AZ-01 AZ-02 AZ-03 AZ-04 AZ-05 AZ-06 AZ-07 AZ-08 AZ-09
AZ – Sen. Kyrsten Sinema (D) and Sen. Martha McSally (R)
Committees
Kyrsten Sinema is the #11 Democrat on the Senate Committee on Commerce, Science, and Transportation
Committees
Martha McSally is the #7 Republican on the Senate Energy Subcommittee on Energy
Martha McSally is the #9 Republican on the Senate Committee on Energy and Natural Resources
HEP Grants
In the past 9 years, this state has received: 37 HEP grants, totalling $13,928,000
Year Institution Amount ($)
------ ------------------------ ------------
2019 Arizona State University 272,000
2019 University of Arizona 2,534,000
2018 Arizona State University 265,000
2018 University of Arizona 1,270,000
2017 Arizona State University 170,000
2017 University of Arizona 1,160,000
2016 Arizona State University 100,000
2016 University of Arizona 1,960,000
2015 Arizona State University 340,000
2015 University of Arizona 1,047,000
2014 Arizona State University 417,000
2014 University of Arizona 1,236,000
2013 Arizona State University 496,000
2013 University of Arizona 1,232,000
2012 Arizona State University 210,000
2012 University of Arizona 1,219,000
SC Contracts
This state received no SC contracts from 2012-2020
NSF MPS Grants
In the past 9 years, this state has received: 601 NSF MPS grants, totalling $208,775,503
Institution Amount ($)
----------------------------------------------------- ------------
Association Of Universities For Research In Astronomy 82,621,528
University Of Arizona 73,037,330
Arizona State University 43,201,977
Lowell Observatory 5,022,378
Northern Arizona University 2,140,721
Planetary Science Institute 1,594,696
Redacted Due To Pii 700,000
Pii Redacted 400,000
Maricopa County Community College District 56,873
SULI/CCI Interns
From 2014-2018, this state had 40 SULI/CCI interns
# Interns Program College
----------- --------- ----------------------------------------------------
21 SULI Arizona State University
10 SULI University of Arizona
3 SULI Embry-Riddle Aeronautical University-Prescott
3 SULI Northern Arizona University
1 CCI Mesa Community College
1 SULI Embry-Riddle Aeronautical University-Prescott Campus
1 SULI Scottsdale Community College
40 — — Back to top
AZ-01 – Rep. Tom O’Halleran (D) – Wikipedia
Committees
Tom O’Halleran is the #31 Democrat on the House Committee on Energy and Commerce
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
In the past 9 years, this district has received: $7,163,099 in NSF MPS grants.
Year Institution Amount ($)
------ --------------------------- ------------
2020 Northern Arizona University 341,275
2020 Lowell Observatory 808,708
2019 Northern Arizona University 468,036
2019 Lowell Observatory 437,995
2018 Northern Arizona University 210,502
2018 Lowell Observatory 119,564
2017 Lowell Observatory 377,515
2017 Northern Arizona University 133,018
2016 Lowell Observatory 1,159,428
2015 Northern Arizona University 216,161
2015 Lowell Observatory 362,698
2014 Northern Arizona University 24,490
2014 Lowell Observatory 315,044
2013 Northern Arizona University 85,257
2013 Lowell Observatory 743,169
2012 Northern Arizona University 661,982
2012 Lowell Observatory 698,257
SULI/CCI Interns
From 2014-2018, this district had 3 SULI/CCI interns
Term Name College Host Lab Program
----------- --------------- --------------------------- ------------------------------------ ---------
Summer 2015 Stephen Kuluris Northern Arizona University National Renewable Energy Laboratory SULI
Summer 2014 Stephen Kuluris Northern Arizona University National Renewable Energy Laboratory SULI
Spring 2014 Samuel Briggs Northern Arizona University Los Alamos National Laboratory SULI
AZ-02 – Rep. Ann Kirkpatrick (D) – Wikipedia
Committees
Ann Kirkpatrick is the #4 Democrat on the House Appropriations Subcommittee on Energy and Water Development
Ann Kirkpatrick is the #29 Democrat on the House Committee on Appropriations
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
In the past 9 years, this district has received: $2,875,130 in NSF MPS grants.
Year Institution Amount ($)
------ --------------------------- ------------
2019 Planetary Science Institute 938,201
2018 Planetary Science Institute 428,409
2017 Planetary Science Institute 65,798
2016 Planetary Science Institute 162,288
2013 University Of Arizona 1,280,434
SULI/CCI Interns
This district had no SULI/CCI interns from 2014-2018
AZ-03 – Rep. Raúl M. Grijalva (D) – Wikipedia
Committees
Raúl M. Grijalva is the #2 Democrat on the House Subcommittee on Higher Education and Workforce Development
HEP Grants
In the past 9 years, this district has received: $9,207,000 in SC HEP grants.
Institution Amount ($) Start End Principal Investigator Project Title
--------------------- ------------ ------- ------- ------------------------ -----------------------------------------------------------------------------------------------------------------
University of Arizona 750,000 2019 2020 Eifler, Tim Multi-Probe Cosmology with DES and LSST
University of Arizona 750,000 2019 2020 Krause, Elisabeth Joint analyses of lensing, clustering, and galaxy clusters with DES and LSST
University of Arizona 2,430,000 2017 Ongoing JOHNS, KENNENTH University of Arizona High Energy Physics Program
University of Arizona 2,244,000 2016 2020 Johns, Kenneth University of Arizona High Energy Physics Program
University of Arizona 750,000 2016 2020 Rozo, Eduardo Constraining Dark Energy with Galaxy Clusters and Baryon Acoustic Oscillations
University of Arizona 64,000 2014 2016 Toussaint, Douglas Searching for Physics Beyond the Std Model: Strongly-Coupled Field Theories at the Intensity and Energy Frontiers
University of Arizona 2,119,000 2014 2016 johns, kenneth University of Arizona High Energy Physics Program
University of Arizona 100,000 2014 2016 johns, kenneth Cosmic Frontier Research in the University of Arizona High Energy Physics Program
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
In the past 9 years, this district has received: $145,300,070 in NSF MPS grants.
Year Institution Amount ($)
------ ----------------------------------------------------- ------------
2020 Association Of Universities For Research In Astronomy 40,304,207
2020 University Of Arizona 315,142
2019 Association Of Universities For Research In Astronomy 42,317,321
2019 Redacted Due To Pii 100,000
2019 University Of Arizona 9,540,184
2018 Redacted Due To Pii 100,000
2018 University Of Arizona 9,414,921
2017 Pii Redacted 200,000
2017 University Of Arizona 10,968,894
2016 University Of Arizona 7,982,523
2015 University Of Arizona 6,838,799
2014 University Of Arizona 2,992,219
2013 University Of Arizona 14,225,860
SULI/CCI Interns
From 2014-2018, this district had 10 SULI/CCI interns
Term Name College Host Lab Program
----------- ----------------------- --------------------- -------------------------------------- ---------
Summer 2018 Rafael Orozco University of Arizona Lawrence Berkeley National Laboratory SULI
Summer 2018 Jaden Johnston University of Arizona National Renewable Energy Laboratory SULI
Summer 2017 Rafael Orozco University of Arizona National Renewable Energy Laboratory SULI
Summer 2017 Aleksandra Safonova University of Arizona SLAC National Accelerator Laboratory SULI
Summer 2016 Maxwell Xavier Duffy University of Arizona Ames National Laboratory SULI
Summer 2015 Kira Rundel University of Arizona National Renewable Energy Laboratory SULI
Spring 2018 Lawson Liem Fuller University of Arizona Lawrence Berkeley National Laboratory SULI
Spring 2018 Aleksandra Safonova University of Arizona Lawrence Livermore National Laboratory SULI
Fall 2017 Lawson Liem Fuller University of Arizona Lawrence Berkeley National Laboratory SULI
Fall 2017 Erica Christy Clevenger University of Arizona Lawrence Livermore National Laboratory SULI
AZ-04 – Rep. Paul A. Gosar (R) – Wikipedia
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
This district received no NSF MPS grants from 2012-2020
SULI/CCI Interns
From 2014-2018, this district had 4 SULI/CCI interns
Term Name College Host Lab Program
----------- ------------------------ ---------------------------------------------------- -------------------------------------- ---------
Summer 2017 David Randal Bas Senecal Embry-Riddle Aeronautical University-Prescott Oak Ridge National Laboratory SULI
Summer 2017 Stephen Mcmillan Embry-Riddle Aeronautical University-Prescott Lawrence Livermore National Laboratory SULI
Summer 2017 Richard M Reksoatmodjo Embry-Riddle Aeronautical University-Prescott Princeton Plasma Physics Laboratory SULI
Summer 2015 Hayden Smotherman Embry-Riddle Aeronautical University-Prescott Campus Idaho National Laboratory SULI
AZ-05 – Rep. Andy Biggs (R) – Wikipedia
Committees
Andy Biggs is the #6 Republican on the House Committee on Science, Space, and Technology
HEP Grants
In the past 9 years, this district has received: $706,000 in SC HEP grants.
Institution Amount ($) Start End Principal Investigator Project Title
------------------------ ------------ ------- ----- ------------------------ -----------------------------------------------------------------------
Arizona State University<PHONE_NUMBER> 2014 Comfort Search for New Physics in Neutral Kaon Decays
Arizona State University<PHONE_NUMBER> 2014 Krauss Theoretical Research at the High Energy Frontier: Cosmology and Beyond
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
In the past 9 years, this district has received: $7,472,658 in NSF MPS grants.
Year Institution Amount ($)
------ ------------------------ ------------
2019 Redacted Due To Pii 200,000
2018 Redacted Due To Pii 100,000
2012 Arizona State University 7,172,658
SULI/CCI Interns
This district had no SULI/CCI interns from 2014-2018
AZ-06 – Rep. David Schweikert (R) – Wikipedia
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
This district received no NSF MPS grants from 2012-2020
SULI/CCI Interns
From 2014-2018, this district had 1 SULI/CCI intern
Term Name College Host Lab Program
----------- --------------------- ---------------------------- ------------------------------------ ---------
Summer 2017 Ashlee Elizabeth Vise Scottsdale Community College National Renewable Energy Laboratory SULI
AZ-07 – Rep. Ruben Gallego (D) – Wikipedia
HEP Grants
In the past 9 years, this district has received: $2,451,000 in SC HEP grants.
Institution Amount ($) Start End Principal Investigator Project Title
--------------------- ------------ ------- ----- -------------------------------- ------------------------------------------------------------------------------------------------------------------
University of Arizona 1000000 2013 2014 Johns University of Arizona High Energy Physics Program
University of Arizona 0 2013 2014 Johns BOOST 2013 Workshop Hosted by the University of Arizona
University of Arizona 1387000 2012 2014 Rutherfoord, Sarcevic, Toussaint University of Arizona High Energy Physics Program
University of Arizona 64000 2012 2014 Toussaint Searching for Physics Beyond the Std Model: Strongly-Coupled Field Theories at the Intensity and Energy Frontiers
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
In the past 9 years, this district has received: $9,478,354 in NSF MPS grants.
Year Institution Amount ($)
------ --------------------- ------------
2013 University Of Arizona 164,833
2012 University Of Arizona 9,313,521
SULI/CCI Interns
This district had no SULI/CCI interns from 2014-2018
AZ-08 – Rep. Debbie Lesko (R) – Wikipedia
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
This district received no NSF MPS grants from 2012-2020
SULI/CCI Interns
This district had no SULI/CCI interns from 2014-2018
AZ-09 – Rep. Greg Stanton (D) – Wikipedia
HEP Grants
In the past 9 years, this district has received: $1,564,000 in SC HEP grants.
Institution Amount ($) Start End Principal Investigator Project Title
------------------------ ------------ ------- ----- ------------------------ -----------------------------------------------------------------------
Arizona State University 537,000 2018 2020 Vachaspati, Tanmay Theoretical Research at the High Energy Frontier: Cosmology and Beyond.
Arizona State University 170,000 2017 2018 Vachaspati, Tanmay Cosmology and Beyond
Arizona State University 100,000 2016 2018 Lunardini, Cecilia Phenomenology of Astrophysical Neutrinos
Arizona State University 340,000 2015 2017 Krauss, Lawrence Theoretical Research at the High Energy Frontier
Arizona State University 0 2014 2016 Comfort, Joseph Search for New Physics in Neutral Kaon Decays
Arizona State University 417,000 2014 2015 Krauss, Lawrence Theoretical Research at the High Energy Frontier: Cosmology and Beyond
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
In the past 9 years, this district has received: $36,486,192 in NSF MPS grants.
Year Institution Amount ($)
------ ------------------------------------------ ------------
2020 Arizona State University 263,815
2019 Arizona State University 5,645,634
2019 Redacted Due To Pii 100,000
2018 Arizona State University 4,950,729
2018 Maricopa County Community College District 56,873
2018 Redacted Due To Pii 100,000
2017 Arizona State University 6,487,472
2017 Pii Redacted 200,000
2016 Arizona State University 3,914,183
2015 Arizona State University 4,689,825
2014 Arizona State University 3,980,861
2013 Arizona State University 6,096,800
SULI/CCI Interns
From 2014-2018, this district had 22 SULI/CCI interns
Term Name College Host Lab Program
----------- --------------------------- ------------------------ ---------------------------------------------- ---------
Summer 2018 Ryland Gerald Wala Arizona State University Lawrence Berkeley National Laboratory SULI
Summer 2018 Andrea Kraetz Arizona State University Oak Ridge National Laboratory SULI
Summer 2018 Christina Piarowski Arizona State University Argonne National Laboratory SULI
Summer 2017 Jared Carlson Mesa Community College Princeton Plasma Physics Laboratory CCI
Summer 2017 Brianna Fornes Arizona State University Idaho National Laboratory SULI
Summer 2016 Paul Abers Arizona State University Lawrence Berkeley National Laboratory SULI
Summer 2016 Brianna Nicole Thorpe Arizona State University Thomas Jefferson National Accelerator Facility SULI
Summer 2015 Jenna Marie Lynch Arizona State University Los Alamos National Laboratory SULI
Summer 2015 Alexander Reed Munoz Arizona State University Oak Ridge National Laboratory SULI
Summer 2015 Matthew Loyd Catlett Arizona State University Los Alamos National Laboratory SULI
Summer 2015 Brianna Nicole Thorpe Arizona State University Thomas Jefferson National Accelerator Facility SULI
Summer 2014 Eric Morgan Arizona State University Lawrence Berkeley National Laboratory SULI
Summer 2014 Matthew Loyd Catlett Arizona State University Los Alamos National Laboratory SULI
Summer 2014 Christopher Joseph Luna Arizona State University Princeton Plasma Physics Laboratory SULI
Spring 2018 Caralyn Szostkiewicz Arizona State University National Renewable Energy Laboratory SULI
Spring 2017 Nathan Jacques Rodkey Arizona State University National Renewable Energy Laboratory SULI
Spring 2016 Jacqueline Paige Shortridge Arizona State University Lawrence Berkeley National Laboratory SULI
Spring 2014 Aman Sharma Arizona State University Argonne National Laboratory SULI
Spring 2014 Austin Earley Arizona State University National Renewable Energy Laboratory SULI
Fall 2018 Gailyn Monroe Arizona State University Argonne National Laboratory SULI
Fall 2017 Caralyn Szostkiewicz Arizona State University National Renewable Energy Laboratory SULI
Fall 2014 Christopher Joseph Luna Arizona State University Princeton Plasma Physics Laboratory SULI


| Address | |||
| City | |||
| State | Zip code |
Start typing the delivery address in the textbox above, and then select the address from the drop-down list when it appears.
You can also zoom in and tap a building to see its address.
App created by Gavin Wright, 2017-2019. Contact me at gavingt@gmail.com.


--- layout: post title: It's rather extr'ordinary date: 2008-03-18 13:40:48.000000000 -07:00 type: post published: true status: publish categories: - singles - video tags: - single - video meta: _publicize_pending: '1' original_post_id: '18' _wp_old_slug: '18' author: login: falsefridays email<EMAIL_ADDRESS>display_name: Jim Withington first_name: '' last_name: '' ---
Some songs really do just fix everything, always.
This is one of them.
[youtube:http://www.youtube.com/watch?v=n3H4TTNSnO8]


{{> layout/header}}
Already using Headstart, but planning on upgrading to a later version? Check the guides below to see how to upgrade your project files as well.
Upgrading is easy through NPM. You might need sudo
rights for this.
npm update -g headstart
Since newer versions of Headstart might require some changes in your project files, it is advised to check for updates here as well.
Since the downloaded boilerplate files aren't linked to git, you need to initialize.
cd my/project/folder
git init
Then, add and commit your local files so you can merge later on:
git add -A
git commit -m "local"
Note: if you have forked the default boilerplate to make custom changes, the following steps can be used to upgrade your personal copy as well.
Next step for your "repository" is to add a remote that points to the upstream repository on Github:
git remote add upstream https://github.com/flovan/headstart-boilerplate.git
You can check to see if it has been added by running:
git remote -v
Next up is fetching the latest changes:
git fetch upstream
This will pull the branches from the default boilerplate repository down. You can now merge your local copy with the fetched files, without losing your local changes:
git checkout master
git merge upstream/master
Git will now go over all the files, and try to auto-merge changes. It is quite normal that "CONFLICT"s appear in the output. This just means you will have to manually inspect and merge the changes.
To see a better overview on newly added and changed files, run:
git log
And copy the commit hash—which will be a random string of letters and numbers—from the output. For example a2b739325c0e43250fa9374bcf2113e65379c3cf
. Then run:
git diff --name-status <copiedHashGoesHere>
Lines starting with "A" are newly added files. Lines starting with "M" indicate modified files that will need inspecting. Git will have added markers in each conflicting file so you can easily compare and fix issues:
<<<<<<< HEAD
// YOUR LOCAL CODE
=======
// CODE FROM UPSTREAM
>>>>>>> upstream/master
You can go up and down the list with your arrow keys, and quit by typing q
. After checking all files and fixing merge issues, your project should be all set to run with the latest version of Headstart.


Hao is a third-year student majoring in Computer Science at UC San Diego. She sincerely passionate on coding and consider herself do a relatively good job on her CS study.


The accent colors are applied to <button type="submit"> and <input type="submit">
elements.
The accent colors are applied to the dots of the indeterminate progress control and the fill for the determinate progress element.
The accent colors are applied to the high light of the option elements within the select element.
The accent colors are applied to the selection of text within all input elements.
The accent colors are applied to the lower fill of the input range element.
The accent colors are applied to the rating elements that represent the value of the rating control.
The accent colors are applied to the lower fill of toggle control.
The accent colors are applied to the selection state of the ListView items. Select any item to see the selection state.
The accent colors are applied to the AppBar and AppBarCommands. Right click or swipe in from the top or bottom to display AppBar.


This application requires root access, if you don't know what it means, please do not install :)
HEBF (empty heart, full battery)
Feeling heartbroken?  Forget it. Your heart can be empty, do not let your battery get too!
What is HEBF:
HEBF Optimizer is an application that aims to increase the performance of your device, also by focusing on battery performance, we didn't forget it. With a clean, fresh and pleasant user interface, HEBF shows you the explanation of each option right below it. The application also features a useful and smart toolbox that you can use whenever you want.
A brief history:
Created in the midst of a broken heart, on top of this, the my device's battery was discharging fast and increasing my fury. Then the application was created in 2014 (before in shell) intended to improve battery life and performance of your phone. - Ivsom Emídio.
Requirements:
- A rooted phone
- Busybox installed (If it did not come with the root)
- Android 4.1+
Why do we need access to storage?
We need to copy the necessary files for the application works, read them and then run them, furthermore, the "Cleaner" and "logging" feature, only work with this permission
Why do we need busybox?
The busybox assures us that all commands will run on all versions of the system, it is a binary that gathers the most common binaries into one, so if one of them is missing on your system, it will be present in busybox.
Support:
At the XDA Thread: http://forum.xda-developers.com/android/software-hacking/hebf-tweaking-battery-saver-optimizer-t3401341
Brought to you by: Lennoard Silva and Ivsom Emídio from Android VIP


It's be clear and say that only a fool would not want to manage their time better, and be more productive as a consquence.
I am not new to this game and I have tried several many ways to manage my time and increase my prodcutivity over the years. However, I'm always open to novel ideas, well novel to me that is, such as the Pomodoro Technique.
That would be the seven counterintuitive ways to be insanely productivity. I have always found that wanting to achieve an objective too much just doesn't work for me. And besides, flow over structure can lead a person anywhere.
Prayer ... Agility and Flow ... Personal Kanban ... a walk in the woods. In that order. Works for me.
I'll be giving the Pomodoro Technique ago. I hope it will assist me in going down to many rabbit-holes.


placeholder
Room Deficiency Report Log
Project number:
{{project_id}}
The following is a list of Room Deficiency Reports for this project.
Total Reports: {{dirs.length}}
{{#each dirs}}
{{dir}}
{{/each}}


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
Frittata is great for a quick brunch. This dish with bacon, mushroom and chives is filled with protein for the perfect start to your day.
With a GI of 54 this meal is
high protein, low GI
and provides 354 kcal per portion.
Preheat the oven to 180C/160C Fan/Gas 4.
Put the mushrooms on a foil-lined baking sheet and scatter over the garlic. Spray with a little oil and season with salt and freshly ground black pepper. Bake in the oven for 18-20 minutes or until softened. Leave until cool enough to handle.
Meanwhile, preheat the grill to medium-hot. Lay the bacon rashers on a foil-lined grill pan and cook for 5-6 minutes, or until slightly crispy. Cool slightly, then slice thickly. Leave the grill on for the frittata.
Put the eggs, chives and mustard in a bowl, beat together lightly and season with freshly ground black pepper.
Heat a large, nonstick frying pan with an ovenproof handle over a medium heat. Add the butter, when the butter is foaming pour in the egg mixture and cook for 1-2 minutes, then add the bacon and whole mushrooms, stalk side up. Cook for a further 2-3 minutes or until almost set. Place the pan under the grill and cook the frittata for 2-3 minutes, or until set. Allow to cool slightly.
Toast the bread and arrange on serving plates. Cut the frittata into wedges and serve on the toasted sourdough. Garnish with chives and serve.


Class EdgeDisplaySettings
Settings for how a curve or line should be displayed.
Inheritance
Inherited Members
Namespace: Elements
Assembly: Hypar.Elements.dll
Syntax
public class EdgeDisplaySettings
Properties
DashMode
Whether and how to display dashes along the line.
Declaration
public EdgeDisplayDashMode DashMode { get; set; }
Property Value
| Type | Description |
|---|---|
| EdgeDisplayDashMode |
DashSize
The size of the dash. If Mode is set to None, this value will be ignored. Note that the units for this value (screen vs world) are affected by the choice of Dash Mode.
Declaration
public double DashSize { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Double |
GapSize
The size of the gaps between dashes. If Mode is set to None, this value will be ignored. If this value is set to null, DashSize will be used. Note that the units for this value (screen vs world) are affected by the choice of Dash Mode.
Declaration
public double? GapSize { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Nullable<System.Double> |
LineWidth
The width of the line. If Mode is set to Screen Units, this will be in pixels (and rounded to the nearest integer). If set to World Units, this will be in meters.
Declaration
public double LineWidth { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Double |
WidthMode
How the Width should be interpreted. If set to Screen Units, Width is interpreted as a constant pixel width (and rounded to the nearest integer). If set to World Units, Width is interpreted as a constant meter width.
Declaration
public EdgeDisplayWidthMode WidthMode { get; set; }
Property Value
| Type | Description |
|---|---|
| EdgeDisplayWidthMode |


Gentown Community Inclusion Hub
Staff intranet
Start here
© C4DE & RMIT University
V1.0 Build 2.7.4, released September 2019


{% extends 'layouts/base.html' %} {% import 'macros/form_macros.html' as f %} {% block content %}
{{ _('Through this form, you will contact the administrators to ask them for
making this control list public. A public control list can not be made private again.') }}
{{ _('By making this
list public, other users will be able to freely access this control list. They will be able
to contact the administrators of this list to propose new allowed values such as lemma.') }}


|
nrf51AdvLogger
1.0
|
Interrupt priority definitions for all the peripherals. More...
Files | |
| file | irq_priority.h |
| This file contains the Interrupt priority definitions for all the peripherals. | |
Definitions of the interrupt priority of all the peripherals | |
If a peripheral does not use interrupts, its priority is defined without a value. Used in UART Driver, Milli-Second RTC Timer and Profile Timer module | |
| #define | PRIORITY_POWER_CLOCK_IRQn |
| #define | PRIORITY_RADIO_IRQn 3 |
| #define | PRIORITY_UART0_IRQn 4 |
| #define | PRIORITY_SPI0_TWI0_IRQn |
| #define | PRIORITY_SPI1_TWI1_IRQn |
| #define | PRIORITY_GPIOTE_IRQn |
| #define | PRIORITY_ADC_IRQn |
| #define | PRIORITY_TIMER0_IRQn |
| #define | PRIORITY_TIMER1_IRQn |
| #define | PRIORITY_TIMER2_IRQn |
| #define | PRIORITY_RTC0_IRQn |
| #define | PRIORITY_TEMP_IRQn |
| #define | PRIORITY_RNG_IRQn |
| #define | PRIORITY_ECB_IRQn |
| #define | PRIORITY_CCM_AAR_IRQn |
| #define | PRIORITY_WDT_IRQn |
| #define | PRIORITY_RTC1_IRQn 2 |
| #define | PRIORITY_QDEC_IRQn |
| #define | PRIORITY_LPCOMP_COMP_IRQn |
| #define | PRIORITY_SWI0_IRQn |
| #define | PRIORITY_SWI1_IRQn |
| #define | PRIORITY_SWI2_IRQn |
| #define | PRIORITY_SWI3_IRQn |
| #define | PRIORITY_SWI4_IRQn |
| #define | PRIORITY_SWI5_IRQn |
Interrupt priority definitions for all the peripherals.
Having all the definitions at one place provides a single glance understanding of the priority levels used by various peripherals


three.js
- instanced circle billboards - colors
Sorry, your graphics card + browser does not support hardware instancing


JobsExplorer.in
Designed a simple web app built to quickly find jobs in India. search keywords and locationto find job listings from 3 of the most popular job sites in India. I built JobsExplorer to help people search their desired jobs with ease and clarity.
Tech: NodeJS, Amazon Web Services, JQuery, NGiNX
UNI Share.in
Created a web application to help students share their books, projects and other academic related stuff with each others in a University Campus. In short, It's a University OLX.
Tech: Django, Amazon Web Services, Python, NGiNX, RDBMS


Class br.com.armange.commons.thread.async.CallableTryAsyncBuilderTest
Tests
| Test |
Duration |
Result |
| shouldTryCallableWithExceptionAndCatchingItAndFinalizer |
1.047s |
passed |
| shouldTryCallableWithExceptionAndCatchingItWithoutFinalizer |
1.002s |
passed |
| shouldTryCallableWithExceptionWithoutCatchingItAndFinalizer |
1.002s |
passed |
| shouldTryCallableWithExceptionsAndCatchingItAndRethrowingAndFinalizer |
7.548s |
passed |
| shouldTryCallableWithoutExceptionWithFinalizer |
1.004s |
passed |
| shouldTryCallableWithoutExceptionsWithoutFinalizer |
1.019s |
passed |
Standard output
SimpleRunnable class was executed successfully
SimpleRunnable class was executed successfully
SimpleRunnable class was executed successfully
CallableWithDeay class was executed successfully
SimpleRunnable class was executed successfully
CallableWithDeay class was executed successfully
SimpleRunnable class was executed successfully
SimpleRunnable class was executed successfully
java.util.concurrent.ExecutionException -> This Exception was expected.
SimpleRunnable class was executed successfully
SimpleRunnable class was executed successfully
java.util.concurrent.ExecutionException -> This Exception was expected.
Standard error
[Thread-4] ERROR br.com.armange.commons.thread.core.ScheduledThreadBuilderExecutor - java.util.concurrent.ExecutionException: java.lang.RuntimeException: -> This Exception was expected.
java.lang.RuntimeException: java.util.concurrent.ExecutionException: java.lang.RuntimeException: -> This Exception was expected.
at br.com.armange.commons.thread.ExceptionConsumerThrowingException.accept(ExceptionConsumerThrowingException.java:8)
at br.com.armange.commons.thread.ExceptionConsumerThrowingException.accept(ExceptionConsumerThrowingException.java:5)
at br.com.armange.commons.thread.async.AbstractTryAsyncBuilder.lambda$catchException$5(AbstractTryAsyncBuilder.java:188)
at java.base/java.util.Optional.ifPresent(Optional.java:183)
at br.com.armange.commons.thread.async.AbstractTryAsyncBuilder.lambda$consumeExceptionIfExists$3(AbstractTryAsyncBuilder.java:152)
at br.com.armange.commons.thread.builder.AbstractThreadBuilder.lambda$handleException$5(AbstractThreadBuilder.java:320)
at br.com.armange.commons.thread.core.ScheduledThreadBuilderExecutor.lambda$afterExecute$0(ScheduledThreadBuilderExecutor.java:89)
at java.base/java.lang.Iterable.forEach(Iterable.java:75)
at br.com.armange.commons.thread.core.ScheduledThreadBuilderExecutor.afterExecute(ScheduledThreadBuilderExecutor.java:87)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1129)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
at java.base/java.lang.Thread.run(Thread.java:829)
Caused by: java.util.concurrent.ExecutionException: java.lang.RuntimeException: -> This Exception was expected.
at java.base/java.util.concurrent.FutureTask.report(FutureTask.java:122)
at java.base/java.util.concurrent.FutureTask.get(FutureTask.java:191)
at br.com.armange.commons.thread.builder.AbstractThreadBuilder.consumesFuture(AbstractThreadBuilder.java:342)
at br.com.armange.commons.thread.builder.AbstractThreadBuilder.lambda$handleException$5(AbstractThreadBuilder.java:316)
... 6 more
Caused by: java.lang.RuntimeException: -> This Exception was expected.
at br.com.armange.commons.thread.CallableWithDelayAndException.call(CallableWithDelayAndException.java:17)
at br.com.armange.commons.thread.CallableWithDelayAndException.call(CallableWithDelayAndException.java:8)
at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)
at java.base/java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:304)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
... 2 more


This past week was packed with new partnerships, ongoing progress in the development of the new frontend for Utopian.io, and updates on our ongoing @fundition campaign to fund the SteemSTEM and Utopian.io international meetup in Italy.
In this post:
- Collaboration and Partnership with @fundition
- Utopian.io v2 Beta Tester Update
- @utopian-io with 10,000+ Followers on Steem
- Meetup Fundition Campaign Updates
- Adding @oracle-d to the Utopian.io Innovation Trail
- Utopian.io Open Source Radio Show on MSP Waves
Utopian.io & Fundition Partnership
A partnership between two like-minded initiatives like crowdfunding platform like Fundition and Utopian.io is only natural, so this week we were proud to announce an official collaboration plan between the two.
With the aim of empowering open source projects and initiatives, Utopian.io offers rewards to open source projects crowdfunding their development on Fundition. Joining is simple, and you can find all the information in @fundition ‘s latest post.
Utopian.io v2 Beta Testers Chosen!
The massive support of - and response to - our announcement of the closed beta for Utopian.io v2.0 has taken us by surprise! While we may, eventually, be able to accommodate all volunteers, we have selected a handful and have contacted them already to get them started with their initial review.
We hope to expand the beta to additional members of the community as soon as we’ve located and fixed critical bugs and issues that will surely be spotted by our selected beta-testers.
Ten Thousand Followers Strong!
On July 24th (this week) the @utopian-io account marked 10,000 followers on Steem. Although this number is comprised of a percentage of inactive users and bots, we still feed accomplished at having made it into the 5-digit-number-of-followers club.
Fundition Campaign Updates: Accommodations Booked!
As the SteemSTEM and Utopian.io Italian meetup Fundition campaign continues, we make sure to keep our supporters (that’s you!) updated. This week we booked the accommodations for the Utopian.io team to also serve as the location of the Utopian.io mini-hackathon, scheduled to take place during our stay in the villa.
The location chosen, between Pisa and Florence and within short driving distance from Vigro Labs, is the scenic and historic town of Lucca. We invite you to support the campaign and discover all about Lucca in our latest update post to the campaign.
Oracle-D Joins the Utopian.io Innovation Trail
Oracle-D, founded by @anarchotech and @starkerz, is a platform connecting STEEM investors with content creators to provide professional content and marketing services to the agency clients. To help @oracle-d grow and to encourage content creators to cover the topics of technology, innovation and open source, we’ve added @oracle-d to the Utopian.io Innovation Trail.
It is important to note that only relevant content upvoted by @oracle-d will be included in the upvote trail, same as with all other initiatives included in the Innovation Trail.
Utopian.io Open Source Radio Show on MSP Waves
Our fun and crazy radio show is growing in audience and it’s not just because of the awesome giveaways raffled live on air to audience members. This week we hosted @punqtured to speak about the Byteball airdrop to Steemians and @holger80, author of open source Steem Python module Beem.
As always, we raffled out two Udemy courses to members of the audience, listened to open source news, and had a great time.
Be sure to tune in next week at 6PM UTC on Wednesday on MSP Waves!
First Time Contributing in Utopian.io?
Learn how to contribute on our website
Utopian Witness
This page is synchronized from the post: Utopian.io Weekly - [July 27th 2018] - Partnering Up with Fundition, Celebrating a Record in Number of Followers and more


The album comes compressed in a zip folder so you can have the whole package in one go! If you are unfamilliar with zip folders there is a youtube video explaining how to unzip it for WINDOWS HERE and for MAC HERE


The following operators are supported, listed in order of precedence:
|
New,First,Last |
custom type operators (unary) |
|
Before,After |
object operators (unary) |
|
Int,Float,Str |
type conversion operators (unary) |
|
+,-,~ |
arithmetic posate(?), negate, bitwise complement (unary) |
| ^ | arithmetic 'to-the-power-of' (binary) |
| *,/,Mod | arithmetic multiply, divide, remainder (binary) |
|
Shl,Shr,Sar |
bitwise shift operators (binary) |
|
+,- |
arithmetic add, subtract (binary) |
|
<,>,<=,>=,=,<> |
comparison operators (binary) |
|
And,Or,Xor |
bitwise And, Or and Exclusive Or (binary) |
| Not |
logical Not (unary) |
Unary operators take one operand, while binary operators take two.
Arithmetic operators produce a result of the same type as the operands. For example, adding two integers produces an integer result.
If the operands of a binary arithmetic or comparison operator are not of the same type, one of the operands is converted using the following rules:
- If one operand is a custom type object, the other must be an object of the same type, or 'Null'.
Else if one operand is a string, the other is converted to a string.
Else if one operand is floating point, the other is converted to floating point.
Else both operands must be integers.
When floating point values are converted to integer, the value is rounded to the nearest integer. When integers and floating point values are converted to strings, an ascii representation of the value is produced.
When strings are converted to integer or floating point values, the string is assumed to contain an ascii representation of a numeric value and converted accordingly. Conversion stops at the first non-numeric character in the string, or at the end of the string.
The only arithmetic operation allowed on string is '+', which simply concatenates the two operands.
Int, Float and Str can be used to convert values. They may be optionally followed by the appropriate type tag - ie: 'Int%', 'Str$' and 'Float#'.
Comparison operators always produce an integer result: 1 for true, 0 for false.
If one of the operators is a custom type object, the other must be an object of the same type, or 'Null', and the only comparisons allowed are '=' and '<>'.
Bitwise and logical operators always convert their operands to integers and produce an integer result.
The Not operator returns 0 for a non-zero operand, otherwise 1. When an expression is used to conditionally execute code - for example, in an 'If' statement - the result is converted to an integer value. A non-zero result means true, a zero result means false.


Configuration¶
The CityIq module and programs require a configuration file that holds credentials and urls. You can generate a default configuration with
ciq_config -w
The generated file is configured for the San Diego system. To use this system, you will just need to add your client id and secret to the file. ( The default file has credentials for San Diego, which may work if they are not expired . )
The Config
object can be constructed on a path
where the config file is location. If none is specified it will look for this
file in several places, in this order:
s
The path specified in the constructor
The path specified by the
CITYIQ_CONFIG
env var.city-iq.yaml
in the current dircity-iq.yaml
in the current dir.city-iq.yaml
in the user’s home dir
Each of the configuration files can be overridden with a keyword in the
Config
object constructor, and each value can be accessed as an attribute
or an index. The nested cache values are special. To access or set them, preceede the key
with cache_.
c = Config(default_zone = 'SD-IE-TRAFFIC', cache_objects='/Volumes/foobar)
print(c.default_zone)
print(c['cache_objects'])
You can also set configuration values with environmental variables, by
uppercasing the variable name and prefixing it with CITYIQ_
:
$ CITYIQ_CACHE_OBJECTS=/tmp/foo/bar ciq_config -p
...
cache_errors: /Volumes/SSD_Extern/cityiq2/errors
cache_meta: /Volumes/SSD_Extern/cityiq2/meta
cache_objects: /tmp/foo/bar
...


When you work with us, you are part of a commited collabotrators.All aspects our software development process are intergrated ,interactive and agile. our approach unifies design , dvelopment and product management to create exceptional products. we start each project by understanding your bussiness goals, the users needs and the product requirements then we help translate them in to effective and elegant solutions.
Before we sign contract or write a single line of code, our team will spend the necessary time needed to understand your product vision the outcome of this scoping session is a proposal of high level statement of work based on your requirements we can provide a complete offering or just the pieces you need.


transient
fields that
are initialized during normal object construction, but whose class does not have a
readObject
method.
As transient
fields are not serialized they need
to be initialized separately in a readObject()
method
during deserialization.
Any transient
fields that
are not initialized during normal object construction are considered to use the default
initialization and are not reported by this inspection.
Example:
class Person implements Serializable {
transient String name = "Default"; //warning, can actually be a null after deserialization
transient String surname; //null is considered the default value and not reported
}


|
SimpleITK
2.0.0
|
Implementation of the N4 bias field correction algorithm. More...
#include <sitkN4BiasFieldCorrectionImageFilter.h>
Public Types | |
| using | PixelIDTypeList = RealPixelIDTypeList |
| using | Self = N4BiasFieldCorrectionImageFilter |
| Public Types inherited from itk::simple::ImageFilter | |
| using | Self = ImageFilter |
| Public Types inherited from itk::simple::ProcessObject | |
| using | Self = ProcessObject |
Public Member Functions | |
| Image | Execute (const Image &image) |
| Image | Execute (const Image &image, const Image &maskImage) |
| double | GetBiasFieldFullWidthAtHalfMaximum () const |
| double | GetConvergenceThreshold () const |
| uint8_t | GetMaskLabel () const |
| std::vector< uint32_t > | GetMaximumNumberOfIterations () const |
| std::string | GetName () const |
| std::vector< uint32_t > | GetNumberOfControlPoints () const |
| uint32_t | GetNumberOfHistogramBins () const |
| uint32_t | GetSplineOrder () const |
| bool | GetUseMaskLabel () const |
| double | GetWienerFilterNoise () const |
| N4BiasFieldCorrectionImageFilter () | |
| Self & | SetBiasFieldFullWidthAtHalfMaximum (double BiasFieldFullWidthAtHalfMaximum) |
| Self & | SetConvergenceThreshold (double ConvergenceThreshold) |
| Self & | SetMaskLabel (uint8_t MaskLabel) |
| Self & | SetMaximumNumberOfIterations (std::vector< uint32_t > MaximumNumberOfIterations) |
| Self & | SetNumberOfControlPoints (std::vector< uint32_t > NumberOfControlPoints) |
| Self & | SetNumberOfControlPoints (uint32_t value) |
| Self & | SetNumberOfHistogramBins (uint32_t NumberOfHistogramBins) |
| Self & | SetSplineOrder (uint32_t SplineOrder) |
| Self & | SetUseMaskLabel (bool UseMaskLabel) |
| Self & | SetWienerFilterNoise (double WienerFilterNoise) |
| std::string | ToString () const |
| Self & | UseMaskLabelOff () |
| Self & | UseMaskLabelOn () |
| virtual | ~N4BiasFieldCorrectionImageFilter () |
| Public Member Functions inherited from itk::simple::ImageFilter | |
| ImageFilter () | |
| virtual | ~ImageFilter ()=0 |
| Public Member Functions inherited from itk::simple::ProcessObject | |
| virtual void | Abort () |
| virtual int | AddCommand (itk::simple::EventEnum event, const std::function< void()> &func) |
| Directly add a callback to observe an event. More... | |
| virtual int | AddCommand (itk::simple::EventEnum event, itk::simple::Command &cmd) |
| Add a Command Object to observer the event. More... | |
| virtual float | GetProgress () const |
| An Active Measurement of the progress of execution. More... | |
| virtual bool | HasCommand (itk::simple::EventEnum event) const |
| Query of this object has any registered commands for event. More... | |
| ProcessObject () | |
| virtual void | RemoveAllCommands () |
| Remove all registered commands. More... | |
| virtual | ~ProcessObject () |
| virtual void | DebugOn () |
| virtual void | DebugOff () |
| virtual bool | GetDebug () const |
| virtual void | SetDebug (bool debugFlag) |
| virtual void | SetNumberOfThreads (unsigned int n) |
| virtual unsigned int | GetNumberOfThreads () const |
| virtual void | SetNumberOfWorkUnits (unsigned int n) |
| virtual unsigned int | GetNumberOfWorkUnits () const |
Private Types | |
| using | MemberFunctionType = Image(Self::*)(const Image *image, const Image *maskImage) |
Private Member Functions | |
| template<class TImageType > | |
| Image | ExecuteInternal (const Image *image, const Image *maskImage) |
Private Attributes | |
| double | m_BiasFieldFullWidthAtHalfMaximum {0.15} |
| double | m_ConvergenceThreshold {0.001} |
| uint8_t | m_MaskLabel {1} |
| std::vector< uint32_t > | m_MaximumNumberOfIterations {std::vector<uint32_t>(4,50)} |
| std::unique_ptr< detail::MemberFunctionFactory< MemberFunctionType > > | m_MemberFactory |
| std::vector< uint32_t > | m_NumberOfControlPoints {std::vector<uint32_t>(3, 4)} |
| uint32_t | m_NumberOfHistogramBins {200u} |
| uint32_t | m_SplineOrder {3u} |
| bool | m_UseMaskLabel {true} |
| double | m_WienerFilterNoise {0.01} |
Friends | |
| struct | detail::MemberFunctionAddressor< MemberFunctionType > |
Additional Inherited Members | |
| Static Public Member Functions inherited from itk::simple::ProcessObject | |
| static bool | GetGlobalDefaultDebug () |
| static void | GlobalDefaultDebugOff () |
| static void | GlobalDefaultDebugOn () |
| static void | SetGlobalDefaultDebug (bool debugFlag) |
| static void | GlobalWarningDisplayOn () |
| static void | GlobalWarningDisplayOff () |
| static void | SetGlobalWarningDisplay (bool flag) |
| static bool | GetGlobalWarningDisplay () |
| static double | GetGlobalDefaultCoordinateTolerance () |
| Access the global tolerance to determine congruent spaces. More... | |
| static void | SetGlobalDefaultCoordinateTolerance (double) |
| Access the global tolerance to determine congruent spaces. More... | |
| static double | GetGlobalDefaultDirectionTolerance () |
| Access the global tolerance to determine congruent spaces. More... | |
| static void | SetGlobalDefaultDirectionTolerance (double) |
| Access the global tolerance to determine congruent spaces. More... | |
| static bool | SetGlobalDefaultThreader (const std::string &threader) |
| Set/Get the default threader used for process objects. More... | |
| static std::string | GetGlobalDefaultThreader () |
| Set/Get the default threader used for process objects. More... | |
| static void | SetGlobalDefaultNumberOfThreads (unsigned int n) |
| static unsigned int | GetGlobalDefaultNumberOfThreads () |
| Set/Get the default threader used for process objects. More... | |
| Protected Member Functions inherited from itk::simple::ImageFilter | |
| void | CheckImageMatchingDimension (const Image &image1, const Image &image2, const std::string &image2Name) |
| void | CheckImageMatchingPixelType (const Image &image1, const Image &image2, const std::string &image2Name) |
| void | CheckImageMatchingSize (const Image &image1, const Image &image2, const std::string &image2Name) |
| Protected Member Functions inherited from itk::simple::ProcessObject | |
| virtual unsigned long | AddITKObserver (const itk::EventObject &, itk::Command *) |
| virtual itk::ProcessObject * | GetActiveProcess () |
| virtual void | OnActiveProcessDelete () |
| virtual void | onCommandDelete (const itk::simple::Command *cmd) noexcept |
| virtual void | PreUpdate (itk::ProcessObject *p) |
| virtual void | RemoveITKObserver (EventCommand &e) |
| Protected Member Functions inherited from itk::simple::NonCopyable | |
| NonCopyable ()=default | |
| NonCopyable (const NonCopyable &)=delete | |
| NonCopyable & | operator= (const NonCopyable &)=delete |
| Static Protected Member Functions inherited from itk::simple::ImageFilter | |
| template<class TImageType > | |
| static void | FixNonZeroIndex (TImageType *img) |
| Static Protected Member Functions inherited from itk::simple::ProcessObject | |
| template<class TImageType > | |
| static TImageType::ConstPointer | CastImageToITK (const Image &img) |
| template<class TPixelType , unsigned int VImageDimension, unsigned int VLength, template< typename, unsigned int > class TVector> | |
| static Image | CastITKToImage (itk::Image< TVector< TPixelType, VLength >, VImageDimension > *img) |
| template<class TImageType > | |
| static Image | CastITKToImage (TImageType *img) |
| static const itk::EventObject & | GetITKEventObject (EventEnum e) |
| template<typename T > | |
| static std::ostream & | ToStringHelper (std::ostream &os, const T &v) |
| static std::ostream & | ToStringHelper (std::ostream &os, const char &v) |
| static std::ostream & | ToStringHelper (std::ostream &os, const signed char &v) |
| static std::ostream & | ToStringHelper (std::ostream &os, const unsigned char &v) |
Implementation of the N4 bias field correction algorithm.
The nonparametric nonuniform intensity normalization (N3) algorithm, as introduced by Sled et al. in 1998 is a method for correcting nonuniformity associated with MR images. The algorithm assumes a simple parametric model (Gaussian) for the bias field and does not require tissue class segmentation. In addition, there are only a couple of parameters to tune with the default values performing quite well. N3 has been publicly available as a set of perl scripts (http://www.bic.mni.mcgill.ca/ServicesSoftwareAdvancedImageProcessingTools/HomePage )
The N4 algorithm, encapsulated with this class, is a variation of the original N3 algorithm with the additional benefits of an improved B-spline fitting routine which allows for multiple resolutions to be used during the correction process. We also modify the iterative update component of algorithm such that the residual bias field is continually updated
Notes for the user:
The basic algorithm iterates between sharpening the intensity histogram of the corrected input image and spatially smoothing those results with a B-spline scalar field estimate of the bias field.
Contributed by Nicholas J. Tustison, James C. Gee in the Insight Journal paper: https://hdl.handle.net/10380/3053
J.G. Sled, A.P. Zijdenbos and A.C. Evans. "A Nonparametric Method for Automatic Correction of Intensity Nonuniformity in Data" IEEE Transactions on Medical Imaging, Vol 17, No 1. Feb 1998.
N.J. Tustison, B.B. Avants, P.A. Cook, Y. Zheng, A. Egan, P.A. Yushkevich, and J.C. Gee. "N4ITK: Improved N3 Bias Correction" IEEE Transactions on Medical Imaging, 29(6):1310-1320, June 2010.
Definition at line 74 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
private |
Setup for member function dispatching
Definition at line 202 of file sitkN4BiasFieldCorrectionImageFilter.h.
Define the pixels types supported by this filter
Definition at line 86 of file sitkN4BiasFieldCorrectionImageFilter.h.
Definition at line 76 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
virtual |
Destructor
| itk::simple::N4BiasFieldCorrectionImageFilter::N4BiasFieldCorrectionImageFilter | ( | ) |
Default Constructor that takes no arguments and initializes default parameters
| Image itk::simple::N4BiasFieldCorrectionImageFilter::Execute | ( | const Image & | image, |
| const Image & | maskImage | ||
| ) |
Execute the filter on the input image
|
private |
|
inline |
Get the full width at half maximum parameter characterizing the width of the Gaussian deconvolution. Default = 0.15.
Definition at line 117 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
Get the convergence threshold. Convergence is determined by the coefficient of variation of the difference image between the current bias field estimate and the previous estimate. If this value is less than the specified threshold, the algorithm proceeds to the next fitting level or terminates if it is at the last level.
Definition at line 97 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
DeprecatedSet/Get mask label value. If a binary mask image is specified and if UseMaskValue is true, only those input image voxels corresponding with mask image values equal to MaskLabel are used in estimating the bias field. If a MaskImage is specified and UseMaskLabel is false, all input image voxels corresponding to non-zero voxels in the MaskImage are used in estimating the bias field. Default = 1.
Definition at line 184 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
Get the maximum number of iterations specified at each fitting level. Default = 50.
Definition at line 107 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inlinevirtual |
Name of this class
Implements itk::simple::ProcessObject.
Definition at line 187 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
Get the control point grid size defining the B-spline estimate of the scalar bias field. In each dimension, the B-spline mesh size is equal to the number of control points in that dimension minus the spline order. Default = 4 control points in each dimension for a mesh size of 1 in each dimension.
Definition at line 150 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
Get number of bins defining the log input intensity histogram. Default = 200.
Definition at line 137 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
Get the spline order defining the bias field estimate. Default = 3.
Definition at line 160 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
Use a mask label for identifying mask functionality. See SetMaskLabel. Defaults to true.
Definition at line 174 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
Get the noise estimate defining the Wiener filter. Default = 0.01.
Definition at line 127 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
Set the full width at half maximum parameter characterizing the width of the Gaussian deconvolution. Default = 0.15.
Definition at line 112 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
Set the convergence threshold. Convergence is determined by the coefficient of variation of the difference image between the current bias field estimate and the previous estimate. If this value is less than the specified threshold, the algorithm proceeds to the next fitting level or terminates if it is at the last level.
Definition at line 92 of file sitkN4BiasFieldCorrectionImageFilter.h.
Set/Get mask label value. If a binary mask image is specified and if UseMaskValue is true, only those input image voxels corresponding with mask image values equal to MaskLabel are used in estimating the bias field. If a MaskImage is specified and UseMaskLabel is false, all input image voxels corresponding to non-zero voxels in the MaskImage are used in estimating the bias field. Default = 1.
Definition at line 179 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
Set the maximum number of iterations specified at each fitting level. Default = 50.
Definition at line 102 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
Set the control point grid size defining the B-spline estimate of the scalar bias field. In each dimension, the B-spline mesh size is equal to the number of control points in that dimension minus the spline order. Default = 4 control points in each dimension for a mesh size of 1 in each dimension.
Definition at line 142 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
Set the values of the NumberOfControlPoints vector all to value
Definition at line 145 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
Set number of bins defining the log input intensity histogram. Default = 200.
Definition at line 132 of file sitkN4BiasFieldCorrectionImageFilter.h.
Set the spline order defining the bias field estimate. Default = 3.
Definition at line 155 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
Use a mask label for identifying mask functionality. See SetMaskLabel. Defaults to true.
Definition at line 165 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
Set the noise estimate defining the Wiener filter. Default = 0.01.
Definition at line 122 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
virtual |
Print ourselves out
Reimplemented from itk::simple::ProcessObject.
|
inline |
Definition at line 169 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
inline |
Set the value of UseMaskLabel to true or false respectfully.
Definition at line 168 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
friend |
Definition at line 206 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
private |
Definition at line 218 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
private |
Definition at line 212 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
private |
Definition at line 234 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
private |
Definition at line 215 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
private |
Definition at line 208 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
private |
Definition at line 227 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
private |
Definition at line 224 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
private |
Definition at line 230 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
private |
Definition at line 232 of file sitkN4BiasFieldCorrectionImageFilter.h.
|
private |
Definition at line 221 of file sitkN4BiasFieldCorrectionImageFilter.h.


When we trigger the button, the JavaScript will try to work out the 43rd Fibonacci sequence.
Whith workers, The UI shread is not block


Chapter 5 Bootstrapping
5.1 Warning
Warning:
This page is for my own personal study purpose. Distribution is prohibited.
5.2 Introduction
The following note is made when I was studying Bret Larget’s note posted online. http://pages.stat.wisc.edu/~larget/stat302/chap3.pdf
He used the data from LOck5data as an example.
## 'data.frame': 500 obs. of 5 variables:
## $ City : Factor w/ 1 level "Atlanta": 1 1 1 1 1 1 1 1 1 1 ...
## $ Age : int 19 55 48 45 48 43 48 41 47 39 ...
## $ Distance: int 10 45 12 4 15 33 15 4 25 1 ...
## $ Time : int 15 60 45 10 30 60 45 10 25 15 ...
## $ Sex : Factor w/ 2 levels "F","M": 2 2 2 1 1 2 2 1 2 1 ...
## [1] 29.11
Now, he sampled a (b X n) table. Note that, the Atlanta data has 500 row, as it has 500 observations (or, people). But, in the following new matrix, it is a (1000 times 500) table. Also, it should be noted that the logic of sample function in R. This webpage provides some insight into this function. Basically, the following R code randomly sample a bigger sample of (1000 times 500) from those 500 data points. After that, the matrix function put such (1000 times 500) data points into a matrix of (1000 times 500).
B = 1000
n = nrow(CommuteAtlanta)
boot.samples = matrix(sample(CommuteAtlanta$Time, size = B * n, replace = TRUE),
B, n)
Next, we need to calculate the mean for each row. Remember, we have 1000 rows. Note that, 1 in the apply function indicates that we calculate means on each row, whereas 2 indicates to each column.
We can then plot all the means.
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 3.6.3
ggplot(data.frame(meanTime = boot.statistics),aes(x=meanTime)) +
geom_histogram(binwidth=0.25,aes(y=..density..)) +
geom_density(color="red")
## [1] 0.9409072
## [1] 1.9
## [1] 27.2 31.0
5.3 Normal distribution, SD, SE
Note, if we do not use bootstraping, we can use the standard CI formula (https://www.mathsisfun.com/data/confidence-interval.html). This formula assumes normal distribution. As we can see, this is close to the result based on the bootstrapping method.
\[\overline{X} \pm Z \frac{S}{\sqrt{n}}=29.11 \pm 1.96 \frac{20.72}{\sqrt{500}}=27.29, 30.93\]
Note that, in the following, the author used 2 times SE to calculate the CI. The relationship between SD and SE:
“Now the sample mean will vary from sample to sample; the way this variation occurs is described by the “sampling distribution” of the mean. We can estimate how much sample means will vary from the standard deviation of this sampling distribution, which we call the standard error (SE) of the estimate of the mean. As the standard error is a type of standard deviation, confusion is understandable. Another way of considering the standard error is as a measure of the precision of the sample mean." (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1255808/)
boot.mean = function(x,B,binwidth=NULL)
{
n = length(x)
boot.samples = matrix( sample(x,size=n*B,replace=TRUE), B, n)
boot.statistics = apply(boot.samples,1,mean)
se = sd(boot.statistics)
require(ggplot2)
if ( is.null(binwidth) )
binwidth = diff(range(boot.statistics))/30
p = ggplot(data.frame(x=boot.statistics),aes(x=x)) +
geom_histogram(aes(y=..density..),binwidth=binwidth) + geom_density(color="red")
plot(p)
interval = mean(x) + c(-1,1)*2*se
print( interval )
return( list(boot.statistics = boot.statistics, interval=interval, se=se, plot=p) )
}
## [1] 16.93922 19.37278
5.4 Sample function
To understand the function of sample in R.
## [1] 2 10 10 15 7 15 17 8 11 15 1 11 6 13 9 3 7 13 7 10
The following uses loop to do the resampling. It uses sample function to index the numbers that they want to sample from the original sample. That is, [] suggests the indexing.
n = length(CommuteAtlanta$Distance)
B = 1000
result = rep(NA, B)
for (i in 1:B)
{
boot.sample = sample(n, replace = TRUE)
result[i] = mean(CommuteAtlanta$Distance[boot.sample])
}
with(CommuteAtlanta, mean(Distance) + c(-1, 1) * 2 * sd(result))
## [1] 16.98167 19.33033
5.5 Proportion
So far, we have dealed with means. How about porpotions?Remember that, when calculating means, it starts with a single column of data to calculate the mean. Similarly, when calculating porpotions, you can just use a single column of data.
## [1] 0.1905222 0.5428111
However, if we have 48 students (i.e., 48 observations) and thus we have a bigger sample. However, how can we do re-sampling? Based on the note, it is kind of simple. They group them together and then resample from it. Note that, when they re-sampling, the programming do not distinguish the difference between 48 observations. But just combined them as a single column (741+699=1440), and then generate a very long column (1440 times 1000) and then reshape it into a matrix (1440 time 1000). This is the basic logic of the boot.mean function.
## [1] 0.4890827 0.5400839
5.6 boot package
After having a basic idea of boostrapping, we can then use the package of boot.
## Warning: package 'boot' was built under R version 3.6.3
data(CommuteAtlanta)
my.mean = function(x, indices)
{
return( mean( x[indices] ) )
}
time.boot = boot(CommuteAtlanta$Time, my.mean, 10000)
boot.ci(time.boot)
## Warning in boot.ci(time.boot): bootstrap variances needed for studentized
## intervals
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 10000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = time.boot)
##
## Intervals :
## Level Normal Basic
## 95% (27.29, 30.93 ) (27.23, 30.87 )
##
## Level Percentile BCa
## 95% (27.35, 30.99 ) (27.44, 31.12 )
## Calculations and Intervals on Original Scale
5.7 Concept of Percentile
require(Lock5Data)
data(ImmuneTea)
tea = with(ImmuneTea, InterferonGamma[Drink=="Tea"])
coffee = with(ImmuneTea, InterferonGamma[Drink=="Coffee"])
tea.mean = mean(tea)
coffee.mean = mean(coffee)
tea.n = length(tea)
coffee.n = length(coffee)
B = 500
# create empty arrays for the means of each sample
tea.boot = numeric(B)
coffee.boot = numeric(B)
# Use a for loop to take the samples
for ( i in 1:B )
{
tea.boot[i] = mean(sample(tea,size=tea.n,replace=TRUE))
coffee.boot[i] = mean(sample(coffee,size=coffee.n,replace=TRUE))
}
boot.stat = tea.boot - coffee.boot
boot.stat
## [1] 12.9636364 22.3000000 6.8272727 13.4909091 14.2363636
## [6] 11.2545455 11.4818182 14.0818182 23.4272727 20.9727273
## [11] 28.1090909 17.1818182 22.4454545 14.6363636 14.6636364
## [16] 1.3454545 10.8181818 12.9090909 30.2545455 28.9181818
## [21] 14.7363636 13.6909091 -1.0090909 21.9545455 22.9909091
## [26] 20.8000000 7.0454545 35.7727273 1.7090909 11.7363636
## [31] 27.1454545 8.5181818 26.7545455 19.4363636 11.3181818
## [36] 20.6909091 11.6727273 13.0363636 8.3818182 26.4000000
## [41] 7.2818182 16.6090909 18.8090909 12.2727273 15.8818182
## [46] 21.0454545 2.8909091 20.5181818 14.1090909 15.2454545
## [51] 17.2909091 16.8181818 3.4363636 14.9181818 29.0545455
## [56] 13.0363636 30.0090909 8.6545455 2.0818182 16.8909091
## [61] 27.1818182 35.0090909 17.0909091 21.4909091 16.7000000
## [66] 19.9818182 5.4363636 -2.1727273 24.3090909 29.1545455
## [71] 29.1363636 12.8545455 16.7545455 16.7818182 20.7818182
## [76] 25.7636364 18.0636364 25.3272727 19.8818182 13.3454545
## [81] 15.7000000 19.3181818 2.7545455 13.2272727 22.4727273
## [86] 16.9454545 25.3727273 4.7636364 23.9454545 22.4727273
## [91] 20.4636364 27.4909091 15.9636364 7.2272727 2.1454545
## [96] 18.0000000 6.2909091 17.4000000 23.3090909 15.7272727
## [101] 10.1454545 7.7272727 18.3000000 11.7363636 26.3454545
## [106] 24.6818182 17.4363636 23.0181818 32.7909091 4.8818182
## [111] 23.9636364 0.8272727 20.0545455 15.6636364 14.5454545
## [116] 12.0818182 21.2545455 10.6545455 10.3090909 18.9545455
## [121] 3.9545455 14.7545455 31.0181818 15.0363636 12.2090909
## [126] 17.0000000 11.7636364 20.1909091 13.3000000 13.5272727
## [131] 7.7636364 15.2272727 12.8363636 23.4818182 14.3181818
## [136] 21.9909091 16.2000000 14.9636364 28.8909091 39.7727273
## [141] 17.9545455 5.4272727 24.9545455 22.3636364 16.3454545
## [146] 9.9909091 12.0272727 9.9363636 23.3636364 6.2636364
## [151] 12.0090909 19.4818182 9.1272727 9.5636364 26.6545455
## [156] 15.5454545 19.7363636 23.2727273 20.2272727 22.7090909
## [161] 16.8090909 22.2818182 24.8818182 14.4727273 3.5363636
## [166] 30.0545455 21.9363636 16.6545455 19.8181818 18.3363636
## [171] 6.4909091 6.4818182 22.2818182 9.3181818 28.8272727
## [176] 20.2909091 19.7545455 8.7909091 24.3545455 31.1363636
## [181] 15.4272727 4.5363636 22.0363636 20.1818182 12.5272727
## [186] 11.7818182 23.1000000 18.2363636 20.6545455 17.4454545
## [191] 33.5454545 17.3181818 22.8545455 13.0818182 10.5363636
## [196] 14.4909091 16.1727273 9.8363636 13.8818182 3.3090909
## [201] 8.6818182 24.9636364 21.8636364 27.9363636 25.6909091
## [206] 21.0818182 24.9636364 27.6090909 8.0363636 8.0454545
## [211] 20.3272727 1.1909091 11.2909091 16.4181818 25.5000000
## [216] 16.9272727 18.3272727 17.2909091 13.7636364 11.2818182
## [221] 16.2727273 4.5909091 12.9727273 24.8454545 14.3272727
## [226] 4.4727273 34.7181818 7.3272727 26.3090909 13.0727273
## [231] 19.8636364 15.5363636 23.7545455 27.3727273 22.1636364
## [236] 21.8000000 13.3363636 19.4818182 16.6090909 12.8909091
## [241] 2.9636364 32.4181818 -10.1181818 26.1090909 13.9090909
## [246] 6.7909091 10.9090909 19.2454545 17.5181818 9.1636364
## [251] 0.2545455 20.9181818 9.7090909 15.0363636 15.3818182
## [256] 17.9363636 12.2181818 13.2636364 26.3454545 6.6909091
## [261] 14.4363636 12.9636364 20.6000000 0.8636364 5.0909091
## [266] 25.1818182 2.3727273 30.5272727 3.9272727 34.3818182
## [271] 6.4272727 28.4636364 24.8454545 16.3636364 18.9272727
## [276] 16.6090909 2.7181818 28.8454545 18.9272727 4.5181818
## [281] 11.7545455 17.4636364 21.4363636 20.0181818 13.6727273
## [286] 19.5818182 1.8818182 29.5909091 25.1363636 20.2727273
## [291] 24.6363636 19.4363636 22.7818182 23.1090909 25.5727273
## [296] 13.5636364 15.6363636 20.5181818 24.2636364 9.4000000
## [301] 23.6272727 9.7636364 29.7636364 4.6181818 18.3363636
## [306] 8.5818182 11.2454545 24.6181818 17.6000000 11.2818182
## [311] 1.6272727 25.8818182 21.7636364 32.9272727 11.3090909
## [316] 16.5181818 17.1636364 17.0818182 10.5545455 21.0181818
## [321] 26.3909091 19.1000000 20.3909091 11.6636364 6.4181818
## [326] 12.9636364 16.8909091 13.7818182 15.9363636 20.5545455
## [331] 19.2545455 10.9454545 20.6363636 21.0545455 27.9454545
## [336] 16.3000000 27.6272727 18.1636364 41.1090909 12.5181818
## [341] 8.6818182 26.4727273 15.7454545 7.4818182 15.9181818
## [346] 26.7727273 17.3181818 17.3272727 19.1545455 13.3363636
## [351] 25.3636364 16.4454545 11.6545455 23.9000000 16.2636364
## [356] 21.6636364 15.9090909 19.8727273 18.3363636 15.9909091
## [361] 12.9272727 12.1909091 6.6727273 20.3454545 27.7818182
## [366] 26.9727273 24.2000000 2.4272727 17.5636364 22.4181818
## [371] 17.9909091 29.8545455 22.6909091 29.8272727 23.5454545
## [376] 16.2727273 12.3636364 14.0818182 20.3181818 31.6000000
## [381] 19.5090909 26.3727273 20.8454545 12.1909091 17.0090909
## [386] 12.2454545 18.4818182 27.8000000 24.1363636 16.6272727
## [391] 23.6727273 21.6909091 -0.2636364 24.6727273 35.4545455
## [396] 17.5000000 2.9000000 24.2454545 31.1727273 12.0636364
## [401] 27.9363636 20.5000000 0.9545455 15.7727273 17.0090909
## [406] 22.8090909 15.1727273 16.3181818 15.2090909 15.0090909
## [411] 14.9909091 7.2454545 16.3363636 17.7545455 23.5272727
## [416] 16.4000000 22.7272727 21.2454545 25.9909091 24.7727273
## [421] 25.1545455 14.3090909 22.0727273 19.8818182 6.8272727
## [426] 25.7818182 30.4636364 11.1000000 7.0363636 24.6818182
## [431] 21.1727273 14.5000000 26.3000000 28.1181818 17.2909091
## [436] 11.9272727 24.2636364 13.4909091 9.4545455 21.1636364
## [441] 20.9363636 11.6636364 25.6000000 20.7272727 23.4272727
## [446] 4.2545455 22.9545455 6.3363636 14.1181818 23.8272727
## [451] 21.5909091 21.3363636 15.7363636 23.6909091 16.1181818
## [456] 11.8363636 11.6909091 18.1545455 9.9818182 25.2000000
## [461] 17.7000000 19.0727273 16.0181818 14.4909091 5.5909091
## [466] 11.6636364 15.2000000 21.9818182 5.5363636 16.5181818
## [471] 15.4545455 11.8454545 25.7545455 13.3909091 11.8545455
## [476] 11.4090909 8.6727273 29.4636364 17.4272727 13.2363636
## [481] 15.9636364 17.8454545 25.4818182 23.0818182 6.9545455
## [486] 9.7363636 17.2000000 16.1909091 19.7363636 22.8909091
## [491] 3.4818182 23.1909091 19.5727273 8.1818182 21.4545455
## [496] 23.6636364 24.3000000 13.2636364 20.3363636 11.5818182
# Find endpoints for 90%, 95%, and 99% bootstrap confidence intervals using percentiles.
# 90%: 5% 95%
quantile(boot.stat,c(0.05,0.95))
## 5% 95%
## 3.533636 29.170000
## 2.5% 97.5%
## 1.976818 31.155455
## 0.5% 99.5%
## -0.6400909 35.6152273
5.8 Bootstrapping for correlation interval
Some data and code are from: https://blog.methodsconsultants.com/posts/understanding-bootstrap-confidence-interval-output-from-the-r-boot-package/
## Student LSAT GPA
## 1 1 576 3.39
## 2 2 635 3.30
## 3 3 558 2.81
## 4 4 578 3.03
## 5 5 666 3.44
## 6 6 580 3.07
## 7 7 555 3.00
## 8 8 661 3.43
## 9 9 651 3.36
## 10 10 605 3.13
## 11 11 653 3.12
## 12 12 575 2.74
## 13 13 545 2.76
## 14 14 572 2.88
## 15 15 594 2.96
##
## Pearson's product-moment correlation
##
## data: data_correlation$LSAT and data_correlation$GPA
## t = 4.4413, df = 13, p-value = 0.0006651
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4385108 0.9219648
## sample estimates:
## cor
## 0.7763745
In the following, I will write my own code to execute the bootstrapping. I set the bootstrapping number only 500, for illustrative purposes. As we can see, the distribution is not symmetrical.
As we can see, the quantile result and c(-1, 1) X 2 are not the same, as the latter assumes symmetrical distribution. However, based on the histogram, we know it is not the case. Thus, quantile would be more appropriate. You can compare the result with that from the boot function.
## [1] 15
set.seed(12345)
B = 500
result = rep(NA, B)
for (i in 1:B)
{
boot.sample = sample(n_row, replace = TRUE)
result_temp = cor.test(data_correlation[boot.sample,]$LSAT,data_correlation[boot.sample,]$GPA)
result[i]=result_temp$estimate
}
hist(result)
## 2.5% 97.5%
## 0.4369293 0.9556859
## [1] 0.1342631
## [1] 0.5107704 1.0370816
## [1] 0.7763745
## [1] 0.5132189 1.0395301
# why add 0.005? Not sure. The following is from the webpage. Later note: please refer to the webpage, as it provides the logic of basic interval.
0.776+0.005+c(-1, 1) * 1.96 * 0.131
## [1] 0.52424 1.03776
In the blog mentioned above, the author used the boot function in R. For the logic of basic interval, please refer to: https://blog.methodsconsultants.com/posts/understanding-bootstrap-confidence-interval-output-from-the-r-boot-package/
library(boot)
get_r <- function(data, indices, x, y) {
d <- data[indices, ]
r <- round(as.numeric(cor(d[x], d[y])), 3)
r}
set.seed(12345)
boot_out <- boot(
data_correlation,
x = "LSAT",
y = "GPA",
R = 500,
statistic = get_r
)
boot.ci(boot_out)
## Warning in boot.ci(boot_out): bootstrap variances needed for studentized
## intervals
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 500 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = boot_out)
##
## Intervals :
## Level Normal Basic
## 95% ( 0.5247, 1.0368 ) ( 0.5900, 1.0911 )
##
## Level Percentile BCa
## 95% ( 0.4609, 0.9620 ) ( 0.3948, 0.9443 )
## Calculations and Intervals on Original Scale
## Some BCa intervals may be unstable


SBI cuts MCLR by 5 bps across all tenors, reduces fixed deposit rates
With this reduction, the one year MCLR, to which most of its loan prices are linked, will come down to 8 per cent
by Press Trust of IndiaCountry's largest lender State Bank of India on Friday reduced its marginal cost of fund based lending rate (MCLR) by 5 basis points across all tenors, effective November 10, and sharply slashed the deposits pricing between 15 and 75 basis points.
This is the seventh consecutive cut in lending rates by the bank this fiscal.
With this reduction, the one year MCLR, to which most of its loan prices are linked, will come down to 8 per cent, the bank said in a statement.
The bank also revised its interest rates on term deposits on account of adequate liquidity in the system. The new deposit rates will also be effective from November 10.
It has reduced interest rate on retail term deposit by 15 basis points for one year to less than two years' tenor.
Bulk term deposit interest rates have been reduced by 30 to 75 bps across tenors, the bank said.
(Only the headline and picture of this report may have been reworked by the Business Standard staff; the rest of the content is auto-generated from a syndicated feed.)


Recently, Andrew Gelman (@StatModeling at Twitter) published a post with this title - ““Dear Major Textbook Publisher”: A Rant”.
In essence, he discussed how a good stats intro text book should be like. And complained about the low quality of some many textbooks out there.
As I am also in the business guilty of coming up with stats curriculum for my students (applied courses for business type students mostly), I discuss some thoughts for “stats curriculum developers” (like myself).
Of course, the GAISE report provides an authoritative overview on what is considered helpful (by the ASA and by many others). My compilation builds on that but adds/drops some points.
Principles in stats curriculum
-
statistical thinking above anything else. What is statistics all about?  But wait, there is variability, stuff comes in different shades of grey. How certain (or rather uncertain) are we about some hypothesis? Ah, that’s why probability comes into play. My grand grandpa did smoke, loved his beer, and see, turned 94 recently, and you keep telling me that would not be enough to “demonstrate”…. Ah, OK… What does statistics say about causality? (not much, to my mind.) This commitment implies that procedural know-how need be downgraded (we cannot have the cake and eat it).
-
Problem solving. Give students some dataset, some question, and let them investigate. Discuss, refine, correct their actions. Explain afterwards better ways (I heard this was called inductive learning).
-
Real data, real problems. Don’t stay with urns and coin tosses, fine as a starter, but then go on to real data, and real problems. For example, I used extraversion and number of Facebook friends as a running example; based on the data students gave to this survey. So they analyzed their own data. If you want to make use of the data: you are welcome, here it is (free as in free).
-
A lot of ‘guide at your side’ (activation). Rather then ‘sage on stage’. However, I found that surprisingly many students appear overwhelmed if the “activation dose” gets too strong. So I try to balance the time when I speak, students work on their own or when they work in groups. Honestly, what’s the advantage of listening to me in person compared to watching a Youtube video? OK, at times, I may respond (Youtube not so much), but the real benefit would be joint problem solving. So let’s do that.
-
Technology (R). Computers are already ubiquitous, and penetrating even more in everyday life. So we as teachers should not dream of the “good old days” where we solved triple integrals with formulas as wide as my arm span (I never did). Future is likely to dominated by computers when it comes to our working style or working tools. So let’s use them and show them how to code. A bit, at least (many are frightened though initially). R is a great environment for that purpose.
-
Ongoing selfassessment. Cognitive and educational psychology points out that frequent assessment helps a lot to learn some stuff [citation missing]. So let’s give the students ongoing feedback. For example, what I do is I use a Google form with quiz feature for that purpose. Minimal preparation from my side for tomorrow’s class, because that’s all pre-prepared.
-
Focus on/start with intuition. Don’t start by throwing some strange (\LaTeX typeset, of course) equation in the students’ faces (unless you are teaching in some math heavy fields, but not in business). Start by explaining the essence in simple words, and giving an intuition. For example, variance can be depicted as (something like) “average deviation sticks”; correlation as “average deviation rectangles” (see this post). When the essence is understood, refine. Now come your long equations. This post speaks of a refinement process from “sorta-true” to very true. Quite true.
-
Be nice. I think there is no point in being overly austere; surely you must be joking, Mr. Feynman. Even stats classes can be fun… Personally, I find Andy Field’s tone in his textbook quite refreshing.
Aims of a stats curriculum
If your class belongs to a business type of field, don’t expect they all will turn to science as a career now. More realistically, they will have to face some stats questions in their working live. Two things may happen then.
First, they hire someone who really knows what to do (at least, someone who thinks so). Then your students of today need be prepared to speak to that expert. They need enough understanding to get the gist of the experts strategy. They need be able to ask some good questions. But the clearly do not need to understand many details, if any.
Second scenario. The future ego of your today-student will by her or himself do some stats problem solving. I think even our applied business students should have some working knowledge, maybe on regression plus some Random Forests, based on some general ideas plus visualization and data wrangling.
In sum, critical understanding is the first and some partial, locally constrained actionable know-how is second.
Content
Some thought blobs.
-
Descriptive stats can be paired with some cool visualization. I like
qplot
fromggplot2
because it is easy start, but allows to combine different steps, which fosters creative problem solving (I hope). -
Data wrangling.
dplyr
is great because with ~5 functions you can rule them all see here for an intro. This is, a couple of easy functions, such asfilter
,summarise
, orcount
do the bulk of typical data preparation tasks (yes, these functions pretty much do what their name suggests). -
wee p, I tend(ed) to focus on this point in class, not because I love it, but because it is still the way papers are built. So students need to understand it. And it is not easy to comprehend. If they do not understand, how can the new generation overcome the problems of the past. I try to lay bare the problems of the p-value (mainly that it is not the answer we need).
-
Bayes. It is (probably) too strong to state that without understanding Bayes, one cannot understand the p-value. But understanding posterior probabilities is a relief, as the p-value can now be accepted as what it is: some conditional probability (of some data given some hypothesis). Bayes puts p into context. But Bayes, I fear – not being deeply based in Bayes – can necessitate a lot of ground work, time consuming fundamentals, if you really want some working knowledge. That’s why I, for the moment at least, take some shortcut, and present only the basic concept, alongside, maybe, some off-the-shelf software such as JASP.
-
Basic statistical learning ideas. Overfitting is an essential concept; it should be grasped. There are a number of similarly important points (particularly resampling, cross-validation, and bias-variance trade-off, for example); the book Introduction to Statistical Learning is of great help here.
-
Algorithmic methods, in the sense of Breiman’s two cultures. One particular nice candidate here are tree-based methods, including notorious Random Forests. Conceptually easy yet high predictive performance in some situations.
-
Text mining. 90% of all is something, I mean, 90% of all information is unstructured, mostly image and text, some say. So let’s look at that. That’s fun, not yet so much looked at, and for business students of some relevance. Julia Silge’s and David Robinsons’s book on Textmining appears great (and vast, for that matter).
-
Repro crisis. Let’s not be silent about the problems we face in Science. True, there are great challenges, but hey, what we need is not less, but more science. A good dose of open science can and should be plugged in here.
Byebye Powerpoint
In my experience, it can be difficult by bypass PowerPoint, even when you are preparing a curriculum.
But for the next curriculum, my plan is to use RMarkdown to write a real scriptum, not only slides. That will be lots of work. But then it will be easy to come up with lean slides, without much text, because there will be the script for the details. Then, slides, can be used what they are intended for: convey one idea per slide, no or little text, primarily consisting of graphics or schema. The slides can be prepared using RMarkdown, too. RMarkdown is not particularly fit for that purpose, though. I mean, you will not have the full features of PPT, and not the ease. But you will not need it. Simple slides can very well be made using RMarkdown. Once there is a real “mini-book”, aka script, no detailed slides are needed any more.
Git and friends can then be used for version control, collaboration, and so on…
However, a lot of colleagues will complain, I fear. Let’s see. I hope to convince them that this is a far better way. Bye bye PowerPoint.
Conclusions
All that here are some quick thoughts in progress. They are more to make up my mind, and to stimulate your thinking. I believe all that written here, but I am sure, I missed a lot and made a number of mistakes. Let me know your thoughts!
Acknowledgements
I learnt a lot, both statistically as well as didactically, from my colleague Karsten Luebke, whose thoughts also went into this post.


| (MV) MALDIVES |
| Subdivision | 1 capital (en) / capitale (fr) / verirash (dv); 7 provinces (en) / province (fr) / provins (dv); 20 administrative atolls (en) / atoll administratif (fr) / atholhu (dv) |
| List Source | IGN 1992; http://en.homeaffairs.gov.mv/; http://en.wikipedia.org/wiki/Administrative_divisions_of_the_Maldives ; Statoids (March, 2009); Official Atlas of the Maldives published in 2008 by the Ministry of Planning and Development |
| Code Source | IATA (for the capital); ISO/TC 46/WG 2 Secretariat (*) + ISO 3166/MA |
| Romanization system | Maldivian BGN/PCGN 1988 |
| Country | Subdivision | Name | Level |
| MV | 00 | Alifu Dhaalu | Administrative atoll |
| MV | 01 | Seenu | Administrative atoll |
| MV | 02 | Alifu Alifu | Administrative atoll |
| MV | 03 | Lhaviyani | Administrative atoll |
| MV | 04 | Vaavu | Administrative atoll |
| MV | 05 | Laamu | Administrative atoll |
| MV | 07 | Haa Alifu | Administrative atoll |
| MV | 08 | Thaa | Administrative atoll |
| MV | 12 | Meemu | Administrative atoll |
| MV | 13 | Raa | Administrative atoll |
| MV | 14 | Faafu | Administrative atoll |
| MV | 17 | Dhaalu | Administrative atoll |
| MV | 20 | Baa | Administrative atoll |
| MV | 23 | Haa Dhaalu | Administrative atoll |
| MV | 24 | Shaviyani | Administrative atoll |
| MV | 25 | Noonu | Administrative atoll |
| MV | 26 | Kaafu | Administrative atoll |
| MV | 27 | Gaaf Alifu | Administrative atoll |
| MV | 28 | Gaafu Dhaalu | Administrative atoll |
| MV | 29 | Gnaviyani | Administrative atoll |
| MV | MLE | Male | Capital |
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Scroll a little every frame. This will be slower as the virtual scroller needs to unlock the correct element(s) but hopefully fast enough to avoid dropping a frame.


Stereotype Threat
How stereotypes shape intellectual identity and performance!!!Date: 11/15/2014
Stereotype threat is a situational predicament in which people are or feel themselves to be at risk of confirming negative stereotypes about their social group.What Is Stereotype Threat?
Stereotype threat is a self-confirming belief that one may be evaluated based on a negative stereotype. Because of stereotype threat, Person who are reminded of negative stereotypes about their race or gender before taking a test perform worse, especially if the negative stereotype is one that makes them feel inferior. The anxiety of confirming a negative stereotype seems to be the driving force behind stereotype threat. That anxiety causes they to perform worse than they would have otherwise. Stereotype threat is a reminder of how social forces can influence test scores, including intelligence scores.
My Personal Stereotype Threat?
I am fortunate enough to have not experienced Stereotype Threat in my childhood; But I have experienced it! while started my flying carrer back in New Zealand.
I was not aware of any difficulties as pilot. When I started with my career I realized flying an aircraft is not easy, always there is challenging situation with changing weather and mechanical failure. Due to anxiety my confidence level decreased and I was taking longer time to learn flying maneuvers. I realized my problem and started reading Human factors and discussing with my instructor. It seems that becoming aware of stereotype threat can help reduce some of the effects. While going for flying I started adopting positive role model and more over I am thankful to my instructor by encouraging me, that I can handle difficult task and achieve high standards.
My Realization
I realize nothing is impossible if tried hard enough to achieve. I am simply an individual who happens to not have any flying experience. I am a student at Flight training manawatu learning to fly many people can’t even think of. I was struggling because it was new to me, and that doesn't necessarily have to have meaning. Although it's very elementary, my biggest realization is that I am who I am at this point in time. All I can do is strive to be better than I was yesterday. I keep my motivated and dont give up easily.
Tips to avoid Stereotype Threat
- Providing role models.
- Emphasizing high standards with assurances about capability for meeting them.
- Encouraging self-affirmation.
- Emphasizing an incremental view of intelligence.
- Providing external attributions for difficulty.


Ardenne High past student is 2020 Rhodes Scholar
Samuel Bailey is the 2020 Rhodes Scholar.
Governor General Sir Patrick Allen made the announcement moments ago at a ceremony at King's House.
The 23-year-old attended Ardenne High School before moving on to the University of the West Indies, Mona and then to the Cave Hill Campus in Barbados where he studied law.
He then graduated from the Norman Manley Law School and was called to the bar in Jamaica last Friday.
He is now employed at the Caribbean Court of Justice in Trinidad and Tobago.
"Honestly, it has not fully settled in yet, because I had gone for it last year and got the runners up spot. So I wasn't even anticipating getting it, but now I'm relieved and happy," Bailey told The Gleaner.
The Rhodes Scholarship is an international postgraduate award for students to study at the University of Oxford.
It was established in 1902
Bailey joins a long list of Jamaicans to be selected for the prestigious scholarship including Ronald Thwaites in 1968, Delroy Chuck in 1973 and current finance and planning minister Dr Nigel Clarke.


|
Class Summary |
| AbsoluteSchedule |
This implementation of Schedule represents a Schedule for an
job that occurs once, at a specific time. |
| CalendarSchedule |
A CalendarSchedule expresses a repeating job in terms of months,
days, hours, and minutes. |
| PeriodicSchedule |
This implementation of Schedule represents a Schedule for an
job that repeats at regular intervals. |
| RelativeSchedule |
This implementation of Schedule represents a Schedule for an
job that occurs once, after a specified delay from the current
time. |
| SchedulableService |
A convenience base class for generic services that are schedulable. |
| ScheduledJob |
A ScheduledJob represents all the information about a job
in the scheduler. |
| SchedulePropertyValueParser |
This parser will turn a String into one of the implementations
of Schedule. |
| SchedulePropertyValueParser.ScheduleFactoryImpl |
This is a generic implementation of the schedulefactory that just checks
the first work of the schedule string and if it is what is expected,
returns true. |
| Scheduler |
A Scheduler keeps track of ScheduledJobs and executes those jobs
according to their Schedules. |
| SingletonSchedulableService |
This class extends SchedulableService to allow multiple Dynamo servers to
run the same scheduled service, while guaranteeing that only one instance
of the service will perform the scheduled task at any given time. |


Draw the plan of your space. Click on the canvas to create the corners.
Hit
Enter
to finish! Hit
Backspace
to delete the shape. Hit
Arrow Left
to Undo.
Continue


|
This is very simple theme for webmin based on Lynx
appearence. It reduces the presence of images to achieve faster
navigation, and tries to simplify webmin look-and-feel to suit
Lynx browser users.
Webmin theme based on Lynx
Note: It keeps module index visible in every webmin page
download
|


One of the cool / frustrating aspects of low level language programming is dealing with the attributes of the OS itself. The thought occurred to me that this stuff starts to take on an indirect mystical sort of reverence.
Perform a calloc blessing of the enigmatic object. Invoke the reference of attribute. Set the value of variable. Withdraw the memory of reference. End with the rites of free, restoring to null.
A programmer is a bit like a machine, no?
The philosophy de jure endorsed by golang et al. is probably the right one. Hide the details and let people get down to business.
Still, I feel like something is lost from the equation. The kernel is a real element functioning off of very real hardware. Another layer. That makes it tricky. Speaking of hardware tricks...
One day I was playing around with the Device Tree and OpenFirmware module, and I noticed the following error messages suddenly appear upon running certain unit tests included with the mentioned kernel module:
## dt-test ### start of unittest - you will see error messages
OF:/testcase-data/phandle-tests/consumer-a: could not get #phandle-cells-missing for
OF:/testcase-data/phandle-tests/consumer-a: could not get #phandle-cells-missing for
OF:/testcase-data/phandle-tests/consumer-a: could not find phandle
OF:/testcase-data/phandle-tests/consumer-a: could not find phandle
OF:/testcase-data/phandle-tests/consumer-a: arguments longer than property
OF:/testcase-data/phandle-tests/consumer-a: arguments longer than property
irq: no irq domain found for /testcase-data/interrupts/intc0 !
OF: overlay: overlay_is_topmost: #5 clashes #6 @/testcase-data/overlay-node/test-bus
OF: overlay: overlay #5 is not topmost
## dt-test ### FAIL of_unittest_overlay_high_level():2131 overlay_base_root not init
## dt-test ### end of unittest - 148 passed, 1 failed
Initially I had expected this might be a hardware issue, but I quickly ruled that out. Since I was using a custom kernel, a fast grep over the code found the answer. There were some newly added unit tests, which had a few bugs that were more or less immediately patched by the developers who had wrote them.
The warning messages thrown seem to be related to this and so the solution in my case was simply to disable the tests and rebuild the kernel. Done and done.
Actually kind of amazing how quick progress occurs in a project of this size. Frankly impressed there aren't more errors, and the code was reasonably clean too.
I sort of felt motivated to keep exploring. So I glanced around at other mild error messages in hopes of gleaming a few nifty tidbits, and found this one:
AMD-Vi: Event logged [ IO_PAGE_FAULT device=09:00.0 domain=0x0003 address=0x000000f400089ac0 flags=0x0010 ]
This particular log entry, might it have some origin in hardware? Or a sad blobby firmware?
A simple `lspci -v` noted the device in question (09:00.0) pointed to an AMD graphics card using the opensource amdgpu module.
Grepping around the driver directory did not turn up too many clues. Some of the error messages were quite helpfully verbose, or perhaps the opposite.
[AVFS] Something is broken. See log!
Regarding the IO_PAGE_FAULT messages, I noticed quite a number of them, one for every group of addresses on that PCIe bus of the GPU. A little too convenient to simply be hardware errors.
Hmmm... well the event was originally printed by a driver for the AMD IOMMU. I had hoped there wouldn't be anything physically wrong with the component, but at this point I thought why not?
Now this one was slightly trickier to pin down, there were at least two files "amd_iommu.c" and "amd_iommu_v2.c" that looked like they might be important. Possibly more.
It ended up related to an event being polled on the IOMMU itself, printed into the logs by the following code:
case EVENT_TYPE_IO_FAULT:
printk("IO_PAGE_FAULT
device=%02x:%02x.%x"
"domain=0x%04x address=0x%016llx
flags=0x%04x]\n",
PCI_BUS_NUM(devid), PCI_SLOT(devid),
PCI_FUNC(devid),
domid, address, flags);
break;
The faults seemed to occur once-in-a-while on bootup, so maybe it was related to that?
I recalled that certain boot functionality on Linux was controllable via boot arguments. A dozen hasty searches later and I found the kernel parameters document.
As an aside, the online Linux Admin Guide from Kernel.org is a decent read, abet with elements out-of-date or *really* out-of-date. Such is the fate of good documentation:
Linux beats them ALL! While all other OS's are TALKING about direct support of Java Binaries in the OS, Linux is doing it!
Still got your attention?
Since the IOMMU is point of focus, I glanced at the relevant documentation section.
iommu= [x86]
off
force
noforce
biomerge
panic
nopanic
merge
nomerge
forcesac
soft
pt [x86, IA-64]
nobypass [PPC/POWERNV] -
Disable IOMMU bypass, using IOMMU for PCI devices.
Oh boy, around here it's not just the memory mapping that's sparse. Some of them seem obvious, like 'off' or 'force', and I suppose the 'panic' means that if an error or fault occurs, then throw in the towel.
The 'pt' option looks like it means pass-through, and a search of the mailing list confirms this. It can be accomplished via adding it to your bootloader of choice and regenerating the config. In my case, it was grub2:
GRUB_CMDLINE_LINUX_DEFAULT="quiet iommu=pt"
After reboot there were no more IO_PAGE_FAULT errors in the logs. Problem solved.
That was kind of exciting. I think I might dig deeper in the future to better understand these things. I hope you do too.


---
layout: default
title: MaxQ
permalink: /projects/MaxQ
footer: true
---
{% include base.html %}
The Project
The requests made by the client:
- - GPU Utilization
- - Cloud Costs
- - Waiting time during CI/CD
- + Queuing
- + Automation
- + Tests
- + Security
- + Monitoring
The Challenge
All the tricky technical details that bug us:
- On-demand CI/CD: Jenkins stands up running for months and costs way too much according to pace of Integration/Deployment shown by the company. A solution would be to build a role via Ansible for AWS Provision and plug it to Jenkins. That would reduce costs by a double digit number at least. Another solution implies scanning cloud costs, going through each via an automated python script, and define what could be reduced/removed.
- Jenkins Scalp: Go through Pipeline, Determine Imperative/Declarative, Implement Parallel CI, Define Delivery, AquaSec.
- Testing: Help and add UI testing.
- Handle Machine: Look through GPU use, add Queuing and complete system automation.
- Monitoring: Prometheus and Grafana to the rescue.
The Stack
All these technologies were used for this job:


Pooling¶
BasePool¶
-
class
paddle.v2.pooling.
BasePool
(name) Base Pooling Type. Note these pooling types are used for sequence input, not for images. Each PoolingType contains one parameter:
参数: name (basestring) – pooling layer type name used by paddle.
Avg¶
-
class
paddle.v2.pooling.
Avg
(strategy='average') Average pooling.
Return the average values for each dimension in sequence or time steps.
\[sum(samples\_of\_a\_sequence)/sample\_num\]
Max¶
-
class
paddle.v2.pooling.
Max
(output_max_index=None) Max pooling.
Return the very large values for each dimension in sequence or time steps.
\[max(samples\_of\_a\_sequence)\]参数: output_max_index (bool|None) – True if output sequence max index instead of max value. None means use default value in proto.
Sum¶
-
class
paddle.v2.pooling.
Sum
Sum pooling.
Return the sum values of each dimension in sequence or time steps.
\[sum(samples\_of\_a\_sequence)\]
SquareRootN¶
-
class
paddle.v2.pooling.
SquareRootN
Square Root Pooling.
Return the square root values of each dimension in sequence or time steps.
\[sum(samples\_of\_a\_sequence)/sqrt(sample\_num)\]
CudnnMax¶
-
class
paddle.v2.pooling.
CudnnMax
Cudnn max pooling only support GPU. Return the maxinum value in the pooling window.


start
Oscillator1 Magnification
Oscillator1 Wave Type
Oscillator2 Magnification
Oscillator2 Wave Type
Low Pass Filter
High Pass Filter
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