We are excited to influence political decisions in the great state of South Carolina.
Join many strong women who are claiming their right to direct public policy and be heard!
Established in November, 2018
Indian Land Republican Women was formed to establish a republican presence in Indian Land, SC. A place where individuals can come to learn about politics in a friendly arena where we do not bash the other parties, instead, we set example of how republicans treat others. In hopes this openness will grow our party, increase local women candidates and increase turn out at the polls.
Come and listen to Sandy McGarry, Chairwoman of the Lancaster County Republican Party. She will be reporting on the current state of the Republican party, changes in Indian Land and about the reorganization of the Indian Land precincts. So if you missed the main meeting on March 28th, please come and learn more!
Also, our County Treasurer, Carrie Helms will be speaking with is as well.
Please come and bring a friend. 2020 is just around the corner so we need to rally together and be strong!


Korean film festival inaugurated
by Special CorrespondentThe Korean Film Festival 2019 was inaugurated in the city on Thursday, marking 100 years of Korean cinema.
Inaugurating the festival, Consul General of the Republic of Korea Hyung Tae-kim said the film festival would be a platform to share cultures of both countries.
“Movies are the universal language of humankind. The film festival coincides with 100th anniversary of the Korean film history,” said Mr. Hyung.
N. Sathya Moorthy, Commissioner of Income Tax, said Korean filmmakers had won critical acclaim. AVM Productions joint managing director AVM K. Shanmugam said Korea and Tamil Nadu had a lot of cultural similarities.
On Thursday, the film Anarchist from Colony was screened. The fatal encounter and Wonderful nightmare will be screened on Friday. On Saturday, films such as Quiet dream and Untouchable lawmen will be screened. Admission is free.


| G.A.R. 45 | |
| (See rule 180) | |
| RECEIPTED BILL | |
| Part I | |
| Received the sum of Rs.....................(..........................) under the All India Services Group Insurance Rule, 1981, being the total of entitlement of Rs.....................from the *Insurance Fund and/or of Rs.................. from the Savings Fund, accrued to— | |
| Name ....................................................Service to which I/he* belonged...................... | |
| Designation.. ............................................................................ | |
| Name of State on whose cadre borne....................................................... | |
| Date.......................... | |
| Signature (s) of Recipient(s)) | |
| (Name in Block letters) | |
| Part II | |
| Endorsement to be recorded by the Designated Drawing Officer of State/ Union Territory or by D.D.O. of concerned Central Ministry/ Department in respect of an officer on deputation to centre. | |
| (a) Date on which the officer became a member of the Scheme.............................................. | |
| (b) Description of the event (retirement, resignation, death etc. and date thereof........................................ | |
| (c) Countersigned for payment of Rs.................... .(Rupees........................................) to claimants | |
| Crossed cheque/demand draft to be issued in favour of claimant (s): | |
| Signature................ ........ | |
| Date.............................. | |
| Designation of D.D.O.............. | |
| Government of.................... | |
| Part III | |
| Endorsement to be recorded by the D.D.O. of Department of Personal and Administrative Reforms | |
| Certified that the above details (including entitlement under savings fund) have been verified and found to be correct. | |
| Signature................ ........ | |
| Date.............................. | |
| D.D.O., D.P.&A.R............... | |
| Part IV | |
| FOR USE IN PAY AND ACCOUNTS OFFICE | |
| Passed for payment of Rs............................. (Rupees.......................) | |
| Payment through Cheque(s) No.(s)........................ date................ |
|
| Accounts Officer | |
| * Delete whichever is inapplicable. |


{% extends "layout_unbranded.html" %} {% block page_title %} Your money claims account {% endblock %} {% block content %} Back Contact the right helpdesk We're building a new service to replace Money Claim Online (MCOL). It's currently in the beta phase being tested with users. Both services are currently live. You need to contact the helpdesk for the service that you used. Money Claims - new beta service https://www.moneyclaims.service.gov.uk Money Claims<EMAIL_ADDRESS>Telephone: 0300 123 7050 Monday to Friday, 9am to 5pm Find out about call charges Money Claim Online (MCOL) https://www.moneyclaim.gov.uk Money Claim Online helpdesk<EMAIL_ADDRESS>Telephone: 0300 123 1057 Monday to Friday, 8:30am to 5pm Find out about call charges {% endblock %}
We're building a new service to replace Money Claim Online (MCOL). It's currently in the beta phase being tested with users.
Both services are currently live. You need to contact the helpdesk for the service that you used.
Money Claims<EMAIL_ADDRESS>Telephone: 0300 123 7050 Monday to Friday, 9am to 5pm Find out about call charges
Money Claim Online helpdesk<EMAIL_ADDRESS>Telephone: 0300 123 1057 Monday to Friday, 8:30am to 5pm Find out about call charges


FIFA says Iraq unsafe to host World Cup qualifying games
ZURICH (AP) - FIFA says Iraq is not safe enough to host World Cup qualifying games against Iran and Bahrain.
FIFA says it asked the Iraq soccer federation “to nominate a neutral venue” for the matches on Nov. 14 and 19.
The games were to be played in Basra, the southern city which hosted Iraq’s return last month to playing competitive games at home.
Iraq has rarely staged home games since the 1980s because of security concerns.
Turmoil in Iraq in recent weeks has been fueled by economic problems and dissatisfaction with Iran’s political influence on its neighbor.
Iraq’s 2-0 win over Hong Kong in Basra last month left the national team at the top of its five-team qualifying group. Two teams advance to the next round of Asian qualifying for the 2022 World Cup in Qatar.
More AP soccer: https://apnews.com/Soccer and https://twitter.com/AP_Sports
Sign up for Daily Newsletters
Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


{% extends "pyoslc_oauth/base.html" %} {% from "pyoslc_oauth/macros.html" import render_form %} {% block form %}
Client {{ client.name }} is requesting:


A replication of the Dwitter API for local development.
u(t) is called 60 times per second. t: Elapsed time in seconds. S: Shorthand for Math.sin. C: Shorthand for Math.cos. T: Shorthand for Math.tan. R: Function that generates rgba-strings, usage ex.: R(255, 255, 255, 0.5) c: A 1920x1080 canvas. x: A 2D context for that canvas.


m.dox
- Function link: Utility::Directory::mkpath()
- Class link: Interconnect::Emitter
- Page link: Downloading and building
- Custom link title
- Page link with custom title
- Link to index page
- Link to class documentation section
- Link to index page with hash after
- Link to page with hash after
- Link to class with query and hash after
- Flat link: Plugin management tutorial
These should produce warnings:
- Link to nonexistent name will be rendered as code:
nonExistent()
- Link to nonexistent name with custom title will be just text
- Link to a section that doesn't have a title will keep the ID (this may break on tagfile update, watch out): corrade-cmake-add-test
- Link to index page without title will have the tag file basename: corrade


Development
Community
a2ml.api.a2ml_dataset.
A2MLDataset
Bases: object
object
Contains the dataset CRUD operations that interact with provider.
create
delete
list
select
a2ml.api.a2ml_experiment.
A2MLExperiment
history
leaderboard
start
stop
a2ml.api.a2ml_model.
A2MLModel
actual
deploy
predict
a2ml.api.a2ml_project.
A2MLProject


{% extends "index.html" %} {% block main_container %}
This app is my playground for exploring the functionalities of ThreeJS. I have not studied geomatics engineering so this app is not an super accurate 'satellite positioning tool', it's mostly just for fun.
The source code to this app can be found on my github page. Any bugs and problems can be freely posted as issues.


Temps are dropping. TGR movies have been consumed. Your toes are itching to be cramped into boots. It’s that magical time of year. And you’re ready.
But your kit? It’s beat up. Well-loved from the epic days of last season, but in no shape to take you to May 2018.
 Re-built from the ground up, our designers focused on the essentials athletes need for winter. The result? Eight unique, immaculately crafted pieces designed to work as one. We call it our Mission Kit.
SO, WHICH KIT CALLS TO YOU?


Blogpost2| My experience with Ubuntu for cuda and cupy
Installing Ubuntu
Since I have an nvidia GPU on my macbook pro, I have decided that I wanted to install Ubuntu. This would allow me to run Cupy and use the GPU for neural networks while using the Chainer framework.
First time installing
After following a tutorial online on how to partition my harddrive and install ubuntu on my macbook properly, I was super happy to see this when booting up:
So my ultimate goal was to use cupy, which is supported by cuda 10.0 (or older), and cuda 10.0 or older is supported by Ubunutu 18.04.1 (or older). I didn’t know this so I first installed the newest Ubuntu 18.04.2 and then after a while found out I couldn’t install cuda 10.0 properly. This is because on the cuda 10.0 installation guide, it says I needed the kernel version 14.15.0 (and I had 124.18.0). I tried different things to fix this problem (such as changing the kernel version to an older version) before I finally resolved to reinstalling an older Ubuntu.
Pretty much, I installed the wrong version of Ubuntu!
When I installed Ubuntu from it’s home page it was 18.04. Little did I know that I needed 18.04.1 instead of 18.04.2! So yes, I decided to reinstall.
Second time installing
The second time I installed Ubuntu, I specifically chose the 18.04.2 version and finally was able to install cuda 10.0. However, I thought to myself, I wonder how it would look like if I changed my screen resolution… Since I had an extra screen connected, I decided to play around with the different resolution possibilities.
Turns out changing the screen resolution made my whole screen black… and then green!
Everytime I rebooted it turned completely black and green on both screens :’( I googled everywhere and tried all possible solutions that were offered on Stackoverflow. I was able to access the root terminal but still, but still nothing worked. I decided to install Ubuntu once again.
Third time is the charm
I WAS SO ANNOYED AT THIS POINT. I started questioning whether I even still wanted to use Chainer anymore, perhaps it is easier to rewrite all my codes to PyTorch.. (probably it was and still is a good idea). But I thought, this is a good way to learn things. Besides, I already have put some time in this, might as well finish the job… So finally.. the third time installing Ubuntu, everything works. I was able to install cuda properly, and then cupy and then everything else. It was a relieving moment, to see I was able to import cupy into python.
UNTIL…
OUT OF MEMORRY!
Until I realized my model architecture is way too heavy for my tiny GPU… OUT OF MEMORY!!
Well…. I guess I cannot do much about this. It is nice to know how to use my GPU I guess.. And perhaps one day I should buy an external GPU.. but they’re not cheap.
Lessons learned
- Think twice or three times before installing the latest version of everything.
- Dont just change your screen resolution on ubuntu.
- Everything you do you can learn from.
Now that I have let out my frustrations on this blog, I would like to thank whomever is reading this for listening (probably nobody). This was very therapeutical to write.


Click or drag file here to get the Module Name and Version
upload 2.0 result
upload 3.0 result
upload 2.0 logs
upload 3.0 logs
Click following URL to copy it to GUI members for checking:
{{tokenUrl}}


---
layout: archive
title: "Research Projects"
permalink: /research/
author_profile: true
---
*Wordcloud generated from thesis projects and conferences
Perception of Meaning and Happiness
- What is a meaningful life? Is a meaningful life the same as a happy life? We examined the role of benefits to society and benefits to oneself in people’s intuitions of meaning and happiness. We found that people emphasized benefits to society when making judgments about meaning, but consider benefits to society and benefits to oneself equally important when making judgments about happiness.
- Master Thesis (Manuscript in production)
How Do People Regulate Their Guilt?
- Guilt has been proven to motivate reparative behaviors. However, guilt decreases over time and what cognitive process is involved is unknown. Thus, the present study examined two possible cognitive strategies (rationalization and social norm) that people use to reduce the feeling of guilt. Though there was no significant difference between these two strategies, we found that the level of guilt was positively correlated with people's willingness towards prosocial behaviors.
- Honors Thesis
Social Comparison and Academic Self-Concept
- This honors course project sought to assess whether the direction of social comparison (i.e. upward/downward) and type of social comparison (i.e. comparing similarities/comparing differences) influenced the academic self-concept (Wigfield & Karpathian, 1991) of college undergraduate students.
- Psychology Honors Conference Poster


Data from the Minnesota Legislature via the Open States Project. Geocoding provided by Mapquest. Note that some legislators are not properly linked to bills they sponsor, which may affect the accuracy of the address search. All icons from The Noun Project: Capital by Jonathan Keating; Wind Farm by Iconathon, Cree; House by Maurizio Pedrazzoli; Checkbox by The Noun Project; Tree by The Noun Project; Microscope by Stuart McCoy; Soldier by Simon Child; Piggy Bank by Brock Kenzler; Stock Exchange by Nacho; Theater by Karl Turner; Playing Cards by Jonathan C. Dietrich; Siren by ___Lo; Gavel by Connie Shu.


The Tesseract (also called the Cube) is a crystalline cube-shaped containment vessel for an Infinity Stone, one of six singularities that predate the universe and possesses unlimited energy. Once the universe came into existence, it changed form to a stone. It was used by various ancient civilizations before coming into Asgardian hands who brought it to Earth. After the Battle of New York, it was returned to Asgard. The Tesseract contains the Space Stone.


|
|||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object com.darwinsys.swingui.layout.ColumnLayout
ColumnLayout, a Columnar Layout Manager for AWT/Swing. Sort of a cross between BoxLayout and GridLayout(0, x). Displays components in a single row or column based on the alignment given to the constructor, with optional padding.
There is a utility method for adding space between the previous and next components, which (like Menu.addSeparator()) adds a fixed-size non-visible component between two added components.
Note: The current version of ColumnLayout doesn't resize.
| Nested Class Summary | |
protected class |
ColumnLayout.Spacer
Class to represent a spacer, like Menubar.separator. |
| Field Summary | |
protected int |
alignment
The alignment for this ColumnLayout |
protected int |
hPadding
The X padding for this ColumnLayout |
protected int |
minh
The minimum height of each component |
protected int |
minw
The minimum width of each component |
protected int |
vPadding
The Y padding for this ColumnLayout |
static int |
X_AXIS
Constant for X AXIS (horizontal column) alignment |
static int |
Y_AXIS
Constant for Y AXIS (vertical column) alignment |
| Constructor Summary | |
ColumnLayout(int dirn)
Construct a ColumnLayout given only an alignment. |
|
ColumnLayout(int dirn,
int pad)
Construct a ColumnLayout given an alignment and a padding amount. |
|
ColumnLayout(int dirn,
int hpad,
int vpad)
Construct a ColumnLayout given an alignment and h,v padding amounts. |
| Method Summary | |
void |
addLayoutComponent(java.lang.String name,
java.awt.Component c)
Called by AWT when the user uses the form add(name, Component). |
void |
addSpacer(java.awt.Container target)
Utility method to add a "spacer" |
protected java.awt.Dimension |
doLayout(java.awt.Container target)
Used internally: compute the layout and the maximal preferred width & height TODO XXX NEED TO SCALE BY TARGSIZE? |
void |
layoutContainer(java.awt.Container target)
Called by AWT to lay out the components in the target Container at its current size. |
java.awt.Dimension |
minimumLayoutSize(java.awt.Container target)
Called from AWT to calculate the minimum size dimensions for the target panel given the components in it. |
java.awt.Dimension |
preferredLayoutSize(java.awt.Container target)
Called by AWT to compute the preferred size for the target panel given our list of the components that it contains. |
void |
removeLayoutComponent(java.awt.Component c)
Called by AWT to remove a given component from the layout. |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
public static final int X_AXIS
public static final int Y_AXIS
protected final int alignment
protected final int hPadding
protected final int vPadding
protected int minw
protected int minh
| Constructor Detail |
public ColumnLayout(int dirn)
public ColumnLayout(int dirn, int pad)
public ColumnLayout(int dirn, int hpad, int vpad)
| Method Detail |
public void addLayoutComponent(java.lang.String name, java.awt.Component c)
addLayoutComponent
in interface java.awt.LayoutManager
name
- String with location for component cc
- Component to be added.public void layoutContainer(java.awt.Container target)
layoutContainer
in interface java.awt.LayoutManager
target
- Container whose components are to be laid out.protected java.awt.Dimension doLayout(java.awt.Container target)
public java.awt.Dimension minimumLayoutSize(java.awt.Container target)
minimumLayoutSize
in interface java.awt.LayoutManager
target
- Container to calculate forpublic java.awt.Dimension preferredLayoutSize(java.awt.Container target)
preferredLayoutSize
in interface java.awt.LayoutManager
target
- Container to calculate forpublic void addSpacer(java.awt.Container target)
public void removeLayoutComponent(java.awt.Component c)
removeLayoutComponent
in interface java.awt.LayoutManager
c
- Component to be removed
|
|||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


About
Spoorthi Usha is Chief Executive Officer of InspiredMemories. Before being named CEO in June 2016, Spoorthi held innovative roles in both enterprise and consumer businesses across places.
Founding InspiredMemories in 2016, she quickly became known as a leader who could span a breadth of technologies and businesses to transform some of InspiredMemories’s biggest offerings with Innovation.
Most recently, Spoorthi was executive of InspiredMemories’s Program InspiredMinds. In this role she led the transformation to the Education field and product business, which gained the market and took inspiration from competition. Before Founding InspiredMemories, Spoorthi was Graduated from Visheshwaraya Technological University, Belgaum.
Originally from Karnataka, India, Spoorthi lives in Tumkur, Karnataka. She earned a bachelor’s degree in electronics and communication from KNSIT, Bangalore, Spoorthi serves on the board of directors to InspiredMemories as well.
Fields
Technology Programs
Working in the technology part as well compare to the other management skills. Her level of design guidelines is simply beautiful and loving.
Business Administration
The company's growth requires advanced level of planning and executing the ideas. She plays an important role in making understand the business and to proceed further.
Application of Skills
Being CEO doesn't shows anyone as a dictator, She Inspires the team to work together and stick to the plans at crisis and works together to solve the problems with our team.
Project Experiences
Building Applications for Clients
Across Globe
Adopted DevOps
At the initial stages of the company, she use to assist the team in building web apps and android apps to serve them better. Her contribution in this field is immense. In total, she helped to serve 15 Web Apps to different customers along with 6 android apps in the span of 180 days.
Creating a product business plan
Market research
Company Business Development
After success in servicing customers, company has decided to make its own product which will be launched in late 2018s for the development of the product Planning, Communication and Execution has been taken care by her in meantime, she also concentrated towards her creative side of building things in a smooth way.


|
CppFactory
0.1.0
Modern c++ object factory implementation in <200 lines
|
Represents an Object that has a global lifetime, meaning it doesn't get destroyed when it leaves scope More...
#include <CppFactory.hpp>
Static Public Member Functions | |
| template<int TZone = 0> | |
| static std::shared_ptr< TObject > | Get () |
Gets (and allocates, if needed) a global object (optionally from a particular zone) for type TObject More... | |
| template<int TZone> | |
| static void | Reset () |
Resets the global object cache for all zones for type TObject More... | |
| static void | Reset () |
Resets the global object cache for all zones for type TObject More... | |
Represents an Object that has a global lifetime, meaning it doesn't get destroyed when it leaves scope
| TObject | The type of object |
|
inlinestatic |
Gets (and allocates, if needed) a global object (optionally from a particular zone) for type TObject
| TZone | The zone to get from |
GlobalObject<TObject>::Get();
|
inlinestatic |
Resets the global object cache for all zones for type TObject
| TZone | The zone to reset |
GlobalObject<TObject>::Reset<1>();
|
inlinestatic |
Resets the global object cache for all zones for type TObject
GlobalObject<TObject>::Reset();


{{title}} {{droppointDescription}} {{item}}, {{droppointData.geoLocation.town}}, {{droppointData.geoLocation.postCode}} Distance {{droppointData.localizedDistance}} Opening Hours {{item.day}} {{item.fromTime}} - {{item.toTime}} {{displayButtonText}}


--- layout: post status: publish published: true title: Podcast 3.40 - Is it still baseball season? author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 6206 wordpress_url: http://ivyenvy.com/?p=6206 date: '2011-09-12 22:00:11 +0000' date_gmt: '2011-09-13 03:00:11 +0000' categories: - Podcast tags: - Podcast comments: [] ---
Andy, Corey and Jeremy take a look at the teams in the Cubs' minor league system and how they fared in 2011. We continue to look at the call-ups and consider a starting line-up full of them next season.
We ask ourselves "What went wrong in 2011?". And we wrap up the show with the photo caption contest.


FAMILY FEUD
Player One
Player Two
Submit
BONUS ROUND
If you see this, I'm sorry
MULTIPLIER
1
2
3
4
5
Guess
Player 1
Score: 0
Player 2
Score: 0


This coordinates the compute nodes for a compute group. It assigns new compute tasks based on the current best route and coordinates the annealing process.


{% extends 'basic.html' %}
{% block title %} MediCare | Upload Blood Dooners{% endblock %}
{% block body %}
{% load static %}
Here You Can Make a Profile of Yours Blood donner's
Which You Can Store Through A List On Other Page Named "..."
Type Date As YYYY-MM-DD Formate For Last Donate.


Backyard (house-side)
data loading...
Backyard (fence-side)
data loading...
Front Garden
data loading...
Tröpfchen/Sprühnebel
data loading...
Intervall
Front Garden Lights
(not activated)
Backyard Lights
(not activated)
Intervall
Copyright © Dr. Christian Benjamin Ries


Your browser doesn't support the features required by impress.js, so you are presented with a simplified version of this presentation.
For the best experience please use the latest Chrome, Safari or Firefox browser.
我要开始熬鸡汤了~~
前端发展迅速，各种框架、思想层出不穷，要保证持续学习，才能跟上脚步


--- layout: archive title: Zulip Chat Archive permalink: /stream/116395-maths/topic/quotient.20classes.html ---
Can I suggest the creation of something a bit like this. quotient_map
can be extended with group_hom
, linear_map
localisation_map
, etc., and used to do definitions on quotient objects that are not defined using the quot
constant. I'm considering a major refactoring using something like this.
import logic.function data.quot open function structure quotient_map (α β : Type*) := (to_fun : α → β) (inv_fun : trunc {f : β → α // right_inverse f to_fun})
I don't think you can prove that quotienting by a setoid is a quotient_map
I think this is not the right assumption. The inverse map should be B -> quotient (to_fun ⁻¹'o eq)
and it should be an equiv when paired with the lift of to_fun
(this might be a use for is_equiv
, if that was ever added for the category stuff)
quot
is important because it is the only thing that can reach other universes
I think you actually mean set.preimage f ∘ eq ∘ f
, though I've no idea why you'd write it like this.
Or even better (∘ f) ∘ eq ∘ f
for some interpretations of "better", at least :-)
I admit what I wrote is not quite type correct, but I do mean what I wrote. (That notation is order.preimage
.) We should have a setoid construction for this case since it's a very common situation (it is also universal, any equivalence relation can be written as f x = f y for an appropriate f), but there is already an easy proof using preimage.equivalence
and eq.equivalence
.
Last updated: Apr 24 2020 at 19:02 UTC


Type:
Fs:
Fc:
Fc2:
Biquad cascades:
Transform:
Bilinear Characteristic:
Bilinear Type:
MatchedZ Characteristic:
Impulse Response Length:
preGain:
Gain (dB):
FIR order:
Calculation Attenuation (dB):
Magnitude Percent
Magnitude dB
Group Delay
Impulse Response
PoleZero
a
Input
Run
Simulation
Coefficients:
a


Actions, resources, and condition keys for Amazon Fraud Detector
Amazon Fraud Detector (service prefix:
frauddetector
) provides the following service-specific resources, actions, and condition context
keys for use in IAM permission policies.
References:
-
Learn how to configure this service .
-
View a list of the API operations available for this service .
-
Learn how to secure this service and its resources by using IAM permission policies.
Topics
Actions defined by Amazon Fraud Detector
You can specify the following actions in the
Action
element of an IAM policy statement. Use policies to grant permissions to perform
an operation in AWS. When you use an action in a policy, you usually allow or
deny access to the API operation or CLI command with the same name. However,
in some cases, a single action controls access to more than one operation. Alternatively,
some operations require several different actions.
The
Resource types
column indicates whether each action supports resource-level permissions. If
there is no value for this column, you must specify all resources ("*") in the
Resource
element of your policy statement. If the column includes a resource type, then
you can specify an ARN of that type in a statement with that action. Required
resources are indicated in the table with an asterisk (*). If you specify a resource-level
permission ARN in a statement using this action, then it must be of this type.
Some actions support multiple resource types. If the resource type is optional (not
indicated as required), then you can choose to use one but not the other.
For details about the columns in the following table, see The actions table .
| Actions | Description | Access level | Resource types (*required) | Condition keys | Dependent actions |
|---|---|---|---|---|---|
| BatchCreateVariable | Creates a batch of variables. | Write | |||
| BatchGetVariable | Gets a batch of variables. | List | |||
| CreateDetectorVersion | Creates a detector version. The detector version starts in a DRAFT status. | Write | |||
| CreateModel | Creates a model using the specified model type. | Write | |||
| CreateModelVersion | Creates a version of the model using the specified model type and model id. | Write | |||
| CreateRule | Creates a rule for use with the specified detector. | Write | |||
| CreateVariable | Creates a variable. | Write | |||
| DeleteDetector | Deletes the detector. Before deleting a detector, you must first delete all detector versions and rule versions associated with the detector. | Write | |||
| DeleteDetectorVersion | Deletes the detector version. You cannot delete detector versions that are in ACTIVE status. | Write | |||
| DeleteEntityType | Deletes an entity type. You cannot delete an entity type that is included in an event type. | Write | |||
| DeleteEvent | Deletes the specified event. | Write | |||
| DeleteEventType | Deletes an event type. You cannot delete an event type that is used in a detector or a model. | Write | |||
| DeleteExternalModel | Removes a SageMaker model from Amazon Fraud Detector. You can remove an Amazon SageMaker model if it is not associated with a detector version. | Write | |||
| DeleteLabel | Deletes a label. You cannot delete labels that are included in an event type in Amazon Fraud Detector. You cannot delete a label assigned to an event ID. You must first delete the relevant event ID. | Write | |||
| DeleteModel | Deletes a model. You can delete models and model versions in Amazon Fraud Detector, provided that they are not associated with a detector version. | Write | |||
| DeleteModelVersion | Deletes a model version. You can delete models and model versions in Amazon Fraud Detector, provided that they are not associated with a detector version. | Write | |||
| DeleteOutcome | Deletes an outcome. You cannot delete an outcome that is used in a rule version. | Write | |||
| DeleteRule | Deletes the rule. You cannot delete a rule if it is used by an ACTIVE or INACTIVE detector version. | Write | |||
| DeleteVariable | Deletes a variable. You cannot delete variables that are included in an event type in Amazon Fraud Detector. | Write | |||
| DescribeDetector | Gets all versions for a specified detector. | Read | |||
| DescribeModelVersions | Gets all of the model versions for the specified model type or for the specified model type and model ID. You can also get details for a single, specified model version. | Read | |||
| GetDetectorVersion | Gets a particular detector version. | List | |||
| GetDetectors | Gets all detectors or a single detector if a detectorId is specified. This is a paginated API. If you provide a null maxResults, this action retrieves a maximum of 10 records per page. If you provide a maxResults, the value must be between 5 and 10. To get the next page results, provide the pagination token from the GetDetectorsResponse as part of your request. A null pagination token fetches the records from the beginning. | List | |||
| GetEntityTypes | Gets all entity types or a specific entity type if a name is specified. This is a paginated API. If you provide a null maxResults, this action retrieves a maximum of 10 records per page. If you provide a maxResults, the value must be between 5 and 10. To get the next page results, provide the pagination token from the GetEntityTypesResponse as part of your request. A null pagination token fetches the records from the beginning. | List | |||
| GetEventPrediction | Evaluates an event against a detector version. If a version ID is not provided, the detector’s (ACTIVE) version is used. | Read | |||
| GetEventTypes | Gets all event types or a specific event type if name is provided. This is a paginated API. If you provide a null maxResults, this action retrieves a maximum of 10 records per page. If you provide a maxResults, the value must be between 5 and 10. To get the next page results, provide the pagination token from the GetEventTypesResponse as part of your request. A null pagination token fetches the records from the beginning. | List | |||
| GetExternalModels | Gets the details for one or more Amazon SageMaker models that have been imported into the service. This is a paginated API. If you provide a null maxResults, this actions retrieves a maximum of 10 records per page. If you provide a maxResults, the value must be between 5 and 10. To get the next page results, provide the pagination token from the GetExternalModelsResult as part of your request. A null pagination token fetches the records from the beginning. | List | |||
| GetKMSEncryptionKey | Gets the encryption key if a Key Management Service (KMS) customer master key (CMK) has been specified to be used to encrypt content in Amazon Fraud Detector. | Read | |||
| GetLabels | Gets all labels or a specific label if name is provided. This is a paginated API. If you provide a null maxResults, this action retrieves a maximum of 50 records per page. If you provide a maxResults, the value must be between 10 and 50. To get the next page results, provide the pagination token from the GetGetLabelsResponse as part of your request. A null pagination token fetches the records from the beginning. | List | |||
| GetModelVersion | Gets the details of the specified model version. | List | |||
| GetModels | Gets one or more models. Gets all models for the AWS account if no model type and no model id provided. Gets all models for the AWS account and model type, if the model type is specified but model id is not provided. Gets a specific model if (model type, model id) tuple is specified. | List | |||
| GetOutcomes | Gets one or more outcomes. This is a paginated API. If you provide a null maxResults, this actions retrieves a maximum of 100 records per page. If you provide a maxResults, the value must be between 50 and 100. To get the next page results, provide the pagination token from the GetOutcomesResult as part of your request. A null pagination token fetches the records from the beginning. | List | |||
| GetRules | Get all rules for a detector (paginated) if ruleId and ruleVersion are not specified. Gets all rules for the detector and the ruleId if present (paginated). Gets a specific rule if both the ruleId and the ruleVersion are specified. | List | |||
| GetVariables | Gets all of the variables or the specific variable. This is a paginated API. Providing null maxSizePerPage results in retrieving maximum of 100 records per page. If you provide maxSizePerPage the value must be between 50 and 100. To get the next page result, a provide a pagination token from GetVariablesResult as part of your request. Null pagination token fetches the records from the beginning. | List | |||
| ListTagsForResource | Lists all tags associated with the resource. This is a paginated API. To get the next page results, provide the pagination token from the response as part of your request. A null pagination token fetches the records from the beginning. | List | |||
| PutDetector | Creates or updates a detector. | Write | |||
| PutEntityType | Creates or updates an entity type. An entity represents who is performing the event. As part of a fraud prediction, you pass the entity ID to indicate the specific entity who performed the event. An entity type classifies the entity. Example classifications include customer, merchant, or account. | Write | |||
| PutEventType | Creates or updates an event type. An event is a business activity that is evaluated for fraud risk. With Amazon Fraud Detector, you generate fraud predictions for events. An event type defines the structure for an event sent to Amazon Fraud Detector. This includes the variables sent as part of the event, the entity performing the event (such as a customer), and the labels that classify the event. Example event types include online payment transactions, account registrations, and authentications. | Write | |||
| PutExternalModel | Creates or updates an Amazon SageMaker model endpoint. You can also use this action to update the configuration of the model endpoint, including the IAM role and/or the mapped variables. | Write | |||
| PutKMSEncryptionKey | Specifies the Key Management Service (KMS) customer master key (CMK) to be used to encrypt content in Amazon Fraud Detector. | Write | |||
| PutLabel | Creates or updates label. A label classifies an event as fraudulent or legitimate. Labels are associated with event types and used to train supervised machine learning models in Amazon Fraud Detector. | Write | |||
| PutOutcome | Creates or updates an outcome. | Write | |||
| TagResource | Assigns tags to a resource. | Tagging | |||
| UntagResource | Removes tags from a resource. | Tagging | |||
| UpdateDetectorVersion | Updates a detector version. The detector version attributes that you can update include models, external model endpoints, rules, rule execution mode, and description. You can only update a DRAFT detector version. | Write | |||
| UpdateDetectorVersionMetadata | Updates the detector version's description. You can update the metadata for any detector version (DRAFT, ACTIVE, or INACTIVE). | Write | |||
| UpdateDetectorVersionStatus | Updates the detector version’s status. You can perform the following promotions or demotions using UpdateDetectorVersionStatus: DRAFT to ACTIVE, ACTIVE to INACTIVE, and INACTIVE to ACTIVE. | Write | |||
| UpdateModel | Updates a model. You can update the description attribute using this action. | Write | |||
| UpdateModelVersion | Updates a model version. Updating a model version retrains an existing model version using updated training data and produces a new minor version of the model. You can update the training data set location and data access role attributes using this action. This action creates and trains a new minor version of the model, for example version 1.01, 1.02, 1.03. | Write | |||
| UpdateModelVersionStatus | Updates the status of a model version. | Write | |||
| UpdateRuleMetadata | Updates a rule's metadata. The description attribute can be updated. | Write | |||
| UpdateRuleVersion | Updates a rule version resulting in a new rule version. Updates a rule version resulting in a new rule version (version 1, 2, 3 ...). | Write | |||
| UpdateVariable | Updates a variable. | Write |
Resource types defined by Amazon Fraud Detector
The following resource types are defined by this service and can be used in the
Resource
element of IAM permission policy statements. Each action in the
Actions table
identifies the resource types that can be specified with that action. A resource
type can also define which condition keys you can include in a policy. These
keys are displayed in the last column of the table. For details about the columns
in the following table, see
The resource types table
.
Condition keys for Amazon Fraud Detector
Amazon Fraud Detector defines the following condition keys that can be used in the
Condition
element of an IAM policy. You can use these keys to further refine the conditions
under which the policy statement applies. For details about the columns in the
following table, see
The condition keys table
.
To view the global condition keys that are available to all services, see Available global condition keys .
| Condition keys | Description | Type |
|---|---|---|
| aws:RequestTag/${TagKey} | Filters actions based on the tags that are passed in the request | String |
| aws:ResourceTag/${TagKey} | Filters actions based on the tags associated with the resource | String |
| aws:TagKeys | Filters actions based on the tag keys that are passed in the request | String |


Apply to study in Canada as an international student, extend your study permit and find out about working while you study or after you graduate.
Answer a few questions to learn whether you need a study permit to study in Canada
Who can apply, how to submit your application, processing times and what to expect when you get to Canada
If you live in China, India, Morocco, Pakistan, the Philippines, Senegal or Vietnam, you may be able to get your study permit faster
How to renew your study permit or restore your status
What minor children need to go to school
Who can work while they study, how much and where
Get a work permit to stay in Canada after you graduate and how to apply for permanent residence
How and when a study permit holder should apply
Where to study, how to apply to schools in Canada, and the documents you need
Tips on living in Canada as an international student
Awards for students, postdoctoral researchers, faculty and schools
All forms were uploaded successfully begin the automation now


This is the first game made by XS Studios.
This game is still in dev.
The point is to reach the goal by clicking the brownie.
With upgrades to help.


{% extends "ProjectCentricMail/base.html" %} {% block body %}
welcome to Project Centric mail , A platform which help in organization of mails to a more industrial approach.
Learn more
...
Button


Many people know that inflation reduces the real value of their savings every year. For example, using CPI data, $1 had the same spending power in year 1950 as $7.13 did in year 2000. Therefore, when considering benefits over multiple decades, it is worth understanding how inflation will impact those benefits.
The short answer is that the Social Security program is designed to account for inflation, as is this tool. The benefit estimates that the Social Security Administration and this tool will give you are in "today's dollars" meaning that the actual benefits will be higher based on the inflation between today and the benefit date, but will have the same purchasing power at that time as the estimate has today.
For example, if this tool estimates your benefit at $1,000 per year starting in 20 years, but there is 350% inflation during those 20 years, then the actual benefit will be $3,500.
While unnecessary, if you want to understand this in even more detail, read on below to understand how different measures of inflation affect different parts of the social security computation.
Consider the following example earnings record from 2005 to 2017:
This person recieves a 2.5% raise every year to keep up with inflation. In 2005, they earned $40,000 and in 2016 they earned $56,483, a 31.2% increase.
The "Primary Insurance Amount" for this person, which is the earnings computation upon which their benefit is based, is computed from the rightmost column, labelled Indexed Earnings. Note that the indexed earnings value in 2005 is roughly the same as in 2016.
This difference in Indexed Earnings is because of the multiplier applied. That multiplier is 1.32 in 2005, representing the fact that there has been ~32% inflation between 2005 and 2016. As such, older earnings records get adjusted upwards for inflation. Every year until you first collect your benefit, the multiplier for all previous years will be adjusted upwards further.
This adjustment is made based on the Average Wage Index (AWI). That is, if the average wage increased by 3% in a year, so will the Average Wage Index and thus all of the prior year's indexed earnings values.
Once you have filed for your benefit, you no longer recieve additional Wage Growth Adjustments. At this point, your Primary Insurance amount, and thus your benefit, are adjusted annually by Cost of Living Adjustments.
These adjustments are based on the Consumer Price Index (CPI-W). Every year, benefits are increased by a few percent. There has never been a decrease, but there have been years with no increase when there was little to no actual inflation.
Future Cost of Living Adjustments are not shown in this calculator since the actual adjustment value won't be known until the future. Therefore any benefits shown by this calculator are displayed in "today's dollars".


--- layout: default title: "Teaching" description: "Teaching" group: navigation navorder: 1 --- {% include JB/setup %}


Will Moore
---- Before deploying on nightshade ----
Save to OMERO (and open saved Figures) Done
Free-text, figure name Done
Open files list Done
Drag to select panels Done
Arrow keys to nudge selected panels Done
Show actual times Done
---- Other feedback RFEs ----
Address interpolation issues
Landscape canvas
Set Image ID Done
Max Intensity Projection
Copy viewport location -> Rectangle
User-defined spacing
Attach pdf to ALL source images Done
2nd page of pdf with image links etc
Vertical labels Done
Keep-alive ping from webfigure Done
Bug: Zoom centering Done
Grid alignment with labels
Label edits saved with loss of focus Done
Free label for titles/row headers etc.
---- Will's other TODOs ---
Panel Borders
Export tiffs with pdf (tiffs -> pdf)
Export to image (png/jpeg)
Send to Panel to Back / Bring to Front
Set x, y, w, h
Alignment guides on drag / resize
Rotate Image within panel
Shapes from OMERO ROIs
panel = {
"imageId":205,
"name":"121-Control siRNA.ome.tiff",
"orig_width":163,
"orig_height":194,
"sizeT":23,
"sizeZ":6,
"theZ":2,
"theT":17,
"pixel_size":0.3312,
"zoom":188,
"dx":9.15,
"dy":12.7,
"width":102.4,
"height":102.5,
"y":641.1,
"x":191.9
"labels":[{"time":"hrs:mins","size":"14","position":"top","color":"000000"}],
"channels":[{"color":"FFFFFF","active":true,"window":{"max":743,"min":87,"end":345,"start":119},"emissionWave":528,"label":"528"}],
"deltaT":[5744,6047,6348,6649,6951,7254,7556,7858,8162,8464,8764,9065,9368,9671,9973,10274,10577,10879,11178,11483,11786,12090,12387],
"selected":false,
"datasetId":"",
"datasetName":"Multiple",
}
| Javascript (Backbone.Model) |
| ↕ |
| json |
| ↕ |
| POST json string |
| ↕                         ↓ |
| Save as Orig File (retrieved by namespace) |
Export to PDF (attached to all images) |


Welcome to Neeraj Bhurewar homework 2 project.
This project shows us usage of hammer.js to drag and drop pictures,and create a webresponsive page


Exactly What It Says on the Tin, a cousin who hails from the rural parts of the nation. This trope usually comes into play on shows set in the suburbs or the city, and it usually takes the form of the cousin (or cousins) coming to visit or one of the show's main characters going to the country to visit the cousin. Either way Hilarity Ensues.
Doesn't necessarily have to be a cousin; it could be any relative. Cousin is just being used as a general term here, since it is somewhat of a common phrase.
Compare Country Mouse; while that trope usually applies to characters who are naive and central to the cast, this more applies to a character with more or less any personality (though stereotypes applying to the country will usually be apparent) and is at most a Recurring Character. May overlap with Half-Witted Hillbilly in comedic works.
See also The City vs. the Country.
Examples:
- In Garfield, Jon's family lives on a farm. He and Garfield have occasionally gone to visit them for an arc.
- Rudi (from the German comic with the same name) has Alfi from deepest Bavaria.
- In the Disney Ducks Comic Universe, Donald's grandma and his cousin Gus Goose live on a farm.
- The Fabulous Furry Freak Brothers have their Cousin Country Cowfreak. Two guesses what he grows on his farm.
- Wonder Woman: Etta Candy's family lives on a ranch in Texas and Etta, Diana and on at least one occasion Steve have traveled there for a handful of story arcs in the country.
- The Griswolds in National Lampoon'sVacation have another side of the family living in rural Missouri. They're not unintelligent by any means (even though they force the Griswolds to take their grandmother to Phoenix, believing they knew about the "surprise" beforehand), but dirt poor and relying on SSI income and multiple jobs to keep their bloated family off the rather-empty streets.
- The story of the city mouse and the country mouse (a theme/idea of Aesop, written into a poem in 1948 by Lauri Pohjanpää). The story: the city mouse visits the country mouse. Country cousin offers the best it has, but City cousin sniffs its nose at everything and goes on about how everything's better in the city. So Country cousin later visits City cousin. City cousin lives in a castle, where the food is marvellous, but the mice have to be ever alert and run and hide, and getting a treat is a tough task. Country mouse thinks it's better to eat humble food and live at peace, than to sneak around and eat handsomely, and returns home.
- Various county cousins, friends, etc. appear on The Beverly Hillbillies. Of course, the main characters are from the country, so this is a Justified Trope.
- In I Love Lucy, Lucy's Cousin Ernie comes from rural Tennessee and quickly overstays his welcome. Getting him to leave takes 2 episodes and, later in the series, Ricky and Lucy see him in Tennessee. Cousin Ernie was played by Composer and Actor Tennessee Ernie Ford.
- That's So Raven has an episode (aptly named "Country Cousins") in which Raven visits her cousins in the country. Gravy is their lifeblood.
- In Married... with Children, Peggy's family consists of country hillbillies who live in the middle of nowhere. They appeared in a few episodes where the Bundys visited them, and when Katey Sagal took a break from the show while pregnant, the character went to Wisconsin to stay with her family.
- In Justified, Raylan's late mother Francis descended from Kentucky hill-folk, much to Arlo's embarassment. His blood ties to Mary, the matriarch of Cope's clan, end up saving his life in "Kin".
- In It's Always Sunny in Philadelphia, the season 9 episode Mac Day has the character Country Mac who, as you might have guessed, is Mac's cousin from the country, who comes for a visit.
- Spoken, not particularly straight example: Bon Jovi's song "We Got It Going On" (from the band's attempt at a country album, Lost Highway) features Big & Rich, who are introduced by Jon saying "And now a Public Service Announcement from my Country Cousins. Big Kenny, talk to the people!"
- On a 30s episode of the Kraft Music Hall sidekick Bob Burns claimed to have visited Bing Crosby's country relatives in the state of Washington.
- This was the premise of Our American Cousin, the play that Abraham Lincoln was watching when he was murdered. An American country bumpkin goes to England to visit his fancy British relations. Hilarity Ensues (assuming no one gets shot).
- The Flintstones had an episode in which Fred's country cousins (who just so happen to be over-the-top Stone Age hilbillies) come to visit for a weekend...and then continue to stay for months afterwards, thinking that Fred really means it when he says that he wishes they could stay longer.
- Hey Arnold! has Arnold's country cousin, Arnie, who is exactly like Arnold in every way, except that he is completely bland, dull, and vanilla-flavored. His hometown, however, is a Bizarro Universe version of Arnold's, where Lulu has a crush on him, but Arnold prefers Hilda. It turns out to be All Just a Dream, though.
- Mikey Hoggins from Pig City is the cousin of Martha and Reggie DeBoar, who live in the city. Mikey went to live with them because his parents thought a city education would be better for him.
- On SpongeBob SquarePants, Plankton call his relatives to help him get the Krabby Patty formula in "Plankton's Army". He expects them to be Evil Geniuses like him, so he's shocked to find that they're all unsophisticated hillbillies. One of them even admits to being illiterate.
- Spoofed in The Simpsons, where Homer's country "cousin" (actually only their dogs are related) is way more sophisticated than him, whereas Homer claims it's the other way round, adhering to the stereotypes concerning country people.
- Scooby-Doo has one of these in Scooby-Dum. You can probably guess what his schtick is.
- Inverted in South Park with Kyle's cousin Kyle, from New York. He's very much the stereotypical Jew/nerd (nasal voice, good with math and money, and heavily allergic to EVERYTHING). In the end, he calls Kyle and his friends hick jocks.
- The Silly Symphonies short "The Country Cousin", which tells Aesop's story Walt Disney style.
- Another take on the same premise was "Little Rural Riding Hood", where the country cousin visits his city-cousin wolf to get away from his countrified Abhorrent Admirer, Red, only to fall for the city version of Red. Once the City Wolf takes him back, he then has the same reaction to Country Red.
- Garfield and Friends: Jon got a visit from a literal country cousin. It was a case of Name's the Same as the cousin's name is Roscoe Arbuckle.
- Mudman, one of Joey and AP's foes in Atomic Puppet, has one in Dirtbag, an anthropomorphic sack of soil and another member of the duo's Rogues Gallery.
- In My Little Pony: Friendship Is Magic, Applejack was a Country Niece to her Aunt and Uncle Orange in Manehatten, whom she lived with for a brief period as a filly, but quickly found that she missed her more immediate family, and the farm.
- Inverted with Babs Seed, who visits her cousins Applejack and Apple Bloom in the rural Ponyville from Manehatten.
- In Oggy and the Cockroaches, there's Elvis, the cockroaches' older cousin. He's a farmer with a disgusting food palate and, much to the disdain of his cousins, manages to get on the good side of Oggy.
- The Ren & Stimpy Show: In the episode "A Friend in your Face", Ren finds the country cousin of the little man living in Stimpy's head has moved into his head.
- The Country Mouse and City Mouse Adventures is about two mice: The more urban mouse Alexander and his rural cousin Emily. They travel around the world together.'
- Kim Possible has a cousin Joss that lives on a ranch with her father and is a Hero-Worshipper of her older cousin.


After building the Naive Bayes classifier, we need to check its performance. We can use a confusion matrix for this. A confusion matrix is a table that describes the performance of a classification model. This is an example of a confusion matrix:
[[True Negative False Positive]
[False Negative True Positive]]
The accuracy of a classification matrix is the total number of correct predictions divided by the total number of predictions. So it can be calculated like this:
\[Accuracy = {TP + TN\over TP + TN + FP + FN}\]Other classification metrics include:
\[Recall = {TP \over TP + FN}\]
\[ Precision = {TP \over TP + FP}\]And finally:
\[F1 = {2.precision.recall \over precision + recall}\]If we use these metrics, we will see that the test dataset contains 85% ham and 15% spam. If we try to predict whether a word is ham or not for every database cell, the accuracy would be 85%. In this case, our algorithm has higher accuracy, precision, recall, and F1 scores than a model that predicts whether a word is ham for each row in the test dataframe.
If you're interested in more information on these metrics, take a look at the Accuracy, Precision, Recall or F1? article by Towards Data Science.
In this stage, your program should:
The greater-than symbol followed by a space (>
) represents the user input. Note that it's not part of the input.
Example 1: An example of your program output
{'Accuracy': 0.5838565022424515, 'Recall': 0.5324324324324315, 'Precision': 0.6452664294520508, 'F1': 0.4387755102040817}


This experiment was designed to determine vector ion drift velocities, ion concentration
and temperature, and spacecraft potential. An ionospheric irregularity index was also obtained from
the ion concentration sensor. The experiment consisted of a retarding potential analyzer with four
planar sensor heads. The sensor head used for ion drift measurements was co-located with another head,
and all heads were spaced almost equally, looking outward from the satellite equator. Since the
satellite spin axis was perpendicular to the orbit plane, these heads could observe along the spacecraft
velocity vector in either the spin or despun mode of the spacecraft. The primary purpose of this
experiment was to provide accurate ion temperatures with other measurements being of secondary importance.
Three of the sensor heads were similar: they had two grounded entrance grids, two retarding grids, a
suppressor grid, a shield grid, and a collector. A linear sweep voltage (32 or 22 to 0 V, up or down)
was normally applied to the retarding grids in 0.75 s. Interpretation of the resulting current-voltage
profiles provided the ion temperature, the ion and electron concentration, some ion composition
information, vehicle potential and plasma drift velocity parallel to the velocity vector. Two of
the three similar sensors had an additional grid between the entrance and retarding grids in order
to protect inner grids from ion bombardment during electron measurements. The other significant
feature of these two sensors was that a small positive collector bias could be applied to assure
adequate access of thermal electrons to the collector. With the retarding grid at constant zero volts,
current changes could be observed for 3-s periods to obtain gradients of ion concentration.
Electron parameters were measured in a manner similar to ions. Ions in mass ranges 1 to 4, 14 to 16,
24 to 32 and greater than 40 atomic mass units could be identified. The fourth sensor head was for
the ion-drift velocity measurements, and consisted of four grounded grids, a negatively biased
suppressor grid, and a 4-segment collector. Differences in various collector segment currents provided
ion-drift directional component information. More details of this experiment are available in W. B.
Hanson et al., Radio Sci., v. 8, n. 4, p. 333, 1973. NSSDC has all the useful data that exist from
this investigation.
Version:2.0.0
This experiment was designed to determine vector ion drift velocities, ion concentration
and temperature, and spacecraft potential. An ionospheric irregularity index was also obtained from
the ion concentration sensor. The experiment consisted of a retarding potential analyzer with four
planar sensor heads. The sensor head used for ion drift measurements was co-located with another head,
and all heads were spaced almost equally, looking outward from the satellite equator. Since the
satellite spin axis was perpendicular to the orbit plane, these heads could observe along the spacecraft
velocity vector in either the spin or despun mode of the spacecraft. The primary purpose of this
experiment was to provide accurate ion temperatures with other measurements being of secondary importance.
Three of the sensor heads were similar: they had two grounded entrance grids, two retarding grids, a
suppressor grid, a shield grid, and a collector. A linear sweep voltage (32 or 22 to 0 V, up or down)
was normally applied to the retarding grids in 0.75 s. Interpretation of the resulting current-voltage
profiles provided the ion temperature, the ion and electron concentration, some ion composition
information, vehicle potential and plasma drift velocity parallel to the velocity vector. Two of
the three similar sensors had an additional grid between the entrance and retarding grids in order
to protect inner grids from ion bombardment during electron measurements. The other significant
feature of these two sensors was that a small positive collector bias could be applied to assure
adequate access of thermal electrons to the collector. With the retarding grid at constant zero volts,
current changes could be observed for 3-s periods to obtain gradients of ion concentration.
Electron parameters were measured in a manner similar to ions. Ions in mass ranges 1 to 4, 14 to 16,
24 to 32 and greater than 40 atomic mass units could be identified. The fourth sensor head was for
the ion-drift velocity measurements, and consisted of four grounded grids, a negatively biased
suppressor grid, and a 4-segment collector. Differences in various collector segment currents provided
ion-drift directional component information. More details of this experiment are available in W. B.
Hanson et al., Radio Sci., v. 8, n. 4, p. 333, 1973. NSSDC has all the useful data that exist from
this investigation.
| Role | Person | |
|---|---|---|
| 1. | PrincipalInvestigator | spase://SMWG/Person/William.B.Hanson |
Information about the Retarding Potential Analyser/Drift Meter (RPA) experiment on the AE-D mission.
Detailed information about the Retarding Potential Analyser/Drift Meter (RPA) experiment on the AE-D mission.


This is a simple hero unit, a simple jumbotron-style component for calling extra attention to featured content or information.
Learn More
Example block-level help text here.


vignettes/not-built-vignettes/tb-data-base-pos-imputation.Rmd
tb-data-base-pos-imputation.Rmd
In this vignette, we repeat the prior analysis but impute the smear covariates to be positive instead.
Briefly recap the data used in the analysis
Fit a series of nested models to the data using our base model
Repeat the model fitting with the Multiple Outside Transmission model
Examine standard errors
The data can be accessed using data(tb_clean)
. The clusters are uniquely ID’d with the variable group
The data consists of 159 clusters where each cluster contains between 1 and 25 individuals. These individuals have covariates corresponding to their smear status (+/-/NA), HIV status (+/-/unknown), date of sputum collection, and race (Asian/Black/White). Please see ?tb_clean
for more information.
There are only individuals who have smear status NA, and in this analysis we impute those to be smear -. We transform date of sputum collection into a variable we call relative time, which is the time in years between the reference sputum collection date and the first observed sputum within a cluster. Therefore, all relative time values of singleton clusters will have the value 0.
We consider HIV status and race to be categorical variables and use “HIV+” and “White” as the reference groups, respectively.
We use the below code to format tb_clean
to use in our model fitting.
clusters <- tb_clean %>% dplyr::mutate(smear = ifelse(spsmear == "Positive", 1, 0), cluster_id = group, hiv_f = hivstatus) %>% dplyr::mutate(hiv_neg_pos = ifelse(hiv_f == "neg", 1, 0), hiv_unk_pos = ifelse(hiv_f == "unk", 1, 0)) %>% dplyr::group_by(cluster_id) %>% dplyr::mutate(rel_time = rel_time / 365) %>% dplyr::mutate(cluster_size = dplyr::n()) %>% dplyr::ungroup() %>% mutate(smear = ifelse(spsmear == "Negative", 0, 1), cluster_id = group) %>% mutate(hiv_neg_pos = ifelse(hiv_f == "neg", 1, 0), hiv_unk_pos = ifelse(hiv_f == "unk", 1, 0)) %>% group_by(cluster_id) %>% mutate(cluster_size = n()) %>% ungroup() %>% mutate(race_f = fct_collapse(race, white = "White", black = "Black or African American", asian = "Asian")) %>% mutate(race_asian_white = ifelse(race_f == "asian", 1, 0), race_black_white = ifelse(race_f == "black", 1, 0)) %>% select(cluster_id, smear, hiv_neg_pos, hiv_unk_pos, rel_time, race_asian_white, race_black_white, cluster_size)
We analyze the series of nested models:
Model 1: \(logit(p_i) = \beta_0\)
Model 2: \(logit(p_i) = \beta_0 + \beta_{1}x_{i,{smear pos}}\)
Model 3: \(logit(p_i) = \beta_0 + \beta_{1}x_{i,smear pos} + \beta_{2}x_{i,{HIV neg}}+ \beta_{3}x_{i,{HIV unk}}\)
Model 4: \(logit(p_i) = \beta_0 + \beta_{1}x_{i,{smear pos}} + \beta_{2}x_{i,{HIV neg}} + \beta_{3}x_{i,{HIV unk}} + \beta_{4}x_{i,{rel. time}}\)
Model 5: \(logit(p_i) = \beta_0 + \beta_{1}x_{i,{smear pos}} + \beta_{2}x_{i,{HIV neg}} + \beta_{3}x_{i,{HIV unk}} + \beta_{4}x_{i,{rel. time}} + \beta_{5}x_{i,{race Asian}} + \beta_{6}x_{i,{race Black}}\)
We first fit our base model described in the model overview, where we assume that the infections within a cluster can be traced back to a root individual within the cluster.
We use the below code to fit each of the models where we use \(K=1000\) MC samples for each cluster in the data. We then report the log likelihood and AIC for each of the models.
Note that for the full results, we use \(K=10000\), which takes about ~3 hour to run on a PC.
K <- 1000 my_seed <- 42 set.seed(my_seed) ## MODELS ## models covariate_list <- vector(mode = "list", length = 5) covariate_list[[1]] <- NA covariate_list[[2]] <- "smear" covariate_list[[3]] <- c("smear", "hiv_neg_pos", "hiv_unk_pos") covariate_list[[4]] <- c("smear", "hiv_neg_pos", "hiv_unk_pos", "rel_time") covariate_list[[5]] <- c("smear", "hiv_neg_pos", "hiv_unk_pos", "rel_time", "race_asian_white", "race_black_white") ## Set up outputs loglike_df <- data.frame(model = 1:length(covariate_list), n_params = c(1, 2, 4, 5, 7), loglike = 0, aic = 0) beta_mat1 <- matrix(0, nrow = 1, ncol = 4) rownames(beta_mat1) <- c("Intercept") colnames(beta_mat1) <- c("Est.", "lower", "upper", "SE") beta_list <- vector(mode = "list", length = length(covariate_list)) beta_list[[1]] <- beta_mat1 for(ii in 2:length(covariate_list)){ mat <- matrix(0, nrow = length(covariate_list[[ii]]) + 1, ncol = 4) rownames(mat) <- c("Intercept", covariate_list[[ii]]) colnames(mat) <- c("Est.", "lower", "upper", "SE") beta_list[[ii]] <- mat }
t_init <- proc.time()[3] ## Sample MC trees all at once t0 <- proc.time()[3] ## Sample all MC clusters at once for each of the 5 models mc_trees <- sample_mc_trees(clusters, B = K, multiple_outside_transmissions = FALSE, covariate_names = covariate_list[[length(covariate_list)]]) print(proc.time()[3] - t0) ## Fit each of the models for(jj in 1:length(covariate_list)){ covariate_names <- covariate_list[[jj]] print("Model:") print(covariate_names) if(is.na(covariate_names[1])){ init_params <- 0 } else{ init_params <- rep(0, length(covariate_names) + 1) } ## Optimize print("Optimizing") bds <- rep(-5, length(init_params)) if(length(covariate_names) > 5){ bds <- rep(-4, length(init_params)) } lower_bds <- bds upper_bds <- -bds cov_mat <- covariate_df_to_mat(mc_trees, cov_names = covariate_names) t1 <- proc.time()[3] best_params <- optim(par = init_params, fn = general_loglike, mc_trees = data.table::as.data.table(mc_trees), return_neg = TRUE, cov_mat = cov_mat, cov_names = covariate_names, use_outsider_prob = FALSE, multiple_outside_transmissions = FALSE, method = "L-BFGS-B", lower = lower_bds, upper = upper_bds, hessian = TRUE ) t2 <- proc.time()[3] - t1 print(paste("Optimization time:", round( t2 / 60, 3), "min")) beta_list[[jj]][, 4] <- sqrt(diag(solve(best_params$hessian))) ## SE from Fisher info ## Likelihood Profile ests print("Likelihood profiling") lp_ests <- likelihood_profs(best_pars = best_params$par, max_loglike = -best_params$value, mc_trees = mc_trees, covariate_names = covariate_names, alpha = .05, multiple_outside_transmissions = FALSE) beta_list[[jj]][,-4] <- lp_ests print("best params:") print(beta_list[[jj]]) loglike_df$loglike[jj] <- -best_params$val print(paste("Total time:", round( (proc.time()[3] - t_init) / 3600, 3), "hrs")) }
loglike_df <- loglike_df %>% mutate(aic = -loglike + 2 * n_params, model = 1:5) %>% select(model, everything()) loglike_df %>% kable(digits = 2, col.names = c("Model", "# params.", "Log like.", "AIC")) %>% kable_styling(bootstrap_options = c("condensed", "hover", "striped", "responsive"), full_width = FALSE, position = "center")
| Model | # params. | Log like. | AIC |
|---|---|---|---|
| 1 | 1 | -409.40 | 411.40 |
| 2 | 2 | -409.23 | 413.23 |
| 3 | 4 | -407.44 | 415.44 |
| 4 | 5 | -396.22 | 406.22 |
| 5 | 7 | -395.82 | 409.82 |
Note that log likelihood increases as the model number increases, which should be the case since the models are nested. The best model according to AIC is model 4 which corresponds to the variables smear, hiv_neg_pos, hiv_unk_pos, rel_time.
The estimated paramters for this model are
beta_list[[4]] %>% kable(digits = 2) %>% kable_styling(bootstrap_options = c("condensed", "hover", "striped", "responsive"), full_width = FALSE, position = "center")
| Est. | lower | upper | SE | |
|---|---|---|---|---|
| Intercept | -0.72 | -0.89 | -0.55 | 0.38 |
| smear | -0.11 | -0.37 | 0.13 | 0.31 |
| hiv_neg_pos | -0.36 | -0.59 | -0.14 | 0.36 |
| hiv_unk_pos | -0.56 | -1.41 | 0.06 | 0.49 |
| rel_time | 0.35 | 0.27 | 0.42 | 0.07 |
where lower
is the lower boundary for 95% likelihood profiling CI and upper
is the upper boundary. The variable SE
is the estimated standard error using the Hessian from the optimization process as an estimate for the Fisher Information. Using these boundaries, we see that HIV- compared to HIV+ and relative time are both significant at the \(\alpha = .05\) level because 0 is not included in the likelihood profiling CI.
Fitting the multiple outside transmissions (MOT) model is as easy as fitting with the base model, we only need to change one argument in two different functions. We fit the above 5 models. Here we use \(K=1000\) MC samples but for our full results, we us \(10000\).
K <- 1000 my_seed <- 24 set.seed(my_seed) ## MODELS ## models covariate_list <- vector(mode = "list", length = 5) covariate_list[[1]] <- NA covariate_list[[2]] <- "smear" covariate_list[[3]] <- c("smear", "hiv_neg_pos", "hiv_unk_pos") covariate_list[[4]] <- c("smear", "hiv_neg_pos", "hiv_unk_pos", "rel_time") covariate_list[[5]] <- c("smear", "hiv_neg_pos", "hiv_unk_pos", "rel_time", "race_asian_white", "race_black_white") ## Set up outputs loglike_df <- data.frame(model = 1:length(covariate_list), n_params = c(1, 2, 4, 5, 7), loglike = 0, aic = 0) beta_mat1 <- matrix(0, nrow = 1, ncol = 4) rownames(beta_mat1) <- c("Intercept") colnames(beta_mat1) <- c("Est.", "lower", "upper", "SE") beta_list <- vector(mode = "list", length = length(covariate_list)) beta_list[[1]] <- beta_mat1 for(ii in 2:length(covariate_list)){ mat <- matrix(0, nrow = length(covariate_list[[ii]]) + 1, ncol = 4) rownames(mat) <- c("Intercept", covariate_list[[ii]]) colnames(mat) <- c("Est.", "lower", "upper", "SE") beta_list[[ii]] <- mat }
t_init <- proc.time()[3] ## Sample MC trees all at once t0 <- proc.time()[3] ## Sample all MC clusters at once for each of the 5 models mc_trees <- sample_mc_trees(clusters, B = K, multiple_outside_transmissions = TRUE, covariate_names = covariate_list[[length(covariate_list)]]) print(proc.time()[3] - t0) ## Fit each of the models for(jj in 1:length(covariate_list)){ covariate_names <- covariate_list[[jj]] print("Model:") print(covariate_names) if(is.na(covariate_names[1])){ init_params <- 0 } else{ init_params <- rep(0, length(covariate_names) + 1) } ## Optimize print("Optimizing") bds <- rep(-5, length(init_params)) if(length(covariate_names) > 5){ bds <- rep(-4, length(init_params)) } lower_bds <- bds upper_bds <- -bds cov_mat <- covariate_df_to_mat(mc_trees, cov_names = covariate_names) t1 <- proc.time()[3] best_params <- optim(par = init_params, fn = general_loglike, mc_trees = data.table::as.data.table(mc_trees), return_neg = TRUE, cov_mat = cov_mat, cov_names = covariate_names, use_outsider_prob = FALSE, multiple_outside_transmissions = TRUE, method = "L-BFGS-B", lower = lower_bds, upper = upper_bds, hessian = TRUE ) t2 <- proc.time()[3] - t1 print(paste("Optimization time:", round( t2 / 60, 3), "min")) beta_list[[jj]][, 4] <- sqrt(diag(solve(best_params$hessian))) ## SE from Fisher info ## Likelihood Profile ests print("Likelihood profiling") lp_ests <- likelihood_profs(best_pars = best_params$par, max_loglike = -best_params$value, mc_trees = mc_trees, covariate_names = covariate_names, alpha = .05, multiple_outside_transmissions = TRUE) beta_list[[jj]][,-4] <- lp_ests print("best params:") print(beta_list[[jj]]) loglike_df$loglike[jj] <- -best_params$val print(paste("Total time:", round( (proc.time()[3] - t_init) / 3600, 3), "hrs")) }
loglike_df <- loglike_df %>% mutate(aic = -loglike + 2 * n_params, model = 1:5) %>% select(model, everything()) loglike_df %>% kable(digits = 2, col.names = c("Model", "# params.", "Log like.", "AIC")) %>% kable_styling(bootstrap_options = c("condensed", "hover", "striped", "responsive"), full_width = FALSE, position = "center")
| Model | # params. | Log like. | AIC |
|---|---|---|---|
| 1 | 1 | -473.20 | 475.20 |
| 2 | 2 | -473.03 | 477.03 |
| 3 | 4 | -467.47 | 475.47 |
| 4 | 5 | -449.24 | 459.24 |
| 5 | 7 | -449.20 | 463.20 |
Note that log likelihood increases as the model number increases, which should be the case since the models are nested. The best model according to AIC is model 4 which corresponds to the variables smear, hiv_neg_pos, hiv_unk_pos, rel_time.
The estimated paramters for this model are
beta_list[[4]] %>% kable(digits = 2) %>% kable_styling(bootstrap_options = c("condensed", "hover", "striped", "responsive"), full_width = FALSE, position = "center")
| Est. | lower | upper | SE | |
|---|---|---|---|---|
| Intercept | -1.72 | -1.91 | -1.54 | 0.48 |
| smear | 0.54 | 0.30 | 0.77 | 0.41 |
| hiv_neg_pos | -0.60 | -0.85 | -0.37 | 0.33 |
| hiv_unk_pos | -1.34 | -3.72 | -0.32 | 0.77 |
| rel_time | 0.52 | 0.46 | 0.59 | 0.08 |
where lower
is the lower boundary for 95% likelihood profiling CI and upper
is the upper boundary. The variable SE
is the estimated standard error using the Hessian from the optimization process as an estimate for the Fisher Information. Using these boundaries, we see that HIV- compared to HIV+, HIV unknown compared to HIV+, and relative time are all significant at the \(\alpha = .05\) level because 0 is not included in the likelihood profiling CI. Here we see smear is significant, but this is lost when we use \(K= 10000\) MC samples instead of \(K=1000\) MC samples.
We see that the standard errors are large compared to the likelihood profiling CI widths. If we multiplied the standard errors by \(2 \times 1.96\), the width of a 95% CI for a normal distributed variable, then this width is much larger than the likelihood profiling estimate.
To see which estimate is closer to the truth, we can bootstrap our data to get a second standard error and third CI estimate. We provide the function bootstrap_clusters()
to resample clusters from our data. The analysis can be repeated on these bootstrap data sets. Below, we see that our new sampled data set contains the same amount of clusters as the original data but now has a different number of individuals compared to the original 389.
bootstrap_data <- bootstrap_clusters(clusters) dim(bootstrap_data)
## [1] 371 9


|
SUKKUR IBA UNIVERSITY Merit-Quality-Excellence DEPARTMENT OF ELECTRICAL ENGINEERING |
"I can state flatly that heavier than air flying machines are impossible." —Lord Kelvin, 1895.
|
Semester Project Quadcopter |
| Background Of Project: |
| The world is always fascinated by the stupendous revolution that science has brought through inventions and innovations. There are hundreds and thousands of bizarre tools that science has created for the betterment of mankind, like engines, computers, internet, medicines etc. All of these technological revolutions have their own role in human development. Besides this, one of the intriguing works of science is in the field of aviation, more specifically, in the development of Unmanned Aerial Vehicles (UAV's). An unmanned aerial vehicle is an aircraft with or without a human pilot on board. There are several sensors and devices attached with these machines that provide the ability to fight autonomously and also capability to control them from ground. These days many universities all around the globe are in the race to make drones which are efficient, autonomous,cheap,more reliable and are able to perform complex maneuvers. There are already several models available in the market that can be used for different purposes: defence, emergency response, agriculture, home delivery, health care and many more. With the exponential increase in the uses of these autonomous vehicles, the drone market is skyrocketing! According to the Drone Market Report 2020, the global drone market will grow from $22.5 billion in 2020 to over $42.8 billion in 2025. For more details Click Here! |
| Our Goal: |
| By taking all of the above facts into consideration I, along with my three classmates, decided to choose a project that helps us to understand the core idea behind the working of UAV's: To make a drone. This was an intricate and exciting task! We had to complete this project within the period of four months and we were supposed to make everything from scratch—like a flight controller, case for drone etc. Beside these conundrums we were so optimistic and keen to make this project mark up to a standard. |
| Purpose Of This Report: |
|
This report presents our whole journey of failures and achievements while working on this project. In this report we have tried our best to cover everything step by step, like how we tackle the intricacies in circuit design, components interface, coding, case design etc, so that it can be a perfect guide for other aspirants of drones. We have provided the links for softwares, all the essential drivers, references for literature review, and complete description about components which are essential for this project. |
|
Drone Sketch: |
|
When we started our project, the first thing that we
considered was a sketch of how our drone would look
like. This was an important step because we had to make
our drone as much lighter as possible, and for this a
clever design was a necessity. So, we all searched on
multiple sources and analyzed some pre-existing mini
drones. We found hundreds of designs—some were
with hard frames and complex structure, some were
light weighted but without propellers guards. Finally,
we all sat together and shared our sketches. All of
them were intriguing, but the one that seemed
compelling for all of our team members was the
one sketched below. With this design we could reduce
much of the material from frame so that it can be
lighter, and also placement of components like flight
controller and motors, was the best in this masterpiece. |
|
Project Requirements: |
|
As drone is a type of vehicle which has capability
to fly, to perform complex maneuvers, move autonomously
to a targeted place, so it requires several components
depending upon how advanced one wants to design it.
In our case, we don’t want to make anything
complicated—one that requires a lot of resources and
time, rather we want to make a small prototype: a miniquadcopter.
The components that we needed for successful compilation
of this project, which are explained in the label
About Components , are:
1: Four motors 2: Four propellers 3: A battery 4: A transmitter 5: A receiver 6: A gyro sensor 7: Flight controller (The components need for flight controller and its designing procedure is available Here!) 8: A frame to hold the components. (Check out CAD Design label, Click Here!) |
|
Block Diagram: |
|
Above block diagram represents how different components interact with one another. At the center there is the flight controller—brain of the drone, which controls all the process. At the top-left side there is a receiver that communicates with the transmitter to receive commands from the user and then it feeds the received data into the flight controller. The Flight controller, more specifically, the microcontroller receives this data and changes the magnitude of the control signal (duty cycle of PWM signal) accordingly. On the top-left side there is a battery—in our case LiPo battery, that supplies power both to the flight controller and to motors. One thing that one might need to bear in mind is that battery terminals are not directly connected to the motors rather one terminal is connected though flight controller, and the other one is directly connected with the battery. This is more explicitly explained in the components connection below. On the bottom-left side there is gyro—this is actually gyro and accelerometer together, it gives the flight controller information about the orientation of the drone (for more details Click Here!). So, by using the data that is continuously feeded into the flight controller—both from gyro and receiver, the flight controller adjusts the speed of motors. |
|
Electrical Schematic |
|
Above diagram is a crystal clear representation of how different components are physically connected to one another—through wires. This was one of the intricate parts of our journey to dig out the right connections between the components that we had to used. It took almost a week, but finally we came up with the above components configuration. In the above figure we can see there is an Arduino Nano that is placed at the center and the rest of components are connected to it. Though in our case, we have made our own flight controller but the microcontroller that we have used is the same as the one used in the Arduino Nano. Thus the pins, like GPIO, PWM, SCL and SDA, are the same—due to same microcontroller ATmega328p. For more details Click here! There are four mosfets connected to each of the four motors to control their speed. One must note here that the positive terminal of the battery is directly connected to each corresponding terminals of motors, but negative connection is provided through the drain of the mosfet. This configuration actually allows to control the magnitude of signal from source to drain by applying a control signal at the gate terminal. Thus we can control the speed of motors by changing the duty cycle of PWM signals. On the top side there is an MPU6050 sensor which is connected to SCL and SDA pins of Arduino Nano and it is powered by a battery through wires connected to its Vcc and Gnd. As we can see there are four other pins which are not connected to any other component. We can't use these pins in our project but they are useful for some other purposes. On the bottom side there is receiver FS-A8S which is connected to the interrupt pin of Arduino Nano (pin 02). As this receiver is PPM based, so it requires only a single wire to transfer all the data of eight channels collectively. Click here for more details about the components specifications and working principles. |
| Next |


--- layout: default title: Americans with Disablilites Act Compliance description: "My philosophical approach to software development, with a bias towards developing modular web components." class: ada parent: ADA ---
Title III of the Americans with Disabilities Act of 1988 provides for equal access for persons with disabilities in places of public accommodation. It’s difficult to argue that the ADA has not made public spaces more accessible to more people in its nearly 30 years in existence. Its effects are present in virtually all businesses – large and small - today. In recent years, there has been movement to extend the conformance requirement to the digital presence of places of accommodation. This will apply not just to your company’s website, but all online digital media published by your company.
Over the past few years, the number of Title III complaints and lawsuits over website accessibility has been increasing. Some cases have included retailers including Target, Peapod and Winn Dixie. Fortunately for businesses, Title III limits liability to injunctive relief (the court will order a change or remedy a behavior - in this case the non-complying website) and legal fees for prevailing parties. Damages are not permitted. It also creates opportunities to dismiss claims by quickly eliminating barriers or through remediation and education.
Qoppa, LLC continuously monitors and implements technologies and processes that comply with the standards for website accessibility. Following, we will discuss the basics of ADA Title III compliance and how it applies to your company’s digital media,
General lawsuits over Title III compliance grew by 37% in 2016, with over 6000 cases being brought before courts; about 250 of these were specific to websites. This does not include simple demand letters that are sent without legal action. At present, these numbers are generally low; however, it is a trend that is expected to continue. And while different federal courts have taken different stances on website inclusion in Title III, ranging from outright dismissal to injunctions for compliance, it is likely that this trend, too, will continue as the U.S. Department of Justice (DOJ) takes additional time to publish guidelines for compliance.
The ADA stipulates that individuals with disabilities be extended the “goods, services, facilities, privileges, advantages or accommodations of any place of public accommodation.” These disabilities include blindness and low vision, deafness and hearing loss, learning disabilities, cognitive limitations, limited movement, speech disabilities, photosensitivity, and combinations of these.
As of summer 2017, the DOJ has not adopted specific guidelines for compliance with Title III although a ruling has been promised for 2018. DOJ has on numerous occasions pointed to the World Wide Web Consortium’s (W3C) Web Content Accessibility Guidelines 2.0 (WCAG 2.0 level AA) in statements and briefs, which is why we expect components of it to be in an eventual final ruling.
WCAG 2.0 is a standard that outlines website accessibility for a broad range of disabilities. It spells out capabilities that conform to accessibility devices and software and is very likely to be part of the DOJ final guidelines. Its goal is to provide a single standard for web content accessibility that meets the needs of individuals, organizations and governments internationally. While WCAG 2.0 is not the law, most legal experts will recommend complying with the DOJ statements that reference WCAG 2.0 level AA.
While ADA covers a broad range of disabilities, frequently hearing and vision impaired individuals use assistive devices to read and interpret the Internet. These devices may read aloud, generate braille text, magnify pages or allow users who cannot use a mouse to navigate with only a keyboard or touchscreen. What’s important is that your media be accessible and readable by these devices. This includes images, which require accessible descriptive text to convey the meaning of the image.
In the case of a complaint, as is similar with physical property complaints, an individual or an advocacy group usually makes a demand for compliance with Title III. It is important to acknowledge such demands and assess your state of compliance. There has been at least one case where a retailer dismissed such requests and ended up with an unfavorable judgment. Coming into compliance may not be very difficult, especially for Qoppa customers.
If you are a client of Qoppa, we have fully compliant solutions that meet WCAG 2.0 level AA guidelines. We are closely monitoring the evolution to WCAG 2.1 and the emerging WCAG 3.0 standard. These standards evolve over a period of years and we monitor and adapt as they do. And while Qoppa does not provide legal indemnity for ADA claims, we maintain high awareness and commitment to help clients develop compliant digital programs and services.
WCAG 2.0 level AA Guidelines provide technical standards on the design and features of websites to enable access to individuals with a disability. The WCAG 2.0 level AA Guidelines ask operators of websites to provide:
Qoppa has been monitoring, and will continue to monitor developments in ADA website standards. Accordingly, we continue to grow and advance our systems and capabilities to accommodate the ever-evolving standards.
We believe the situation will remain in limbo under the current DOJ due to the Executive Order limiting new regulation. This will continue to confuse all stakeholders. The rise of lawsuits/complaints stems from the ambiguity of what the rules are. Until legal guidelines are published, this will, unfortunately, be determined through lawsuits with intermittent DOJ briefs.
We believe that regardless of the disposition of the current administration or the longstanding delay of concrete guidelines by the DOJ, businesses should:
However, it’s more than compliance.
Qoppa believes that while the risk of a compliance issue is low in terms of likelihood and cost, it remains the right thing to do for your business. You’ve worked hard to make your physical environment welcoming and inclusive of all members of society. We think it only natural that this would extend to your company’s digital footprint as well.
Qoppa is happy to work with you to develop an ADA Compliance strategy for your digital media.


--- layout: default title: Coloring Pages permalink: /coloring-pages/ files: - file: coloring-pages/coloringpage_001.png image_small: coloring-pages/coloringpage_001_small.jpg title: Coloring page of Blathers from Animal Crossing caption: Coloring page of Blathers from Animal Crossing activated: false - file: coloring-pages/coloringpage_002.png image_small: coloring-pages/coloringpage_002_small.jpg title: Coloring page of Bruni from Frozen 2 caption: Coloring page of Bruni from Frozen 2 activated: true - file: coloring-pages/coloringpage_003.png image_small: coloring-pages/coloringpage_003_small.jpg title: Coloring page of Mike and Sully caption: Coloring page of Mike and Sully activated: false - file: coloring-pages/coloringpage_004.png image_small: coloring-pages/coloringpage_004_small.jpg title: Coloring page of SquidGirl from Splatoon caption: Coloring page of Inkling Girl from Splatoon activated: false - file: coloring-pages/coloringpage_005.png image_small: coloring-pages/coloringpage_005_small.jpg title: Coloring page of Geralt of Rivia from The Witcher Series caption: Coloring page of Geralt of Rivia from The Witcher Series activated: false ---
If you can't have too much coloring pages in life, here's your section! From well known characters to original designs, you can print them out and enjoy some stress-free coloring time! I will announce new pages on my social medias! Don't forget to send me a message with the final result! 😉
The pages are scaled at letter format (8.5 in x 11 in).


'Austin Powers: The Spy Who Shagged Me' (1999)
A movie review by Walter Frith
<EMAIL_ADDRESS>Member of the 'Online Film Critics Society' http://www.ofcs.org
'Austin Powers: The Spy Who Shagged Me' is one of the most schizophrenic films I've ever seen. They thrown in some new characters that work and some that don't. Between the three characters that Mike Myers plays, one is horrible, one is excellent and one is somewhere in the middle. Throw in a leading lady with a poor sense of comic timing and given very little direction, and this sequel to the 1997 film caught me by surprise with just how tastelessly bizarre and unfunny it actually is in many places.
The film opens pretty much where it left off and Dr. Evil (Mike Myers character #1) is making a return to the Earth where he plots revenge against Austin Powers (Mike Myers character #2) for foiling his plot in the first film. Austin and his new bride Vanessa (Elizabeth Hurley in a brief cameo in bizarre contrast to the first film) are on their honeymoon when things go awry and from there we learn that Dr. Evil's plan is to go back to 1967, the time when both he and Austin Powers were both cryogenically frozen in the first film, and steal Austin's "mojo". This is apparently a chemical compound that gives Austin his groove, his swinging sense of style and his ability to do his job as a super agent with these qualities intact. Once weakened, Dr. Evil will attempt to blackmail world forces out of 100 billion dollars or he will turn his deadly laser gun loose on the Earth from outer space and will start by destroying Washington, D.C.
Dr. Evil's plan is to add some new and somewhat lethal characters to his roster and they include one called Fat Bastard, a totally repulsive and awful character played by Myers and an ingenious and totally whacked out and hilarious creation Dr. Evil calls Mini-me, a clone 1/8 the size of himself (Verne Troyer). One of Dr. Evil's right hand men, Number 2 (Robert Wagner) is portrayed in 1967 by Rob Lowe as a younger version of that character and it took me a while to figure out if it was Rob Lowe speaking with Robert Wagner's voice over dubbed or Lowe doing his own impression. It sounded like the latter which was mildly amusing. Seth Green is back as Dr. Evil's son Scott as is Mindy Sterling returns as the Nazi-ish Frau Farbissina.
On Austin's side of the playing field, there is the return of Basil Exposition (Michael York), Austin's boss and a somewhat cardboard character and this time, filling in for Vanessa Kensington is Felicity Shagwell (Heather Graham) who looks great but is an extremely bland leading lady with little life or zest for her role.
The only parts of this installment in the Austin Powers series I enjoyed were the ones where Dr. Evil is on screen. Austin Powers is only average in this one and the fat Bastard character is repulsive, unfunny and disgusting and may actually and literally make you sick. Mini-me is a charmer and the rest of the film's new characters are only average. 'Austin Powers: The Spy Who Shagged Me' tries to copy 1998's 'There's Something About Mary' with some gross-out gags that are unnecessary, jaw-dropping and unbelievably repellent. Perhaps they figured with such a different look, many wouldn't notice but those who enjoy film viewing as a hobby can't help but notice.
The film intentionally leaves the door open for a third film and perhaps a third film would do better if it relied on creating some original humour rather than lampooning films like the 007 series, 'Star Wars', 'Apollo 13', the Matt Helm series and others as well. With all of its campy and silly infrastructure, perhaps it might be better to get a new director as well. The director of the two Austin Powers movies, Jay Roach, looks like he'd be better at directing mindless and insulting television commercials. How about getting David or Jerry Zucker ('Airplane!', 'The Naked Gun') or even John Landis who is great at irreverent comedy. At least there would be something to actually laugh at if these guys were at the controls.
OUT OF 5 > * * 1/2
Visit FILM FOLLOW-UP by Walter Frith http://www.cgocable.net/~wfrith/movies.htm
* * * * * - a must see * * * * 1/2 - don't miss it * * * * - an excellent film * * * 1/2 - a marginal recommendation * * * - can't quite recommend it * * 1/2 - don't recommend it * * - avoid it * 1/2 - avoid it seriously * - avoid it AT ALL COSTS 1/2 - see it at your own risk zero - may be hazardous to your health
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