@() @main("API Documentation") {
limit
limit
parameter for the info
and dataset
endpoints. The limit
endpoint will simply return the value that has been set, or else null
if there is no limit.
{
"series_limit_csv": 50,
"series_limit_json": 100
}
subjects
List available subjects.
| Parameter | Optional | Multiple | Description |
|---|---|---|---|
format |
✔ | ✗ | Output format. One of CSV or JSON . Defaults to JSON . |
subjectCode |
✔ | ✗ | Subject code. For example, HLF , LCI , BOP , CPI , etc. |
subjectKeyword |
✔ | ✔ | Search term for subject. |
The basic format is:
For example:
[
{
"subject_code": "HLF",
"title_text": "Household Labour Force Survey - HLF"
}
]
or:
[
{
"subject_code": "HLF",
"title_text": "Household Labour Force Survey - HLF"
},
{
"subject_code": "LCI",
"title_text": "Labour Cost Index - LCI"
}
]
families
List available families.
| Parameter | Optional | Multiple | Description |
|---|---|---|---|
format |
✔ | ✗ | Output format. One of CSV or JSON . Defaults to JSON . |
subjectCode |
✔ | ✗ | Subject code. For example, HLF , LCI , BOP , CPI , etc. |
familyCode |
✔ | ✗ | Family code. For example, SA . |
familyNbr |
✔ | ✗ | Family number. |
subjectKeyword |
✔ | ✔ | Search term for subject. |
familyKeyword |
✔ | ✔ | Search term for family. |
The basic format is:
For example:
[
{
"subject_code": "HLF",
"family_code": "SA",
"family_nbr": 7,
"title_text": "Persons Employed, Unemployed, Not in Labour Force by Sex by Age Group"
},
{
"subject_code": "HLF",
"family_code": "SA",
"family_nbr": 4327,
"title_text": "SA - Labour Force Status by Sex by Age Group - Sampling Errors"
}
]
or:
[
{
"subject_code": "BAS",
"family_code": "SA",
"family_nbr": 4682,
"title_text": "Building activity by region"
},
{
"subject_code": "BLD",
"family_code": "SF",
"family_nbr": 4686,
"title_text": "Building consents by region"
}
]
info
Description of available time series.
| Parameter | Optional | Multiple | Description |
|---|---|---|---|
format |
✔ | ✗ | Output format. One of CSV , or JSON . Defaults to JSON . |
subjectCode |
✔ | ✔ | Subject code. For example, HLF , LCI , BOP , CPI , etc. |
familyCode |
✔ | ✔ | Family code. For example, SA . |
familyNbr |
✔ | ✔ | Family number. |
seriesCode |
✔ | ✔ | Series code. For example, HLFQ.SAA1AZ . |
subjectKeyword |
✔ | ✔ | Search term for subject. |
familyKeyword |
✔ | ✔ | Search term for family. |
seriesKeyword |
✔ | ✔ | Search term for series. |
interval |
✔ | ✗ | Interval. 1 denotes monthly, 3 denotes quarterly, and 12 denotes annual. |
offset |
✔ | ✗ | Offset month. For example, 3 denotes the March quarter for quartely data. |
limit |
✔ | ✗ | Series limit. Limits the number of results returned. |
drop |
✔ | ✗ | Used in conjunction with limit to access results in batches. For example, setting limit to 100 and drop to 100 would yield results 101 through 200. |
The basic format is:
For example:
[
{
"subject_code": "HLF",
"family_code": "SA",
"family_nbr": 7,
"series_code": "HLFQ.SAF2AA",
"series_nbr": 2059,
"subject_title": "Household Labour Force Survey - HLF",
"family_title": "Persons Employed, Unemployed, Not in Labour Force by Sex by Age Group",
"interval": 3,
"offset": 3,
"magnitude": 0,
"unit": "Percent",
"variables": [
"Persons Employed, Unemployed, Not in Labour Force (for current quarter)",
"Sex",
"Age Group"
],
"outcomes": [
"Unemployment Rate",
"Female",
"Aged 15-19 Years"
]
},
{
"subject_code": "HLF",
"family_code": "SA",
"family_nbr": 7,
"series_code": "HLFQ.SAH2AA",
"series_nbr": 3099244,
"subject_title": "Household Labour Force Survey - HLF",
"family_title": "Persons Employed, Unemployed, Not in Labour Force by Sex by Age Group",
"interval": 3,
"offset": 3,
"magnitude": 0,
"unit": "Percent",
"variables": [
"Persons Employed, Unemployed, Not in Labour Force (for current quarter)",
"Sex",
"Age Group"
],
"outcomes": [
"Employment Rate",
"Female",
"Aged 15-19 Years"
]
}
]
count
Count individual time series.
| Parameter | Optional | Multiple | Description |
|---|---|---|---|
format |
✔ | ✗ | Output format. One of CSV , or JSON . Defaults to JSON . |
subjectCode |
✔ | ✔ | Subject code. For example, HLF , LCI , BOP , CPI , etc. |
familyCode |
✔ | ✔ | Family code. For example, SA . |
familyNbr |
✔ | ✔ | Family number. |
seriesCode |
✔ | ✔ | Series code. For example, HLFQ.SAA1AZ . |
subjectKeyword |
✔ | ✔ | Search term for subject. |
familyKeyword |
✔ | ✔ | Search term for family. |
seriesKeyword |
✔ | ✔ | Search term for series. |
interval |
✔ | ✗ | Interval. 1 denotes monthly, 3 denotes quarterly, and 12 denotes annual. |
offset |
✔ | ✗ | Offset month. For example, 3 denotes the March quarter for quartely data. |
The basic format is:
For example:
22867
series
Time series data by identifier.
| Parameter | Optional | Multiple | Description |
|---|---|---|---|
format |
✔ | ✗ | Output format. One of CSV , JSON , or CHART . CHART produces a highchart. Defaults to JSON . |
seriesCode |
✔ | ✔ | Series identifier. For example, HLFQ.SAA1AZ . |
start |
✔ | ✗ | Start date. For example, 1986.03 . |
end |
✔ | ✗ | End date. For example, 2018.12 . |
head |
✔ | ✗ | How many values at the head of each series to keep. |
tail |
✔ | ✗ | How many values at the tail of each series to keep. |
title |
✔ | ✗ | A title to be used if selected format is CHART . |
The basic format is:
For example:
[
{
"subject_code": "HLF",
"subject_title": "Household Labour Force Survey - HLF",
"family_code": "SA",
"family_nbr": 7,
"family_title": "Persons Employed, Unemployed, Not in Labour Force by Sex by Age Group",
"series_code": "HLFQ.SAA1AZ",
"series_nbr": 1878,
"interval": 3,
"magnitude": 3,
"offset": 3,
"units": "Number",
"variables": [
"Persons Employed, Unemployed, Not in Labour Force (for current quarter)",
"Sex",
"Age Group"
],
"outcomes": [
"Persons Employed in Labour Force",
"Male",
"Total All Ages"
],
"period": [
"2017.03",
"2017.06",
"2017.09",
"2017.12",
"2018.03",
"2018.06",
"2018.09",
"2018.12"
],
"value": [
1352,
1341.7,
1360.1,
1381.7,
1387.1,
1381.1,
1388.1,
1392.1
],
"status": [
"FINAL",
"FINAL",
"FINAL",
"FINAL",
"FINAL",
"FINAL",
"FINAL",
"FINAL"
]
},
{
"subject_code": "HLF",
"subject_title": "Household Labour Force Survey - HLF",
"family_code": "SA",
"family_nbr": 7,
"family_title": "Persons Employed, Unemployed, Not in Labour Force by Sex by Age Group",
"series_code": "HLFQ.SAA2AZ",
"series_nbr": 1890,
"interval": 3,
"magnitude": 3,
"offset": 3,
"units": "Number",
"variables": [
"Persons Employed, Unemployed, Not in Labour Force (for current quarter)",
"Sex",
"Age Group"
],
"outcomes": [
"Persons Employed in Labour Force",
"Female",
"Total All Ages"
],
"period": [
"2017.03",
"2017.06",
"2017.09",
"2017.12",
"2018.03",
"2018.06",
"2018.09",
"2018.12"
],
"value": [
1195,
1188.8,
1214.6,
1237.8,
1239.3,
1242.3,
1259.8,
1252
],
"status": [
"FINAL",
"FINAL",
"FINAL",
"FINAL",
"FINAL",
"FINAL",
"FINAL",
"FINAL"
]
}
]
or (and note that the chart
format is a very minimal implementation, for illustrative purposes only):
dataset
Time series data.
| Parameter | Optional | Multiple | Description |
|---|---|---|---|
format |
✔ | ✗ | Output format. One of CSV , or JSON . Defaults to JSON . |
subjectCode |
✔ | ✔ | Subject code. For example, HLF , LCI , BOP , CPI , etc. |
familyCode |
✔ | ✔ | Family code. For example, SA . |
familyNbr |
✔ | ✔ | Family number. |
seriesCode |
✔ | ✔ | Series code. For example, HLFQ.SAA1AZ . |
subjectKeyword |
✔ | ✔ | Search term for subject. |
familyKeyword |
✔ | ✔ | Search term for family. |
seriesKeyword |
✔ | ✔ | Search term for series. |
interval |
✔ | ✗ | Interval. 1 denotes monthly, 3 denotes quarterly, and 12 denotes annual. |
offset |
✔ | ✗ | Offset month. For example, 3 denotes the March quarter for quartely data. |
limit |
✔ | ✗ | Series limit. Limits the number of results returned. |
drop |
✔ | ✗ | Used in conjunction with limit to access results in batches. For example, setting limit to 100 and drop to 100 would yield results 101 through 200. |
head |
✔ | ✗ | How many values at the head of each series to keep. |
tail |
✔ | ✗ | How many values at the tail of each series to keep. |
The basic format is:
For example:
[
{
"subject_code": "HLF",
"subject_title": "Household Labour Force Survey - HLF",
"family_code": "SA",
"family_nbr": 7,
"family_title": "Persons Employed, Unemployed, Not in Labour Force by Sex by Age Group",
"series_code": "HLFQ.SAB2AA",
"series_nbr": 1915,
"interval": 3,
"magnitude": 3,
"offset": 3,
"units": "Number",
"variables": [
"Persons Employed, Unemployed, Not in Labour Force (for current quarter)",
"Sex",
"Age Group"
],
"outcomes": [
"Persons Unemployed in Labour Force",
"Female",
"Aged 15-19 Years"
],
"period": [
"2018.03",
"2018.06",
"2018.09",
"2018.12"
],
"value": [
13.9,
12.9,
9.5,
13
],
"status": [
"FINAL",
"FINAL",
"FINAL",
"FINAL"
]
},
{
"subject_code": "HLF",
"subject_title": "Household Labour Force Survey - HLF",
"family_code": "SA",
"family_nbr": 4327,
"family_title": "SA - Labour Force Status by Sex by Age Group - Sampling Errors",
"series_code": "HLFQ.SASEB2AA",
"series_nbr": 3265428,
"interval": 3,
"magnitude": 3,
"offset": 3,
"units": "Number",
"variables": [
"Sampling Errors",
"Persons Employed, Unemployed, Not in Labour Force (for current quarter)",
"Sex",
"Age Group"
],
"outcomes": [
"Sampling errors",
"Persons Unemployed in Labour Force",
"Female",
"Aged 15-19 Years"
],
"period": [
"2018.03",
"2018.06",
"2018.09",
"2018.12"
],
"value": [
2.9,
2.6,
2.5,
2.6
],
"status": [
"FINAL",
"FINAL",
"FINAL",
"FINAL"
]
}
]
}


AP Top Science News at 6:43 a.m. EST
Spacewalking astronauts slice into cosmic ray detector
Egypt unveils animal mummies of lion cubs, crocodiles, birds
UK treasure hunters jailed for stealing Viking-era hoard
Carcass of giant blue whale brought to surface for study
Bacteria-infected mosquitoes take bite out of deadly dengue
Vape debate: Are e-cigarettes wiping out teen smoking?
Study: Yellowstone bison mow, fertilize their own grass
Boeing names space test dummy Rosie after WWII riveter
US-born Bei Bei settles into new home at Chinese panda base
2 insect species classified as threatened as glaciers melt
Copyright 2019 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.


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Cartpole Dynamics and Control
Introduction
As is the case for a lot of PhDs, my main work focuses on a small part of robotics. There are many areas of robotics that I would love to work on but do not. As part of my ongoing effort to learn more about these areas, I work on accessible problems that interest me. One area of robotics that fascinates me is that of rigid body dynamics and control. Regardless of how many times I see it, there is something magical about using mathematics to make correct predictions about the world. Newtonian mechanics - the science that powers most modern robots - is many hundreds of years old, but I can still feel the magic when the equations come to life.
One toy system that is commonly used to learn about dynamics and control theory is the cart-pole or inverted pendulum. Over the past few weeks, I’ve been working through simulating the dynamics of the system. I alse tried out some common control strategies to perform the task of balancing the pendulum with its center of mass above the joint. In this article I’ll go through the process from beginning to end. I think the cart pole system is very convenient as it is simple enough that its dynamics can be worked out by hand. Concomitantly, it is complex enough to warrant applying some advanced control strategies.
Modeling and Simulating the Cart-Pole System
The diagram above (lifted from the Wikipedia article) shows what the system looks like. It is assumed that the cart is on a fixed linear rail (frictionless of course). This can be considered a prismatic joint in robotics terminology. The pendulum is mounted on the cart with a revolute joint that has a single rotational degree of freedom. There are a few ways to model the pendulum. Some model it as a uniform bar/cylinder and some as a concentrated mass at the end of a massless link. For the purposes of this article, I’ll be modeling it as a thin cylindrical rod.
Lagrangian Mechanics
Lagrangian mechanics is a fascinating and beautiful formulation of classical mechanics. Rather than the three laws of motion, Lagrangian mechanics is interested in a scalar quantity called the action which is a function of the state of the mechanical system. By applying a simple constraint known as the Principle of Least Action, you can get the equations of motion for any system in any coordinate system.
To apply Lagrangian Mechanics to the cart-pole system, I need to first write down the Lagrangian of the system which is defined as the difference between the potential and kinetic energies of the system as functions of the state.
Since the cart moves perpedicular to gravity, the potential energy $V$ only depends on the vertical position of the pendulum.
$$ V = -m_{p}gl\cos{\theta} $$
The Kinetic Energy of the system is a bit more involved. It’s the sum of the kinetic energies of the cart and the pole.
$$ \begin{align} T &= \frac{1}{2} m_c \dot{x}^2 + \frac{1}{2}m_p \left[ (\dot{x} + l\dot{\theta}\cos{\theta})^2 + (l\dot{\theta} \sin{\theta})^2\right] \\ &= \frac{1}{2} (m_c + m_p) \dot{x}^2 + \frac{1}{2} m_p l^2 \dot{\theta}^2 (\cos^2{\theta} + \sin^2{\theta}) + m_p l \dot{x}\dot{\theta}\cos{\theta} \\ &= \frac{1}{2} (m_c + m_p) \dot{x}^2 + \frac{1}{2} m_p l^2 \dot{\theta}^2 + m_p l \dot{x}\dot{\theta}\cos{\theta}\end{align} $$
The Lagrangian is then:
$$ \mathcal{L} = T - V $$
In Lagrangian mechanics, as the system evolves over time, a quantity called action is defined as the integral of the Lagrangian.
$$ S = \int_{t_1}^{t_2} \mathcal{L} dt$$
According to the Principle of Least Action, the dynamics of the system evolves so that this quantity - the action - is minimized. Once I have the action I can use the Euler-Lagrange equations to find the equations of motion of the system.
$$ \frac{d}{dt} \frac{\partial \mathcal{L}}{\partial \dot{q}_i} - \frac{\partial \mathcal{L}}{\partial q_i} = F_i$$
The $q_i$ represent the state variables of the system and $F_i$ generalized forces. By applying these equations once for each state variable I get the full equations of motion of the system. In the case of the cart pole system, the state variables are the position of the cart and the angle between the cart and the pendulum. The resulting two equations are:
$$ \begin{align} -m_p l \dot{\theta} \sin{\theta} + (m_c + m_p) \ddot{x} + m_p l \ddot{\theta} \cos{\theta} = F_1 \\ m_p g l \sin{\theta} + m_p l \ddot{x} \cos{\theta} + m_p l^2 \ddot{\theta} = F_2 \end{align} $$
The central equation of study in robot dynamics are the manipulator equations. These equations are a general way to express the dynamics of a multi-link rigid body. By inspecting the equations of motion derived above, they can be expressed in the manipulator equation form:
$$ H(q)\ddot{q} + C(q, \dot{q})\dot{q} + G(q) = F $$
Where
$$ H = \begin{bmatrix} m_c + m_p & m_p l \cos{\theta} \\ m_p l \cos{\theta} & m_p l^2 \end{bmatrix}$$ $$ C = \begin{bmatrix} 0 & -m_p l \dot{\theta} \sin{\theta} \\ 0 & 0 \end{bmatrix} $$ $$ G = \begin{bmatrix} 0 \\ m_p g l \sin{\theta} \end{bmatrix} $$ $$ q = \begin{bmatrix} x \\ \theta \end{bmatrix} $$
Simulating the Cart Pole System
There are a few ways to simulate a mechanical system once you have the equations of motion. Python is an excellent option. The scipy package has a good set of numerical integrators.  However, for this project, I chose to use MATLAB. Mathworks decided to make the student (and personal) version of MATLAB available for ridiculously cheap prices (compared to their organizational licenses). So about a year ago I bought their student license so I could learn to use it. Everything I do in this article can be done just as well using Python and scipy. However, I do admit that using MATLAB makes the work a little smoother.
For simulating the cart-pole I will be using the Simscape Multibody toolbox for Simulink.
Without any kind of control system active, we can see the dynamics of the free system quite nicely.
Balancing Control
The balancing control task is that of having the pendulum balance with its center of mass above the cart by only moving the cart (there is no actuator on the revolute joint). The control input in this task is a force that is applied to the cart (through electric motors or rocket engines or some other means of actuation).
The cart pole system is a highly non-linear system. However, just like how the motion of the simple pendulum can be approximated by a linear system for small angles, the cart pole system can be linearized too. Sometimes in engineering, the approach of “fiddling with it till it works” really works! So the first thing I tried to do is to implement a simple PD controller. If the system is linear around the balancing point (which it is in this case) and long as the states aren’t too far off, this approach will work.
Hand-Tuned Control Gains
So the very first thing I tried are hand tuned control gains. I just assumed that I could achieve what I wanted by using the control signal $u = K\cdot s$ where $s$ is the state vector with the positions and velocities: $s = \begin{bmatrix} x & \dot{x} & \theta & \dot{\theta}\end{bmatrix}^T$. After a bit (a lot) of fiddling, I did manage to find a set of control gains that worked. Here’s what the controller looks like when it tries to balance the pendulum.
LQR
To avoid wasting all that time fiddling, control engineers invented a type of controller called a Linear Quadratic Regulator. LQR controllers formalize the process of fiddling by defining a cost function on the states and control inputs. The optimal control gain vector $K$ is the vector that minimizes this cost function. LQR is fundamental enough in control theory that standard functions exist to calculate the control gains if you have the linearized system. Such functions exist in both MATLAB and scipy or specialized control libraries.
To do LQR, we first need to linearize the system around an “operating point”. This process can be little (but not too much) involved if you try to do it by hand so I applied liberal amounts Mathematica to help me out (you can do the same using SymPy in Python). I first expressed the equations of motion in the form $\dot{s} = f(s, u)$. This can be done by rearranging the manipulator equations. Once you have the dynamics in this form, the linearized equations of motion can be expressed as:
$$ \dot{x} = A(s - s^*) + B(u - u^{ *}) $$
Where
$$ A = \frac{\partial f}{\partial s}\Bigr\rvert _{s=s^{*}} $$ $$ B = \frac{\partial f}{\partial u}\Bigr\rvert _{u=u^{ *}} $$
For the cart pole system, the matrices come out as:
$$ A = \begin{bmatrix} 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \\ 0 & \frac{m_p g}{m_c} & 0 & 0 \\ 0 & \frac{(m_c + m_p)g}{m_c l} & 0 & 0 \end{bmatrix} $$
$$ B = \begin{bmatrix} 0 \\ 0 \\ \frac{1}{m_c} \\ \frac{1}{m_c l}\end{bmatrix} $$
To compute the optimal LQR control gains, I need to define a few more matrices. As I said before, LQR finds the gain matrix $K$ that optimizes the cost function
$$ J = \int_0^{\infty} (s^TQs + u^TRu + 2x^TNu) dt$$
I need to define the matrix $Q$, $R$ and $N$. You can think of $Q$ as a matrix of weights that tell you how much to “penalize” certain combinations of the state. For instance the first term in the diagonal of the $Q$ matrix tells you how much to penalize the value of the state variable $x$. The same goes for other matrices. Having the $Q$ matrix as a scalar multiple of the identity matrix is a good default. $R$ is just a single scalar value. $N$ can be skipped in most cases. With these I got a control gain matrix that also successfully drove the states to my target. It looked quite similar to my hand tuned control gains actually. On close inspection you can see that the system does reach it’s target slightly faster.
Conclusion
Dynamics is an important part of a roboticist’s toolkit. Learning dynamics from a textbook can be a bit daunting. For me, implementing the ideas in code and generating nice animations helps me to get a better intuition of the mathematics. Now that I have the environment set up, I’m considering trying my hand at some swing-up control using energy shaping methods or reinforcement learning.


Oman begins preparations for Gulf Cup
by Times News ServiceMuscat: Oman’s national team is preparing for the upcoming Gulf Cup which is scheduled to be held in Qatar’s capital Doha from November 24 to December 6.
Five teams
Muscat: Oman’s national team is preparing for the upcoming Gulf Cup which is scheduled to be held in Qatar’s capital Doha from November 24 to December 6.
Five teams


Active & Disabled Buttons
Use
.active
and
.disabled
to control state.
Active Outlined Primary
Disabled Outlined Primary


| rigz.max2dext: | Functions | Modinfo | Source |
Max2D module provides a few extra features for the standard max2d, mainly the loading of images in PreMultipliedAlpha mode which enables and additional blend mode - screen blend, plus making it a lot easier to grab a screen with the lpaha channel intact.
In order to use this properly for your own use (this is mainly for use internally by timelinefx you need to follow a few rules:
| GetBlendEXT | get the current Blend Mode of SetBlendEXT. |
| GrabPixmapEXT | Grab pixmap. |
| InitEXTGraphics | Initialise the Extended Graphics Driver. |
| LoadAnimImageEXT | Load a multi-frame image. |
| LoadImageEXT | Load an image. |
| SetAlphaEXT | Set current alpha level. |
| SetBlendEXT | Set current blend mode. |
| SetClsColorEXT | Reset the state_blend of the normal SetBlend command. |
| SetColorEXT | Set current color. |
| Function GetBlendEXT:Int() | |
| Returns | the current blendmode. |
| Description | get the current Blend Mode of SetBlendEXT. |
| Function GrabPixmapEXT:TPixmap(x:Int, y:Int, width:Int, height:Int) | |
| Description | Grab pixmap. |
| Function InitEXTGraphics() | |
| Description | Initialise the Extended Graphics Driver. |
| Information | It's important that you run this before attemtping to use any of the EXT commands. |
| Function LoadAnimImageEXT:TImage(url:Object, cell_width:Int, cell_height:Int, first_cell:Int, cell_count:Int, flags:Int = -1, PreMultiAlpha:Int = False) | |
| Returns | An image object. |
| Description | Load a multi-frame image. |
| Information | This will load an animation image and pre multiply all it's colour with the alpha channel PreMultiAlpha is set to true. |
| Function LoadImageEXT:TImage(url:Object, flags:Int = -1, ChangeImage:TPixmap(pixmap:TPixmap)) | |
| Returns | A new image object. |
| Description | Load an image. |
| Information | This will load an image and pre multiply all it's colour with the alpha channel if PreMultiAlpha is set to True. |
| Function SetAlphaEXT(alpha:Float) | |
| Description | Set current alpha level. |
| Information | Use this when working with images that have premultiplied alpha
alpha should be in the range 0 to 1.
alpha controls the transparancy level when the ALPHABLEND blend mode is in effect. The range from 0.0 to 1.0 allows a range of transparancy from completely transparent to completely solid. |
| Function SetBlendEXT(blend:Int) | |||||||||||||||
| Description | Set current blend mode. | ||||||||||||||
| Information | SetBlend controls how pixels are combined with existing pixels in the back buffer when drawing
commands are used in BlitzMax.
blend should be one of:
|
| Function SetClsColorEXT(red:Int, green:Int, blue:Int, alpha:Float = 1) | |
| Description | Reset the state_blend of the normal SetBlend command. |
| Information | After using SetBlendEXT, Max2d won't know whether it's blendmode is the same anymore, so use this to reset it and ensure it works properly
next time you use it.
end rem
Function ResetBlendMode()
SetBlend 10
SetBlend GetBlend()
End Function
rem bbdoc: Reset the state_blend of the SetBlendEXT command about: After using SetBlend, Max2dEXT won't know whether it's blendmode is the same anymore, so use this to reset it and ensure it works properly next time you use it. end rem Function ResetBlendModeEXT() SetBlendEXT 10 SetBlend GetBlendEXT() End Function Rem bbdoc: Set current Cls color plus the alpha level about: The red, green and blue parameters should be in the range of 0 to 255. Alpha should be in the range 0 to 1. The default cls color is black. |
| Function SetColorEXT(red:Int, green:Int, blue:Int) | |
| Description | Set current color. |
| Information | Use this when working with images that have premultiplied alpha
The red, green and blue parameters should be in the range of 0 to 255. |
| Version | 1 |
|---|---|
| Author | Peter Rigby |
| License | MIT |
| Copyright | RigzSoft |


class: center, middle, inverse, title-slide # Introduction to Statistics ## Part I: Introduction to Statistical Inference ### Stéphane Guerrier & Yuming Zhang ### 18 December 2020 --- # Introduction Welcome to the class ".hi-purple[Introduction to Statistics]"! Throughout this class, we will use a platform called .hi-pink[Piazza] for the share of all the teaching materials and Q&A.  - Signup Link: [https://piazza.com/configure-classes/winter2020/sisu](https://piazza.com/configure-classes/winter2020/sisu) - Access Code: .purple[statisfun] --- # R and RStudio .pull-left[ In this class, we will use the statistical software .hi-pink[R] together with the integrated development environment .hi-pink[R Studio], which can be downloaded with the following: - Latest version of R: [https://cran.r-project.org/](https://cran.r-project.org/) - Latest version of R Studio: [https://www.rstudio.com/](https://www.rstudio.com/) .hi-purple[Note:] You cannot use RStudio without having installed R on your computer. ] .pull-right[  ] --- # What is statistics? .pull-left[ .smaller[.hi-pink[Statistics] is a science that uses mathematics and computer science to deal with the collection, analysis, interpretation, and presentation of masses of numerical data. Informally, it is the .pink[science of learning from data].]  .tiny[Source: [luminousmen](luminousmen.com)] ] .pull-right[ .smaller[.hi-purple[Statistics] is a crucial part of our life. However, .purple[statistical methods are often consciously (or not) misused]. This can lead to contradictory studies and conclusions (as seen during the current COVID-19 pandemic).]  .tiny[Source: [Atoz Markets](https://atozmarkets.com/news/untold-reality-of-p-hacking-in-finance/)] ] --- # How can statistics be useful? .smallest[Statistics can be used (among others) to 1. .purple[Visualize data] (e.g. propagation of COVID-19 in different countries). 2. .purple[Understand and interpret data] (e.g. main causes of cancer). 3. .purple[Assess the validity of a hypothesis] (e.g. is a drug working?). 4. .purple[Make predictions] (e.g. predicting unemployment or risk indices).] .smallest[Learning more about statistics allows to 1. Better understand .pink[arguments based on data.] 2. Be able to apply .pink[critical thinking] about .pink[statistics used as evidence]. 3. Understand how statistical associations are used to .pink[evaluate claims (hypotheses)] and .pink[assess causal connections.]] .smallest[.purple[Understanding and knowing how to interpret statistical analyses is therefore becoming an increasingly vital skill.]] --- # Simpson's Paradox .pull-left[.smallest[.hi-pink[Statistical analysis can be tricky.] Here we give an example of a study of gender bias among graduate school admissions to University of California, Berkeley, for the fall of 1973.]  ] .pull-right[ .smallest[ The data show that .purple[among 8,442 men applicants 44% were admitted] while .pink[among 4,321 women applicants only 35% were admitted]. The overall admission rate was around 41%. The difference is quite large (9%) and it is a large sample with 12,763 applicants, so it is unlikely that this is due to chance. Therefore, the data suggest that .purple[men applying were more likely to be admitted than women]. However, when people looked more into the data, they found that this conclusion is actually completely incorrect. In fact, a correct analysis showed that .pink["small but statisticall significant bias in favor of women"]. But why? 😮 ] ] --- # Simpson's Paradox .pull-left[  ] .pull-right[  ] - .smallest[Men applicants tended to apply for "easy" departments, i.e. departments that had high admission rates.] - .smallest[Women applicants tended to apply to "hard" departments, i.e. departments that had low admission rates.] - .smallest[So it turns out that most of the departments actually had a slightly higher success rate for women.] - .smallest[This phenomenon is actually very common and known as .hi-purple[Simpson's Paradox]. ] --- # How does it work? .smallest[ - Statistical methods are based on several fundamental concepts, the most central of which is to consider the information available (in the form of data) resulting from a .purple[random process]. - As such, the data represent a .hi-pink[random sample] of a totally or conceptually accessible .hi-pink[population]. - Then, .purple[statistical inference] allows to infer the properties of a population based on the observed sample. This includes deriving estimates and testing hypotheses. ]  .tiny[Source: [luminousmen](luminousmen.com)] --- # Outline In this class, we plan to cover the following topics: - Introduction to .pink[statistical inference] (concepts of random variables, confidence interval and p-values). - Introduction to the .purple[statistical software R]. - .pink[Tests for comparing the mean of two groups] (descriptive analysis, t-test, rank-based methods). - .purple[Tests for comparing the mean of multiple groups] (descriptive analysis, ANOVA and non-parametric counterparts, discussion on multiple testing). - Introduction to .pink[regression] (correlation vs causality, descriptive analysis, linear regression and going beyond linear regression). - .purple[Pitfalls] for statistical analysis and remedies (p-hacking/HARKing and how to avoid it, replicability crisis). --- # Population and Sample - Example .smallest[To fix ideas we will consider a simple example. The 2020 United States presidential election was the 59th quadrennial presidential election, held on November 3, 2020. According to the latest estimates, .hi.pink[Biden's team received 51.3% of the votes while Trump's received 46.8%] .smallest[👋]. Naturally, the result of American elections is not determined by the popular vote but suppose that we were interested in collecting data .hi-purple[before the vote] to assess if Biden's team will receive more than 50% of the votes].  .tiny[Source: Adapted from [fivethirtyeight.](https://projects.fivethirtyeight.com/trump-biden-election-map/)] .footnote[.smallest[👋 More details on the results can be found [here](https://en.wikipedia.org/wiki/2020_United_States_presidential_election).]] --- # Population and Sample - Example .pull-left[.smaller[In this example, we will make the .purple[following assumptions for simplicity]: - The American population of voters is composed of 1200 individuals (616 for Biden, 561 for Trump and 23 independents). - We can perfectly sample the population (everyone is available, no double sampling, and the sampling is random). - People don't change their mind and they don't lie. ]] .pull-right[  ] --- # Proportion Estimation Suppose we ask `\(n\)` voters for which candidate (Biden, Trump or independent) they intend to vote in order to estimate the proportion of voters for Biden. For that we define the .hi.purple[random variables] `\(X_1, ..., X_n\)` where `\(X_i\)` is defined as: .saller[ .center[ `\( X_i = \left\{ \begin{array}{ll} 1 & \mbox{if voter } i \mbox{ intends to vote for Biden}\\ 0 & \mbox{otherwise.} \end{array} \right. \)` ] ] The random variables `\(X_1, ..., X_n\)` are called a (random) .hi.purple[sample] and we refer to `\(n\)` as the .hi.purple[sample size]. Let `\(p\)` denote the (true) proportion of voters for Biden (which in this case is 51.3%), we then write $$ `\begin{align} \color{#e64173}{\Pr \Big(} {X_i = 1} \color{#e64173}{\Big)} = p, \end{align}` $$ where `\(\color{#e64173}{\Pr (} A \color{#e64173}{)}\)` denotes the .pink[probability] of the .purple[event] `\(A\)`. --- # Proportion Estimation Using the random variables `\(X_1, ..., X_n\)` we can define an .hi.purple[estimator] of `\(p\)`, which we often write as `\(\hat{p}\)` and is given by `$$\hat{p} = \frac{1}{n} \sum_{i=1}^n X_i = \frac{\color{#e64173}{m}}{\color{#6A5ACD}{n}},$$` where `\(\color{#e64173}{m}\)` denotes the number of voters in our sample in favor of Biden, and `\(\color{#6A5ACD}{n}\)` is the sample size (as described previously). An estimator is defined as a function of the data (i.e. `\(X_1, ..., X_n\)`), and therefore, theoretically any function of `\(X_1, ..., X_n\)` can be an estimator. However, in this case `\(\hat{p}\)` is the best possible estimator of `\(p\)` .smallest[👋] and therefore it is not useful (in this case) to search for better estimators. .footnote[.smallest[👋] More precisely, this estimator is unbiased [(more info.)](https://en.wikipedia.org/wiki/Bias_of_an_estimator) and has the smallest possible variance [(more info.)](https://en.wikipedia.org/wiki/Variance) as it attains the Cramér–Rao bound [(more info.)](https://en.wikipedia.org/wiki/Cram%C3%A9r%E2%80%93Rao_bound).] --- # Population and Sample - Example .pull-left[ .smallest[ Consider a sample of `\(n = 10\)` voters (randomly chosen from the population) as shown here 👉. In this case, we have 5 voters for Biden and 5 for Trump. We say that `$$x_1 = 1, ..., x_5 = 1, x_6 = 0, ..., x_{10} = 0$$` are .hi.purple[realizations] of the random variables `\(X_1, ..., X_{10}\)`. We can now compute our estimator on the observed data (i.e. the realizations) and we obtain `\(\hat{p} = 0.5\)`. Therefore, .pink[our best guess] based on the available data is that 50% of the voters will vote for Biden. Unfortunately, this doesn't really help us. So let's try with a bigger sample size... say `\(n = 40\)`. ]] .pull-right[  ] --- # Population and Sample - Example  --- # Population and Sample - Example .center[![](GIF/sample.gif)] --- # Population and Sample - Example  --- # Population and Sample - Example  --- # Population and Sample - Example - In our example, we are interested in knowing if `\(p\)`, a .pink[population-level quantity], is bigger or smaller than 50%. Unfortunately, `\(p\)` is generally unknown as we cannot access the whole population (otherwise let's not bother with Statistics! 🤪). Therefore, we use `\(\hat{p}\)` instead, a .purple[sample-dependent quantity]. - However, as we can see, `\(\hat{p}\)` is .turquoise[random] in the sense that it can change depending on the collected sample (e.g. we get different answers when `\(n = 200\)`, where Trump is leading, and when `\(n = 600\)`, where Biden is leading). - To address this issue, we need to assess the .hi.pink[uncertainty] of `\(\hat{p}\)` (i.e. assess how different `\(\hat{p}\)` and `\(p\)` can be). - Statistics can provides us many tools allowing to determine uncertainty as well as the associated .pink[decision-making risks]. --- # How to measure uncertainty? Uncertainty can be measured in many different ways. A common approach (in statistics) is to use .hi-purple[confidence intervals], which rely on the .hi.pink[Central Limit Theorem (CLT)] that states: .center[.turquoise["The sampling distribution of the sample mean approaches to a normal distribution as the sample size gets larger."]] Loosely speaking, we can translate the CLT as `$$\bar{X} = \frac{1}{n} \sum_{i = 1}^n X_i \color{#e64173}{\overset{\cdot}{\sim}} \color{#6A5ACD}{\mathcal{N}(\mu, \sigma^2)},$$` where `\(\color{#6A5ACD}{\mathcal{N}(\mu, \sigma^2)}\)` denotes a normal distribution with mean `\(\mu\)` and variance `\(\sigma^2\)` (typically computed using the data).smallest[👋]. Here `\(\bar{X}\)` denotes the sample mean and `\(\color{#e64173}{\overset{\cdot}{\sim}}\)` represents ".pink[approximately distributed as]". .footnote[.smallest[👋] Check out [expected value](https://en.wikipedia.org/wiki/Expected_value) and [variance](https://en.wikipedia.org/wiki/Variance).] --- # How to measure uncertainty? In our example, we have `$$\hat{p} \overset{\cdot}{\sim} \mathcal{N}\Bigg(p, \frac{p(1-p)}{n}\Bigg).$$` .pink[How to understand the practical implications of the CLT?] Informally, it means that when a measurement can be thought of as the sum (or the average) of .hi.purple[numerous] factors, its distribution tends to go to a normal distribution. For example, the height of adults can be thought of as the sum of their genetic information, diet, life style, ... --- # Distribution of heights  --- # Central Limit Theorem - Example  --- # Central Limit Theorem - Example  --- # Central Limit Theorem - Example  --- # Central Limit Theorem - Example .center[![](GIF/sample_clt.gif)] --- # Central Limit Theorem - Example  --- # Central Limit Theorem - Example .center[![](GIF/sample_clt2.gif)] --- # Central Limit Theorem - Example  --- # Central Limit Theorem - Example People have used .pink[the Galton Board] as a practical device to demonstrate the CLT, in particular that with sufficient sample size the binomial distribution approximates a normal distribution.

 --- # Confidence Intervals Now that we learn about CLT, how can we use it to construct .hi-purple[confidence intervals]? - .smaller[Confidence intervals correspond to .pink[a range of values that are likely to include the population value with a certain level of confidence]. The level of confidence is a probability expressed as a percentage (%).] - .smaller[In our example, we are interested in the true (population) proportion of voters in favor of Biden (51.3%). Using our sample of] `\(\small n=200\)` .smaller[, we had] `\(\small m =95\)` .smaller[and we can construct the following 95% confidence interval]: .pull-left[  ] .smaller[.pull-right[ So what does it mean? 🤔 It means that with a .hi-purple[probability of 95%], the true proportion of voters for Biden (51.3% in this case) is between 40.58% and 54.42%.] ] --- # Confidence Intervals How is this confidence interval computed? We recall that by CLT, `$$\hat{p} \overset{\cdot}{\sim} \mathcal{N}\Bigg(p, \frac{p(1-p)}{n}\Bigg).$$` However, as we .hi.purple[do not know] the true value of `\(p\)` in practice, we replace it with the .hi.purple[best guess] we have, which is `\(\hat{p}\)`. We then can write `$$\hat{p} \overset{\cdot}{\sim} \mathcal{N}\Bigg(\color{#e64173}{\hat{p}}, \color{#6A5ACD}{\frac{\hat{p}(1-\hat{p})}{n}}\Bigg).$$` The `\(1-\alpha\)` confidence interval for `\(p\)` is then given by `$$\color{#e64173}{\hat{p}} \pm Z_{1-\alpha/2}\color{#6A5ACD}{\sqrt{\frac{\hat{p}(1-\hat{p})}{n}}}.$$` But what is `\(Z_{1-\alpha/2}\)`? 🤓 --- # Confidence Intervals `\(Z_{1-\alpha/2}\)` corresponds to the `\(1-\alpha/2\)` quantile of a standard normal distribution `\(\mathcal{N}(0,1)\)`, that is, .center[ `\(Z_{1-\alpha/2}\)` is such that `\(\Pr(Z \leq Z_{1-\alpha/2}) = 1-\alpha/2\)` where `\(Z \sim \mathcal{N}(0,1)\)`. ] .pull-left[  ] .pull-right[ - For 90% confidence interval, `\(Z_{0.95} \approx 1.64\)`. - .pink[For 95% confidence interval,] `\(\color{#e64173}{Z_{0.975} \approx 1.96}\)`.pink[.] - For 99% confidence interval, `\(Z_{0.995} \approx 2.58\)`. ] --- # Confidence Intervals Therefore, a confidence interval corresponds to a range of values that contains the true unknown population-level quantity we are considering with a probability of approximately `\(1-\alpha\)` (typically 95%). Basically, we have `$$\Pr \left(p \in \color{#e64173}{\hat{p}} \pm Z_{1-\alpha/2}\color{#6A5ACD}{\sqrt{\frac{\hat{p}(1-\hat{p})}{n}}} \right) \approx 1-\alpha.$$` ⚠ This means that a fraction of `\(100\times\alpha\)`% of confidence intervals .hi.pink[don't include] `\(p\)`. --- # Confidence Intervals .center[![](GIF/sample_clt3.gif)] --- # Confidence Intervals  --- # How to compute confidence intervals .panelset[ .panel[.panel-name[Example] .smallest[Previously, we said that in a sample of] `\(\small n=200\)`.smallest[, we had] `\(\small m = 95\)`.smallest[. In this case, we mentioned that (40.58%, 54.42%) was a 95% confidence interval. But how are these numbers computed? We have] $$ \small \color{#e64173}{\hat{p}} = \frac{m}{n} = \frac{95}{200} = \color{#e64173}{47.5\%}.$$ .smallest[Then, we have] `$$\small\color{#6A5ACD}{\sqrt{\frac{\hat{p}(1-\hat{p})}{n}}} \approx \color{#6A5ACD}{0.0353}.$$` .smallest[To obtain a 95% confidence interval we used] `\(\small Z_{1-\alpha/2} = Z_{0.975} \approx 1.96\)` .smallest[ and we get] `$$\small \color{#e64173}{\hat{p}} \pm Z_{1-\alpha/2}\color{#6A5ACD}{\sqrt{\frac{\hat{p}(1-\hat{p})}{n}}} \approx \color{#e64173}{\frac{51.728}{100}} \pm 1.96 \times \color{#6A5ACD}{0.0353} = (40.58\%, 54.42\%).$$` ] .panel[.panel-name[`R` Code - 💪] ```r n = 200 # Sample size m = 95 # Number of boys alpha = 0.05 # Confidence level (1-alpha) p_hat = m/n # Estimated proportion # Compute CI Z = qnorm(1 - alpha/2) delta = sqrt(p_hat*(1 - p_hat)/n) CI = p_hat + c(-1, +1)*Z*delta print(paste("The estimated proportion is ", round(100*p_hat, 2), "%", sep = "")) ``` ``` #> [1] "The estimated proportion is 47.5%" ``` ```r print(paste("A ", (1 - alpha)*100, "% confidence interval is given by (", round(100*CI[1], 2), "%, ", round(100*CI[2], 2), "%)", sep = "")) ``` ``` #> [1] "A 95% confidence interval is given by (40.58%, 54.42%)" ``` ] ] --- # Confidence Interval with `\(\alpha = 20\%\)`  --- # Confidence Interval with `\(\alpha = 20\%\)`  --- # Confidence Interval with `\(\alpha = 20\%\)`  --- # Confidence Intervals with `\(\alpha = 20\%\)` .center[![](GIF/sample_ci.gif)] --- # Why is 20%? Why not 5%? .center[![](GIF/sample_ci5.gif)] --- # What if I want to be certain? 😳 .center[![](GIF/sample_ci0.gif)] --- # ⚠ Take home message - Since the data is available through sampling, it is .hi-purple[random]. .pink[Therefore, a decision or prediction can never be made with certainty!] - The only certainty one can have is that, for example, a proportion will always be included in the interval from 0% to 100%. .hi-purple[However, this is neither informative nor useful] and it does not even depend on the data. - There exists a trade-off between .hi-pink[risk] as measured by `\(1-\alpha\)` (typically 95%) the confidence level, and the .hi-pink[precision of the conclusion] as measured, for example, by the confidence interval length. - Moreover, the larger the sample size, the more precise the conclusion, for the same confidence level. - Therefore, .purple[every decision based on statistical methods has a risk and how much risk is acceptable depends on the context] (e.g. safety in airplanes vs which soft drink tastes better). --- # Example: Are there more 👦 than 👧? .panelset[ .panel[.panel-name[Problem] .smallest[An American found 13,173 boys were born among 25,468 newborn children. Is this sample an evidence that the birth of boys may be more common than the birth of girls in the entire population?] .smallest[So, we have] `\(\small n = 25,468\)` .smallest[and] `\(\small m = 13,173\)`. .smallest[Therefore, we have] $$ \small \color{#e64173}{\hat{p}} = \frac{m}{n} = \frac{13173}{25468} \approx \color{#e64173}{51.728\%}.$$ .smallest[Then, we have] `$$\small\color{#6A5ACD}{\sqrt{\frac{\hat{p}(1-\hat{p})}{n}}} \approx \color{#6A5ACD}{0.003131}.$$` .smallest[To obtain a 95% confidence interval we used] `\(\small Z_{1-\alpha/2} = Z_{0.975} \approx 1.96\)` .smallest[ and we get] `$$\small \color{#e64173}{\hat{p}} \pm Z_{1-\alpha/2}\color{#6A5ACD}{\sqrt{\frac{\hat{p}(1-\hat{p})}{n}}} \approx \color{#e64173}{\frac{51.728}{100}} \pm 1.96 \times \color{#6A5ACD}{0.003131} = (51.11\%, 52.34\%).$$` ] .panel[.panel-name[`R` Code - 💪] ```r n = 25468 # Sample size m = 13173 # Number of boys alpha = 0.05 # Confidence level (1-alpha) p_hat = m/n # Estimated proportion # Compute CI Z = qnorm(1 - alpha/2) delta = sqrt(p_hat*(1 - p_hat)/n) CI = p_hat + c(-1, +1)*Z*delta print(paste("The estimated proportion is ", round(100*p_hat, 2), "%", sep = "")) ``` ``` #> [1] "The estimated proportion is 51.72%" ``` ```r print(paste("A ", (1 - alpha)*100, "% confidence interval is given by (", round(100*CI[1], 2), "%, ", round(100*CI[2], 2), "%)", sep = "")) ``` ``` #> [1] "A 95% confidence interval is given by (51.11%, 52.34%)" ``` ] .panel[.panel-name[`R` Code - 🤖] ```r n = 25468 # Sample size m = 13173 # Number of boys prop.test(x = m, n = n) ``` ``` #> #> 1-sample proportions test with continuity correction #> #> data: m out of n, null probability 0.5 #> X-squared = 30.2, df = 1, p-value = 3.897e-08 #> alternative hypothesis: true p is not equal to 0.5 #> 95 percent confidence interval: #> 0.5110785 0.5233910 #> sample estimates: #> p #> 0.5172373 ``` ] ]


Government is on its last legs, says BNP's Mirza Fakhrul
by bdnews24.comBNP leader Mirza Fakhrul Islam Alamgir believes the end is near for the ruling Awami League government.
“The farewell bell for the government has begun to toll. The other day Khanadaker Mosharraf Hossain said that she (Sheikh Hasina) inaugurated a cricket match by ringing a bell; now the farewell bell for the government is being heard everywhere,” he said in a discussion meeting at the National Press Club on Friday.
According to the BNP secretary general, the government has brought about its own downfall through its failures.
“I urge the government to refrain from false propaganda. Stop talking about the agricultural sector, or the economic growth. Today, onion and salt prices are escalating and you failed to contain it.”
Marginal farmers are being deprived of a fair price for their produce, said Fakhrul.
“I came back from Thakurgaon the other day and a maund of paddy is being sold for Tk 550 which takes Tk 650-750 for a farmer to produce. The sufferings of the farmers are unbelievable.”
Under the circumstances, the BNP leader urged the people unite in order to alleviate the situation.
“The government which is acting like a monster and destroying all of our achievements must be forced to leave.”
“Many people have tried to break the BNP since its inception; Ershad tried it too but failed. They (Awami League) have been trying to do it for the last 10 years but couldn’t move a single person out of it. They won’t ever able to do that,” said Fakhrul in reply to Awami League Secretary General Obaidul Quader’s remark that the BNP will fall apart.
Fakhrul highlighted Ziaur Rahman’s contributions to the development of the agricultural sector along with the different initiatives taken by the BNP government led by Khaleda Zia and the vision of Acting BNP Chairperson Tarique Rahman.
BNP ‘SATISFIED’ WITH HOLEY ARTISAN VERDICT
Fakhrul also expressed satisfaction with the verdict of the special anti-terrorism tribunal which sentenced seven militants to death for their roles in the gruesome terror attack on Dhaka's Holey Artisan Bakery in 2016.
“We’re satisfied with the Holey Artisan verdict,” said Mirza Fakhrul Islam Alamgir. “We hope to prevent such incidents in future.”
The BNP secretary general cited a different reason behind the emergence of terrorism in the country.
“The problem lies in some other part.  They have pushed the state to such a point that it can’t be called a democratic state anymore. The conspiracy to turn Bangladesh into a failed state is almost ready,” he said criticising the government.


Do you like Hoodie and now want to tell other people about it? Wow, that’s amazing to hear! We know that preparing talks takes a lot of time. To support you and your efforts, we have prepared some talks about Hoodie for you (including presenter notes.) Just click on the format of your choice, download them, adjust them to your needs, and help us spread the word! Our currently available talks are (zip archive including fonts):
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Description
The Off-Score project is a music album that you can interact with. Off-Score Project consists of 5 tracks. Each track comes with a song and a game. On each track, we experiment with different ways and game mechanics to invite the player to listen to the lyrics of the song. The interactive tracks will lead you through different stories, mood and topics, from a piece of paper inside a copy machine which represent the society, to an elephants hunter that struggles with regrettable memories. "Pay attention to the music in order to advance in the game; play through the game in order to understand the story behind the song." This is the vision of the Off-Score Project. We hope to provide a new way of storytelling with games and music together.
History
Singer-songwriter YenTing Lo noticed that most music listeners don't listen to the lyrics of a song. When she was planning to record her own music EP in 2017, this problem came to her mind because the lyrics are the main soul of her songs. Having worked and discussed with Ferran, a game developer who's been interested in new ways of combining music and games, they agreed that the interactivity of games could provide listeners with an experience that highlights the lyrics and enhances the storytelling of the songs. After approaching Ferran's co-worker Vanja for the visual art, the Off-Score Project began its journey.
The name Off-Score comes from our vision to take people out of the "game score" and "music score".
Features
- Interactive Music Album
- 5 tracks with unique gameplay, story and mood
- Original jazz fusion songs with allegorical lyrics
- Aim to make listeners engage more with the music
Tracks
- Copy Machine
- Elephants Remember
- Late-coming puberty
- Relativity
- Copy Machine
Videos
Copy Machine Release Trailer — YouTube
Elephants Remember Release Trailer — YouTube
Application for Stugan 2018 — YouTube
Images
Logo & Icon
Team & Repeating Collaborator
- Ferran Bertomeu Castells
- Programming, Design
- YenTing Lo
- Music, Design
- Vanja Mrgan
- Visual Art, Design
Contact
- Inquiries
-<EMAIL_ADDRESS>- twitter.com/OffScoreProject
- Skype
- fonserbc
- Web
- offscore.itch.io
- Presskit
- fonserbc.github.io/offscore


‘The Walking Dead’ will reportedly end after Season 12
The 10th season of the AMC show is currently airing
The Walking Dead may be set to finish after season 12, new reports state.
The show’s 10th season is currently airing on AMC.
The first episode of season 10, which aired at the start of October, saw The Walking Dead drop to its all-time ratings low.
Now, We Got This Covered have revealed that sources say the 12th season of the show may be its last, with AMC instead focusing on the show’s spin-off series’.
The first Walking Dead spin-off, Fear The Walking Dead, first aired in 2015 and shared its fifth season earlier this year, while a new spin-off is also in the works.
Ryan Hurst as Beta and Samantha Morton as Alpha in ‘The Walking Dead’
The report reveals that “the current thinking at AMC is to do two more seasons of The Walking Dead before bringing things to a close and focusing on the spinoffs”.
The latest episode of season 10 of The Walking Dead, episode 7, aired on Sunday night (November 18), and saw the Whisperer mole finally unmasked, confirming a Walking Dead fan theory in the process.
Another recent easter egg from the show linked Beta and Beth, with a song called ‘The Turtle and the Monkey’, which is written and sung by former Walking Dead star Emily Kinney, who played Beth Greene on the show, playing during a recent episode.


A <tc:sheet/> can contain a <tc:row/> tag with a containing <tc:event/> tag. The event type can be set by the event attribute. Possible values are 'click' and 'dblclick'.
<tc:sheet/>
<tc:row/>
<tc:event/>
event
Select an object from the SolarObjects list.


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All items
Bookmark edit
Tidy or "lint" the data
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I have been contemplating how to best start my OEM project as I was recently setback by one of my Nanodes having its radio quit working (I was going to do a NanodeRF+NanodeTXshield. I was going to take advantage of the problem and upgrade the emonTX part to an Arduino Due for the better ADC and more inputs.
Has anyone done any work combining the emonTX and emonbase packages? I know icboredman did some work on getting emonlib ready for use with the Due. But are there any other shoulders I can stand on to get this working? Or warnings on potential problems?
Emon All-In-One
Submitted by officeboy on Tue, 24/03/2015 - 22:07I have been contemplating how to best start my OEM project as I was recently setback by one of my Nanodes having its radio quit working (I was going to do a NanodeRF+NanodeTXshield. I was going to take advantage of the problem and upgrade the emonTX part to an Arduino Due for the better ADC and more inputs.
Then I found the spark core (well photon actually, $19!) ARM core, 8x 12-bit analog inputs, and built in WiFi.
Has anyone done any work combining the emonTX and emonbase packages? I know icboredman did some work on getting emonlib ready for use with the Due. But are there any other shoulders I can stand on to get this working? Or warnings on potential problems?


"SAFFRON" Hebrews karkom, Arab. zafran (i.e., “yellow”), mentioned only in Cant. 4:13, 14; the Crocus sativus. Many species of the crocus are found in Palestine. The pistils and stigmata, from the centre of its flowers, are pressed into “saffron cakes,” common in the East. “We found,” says Tristram, “saffron a very useful condiment in travelling cookery, a very small pinch of it giving not only a rich yellow colour but an agreable flavour to a dish of rice or to an insipid stew.”
"SAFFRON" Hebrews karkom, Arab. zafran (i.e., “yellow”), mentioned only in Cant. 4:13, 14; the Crocus sativus. Many species of the crocus are found in Palestine. The pistils and stigmata, from the centre of its flowers, are pressed into “saffron cakes,” common in the East. “We found,” says Tristram, “saffron a very useful condiment in travelling cookery, a very small pinch of it giving not only a rich yellow colour but an agreable flavour to a dish of rice or to an insipid stew.”


change selection trait climate studies study [], phenological responses adaptive model phenotypic data dataset climatic mean population annual fitness species traits grid.. response temperature WMSD linear time variable values PRCS assess University models used (Fig. changes morphological fitted Fig. using variance variation PRC differentials phenology year (Supplementary Data Department effects http://orcid.org/--- mixed-effects random conditions {border-style (i.e. assessed critical Supplementary df = , effect px;} = ., #ffffff;} actual climate-driven global reported studies, warming abundance associated condition species, lag slope zero Nature environmental observed populations taxa temporal Climate Research analyses negative publication quantitative time, Chang. Nat. Source \documentclass[pt]{minimal} \setlength{\oddsidemargin}{-pt} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amsmath} \usepackage{amssymb} \usepackage{mathrsfs} \usepackage{upgreek} \usepackage{wasysym} body datasets example, following heterogeneity identity phenotype years birds did estimates included maladaptive significant temperatures variables Gaussian Science \varepsilon bird calculated conducted dataset, different proportion rate slopes test traits, traits. Biology, Cornell Ecol. LINK Lab Sciences, Trait Year considered direction including information majority package product recent shifts size study, (e.g. Clim Institute Library Ornithology average extinction literature measured meta-analysis natural optimum provided publications px; results search shown years, {font-family (Methods). ./nature ./nclimate Biol. Clim. Evolution Lynch University, Zoology, \times account advanced available covariate, directional distribution estimated extracted font-size framework genetic growth indicate meta-analyses persistence range residual risk showing sign strength type weighted ./j.-...x Biological Biology Bürger Change Council Ecology Evol. Glob. Sel analysis animal assessing breeding change, demonstrate differed distributions double; events evidence expected findings fixed focused higher lags line measure necessary performed pictures qualitative selection, species. subset taken tested (LRT ,$$\end{document} Am. Arnold Ecology, Evolutionary However, Note Parmesan \alpha \beta adaptive, advancement advancing affected and, assembled autocorrelation based biased border-color border-width changed constant contained defining differ difference earlier evolutionary explanatory extent factor file fixed-effect grid..f independent lack means model, obtained occurred original proportional publications, published red refitted reflecting reproduction research sensitive solid; species’ standardized study. suggests taxon testing that, variable, wild {font-style {font-weight (ML), (which ./s--- Adaptive Cambridge Charmantier Commons Creative Environmental European Figs. First, LRT: Macaulay Merilä National School Ser. Soc. Thomas according acting adaptation affects approach benefits between-study binomial biodiversity bold category change. changing climate, climate. components courier; data. dataset. depended depends detailed determine differential, directly em; estimate events, factors function general generally identified increase increased indicates infer influencing intercept. larger length lines lower mainly mammals meta-analytic names phenology, potential predicted probability provide pt;} rates ref. reflect reflects regression respective responses. review shared showed single sizes step, structure systematic t). tendency timing total trend trends value version viability vs. was: {\mathrm{Year}}_t −. ± . SD


{% load static %}
"We are dedicated to finding to the latest vulnerbilities for the home user"
SOFTWARE DEVELOPER
As a software developer with a background in operations management, I want to combine my experience as COO of two startups with my full-stack Python skill-set to make businesses more efficient and reach a greater profit potential.
SOFTWARE DEVELOPER
As a software developer with a background in operations management, I want to combine my experience as COO of two startups with my full-stack Python skill-set to make businesses more efficient and reach a greater profit potential.
SOFTWARE DEVELOPER
Spent my time in high school coding as a hobby and then attended classes to get an advanced education in Python
SOFTWARE DEVELOPER
Spent my time in high school coding as a hobby and then attended classes to get an advanced education in Python


-
ISIS vs. the Kurds
www.newyorker.com
The U.S. wants the Kurds to do two potentially incompatible things: fight ISIS and resist seceding from Iraq. Dexter Filkins reports.
-
Should We All Take a Bit of Lithium? - NYTimes.com
www.nytimes.com
THE idea of putting a mind-altering drug in the drinking water is the stuff of sci-fi, terrorist plots and totalitarian governments. Considering the ...
-
Smart Phones for Smart Kids
www.kpcb.com:80
In the coming weeks, 55 million U.S. students in grades K-12 will go back to school. In their backpacks they will carry pens, No. 2 pencils, 3-ring ...
-
I turned my apartment into a smart home... and all I got was bored
pando.com
After a week of dabbling with making my home smarter, I came to the conclusion that maybe my home was smart enough already... you know, with two ...
-
Salvadoran kids love their TOMS—but they already have shoes
qz.com
"In 2006, American traveler Blake Mycoskie befriended children in a village in Argentina and found they had no shoes to protect their feet." That's ...
-
The War Nerd: Bombs away in the Middle East! But why is Israel so quiet?
pando.com
Where’s Israel? That’s one of the most interesting, and widely ignored, questions to come out of the US bombing campaign against Islamic State (IS) ...
-
National news from McClatchy DC News | Washington DC
www.mcclatchydc.com
Read news from around the country provided by McClatchyDC.com in Washington DC.
-
Why We Sit Back and Let Apps Do Our Chores - NYTimes.com
www.nytimes.com
Near the top of the list of tiresome tasks that the Internet has yet to solve is this one: trekking to the post office.
-
Fashion as a Inescapable Institution
www.psmag.com
Like it or not, fashion is an institution because we can no longer feasibly make our own clothes.
-
Jean Baudrillard - Simulacra and Simulations - I. The Precession of ...
www.egs.edu
The simulacrum is never what hides the truth - it is truth that hides the fact that there is none. The simulacrum is true. -Ecclesiastes
-
Neurocomic: A Graphic Novel About How the Brain Works
www.brainpickings.org
From the caves of memory to the castles of deception, by way of naughty neurotransmitters and giddy ganglia. Scientists are only just beg
-
Syria's civil war created 2.5 million refugees last year — the US let in 36
www.vox.com
Neighboring countries are struggling to house them in nightmarish refugee camps
-
U.S. Outgunned by Extremists on New YouTube, Twitter Battlefield - ...
www.bloomberg.com
U.S. Outgunned by Extremists on New YouTube, Twitter Battlefield
-
The Man on a Quest to Open-Source Cancer Research | WIRED
www.wired.com
Isaac Yonemoto is a chemist, but he's been writing software code since he was a kid. He calls himself a "semi-recreational" programmer, and now, he's ...
-
Explore – Treat your soul to Mary Oliver’s exquisite reading...
explore.noodle.com
Treat your soul to Mary Oliver’s exquisite reading of “Wild Geese”
-
Human Contact Patterns in High Schools Hint at Epidemic Containment ...
www.technologyreview.com
The first study of contact patterns between high school students suggests new ways to halt the spread of disease.
-
Here’s what Stanford MBAs think about the tech bubble
qz.com
Rumblings of the next tech bubble have been omnipresent throughout Silicon Valley and the media. So we surveyed approximately 30 MBA students at one ...
-
Selling Prozac as the Life-Enhancing Cure for Mental Woes - NYTimes.com
www.nytimes.com
Retro Report
-
Facebook prepares to bolster its stalking abilities with new ad tools
pando.com
Facebook wants to stalk you around the Web, and soon you won't be able to throw it off your digital trail even if you switch between your smartphone, ...
-
How to Beat Cancer with Yogurt Bacteria [feature] - OpenMind
www.bbvaopenmind.com
Beating cancer is one of the primary goals of medicine. What are the most advanced and most likely strategies? How to beat cancer
-
We are more rational than those who nudge us – Steven Poole – Aeon
aeon.co
We are told that we are an irrational tangle of biases, to be nudged any which way. Does this claim stand to reason?
-
Naomi Klein On ‘Capitalism Vs. The Climate’
onpoint.wbur.org
Big climate protests in New York before a big UN summit. Activist and author Naomi Klein says change the economy or die. She's with us.
-
The NFL, the Military, and Problem With Masculine Institutions
www.psmag.com
Both the NFL and the U.S. military cultivate and reward a form of hyper-violent masculinity. The consequences of doing so have never been more ...
-
Top-level turnover makes it harder for DHS to stay on top of evolving ...
www.washingtonpost.com
High turnover among Homeland Security leaders is a result of what employees describe as a dysfunctional work environment, abysmal morale and the lure ...
-
Islamic State recruitment soaring in wake of U.S. bombing - Middle ...
www.haaretz.com
Most of the group's foreign recruits cross into Syrian over the porous border from Antalya in Turkey.
-
Climate protesters to 'flood' Wall Street on Monday
money.cnn.com
As world leaders gather in New York for talks on climate change, hundreds of protesters are expected to target Wall Street Monday to agitate for the ...
-
AP Enterprise: al-Qaida's Syrian cell alarms US
bigstory.ap.org
WASHINGTON (AP) — While the Islamic State group is getting the most attention now, another band of extremists in Syria — a mix of hardened jihadis ...


- Easy to use control panel
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Basics of an Algo Order
An Algo order is a regular Order that has an AlgoOrderData section added in the Place Order or Change Order requests.Get the strategy guide here and the developer description at the Developer Portal.
Order tickets must warn before trading a complex product, provide a KID download and the cost breakdown. See the sample.
Algo-orders can be used for pre-market trading in US Stocks.
!! IMPORTANT !! Algo Orders only partly work on SIM (no executions), so Live testing is necessary!
The '?env=live' option to test on production uses a readonly app. So best is to use a token from your own live app.
POST body containing order parameters:
Response:
Click button to launch function.
JS code:
Click button to show code.


|
VirtualSCADA
1.0
VirtualSCADA Senior Design Project
|
Functions | |
| def | modbusRead (master) |
| def | modbusWrite (master, theNewValues) |
| def | guiInit (Values) |
| def | redrawGui (TheValues) |
| def | modeOff () |
| def | modeMan () |
| def | modeAuto () |
| def | controlPump () |
| def | controlSol () |
| def | pumpOff () |
| def | pumpOn () |
| def | solClose () |
| def | solOpen () |
| def | newValuesInit () |
| def | csvWrite (TheValues) |
| def | getNewValues () |
| def | infiniteLoop () |
Variables | |
| tuple | modbusMaster = modbus_tcp.TcpMaster() |
| tuple | TheValues = modbusRead(modbusMaster) |
| tuple | TheGuiObjects = guiInit(TheValues) |
| tuple | theNewValues = newValuesInit() |
| tuple | theOldValues = newValuesInit() |
| tuple | f = open('measurement.csv', 'w') |
| def pipelineGui.controlPump | ( | ) |
Definition at line 337 of file pipelineGui.py.
| def pipelineGui.controlSol | ( | ) |
Definition at line 341 of file pipelineGui.py.
| def pipelineGui.csvWrite | ( | TheValues | ) |
Definition at line 374 of file pipelineGui.py.
| def pipelineGui.getNewValues | ( | ) |
Definition at line 387 of file pipelineGui.py.
| def pipelineGui.guiInit | ( | Values | ) |
Definition at line 118 of file pipelineGui.py.
| def pipelineGui.infiniteLoop | ( | ) |
Definition at line 413 of file pipelineGui.py.
| def pipelineGui.modbusRead | ( | master | ) |
Definition at line 15 of file pipelineGui.py.
| def pipelineGui.modbusWrite | ( | master, | |
| theNewValues | |||
| ) |
Definition at line 75 of file pipelineGui.py.
| def pipelineGui.modeAuto | ( | ) |
Definition at line 333 of file pipelineGui.py.
| def pipelineGui.modeMan | ( | ) |
Definition at line 329 of file pipelineGui.py.
| def pipelineGui.modeOff | ( | ) |
Definition at line 325 of file pipelineGui.py.
| def pipelineGui.newValuesInit | ( | ) |
Definition at line 361 of file pipelineGui.py.
| def pipelineGui.pumpOff | ( | ) |
Definition at line 345 of file pipelineGui.py.
| def pipelineGui.pumpOn | ( | ) |
Definition at line 349 of file pipelineGui.py.
| def pipelineGui.redrawGui | ( | TheValues | ) |
Definition at line 223 of file pipelineGui.py.
| def pipelineGui.solClose | ( | ) |
Definition at line 353 of file pipelineGui.py.
| def pipelineGui.solOpen | ( | ) |
Definition at line 357 of file pipelineGui.py.
| tuple f = open('measurement.csv', 'w') |
Definition at line 439 of file pipelineGui.py.
| tuple modbusMaster = modbus_tcp.TcpMaster() |
Definition at line 427 of file pipelineGui.py.
Definition at line 436 of file pipelineGui.py.
| tuple theNewValues = newValuesInit() |
Definition at line 437 of file pipelineGui.py.
| tuple theOldValues = newValuesInit() |
Definition at line 438 of file pipelineGui.py.
| tuple TheValues = modbusRead(modbusMaster) |
Definition at line 435 of file pipelineGui.py.


:(
Your PC ran into a problem that it couldn't handle, and now it needs to restart.
You can search for the error online: HAL INITIALIZATION FAILED


Popcorn Maker makes it easy to enhance, remix and share web video. Use your web browser to combine video and audio with content from the rest of the web — from text, links and maps to pictures and live feeds.
Watch these three episodes that chronicle the Internet 2012 Bus Tour - a campaign to address issues related to the Open Internet during the US Presidential campaign.


When a creature takes damage from a single source equal to or greater than half its hit point maximum, it must succeed on a DC 15 Constitution saving throw or suffer a random effect determined by a roll on the System Shock Table. For example, a creature that has a hit point maximum of 30 must make that Constitution save if it takes 15 damage or more from a single source.
| d10 |
Effect |
| 1 |
The creature drops to 0 hit points. |
| 2-3 |
The creature drops to 0 hit points but is stable. |
| 4-5 |
The creature is stunned until the end of its next turn. |
| 6-7 |
The creature can't take reactions and has disadvantage on attack rolls and ability checks until the end of its next turn. |
| 8-10 |
The creature can't take reactions until the end of its next turn. |


| Interface | Description |
|---|---|
| DataEntry |
A markup interface used for marking classes to be POJO classes
|
| WorldData |
An interface that points out that certain classes are POJO classes
of World Data Bank giving global data, have a map for each pojo object
|
| Class | Description |
|---|---|
| AirportEntry |
AirportEntry
|
| EarthQuakeEntry |
EarthQuakeEntry is a POJO class that consumes all the data about a single Earthquake event
which is used by the rest of the program.
|
| LifeExpectancyEntry |
LifeExpectancyEntry is a POJO class that consumes all the data about a single Life Expectancy Survey for a country
which is used by the rest of the program.
|


Markdown basics:
# Header 1
## Header 2
### Header 3
#### Header 4
##### Header 5 (looks like header 4 in most browsers)
This is a paragraph, which is text surrounded by whitespace. Paragraphs
can be on one line (or many), and can drone on for hours.
### Links
Here is a Markdown link to [Apache CouchDB](http://couchdb.apache.org),
and a literal <http://link.com/>.
You can link to other pages in the wiki like
this: [another page in the wiki](another-page)
**note the wiki page id is just the thing in the parentheses**
Here's a link to [the wiki home page](index).
You can also create links with CamelCase or with [[double-bracket-links]]
### Emphasis
Now some inline markup like _italics_, **bold**, and `code()`.
### Images are easy
![picture alt](/_utils/image/logo.png "Title is optional")
> Blockquotes are like quoted text in email replies
>> And, they can be nested
Another paragraph, this one is longer: Lorem ipsum dolor sit amet, consectetur
adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna
aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi
ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in
voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint
occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim
id est laborum.
* Bullet lists are easy too
- Another one
+ Another one
### Horizontal rules
* * * *
****
--------------------------
And now some code:
// Code is just text indented a bit
which(is_easy) to_remember();
// it's indented by 4 spaces
Text with
two trailing spaces
(on the right)
can be used
for things like poems
_That's it, folks!_


| Name: ms. kavyashree | Reg No: 11-4SMMHC18 |
|---|---|
| Age / Sex:15/f | Contact No:9980035203 |
| Marital Status:single | Date:16/4/18 |
| Occupation:10th over. | Dr. PJFP |
Address: Native sathlurur, ulsoor gramahalli |
DIAGNOSIS- |
LMP: 4/11/17. IRREGULAR PERIODS. MENARCHE: 12 YEARS. SINCE THEN ONCE IN 5 MONTHS OR 6 MONTHS KEEPS GETTING PERIOD. MORE BLLEEDING BUT NO PAIN. ALWAYS SLEEPING . ANGRY IS SAYS ANYTHNG. HAIRFALL. WONT DO ANY WORK.
AT THE AGE OF 12 YEARS BEFOE MENARCHE USE TO GET EPISTAXIS, BLEEDIGN PER RECTUM DURING STOOLS AND SWELLING THROAT FR THIS CHECKED THYROID LEVELS SINCE THEN HYPOTHYROIDISM. ON 25MCG ELTROXIN.
GT ADMITTED IN HOSPITAL A WEEEK BACK. RECC. TONSILLITIS. FEVER.
HAS ONE ELDER SISTER 17 YEARS. …
PAST H/O:
NS
PERSONAL H/O:
LIKES OIL ITEMS MORE, CHICKEN, EGG. DOESN’T LIKE SWEET MUCH.
DRINKS WHILE EATING ONLY . LESS WATER. LIKES COLD DRINKS. LIKES JUICE.
SWEATS WHILE WORKING.
RX:
CALC.CARB 200 – 3 DOSES – 5 DAYS ONCE.
BELL 200 4-4-4-4 DISC.
BAPTISIA 200 – DISC 4-4-4-4.
28/4/18: hairfall slightly better no other change.
Rx:
Calc. carb 200 – 3 doses – 5 days once.
Rhus tox 30 – 2 dram pills 4-4-4
Baptisia 200 – 4-4-4-4.
9/6/18: lmp: 27/5/18. Bleeding not stopping. 3 pads a day. Clots. Smell. Painless. Giddy from yesterday. Obstinate if says to work as she is growing weight. Denies that there is no change. more sweat. Feels hairfall slightly better. Dry tongue.
Rx:
Bell 1m – 4 pills 3 hourly. Fr 3 days.
After 2 days:
Calc carb 1m – 1 dose
Rub 15 – night 1 dose b/food.
Pills 4-4-4-4. 2 dram


My name is Tricia Appleby. I am 35 years old, and married for 12 years. I have 2 boys, William and Connor. I have 2 cats and a newly acquired crawdad. Up until recently I was an assistant preschool teacher to 2 year old.


|
|
NHS maternity exemption certificate
Jane Doe This allows you to get free NHS prescriptions, dental exams and dental treatment until your child's first birthday. Warning You may have to show your health care professional this email as proof that you don't have to pay Further informationYou can find out about other reasons why you don't have to pay for glasses or contact lenses: https://www.nhs.uk/help-with-health-costs
Read our privacy policy, on how we use your infomation. |


| arrow.optics.typeclasses.At | |
| arrow.optics.typeclasses.Conj | |
| arrow.optics.typeclasses.Cons | |
| arrow.optics.typeclasses.Each | |
| arrow.core.Either (extensions in package arrow.optics.extensions.either.each) | |
| arrow.core.Either (extensions in package arrow.optics) | |
| arrow.core.Either (extensions in package arrow.optics.extensions) | |
| arrow.optics.extensions.EitherEach | Each instance for Either that has focus in each Either.Right. |
| arrow.optics.typeclasses.FilterIndex | |
| arrow.optics.Fold | A Fold is an optic that allows to focus into structure and get multiple results. |
| arrow.optics.FoldKindedJ | |
| arrow.optics.FoldOf | |
| arrow.optics.FoldPartialOf | |
| arrow.optics.ForFold | |
| arrow.optics.ForGetter | |
| arrow.optics.ForIso | |
| arrow.optics.ForLens | |
| arrow.optics.ForOptional | |
| arrow.optics.ForPIso | |
| arrow.optics.ForPLens | |
| arrow.optics.ForPOptional | |
| arrow.optics.ForPPrism | |
| arrow.optics.ForPrism | |
| arrow.optics.ForPSetter | |
| arrow.optics.ForPTraversal | |
| arrow.optics.ForSetter | |
| arrow.optics.ForTraversal | |
| arrow.optics.Getter | A Getter is an optic that allows to see into a structure and getting a focus. |
| arrow.optics.GetterKindedJ | |
| arrow.optics.GetterOf | |
| arrow.optics.GetterPartialOf | |
| arrow.core.Id (extensions in package arrow.optics) | |
| arrow.optics.typeclasses.Index | |
| arrow.optics.Iso | Iso is a type alias for PIso which fixes the type arguments and restricts the PIso to monomorphic updates. |
| arrow.optics.IsoKindedJ | |
| arrow.optics.IsoOf | |
| arrow.optics.IsoPartialOf | |
| arrow.Kind (extensions in package arrow.optics) | |
| arrow.optics.Lens | Lens is a type alias for PLens which fixes the type arguments and restricts the PLens to monomorphic updates. |
| arrow.optics.LensKindedJ | |
| arrow.optics.LensOf | |
| arrow.optics.LensPartialOf | |
| kotlin.collections.List (extensions in package arrow.optics.extensions.list.cons) | |
| arrow.optics.extensions.list.cons.List | |
| arrow.optics.extensions.list.each.List | |
| arrow.optics.extensions.list.filterIndex.List | |
| arrow.optics.extensions.list.index.List | |
| kotlin.collections.List (extensions in package arrow.optics.extensions.list.snoc) | |
| arrow.optics.extensions.list.snoc.List | |
| arrow.optics.extensions.ListCons | Cons instance definition for List. |
| arrow.optics.extensions.ListEach | Each instance definition for List that summons a Traversal to focus in each A of the source List. |
| arrow.core.ListExtensions (extensions in package arrow.optics) | |
| arrow.core.ListExtensions (extensions in package arrow.optics.extensions) | |
| arrow.optics.extensions.ListFilterIndex | FilterIndex instance definition for List. |
| arrow.optics.extensions.ListIndex | Index instance definition for List. |
| arrow.data.ListK (extensions in package arrow.optics.extensions.listk.cons) | |
| arrow.data.ListK (extensions in package arrow.optics.extensions.listk.each) | |
| arrow.data.ListK (extensions in package arrow.optics.extensions.listk.filterIndex) | |
| arrow.data.ListK (extensions in package arrow.optics.extensions.listk.index) | |
| arrow.data.ListK (extensions in package arrow.optics.extensions.listk.snoc) | |
| arrow.data.ListK (extensions in package arrow.optics) | |
| arrow.data.ListK (extensions in package arrow.optics.extensions) | |
| arrow.optics.extensions.ListKCons | Cons instance definition for ListK. |
| arrow.optics.extensions.ListKEach | Each instance definition for ListK. |
| arrow.optics.extensions.ListKFilterIndex | FilterIndex instance definition for ListK. |
| arrow.optics.extensions.ListKIndex | Index instance definition for ListK. |
| arrow.optics.extensions.ListKSnoc | Snoc instance definition for ListK. |
| arrow.optics.extensions.ListSnoc | Snoc instance definition for List. |
| arrow.optics.extensions.ListTraversal | Traversal for List that focuses in each A of the source List. |
| arrow.optics.extensions.MapAt | At instance definition for Map. |
| arrow.optics.extensions.MapEach | Each instance definition for Map. |
| arrow.optics.extensions.MapFilterIndex | FilterIndex instance definition for Map. |
| arrow.optics.extensions.MapIndex | Index instance definition for Map. |
| arrow.core.MapInstances (extensions in package arrow.optics.extensions) | |
| arrow.data.MapK (extensions in package arrow.optics.extensions.mapk.at) | |
| arrow.data.MapK (extensions in package arrow.optics.extensions.mapk.each) | |
| arrow.data.MapK (extensions in package arrow.optics.extensions.mapk.filterIndex) | |
| arrow.data.MapK (extensions in package arrow.optics.extensions.mapk.index) | |
| arrow.data.MapK (extensions in package arrow.optics) | |
| arrow.data.MapK (extensions in package arrow.optics.extensions) | |
| arrow.optics.extensions.MapKAt | At instance definition for MapK. |
| arrow.optics.extensions.MapKEach | Each instance definition for Map. |
| arrow.optics.extensions.MapKFilterIndex | FilterIndex instance definition for Map. |
| arrow.optics.extensions.MapKIndex | Index instance definition for Map. |
| arrow.optics.extensions.MapTraversal | Traversal for Map that focuses in each V of the source Map. |
| arrow.data.NonEmptyList (extensions in package arrow.optics.extensions.nonemptylist.each) | |
| arrow.data.NonEmptyList (extensions in package arrow.optics.extensions.nonemptylist.filterIndex) | |
| arrow.data.NonEmptyList (extensions in package arrow.optics.extensions.nonemptylist.index) | |
| arrow.data.NonEmptyList (extensions in package arrow.optics) | |
| arrow.data.NonEmptyList (extensions in package arrow.optics.extensions) | |
| arrow.optics.extensions.NonEmptyListEach | Each instance definition for NonEmptyList. |
| arrow.optics.extensions.NonEmptyListFilterIndex | FilterIndex instance definition for NonEmptyList. |
| arrow.optics.extensions.NonEmptyListIndex | Index instance definition for NonEmptyList. |
| arrow.core.Option (extensions in package arrow.optics.extensions.option.each) | |
| arrow.core.Option (extensions in package arrow.optics) | |
| arrow.core.Option (extensions in package arrow.optics.extensions) | |
| arrow.optics.Optional | Optional is a type alias for POptional which fixes the type arguments and restricts the POptional to monomorphic updates. |
| arrow.optics.OptionalKindedJ | |
| arrow.optics.OptionalOf | |
| arrow.optics.OptionalPartialOf | |
| arrow.optics.extensions.OptionEach | Each instance definition for Option. |
| arrow.optics.PIso | An Iso is a loss less invertible optic that defines an isomorphism between a type S and A i.e. a data class and its properties represented by TupleN |
| arrow.optics.PIsoKindedJ | |
| arrow.optics.PIsoOf | |
| arrow.optics.PIsoPartialOf | |
| arrow.optics.PLens | A Lens (or Functional Reference) is an optic that can focus into a structure for getting, setting or modifying the focus (target). |
| arrow.optics.PLensKindedJ | |
| arrow.optics.PLensOf | |
| arrow.optics.PLensPartialOf | |
| arrow.optics.POptional | An Optional is an optic that allows to see into a structure and getting, setting or modifying an optional focus. |
| arrow.optics.POptionalKindedJ | |
| arrow.optics.POptionalOf | |
| arrow.optics.POptionalPartialOf | |
| arrow.optics.PPrism | A Prism is a loss less invertible optic that can look into a structure and optionally find its focus. Mostly used for finding a focus that is only present under certain conditions i.e. list head Prism<List, Int> |
| arrow.optics.PPrismKindedJ | |
| arrow.optics.PPrismOf | |
| arrow.optics.PPrismPartialOf | |
| arrow.optics.Prism | Prism is a type alias for PPrism which fixes the type arguments and restricts the PPrism to monomorphic updates. |
| arrow.optics.PrismKindedJ | |
| arrow.optics.PrismOf | |
| arrow.optics.PrismPartialOf | |
| arrow.optics.PSetter | A Setter is an optic that allows to see into a structure and set or modify its focus. |
| arrow.optics.PSetterKindedJ | |
| arrow.optics.PSetterOf | |
| arrow.optics.PSetterPartialOf | |
| arrow.optics.PTraversal | A Traversal is an optic that allows to see into a structure with 0 to N foci. |
| arrow.optics.PTraversalKindedJ | |
| arrow.optics.PTraversalOf | |
| arrow.optics.PTraversalPartialOf | |
| arrow.optics.extensions.sequence.each.Sequence | |
| arrow.optics.extensions.sequence.filterIndex.Sequence | |
| arrow.optics.extensions.sequence.index.Sequence | |
| arrow.data.SequenceK (extensions in package arrow.optics.extensions.sequencek.each) | |
| arrow.data.SequenceK (extensions in package arrow.optics.extensions.sequencek.filterIndex) | |
| arrow.data.SequenceK (extensions in package arrow.optics.extensions.sequencek.index) | |
| arrow.data.SequenceK (extensions in package arrow.optics.extensions) | |
| arrow.optics.extensions.SequenceKEach | Each instance definition for SequenceK. |
| arrow.optics.extensions.SequenceKFilterIndex | FilterIndex instance definition for SequenceK. |
| arrow.optics.extensions.SequenceKIndex | Index instance definition for SequenceK. |
| arrow.optics.extensions.set.at.Set | |
| arrow.optics.extensions.SetAt | At instance definition for Set. |
| arrow.core.SetExtensions (extensions in package arrow.optics) | |
| arrow.data.SetK (extensions in package arrow.optics.extensions.setk.at) | |
| arrow.optics.extensions.SetKAt | At instance definition for SetK. |
| arrow.optics.Setter | Setter is a type alias for PSetter which fixes the type arguments and restricts the PSetter to monomorphic updates. |
| arrow.optics.SetterKindedJ | |
| arrow.optics.SetterOf | |
| arrow.optics.SetterPartialOf | |
| arrow.optics.typeclasses.Snoc | |
| kotlin.String (extensions in package arrow.optics) | |
| kotlin.String (extensions in package arrow.optics.extensions) | |
| arrow.optics.extensions.StringCons | |
| arrow.optics.extensions.StringEach | Each instance for String. |
| arrow.optics.extensions.StringFilterIndex | FilterIndex instance for String. It allows filtering of every Char in a String by its index’s position. |
| arrow.optics.extensions.StringIndex | Index instance for String. It allows access to every Char in a String by its index’s position. |
| arrow.optics.extensions.StringSnoc | |
| arrow.optics.Traversal | Traversal is a type alias for PTraversal which fixes the type arguments and restricts the PTraversal to monomorphic updates. |
| arrow.optics.TraversalKindedJ | |
| arrow.optics.TraversalOf | |
| arrow.optics.TraversalPartialOf | |
| arrow.core.Try (extensions in package arrow.optics.extensions.try.each) | |
| arrow.core.Try (extensions in package arrow.optics) | |
| arrow.core.Try (extensions in package arrow.optics.extensions) | |
| arrow.optics.extensions.TryEach | Each instance definition for Try. |
| arrow.core.Tuple10 (extensions in package arrow.optics) | |
| arrow.core.Tuple2 (extensions in package arrow.optics) | |
| arrow.core.Tuple3 (extensions in package arrow.optics) | |
| arrow.core.Tuple4 (extensions in package arrow.optics) | |
| arrow.core.Tuple5 (extensions in package arrow.optics) | |
| arrow.core.Tuple6 (extensions in package arrow.optics) | |
| arrow.core.Tuple7 (extensions in package arrow.optics) | |
| arrow.core.Tuple8 (extensions in package arrow.optics) | |
| arrow.core.Tuple9 (extensions in package arrow.optics) | |
| arrow.data.Validated (extensions in package arrow.optics) |


NBC’s issues aren’t addressed despite candidates’ letter
NEW YORK (AP) - A day after four Democratic presidential candidates called on NBC News to support an independent investigation into its handling of sexual misconduct, the issue remained offstage at Wednesday’s debate on MSNBC.
The #MeToo movement was brought up in only one question, and not to a candidate who had addressed NBC’s case. Moderator Kristen Welker asked former Vice President Joe Biden how he would address sexual violence against women.
Candidates Cory Booker, Kamala Harris, Bernie Sanders and Elizabeth Warren had signed a letter calling for the probe. A book by Ronan Farrow called into question NBC’s finding that its executives weren’t aware of former “Today” host Matt Lauer’s behavior before the rape allegation that led to his firing two years ago. Lauer has called the relationship consensual.
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Dogs and Cats Machine Learning Audio Prediction
Cat or Dog?
Select Audio File:
Process Audio
Result shows here


Analyze the short text below!
This is a small test, where
Thomas Unknown
and Barack Obama sing a song.
Found
entities. Out of which
are persons.
Add a fake name of B. Obama.
Annotate the task below.
Thomas Unknown
should implement something cool.
Persons:
Tasks:


Pragmatic Programmer, The: From Journeyman to Master
Foreword
Preface
Who Should Read This Book?
What Makes a Pragmatic Programmer?
Individual Pragmatists, Large Teams
It's a Continuous Process
How the Book Is Organized
What's in a Name?
1. A Pragmatic Philosophy
The Cat Ate My Source Code
Software Entropy
Stone Soup and Boiled Frogs
Good-Enough Software
Your Knowledge Portfolio
Communicate!
Summary
2. A Pragmatic Approach
The Evils of Duplication
Orthogonality
Reversibility
Tracer Bullets
Prototypes and Post-it Notes
Domain Languages
Estimating
3. The Basic Tools
The Power of Plain Text
Shell Games
Power Editing
Source Code Control
But My Team Isn't Using Source Code Control
Source Code Control Products
Debugging
Text Manipulation
Exercises
Code Generators
4. Pragmatic Paranoia
Design by Contract
Dead Programs Tell No Lies
Assertive Programming
When to Use Exceptions
How to Balance Resources
Objects and Exceptions
Balancing and Exceptions
When You Can't Balance Resources
Checking the Balance
Exercises
5. Bend or Break
Decoupling and the Law of Demeter
Metaprogramming
Temporal Coupling
It's Just a View
Blackboards
6. While You Are Coding
Programming by Coincidence
Algorithm Speed
Refactoring
Code That's Easy to Test
Evil Wizards
7. Before the Project
The Requirements Pit
Solving Impossible Puzzles
Not Until You're Ready
The Specification Trap
Circles and Arrows
8. Pragmatic Projects
Pragmatic Teams
Ubiquitous Automation
Ruthless Testing
It's All Writing
Great Expectations
Pride and Prejudice
A. Resources
Professional Societies
Building a Library
Internet Resources
Bibliography
B. Answers to Exercises


Metamorphic rocks are made by the existing rocks types, and the process called metamorphism. Metamorphic rocks make up a large part of Earth's crust and are classified by texture, chemical and mineral assemblage. They can form from tectonic processes such as continental collisions, which cause horizontal pressure, friction and distortion. They are also formed when rock is heated up by the intrusion of hot molten rock called magma from the Eearth's interior.
There are two basic types of metamorphic rocks. Foliated metamorphic rocks such as gneiss, phyllite, schist, and slate have a layered or banded appearance that is produced by exposure to heat and directed pressure.
Non-foliated metamorphic rocks such as hornfels, marble, quartzite, and novaculite do not have a layered or banded appearance.
Metamorphic rock are classified on the basis of texture and mineral composition. The texture can be foliated or non-foliated. Foliated metamorphic rocks are appeared banded or layered. Foliated rocks can be ordered in terms of increasing metamorphism. Non-foliated metamorphic rock usually contains one mineral.
Slate is a fine-grained, foliated, homogenous metamorphic rock composed of clay or volcanic ash through low-grade regional metamorphism. Slate is the finest grained foliated metamorphic rock. Slate is mainly composed of the minerals quartz and muscovite or illite, often along with biotite, chlorite, hematite, and pyrite. Slate can be made into roofing slates, a type of roof tile, which are installed by a slater.
Gneiss is a foliated metamorphic rock identified by its bands and lenses of varying composition, while other bands contain granular minerals with an interlocking testure. Gneiss usually forms by regional metamorphism at convergent plate boundaries. Gneiss can form in several different ways. Regional metamorphism can transform shale into slate, then phyllite, then schist, and finally goes into gneiss.
Marble is a hard crystalline metamorphic form of limestone, typically white with coloured mottlings or streaks, which meay be polished and is used in sculpture and architecture. e.g. The spotless white marble of the Taj Mahal. Marble can be foliated or non-foliated, composed of recrystallized carbonate minerals, most commonly calcite or dolomite.
Schist is a coarse-grained metamorphic rock which consists of a layers of different minerals and can be split into thin irregular plates. Schist is formed by the metamorphosis of mudstone, or some types of igneous rocks. The resulting foliation is coarser and more distinct than that of slate due to the higher degree of crystallization of mica minerals forming larger crystals, and is often reffred to as schiosity.
Quartzite is a non-foliated metamorphic rock composed almost entirely of quartz. Quartzite is usually in gray color. Some rock units that are stained by iron can be pink, red, or purple. Most quartzite forms during mountain-building events at convergent plate boundaries. Quartzite is an important rock type in folded mountain ranges throughout the world.


Hi! I'm a graduate student of SFSU Computer Science department. This is my second semester in Master's Program. I am from India. I have done my bachelors in Electrical field. I used to work with 'Aker Solutions Pvt.Ltd.' company which deals with oil and gas sector.
Some of the programming languages that I have worked with include, C, C++, Java, Python, HTML/CSS and MatLab. I am enjoying school life here in San Francisco and I am looking forward to expanding my knowledge in Computer Science field and apply it to the real world problems.
Apart from studies, I like hiking, traveling, reading books and watching movies.


Hulu+ Live TV to raise monthly prices to $54.99
(Reuters) - Hulu+ Live TV is increasing its monthly base price to $54.99 from $44.99 per month from Dec. 18, the company said in a statement on Friday.
Hulu is jointly owned by Walt Disney Co and Comcast's NBCUniversal, and Disney is in the process of taking full control of Hulu in a deal with Comcast. (hulu.tv/2Oera5r)
The bundle, consisting of Hulu and Live TV services, offers more than 60 channels like ESPN, Fox Sports and CNN among other major broadcast networks along with its on-demand video service.