Taking the Coursera Machine Learning course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng.
Assumes you have knowledge of Week 4.
Table of Contents
- Lecture notes:
Neural Networks: Learning
Cost Function and Backpropagation
Cost Function
Let’s define a few variables that we will need to use.
- $L$ = the total number of layers in the network
- $s_l$ = number of units (not counting bias unit) in layer $l$
- $K$ = number of output units or classes
Recall that in neural networks, we may have many output nodes. We denote $h_\Theta(x)_k$ as being a hypothesis that results in the $k^{\text{th}}$ output. Our cost function for neural networks is going to be a generalization of the logistic regression cost function. Recall the regularized logistic regression cost function:
$$ J(\theta) = -\dfrac{1}{m} \sum\limits_{i=1}^{m} [y^{(i)} \log(h_\theta(x^{(i)})) + (1-y^{(i)}) \log(1 - h_\theta(x^{(i)})) ] + \dfrac{\lambda}{2m} \sum\limits_{j=1}^{n} \theta_j^2 $$
For neural networks, our cost function is the following:
$$ J(\Theta) = -\dfrac{1}{m} \sum\limits_{i=1}^{m} \sum\limits_{k=1}^{K} [y_k^{(i)} \log((h_{\Theta}(x^{(i)}))_k) + (1 - y_k^{(i)}) \log(1-(h_\Theta(x^{(i)}))_k)] + \dfrac{\lambda}{2m} \sum\limits_{l=1}^{L-1} \sum\limits_{i=1}^{s_l} \sum\limits_{j=1}^{s_{l+1}} (\Theta_{j,i}^{(l)})^2 $$
Multiple output nodes are accounted for by the nested summations. In the first part of the equation, before the square brackets, an additional nested summation loops through the number of output nodes. In the regularization term after the square brackets, we account for multiple theta matrices. The number of columns in the current theta matrix is equal to the number of nodes in the current layer (including the bias unit). The number of rows in the current theta matrix is equal to the number of nodes in the next layer (excluding the bias unit). As before with logistic regression, we square every term.
Note:
- The double sum simply adds up the logistic regression costs calculated for each cell in the output layer.
- The triple sum simply adds up the squares of all the individual $\Theta$s in the entire network.
- The $i$ in the triple sum does not refer to training example $i$
Backpropagation Algorithm
“Backpropagation” is neural network terminology for minimizing our cost function. This is similar to what we did with gradient descent in logistic and linear regression. Our goal is to compute:
$$ \min_\Theta J(\Theta) $$
That is, we want to minimize our cost function $J$ using an optimal set of parameters in theta. In this section, we will look at the equations we used to compute the partial derivative of $J(\Theta)$:
$$ \dfrac{\delta}{\delta\Theta_{i,j}^{(l)}} J(\Theta) $$
To do so, we use the following algorithm:
Given training set ${ (x^{(1)}, y^{(1)}), \dots, (x^{(m)}, y^{(m)}) }$, set $ \Delta_{i,j}^{(l)} := 0 $ for all $ (l, i, j) $. This gives us a matrix full of zeros.
For training example $t = 1$ to $m$:
- Set $ a^{(l)} := x^{(t)} $
- Perform forward propagation to compute $ a^{(l)} $ for $ l = 2, 3, \dots, L $
Using $y^{(t)}$, compute $ \delta^{(L)} = a^{(L)} = y^{(t)} $
Where $L$ is our total number of layers and $a^{(L)}$ is the vector of outputs of the activation units for the last layer. Our “error values” for the last layer are simply the differences of our actual results in the last layer and the correct outputs in y. To get the delta values of the layers before the last layer, we can use an equation that steps us back from right to left:
Compute $ \delta^{(L-1)}, \delta^{(L-2)}, \dots, \delta^{(2)} $ using $ \delta^{(l)} = ((\Theta^{(l)})^T \delta^{(l+1)}) .* a^{(l)} .* (1 - a^{(l)}) $
The delta values of layer $l$ are calculated by multiplying the delta values in the next layer with the theta matrix of layer $l$. We then element-wise multiply that with a function called $g’$ (g-prime), which is the derivative of the activation function $g$ evaluated with the input values given by $z^{(l)}$.
The g-prime derivative terms can also be written out as:
$$ g’(z^{(l)}) = a^{(l)} .* (1-a^{(l)}) $$
$ \Delta_{i,j}^{(l)} := \Delta_{i,j}^{(l)} + a_j^{(l)} \delta_i^{(l+1)} $ or, the vectorized approach, $ \Delta^{(l)} := \Delta^{(l)} + \delta^{(l+1)}(a^{(l)})^T $
We update our new $\Delta$ matrix.
- $ D_{i,j}^{(l)} := \dfrac{1}{m}(\Delta_{i,j}^{(l)} + \lambda\Theta_{i,j}^{(l)} ) \hspace{2em} \text{ if } j \neq 0 $
- $ D_{i,j}^{(l)} := \dfrac{1}{m} \Delta_{i,j}^{(l)} \hspace{2em} \text{ if } j = 0 $
The capital-delta matrix $D$ is used as an “accumulator” to add up our values as we go along and eventual compute our partial derivative. Thus, we get $ \dfrac{\delta}{\delta\Theta_{i,j}^{(l)}} J(\Theta) = D_{i,j}^{(l)} $
Backpropagation Intuition
Recall the cost function for a neural network:
$$ J(\Theta) = - \dfrac{1}{m} \sum\limits_{t=1}^{m} \sum\limits_{k=1}^{K} [y_k^{(t)} \log(h_\Theta (x^{(t)}))_k + (1-y_k^{(t)}) \log(1-h_\Theta(x^{(t)})_k) ] + \dfrac{\lambda}{2m} \sum\limits_{l=1}^{L-1} \sum\limits_{i=1}^{s_l} \sum\limits_{j=1}^{s_{l+1}} (\Theta_{j,i}^{(l)})^2 $$
Considering the simple non-multiclass classification $ (k = 1) $ and disregarding regularization, the cost is computed with:
$$ \text{cost}(t) = y^{(t)} \log(h_\Theta(x^{(t)})) + (1-y^{(t)}) \log(1-h_\Theta(x^{(t)})) $$
Intuitively, $\delta_j^{(l)}$ is the ‘error’ for $a_j^{(l)}$ (unit j in layer l). More formally, the delta values are the derivative of the cost function:
$$ \delta_j^{(l)} = \dfrac{\partial}{\partial z_j^{(l)}} \text{cost}(t) $$
Recall the derivative is the slope of a line tangent to the cost function. The steeper the slope, the more incorrect we are. Consider the following neural network below and see how we could calculate $ \delta_j^{(l)}$:
In the image above, to calculate $\delta_2^{(2)}$ we multiply the weights $\Theta_{12}^{(2)}$ and $\Theta_{22}^{(2)}$ by their respective $\delta$ values found to the right of each edge. This gives us $ \delta_2^{(2)} = \Theta_{12}^{(2)} * \delta_1^{(3)} + \Theta_{22}^{(2)} * \delta_2^{(3)} $. To calculate every single possible $ \delta_j^{(l)} $ we need to start from the right of our diagram. We can think of the edges as our $ \Theta_{ij} $.Going from right to left, to calculate the value of $ \delta_j^{(l)} $ we can take over all sums of each weight times the $\delta$ it is coming from. Another example here is $ \delta_2^{(3)} = \Theta_{12}^{(3)}*\delta_1^{(4)} $.
Backpropagation in Practice
Implementation Note: Unrolling Parameters
With neural networks, we are utilizing sets of matrices:
$$ \Theta^{(1)}, \Theta^{(2)}, \Theta^{(3)}, \dots $$ $$ D^{(1)}, D^{(2)}, D^{(3)}, \dots $$
In order to use optimizing functions such as fminunc()
, we want to unroll all elements and put them into one long vector.
thetaVector = [ Theta1(:); Theta2(:); Theta3(:); ];
deltaVector = [ D1(:); D2(:); D3(:); ];
If the dimensions of Theta1 is 10 by 11, Theta2 is 10 by 11, and Theta3 is 1 by 11, we can get back our original matrices from the unrolled versions as follows:
Theta1 = reshape(thetaVector(1:110), 10, 11);
Theta2 = reshape(thetaVector(111:220), 10, 11);
Theta3 = reshape(thetaVector(221:231), 1, 11);
To summarize:
Gradient Checking
Gradient checking will assure that backpropagation works as intended. We approximate the derivative of our cost function with:
$$ \dfrac{\partial}{\partial\Theta} J(\Theta) \approx \dfrac{ J(\Theta + \epsilon) - J(\Theta - \epsilon) }{2\epsilon} $$
With multiple theta matrices, we can approximate the derivative with respect to $\Theta_j$ as follows:
$$ \dfrac{\partial}{\partial\Theta_j} J(\Theta) \approx \dfrac{ J(\Theta_1, \dots, \Theta_j + \epsilon, \dots, \Theta_n) - J(\Theta_1, \dots, \Theta_j - \epsilon, \dots, \Theta_n) }{2\epsilon} $$
A small value for $\epsilon$ (epsilon) such as $\epsilon = 10^{-4}$ guarantees that the math works out properly. If the value for $\epsilon$ is too small or too large, we can end up with numerical problems.
Hence, we are only adding or subtracting epsilon to the $\Theta_j$ matrix. In octave, we can do that as follows:
epsilon = 1e-4;
for i = 1:n,
thetaPlus = theta;
thetaPlus(i) += epsilon;
thetaMinus = theta;
thetaMinus(i) -= epsilon;
gradApprox(i) = (J(thetaPlus) - J(thetaMinus))/(2*epsilon)
end
We previously saw how to calculate the delta vector. Once we compute our gradApprox vector, we can check that $ \text{gradApprox} \approx \text{deltaVector} $. Once backpropagation is verified to be correct, you do not need to compute gradApprox again. Code to compute gradApprox can be very slow.
Random Initialization
Initializing all theta weights to zero does not work with neural networks. When backpropagation occurs, all nodes will update to the same value repeatedly. Instead, we should randomly initialize our weights for our $\Theta$ matrices using the following method:
We initialize each $\Theta_{ij}^{(l)}$ to a random value between $[-\epsilon,\epsilon]$. Using the above formula guarantees that we will get the desired bound. The same procedure applies to all of the $\Theta$’s. Below is some working code for experimentation:
INIT_EPSILON = 1e-2;
% If the dimensions of Theta1 is 10x11, Theta2 is 10x11 and Theta3 is 1x11.
Theta1 = rand(10,11) * (2 * INIT_EPSILON) - INIT_EPSILON;
Theta2 = rand(10,11) * (2 * INIT_EPSILON) - INIT_EPSILON;
Theta3 = rand(1,11) * (2 * INIT_EPSILON) - INIT_EPSILON;
The rand(x,y)
function will initialize a matrix of random real numbers between 0 and 1.
The INIT_EPSILON
value is unrelated to the epsilon from Gradient Checking.
One effective strategy for choosing $ \epsilon_{\text{init}} $ is to base it on the number of units in the network. A good choice of $ \epsilon_{\text{init}} $ is $ \epsilon_{\text{init}} = \dfrac{\sqrt{6}}{\sqrt{L_\text{in} + L_\text{out}}} $ , where $ L_\text{in} = s_l $ and $ L_\text{out} = s_{l+1} $ are the number of units in the layers adjacent to $\Theta^{(l)}$.
Putting it Together
First, pick a network architecture. Choose the layout of your neural network, including how many hidden units in each layer and how many layers in total you want to have.
- Number of input units = dimension of features $x^{(i)}$
- Number of output units = number of classes
- Number of hidden units per layer = usually more is better (but computation increases)
- Defaults: 1 hidden layer. If more than 1 hidden layer, it is recommended that you have the same number of units in every hidden layer.
Training a Neural Network
- Randomly initialize the weights
- Implement forward propagation to get $h_Theta(x^{(i)})$ for any $x^{(i)}$
- Implement the cost function
- Implement backpropagation to compute partial derivatives
- Use gradient checking to confirm backpropagation works. Disable gradient checking afterwards
- Use gradient descent or a built-in optimization function to minimize the cost function with the weights in theta.
When we perform forward and back propagation, we loop on every training example:
for i = 1:m,
% Perform forward propagation and backpropagation using example (x(i),y(i))
% (Get activations a(l) and delta terms d(l) for l = 2,...,L)
The following image gives us an intuition of what is happening as we implement the neural network:
Ideally, you want $ h_\Theta(x^{(i)}) \approx y^{(i)} $. This will minimize our cost function. However, keep in mind that $J(\Theta)$ is not convex and we could end up in a local minimum instead.
Application of Neural Networks
Autonomous Driving
See Dean Pomerleau (Carnagie Mellon) autonomous driving neural network. (This was done already in 1992).
Move on to Week 6.


geometry.Segment()¶
-
class
tkgeom.geometry.
Segment
(p, q)¶ A class used to represent a Segment.
-
slope
¶ slope of the line (np.Inf if vertical)
- Type
float
-
intercept
¶ intercept on the y axis (intercept on the x axis if vertical)
- Type
float
-
points
¶ list of p and q
- Type
list of Point()
-
intersection
(other)¶ The function to find the intersection of two Segment objects.
- Parameters
other (Segment()) – The Segment() for which the intersection is calculated
- Returns
list of the intersections
- Return type
list of Point()
Notes
The list has one element if the Segments intersect (even if only through their end points).
An empty list is returned if the Segments do not have an intersection.
Examples
>>> s1=Segment(Point(2,2),Point(-2,-2)) >>> s2=Segment(Point(-2,2),Point(2,-2)) >>> s1.intersection(s2) [Point(0.000, 0.000)] >>> s3=Segment(Point(-3,-4),Point(-6,-7)) >>> s1.intersection(s3) []
-


@* * This template takes a single argument, a String containing a * message to display. *@ @() @* * Call the `main` template with two arguments. The first * argument is a `String` with the title of the page, the second * argument is an `Html` object containing the body of the page. *@ @main("Encryption service") { @helper.form(action = routes.EncryptionController.upload(), 'enctype -> "multipart/form-data", 'class -> "pre-process"){


| Fig 1 |
Model flow chart of the rearing period. Aggreg = aggregation ... |
| Fig 2 |
Effect of aggregation level of food resources. Effect (mean ... |
| Fig 3 |
Effects of distance to the resource (Dist) on foraging behav ... |
| Fig 4 |
Effect of the memorization on mortality rates. Effect (mean ... |
| Fig 5 |
Distribution and frequency of fishing events. Simulation res ... |
| Fig 6 |
Effects of female body length on survival and foraging behav ... |
| Fig 7 |
Identifying optimal body length. (a) (Energetic cost) / (suc ... |


Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
| Name | Dosage | Banned |
|---|


DH_UNDOCUMENTED(1) Debhelper DH_UNDOCUMENTED(1) NAME dh_undocumented - undocumented.7 symlink program (deprecated no-op) SYNOPSIS Do not run! DESCRIPTION This program used to make symlinks to the undocumented.7 man page for man pages not present in a package. Debian policy now frowns on use of the undocumented.7 man page, and so this program does nothing, and should not be used. SEE ALSO debhelper(7) This program is a part of debhelper. AUTHOR Joey Hess<EMAIL_ADDRESS>9.20120909 2011-09-12 DH_UNDOCUMENTED(1)


kcs is a sample to demonstrate the use of kernel mode perflib API. The driver does not control any hardware; it simple provides counters. The code contains comments to explain what each function does.
To test the Kcs counter provider, use the following procedure.
lodctr.exe /m:kcs.man
sc create kcs binpath= %windir%\system32\kcs.sys type= kernel start= demand
net start kcs
perfmon.exe
net stop kcs
unlodctr.exe /m:kcs.man
To sign the simple driver (kcs.sys), use the following procedure.
makecert -r -pe -ss PrivateCertStore -n CN=Contoso.com(Test) kcs.cer
[CatalogHeader]
Name=kcs.cat
PublicVersion=0x0000001
EncodingType=0x00010001
CATATTR1=0x10010001:OSAttr:2:6.0
[CatalogFiles]
<hash>File1=kcs.pdb
<hash>File2=kcs.sys
makecat -v kcs.cdf
certmgr.exe /add kcs.cer /s /r localMachine root
Signtool sign /v /s PrivateCertStore /n Contoso.com(Test) /t http://timestamp.verisign.com/scripts/timestamp.dll kcs.sys
Signtool verify /pa /v kcs.sys
This sample driver should not be used in a production environment.
Kcs is designed for Windows 7 and later versions of Windows.
kcs.h
makefile
sources
kcs.c
kcs.rc
The files that comprise Kcs are included in the Windows DDK in the \src\general\perfcounters\kcs directory.
We welcome your comments,
problem reports, and wish-list requests. Please submit them by visiting the Windows DDK
Web site.
|
|
© Microsoft
Corporation


---
layout: default
title: Daniel Thoms
image: /resources/paintings/selfport.jpg
---
Fay Canyon, Sedona Arizona – Oil on Canvas 10×10"
untitled – Oil on canvasboard 9×12"
untitled – Oil on canvasboard 9×12"
Bear Mountain and Junipers, Sedona, Arizona – Oil on Canvas 8×12"
untitled – Oil on canvasboard 8×12"
untitled – Oil on canvasboard 12×16"
untitled – Oil on canvas 40×19"
June Lake in the morning sun, California – Oil on paper 9×11″
Eastern Sierra from Green Church Hotsprings, California – Oil on Canvas 10x10″
Low Desert Wash, Arizona – Oil on Canvas 8x12"
Untitled – Oil on canvas 20×24″
Dry Creek, Sedona, Arizona – Oil on Canvas 8×12"
Smith River, California – Oil on Canvas 9×12″
Untitled – Oil on Board 8×12"
Sitges – Oil on Canvas 12x16"
Untitled – Oil on Board 8×12"
Untitled – Oil on Paper 8×12"
❯
❮


[§39A‑242] Sunset provision. After June 30, 2015, no new special purpose revenue bonds shall be issued under this part. [L 1994, c 280, pt of §6]


KYA is an upgraded version of the CO tracker which I made months before. KYA is installed with the most advanced SGP30 and particle electron (Cellular). SGP30 is capable of sensing CO2 and TVOC levels in the air. This device updates 15 minutes once to the cloud. though SGP30 is costly, I chose it for its least time for heating and accuracy. As usual, I have chosen Ubidots for dashboard and storage. I recommend to use ubidots services for their stunning dasboard templates.
Project SAC is Smart Animal Collar which is an advanced animal tracking and activity monitoring device. SAC has features like GPS, LoRa, Accelerometer, Gyroscope, Temperature Sensor and Acoustic Sensor. Initially, SAC is designed only for elephants. Wild Elephants and its Musth is a big problem for the forest officials. Using the accelerometer and Gyroscope, the elephant's musth can be easily found out using machine learning. The reason for the acoustic sensor is for identifying the situation of the elephant rumbles. Currently, the project is at the initial fabrication stage.
created with
Nicepage .


| Wednesday | Thursday | Friday | Saturday | Sunday |
|
Schedule is subject to change without notice. |
February 8-10 In a Lonely Place (1950) 7:30 (plus 3:50 Sat/Sun) Casablanca (1942) 5:35, 9:15 |
|||
|
February 13-14 The Big Sleep (1946) 7:30 Dark Passage (1947) 5:30, 9:35 |
February 15-17 The African Queen (1951) 7:30 (plus 3:55 Sat/Sun) Beat the Devil (1953) 5:50, 9:25 |
|||
|
February 20-21 Key Largo (1948) 7:30 Brother Orchid (1940) 5:50, 9:25 |
February 22-24 The Treasure of the Sierra Madre (1948) 7:30 (plus 3:40 Sat/Sun) Black Legion (1937) 5:55, 9:45 |
|||
|
February 27-28 They Drive By Night (1940) 7:30 Action in the North Atlantic (1943) 5:10 only (Weds); 5:10, 9:15 (Thurs) |
March 1-3 The Caine Mutiny (1954) 7:30 (plus 3:15 Sat/Sun) We're No Angels (1955) 5:30, 9:45 |
|||
| Wednesday | Thursday | Friday | Saturday | Sunday |


This document contains the release notes for the LLVM Compiler Infrastructure, release 2.8. Here we describe the status of LLVM, including major improvements from the previous release and significant known problems. All LLVM releases may be downloaded from the LLVM releases web site.
For more information about LLVM, including information about the latest release, please check out the main LLVM web site. If you have questions or comments, the LLVM Developer's Mailing List is a good place to send them.
Note that if you are reading this file from a Subversion checkout or the main LLVM web page, this document applies to the next release, not the current one. To see the release notes for a specific release, please see the releases page.
The LLVM 2.8 distribution currently consists of code from the core LLVM repository (which roughly includes the LLVM optimizers, code generators and supporting tools), the Clang repository and the llvm-gcc repository. In addition to this code, the LLVM Project includes other sub-projects that are in development. Here we include updates on these subprojects.
Clang is an LLVM front end for the C, C++, and Objective-C languages. Clang aims to provide a better user experience through expressive diagnostics, a high level of conformance to language standards, fast compilation, and low memory use. Like LLVM, Clang provides a modular, library-based architecture that makes it suitable for creating or integrating with other development tools. Clang is considered a production-quality compiler for C and Objective-C on x86 (32- and 64-bit).
In the LLVM 2.8 time-frame, the Clang team has made many improvements:
The Clang Static Analyzer project is an effort to use static source code analysis techniques to automatically find bugs in C and Objective-C programs (and hopefully C++ in the future!). The tool is very good at finding bugs that occur on specific paths through code, such as on error conditions.
In the LLVM 2.8 time-frame,
The VMKit project is an implementation of a JVM and a CLI Virtual Machine (Microsoft .NET is an implementation of the CLI) using LLVM for static and just-in-time compilation.
With the release of LLVM 2.8, ...
The new LLVM compiler-rt project is a simple library that provides an implementation of the low-level target-specific hooks required by code generation and other runtime components. For example, when compiling for a 32-bit target, converting a double to a 64-bit unsigned integer is compiled into a runtime call to the "__fixunsdfdi" function. The compiler-rt library provides highly optimized implementations of this and other low-level routines (some are 3x faster than the equivalent libgcc routines).
All of the code in the compiler-rt project is available under the standard LLVM License, a "BSD-style" license. New in LLVM 2.8: Soft float support
DragonEgg is a port of llvm-gcc to gcc-4.5. Unlike llvm-gcc, which makes many intrusive changes to the underlying gcc-4.2 code, dragonegg in theory does not require any gcc-4.5 modifications whatsoever (currently one small patch is needed). This is thanks to the new gcc plugin architecture, which makes it possible to modify the behaviour of gcc at runtime by loading a plugin, which is nothing more than a dynamic library which conforms to the gcc plugin interface. DragonEgg is a gcc plugin that causes the LLVM optimizers to be run instead of the gcc optimizers, and the LLVM code generators instead of the gcc code generators, just like llvm-gcc. To use it, you add "-fplugin=path/dragonegg.so" to the gcc-4.5 command line, and gcc-4.5 magically becomes llvm-gcc-4.5!
DragonEgg is still a work in progress. Currently C works very well, while C++, Ada and Fortran work fairly well. All other languages either don't work at all, or only work poorly. For the moment only the x86-32 and x86-64 targets are supported, and only on linux and darwin (darwin needs an additional gcc patch).
2.8 status here.
The LLVM Machine Code (aka MC) sub-project of LLVM was created to solve a number of problems in the realm of assembly, disassembly, object file format handling, and a number of other related areas that CPU instruction-set level tools work in. It is a sub-project of LLVM which provides it with a number of advantages over other compilers that do not have tightly integrated assembly-level tools. For a gentle introduction, please see the Intro to the LLVM MC Project Blog Post.
2.8 status here
An exciting aspect of LLVM is that it is used as an enabling technology for a lot of other language and tools projects. This section lists some of the projects that have already been updated to work with LLVM 2.8.
This release includes a huge number of bug fixes, performance tweaks and minor improvements. Some of the major improvements and new features are listed in this section.
In addition to changes to the code, between LLVM 2.7 and 2.8, a number of organization changes have happened:
LLVM 2.8 includes several major new capabilities:
LLVM IR has several new features for better support of new targets and that expose new optimization opportunities:
In addition to a large array of minor performance tweaks and bug fixes, this release includes a few major enhancements and additions to the optimizers:
We have put a significant amount of work into the code generator infrastructure, which allows us to implement more aggressive algorithms and make it run faster:
New features of the X86 target include:
New features of the ARM target include:
This release includes a number of new APIs that are used internally, which may also be useful for external clients.
Other miscellaneous features include:
If you're already an LLVM user or developer with out-of-tree changes based on LLVM 2.7, this section lists some "gotchas" that you may run into upgrading from the previous release.
In addition, many APIs have changed in this release. Some of the major LLVM API changes are:
LLVM is known to work on the following platforms:
The core LLVM infrastructure uses GNU autoconf to adapt itself to the machine and operating system on which it is built. However, minor porting may be required to get LLVM to work on new platforms. We welcome your portability patches and reports of successful builds or error messages.
This section contains significant known problems with the LLVM system, listed by component. If you run into a problem, please check the LLVM bug database and submit a bug if there isn't already one.
The following components of this LLVM release are either untested, known to be broken or unreliable, or are in early development. These components should not be relied on, and bugs should not be filed against them, but they may be useful to some people. In particular, if you would like to work on one of these components, please contact us on the LLVMdev list.
The only major language feature of GCC not supported by llvm-gcc is the __builtin_apply family of builtins. However, some extensions are only supported on some targets. For example, trampolines are only supported on some targets (these are used when you take the address of a nested function).
A wide variety of additional information is available on the LLVM web page, in particular in the documentation section. The web page also contains versions of the API documentation which is up-to-date with the Subversion version of the source code. You can access versions of these documents specific to this release by going into the "llvm/doc/" directory in the LLVM tree.
If you have any questions or comments about LLVM, please feel free to contact us via the mailing lists.


The user is a parent with a child diagnosed with pediatric epilepsy who needs assistance in administering the ketogenic diet for seizure control.
The ketogenic diet is a high fat, low carb diet that that puts the body into a state of ketosis (when the body gets energy from burning fat rather than carbs).
When using the Ketogenic Diet as a treatment for Epilepsy, the meals must strictly adhere requirements set by the nutritionist. This often means measuring food ingredients down to the tenth of a gram. Parents must also keep a diary of each meal, Ketone levels (to see that ketosis is maintained) as well as seizure counts. This helps the both the neurologist and the nutritionist gain insight into what might be helping with seizure control and what is not helping.
Extensive qualitative user research (primarily user feedback, user observation, and interviews) as well as competitive analysis of the only (official) keto calculator lead to a period of discovery where I could gain insight into the users' needs.
I also did research to see if there were any design solutions for other food trackers that may work successfully with this keto calculator.
Next I set about exploring the different possibilities through various sketches and wireframes.
Below is a Red Route (likely the most common path) design for this app. I used this Red Route Design panel as a storyboard when talking to some potential users about some of the features that this app might have. I included one high fidelity screen so that potential users could see the potential look/feel of the app. During followup interviews where I showed a high fidelity frame, I also tested various color schemes. A mostly purple theme tested the best of the several color iterations, as it holds some sentimental value to users. Purple is the official Epilepsy awareness color.
This is still a work in progress. I will update this project as more work is completed.


In case you don't fit into the standard requirements or feel that you need to brush on your skills before deciding to join us, then we have provided some online course suggestions. These suggestions are of renowned virtual establishments; hence, we are certain that they will be of great use and benefit to you.
Khan Academy
Khan Academy is a free website which not only allows you to proficiently learn coding, but other core academic subjects too.
Codecademy
Codecademy is a well-known site. It is free to use and preserves over 45 million code learners.
W3Schools
W3Schools is also a free website which is effective for learning coding languages such as HTML, CSS, JS and much more.
Web Development Recruitment Agency based in Reading, Berkshire.
Hotbeans ©2018. All Rights Reversed


Welcome to LWF Portal
Long-term Forest Ecosystem Research Programme LWF LWF Logo Swiss forests are exposed to changing environmental conditions. The consequences of air pollution and climate change on the forest are important questions which the Long-term Forest Ecosystem Research Programme LWF seeks to answer. Within a legally set framework of the LWF programme, Switzerland partakes in international research programmes such as The International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests ICP-Forests or the Long-Term Ecosystem Research.
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json
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{% extends "layout.html" %} {% block content %} You think I have time to deal with terms and conditions? Ain't nobody got time for that. {% endblock %}


Aki :: Profile
Aki is a start-up dreamer in Silicon Valley with a passion to tinker and experiment with web applications. Having versatile skills in business, technology, and operations; Aki's is a seasoned expert in partner management in digital content/entertainment, program and project management for SaaS development, and business operations for world-class digital services.
Career Summary
- 14+ years in launching & managing digital entertainment/SaaS services
- Strategic facilitator of global partnerships out of US and Japan
- Seasoned business manager of digital business operations
- Trusted leader, team builder, and mentor
- Passionate experimenter & bootcamper developing modern apps
Experience
July 2012 to Present
Business Operations, VERIZON OnCue
Nov 2007 to Dec 2012
Founder/CEO, JAFINITY
July 2006 to July 2012
Business Development / Program Management / Business Operations, MICROSOFT
Jan 2005 to Mar 2006
Marketing and Merchandising, MTV NETWORKS
Apr 2000 to Sep 2004
Producer, Digital Content & Smart TV, PANASONIC
Education
Dev Bootcamp
Web Developer Extraordinaire (Working on it!)
-- Ruby on Rails, HTML5, CSS, JavaScript/Node.js, jQuery, Coffeescript, SASS, HAML, AJAX, Git
Stanford University
Stanford Innovation and Entrepreneurship Certificate (Working on it!)
Stanford Advanced Project Management Certificate
New York University
M.A., Music Business
Keio University
B.A., Environmental Information
Noteworthy Skills & Expertise
- Start-ups and Entrepreneurial Spirit
- Global Business Development and Partner Management
- Building and Managing SaaS Platforms for Entertainment/ECommerce
- Digital Supply Chain/Business Process Architecture
- Content Operations and Partner/Product Onboarding
- Program/Project Management for Software + Services
- Building and Leading High Performing Teams
- Tech: HTML5, CSS, Ruby on Rails, JavaScript, SQL, Git/Github
- Languages: English, Japanese, and Limited Mandarin Chinese
Patents
- Tiered Subscription Management Model (pending)
- Automated Offer Management System (pending)
- Reputation Scoring for Online Storefronts<PHONE_NUMBER>0)
- System for Revenue Sharing for Online Storefronts<PHONE_NUMBER>0)
- Moving Picture Recommendation Processing Device (7765461)
Certifications
- SCPM - Stanford Certified Project Manager
- CSM - Certified ScrumMaster


Caroline Flack gives first glimpse at Winter Love Island rocking dazzling yellow
The presenter showed off her tanned arms and sun-kissed hair as she shoots promos in South Africa for the first series of Winter Love Island
by Irishmirror.ieLove Island'sCaroline Flack has been teasing fans ahead of the upcoming Winter Love Island.
The show, which is expected for a January release date, will be the first of its kind and means that fans will be getting double helpings of Love Island in 2020.
Now Caroline's out in Cape Town filming promos for the show, and stunning in a bright yellow summer dress. The star added that she got her hair and makeup done by celeb hairstylist Gemma Wheatcroft.
"Day 1 shoot," the blond beauty wrote, "Hair and makeup by @gemflossi."
Fans rushed to the comments section to gush over the 40 year old.
"Naturally beautiful. Wow," one wrote.
 Aging like fine wine haha x."
A third poured their heart out to Caroline, writing: "Unreal Miss Flack, looking on fire again babe."
Others just wanted to know about Love Island.
"When does Love Island start again?" one demanded.
"Love Island????" another needed to know.
She also shared some glimpses of South Africa on her story, including one of her looking over her bare shoulder in a sultry pose.
"So we've just landed in Cape Town," she told fans as she was getting her makeup done, "We're here to film the advert for Love Island season six. We're not really sure what to expect, because we're not used to filming for Love Island during the winter."
Later she added: "Just finished the first shot of the day.
"To begin with a had this yellow dress on, it looked so weird. I looked by I'd been run over by a roller and someone tried to plump me back up again.
"I'm never wearing that dress again."
While an official date is yet to be confirmed, it's being widely reported that the show will be hitting our screens as soon as 8th January.
MailOnline have reported that staff for the show will be jetting out on New Year's Day, with the show set to kick off a week later.
The sources also told the publication that fans can expect a two week shorter edition of the show, and that producers will be using "test Islanders" ahead of the main cast's arrival.
They said: "It has been decided that the show will run six weeks instead of the usual eight. The new cast of singletons fly out just days before the launch but bosses work with test Islanders to uncover the best spots in the villa to create drama."
As well as Caroline, fans will be happy to know that comedian Iain Stirling will again be lending his voice to the show as the drama unfolds.
"Do you know what?" he told Good Morning Britain, "Winter Love Island is gonna come back sooner that you thought and it's gonna be enjoyable."


--- layout: post title: 'BlakeTV: Version 2' date: 2011-07-28 17:18:39.000000000 -05:00 categories: - BlakeTV tags: - blaketv - bugfix - projects - tv - update - zend status: publish type: post published: true meta: _edit_last: '1' author: login: Blake email<EMAIL_ADDRESS>display_name: Blake first_name: Blake last_name: Harley ---
In a rather stunning turn of events, I have managed to completely rewrite BlakeTV in under 3 days. After professing my love of frameworks, I decided that it was time to start using them in my personal projects more often. Specifically, Zend Framework. Transferring and fixing up an old project seemed like the best way to focus on the setup of Zend Framework and less on the project itself. Which is not to say I didn't put any effort into the rewriting of BlakeTV---because I did. Besides a massive amount of bug squashing, the core API code didn't change a whole lot. This allowed me to focus on creating a personal Zend Framework toolset that can I can use between all of my Zend Framework projects in the future.
In addition to moving over to Zend Framework, I'm going to start logging all major and some minor changes to BlakeTV. I'll also be tracking a rough version number.
New Features:
Changes:
Bugfixes:
Planned Future Additions:
Now that I've spent all this time and effort updating BlakeTV, you should sign up for an account and use it.
Do it.
If you have any questions, comments, suggestions or find any problems with BlakeTV, please leave a comment here.


system('start ruby.exe C:\script.rb')This will create a new Ruby window, which will run the specified script, and close when it is complete.
--- layout: post title: "Create a New Ruby Process in Windows" date: 2012-01-10T18:20:00-08:00 categories: - Ruby - Windows - parallel ---
system('start ruby.exe C:\script.rb')This will create a new Ruby window, which will run the specified script, and close when it is complete.


B. Verkin ILTPE of NASU –
B. Verkin Institute for Low Temperature Physics and Engineering
of the National Academy of Sciences of Ukraine, 47 Nauky Ave.,
Kharkiv, 61103
Date of Birth 30.09. 1954
Graduated from Kharkiv State University in 1977.
Started work at B. Verkin ILTPE of NASU in 1977,
PhD (Phys & Math), the same institute, 1990.
From 2004 – senior researcher.
Senior Researcher (Theoretical Physics, 2007)


Hey, there! Thanks for stopping by!
I am a graduate of Full Sail University with a Bachelor of Science in Web Design & Development.
I enjoy graphic design, front end design & development, and user interface/user experience design & integration.
MelonySmith.net… for all of your front-end needs. Offering graphic design, logo design & branding, responsive layout design & implementation services with a focus on a complete and successful user experience.
Dedicated to providing quality customer service with an emphasis on timely results & your complete satisfaction.
- Adobe Photoshop & Illustrator
- Layouts, colors, graphics, & typography
- Logo creation & branding
- User friendly design
- Client-side scripting: HTML5, CSS3, Javascript, & JQuery
- Cross browser & multi device compatibility
- User friendly experience
- Server-side scripting: PHP
- Relational database management system: MySQL
- Social media branding & website integration
- 100% customer satisfaction
Most Recent Projects
Personal, Pet, & Sports Photography by Chan Hebert
HTML5, CSS3, Javascript, & Adobe Photoshop
McLennan Animal Rescue Coalition
HTML5 & CSS3
Personal, Pet, & Sports Photography by Chan Hebert
HTML5, CSS3, Javascript, jQuery, Fancybox, & Bootstrap
Heart & Soul Animal Rescue
coming soon
Dedicated to the rescue and rehabilitation of stray, abandoned, orphaned, & unwanted animals.
Adobe Photoshop & Adobe Illustrator
Mixing the Old with the New
Adobe Photoshop & Adobe Illustrator
T-Shirt Design
MacPractice employee t-shirt design contest
Adobe Photoshop


object sybase_fetch_field ( resource $result [, int $field_offset = -1 ] )
sybase_fetch_field() can be used in order to obtain information about fields in a certain query result.
Returns an object containing field information. The properties of the object are: name - column name. if the column is a result of a function, this property is set to computed#N, where #N is a serial number. column_source - the table from which the column was taken max_length - maximum length of the column numeric - 1 if the column is numeric type - datatype of the column


Congratulations on your purchase of the PushButton Networking Kit! PBNetworking makes it easy to build networked Flash games.
You will need at least PushButton Engine revision 66 to use the PBNetworking components.
Please visit the beta forums for support, feedback, and questions.
PushButton Networking is a high-efficiency, low-latency networking library for ActionScript 3 Flash games. It lets you build bigger, better, faster networked games more quickly. PushButton Networking uses the same networking techniques found in fast-action AAA games such as Quake, Tribes, Far Cry, and Unreal.
PushButton Networking is focused on making the connection between client and server as lean, efficient, and cheater-free as possible. It comes with a server binary that lets you write your game server in ActionScript and run it with no extra work.
Note: MMOs require high availability, clustered backends. You may find that other technologies will be better for building this sort of backend than the server which comes with PushButton Networking. For communicating between your servers and client, PushButton Networking is still an excellent choice (contact us for versions in other languages).
There are three projects that come with this kit:
The networking library comes in two pieces: a native C++ server binary based on Tamarin (in PBNetworking/Server), and a networking library written in ActionScript that runs on client and server (in PBNetworking/Source).
We have backends in several other languages as well, including Java. Contact us for details on getting ahold of these.
Three kinds of networking are supported by this kit. Each kind builds on the previous.
Many networking systems either overspecify or underspecify their protocols. An underspecified protocol is one where information is sent that could be inferred. For instance, nearly every XML protocol will send both the name of a field and the data contained in it, ie, <name>Bob</name>. The specification for the protocol is not precise enough, so extra data must be sent for the other end to make sense of it.
An overspecified protocol is one where not enough information is provided for every party to process the data. For instance, some protocols are dependent on the two parties having exactly the same binary - and if you rebuild the game, then the protocol gets out of sync! The specification of the protocol is so detailed and precise - since it is encoded right in the compiled source code of the application - that it is hard for anyone else to make sense of data coming over the wire.
If you have 5kb/sec of bandwidth to spend on a client, would you rather be able to fit 50 updates or 500? The goal of PBNetworking is to let you specify exactly what should be sent over the wire without incurring any overhead, and without being tied to a specific binary. This is done by building an XML description of how data should be serialized - specifying how many bits a number should be packed into, when certain pieces should be sent, how strings should be sent, etc. Then, anyone who has the protocol XML description and a little bit of specialized knowledge can communicate with anyone else who has the protocol XML file.
A protocol XML file looks like this: (This example is taken from the PBNetworkDemo.)
<!-- The root tag. --> <library> <!-- The type can be event, ghost, or fragment. Event tags describe events, ghost fragments are used for most-recent-state updates, and fragments are for miscellaneous use. --> <!--This tag defines the event that is used to send chat message. It is sent both ways - client to server and back. --> <event name="chat"> <!-- Each child tag defines a field type and has attributes indicating the name and how the data will be encoded. Data is always written into the packet in the order of the tags. --> <!-- In this case, this string is used to send the chat message. --> <string name="message"/> </event> <!-- This event is sent when the user clicks, ordering the circles to move somewhere. It is sent from the client to the server. --> <event name="circle"> <!-- A rangedInt encodes an integer value that can range from min to max. In this case, the x and y positions where the client clicked are sent. --> <rangedInt name="x" min="0" max="1000"/> <rangedInt name="y" min="0" max="1000"/> </event> <!-- This is the protocol description for most recent state updates for the moving circles. Whenever the goal for a circle changes, the this update is sent. --> <ghost name="CircleGhost"> <!-- Some fields can contain other fields. A dirtyFlag contains other fields. It tracks when they are changed, and only sends them if they are dirty. It is only meaningful in a ghost update; otherwise it acts like a flag field, which sends its children when it is set to true. --> <dirtyFlag name="flag1"> <rangedInt name="x" min="0" max="1000"/> <rangedInt name="y" min="0" max="1000"/> <flag name="state"/> </dirtyFlag> </ghost> </library>
All of this protocol stuff is dealt with by the NetRoot class. You load protocol data into NetRoot via the static method NetRoot.LoadNetProcotol. Then, as needed, you can request protocol data via NetRoot.GetByName. This method returns a NetRoot instance, which tracks all the data that will be sent across the wire and how it will be sent. All the code using it needs to know is what the names of the fields are, so it can indicate to the NetRoot what their values are.
Getting/setting fields is easy. The following code snippet shows a simple protocol description and code to get/set fields on it.
// Simple example event - load the definition. var protocolXML:XML = <things> <event name="TestEvent"> <rangedInt name="MyIntegerField"/> <float name="MyFloatField" bitCount="10"/> <flag name="MyBooleanField"> <string name="MyStringField"/> </flag> </event> </things>; NetRoot.LoadNetProtocol(protocolXML.toXMLString()); // Now, get it in NetRoot form. var root:NetRoot = NetRoot.GetByName("myevent"); // We can get/set values. (root.GetElement("MyIntegerField") as IIntegerNetElement).SetValue(1); (root.GetElement("MyFloatField") as IFloatNetElement).SetValue(0.2); (root.GetElement("MyStringField") as IStringNetElement).SetValue("hello"); (root.GetElement("MyBooleanField") as IBooleanNetElement).SetValue(true); // To write to a BitStream, just pass it to the NetRoot. var bs:BitStream = new BitStream(1024); root.Serialize(bs); // You can read from the BitStream just as easily. This updates the // elements based on what was just read. root.Deserialize(bs);
Most of the time you won't need to work with NetRoot directly. There are two places that use this protocol data. And we'll discuss them in the next two sections.
Most of the time you will subclass your event from GenericNetEvent. It provides a convenient way to implement new events with minimal work. If you need custom behavior, you can also directly subclass NetEvent.
Here's a simple example of a chat event. Notice you have to provide both a protocol definition, as well as an AS3 class to make this work.
<!-- The protocol XML fragment for the event. --> <event name="chat"> <string name="message"/> </event>
// The class implementing the chat event. It extends GenericNetEvent, which // does the bulk of the work. public class ChatEvent extends GenericNetEvent { public var message:String; // The constructor lets you easily create a new chat event by doing: // var ce:ChatEvent = new ChatEvent("Hey world!"); // However it must always be possible to call with no arguments so // that the event can be created in response to incoming data. public function ChatEvent(msg:String = null) { // Indicate the protocol element we will be using to // the superclass. super("chat"); // Register the message member of this event; GenericNetEvent will look // for a matching field in the protocol XML (shown above). registerField("message"); message = msg; } // This is called when the event is received. public override function process(conn:EventConnection):void { // Because message was registered with registerField, GenericNetEvent // will have already deserialized our event's data and set the field // for us by the time process is called. Logger.Print(this, "Chat received: " + message); } } // You also have to register the ChatEvent so the networking system knows // to instantiate it in response to "chat" events. NetEvent.registerClass("chat", ChatEvent);
That's how you create a new event type. To send it, all you have to do is get ahold of a connected EventConnection and queue the event for transmission. Here's how you would send a chat event out to everyone connected to your server:
NetworkInterface.Instance.ForEachConnection(function (nc:NetworkConnection):void { if(nc is EventConnection) (nc as EventConnection).PostEvent(new ChatEvent("Hey buddy!")); });
Ghosting objects is fairly simple. However, even with the aid of our components, it takes care to produce a really world-class networked game. Ultimately, the game itself must be aware of the fact it is networked and take steps to mitigate latency to give the best experience. Despite this, for many games, ghosting will "just work" without a lot of extra effort.
First, build your game object normally, and identify what fields need to be networked. The more you network, the fewer updates can be sent over a fixed amount of bandwidth, and the less responsive the game will be. In addition, the fields you identify will be updated at irregular intervals on the client, so it is best to choose fields that won't cause lots of popping for the user.
For instance, in the PBNetworkingDemo, we network the goal position, not the actual position. Then, the circle entities smoothly interpolate towards that point at a fixed rate. This effectively hides latency, while still giving responsive motion when the server updates the goal position.
In any case, once you know what fields should be networked, you add a GhostComponent and identify those fields and what field in a NetRoot they should be mapped to. You also need to provide a protocol description. For the circles this looks like this:
<!-- Protocol description for the circle --> <ghost name="CircleGhost"> <dirtyFlag name="flag1"> <rangedInt name="x" min="0" max="1000"/> <rangedInt name="y" min="0" max="1000"/> <flag name="state"/> </dirtyFlag> </ghost>
<!-- The ghost component as seen on the Circle entity. --> <component type="PBLabs.Networking.Ghosting.GhostComponent" name="Ghost"> <GhostInstance> <!-- This is used to indicate what entity the clients should instantiate to show the circle. It must have a matching GhostComponent entity on it. --> <PrototypeName>ClientCircle</PrototypeName> <!-- The protocol block to use. --> <ProtocolName>CircleGhost</ProtocolName> <!-- The list of properties to use and what field on the protocol they map to. --> <TrackedProperties> <_><EMAIL_ADDRESS><ProtocolField>x</ProtocolField> </_> <_><EMAIL_ADDRESS><ProtocolField>y</ProtocolField> </_> </TrackedProperties> </GhostInstance> </component>
That's everything you need to send most recent state updates for the circle across the wire to a client. However, you also have to have some support logic in your game code, so that the networking components know which entities to transmit state for. This requires a little setup code when you establish the connection.
var gc:GhostConnection; // On client and server, you must call ActivateGhosting to turn on ghosting. gc.ActivateGhosting(true); // You must also specify a scope object on the server end: gc.ScopeObject = someObject;
The scope object implements IScoper, which defines a single method, ScopeObjects, which is passed a GhostManager. The scope object is required to iterate over active game entities and mark them as in scope. Marking an entity in scope is done by getting its Ghost from its GhostComponent, calling CheckTrackedProperties() to update what fields need to be transmitted if any, and then calling MarkGhostInScope on the GhostManager, passing the GhostComponent. Or in other words:
// Marking an entity as in scope, in response to a scope query. var g:Ghost = (inScopeEntity.LookupComponentByType(GhostComponent) as GhostComponent).GhostInstance; g.CheckTrackedProperties(); ghostManager.MarkGhostInScope(g, 1.0);
The PBNetworkingDemo is the best example for how all these pieces need to interact. Once you get the hang of it, it's straightforward - and of course, you can always ask for help, get clarification, or complain about vague documentation on the beta forums!


This is a simple data collection practice using MPU-6050 and MMA8452.
Week1: FUTURE TELLING PROGRAM
At first I wanted to create a project about fortune telling. Using sensors to make a dice to collect the dice x y z position data to interact with the program.
Week2: EXPLORE ON MMA8452
This week it is connecting MMA8452 sensor and particle board, trying to read data.
Week3: EXPLORE ON MPU-6050
This week it is connecting MMA8452 sensor and particle board, trying to read data and visualize all the data.


A terminal like linux terminal where your default shell is bash
Open git bash from right click menu contex after you install git
Ensure that you have the git submenuDo you know we can create new text file and write to it by running notepad from terminal ?
.bashrc is a shell script that Bash runs whenever it is started interactively. It initializes an interactive shell session.
You can put any command in that file that you could type at the command prompt.
You put commands here to set up the shell for use in your particular environment,
or to customize things to your preferences.
A common thing to put in .bashrc are aliases that you want to always be available.
export CLICOLOR=1
export LANG="en_US.UTF-8"
alias cp="cp -i"
alias ls="ls --color=auto"
export PS1="\[\033[01;32m\]\u@\h\[\033[00m\]:\[\033[01;34m\]\w\[\033[00m\]\$ "
export EDITOR="vim"
That's all you really know to get started
So can we simplify editing .bashrc using notepad ?
of course we can just type notepad ~/.bashrc
If the file not there you will be prompted to create it so go ahead
once created you can start put the alias and path you need
As example you can copy and paste these on the file and save
export PATH="$PATH:~/Anaconda3:~/Anaconda3/Scripts"
echo 'alias python="winpty python.exe"'


DRAW FROM A HAT
Name drawn will show here
Enter names each at a time or separated by commas
NAMES
WINNERS


We, as Tezos contributors, will refuse to KYC. We won't capitulate to the latest unethical, dangerous, humiliating, retroactive request from Tezos Foundation. This is a call to fight for privacy, and to defend our rights refusing the new, incredibly unfair Terms. #tezos #...


| First Name | Last Name | Job Title | Address | Gender | Birth Date |
|---|---|---|---|---|---|
| Nikunj | Savani | ASP .NET Developer | A-201 Rivera apartment, near CNG pump, sudama chowk, mota varachha | Male | 5/4/1988 12:00:00 AM |
| Bhavesh | Patel | ASP .NET Developer | A-201 Rivera apartment, near CNG pump, sudama chowk, mota varachha | Male | 5/4/1988 12:00:00 AM |
| Manish | Parakhiya | ASP .NET Developer | A-201 Rivera apartment, near CNG pump, sudama chowk, mota varachha | Male | 5/4/1988 12:00:00 AM |


{% extends "menu/megamenu_extra_base.html" %} {% load i18n %} {% block megamenu_extra_num_cols %}threecol{% endblock %} {% block megamenu_extra %}
{% blocktrans %} Create an amazing story in minutes. Start by choosing a template -- like photo voice, explainer, or data story. Customize it with your text, photos, video, audio, data and more. Include a call to action to inspire your readers to get involved. {% endblocktrans %}


Code base¶
Notes on the code base
linguistica/__main__.py
¶
For running GUI and CLI
linguistica/__init__.py
¶
__init__.py
sets up the functions for reading data.
linguistica/release.py
¶
Metadata of the library (version etc)
linguistica/util.py
¶
Constants and various utility functions.
linguistica/lexicon.py
¶
The Lexicon
class.
linguistica/ngram.py
¶
This ngram
module is to get the word ngrams.
linguistica/signature.py
¶
- Morphological signatures should really be sets, but they are tuples
(e.g.
('NULL', 's')
) with affixes ordered alphabetically. The signatures are very often the keys in some dicts, and Python doesn’t allow sets to be dict keys...
linguistica/phon.py
¶
The phon
module is to perform various phonology-related computations.
linguistica/trie.py
¶
Left-to-right and right-to-left tries. Successors. Predecessors.
linguistica/manifold.py
¶
Syntactic word neighbors
linguistica/fsm.py
¶
(Forthcoming)


IKProgressView
Example
To run the example project, clone the repo, and run pod install
from the Example directory first.
Requirements
iOS 8.0 and higher
Installation
IKProgressView is available through CocoaPods. To install it, simply add the following line to your Podfile:
pod "IKProgressView"
Example
Features
Cool radial animated progress view.
Use
setProgress(XXX, animated: YYY, ZZZ)
- method for setting progress animated or not. Completion is required due to animation performs with redraw whole progress view. If you know how to implement this stuff using CoreAnimation, feel free to contact me.filledView
- mean that progress view will use whole view, measure it sizes itselfinteriorR
&exteriorR
- radiuses for progress view, only usable within turn off-edfilledView
animated
- static view, or active redrawing with gradient animation.
There are some inner setting, you can play with.
Author
Igor Kislyuk<EMAIL_ADDRESS>License
IKProgressView is available under the MIT license. See the LICENSE file for more info.


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Enumerations
The following enumerations are available globally.
-
The defined anti-cloning types.
- - Since: 5.3
Seals the user’s credentials. A factory reset/initialization of the device will make the credentials unusable.
Requirements: None @warning: IfEMFastTrackDeviceFingerprintTypeSoft
orEMFastTrackDeviceFingerprintTypeHardwareKey
is used, user can not use the restored data after doing a factory reset and re-initializing to the same device.
Declaration
Objective-C
enum EMFastTrackDeviceFingerprintType {}
Swift
enum EMFastTrackDeviceFingerprintType : UInt
- - Since: 5.3
Seals the user’s credentials. A factory reset/initialization of the device will make the credentials unusable.
-
Declaration
Objective-C
enum EMFastTrackErrorCode {}
Swift
typealias EMFastTrackError.Code._ErrorType = EMFastTrackError
-
Declaration
Objective-C
enum EMIncomingMessageType {}
Swift
enum EMIncomingMessageType : UInt
-
The mobile messenger policy of the Mobile Protector SDK when the physical device is detected as being jailbroken.
- - Since: 5.3
Declaration
Objective-C
enum EMMobileMessengerJailbreakPolicy {}
Swift
enum EMMobileMessengerJailbreakPolicy : Int
-
Declaration
Objective-C
enum EMOtpType {}
Swift
enum EMOtpType : UInt
-
Declaration
Objective-C
enum EMFastTrackProvisioningProtocol {}
Swift
enum EMFastTrackProvisioningProtocol : Int32
-
The token policy of the Mobile Protector SDK when the physical device is detected as being jailbroken.
- - Since: 5.3
Declaration
Objective-C
enum EMMobileProtectorTokenJailbreakPolicy {}
Swift
enum EMMobileProtectorTokenJailbreakPolicy : Int
-
Declaration
Objective-C
enum EMSystemBiometricType {}
Swift
enum EMSystemBiometricType : UInt
-
Declaration
Objective-C
enum EMOutgoingMessageType {}
Swift
enum EMOutgoingMessageType : UInt
-
Declaration
Objective-C
enum EMFastTrackOathHashAlgorithm {}
Swift
enum EMFastTrackOathHashAlgorithm : Int
-
Supported types of Time step. Choose
EMFastTrackOathTimestepTypeNone
if not used.- - Since: 5.3
Declaration
Objective-C
enum EMFastTrackOathTimestepType {}
Swift
enum EMFastTrackOathTimestepType : Int
-
Declaration
Objective-C
enum EMTransactionSigningOtpMode {}
Swift
enum EMTransactionSigningOtpMode : UInt
-
Declaration
Objective-C
enum EMTransactionSigningResponseValue {}
Swift
enum EMTransactionSigningResponseValue : UInt
-
Declaration
Objective-C
enum EMTransactionVerifyResponseValue {}
Swift
enum EMTransactionVerifyResponseValue : UInt


Step 3: Test Run and Data
In this step, we’re going to test our applications are working and add some data before deployment. For this step, you’ll need your mobile device, and to download the Ionic View app to your device.
Next, we’ll need to open the Node.js command prompt
that was installed with Ionic. Once you’ve located this, you’ll need to
do the following.

You can do this by opening it in a file explorer, then copying and pasting the location path.
-
$cd YOUR FRONTEND FILE PATH HERE
Then, type the following command and when prompted, enter your Ionic account details. Once that’s completed, a testing version of your customer application, using the sample data provided in the sampledata JSON will be available for you to download.
-
$cd ionic upload
Then we'll repeat this process for our business application too. Once these are working, you can go ahead and supply your own menu and design features through the business application.
When you set up your design, Navigate to your business application’s Settings tab.
From there, you can select the image that forms the background of your login and signup screens,
the image that’ll be displayed as your logo across the app, and a pair of panels for the account settings and navigation bar.
Finally, to set up your menu,
add your items using the form accessed in the top-right corner.
You should then delete the samples so they aren’t showing up on the menu for your users.
You can also edit the existing items with the Edit option.


|
MoraLife
1.3.4
A Digital Conscience for your iOS device.
|
Conscience character. More...
#import <ConscienceView.h>
Instance Methods | |
| (id) | - initWithFrame:withBody:withAccessories:withMind: |
| Change eye state to imitate winking/blinking Values are open and closed. More... | |
| (void) | - changeDimplesExpressions: |
| Change dimples expression Values are happy, normal, sad. More... | |
| (void) | - changeLipsExpressions: |
| Change lips expression. More... | |
| (void) | - changeTeethExpressions: |
| Change teeth expression. More... | |
| (void) | - changeTongueExpressions: |
| Change tongue expression. More... | |
| (void) | - changeBubble |
| Changes color and speed of the bubble surrounding the Conscience. More... | |
| (void) | - changeEyeDirection |
| Changes direction that Conscience is looking. More... | |
| (void) | - timedMouthExpressionChanges |
| Determine which emotion to ask for mouth change. More... | |
| (void) | - removeConscienceInvisibility |
| Make Conscience reappear. More... | |
| (void) | - timedEyeChanges |
| Determine which direction/emotion to ask for eye change. More... | |
| (void) | - setTimers |
| Begin timers for eyes/mouth changes. More... | |
| (void) | - stopTimers |
| Stop timers for eyes/mouth changes. More... | |
| (void) | - makeConscienceVisible |
| Make Conscience reappear. More... | |
| (CAKeyframeAnimation *) | - shakeAnimation |
| Make Conscience reappear. More... | |
Properties | |
| int | directionFacing |
| Which way the Conscience is currently facing. More... | |
| BOOL | isExpressionForced |
| allow for forcing of an expression More... | |
| UIView * | conscienceBubbleView |
| UIView that contains all other Views. More... | |
| ConscienceBody * | currentConscienceBody |
| Current Conscience visual configuration. More... | |
| ConscienceAccessories * | currentConscienceAccessories |
| Current Conscience visual possessions. More... | |
| ConscienceMind * | currentConscienceMind |
| Current Conscience mood/enthusiasm. More... | |
Conscience character.
Implementation: ConcienceView can display the User's Conscience or a Antagonist's Conscience.
Combination of ConscienceAccessories, ConscienceBody and ConscienceMind
User's Conscience is customizable by the User and populated by userConscienceBody and userConscienceMind.
Antagonist Conscience is determined by System and assigned to currentConscienceBody and currentConscienceMind.
Actual facial changes are requested by ViewController.
| - (void) changeBubble |
Changes color and speed of the bubble surrounding the Conscience.
Implementation: Actual values to set for the bubble are populated in currentConscienceBody, currentConscienceMind Color determined by User.
Animation determined by Conscience Mood/Enthusiasm
| - (void) changeDimplesExpressions: | (NSString *) | expression |
Change dimples expression Values are happy, normal, sad.
Implementation: Display dimples.
| expression | NSString layerName of dimple ConscienceLayer to be selected |
Dimple choices determined by User's mouth choice expression determined by Conscience Mood/Enthusiasm
| - (void) changeEyeDirection |
Changes direction that Conscience is looking.
| - (void) changeLipsExpressions: | (NSString *) | expression |
Change lips expression.
Implementation: Display lips.
| expression | NSString layerName of lips ConscienceLayer to be selected |
Lip choices determined by User's mouth choice expression determined by Conscience Mood/Enthusiasm
| - (void) changeTeethExpressions: | (NSString *) | expression |
Change teeth expression.
Implementation: Display teeth.
| expression | NSString layerName of teeth ConscienceLayer to be selected |
Teeth choices determined by User's mouth choice expression determined by Conscience Mood/Enthusiasm
| - (void) changeTongueExpressions: | (NSString *) | expression |
Change tongue expression.
Implementation: Display tongue.
| expression | NSString layerName of tongue ConscienceLayer to be selected |
Tongue choices determined by User's mouth choice expression determined by Conscience Mood/Enthusiasm
| - (id) initWithFrame: | (CGRect) | frame | |
| withBody: | (ConscienceBody *) | argBody | |
| withAccessories: | (ConscienceAccessories *) | argAccessories | |
| withMind: | (ConscienceMind *) | argMind | |
Change eye state to imitate winking/blinking Values are open and closed.
| frame | CGRect encompassing entire Conscience |
| argBody | ConscienceBody object to display |
| argAccessories | ConscienceAccessories currently outfitted |
| argMind | ConscienceMind containing Conscience's current state of mind |
| - (void) makeConscienceVisible |
Make Conscience reappear.
Implementation: Reset Conscience alpha.
| - (void) removeConscienceInvisibility |
Make Conscience reappear.
Implementation: Determine Conscience direction facing.
Reverse Conscience if necessary. Call function to make Conscience visisble.
| - (void) setTimers |
Begin timers for eyes/mouth changes.
Implementation: Determine if Conscience is awake.
Set timers to change eye and lip expresssions.
| - (CAKeyframeAnimation *) shakeAnimation |
Make Conscience reappear.
| - (void) stopTimers |
Stop timers for eyes/mouth changes.
Implementation: Cancel timers handling mouth/eye expressions and any transient blinks.
| - (void) timedEyeChanges |
| - (void) timedMouthExpressionChanges |
Determine which emotion to ask for mouth change.
Implementation: Determine which mouth expression to enable along with teeth, dimple and tongue selection.
|
readwritenonatomicstrong |
UIView that contains all other Views.
|
readwritenonatomicstrong |
Current Conscience visual possessions.
|
readwritenonatomicstrong |
Current Conscience visual configuration.
|
readwritenonatomicstrong |
Current Conscience mood/enthusiasm.
|
readwritenonatomicassign |
Which way the Conscience is currently facing.
|
readwritenonatomicassign |
allow for forcing of an expression


| Beatle is a quirky loner who refuses to live by society's rules. Her business cards read "Towing/Assassination," and she even has a hit man infomercial to go along with it. Her only human contact is with her psychiatrist and the stripper/hooker she employs once a week. Everything changes the night she meets Athena Klendon, a suicidal blonde with a secret. With a demented killer on their tail, courtesy of Athena's crooked former employer, the two quickly strike a bargain. Athena will change her life insurance policy to reflect Beatle as the beneficiary in exchange for her own execution. And while they wait for the paperwork to come through, Beatle will provide room and board and get some extra help on her infomercial. The two have no idea that Detective Holt has been on their case reviewing their every move. Just when he thinks he's got Beatle pinned beyond a shadow of a doubt, this comedic mystery takes a wild and unexpected turn. | |
| Genres: | Comedy, Drama, Thriller |
| Director: | Donna Robinson |
| Writer: | Katherine Brooks, Donna Robinson |
Cast: | Athena - Ever Carradine Beatle - Michele Hicks Bruce - Jeffrey Dean Morgan Kyle Sullivan - Ben Mendelsohn Parking Violations Bureau Agent - Tony Shalhoub Carla - Sarah Shahi Dr. Vanderark - Orlando Jones Friday Green - Ivana Milicevic Secretary - Brooke Adams Detective Holt - Raffaello Degruttola Waitress - Jamie Donnelly Patient - Cullen Douglas Liquor Store Clerk - William Stanford Davis Detective - Mickey Breitenstein Salesman - Jeff Enden Old Man - Jerry Jones Patient #2 - Mark Kelly Nick - Wayne Douglas Morgan Police Office / Detective - Tyson Power Bartender - Katie Sills Girl in the Park - Jamie Franklin |


A simple static nodejs server boilerplate
Use this gist to get started with a dead simple nodejs static web server.


Consider this array:
int[] numbers = { 0, 1, 1, 2, 3, 5, 8 };
When you ask for numbers[3]
, internally the computer does this fake pseudo-code:
func array_access(index) {
address = numbers; // numbers is actually an address!
shifted_address = address + index * size_of_integer;
return value_at_memory_location(shifted_address)
}
add
or push
to add new things to the endremove
to remove an item at a particular index
L = ['a', 'b', 'c']
L.append('d') # add onto the end
L.extend(['e', 'f', 'g']) # add multiple items
L.insert(1, 'sneaky') # insert 'sneaky' at index 1
L.pop(1) # remove the item at index 1
L.pop() # remove the item at the end
pop()
and append
pop(0)
and append
Given two sets S and T
S = set([1, 2, 3])
T = set([3, 4, 5])
S & T # Intersection, gives set([3])
S | T # Union, gives set([1, 2, 3, 4, 5])
S - T # Difference, gives set([1, 2])
2 in S # Boolean, is 2 in the set?
6 not in S # Boolean, is 6 not in the set
A = set(['a', 'b', ...])
B
A - B
''.join(sorted(A - B))
D = {} # empty dictionary declaration
D = { 'Georgia' : 'GA', 'Florida' : 'FL' } # declaring a dictionary with data
D['New York'] = 'NY' # adding a new entry
D['Georgia'] # Spits out 'GA'
'Alabama' in D # Is 'Alabama' a key?
D.keys() # provides an iterable over the dictionary's keys are
D.values() # provides an iterable over the dictionary's values
class Node:
def __init__(self, value):
# initially, this node has no parent, nor any children
self.value = value
self.parent = None
self.children = []
def add_child(self, child):
# to add a child, add the passed node to the list of children,
# but also link it back to "self", which is the parent
child.parent = self
self.children.append(child)
def traverse(root, do_something):
# This is the list of nodes we have yet to visit. We start with just
# the root
to_visit = [root]
# while there are more nodes to visit
while len(to_visit) is not 0:
# get the next node to visit.
# Note: to_visit.pop() gives a Depth-First-Search
# to_visit.pop(0) gives a Breadth-First-Search
# check out https://en.wikipedia.org/wiki/Tree_traversal
#next_node = to_visit.pop()
next_node = to_visit.pop(0)
# Add all of the children of this node as nodes to visit
to_visit.extend(next_node.children)
# do something with the node (we passed the "action" as a parameter)
do_something(next_node)
# Building a tree here
A = Node('A')
# First level
B = Node('B')
A.add_child(B)
C = Node('C')
A.add_child(C)
# Second level
D = Node('D')
B.add_child(D)
E = Node('E')
B.add_child(E)
F = Node('F')
C.add_child(F)
# Call the traverse function with a starting node, and a function to
# perform at each node
traverse(A, lambda node : print(node.value))


Map Editor 3
Posted: Mon Jan 29, 2007 1:31 pm
When I release this editor, go ahead and start using it to make "real" maps. I won't be making any more major changes to the map format or models. The only updates for a while will be created by YOU! and re-released by me.
Here's what I'm thinking:
I also plan on updating how the house walls look. I don't really like the system right now, but it's functional, and to keep this project moving forward I've decided to put off making that little detail better. There are no roofs and the positioning is a pain. Sorry.
Model sizes & positions are pretty much standard now, tell me if you find anything wonky.
Here's what I'm thinking:
- Replace all of the Xenimus textures, and put them into PNG format (smaller download, more colors!)
- Create "themed" textures--I'd LOVE to have a texture set for each season! and maybe some for special events, etc. We could release these on occasions as downloads, or build in an auto-update feature.
- Replace static models with custom versions. These will be done by our modelers and through purchases. As content is developed, I'll change references internally and release new versions with the new content. It will definitely have the same dimensions and be the same type, though--for example, if you put down a table now, when it's replaced, it will still be a table approximately the same size.
- Replace animated models. This will happen much later, as this is by far the most annoying part.
I also plan on updating how the house walls look. I don't really like the system right now, but it's functional, and to keep this project moving forward I've decided to put off making that little detail better. There are no roofs and the positioning is a pain. Sorry.
Model sizes & positions are pretty much standard now, tell me if you find anything wonky.


Design, Development
Just Food, Development
Hipster Content, Just Food
News, Computers
Company News, Hipster Content
Just food


{%extends "base/page.html"%} {%block title%}Password reset{%endblock%} {%block contents%}
If you've forgotten your password, you can enter your email address in the field below and we'll email you instructions for setting a new one.


Dragon’s Breath Yoga teachers draw from Forrest, Ashtanga, Prana Flow, Rocket, Yin and Vinyasa practices to create dynamic sequences.
Sign Up for ClassWorkshops allow you to go deeper in your practice by sharing and exploring the passion and expertise of a teacher. Discover more and find out what’s upcoming.
Sign Up for WorkshopsDragon’s Breath Yoga offers mindful yoga that is accessible to all. Teachers bring their magical approach to the practice by sharing their knowledge and passion, focusing on breath, alignment, and strengthening. Based on the foundations of Forrest, Ashtanga, and Vinyasa, Dragon’s Breath offers a range of styles to challenge and inspire any yogi.
The dragon has been misunderstood and made to be the villain. The fire of their breath is thought to be destructive but in reality, it is a controlled burn. Dragons breathe with purpose and with each inhale and exhale, they focus intention. Dragon eyes see all and when they need to focus their strength, power, and magick, they use their breath. When we work with the dragon, we discover our own courage, our passions, and our place in the world. The quest is to ally with the dragon. Never waste another breath.
We offer high quality yoga instruction to yoga practitioners of all levels – no matter who you are, what you look like, who and what you love, or what you believe. Being a member of an active and caring community is important to us and having a lair to explore connection is a guiding principle. Our goal is to make each student, instructor, and staff member feel supported and guided through the evolution and growth of their yoga practice. The journey toward becoming our biggest, brightest, best, most kind, and most magical selves begins when we roll out the mat.
New Student Special: $75 for 1 month.
Single class: $35
5 Class Pass (3 Month Expiration): $95
10 Class Pass (3 Month Expiration): $175
Monthly Membership: $109/mth (3 mth. min. commitment w/ mat storage)
1 Month Unlimited: $140
3 Months Unlimited: $350


Score: {{score}}
Upvote Ratio: {{ratio}}
Embedded URL: {{url}}
Author: {{auth}}
Author Comment Karma: {{authCommentKarm}}
Author Link Karma: {{authLinkKarm}}
Is the user verified?: {{authVerified}}
Comment amount: {{comNum}}
{{comment[0]}}
{{comment[1]}}
Score: {{comment[2]}}
Post Engagement
Comment amount: {{comNum}}
{% if comWithLink|length > 0 %}Comments With Links
{% for comment in comWithLink %}{{comment[0]}}
{{comment[1]}}
Score: {{comment[2]}}
Comments With Keywords
{% for comment in comWithKey %}{{comment[0]}}
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The Official Rulebook for FALLACIES 2020
-Overall Objective
Players scour through the clues and try to uncover the mysteries behind these conspiracy theories.
-Rules/How to play
> Log into your account at fallacies.csivit.com and you will be presented with a hint to start your hunt!
> Enter your answer into the text box and if you’re worthy enough, they'll let you through to the next question.
> Alternatively, you can use our Telegram Bot (brobot) to enter your response.
> Solve as many questions as you can and be the best conspiracy theorist.
> Please refrain from any form of malpractice to maintain the spirit of the game. CSI-VIT holds the right to disqualify any participant involved.
-Scoring
Each conspiracy that you decipher will earn you a point on the leader board.


,
Last checked in on {{me.lastCheckInDate | date: 'MM/dd/yyyy'}} at {{me.lastCheckInDate | date: 'h:mm:ss a'}}
Slide this slider to the right to allow users to get a more accurate idea of where you are. Slide left to create a more undetermined location.
Range Value:
Redeclare location (if you've moved since last time you logged in)


Selection from the Washington and Oregon AVA with unique planting environment. Also includes world-renowned AVA from France, Itatly, and other parts of Europe.
Our wine selections are suitable gifts for various occasions. We also offered customized gift boxes recommended by our winemakers.
We offered a variety of activies which includes lessons for WSET, history of wines, tasting classes, etc. Contact us for more information.
Want to buy wines not listed in our products, contact us and we can try to find you a good deal!
Don't know what kind of wines you're looking for, you can get good recommendations from our experts.
If you are passionate about wines and you wanted to start a career, join our team!


| ApiVersionResponse |
Model for the following API calls responses:
|
| ApiVersionResponse.ApiVersion |
|
| AuditDetailsResponse |
Model for the following API calls responses:
|
| AuditDetailsResponse.ResponseDetails |
|
| AuditDetailsResponse.ResponseDetails.DetailsObject |
|
| AuditMetadataResponse |
Model for the following API calls responses:
|
| AuditMetadataResponse.AuditMetadata |
|
| AuditMetadataResponse.AuditMetadata.Field |
|
| AuditTypesResponse |
Model for the following API calls responses:
|
| AuditTypesResponse.AuditTrail |
|
| AuthenticationResponse |
Model for the following API calls responses:
|
| AuthenticationResponse.Vault |
|
| BinderExportResponse |
Model for Retrieve Binder Export Results
GET /api/{version}/objects/binders/batch/actions/export/{jobid}/results
|
| BinderResponse |
Model for the binder response
|
| BinderResponse.Renditions |
|
| BinderResponse.Version |
|
| BinderSectionResponse |
Model for the binder section response, needed to handle:
1.
|
| BinderTemplateBulkResponse |
Model for the Template responses
|
| BinderTemplateBulkResponse.TemplateResult |
|
| BinderTemplateMetadataResponse |
Model for the following API calls responses:
|
| BinderTemplateNodeBulkResponse |
Model for the Template responses
|
| BinderTemplateNodeBulkResponse.TemplateNodeResult |
|
| BinderTemplateResponse |
Model for the Binder Template responses
|
| BulkWorkflowActionDetailsResponse |
Model for the following API calls responses:
|
| BulkWorkflowActionDetailsResponse.Control |
|
| BulkWorkflowActionDetailsResponse.Prompt |
|
| BulkWorkflowActionsResponse |
Model for the following API calls responses:
|
| BulkWorkflowActionsResponse.ActionItem |
|
| BulkWorkflowJobStartResponse |
|
| BulkWorkflowJobStartResponse.Job |
|
| ComponentContentResponse |
Response model for the Component Content Files
|
| ComponentContentResponse.ComponentContent |
|
| ComponentContentResponse.Link |
|
| DiscoveryResponse |
Model for the following API calls responses:
|
| DiscoveryResponse.DiscoveryData |
|
| DiscoveryResponse.DiscoveryData.AuthProfile |
|
| DiscoveryResponse.DiscoveryData.AuthProfile.AsMetadata |
|
| DocumentActionEntryCriteriaResponse |
Retrieve the entry criteria for a workflow.
|
| DocumentActionEntryCriteriaResponse.Property |
|
| DocumentActionEntryCriteriaResponse.Property.ObjectTypeReferenced |
|
| DocumentActionInitiateResponse |
Response model for the following API calls:
|
| DocumentActionResponse |
Response model for the following API calls:
|
| DocumentActionResponse.LifecycleAction |
|
| DocumentAnnotationResponse |
Model for the document annotation response
|
| DocumentAnnotationResponse.AnchorData |
|
| DocumentAttachmentBulkResponse |
Model for the document response
|
| DocumentAttachmentResponse |
Model for the document response
|
| DocumentAuditResponse |
Model for the following API calls responses:
|
| DocumentAuditResponse.DocumentAudit |
|
| DocumentBulkResponse |
Model for document create API response
|
| DocumentClassificationResponse |
Model for the document classification response
|
| DocumentClassificationResponse.AvailableLifecycle |
|
| DocumentClassificationResponse.RelationshipType |
|
| DocumentClassificationResponse.Template |
|
| DocumentDeletionResponse |
Model for document deletions API response
|
| DocumentDeletionResponse.DeleteDocument |
|
| DocumentDeletionResponse.ResponseDetails |
|
| DocumentEventResponse |
Model for the following API calls responses:
|
| DocumentEventSubtypeResponse |
Model for the following API calls responses:
|
| DocumentEventSubtypeResponse.DocumentEventSubtype |
|
| DocumentEventSubtypeResponse.DocumentEventSubtype.Value |
|
| DocumentEventTypeResponse |
Model for the following API calls responses:
|
| DocumentEventTypeResponse.Event |
|
| DocumentEventTypeResponse.Event.SubType |
|
| DocumentExportResponse |
Model for document export API response
|
| DocumentExportResponse.ExportedDocument |
|
| DocumentFieldResponse |
Model for the document field response
|
| DocumentLifecycleDeleteOverrideRulesResponse |
Model for lifecycle Role Assignment Rules Response API
|
| DocumentLifecycleDeleteOverrideRulesResponse.DeleteOverrideRule |
|
| DocumentLifecycleRoleAssignmentResponse |
Model for the Document Lifecycle Role Assignment response
|
| DocumentLifecycleRoleAssignmentResponse.RoleAssignment |
|
| DocumentLifecycleRoleAssignmentRulesResponse |
Model for lifecycle Role Assignment Rules Response API
|
| DocumentLockResponse |
Response model for the following API calls:
|
| DocumentLockResponse.Lock |
|
| DocumentRelationshipBulkResponse |
Model for the Document Relationship Bulk responses
|
| DocumentRelationshipMetadataResponse |
Model for the following API calls responses:
|
| DocumentRelationshipMetadataResponse.Relationship |
|
| DocumentRelationshipMetadataResponse.Relationship.RelationshipObject |
|
| DocumentRelationshipMetadataResponse.RelationshipProperty |
|
| DocumentRelationshipMetadataResponse.RelationshipType |
|
| DocumentRelationshipMetadataResponse.RelationshipType.TargetDocumentType |
|
| DocumentRelationshipResponse |
Model for the Document/Binder relationship response endpoints:
POST
/api/{version}/objects/documents/{document_id}/versions/{major_version_number_v}/{minor_version_number_v}/relationships
DELETE
/api/{version}/objects/documents/{document_id}/versions/{major_version_number__v}/{minor_version_number__v}/relationships/{relationship_id}
|
| DocumentRelationshipRetrieveResponse |
Model for the Document/Binder relationship response endpoints:
GET
/api/{version}/objects/documents/{doc_id}/versions/{major_version}/{minor_version}/relationships
GET
/api/{version}/objects/documents/{doc_id}/versions/{major_version}/{minor_version}/relationships/{relationship_id}
GET
/api/{version}/objects/binders/{binder_id}/versions/{major_version}/{minor_version}/relationships
GET
/api/{version}/objects/binders/{binder_id}/versions/{major_version}/{minor_version}/relationships/{relationship_id}
|
| DocumentRelationshipRetrieveResponse.Relationship |
|
| DocumentRenditionResponse |
Model for the following API calls responses:
|
| DocumentRenditionResponse.Renditions |
|
| DocumentResponse |
Model for the document response
|
| DocumentResponse.Renditions |
|
| DocumentResponse.Version |
|
| DocumentRoleChangeBulkResponse |
Model for responses to bulk Document Role Requests
|
| DocumentRoleChangeBulkResponse.DocumentRoleChange |
|
| DocumentRoleChangeResponse |
Model for responses to single Document Role Update and Delete requests
|
| DocumentRoleCheckForDCCResponse |
Model for Document Role Check for Document Change Control response
|
| DocumentRoleRetrieveResponse |
Model for responses to Document Role GET requests
|
| DocumentRoleRetrieveResponse.DocumentRole |
|
| DocumentsResponse |
Model for all documents API Response
|
| DocumentSubtypeResponse |
Model for the document subtype response
|
| DocumentSubtypeResponse.DocumentClassification |
|
| DocumentTemplateBulkResponse |
Model for the Template responses
|
| DocumentTemplateBulkResponse.TemplateResult |
|
| DocumentTemplateMetadataResponse |
Model for the following API calls responses:
|
| DocumentTemplateResponse |
Model for the document template response
|
| DocumentTemplatesResponse |
Model for the document templates response
|
| DocumentTokenResponse |
Response model for the following API calls:
|
| DocumentTokenResponse.Token |
|
| DocumentTypeResponse |
Model for the document type response
|
| DocumentTypeResponse.DocumentSubtype |
|
| DocumentTypesResponse |
Model for the all document types response
|
| DocumentTypesResponse.DocumentType |
|
| DocumentVersionResponse |
Response model for the following API calls:
|
| DocumentVersionResponse.Version |
|
| DocumentWorkflowMetadataResponse |
Model for the document response
|
| DocumentWorkflowMetadataResponse.Property |
|
| DocumentWorkflowMetadataResponse.Value |
|
| DomainAuditResponse |
Model for the following API calls responses:
|
| DomainAuditResponse.DomainAuditData |
|
| DomainResponse |
Model for the following API calls responses:
|
| DomainResponse.Domain |
|
| DomainResponse.Domain.DomainVault |
|
| DomainResponse.Domain.DomainVault.VaultFamily |
|
| EDLResponse |
Response model for an EDL API response
|
| EDLResponse.EDLNode |
|
| FileStagingItemBulkResponse |
|
| FileStagingItemBulkResponse.FileStagingItem |
|
| FileStagingItemBulkResponse.ResponseDetails |
|
| FileStagingItemResponse |
|
| FileStagingItemResponse.FileStagingItem |
|
| FileStagingJobResponse |
Response model for the following API calls:
|
| FileStagingJobResponse.Job |
|
| FileStagingSessionBulkResponse |
|
| FileStagingSessionBulkResponse.FileStagingSession |
|
| FileStagingSessionBulkResponse.ResponseDetails |
|
| FileStagingSessionResponse |
|
| FileStagingSessionResponse.FileStagingSession |
|
| GroupResponse |
Model for the following API calls responses:
|
| GroupRetrieveResponse |
Model for the following API calls responses:
|
| GroupRetrieveResponse.GroupNode |
|
| InitiateMultiDocumentWorkflowResponse |
Model for the following API calls responses:
|
| InitiateMultiDocumentWorkflowResponse.MultiDocumentWorkflowData |
|
| JobCreateResponse |
Response model for the following API calls:
|
| JobCreateResponse.Warning |
|
| JobHistoryResponse |
Response model for the API Job History response
|
| JobHistoryResponse.ResponseDetails |
|
| JobMonitorResponse |
Response model for the API Job Monitor response
|
| JobMonitorResponse.ResponseDetails |
|
| JobStatusResponse |
Response model for the API Job Status response
|
| JobTaskResponse |
Response model for the API Job Task response
|
| JobTaskResponse.JobTask |
|
| JobTaskResponse.ResponseDetails |
|
| LoaderResponse |
Model for the following API calls responses:
|
| LoginAuditResponse |
Model for the following API calls responses:
|
| LoginAuditResponse.LoginAuditData |
|
| MdlResponse |
Response model for the following API calls:
|
| MetaDataComponentRecordResponse |
Response model for the following API calls:
|
| MetaDataComponentTypeBulkResponse |
Response model for the following API calls:
|
| MetaDataComponentTypeResponse |
Response model for the following API calls:
|
| MetaDataDocumentLockResponse |
|
| MetaDataGroupResponse |
Response model for the following API call:
|
| MetaDataObjectBulkResponse |
Response model for the following API calls:
|
| MetaDataObjectFieldResponse |
Response model for the following API calls:
|
| MetaDataObjectResponse |
Response model for the following API calls:
|
| MetaDataUserResponse |
|
| MultiDocumentWorkflowDetailsResponse |
Model for the following API calls responses:
|
| MultiDocumentWorkflowDetailsResponse.Control |
For each control, the following data may be returned:
|
| MultiDocumentWorkflowDetailsResponse.MultiDocumentWorkflow |
|
| MultiDocumentWorkflowDetailsResponse.Prompt |
|
| MultiDocumentWorkflowsResponse |
Model for the following API calls responses:
|
| MultiDocumentWorkflowsResponse.MultiDocWorkflow |
|
| OauthTokenResponse |
|
| ObjectAuditResponse |
Model for the following API calls responses:
|
| ObjectAuditResponse.ObjectAuditData |
|
| ObjectRecordActionBulkResponse |
Model for the following API calls responses for the Object Workflow Action
operations:
|
| ObjectRecordActionResponse |
Model for the following API calls responses:
|
| ObjectRecordActionResponse.Action |
|
| ObjectRecordActionResponse.Link |
|
| ObjectRecordAttachmentResponse |
Model for the following API calls responses:
|
| ObjectRecordBulkResponse |
Model for the following API calls responses for the Object Record
CUD operations:
|
| ObjectRecordCollectionResponse |
Model for the following API calls responses for the Object Record CRUD
operations:
|
| ObjectRecordCollectionResponse.ResponseDetails |
|
| ObjectRecordDeletedResponse |
Model for Deleted Object Records recycle bin endpoint:
|
| ObjectRecordDeletedResponse.DeletedRecord |
|
| ObjectRecordResponse |
Model for the following API calls responses for the Object Record CRUD
operations:
|
| ObjectRecordResponse.ResponseDetails |
|
| ObjectRecordRoleChangeResponse |
Model for the Object Record Role API responses:
|
| ObjectRecordRoleChangeResponse.ObjectRoleChange |
|
| ObjectRecordRoleChangeResponse.ObjectRoleChange.ObjectRole |
|
| ObjectRecordRoleResponse |
Model for the Object Record Role API responses:
|
| ObjectRecordRoleResponse.ObjectRole |
|
| ObjectRecordTypeResponse |
Model for the Object Record Type or Object describe API responses:
|
| ObjectRecordTypeResponse.ObjectRecordType |
|
| ObjectRecordTypeResponse.ObjectRecordType.ObjectRecordTypeAction |
|
| ObjectRecordTypeResponse.ObjectRecordType.ObjectRecordTypeField |
|
| ObjectWorkflowActionDetailsResponse |
Model for the following API calls responses for the Object Workflow Action Details
operations:
|
| ObjectWorkflowActionResponse |
Model for the following API calls responses for the Object Workflow Action
operations:
|
| ObjectWorkflowActionResponse.WorkflowAction |
|
| ObjectWorkflowResponse |
Model for the following API calls responses for the Object workflow
operations:
|
| ObjectWorkflowResponse.ObjectWorkflow |
|
| ObjectWorkflowResponse.ResponseDetails |
|
| ObjectWorkflowTaskActionDetailsResponse |
Model for the following API calls responses for the Object Workflow Task Action Details
operations:
|
| ObjectWorkflowTaskActionDetailsResponse.Control |
|
| ObjectWorkflowTaskActionDetailsResponse.Control.Prompt |
|
| ObjectWorkflowTaskActionDetailsResponse.Control.Reason |
|
| ObjectWorkflowTaskActionDetailsResponse.Control.Verdict |
|
| ObjectWorkflowTaskActionDetailsResponse.ObjectWorkflowTaskAction |
|
| ObjectWorkflowTaskResponse |
Model for the following API calls responses for the Object WorkflowTask
operations:
|
| ObjectWorkflowTaskResponse.ObjectWorkflowTask |
|
| ObjectWorkflowTaskResponse.ResponseDetails |
|
| PackageResponse |
Model for the following API calls responses:
|
| PackageResponse.VaultPackage |
|
| PackageResultsResponse |
Model for the following API calls responses:
|
| PackageResultsResponse.ResponseDetails |
|
| PackageResultsResponse.ResponseDetails.DeploymentLog |
|
| PicklistResponse |
Model for the following API calls
|
| PicklistResponse.Picklist |
|
| PicklistResponse.Picklist.UsedIn |
|
| PicklistValueResponse |
Model for the following API calls
GET /api/{version}/objects/picklists/{picklist_name}
|
| PicklistValueResponse.PicklistValue |
|
| QueryResponse |
Model for VQL query API calls
|
| QueryResponse.QueryDescribe |
|
| QueryResponse.QueryDescribe.QueryObject |
|
| QueryResponse.QueryResult |
|
| QueueResponse |
Response model for the following API calls:
|
| QueueResponse.Queue |
|
| QueueResponse.Queue.Connection |
|
| QueueResponse.Queue.Connection.QueueError |
|
| SandboxEntitlementResponse |
|
| SandboxEntitlementResponse.SandboxEntitlement |
|
| SandboxEntitlementResponse.SandboxEntitlement.Entitlement |
|
| SandboxResponse |
|
| SandboxResponse.Limit |
|
| SandboxResponse.Sandbox |
|
| SCIMResponse |
|
| SCIMResponse.AuthenticationScheme |
|
| SCIMResponse.Bulk |
|
| SCIMResponse.ChangePassword |
|
| SCIMResponse.Etag |
|
| SCIMResponse.Filter |
|
| SCIMResponse.Meta |
|
| SCIMResponse.Patch |
|
| SCIMResponse.Resource |
|
| SCIMResponse.Resource.Schema |
|
| SCIMResponse.Sort |
|
| SCIMUserBulkResponse |
|
| SCIMUserResponse |
|
| SDKResponse |
Response model for the following API calls:
|
| SecurityPoliciesResponse |
Model for response to requests to the Retrieve Security Policies endpoint
|
| SecurityPoliciesResponse.SecurityPolicy |
|
| SecurityPolicyResponse |
Model for response to requests to the Retrieve Security Policy endpoint
|
| SecurityPolicyResponse.SecurityPolicy |
|
| SecurityPolicyResponse.SecurityPolicy.PolicyDetails |
|
| SecurityPolicyResponse.SecurityPolicy.PolicySecuritySettings |
|
| SecurityPolicyResponse.SecurityPolicy.PolicySecuritySettings.AuthenticationType |
|
| SystemAuditResponse |
Model for the following API calls responses:
|
| SystemAuditResponse.SystemAuditData |
|
| UserBulkResponse |
|
| UserPermissionResponse |
|
| UserPermissionResponse.UserPermissions |
|
| UserPermissionResponse.UserPermissions.PermissionSet |
|
| UserResponse |
|
| UserRetrieveResponse |
|
| UserRetrieveResponse.UserNode |
|
| ValidatePackageResponse |
Response model for the following API calls:
|
| ValidatePackageResponse.ResponseDetails |
|
| ValidatePackageResponse.ResponseDetails.PackageStep |
|
| VaultResponse |
Base Vault API response message
|
| VaultResponse.APIResponseError |
|


List of winners at the 2019 Country Music Association Awards
NASHVILLE, Tennessee (AP) - Complete list of winners from the 2019 Country Music Association Awards, held Wednesday at the Bridgestone Arena in Nashville, Tennessee.
- Entertainer of the year: Garth Brooks
- Female vocalist of the year: Kacey Musgraves
- Male vocalist of the year: Luke Combs
- New artist of the year: Ashley McBryde
- Album of the year: “GIRL,” Maren Morris
- Song of the year: “Beautiful Crazy,” Luke Combs
- Single of the year: “God’s Country,” Blake Shelton
- Vocal duo of the year: Dan + Shay
- Vocal group of the year: Old Dominion
- Musical event of the year: “Old Town Road (Remix),” Lil Nas X featuring Billy Ray Cyrus
- Music video of the year “Rainbow,” Kacey Musgraves
- Musician of the year: Jenee Fleenor (fiddle)
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L01 - Dynamic List View - Articles - Content Tree
Dynamic List View of child article content items based on child items in Sitecore content tree. Sorted by order in content tree.
Utilized on the following pages:
[list the page types this component is known for]
Interactions
[list interactions, if applicable]
Fields Displayed
Fields displayed include:
- Title
- Short Descriptoin
- Image - Thumbnail version.
- Title is a link, Image is a link.
Notes
- If no image, leave blank. Do not replace with placehlder.
- This is structurally identical to L02 - Dynamic List View - Articles - Tags