h
#include <stddef.h> int triang (int n);
triang2.c
#include <stddef.h> #include "stdlib.h" #include "stack2.h" #include "triang2.h" void push_increasing (struct stack *st, int n) { int i=0; while (i<n) { i++; push(st,i); } } int pop_and_add (struct stack *st, int n) { int i=0; int t, s=0; while (i<n) { t=pop(st); s += t; i++; } return s; } int triang(int n) { struct stack *st; int i,t,s; st = newstack(); push_increasing(st, n); s = pop_and_add(st, n); return s; }
- We will use VSU's specification facilities to enforce locality in the proof;
- One might build the triang module from more than one .c file, with the different .c files referencing "internal" functions from each other. In this case, static won't work in C, but our VSU can still specify the module structure.
Let's verify!
Require Import VST.floyd.proofauto.
Require Import VC.triang2.
Require Import VC.Spec_stdlib.
Require Import VC.Spec_stack.
Require Import VC.triang2.
Require Import VC.Spec_stdlib.
Require Import VC.Spec_stack.
Abstract Predicate Declaration (APD)
Abstract Specification Interface (ASI)
We need to mention the MallocFreeAPD because the triang
function uses mem_mgr in its pre and postconditions.
Although the implementation of this module uses the StackAPD,
that use is purely internal, and does not need to be mentioned
in the external interface specification.
Exercise: 2 stars, standard (triang_spec)
Adjust this funspec until it properly specifies the triang function. Suggestion: the PROP clause of the precondition could very reasonably limit the size of the input to approximately the square root of the maxium integer, so that the triangular number does not overflow.
Definition triang_spec : ident × funspec :=
DECLARE _triang
WITH gv: globals (*... more WITH variables... *)
(* FILL IN HERE *)
PRE [
(* FILL IN HERE *)
]
PROP (
(* FILL IN HERE *)
)
PARAMS(
(* FILL IN HERE *)
)
GLOBALS(gv)
SEP (mem_mgr M gv)
POST [ tint ]
(* Suggestion: this postcondition does not need an EX, but you
may add one if you wish to. *)
PROP (
(* FILL IN HERE *)
)
RETURN (
(* FILL IN HERE *)
)
SEP (mem_mgr M gv).
DECLARE _triang
WITH gv: globals (*... more WITH variables... *)
(* FILL IN HERE *)
PRE [
(* FILL IN HERE *)
]
PROP (
(* FILL IN HERE *)
)
PARAMS(
(* FILL IN HERE *)
)
GLOBALS(gv)
SEP (mem_mgr M gv)
POST [ tint ]
(* Suggestion: this postcondition does not need an EX, but you
may add one if you wish to. *)
PROP (
(* FILL IN HERE *)
)
RETURN (
(* FILL IN HERE *)
)
SEP (mem_mgr M gv).
If you don't get this quite right, you will notice it either when
you prove VSU_triang (verification of triang.c) or when
you prove VSU_main (verification of the client). In that case,
just come back here and adjust the funspec.
☐
Because the functions push_increasing and pop_and_add are not
exported from the module -- they are used only internally by triang,
their funspecs need not be mentioned here.
Now the "Triang Abstract Specification Interface" is just a list of the
exported funspecs
Completing the Triang ASI
And remember that StackASI is parameterized by the Variables of the Section:
Next Chapter: Spec_stdlib
(* 2022-08-28 09:48 *)


Attention; CEO/ President. From; MR. Davis Tutu Tel No; 27 733 624 034 fax No; 27 115 076 062 JOHANNESBURG-SOUTH AFRICA<EMAIL_ADDRESS>Dear Sir/Madam, HIGHLY CONFIDENTIAL Greetings, This letter may come to you as a surprise but I plead with you to patiently read through it before making a decision whether to assist me or not. I wish to solicit for your assistance in this transaction, which I strongly believe, will be of mutual benefit to both of us. My name is DAVIS TUTU, a branch manger of a leading bank in South Africa (ABSA Bank), my colleagues and I wish to seek your assistance in the transfer of some huge sum of money (USD$35M) into a foreign bank account. An American national deposited this money in a fixed deposit account in the bank, six years ago. The length of time for the maturity of the fixed deposit was five years, which expired towards the ending of last year. Incidentally, MR. JEFF STEVENS, who was the beneficiary died in the September 11th bombing of the World Trade Center in America, and he left MR DAVID STEVENS as the Next-of-kin to claim this money, but unfortunately he was said to have died of cancer few months after Jeff's death. All efforts made to trace any known relations of both proved abortive, and no one has written our bank in this regard. All modalities for the successful transfer of this money into an offshore bank account have been put in place and we are looking for a trust worthy foreign partner into whose bank account this fund would be remitted to and it is in this regard that my colleagues have mandated me to provide a partner who will assist us. We have agreed to compensate you with 20% of the total amount if you should accept to assist us. If you are interested in giving us the required assistance, kindly reply this letter stating your Telephone/Fax numbers for further action. Because of the secrecy surrounding this transaction, I suggest that you send a fax me or call and i will contact you for futher details. I am looking forward to a favorable reply from you soon. Feel free to ask any question in this regard. I remain, Yours Faithfully, Davis Tutu


How To Create A New Webpage On Production Server
-
Clone your repository
-
To go into your repository, type harry2474-cmis-wd
-
Type gedit index.html &
-
Type gedit main.css &
-
Type python -m SimpleHTTPServer to start the development server
-
cd .. After you are done with html and styling, you can go check on the development server by type localhost:8000/specific file name
-
In Terminal, type git add (file name) and then click enter
-
In Terminal, type git commit -m "Type what changes you made to the file"
-
In Terminal, type git push then type your usersname, click enter and type your password.
Useful Websites


Spanning all the way back to when I first started school, I have attended five different institutions:
Throughout my life I have worked three seperate jobs:
Now, I have decided to join Epicodus because I am ready for a life change. I took a java class at UW and enjoyed it a lot so I want to pursue programming further. There are jobs I could get from my major in college but nothing that I enjoy as much as coding (so far), I also don't want to work at the bakery for the rest of my life so Epicodus seemed like the right step for me.
I fill up my free time with several different activities:


I'm Daniel Radford, Digital Media student at the University of the West of England.
Alongside my studies, I work at Nymbl creating virtual reality experiences using Unreal Engine and the Oculus Rift, HTC Vive and Samsung Gear.
I enjoy creating audio visualisers and working on live visuals for Crux Bristol.
Visual works include experimental musical arrangements by DXO accompanied by drastic 2D animation created in After Effects.
Currently, I run the 24/7 youtube radio station Cloud Chillout


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WebCafe,an Parisian Pastries that was located in Baguio City, Philippines, is serving some delicious pastries made in paris.
France is famous the world over for its decadent desserts and delightful pastry concoctions. The word “dessert” is actually derived from the French word “desservir” which means “to clear the table”.


SALMON COOKIE'S SALES CALCULATOR
Location Name:
Minimum Customers:
Maximum Customers:
Average Cookies Bought/cutomer:
LOCATION
TOTAL


{%- include head.html -%} {%- include bkgimg.html -%}
{%- include header.html -%}
{{ content }} {%- include render.html -%} {%- include animation.html -%}


WITNESS LIST Government Reform Committee March 12, 2003-8:30A Councils of Government On: Heath, Patrick (Texas Association of Regional Councils) Pitner, Gary (Panhandle Regional Planning Commission) Ray, Jim (Texas Assoc. of Regional Councils)


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Panda3D
|
Public Member Functions | |
| __init__ (const BulletRigidBodyNode node_a, const BulletRigidBodyNode node_b, const TransformState frame_a, const TransformState frame_b, bool use_frame_a) | |
| __init__ (const BulletRigidBodyNode node_a, const TransformState frame_a, bool useFrame_a) | |
| float | getAngularPos () |
| const TransformState | getFrameA () |
| const TransformState | getFrameB () |
| float | getLinearPos () |
| float | getLowerAngularLimit () |
| float | getLowerLinearLimit () |
| float | getMaxAngularMotorForce () |
| float | getMaxLinearMotorForce () |
| bool | getPoweredAngularMotor () |
| bool | getPoweredLinearMotor () |
| float | getTargetAngularMotorVelocity () |
| float | getTargetLinearMotorVelocity () |
| float | getUpperAngularLimit () |
| float | getUpperLinearLimit () |
| setFrames (const TransformState ts_a, const TransformState ts_b) | |
| setLowerAngularLimit (float value) | |
| setLowerLinearLimit (float value) | |
| setMaxAngularMotorForce (float max_force) | |
| setMaxLinearMotorForce (float max_force) | |
| setPoweredAngularMotor (bool on) | |
| setPoweredLinearMotor (bool on) | |
| setTargetAngularMotorVelocity (float target_velocity) | |
| setTargetLinearMotorVelocity (float target_velocity) | |
| setUpperAngularLimit (float value) | |
| setUpperLinearLimit (float value) | |
| Public Member Functions inherited from BulletConstraint | |
| enableFeedback (bool value) | |
| float | getAppliedImpulse () |
| float | getBreakingThreshold () |
| Returns the applied impluse limit for breaking the constraint. More... | |
| float | getDebugDrawSize () |
| float | getParam (BulletConstraint::ConstraintParam num, int axis) |
| BulletRigidBodyNode | getRigidBodyA () |
| BulletRigidBodyNode | getRigidBodyB () |
| bool | isEnabled () |
| Returns TRUE if the constraint is enabled. More... | |
| setBreakingThreshold (float threshold) | |
| Sets the applied impulse limit for breaking the constraint. More... | |
| setDebugDrawSize (float size) | |
| setEnabled (bool enabled) | |
| setParam (BulletConstraint::ConstraintParam num, float value, int axis) | |
| Public Member Functions inherited from TypedObject | |
| TypeHandle | getType () |
| int | getTypeIndex () |
| Returns the internal index number associated with this object's TypeHandle, a unique number for each different type. More... | |
| bool | isExactType (TypeHandle handle) |
| Returns true if the current object is the indicated type exactly. More... | |
| bool | isOfType (TypeHandle handle) |
| Returns true if the current object is or derives from the indicated type. More... | |
| Public Member Functions inherited from ReferenceCount | |
| int | getRefCount () |
| Returns the current reference count. More... | |
| ref () | |
| Explicitly increments the reference count. More... | |
| bool | testRefCountIntegrity () |
| Does some easy checks to make sure that the reference count isn't completely bogus. More... | |
| bool | testRefCountNonzero () |
| Does some easy checks to make sure that the reference count isn't zero, or completely bogus. More... | |
| bool | unref () |
| Explicitly decrements the reference count. More... | |
Static Public Member Functions | |
| static TypeHandle | getClassType () |
| Static Public Member Functions inherited from BulletConstraint | |
| static TypeHandle | getClassType () |
| Static Public Member Functions inherited from TypedReferenceCount | |
| static TypeHandle | getClassType () |
| Static Public Member Functions inherited from TypedObject | |
| static TypeHandle | getClassType () |
| Static Public Member Functions inherited from ReferenceCount | |
| static TypeHandle | getClassType () |
Public Attributes | |
| float | angular_pos |
| ConstPointerToTransformState | frame_a |
| ConstPointerToTransformState | frame_b |
| float | linear_pos |
| float | lower_angular_limit |
| float | lower_linear_limit |
| Limits. More... | |
| float | max_angular_motor_force |
| float | max_linear_motor_force |
| bool | powered_angular_motor |
| bool | powered_linear_motor |
| float | target_angular_motor_velocity |
| float | target_linear_motor_velocity |
| float | upper_angular_limit |
| float | upper_linear_limit |
| Public Attributes inherited from BulletConstraint | |
| float | applied_impulse |
| float | breaking_threshold |
| Returns the applied impluse limit for breaking the constraint. More... | |
| float | debug_draw_size |
| bool | enabled |
| Returns TRUE if the constraint is enabled. More... | |
| BulletRigidBodyNode | rigid_body_a |
| BulletRigidBodyNode | rigid_body_b |
| Public Attributes inherited from TypedObject | |
| TypeHandle | type |
| Returns the TypeHandle representing this object's type. More... | |
| Public Attributes inherited from ReferenceCount | |
| int | ref_count |
| The current reference count. More... | |
Additional Inherited Members | |
| Public Types inherited from BulletConstraint | |
| enum | ConstraintParam { CP_erp = 1, CP_stop_erp = 2, CP_cfm = 3, CP_stop_cfm = 4 } |
| __init__ | ( | const BulletRigidBodyNode | node_a, |
| const BulletRigidBodyNode | node_b, | ||
| const TransformState | frame_a, | ||
| const TransformState | frame_b, | ||
| bool | use_frame_a | ||
| ) |
| __init__ | ( | const BulletRigidBodyNode | node_a, |
| const TransformState | frame_a, | ||
| bool | useFrame_a | ||
| ) |
| float getAngularPos | ( | ) |
|
static |
| const TransformState getFrameA | ( | ) |
| const TransformState getFrameB | ( | ) |
| float getLinearPos | ( | ) |
| float getLowerAngularLimit | ( | ) |
| float getLowerLinearLimit | ( | ) |
| float getMaxAngularMotorForce | ( | ) |
| float getMaxLinearMotorForce | ( | ) |
| bool getPoweredAngularMotor | ( | ) |
| bool getPoweredLinearMotor | ( | ) |
| float getTargetAngularMotorVelocity | ( | ) |
| float getTargetLinearMotorVelocity | ( | ) |
| float getUpperAngularLimit | ( | ) |
| float getUpperLinearLimit | ( | ) |
| setFrames | ( | const TransformState | ts_a, |
| const TransformState | ts_b | ||
| ) |
| setLowerAngularLimit | ( | float | value | ) |
| setLowerLinearLimit | ( | float | value | ) |
| setMaxAngularMotorForce | ( | float | max_force | ) |
| setMaxLinearMotorForce | ( | float | max_force | ) |
| setPoweredAngularMotor | ( | bool | on | ) |
| setPoweredLinearMotor | ( | bool | on | ) |
| setTargetAngularMotorVelocity | ( | float | target_velocity | ) |
| setTargetLinearMotorVelocity | ( | float | target_velocity | ) |
| setUpperAngularLimit | ( | float | value | ) |
| setUpperLinearLimit | ( | float | value | ) |
| float angular_pos |
| ConstPointerToTransformState frame_a |
| ConstPointerToTransformState frame_b |
| float linear_pos |
| float lower_angular_limit |
| float lower_linear_limit |
Limits.
| float max_angular_motor_force |
| float max_linear_motor_force |
| bool powered_angular_motor |
| bool powered_linear_motor |
| float target_angular_motor_velocity |
| float target_linear_motor_velocity |
| float upper_angular_limit |
| float upper_linear_limit |


--- layout: post title: 'Domain-Driven Design: Putting the Model to Work' date: 2010-01-29 14:24:24.000000000 -07:00 type: post published: true status: publish categories: - Community Event - News tags: [] meta: _gcid: '173' _gcuser: Vladimir Gitlevich content_type: announcement Date(s): '2010-02-17T00:00:00' Summary: '2' Link: http://skillsmatter.com/event/java-jee/domain-driven-design-putting-the-model-to-work/ps-596 Location: '2' _bitly_trim: http://dddcommunity.org/?p=84 _edit_last: '3' _dependencies: s:6:"a:0:{}"; _custom_background: a:5:{s:3:"url";s:0:"";s:16:"background-color";s:0:"";s:17:"background-repeat";s:6:"repeat";s:21:"background-attachment";s:6:"scroll";s:19:"background-position";s:8:"left top";} _intro_text: default _intro_custom_banner: a:1:{s:3:"url";s:0:"";} author: login: Vladimir Gitlevich email<EMAIL_ADDRESS>display_name: Vladimir Gitlevich first_name: Vladimir Gitlevich last_name: '' ---
Effective software teams take a torrent of information and knowledge
about their subject matter (the domain) and distill it into a stream of
quality software. Finding and exploiting domain models is a key to
success in projects with complex domains.
Aimed at senior developers, architects and managers, this talk will
outline the foundations of domain-driven design: How models are chosen
and evaluated; How multiple models coexist; How the patterns help avoid
the common pitfalls, such as overly interconnected models; How
developers and domain experts together in a DDD team engage in deeper
exploration of their problem domain and make that understanding
tangible as a practical software design.
This talk will be in English.
Eric will be in Paris from the 15th of Feb teaching his acclaimed "Eric Evans Domain Driven Design Hands-On Immersion" course.


Leave blank to view all databases.
Name for the connection in MonQL.
Leave blank for none.
Use "localhost" for this computer, or an IP address for remote connections.
The port to connect to the database server on.


| Name | Organization / Region | Phone | |
|---|---|---|---|
| U.S. Air Force | |||
| Nick A De Quiroga | Chief, AF Labor Advisors |<EMAIL_ADDRESS>|<PHONE_NUMBER> |
| Kyle Roberts | Eastern Region |<EMAIL_ADDRESS>|<PHONE_NUMBER> |
| Gary Lechman | Central Region |<EMAIL_ADDRESS>|<PHONE_NUMBER> |
| Ward Graham | Western Region |<EMAIL_ADDRESS>|<PHONE_NUMBER> |
| U.S. Army | |||
| Roger W Wilkinson | Office of the Judge Advocate General |<EMAIL_ADDRESS>|<PHONE_NUMBER> |
| U.S. Army Corps of Engineers | |||
| Vanessa Shaw-Jennings | Office of the Chief Counsel |<EMAIL_ADDRESS>|<PHONE_NUMBER> |
| Defense Logistics Agency | |||
| Anne Burleigh |<EMAIL_ADDRESS>|<PHONE_NUMBER> / DSN<PHONE_NUMBER> | |
| Defpartment of Defense | |||
| Larry McLaury | Office of the Under Secretary (ATL) |<EMAIL_ADDRESS>|<PHONE_NUMBER> |
| Department of the Navy | |||
| Navy and Marine Corps |<EMAIL_ADDRESS>|<PHONE_NUMBER> | |
| Naval Facilities Engineering Command | |||
| Lynn Forbes | NAVFAC Labor Advisor |<EMAIL_ADDRESS>|<PHONE_NUMBER> |
| Name | Phone | ||
|---|---|---|---|
| Department of Agriculture | |||
| USDA Labor Advisor |<EMAIL_ADDRESS>| ||
| Department of Energy | |||
| Jeanne Lupardo |<EMAIL_ADDRESS>|<PHONE_NUMBER> | |
| John Sullivan |<EMAIL_ADDRESS>|<PHONE_NUMBER> | |
| General Services Administration | |||
| GSA Labor Advisor |<EMAIL_ADDRESS>| ||
| Department of Homeland Security | |||
| David Duda |<EMAIL_ADDRESS>|<PHONE_NUMBER> | |
| Department of Housing and Urban Development | |||
| Pamela Glekas Spring |<EMAIL_ADDRESS>|<PHONE_NUMBER> | |
| Department of Labor | |||
| This position is currently vacant. | |||
| National Aeronautics and Space Administration (NASA) | |||
| John Brett |<EMAIL_ADDRESS>|<PHONE_NUMBER> | |
| Social Security Administration | |||
| Joan M Smith |<EMAIL_ADDRESS>|<PHONE_NUMBER> | |
| Department of Transportation | |||
| Bonnie Angermann, Office of Chief Conusel |<EMAIL_ADDRESS>|<PHONE_NUMBER> | |
| Joanne Burrus, Maritime Administration |<EMAIL_ADDRESS>|<PHONE_NUMBER> | |
| Greg Carter, Federal Aviation Administration |<EMAIL_ADDRESS>| ||
| Steven Rochlis, Federal Highway Administration |<EMAIL_ADDRESS>|<PHONE_NUMBER> | |
| Stephen Pereira, Federal Transit Administration |<EMAIL_ADDRESS>|<PHONE_NUMBER> | |
| Brandon C Hollingshead, Pipeline and Hazardous Material Safety Administration |<EMAIL_ADDRESS>|<PHONE_NUMBER> | |
| Jean Mulff, Federal Motor Carrier Safety Administration |<EMAIL_ADDRESS>|<PHONE_NUMBER> | |
| RT Baumann, Senior Attorney, Office of Acquisition Management |<EMAIL_ADDRESS>| ||
| Ross Jeffries, Directory, National Highway Traffic Safeway Administration |<EMAIL_ADDRESS>| ||
| Department of Veterans Affairs | |||
| Mitchell L Gasbarra |<EMAIL_ADDRESS>|<PHONE_NUMBER> |


{%extends "main/header.html"%} {% block content%} {% for message in messages %}
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If you don't have an account Register here. {%endblock%}


Technie, a Polyglot , I love exploring and fiddling with new technologies in an attempt to solve the hot computer science problems. I live by the words of Steve Jobs, “Stay hungry. Stay foolish.” and approach everything I do with this in mind to guide my curiosity and goals. I hope to one day take this curiosity and my technical skills to new heights as an established entrepreneur, where I wish to make my mark in the same way as I was able to at Visa this summer, by designing dreams. Amen!


Our Next Free Course at Gilt: Data Visualization
For all of you 9-to-5ers who enjoy your free Gilt tech courses to take place after work, great news: Our next freebie will be an evening course. The focus: data visualization. But not just any data visualization: This three-hour session will cover visualizations for forests, trees and clustering in the context of visualization for multivariate analysis. Leading the way will be Paul Trowbridge, an adjunct instructor at NYU’s Center for Advanced Digital Applications.
Multivariate, high-dimensional data sets pose challenges for low-dimensional visual representation, interpretation and communication. In this class, Paul will explore clustering, trees and Random forests–three common methods for analyzing multivariate data when classification and prediction are key goals of the analysis. He’ll introduce each analysis method, and present visualization strategies and techniques for each method–focusing on Random forests. He’ll review these existing visualization methods, teaching how to produce and interpret each visual. He’ll then proceed to explore some visualization strategies to diagnose and study the quality of the prediction and classification output of a Random forest analysis. Each step in his presentation will draw from relevant example data sets.
The essentials:
When: Thursday, October 23, 2014 from 5-9 PM
Where: Gilt office in Manhattan
Cost: Nothing!
Who: Gilt technologists … and you?
About the instructor: In addition to teaching at NYU, Paul is a PhD student in Statistics at Rutgers and organizer of the meetup group Data Visualization New York, which has +2200 members.
Fill out the form below to submit your contact info. (Note: You must be age 18 or older to attend.) Please keep in mind that submitting this form does not guarantee you a seat. However, it does mean that you’ll receive details about the other courses we’re developing as soon as they become available. We hope you can join us!
REGISTRATION IS CLOSED.


data class Evaluation<TProgram, TOutput : Output<TProgram>>
An evaluation of a program on a set of fitness cases. |
|
data class FitnessCase<out TData>
A case to evaluate a programs fitness on. |
|
abstract class FitnessContext<TData, TOutput : Output<TData>> : Module
Provides a way to map a program to fitness cases using a given fitness function. |
|
class FitnessEvaluator<TData, TOutput : Output<TData>>
Provides a way to evaluate the fitness of a program. |
|
abstract class FitnessFunction<TData, TOutput : Output<TData>>
Provides the functionality to compute the fitness of an individual program on a set of input-output examples. |
|
object FitnessFunctions
A collection of standard fitness functions. |
|
class MultipleOutputFitnessContext<TData> : FitnessContext<TData, Multiple<TData>>
Facilitates fitness evaluation for programs which have multiple outputs. |
|
class SingleOutputFitnessContext<TData> : FitnessContext<TData, Single<TData>>
Facilitates fitness evaluation for programs which have a single output. |
typealias FitnessFunctionProvider<TData, TOutput> = () -> FitnessFunction<TData, TOutput>
A function that provides a FitnessFunction implementation on request. |
|
typealias MultipleOutputFitnessFunction<TData> = FitnessFunction<TData, Multiple<TData>>
A FitnessFunction for lgp.core.program.Programs with multiple outputs. |
|
typealias SingleOutputFitnessFunction<TData> = FitnessFunction<TData, Single<TData>>
A FitnessFunction for lgp.core.program.Programs with a single output. |


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%
'There are at least three types of cards
'Question cards, Technique cards and OBTW cards
'Question cards have the text of the question and the appropriate solution
'Technique cards have the name of the procedure and a list of the required assumptions
'OBTW cards have ( the text of the card and the effect of the card: The effect consists of
' (a) what assumptions are being affected,
' (b) the player being affected (You, One Opponent or All players),
' (c) whether it is a positive or negative effect,
' (d) more could be added later
dim NumQues
dim NumTech
dim NumReq
dim NumCards
dim NumEffects
Dim intInitialize
dim strQuestions()
dim intSolutions()
dim strTechNames()
dim strAssmList()
dim strReqList()
dim strCardText()
dim strEffectList()
NumQues=24
NumTech = 19
NumReq=5
NumCards = 19
intInitialize = 0
Redim strQuestions(NumQues)
redim intSolutions(NumQues)
redim strTechNames(NumTech)
redim strReqList(NumTech,NumReq)
redim strCardText(numcards)
redim strEffectList(NumCards)
Call LoadQuestions
Call LoadRequirements
call LoadCards
Session("NumQues") = NumQues
Session("NumTech") = NumTech
Session("NumReg") = NumReg
Session("NumCards") = NumCards
Session("Initialize") = intInitialize
Session("Questions") = strQuestions
Session("Solutions") = intSolutions
Session("Techniques") = strTechNames
Session("ReqList") = strReqList
Session("CardText") = strCardText
Session("EffectList") = strEffectList
response.write("
" & intInitialize )
Sub LoadQuestions()
Dim Techs_n_Ques()
Dim i
ReDim Techs_n_Ques(NumQues)
'First Load the Questions
Techs_n_Ques(1) = "(18)Does the increase in the mean salary with each year of experience change with the number of employees in the company?"
Techs_n_Ques(2) = "( 6)You wish to estimate all possible diffences in average executives salaries across four types of companies: consumer products, utilities, industrial-high tech, and financial services."
Techs_n_Ques(3) = "( 5)Does the IRS assess the same aveage amount of fines for Manufacturing as opposed to Service Industries?"
Techs_n_Ques(4) = "( 9)What will be the average final exam grade for all students how make a 75 on exam 1 and 92 on exam 2?"
Techs_n_Ques(5) = "( 3)Knowing only sample data what is the average IRS penalties?"
Techs_n_Ques(6) = "(16)After determining that there is no interaction of machine and manufactuer, is there a difference in average repair cost among four types of machines?"
Techs_n_Ques(7) = "(10)What will be the salary of a person with a given number of years experience?"
Techs_n_Ques(8) = "(17)There is interaction of machine and manufactuer, what is differences in average repair cost between combinations of machine and manufacturers?"
Techs_n_Ques(9) = "( 6)What will be average difference in salaries between the positions of assistant vice-president and supervisor?"
Techs_n_Ques(10) = "(13)Among blocking by position (CEOs, CFOs, and CIOs), is there any difference in the average salaries between men and women?"
Techs_n_Ques(11) = "( 8)After adjusting for the GMAT score, what is the change in the average of the final grades for for each hour studied?"
Techs_n_Ques(12) = "( 5)Is there any difference in the average of executive salaries among the different ranks (vice-president, assistant vice-president, or supervisor)?"
Techs_n_Ques(13) = "( 7)Is undergraduate major related to the letter grade in course?"
Techs_n_Ques(14) = "(15)People were sampled from combinations of gender and city. You wish to know if the effect of gender on salary depends on city."
Techs_n_Ques(15) = "(12)For all students with a specific GPA, is there any difference in the average final grades between two majors?"""
Techs_n_Ques(16) = "(14)After blocking by the number of statistics classes (0, 1, or 2), what is the difference in average grade among the majors ?"
Techs_n_Ques(17) = "(11)Can years experience and/or years of education help predict an executive's salary?."
Techs_n_Ques(18) = "(12)Is there evidence of discrimination (do male executives get paid more than women executives with the same experience)?"
Techs_n_Ques(19) = "( 4)Based only on a sample will the average of the final grades for all students who could have taken the class be larger than 70?"
Techs_n_Ques(20) = "( 9)A delivery service wishes to determine the average delivery costs for all deliveries made 20 miles from the store."
Techs_n_Ques(21) = "( 2)A student wishes to know what percent of the time MBA students will use statistics at work."
Techs_n_Ques(22) = "( 1)Given you only know the variation in salaries of all vice-presidents, based on a sample what is the average salaries of all vice-presidents?"
Techs_n_Ques(23) = "( 7)Does a person's opinion about capital punishment depend on their party affiliation?"
Techs_n_Ques(24) = "(19)After determining that there is no interaction of machine and manufactuer, what is the differences in average repair cost among four types of machines?"
'randomize the question order
Call RandomSort(Techs_n_Ques)
'seperate the questions and solution techniqes
For i = 1 To NumQues
strQuestions(i) = Right(Techs_n_Ques(i), Len(Techs_n_Ques(i)) - 4)
intsolutions(i) = Trim(Mid(Techs_n_Ques(i), 2, 2))
' response.write("
")
' response.write( intSolutions(i) & ", " & strQuestions(i) )
Next
end sub
Sub LoadRequirements()
Dim Assms_n_Techs()
Dim i
Dim j
Dim strTemp
Dim strTemp2
Dim strTemp3
ReDim Assms_n_Techs(NumTech)
Assms_n_Techs(1) = "(01100) Z conf. interval (CI) for a mean"
Assms_n_Techs(2) = "(10010) Z CI for a proportion"
Assms_n_Techs(3) = "(01101) t CI for a mean"
Assms_n_Techs(4) = "(01101) t test for a mean"
Assms_n_Techs(5) = "(01111) One Way ANOVA F test"
Assms_n_Techs(6) = "(01111) One-Way ANOVA Tukey "
Assms_n_Techs(7) = "(10010) Chi-Square contingency table"
Assms_n_Techs(8) = "(11111) CI for a Slope"
Assms_n_Techs(9) = "(11111) CI for a mean given Xi values"
Assms_n_Techs(10) = "(11111) Prediction interval for Y | Xi values"
Assms_n_Techs(11) = "(11111) Regression F test of Model"
Assms_n_Techs(12) = "(11111) Dummy Variable Regression"
Assms_n_Techs(13) = "(01111) Randomized Block F test"
Assms_n_Techs(14) = "(01111) Random Block Tukey Procedure"
Assms_n_Techs(15) = "(01111) 2-Way ANOVA F test - Interaction"
Assms_n_Techs(16) = "(01111) 2-Way ANOVA F test - Factor Means"
Assms_n_Techs(17) = "(01111) 2-Way ANOVA Tukey - Treatment Means"
Assms_n_Techs(18) = "(11111) Test of a Slope "
Assms_n_Techs(19) = "(01111) 2 Way ANOVA Tukey Factor Means"
'seperate the questions and solution techniqes
For i = 1 To NumTech
strTechNames(i) = Right(Assms_n_Techs(i), Len(Assms_n_Techs(i)) - (NumReq + 3))
strTemp = Trim(Mid(Assms_n_Techs(i), 2, 5))
strTemp2 = ""
For j = 1 To NumReq
strTemp3 = Mid(strTemp, j, 1)
strReqList(i, j) = strTemp3
strTemp2 = strTemp2 & strReqList(i, j)
Next
' Response.write("
" & strTemp2 & " " & strTechNames(i))
next
end sub
Sub LoadCards()
Dim Cards_n_Effects()
Dim i
Dim j
Dim strTemp
Dim strTemp2
Dim strTemp3
redim cards_n_Effects(NumCards)
Cards_n_Effects(1)="(1122)Oh-By-The-Way: The data is non-normally distributed. Effects: All players must have a Small Sample size and a Moderate Sample size box checked, then they can choose Normality. If any Normality box is checked, it must be unchecked. Next Player's turn."
Cards_n_Effects(2)="(1122)Oh-By-The-Way: The data is severely non-normally distributed. Effects: : All players must have a Small Sample size and a Moderate Sample, and Large Sample size box checked, then they can choose Normality. If any Normality box is checked, it must be unchecked. Next Player's turn."
Cards_n_Effects(3)="(1112)Oh-By-The-Way: The company remembers that they wish to control for the effect of an additional quantitative variable. Effects: All players must be using a technique that allows multiple independent variables. Any Linearity box checked must be unchecked. Next Player's turn. "
Cards_n_Effects(5)="(1132)Oh-By-The-Way: The assumption of equal variance is violated. Effects: All Equal Variance boxes must be unchecked. Next Player's turn. "
Cards_n_Effects(6)="(1112)Oh-By-The-Way: The expected value of Y is not the specified linear function of the Xis. Effects: All Linearity boxes must be unchecked. Next Player's turn. "
Cards_n_Effects(7)="(2362)Oh-By-The-Way: Data was artificially created by experimenter. Effect: An opponent on your choice must uncheck their Sample boxes and Assumption boxes. Go again. "
Cards_n_Effects(8)="(3362)Oh-By-The-Way: Bad data - Too many missing observations. Effect: You must uncheck all your boxes. Next Player's turn. "
Cards_n_Effects(9)="(3362)Oh-By-The-Way: The instructor of your statistics course has a fake diploma. Effect: You must uncheck all your boxes. Next Player's turn."
Cards_n_Effects(10)="(3362)Oh-By-The-Way: A software virus destroys your data. Effect: You must uncheck all your boxes. Next Player's turn. "
Cards_n_Effects(11)="(2122)Oh-By-The-Way: An opponent believes that the data is non-normally distributed. Effects: An opponent must have a Small Sample size and a Moderate Sample size box checked, then they can choose Normality. If his/her Normality box is checked, it must be unchecked. Go again."
Cards_n_Effects(12)="(2322)Oh-By-The-Way: An Opponent believes that the data is severely non-normally distributed. Effects: An opponent must have a Small Sample size and a Moderate Sample, and Large Sample size box checked, then they can choose Normality. If her/his Normality box is checked, it must be unchecked. Go again."
Cards_n_Effects(13)="(2132)Oh-By-The-Way: An opponent believes that the assumption of equal variance is violated. Effects: Her/his Equal Variance boxes must be unchecked. Go again. "
Cards_n_Effects(14)="(2112)Oh-By-The-Way: An opponent believes that the expected value of Y is not the specified linear function of the Xis. Effects: His/her Linearity boxes must be unchecked. Go again. "
Cards_n_Effects(15)="(3251)Oh-By-The-Way: Your statistics professor comes by to visit. Effect: You get to check two of your boxes. Next player's turn."
Cards_n_Effects(16)="(3351)Oh-By-The-Way: You spy on an opponent. Effect: You may check all the boxes that an opponent has checked. Next Player's turn."
Cards_n_Effects(17)="(3171)Oh-By-The-Way: You buy an expert system for statistics. Effect: Starting your next turn you can choose up to two boxes per turn. Next Player's turn."
Cards_n_Effects(18)="(3181)Oh-By-The-Way: You hire a statistical consultant. Effect: Starting next turn you may choose up to two boxes per turn. Next Player's turn."
Cards_n_Effects(19)="(2182)Oh-By-The-Way: An opponent's consultant quits. Effect: If an opponent has a consultant, then they lose that help. Next Player's turn."
Cards_n_Effects(4)="(2172)Oh-By-The-Way: An opponent's expert system crashes. Effect: If an opponent has an expert system, then they lose that help. Go Again."
For i = 1 To Numcards
strCardText(i) = Right( cards_n_Effects(i), Len( cards_n_Effects(i) ) - 6)
strEffectList(i) = mid(cards_n_Effects(i),2,4)
next
end sub
Sub RandomSort(arr)
Dim strTemp
Dim i
Dim j
Dim lngMax
Dim sngRand()
Dim sngTempRand
sngTempRand = 2.01
lngMax = UBound(arr)
ReDim sngRand(lngMax)
For i = 1 To lngMax
randomize
sngRand(i) = Rnd()
Next
For i = 1 To lngMax - 1
For j = i + 1 To lngMax
If sngTempRand > sngRand(j) Then
sngTempRand = sngRand(i)
sngRand(i) = sngRand(j)
sngRand(j) = sngTempRand
strTemp = arr(i)
arr(i) = arr(j)
arr(j) = strTemp
End If
Next
Next
End Sub
%>


3.5 Linear mixed models
We return to our subject and object relative clause data from English (Grodner and Gibson, Expt 1). First we load the data as usual, define relative clause type as a sum coded predictor, and create a new column called so
that represents the contrast coding (\(\pm 1\) sum contrasts), and a column that holds log-transformed reading time.
gg05e1 <- read.table("data/grodnergibsonE1crit.txt",
header = TRUE)
gg05e1$so <- ifelse(gg05e1$condition == "objgap", 1,
-1)
gg05e1$logrt <- log(gg05e1$rawRT)
Recall that these data have multiple measurements from each subject for each condition:
## subject
## condition 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## objgap 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## subjgap 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## subject
## condition 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
## objgap 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## subjgap 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## subject
## condition 34 35 36 37 38 39 40 41 42
## objgap 8 8 8 8 8 8 8 8 8
## subjgap 8 8 8 8 8 8 8 8 8
We can visualize the different responses of subjects:
## `geom_smooth()` using formula 'y ~ x'
It’s clear that different subjects have different effects of the relative clause manipulation: some slopes are positive sloping, some are flat, and some are negatively sloping. There is between-subject variability in the relative clause effect.
Given these differences between subjects, you could fit a separate linear model for each subject, collect together the intercepts and slopes for each subject, and then check if the slopes are significantly different from zero. There is a function in the package that computes separate linear models for each subject: lmList.
One can extract the intercept and slope estimates for each subject. For example, for subject 1:
## (Intercept) so
## 5.76962 0.04352
One can plot the individual lines for each subject, as well as the fit of a simple linear model m0 for all the data taken together; this will show how each subject deviates in intercept and slope from the model m0’s intercept and slope.
To find out if there is an effect of relative clause type, we simply need to check whether the slopes of the individual subjects’ fitted lines taken together are significantly different from zero. A one-sample t-test will achieve this:
##
## One Sample t-test
##
## data: coef(lmlist.fm1)[2]
## t = 2.8, df = 41, p-value = 0.008
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.01745 0.10658
## sample estimates:
## mean of x
## 0.06202
The above test is exactly the same as the paired t-test and the varying intercepts linear mixed model that we fit in the last chapter using the by-subject aggregated data:
bysubj <- aggregate(log(rawRT) ~ subject + condition,
mean, data = gg05e1)
colnames(bysubj)[3] <- "logrt"
t.test(logrt ~ condition, bysubj, paired = TRUE)$statistic
## t
## 2.81
## compare with linear mixed model:
summary(lmer(logrt ~ condition + (1 | subject), bysubj))$coefficients[2,
]
## Estimate Std. Error t value
## -0.12403 0.04414 -2.81021
The above lmList model we just fit is called repeated measures regression. We now look at how to model unaggregated data using the linear mixed model. Incidentally, this repeated measures regression model is now largely of historical interest, and useful only for understanding the linear mixed model, which is the modern standard approach.
We turn next to three main types of linear mixed model; other variants will be introduced in later chapters.
3.5.1 Model type 1: Varying intercepts
The linear mixed model does something related to the above by-subject fits, but with some crucial twists, as we see below. In the model shown below, the statement
\[\begin{equation} (1 \mid \hbox{subject}) \end{equation}\]
adjusts the grand mean estimates of the intercept by a term (a number) for each subject.
Notice that we did not aggregate the data.
Here is the abbreviated output:
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.09983 0.3160
Residual 0.14618 0.3823
Number of obs: 672, groups: subject, 42
Fixed effects:
Estimate Std. Error t value
(Intercept) 5.88306 0.05094 115.497
so 0.06202 0.01475 4.205
One thing to notice in the present example is that the coefficients (intercept and slope) of the fixed effects of the above model are identical to those in the linear model m0
above. What is different between the linear model and the linear mixed model is the standard error. In the latter, the standard error is determined by more than one source of variance, as we explain below.
The intercept adjustments for each subject can be viewed by typing:
## [1] -0.103928 0.077195 -0.230621 0.234198 0.008828
## [6] -0.095363 -0.205571 -0.155371 0.075944 -0.364367
Here is another way to summarize the adjustments to the grand mean intercept by subject. The error bars represent 95% confidence intervals.
## $subject
3.5.2 The formal statement of the varying intercepts model
The model m0.lmer
above prints out the following type of linear model. \(i\) indexes subject, and \(j\) indexes items.
Once we know the subject id and the item id, we know which subject saw which condition:
## subject item condition rawRT so logrt
## 6 1 1 objgap 320 1 5.768
The mathematical form of the linear mixed model is:
\[\begin{equation} y_{ij} = \beta_0 + u_{0i}+\beta_1\times so_{ij} + \varepsilon_{ij} \end{equation}\]
The only new thing here beyond the linear model we saw earlier is the by-subject adjustment to the intercept. These by-subject adjustments to the intercept \(u_{0i}\) are assumed by lmer to come from a normal distribution centered around 0:
\[\begin{equation} u_{0i} \sim Normal(0,\sigma_{u0}) \end{equation}\]
The ordinary linear model m0 has one intercept \(\beta_0\) for all subjects, whereas this linear mixed model with varying intercepts m0.lmer
has a different intercept (\(\beta_0 + u_{0i}\)) for each subject \(i\).
We can visualize the adjustments for each subject to the intercepts as shown below.
An important point is that in this model there are two variance components or sources of variance (cf. the linear model, which had only one):
- \(u_0 \sim Normal(0,\sigma_{u0})\)
- \(\varepsilon \sim Normal(0,\sigma)\)
These two standard deviations determine the standard error of the \(\beta_1\) slope parameter.
3.5.3 Model type 2: Varying intercepts and slopes, without a correlation
Unlike the figure associated with the lmlist.fm1
model above, which also involves fitting separate models for each subject, the model m0.lmer
assumes different intercepts for each subject but the same slope.
We can choose to fit different intercepts as well as different slopes for each subject. To achieve this, assume now that each subject’s slope is also adjusted:
\[\begin{equation} y_{ij} = \beta_0 + u_{0i}+(\beta_1+u_{1i})\times so_{ij} + \varepsilon_{ij} \end{equation}\]
That is, we additionally assume that \(u_{1i} \sim Normal(0,\sigma_{u1})\). The lmer
notation for fitting separate intercepts and slopes is (1+so||subject)
. We will just explain what the double vertical bars represent.
The output of this model will now show that there are not two but three sources of variability. These are:
- \(u_0 \sim Normal(0,\sigma_{u0})\)
- \(u_1 \sim Normal(0,\sigma_{u1})\)
- \(\varepsilon \sim Normal(0,\sigma)\)
In particular, the model estimates the following standard deviations:
- \(\hat\sigma_{u0}=0.317\)
- \(\hat\sigma_{u1}=0.110\)
- \(\hat\sigma = 0.365\).
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.1006 0.317
subject.1 so 0.0121 0.110
Residual 0.1336 0.365
Number of obs: 672, groups: subject, 42
Fixed effects:
Estimate Std. Error t value
(Intercept) 5.8831 0.0509 115.50
so 0.0620 0.0221 2.81
These fits for each subject are visualized below (the gray line shows the model with a single intercept and slope, i.e., our old model m0
):
<IP_ADDRESS> Comparing lmList model with the varying intercepts model
Compare this model with the lmlist.fm1
model we fitted earlier:
What is striking is that each subject’s estimated best fit line is “smooothed out” compared to the lmList fits. This aspect of the linear mixed model is called shrinkage; we return to this point shortly.
<IP_ADDRESS> Visualizing random effects
As before, it is instructive to visualize the by-subjects adjustments to the intercept and slope:
## $subject
What this is showing is wide variability in the mean reading times between subjects, but very little variation in the slope between subjects.
<IP_ADDRESS> The formal statement of varying intercepts and varying slopes linear mixed model
Here is the full statement of the varying intercept and slopes model. Again, i indexes subjects, j items.
\[\begin{equation} y_{ij} = \beta_0 + u_{0i}+(\beta_1+u_{1i})\times so_{ij} + \varepsilon_{ij} \end{equation}\]
There are now three variance components:
- \(u_0 \sim Normal(0,\sigma_{u0})\)
- \(u_1 \sim Normal(0,\sigma_{u1})\)
- \(\varepsilon \sim Normal(0,\sigma)\)
<IP_ADDRESS> Crossed random effects for subjects and for items
The varying intercepts and slopes model doesn’t capture all the sources of variance yet. The items also contribute sources of variance: just like subjects, items may also vary in their reading times or in the extent to which the reading times are impacted by condition. I other words, they might have different intercepts and slopes.
Notice that in this design (as in many designs in psycholinguistics or linguistic research) each subject sees all the items.  Notice that there is a lot less item-level variation; this is often the case in planned experiments in sentence processing, where the experimental items are carefully constructed to vary as little as possible.
In the above models, there is an assumption that there is no correlation between the intercept and slope adjustments by subject, and no correlation between the intercept and slope adjustments by item. It is possible that the intercept and slope adjustments are in fact correlated. We turn to this model next.
3.5.4 Model type 3: Varying intercepts and varying slopes, with correlation
A correlation can be introduced between the intercept and slope adjustments by using a single vertical bar instead of two vertical bars in the random effects structure:
## boundary (singular) fit: see ?isSingular
To understand what this model is doing, we have to recall what a bivariate/multivariate distribution is.
Random effects:
Groups Name Variance Std.Dev. Corr
subject (Intercept) 0.10103 0.3178
so 0.01228 0.1108 0.58
item (Intercept) 0.00172 0.0415
so 0.00196 0.0443 1.00 <= degeneracy
Residual 0.12984 0.3603
Number of obs: 672, groups: subject, 42; item, 16
Fixed effects:
Estimate Std. Error t value
(Intercept) 5.8831 0.0520 113.09
so 0.0620 0.0247 2.51
The correlations (0.58 and 1.00) you see in the model output below are the correlations between the varying intercepts and slopes for subjects and for items. Notice that the variance covariance matrix for items is degenerate: its correlation is 1. This matrix cannot be inverted.
When the correlation is +1 or -1 or near these numbers, this means that the optimizer in lme4 is unable to estimate the correlation parameter, usually due to there not being enough data. If you are in such a situation, you are better off not trying to estimate this parameter with the data you have, and instead fitting one of the simpler models. We will return to this point when discussing model selection. For further discussion, see Barr et al. (2013), Bates et al. (2015), and Matuschek et al. (2017).
<IP_ADDRESS> Formal statement of varying intercepts and varying slopes linear mixed model with correlation
As usual, i indexes subjects, j items. The vector so
is the sum-coded factor levels: +1 for object relatives and -1 for subject relatives. The only new thing in this model is the item-level effects, and the specification of the variance-covariance matrix for subjects and items, in order to include the correlation parameters.
\[\begin{equation} y_{ij} = \alpha + u_{0i} + w_{0j} + (\beta + u_{1i} + w_{1j}) \times so_{ij} + \varepsilon_{ij} \end{equation}\]
where \(\varepsilon_{ij} \sim Normal(0,\sigma)\) and
\[\begin{equation}\label{eq:covmatLM} \Sigma_u = \begin{pmatrix} \sigma _{u0}^2 & \rho _{u}\sigma _{u0}\sigma _{u1}\\ \rho _{u}\sigma _{u0}\sigma _{u1} & \sigma _{u1}^2\\ \end{pmatrix} \quad \Sigma _w = \begin{pmatrix} \sigma _{w0}^2 & \rho _{w}\sigma _{w0}\sigma _{w1}\\ \rho _{w}\sigma _{w0}\sigma _{w1} & \sigma _{w1}^2\\ \end{pmatrix} \end{equation}\]
\[\begin{equation}\label{eq:jointpriordistLM} \begin{pmatrix} u_0 \\ u_1 \\ \end{pmatrix} \sim \mathcal{N} \left( \begin{pmatrix} 0 \\ 0 \\ \end{pmatrix}, \Sigma_{u} \right), \quad \begin{pmatrix} w_0 \\ w_1 \\ \end{pmatrix} \sim \mathcal{N}\left( \begin{pmatrix} 0 \\ 0 \\ \end{pmatrix}, \Sigma_{w} \right) \end{equation}\]
<IP_ADDRESS> Visualizing the random effects
One can visualize the correlation between intercepts and slopes by subjects. The positive correlation of 0.58 between subject intercept and slope adjustments implies that slower subjects show larger effects. However, the dotplot below doesn’t show a convincing indication that such a correlation exists:
The correlation pattern is easier to see if we plot the slope adjustments against the intercept adjustments.
plot(ranef(m3.lmer)$subject[, 1], ranef(m3.lmer)$subject[,
2], xlab = "Intercept adjustments (subject)", ylab = "Slope adjustments")
When we talk about hypothesis testing, we will look at what inferences we can draw from this correlation.
The dotplot showing the item-level effects shows a perfect correlation between intercept and slope adjustments, but as mentioned above these are from a degenerate variance covariance matrix and not meaningful.
References
Barr, Dale J, Roger Levy, Christoph Scheepers, and Harry J Tily. 2013. “Random Effects Structure for Confirmatory Hypothesis Testing: Keep It Maximal.” Journal of Memory and Language 68 (3). Elsevier: 255–78.
Bates, Douglas M., Reinhold Kliegl, Shravan Vasishth, and Harald Baayen. 2015. “Parsimonious Mixed Models.”
Matuschek, Hannes, Reinhold Kliegl, Shravan Vasishth, R. Harald Baayen, and Douglas M. Bates. 2017. “Balancing Type I Error and Power in Linear Mixed Models.” Journal of Memory and Language 94: 305–15. https://doi.org/10.1016/j.jml.2017.01.001.


17-year-old boy critical in hospital with meningococcal B strain
A teenage boy is in a critical condition in hospital after being diagnosed with meningococcal B strain.
The 17-year-old from Adelaide is being treated in hospital while nine people who had contact with him have been prescribed precautionary antibiotics.
There have been 27 cases of invasive meningococcal this year compared with 30 at the same time last year.
Of the 27 cases, 19 have been B strain, four Y strain and four W strain.
The teenage boy’s case has not been linked to any other outbreaks or incidents.


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The dominance of mobile internet use means that users are searching for the right business as
they travel, shop, or sit on their couch at home. Search Engine Optimization (SEO) allows you to
increase your visibility and find the right customers for your business.
Online Reputation Management
The web is full of opinions, and some of these can be negative. Social media allows anyone with
an internet connection to say whatever they want about your business. Online Reputation
Management gives you the control over what potential customers see when they search for your
business.
Social Media Marketing
Social media continues to have a sizable influence on buying habits. Social media marketing
helps you determine which platforms are suited to your brand, using analytics to find the right
markets and increase your lead generation.
Social Media Marketing
Social media continues to have a sizable influence on buying habits. Social media marketing helps you determine which platforms are suited to your brand, using analytics to find the right markets and increase your lead generation.


{% extends "templates/layouts/content-view__partial.html" %}
{% set title = "Schedule | PIXEL UP! 2016 in Johannesburg" %}
{% set meta_description = "An impressive lineup of Speakers who've worked at companies like Apple, Dropbox, Twitter and SalesforceUX." %}
{% block meta %}
Along with the continental breakfast, you’ll get an amazing lunch, beverage breaks and nibbles throughout the day.
{% for i in data.scheduleItems %} {% if (i.startTime | date('dddd'))=="Monday" %} {{ listingItem(i) }} {% endif %} {% endfor %}Along with the continental breakfast, you’ll get an amazing lunch, beverage breaks and nibbles throughout the day.
{% for i in data.scheduleItems %} {% if (i.startTime | date('dddd'))=="Tuesday" %} {{ listingItem(i) }} {% endif %} {% endfor %}Day 3 is workshop day. All day workshops starting at 9am and ending at 4pm. Go beyond inspiration and immerse yourself in groundbreaking design & development skills you won’t find anywhere else. Get your team on the same page with proven methods, tools and clear-cut direction on specific actions and skills.
{% for i in data.scheduleItems %} {% if (i.startTime | date('dddd'))=="Wednesday" %} {{ listingItem(i) }} {% endif %} {% endfor %}Workshops tickets are R3,500.00. Workshop tickets are valid for the workshop only. For the conference days you'll need an extra ticket.


Artists by Movement: Cubism
Europe, 1908-1920
Cubism was developed between about 1908 and 1912 in a collaboration between Georges Braque and Pablo Picasso. Their main influences are said to have been Tribal Art (although Braque later disputed this) and the work of Paul Cezanne. The movement itself was not long-lived or widespread, but it began an immense creative explosion which resonated through all of 20th century art.
The key concept underlying Cubism is that the essence of an object can only be captured by showing it from multiple points of view simultaneously.
Cubism had run its course by the end of World War I, but among the movements directly influenced by it were Orphism, Precisionism, Futurism, Purism, Constructivism, and, to some degree, Expressionism.
| |
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{% extends "form.html"%}
{% block title %}
{{ title }}
{% endblock %}
{% block extra_css %}
{% endblock %}
{% block pre_form_content %}
{% include "horizontal_filter_form.html" with a_form=locations_filter no_button=True filter_id='loc_filter' no_auto_refresh=True%}
{% endblock %}
{% block display_form_fields %}
{% include "naked_form.html" with a_form=form %}
{% include "horizontal_naked_form.html" with a_form=odk_access_form %}
{% include "horizontal-formset.html" with formset=ussd_access_form legend='USSD Access' class='ussd_form' %}
{% endblock %}
{% block javascripts %}
{% endblock %}


{% extends "easydmp/plan/base.html" %} {% load static %} {% block content %}
Invite people to view to this plan
Invite people to edit to this plan


Frequently Asked Questions
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The events that are covered by the reimbursement plan are: university courses, seminars, certification preparation classes, certifications, technical training and other. For more information on other, contact the benefit coordinator.
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The total amount an employee can claim is $1,000.00 in tuition reimbursement per year. The percentage of total cost that will be reimbursed is as follows:
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New Delhi declares public emergency as pollution at year's worst
by Neha DasguptaNEW DELHI (Reuters) - Authorities in New Delhi declared a public health emergency on Friday and closed schools and all construction activity until next week as air pollution in the city hit its worst level this year.
A thick haze has hung over the Indian capital this week caused by plumes of toxic smoke from farm fires raging in neighbouring states.
An index measuring the level of a deadly air pollutant hit 484 on a scale of 500 on Friday, the government’s Central Pollution Control Board, the worst this year.
The index measures the level of PM 2.5, tiny particulate matter that goes deep into the lungs. Anything above 400 poses a risk for people with respiratory illnesses and can also affect even those with healthy lungs.
Some companies advised employees to avoid exposure to toxic air and work from home.
“We have been advised to stay at home on Monday,” said Anuj Rawat, an account director at Kantar, the market research arm of British advertising major WPP. Kantar employs around 400 people at its office in New Delhi, Rawat said.
Related Coverage
The Environment Pollution Control Authority, which is leading the effort to tackle Delhi’s pollution, said: “We have to take this as a public health emergency as air pollution is now hazardous and will have adverse health impacts on all, but particularly our children.”
It banned all construction work in the sprawling metropolis of 20 million people and its neighbouring cities until Nov. 5.
Each year, farmers in the states of Punjab and Haryana burn crop residue to prepare for the sowing season, ignoring government warnings.
According to government-run monitor SAFAR, satellite pictures had captured nearly 3,200 incidents of stubble burning on Thursday in Haryana and Punjab that contributed to 44% percent of Delhi’s pollution.
Delhi Chief Minister Arvind Kejriwal offered free masks on Friday and ordered schools shut till Nov. 5 to protect children.
The toxic air has left several Bangladeshi cricket players with sore throats and itchy eyes ahead of their Twenty20 match against India on Sunday.
“Government knew an emergency situation was approaching and did not take substantive steps on stubble burning or big industrial polluting sources,” said Sunil Dahiya, an energy and air pollution analyst at Greenpeace.
“A public health emergency situation began at least 10 days ago.”


Created by John Murray
Questions you may have about Scala syntax or from the materials sent out before the class?
We'll cover some basic constructs and concepts that you should expect to see in everday Scala code:
for
Expressionforeach
map
Option[T]
flatMap
for
ComprehensionTry[T]
The for
expression is a common pattern for
iterating over data in a collection. It is very similar
to Java's foreach
construct.
By John Murray


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The Golden Retriever is a large-sized breed of dog bred as gun dogs to retrieve shot waterfowl such as ducks and upland game birds during hunting and shooting parties, and were named 'retriever' because of their ability to retrieve shot game undamaged.
- Scientific name: Canis lupus familiaris
- Color: Any shades of gold or cream
- Life Span: Average 10-12 years
- Origin: Scotland
- Temperament: Confident, Friendly, Intelligent, Kind, Reliable, Trustworthy
Although most dogs are loyal, especially if you take good care of them, some can still be independent and aloof. This is what makes the Golden Retriever special.  Best of all, they pay importance to family time, making them the perfect companion and pet for those with children or already starting a family.


Meghan Markle Instagram post deleted by Stella McCartney after backlash: report
by Julius YoungFans reportedly cried foul over a social media post shared Monday by Stella McCartney highlighting Meghan Markle’s outerwear.
According to Page Six, social media followers inundated the comments section of a since-deleted Instagram post on the popular United Kingdom fashion brand’s profile that showed the Duchess of Sussex donning a black-belted, double-breasted overcoat by McCartney, which per the outlet, carries a price tag in excess of $2,300.
Markle wore the ensemble during Remembrance Day services at the Cenotaph in Westminster on Sunday.
PRINCE HARRY TEASES BABY No. 2 WITH MEGHAN MARKLE DURING SURPRISE VISIT TO MILITARY FAMILIES: REPORT
ARCHIE HELPED HEAL ROYAL RIFT OVER MEGHAN MARKLE, ROYAL EXPERT SAYS
“So honoured to have HRH [Her Royal Highness] Duchess of Sussex in our Autumn ’19 coat at Remembrance Day service. x Stella,” the caption reportedly read.
Many commenters on the post complimented Markle on her getup. But others felt it inappropriate for the fashion designer to make mention of her apparel on a day reserved to honor those who've died in service to their country.
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“Remembrance day is to honour those that fought for us not to use for advertising purposes. Pretty shocked,” one follower wrote, while another added, “Using remembrance day to advertise your clothes is a little disrespectful!! People died and you take pictures of people showing respect for advertising purposes. Just wrong.”
A rep for the designer did not immediately respond to Fox News’ request for comment.


Motivation
The tree component aims at displaying tree data structures in a visually meaningful way. It aims to be generic enough to be freely customized, according to the constraints of the chosen visualization. Also it should allow to select plugins, to build a final visualization from a range of visualization additive effects (selectable nodes, foldable nodes, etc.).
API
Tree :: TreeSettings -> Components -> TreeComponent
Description
Creates a TreeComponent
component whose behaviour is parameterized via TreeSettings
. Children
components are components specifying appearance and behaviour of the root node, intermediary
nodes, and leaf nodes, and dealing with the edge case where the tree to visualize is ‘empty’ (i.e.
null
or undefined
).
The parametrization is as follows :
- an array of sink names must be passed to indicate which sinks are to be extracted from the
Components
- the
treeSource
property gives the name of the source which will emit tree data structures - the
localStateSource
property gives the name of the source holding the state of theTreeComponent
. This may include for instance whether a particular node is folded or expanded. - the
localTreeSetting
property is such thatsettings[localTreeSettings]
contains the actual tree to display (reminder : a component isfn(sources, settings)
) defaultUIstateNode
is the default UI state for each node when it is first displayedlocalCommandSpecs
specifies a command sink (hence an eponym command result source), and a command processing function, receiving, and executing the received commands- the incoming value from the
from
configured source is passed to the children component in their settings in a property configured in theas
property of theForEach
component’s settings. lenses
specifies the specific encoding of the tree (cf.fp-rose-tree
library documentation)
The behaviour is as follows :
- A
TreeSource
source emits a tree data structure, which can be traversed thanks to the parameterized lenses - For each new tree, the ui state is re-computed :
- new nodes are given the
defaultUIstateNode
value - existing nodes do not lead to updating the ui state
- the accumulated ui state is a hash whose key is a path to the tree, and value is the ui state for the node at that path
- new nodes are given the
- if the tree data structure is ‘empty’ (i.e. null or undefined), the
TreeEmpty
component is activated - Otherwise, the tree data structure is traversed (post-order traversal), the result of the
traversal being a component which is build recursively from the tree content by matching the leaf
nodes to
TreeLeaf
component, and the rest of the nodes (including the root node) toTreeNode
. That result is then wrapped within theTreeRoot
container component.- Ex : a tree like
[[N0, [N1, N2], N3]]
will correspond to a component of the form… :
- Ex : a tree like
m({...), [ TreeRoot, [
m({...}, [TreeLeaf]),
m({...}, [TreeNode, [
m({...}, [TreeLeaf]),
m({...}, [TreeLeaf])
]]),
m({...}, [TreeLeaf])
]])
- Note as each
TreeNode
acts as a container component forTreeLeaf
and downstreamTreeNode
s - … with the information about
N0
,N1
etc. passed toTreeNode
,TreeLeaf
etc. through settings Tree...
components can :- access the tree settings they are passed due to inheritance. In particular, they have access
to
localTreeSettings
andlocalStateSource
- access the tree data structure through
settings[localTreeSettings]
, - access the path and label for the node they are associated through
settings.path
, orsettings.label
- access the tree UI state through the
sources[localStateSource]
- emit commands through the sink with name given by
localCommandSpecs.source
, and execute those commands through the function passed inlocalCommandSpecs.executeFn
. The result of these commands is passed back the source with name given bylocalCommandSpecs.source
- A typical use of these commands is to fetch data when some events occurs on a given node of the displayed tree
- Note that errors occurring during the execution of commands are passed as is through the source, and terminate it (per Rxjs contract). Hence, if an error can be recovered from or should be ignored, it is better to encode as a regular message, rather than an error notification.
- access the tree settings they are passed due to inheritance. In particular, they have access
to
- UI state and command sinks are not propagated upstream. Other sinks from the
Tree...
components are passed upstream the component tree.
Types
TreeComponent :: Component
Components :: [TreeEmpty, TreeRoot, TreeNode, TreeLeaf]
TreeEmpty :: Component
TreeRoot :: Component
TreeNode :: Component
TreeLeaf :: Component
TreeSettings :: Record {
treeSource :: SourceName
localStateSource :: SourceName
localTreeSetting :: String
defaultUIstateNode :: *
localCommandSpecs :: Record { source :: SourceName, executeFn :: Command -> Observable<Result>}
Optionallenses :: TreeLenses
sinkNames :: Array<SinkName>
}
Contracts
TODO
Example
TODO
Tips
The generic API retained for Tree UI component allows to define plugins reasonably easily, by
combining core TreeRoot
, TreeNode
, and TreeLeaf
components with plugins’.
TODO : example of normal tree, then enhanced with tree with foldable ability


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Filtering¶
Apart from reading and writing, filtering and transforming are the most common operations within data flows. This sections presents various nuts used to filter, partition or group data.
Filter¶
A common task is to remove elements from a data flow. nuts-flow
provides Filter
and FilterFalse
for this purpose. In the
following example all number greater than five are extracted:
>>> from nutsflow import *
>>> Range(10) >> Filter(lambda x: x > 5) >> Collect()
[6, 7, 8, 9]
FilterFalse
is simply the negation of Filter
and extracts
number smaller or equal to five:
>>> Range(10) >> FilterFalse(lambda x: x > 5) >> Collect()
[0, 1, 2, 3, 4, 5]
Filter
and FilterFalse
take a predicate (Lambda) function that
must return a boolean value. If the predicate function is very simple
it can be written shorter using underscore syntax:
>>> from nutsflow import _
>>> Range(10) >> Filter(_ > 5) >> Collect()
[6, 7, 8, 9]
>>> Range(10) >> FilterFalse(_ > 5) >> Collect()
[0, 1, 2, 3, 4, 5]
Partition¶
If both ‘sides’ of a filter, the elements accepted and the elements
rejected, are wanted the Partition
nut can be used:
>>> greater, smaller = Range(10) >> Partition(_ > 5)
>>> greater >> Collect()
[6, 7, 8, 9]
>>> smaller >> Collect()
[0, 1, 2, 3, 4, 5]
>>> odd, even = Range(10) >> Partition(_ % 2)
>>> odd >> Collect()
[1, 3, 5, 7, 9]
>>> even >> Collect()
[0, 2, 4, 6, 8]
Note that Partition
returns a tuple containing two iterators.
GroupBy¶
Similar, but more powerful than Partition
is GroupBy
, which allows
to group the elements of the flow according to a key function:
>>> Range(10) >> GroupBy(_ > 5) >> Collect()
[(False, [0, 1, 2, 3, 4, 5]), (True, [6, 7, 8, 9])]
GroupBy
returns an iterator over the groups, where each group is
a tuple with the result of the key function first and the elements of
the group second. If the result of the key function is not required
the nokey flag can be set to True
:
>>> Range(10) >> GroupBy(_ > 5, nokey=True) >> Collect()
[[0, 1, 2, 3, 4, 5], [6, 7, 8, 9]]
In contrast to Partition
, GroupBy
is not limited to a boolean
key function. For instance, to group by the remainder of the division
by 3 simply call
>>> Range(10) >> GroupBy(_ % 3) >> Collect()
[(0, [0, 3, 6, 9]), (1, [1, 4, 7]), (2, [2, 5, 8])]
GroupBy
loads all data in memory and should be avoided for large data sets.
If the data is sorted GroupBySorted
can be used instead.
TakeWhile and DropWhile¶
Occasionally, it is necessary to run a data flow until a certain
condition is met. TakeWhile(func)
takes elements from the
iterable as long as the predicate function is true.
In the following example all number are collected until
the first negative number is encountered:
>>> [2, 1, -1, 3, 4, -1] >> TakeWhile(_ > 0) >> Collect()
[2, 1]
Similarily, DropWhile(func)
skips all elements while the predicate function
is true and returns the remainder of the iterable:
>>> [2, 1, -1, 3, 4, -1] >> DropWhile(_ > 0) >> Collect()
[-1, 3, 4, -1]


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Statistical data set
The list of providers who have been judged to have outstanding overall effectiveness in an Ofsted inspection.
The page has the latest data from 2015-2016; and files with data from 1993 to 2015.
Outstanding providers are those providers who have been judged to have outstanding overall effectiveness in an Ofsted inspection. There are some instances where some special criteria may apply.
When Ofsted inspects a provider, the inspector will usually make a judgement on the standard of the provider. The very best providers receive a judgement of outstanding overall effectiveness.
Ofsted sends each outstanding provider a certificate and a letter from Her Majesty’s Chief Inspector of Education, Children’s Services and Skills. These are normally posted to the provider during the month after the publication of the inspection report. The provider can use Ofsted’s Outstanding Provider logo on stationery and signage.
Every year, Ofsted publishes a list of outstanding providers for the previous inspection year, which runs September to August. Publication is usually at the same time as the Annual Report.
For more information on the judgement grades, read about being inspected as a:
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Published: 1 December 2015
Updated: 1 December 2016
From: Ofsted
Part of: Ofsted Annual Report 2015/16 Ofsted Annual Report 2014/15