When set to true, this option will force the log severity to be error for all non 2** response statuses.
If set to true, this option will stop the Date header being added to responses.
If set to true, this option will stop the X-Request-ID header being added to responses.
If set to true, this option will stop the X-Response-Time header being added to responses.
Configures pino the use extreme mode for added performance.
Allows you to pass your own instance of Pino preconfigured to use in the logging middleware. This option is not compatible with pinoOptions or extreme, as they don't make sense if you pass a preconfigured pino object. If a preconfigured instance is used requests, responses and errors will not be serialized.
These options will be passed to pino. If no serializers for req, res or err are specified the defaults will be used.
Allows you to override the default request id generation function. By default this is set to the uuidv4 function from the uuid node module.
Generated using TypeDoc
A set of configuration options used to configure a new instance of KoaReqLogger.


Revolut appoints new group chief risk officer Pierre Decote
by Ruby HinchliffeRevolut has appointed Pierre Decote as its new group chief risk officer (CRO).
Decote comes from Prodigy Finance, a fintech which finances international post-graduate students, and has also gathered experience working in analytics as a chief executive at Tandem Bank, Barclaycard and the US’ tenth largest bank by assets Capital One.
The new group CRO will be joined by fellow new recruits ex-Goldman Sachs head Jason Burgess, ex-HSBC director Ed Simmons and ex-Barclays’ chief executive Daniel Gordon, who will be acting as heads of liquidity risk management,corporate structuring and operational risk management respectively.
Read next: Revolut CISO leaves after two months on the job
The neobank has traditionally struggled to hold on to its risk and compliance chief executives. Chief information security officer (CISO) Dinis Cruz left after just two months in early November 2019, and since 2016 the challenger has also lost two chief risk officers, a chief compliance officer and two money laundering reporting officers.
As its new CRO, Pierre will head up Revolut’s global risk function as it expands globally and develops new product lines such as credit and Revolut Youth.
Commenting on the CRO’s years of experience, Revolut’s founder and CEO Nik Storonsky says his appointment “is a major coup in our continued mission to become the world’s first truly global bank”.
Now with more than eight million customers worldwide, the digital bank is gearing up for a $500 million funding round to support its expansion into eight new countries before the end of this year.
Related: Revolut appoints new chairman


try:
out = torch.zeros_like(x)
except AttributeError:
out = x.new(x.size()).zero_()
// unroll inputs and outputs for ease of iteration through elements
condition_ = condition.view(N)
x_ = x.view(N)
def where(condition, x, y):
assert condition.shape == x.shape == y.shape, "Dimension mismatch"
result = zeros_like(condition, **context(x))
result[condition] = x
result[~condition] = y
return result
def all(tensor):
return torch.sum(tensor != 0)


Royalty free stock photos
Popular: Food, Travel, Photography, Nature, Portraits, Mountains, Technology
Popular: Food, Travel, Photography, Nature, Portraits, Mountains, Technology
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Use the install package on the website http://nodejs.org/, or via command line http://howtonode.org/how-to-install-nodejs
Install Grunt Command Line Interface (Grunt CLI) allowing you to execute grunt on the command line.
npm install -g grunt-cli
Prerequisite: install Homebrew: http://brew.sh/
Run command: brew install ImageMagick
Error:
The `brew link` step did not complete successfully The formula built, but is not symlinked into /usr/local Could not symlink include/libltdl/usr/local/include is not writable.
Solution:
Go to the directory cd /usr/local/include
and open permissions on the folder sudo chmod -R 777 .
Run these commands to esure proper linking
brew link imagemagick brew link jpeg brew link libtool brew link libpng brew link freetype
If grunt build.images.web
throws an error still, you may need ghostscript. brew install ghostscript
Used for svg spriting (https://github.com/jkphl/svg-sprite)... grunt build.sprite
npm install svg-sprite -g
$ cd FOLDER/LOCATION/CONTAINING/GRUNTFILE.JS
Install project specific gems from the Gemfile.lock file.
bundle install
Install all necessary packages inside package.json into a node_modules folder.
npm install
Run initial tasks again to gaurd against error when running this through Yeoman.
grunt build.dev
Now you should be able to run grunt watch
to compile css among other commands found in the Quick Reference section.
With Grunt we can minify, lint, watch and build. Preface the following tasks with grunt
in the command line.
watch build build.dev build.prod
watch.css build.css build.css.dev build.css.prod
watch.js lint.js minify.js build.js build.js.dev build.js.prod
watch.images minify.images build.images build.images.sprites build.images.resize (2x, 3x folders) build.images.web build.images.apple build.images.android build.images.windows build.images.firefox build.images.favicon build.images.itunes build.sprite
modernizr
sync sync --url=LOCALSERVERURL


We are here to help you
Are you curious about something? Do you have some kind of problem with our products? As am hastily invited settled at limited civilly fortune me. Really spring in extent an by.  Of so am he remember although required. Bachelor unpacked be advanced at. Confined in declared marianne is vicinity.
Please feel free to contact us, our customer service center is working for you 24/7.
Contact form
Address
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Great Britain
Call center
This number is toll free if calling from Great Britain otherwise we advise you to use the electronic form of communication.
+33 555 444 333
Electronic support
Please feel free to write an email to us or to use our electronic ticketing system.
-<EMAIL_ADDRESS>- Ticketio - our ticketing support platform


Enache Anca
<EMAIL_ADDRESS>+40720460655 London, UK
Work Experience
Pet product - Sales Manager
April 2019 - September 2019
Bucharest, Romania
- Leadership
- Attention to detail
- Business awareness
Vet Endoscopy Training Center - Student Veterinary Nurse
September 2018 - June 2019
Bucharest, Romania
- Accuracy
- Critical thinking
- Empathy
Ringstar Pet Hotel - Pet Hotel Attendant
June 2018 - December 2018
Bucharest, Romania
- Teamwork
- Organization
- Composure
Phillip Morris International via Teleperformance - Customer Care Representative
November 2016 - February 2018
Bucharest, Romania
- Teamwork
- Organization
- Composure
Bravo Dog Training - Social Media Manager
January 2016 - June 2017
Ploiesi, Romania
- Creative mindset
- Strategy planning
- Community management
Fun4Kids - Event Coordinator
June 2015 - December 2015
Ploiesi, Romania
- Organization
- Budgeting
- Problem solving
Additional skills
Driving licence category B
Education
University of Bucharest
October 2014 - June 2016
Bachelor of European Studies and Philosophy
- Applied Ethics
- Argumentation Theory and Argumentative Practices
- Political and Social Philosophy
Languages
- English (highly proficient)
- Romanian (native)
- French (beginner)


Author: {{quote.author}}
publisher: {{quote.publisher}}
published on {{quote.completeDate|dateCount}} days ago


Uploads a file in a specified folder.
File contents as binary
Returns the status of the new file.
Mandatory header Content-Type: application/octet-stream
Datacenter location
If the url for the PUT request does not correspond to the datacenter where the site is located, a 301 response will be returned that will redirect the request to the correct location.
Nested folders
If you use a filePath with nested folders, the API will automatically create the nested folder structure.
The depth of the nested folder structure is limited only by the URL length.
Query params
The API supports an optional query param named version that as the following possible values:
Accepts and returns JSON as Content-Type.
Request:
PUT /api/v2/admin/sites/current/storage/images/test.txt HTTPS/1.1
Accept: application/json
ContentType: "application/octet-stream"
Authorization: c50f6e6be0d1481ca0d8eb0c63642fdd171c17846af04cdd95676a0888141f73
Response:
HTTP/1.1 200 OK
Create a new file in the root folder
var request = $.ajax({
url: "/api/v2/admin/sites/current/storage/testing.txt?version=draft-publish",
type: "PUT",
headers: {
"Authorization": $.cookie('access_token')
},
contentType: "application/octet-stream",
data: "The file's content goes here"
});
Create file (specify the full path)
var request = $.ajax({
url: "/api/v2/admin/sites/current/storage/images/description2.txt?version=draft-publish",
type: "PUT",
headers: {
"Authorization": $.cookie('access_token')
},
contentType: "application/octet-stream",
data: 'New content'
});
Notes:
/
at the beginning of the file path, the system will add this.This method will return the following error codes:
200
- OK301
- Redirect to correct data center url400
- Bad request:
104000
- Generic FileAPI error104002
- Not a file104003
- Not enough privileges 104004
- Invalid login multiple sites 104005
- Exceeded maximum file size 104006
- Invalid editable region name, if a template 104007
- Can't find tag {pagecontent} in editable region, if a template 104008
- Can't find tag {pagecontent}, if a template 104009
- Path not found104010
- Destination path not found 104012
- Web page in workflow updated, if trying to update a page in a workflow 104014
- Name required104015
- Invalid URL 104016
- Name not unique 104019
- Can't find folder 104022
- Destination exists 104023
- Unauthorized access 104028
- File or folder name too long 401
- when authentication token is incorrect


The Vertical Navigation can be used as an alternative to the Horizontal Navigation.
This navigation type is composed by two distinct item levels:
-
First Level items - always visible, can be either navigable in and of themselves or be a container for
Second Level items (it should not be both)
-
Second Level items → Are always contained by a First Level item, and can have their visibility toggled
by this First Level item. They are always only navigable


“I’d really love to be my own boss”. “Wouldn’t it be great to work when I wanted to?”
We have all had these thoughts before, likely without much hope in our tone. These aspiring thoughts probably were followed by this: “I wish there was a way”.
Before Listant, there was no clear cut opportunity to be your own boss. Not without an expensive, timely process at least.
Listant enables your inner entrepreneur by providing the tools for you to function as your own business! Here are 5 benefits of using Listant to be your own boss:
-
Work Without a Schedule: We are often confronted with the problem “I want to work, but no jobs fit my schedule”, but no job will be flexible enough to accommodate your specific needs. Listant allows you to work at your own convenience, instead of on somebody else’s schedule.
-
Enable the Entrepreneur: With Listant, you are your boss.  Listant is about empowering you to control what you want to do and when you want to do it.
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End-to-End Solution: Listant allows you to connect, communicate, and cash out all on one platform. Other platforms involve third parties and inefficient processes, but Listant allows you to complete the process from start to finish.
You have always wanted to take more control of your life, so don’t waste anymore time and start today! Be your own boss, Just Listant!
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public abstract class DataInput extends Object implements Cloneable
| Constructor and Description |
|---|
DataInput
()
|
| Modifier and Type | Method and Description |
|---|---|
Object
|
clone
()
Returns a clone of this stream.
|
abstract byte
|
readByte
()
Reads and returns a single byte.
|
abstract void
|
readBytes
(byte[] b,
int offset,
int len)
Reads a specified number of bytes into an array at the specified offset.
|
void
|
readBytes
(byte[] b,
int offset,
int len,
boolean useBuffer)
Reads a specified number of bytes into an array at the
specified offset with control over whether the read
should be buffered (callers who have their own buffer
should pass in "false" for useBuffer).
|
void
|
readChars
(char[] buffer,
int start,
int length)
Deprecated.
-- please use readString or readBytes
instead, and construct the string
from those utf8 bytes
|
int
|
readInt
()
Reads four bytes and returns an int.
|
long
|
readLong
()
Reads eight bytes and returns a long.
|
short
|
readShort
()
Reads two bytes and returns a short.
|
String
|
readString
()
Reads a string.
|
Map
<
String
,
String
>
|
readStringStringMap
()
|
int
|
readVInt
()
Reads an int stored in variable-length format.
|
long
|
readVLong
()
Reads a long stored in variable-length format.
|
void
|
setModifiedUTF8StringsMode
()
Call this if readString should read characters stored
in the old modified UTF8 format (length in java chars
and java's modified UTF8 encoding).
|
public void setModifiedUTF8StringsMode()
public abstract byte readByte() throws IOException
IOException
DataOutput.writeByte(byte)
public abstract void readBytes(byte[] b, int offset, int len) throws IOException
b
- the array to read bytes into
offset
- the offset in the array to start storing bytes
len
- the number of bytes to read
IOException
DataOutput.writeBytes(byte[],int)
public void readBytes(byte[] b, int offset, int len, boolean useBuffer) throws IOException
BufferedIndexInput
respects this parameter.
b
- the array to read bytes into
offset
- the offset in the array to start storing bytes
len
- the number of bytes to read
useBuffer
- set to false if the caller will handle
buffering.
IOException
DataOutput.writeBytes(byte[],int)
public short readShort() throws IOException
IOException
DataOutput.writeByte(byte)
public int readInt() throws IOException
IOException
DataOutput.writeInt(int)
public int readVInt() throws IOException
IOException
DataOutput.writeVInt(int)
public long readLong() throws IOException
IOException
DataOutput.writeLong(long)
public long readVLong() throws IOException
IOException
public String readString() throws IOException
IOException
DataOutput.writeString(String)
@Deprecated public void readChars(char[] buffer, int start, int length) throws IOException
buffer
- the array to read characters into
start
- the offset in the array to start storing characters
length
- the number of characters to read
IOException
DataOutput.writeChars(String,int,int)
public Object clone()
Clones of a stream access the same data, and are positioned at the same point as the stream they were cloned from.
Expert: Subclasses must ensure that clones may be positioned at different points in the input from each other and from the stream they were cloned from.
public Map < String , String > readStringStringMap() throws IOException
IOException


Message
foo
is not one of the predefined iTunes categories or sub-categories
Explanation
itunes:category can only be populated using iTunes specific catgories or sub-categories, as listed in the specification.
Solution
Change the attribute value to one of the following values:
- Arts
- Design
- Fashion & Beauty
- Food
- Literature
- Performing Arts
- Visual Arts
- Business
- Business News
- Careers
- Investing
- Management & Marketing
- Shopping
- Comedy
- Education
- Education Technology
- Higher Education
- K-12
- Language Courses
- Training
- Games & Hobbies
- Automotive
- Aviation
- Hobbies
- Other Games
- Video Games
- Government & Organizations
- Local
- National
- Non-Profit
- Regional
- Health
- Alternative Health
- Fitness & Nutrition
- Self-Help
- Sexuality
- Kids & Family
- Music
- News & Politics
- Religion & Spirituality
- Buddhism
- Christianity
- Hinduism
- Islam
- Judaism
- Other
- Spirituality
- Science & Medicine
- Medicine
- Natural Sciences
- Social Sciences
- Society & Culture
- History
- Personal Journals
- Philosophy
- Places & Travel
- Sports & Recreation
- Amateur
- College & High School
- Outdoor
- Professional
- Technology
- Gadgets
- Podcasting
- Software How-To
- Tech News
- TV & Film
Not clear? Disagree?
You might be able to find help in one of these fine resources.


Loading...
Note this section only measures CPU time. All values are estimates, and 'Draw calls' will be an underestimate where the browser uses multithreaded rendering. GPU rendering times are not shown, and the framerate may be limited by GPU performance.


Over the years, our team has discovered the importance of outreach. We have fully embraced the ideology that FTC is about more than just the robot. This year we have done more outreach than ever and we are just getting started. We are planning to volunteer at FLL tournaments and create a basic robot instruction manual for rookie teams. Giving back is a big part of what we do because of all we have gained from FTC over the years.
Physics in Action
Date: May 17, 1 hour
reparation: Prepared the robot for demonstration and brought it and the center vortex into school.
Description: Brought the robot into 1st period, SMART Lunch, 3rd period, and 4th period to demonstrate the robot to students in Physics I and II. Julie and I demonstrated the robot, explained the engineering process, and let the kids drive the robot.
Science Fair
Date: April 19, 2 hours
Preparation: Prepared the robot for demonstration and brought the other demonstration pieces (scissor lift, motor we disassembled, etc.)
Description: Contacted the organizer for a local elementary school science fair and asked if we could bring in the robot to show the kids. They agreed and helped us set up in a room where kids and parents circulated around. The kids took turns driving the robot and looking at the other demonstration pieces we laid out on the table. Chloe and the coach talked to students and parents and explained the demonstrations, while Ethan and Mohit helped kids drive the robot.
Children's Day Off
Date: April 27, 2 hours
Preparation: Prepared the robot for demonstration, practiced what I would say, and prepared the other demonstrations.
Description: Brought the robot into Bayer's Take Your Child to Work Day.  Chloe introduced the team and Ethan demonstrated the robot. The audience was then split up into two groups. One group looked at all the demonstration pieces presented by Chloe and the Coach, the other learned more about the robot specifics with Ethan and Mohit.
Cub Scout Day Camp
Date: August 3rd, 2 hours
Preparation: Prepared the robot for demonstration.
Description: Chloe and Ethan spent 2 hours demonstrating the robot to cub scouts and letting them drive the robot.
Going Back to School
Date: March 24th, 3 hours
Preparation: Prepared the robot for demonstration and brought the other demonstration pieces (scissor lift, motor we disassembled, etc.)
Description: A local school reached out to Dr. Bennet about having a robotics team come in so last minute we volunteered to help. We demonstrated the robot in front of a group of kids. We then divided the group, so half could look at the demonstration items we brought such as the scissor lift, a motor that has been taken apart, and the tape measure hook. The other group learned more about the robot and a few even got to drive it. The groups then switched so everyone saw both things.
Girl Scout Demo
Date: October 8th, 2 hours
Preparation: Prepared the robot for demonstration and organized the demonstration materials.
Description: Chloe demonstrated the robot to a Cadet Girl Scout meeting (girls in middle school). All the girls were then given the opportunity to drive the robot. They also looked at the demonstration materials and asked any questions they had. The girls and their leaders were very impressed with the robot.
NetApp
Date: July 26th, 5 hours
Preparation: Reviewed the event so everyone knew what was going to happen and was prepared.
Description: Julie shared her experience with FTC with a group of girls. She then helped the girls build and program Mindstorms Lego robots to complete specific tasks.
Pre-Engineering
Date: June 12, 12 hours
Preparation: We spent 2 hours counting and finding pieces for a kit robot for groups to build. Then we worked with other teams to make sure there were 6 complete kits. Once we had two kits, we pre-assembled the arm to speed up the building process.
Description: We worked with a few teams to help kids build a robot from instructions. In the morning we had one group of kids. The kids then moved on to a different activity and another group came in the afternoon. Due to the complexity of the robot we made modifications as time went on. There was a different set of kids in the morning and afternoon. In the morning we helped the kids build the robot from the construction manual. However, not all groups were able to finish. We also ran into some other problems that the volunteers had to trouble shoot, so we could get all the robots working. In the afternoon, we decided to change the goal and focus on building the fastest robot instead of building the robot in the manual. This meant that Chloe had to create a new program without a servo claw for the teams to use. Overall, we taught the kids how to build robots and introduced some of the problems that are an everyday occurrence for FTC teams.
Girl Scout Robotics Camp
Date: January 6th, 6 hours
Preparation: Replaced broken rubber bands on the rubber band drums. Then put the wheels and servo back on the robot.
Description: Chloe demonstrated the robot to a group of Brownie and Junior Girl Scouts. She then helped the girls create their own Lego robots to complete a challenge from the FLL 2012 challenge Senior Solutions. When the girls had a break from programming, Chloe also helped them drive the robot.


Ententional
From the Glossary
Ententional: A generic adjective coined in this book for describing all phenomena
that are intrinsically incomplete in the sense of being in relationship to, constituted
by, or organized to achieve something non-intrinsic. This includes function, information,
meaning, reference, representation, agency, purpose, sentience, and value
Constitutive absence: A particular and precise missing something that is a critical
defining attribute of 'ententional' phenomena, such as functions, thoughts, adaptations,
purposes, and subjective experiences.
Emergent dynamics: A theory developed in this book which explains how homeodynamic
(e.g., thermodynamic) processes can give rise to morphodynamic (e.g., self-organizing)
processes, which can give rise to teleodynamic (e.g., living and mental) processes.
Intended to legitimize scientific uses of ententional (intentional, purposeful,
normative) concepts by demonstrating the way that processes at a higher level in
this hierarchy emerge from, and are grounded in, simpler physical processes, but
exhibit reversals of the otherwise ubiquitous tendencies of these lower-level processes
Intentional: In common usage, an adjective describing an act that is performed
on purpose. Technically, in twentieth-century philosophy of mind, it is a term
deriving from the medieval Scholastics, reintroduced by the German philosopher
Brentano, to designate a characteristic common to all sensations, ideas, thoughts,
and desires: the fact that they are 'about' something other than themselves
Preformationism: Narrowly, the assumption that the human physique was preformed
from conception. More broadly as used here, the assumption that ententional phenomena
were performed in antecedent phenomena - that, for example, language is preformed
in a universal grammar module, information is preformed in DNA, or that consciousness
is preformed in the mind of God
Ententional
- existing with-respect-to, for-the-sake-of, or in-order-to-generate something that
is absent - p26
- includes a function at one extreme and value at the other - p26
- generic term/adjective - p26
- phenomena that are intrinsically incomplete in the sense of being in relationship
to, constituted by, or organized to achieve something non-intrinsic - p26
- a fundemental relationship to something absent - p26
Ententional phenomena include functions that have
- satisfaction conditions,
- adaptations that have environmental correlates,
- thoughts that have contents,
- purposes that have goals,
- subjective experiences that have a self/other perspective
- values that have a self that benefits or is harmed. -p27
Although functions, adaptations, thoughts, purposes, subjective experiences, and
values each have distinct attributes that distinguish them, they all also have
an orientation to a specific constitutive absence, a particular and precise missing
something that is their critical defining attribute - p27
>> extrinsic?
>> If intentional is the ground, ententional is the background?
Ententional ~ Absential
- Currently, we lack a single term in the English language (or others that I know
of) that captures this more generic sense of existing with-respect-to, for-the-sake-of,
or in-order-to-generate something that is absent that also includes function at
one extreme and value at the other. The recognition that there is a common core
property characterizing how each of these involves a necessary linkage with, and
inclination toward, something absent argues for finding a term to refer to it -
p26
- Ententional phenomena include functions that have satisfaction conditions, adaptations
that have environmental correlates, thoughts that have contents, purposes that
have goals, subjective experiences that have a self/other perspective, and values
that have a self that benefits or is harmed. Although functions, adaptations, thoughts,
purposes, subjective experiences, and values each have distinct attributes that
distinguish them, they all also have an orientation to a specific constitutive
absence, a particular and precise missing something that is their critical defining
attribute - p 26
- Ententional processes and relationships can best be classed as expressions of
final causality in Aristotle's terms. Their absential character is determined by
the end toward which they tend, or the represented concept for the sake of which
they were created. But with this erosion of the plurality of causal concepts, a
place for a form of causal influence derived from something absent was no longer
acceptable - p35-42 << much discussion on the absential follows.
- When a system exhibits ententional properties, it is because something physical
is missing, not added - p42
- By examining this model system more closely, we will be able to demonstrate how
ententional processes acquire this seemingly paradoxical character of efficacious
absence - p311
Ententional ~ Causality
- If ententionally phenomena are causally import then types of things can have real
physical consequences - p29
- Ententional processes and relationships can best be classed as expressions of
final causality in Aristotle's terms - p35
- Their absential character is determined by the end toward which they tend, or
the represented concept for the sake of which they were created - p35
Ententional ~ Conscious
- Conscious experience - most commonplace phenomenon of everyday life - is quintessence
of an ententional phenomenon - p33-34
Ententional ~ Emergence
- ententional and emergence subtly tangled - p32
Ententional ~ figure / background
- Inversion of perspective to pay attention to what is missing resolves dilemma
of ententional. A counterintuitive figure/background reversal, focusing on what
is absent rather than present, offers a means to repair some of the serious inadequacies
in our conceptions of matter, order, life, work, information, representation, and
even consciousness and conceptions of value - p44
Ententional ~ Functionalism / Multiple Realizability
- All ententional phenomena have multiple realizability - p30
Ententional ~ Reduction
- ententional phenomena often particular in embidiment - causal consequences irreeducibly
depend on some general type - p42
Ententional ~ Science
- from physical-chemistry point of view, ententional appears like magic - p3
- science ignores or bans ententional - p31
- to science, ententional phenomena appear invisible - p32
- ententional phenomena are a basic universal property like mas or charge. Everything
is ententional in some respect - p39
- wherever we place the fence between ententional and physical, we need instructions
as how to pass through the fence - p40
some of the below may also be above...
Entention to characterize an internal relationship to a telosean end, or otherwise
displaced - p27
Ententional phenomena
- asymmetrical
- hierarchical
Scholars and scientists differ on meaning of intention and purpose. - p27
Ententional phenomena
- multiple realizability
- can be embodied in highly diverse physical-chemical processes and substrates
- have real physical consequences
- thus realist as opposed to nominalist - p29
- see also functionalism, but:
- interchangeability of consequences even more important
- potential consequences also generals - as well as virtual and absent


|
C-ATTL3
0.1
|
#include <DataProvider.h>
Public Member Functions | |
| InMemoryDataProvider (DataPtr obs, DataPtr obj, bool shuffle=true) | |
| const Dimensions< int, Rank > & | get_obs_dims () const |
| const Dimensions< int, Rank > & | get_obj_dims () const |
| unsigned | instances () const |
| bool | has_more () const |
| DataPair< Scalar, Rank, Sequential > | get_data (unsigned batch_size) |
| void | reset () |
| Public Member Functions inherited from cattle::DataProvider< Scalar, Rank, Sequential > | |
| virtual | ~DataProvider ()=default |
Private Types | |
| typedef Tensor< Scalar, DATA_RANKS > | Data |
| typedef TensorPtr< Scalar, Rank, Sequential > | DataPtr |
Private Attributes | |
| DataPtr | obs |
| DataPtr | obj |
| Dimensions< int, Rank > | obs_dims |
| Dimensions< int, Rank > | obj_dims |
| std::size_t | rows |
| std::array< int, DATA_RANKS > | offsets |
| std::array< int, DATA_RANKS > | data_extents |
| std::array< int, DATA_RANKS > | obj_extents |
Static Private Attributes | |
| static constexpr std::size_t | DATA_RANKS = Rank + Sequential + 1 |
A data provider that reads from the memory. It requires the entire observation and objective data sets to be loaded into memory, but it fetches pairs faster.
|
private |
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private |
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inline |
It constructs a data provider backed by the specified tensors.
| obs | A unique pointer to the tensor containing the observations. |
| obj | A unique pointer to the tensor containing the objectives. |
| shuffle | Whether the 'rows' (first ranks) of the tensors should be randomly shuffled. |
|
inlinevirtual |
Reads and returns the specified number of observation-objective pairs. It also offsets the reader by the specified number.
| batch_size | The maximum number of observation-objective pairs to read and return. |
Implements cattle::DataProvider< Scalar, Rank, Sequential >.
|
inlinevirtual |
A simple constant getter method for the dimensions of the objectives.
Implements cattle::DataProvider< Scalar, Rank, Sequential >.
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inlinevirtual |
A simple constant getter method for the dimensions of the observations.
Implements cattle::DataProvider< Scalar, Rank, Sequential >.
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inlinevirtual |
A simple constant method that returns whether the data provider instance has more data to provide.
Implements cattle::DataProvider< Scalar, Rank, Sequential >.
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inlinevirtual |
A simple constant method that returns the number of observations and objectives handled by the data provider instance.
Implements cattle::DataProvider< Scalar, Rank, Sequential >.
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inlinevirtual |
It resets the reader head to the beginning of the data storage.
Implements cattle::DataProvider< Scalar, Rank, Sequential >.
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private |
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staticprivate |
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private |
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private |
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private |
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Food is a source of health, energy and enjoyment. Few things in life provide as much pleasure as a delicious meal shared with family or friends, and nothing can do more for your total health and well-being - glowing skin, bright clear eyes, your stamina and strength, immune system, even your mood - as the right sort of food and the right proportions.
 Give you vitality and energy. Help you stay at a weight that’s right for you. Boost your immune system. Ward off serious illnesses such as heart disease, certain cancers, type 2 diabetes, high cholesterol and gallbladder disease.
Here I explain best recipies for you.It's healty for us.
And I think you like it for cook with delicious.
Let's go.
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You can see various countries traditional food.
Click HereI think you will try and satisfy.Have a nice day.


{% extends "global/Page.html" %} {% load otree static %} {% block title %} Instructions {% endblock %} {% block content %}
Example: Sender A has received 7 winning tickets (blue), while sender B has received 3. Sender A chooses to send the message 7 while Sender B chooses to send message 9. After observing both messages, but not the actual number of winning tickets, the receiver can choose either A or B.
{% next_button %} {% endblock %}


Class MixedRealityDiagnosticsSystem
Inheritance
MixedRealityDiagnosticsSystem
Assembly: XRTK.dll
Syntax
public class MixedRealityDiagnosticsSystem : BaseEventSystem, IMixedRealityDiagnosticsSystem, IMixedRealityEventSystem, IMixedRealityService, IDisposable, IMixedRealityEventSource, IEqualityComparer
Constructors
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MixedRealityDiagnosticsSystem(MixedRealityDiagnosticsProfile)
Declaration
public MixedRealityDiagnosticsSystem(MixedRealityDiagnosticsProfile profile)
Parameters
Properties
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CpuUseTracker
Declaration
public CpuUseTracker CpuUseTracker { get; }
Property Value
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DiagnosticVisualization
Gets the GameObject that represents the diagnostic visualization
Declaration
public GameObject DiagnosticVisualization { get; }
Property Value
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FpsUseTracker
Declaration
public FpsUseTracker FpsUseTracker { get; }
Property Value
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MemoryUseTracker
Declaration
public MemoryUseTracker MemoryUseTracker { get; }
Property Value
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ShowCpu
Enable / disable cpu profiling when the diagnostic panel is visible.
Declaration
public bool ShowCpu { get; set; }
Property Value
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ShowFps
Enable / disable fps profiling when the diagnostic panel is visible.
Declaration
public bool ShowFps { get; set; }
Property Value
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ShowMemory
Enable / disable memory profiling when the diagnostic panel is visible.
Declaration
public bool ShowMemory { get; set; }
Property Value
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SourceId
The Unique Source Id of this Event Source.
Declaration
public uint SourceId { get; }
Property Value
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SourceName
The Name of this Event Source.
Declaration
public string SourceName { get; }
Property Value
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Visible
Enable / disable the diagnostic display
Declaration
public bool Visible { get; set; }
Property Value
Methods
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Destroy()
Optional Destroy function to perform cleanup of the service before the Mixed Reality Toolkit is destroyed.
Declaration
public override void Destroy()
Overrides
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Equals(Object, Object)
Declaration
public bool Equals(object x, object y)
Parameters
Returns
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GetHashCode(Object)
Declaration
public int GetHashCode(object obj)
Parameters
| Type |
Name |
Description |
| Object |
obj |
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Initialize()
The initialize function is used to setup the service once created.
This method is called once all services have been registered in the Mixed Reality Toolkit.
Declaration
public override void Initialize()
Overrides
Implements
Extension Methods


Country of my home
And now I can breathe
I'm at the gates
Gates to the country of my home
And I feel good
I feel good for the bad things
The bad things that don't make me feel good.
The manerisms of my compatriots:
Desperate passangers
Twisted airport lines
Overly empathetic smiles
The cheap colognes
The tasteless sense of style
Loud families
Physical people The same peculiarities
The same defects
That boils my brain
That makes me want to fly
Are the ones that I'm not alone
Are the ones I'm allowed to err
With all my shared aberrations
With all my quirk attempts
We accept our falls
Without fears
Without second-guesses
An organic margin of error
I get to
Today or tomorrow
As a promise or attempt
To feel
To love
To be.


http://osdir.com/Article6677.phtml
"...Google and O'Reilly came together to begin the first of an annual Open Source Awards to honor five open source people whose contributions over the past year have been exceptional in five categories: Communicator, Evangelist, Diplomat, Integrator, and Hacker.
...
Integrator:
One person was able to get Python, Perl & PHP people to all agree on something. No small feat. They all agreed on the success of SQlite, and began using it in earnest by being small as well as extremely fast for all the most common database functions. Our server budgets also thank you.
Dr Richard Hipp (SQLite)"


Chapter 20 Generalized linear model
20.1 Learning goals
- Logistic regression.
- Logit transform.
- Fitting a logistic regression in R.
- Visualizing and interpreting model predictions.
- Simulating data from a logistic regression.
- Assessing model fit.
- Testing hypotheses.
- Reporting results.
- Mixed effects logistic regression.
20.2 Load packages and set plotting theme
library("knitr") # for knitting RMarkdown
library("titanic") # titanic dataset
library("kableExtra") # for making nice tables
library("janitor") # for cleaning column names
library("broom") # for tidying up linear models
library("lme4") # for linear mixed effects models
library("boot") # for bootstrapping (also has an inverse logit function)
library("ggeffects") # for showing effects in linear, generalized linear, and other models
library("tidyverse") # for wrangling, plotting, etc.
# include references for used packages
knitr::write_bib(.packages(), "packages.bib")
20.3 Load data set
Let’s take a quick look at the data:
## Observations: 891
## Variables: 12
## $ passenger_id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1…
## $ survived <int> 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0,…
## $ pclass <int> 3, 1, 3, 1, 3, 3, 1, 3, 3, 2, 3, 1, 3, 3, 3, 2, 3, 2, 3,…
## $ name <chr> "Braund, Mr. Owen Harris", "Cumings, Mrs. John Bradley (…
## $ sex <fct> male, female, female, female, male, male, male, male, fe…
## $ age <dbl> 22, 38, 26, 35, 35, NA, 54, 2, 27, 14, 4, 58, 20, 39, 14…
## $ sib_sp <int> 1, 1, 0, 1, 0, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0, 0, 4, 0, 1,…
## $ parch <int> 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0, 0, 1, 0, 0,…
## $ ticket <chr> "A/5 21171", "PC 17599", "STON/O2. 3101282", "113803", "…
## $ fare <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8.4583, 51.862…
## $ cabin <chr> "", "C85", "", "C123", "", "", "E46", "", "", "", "G6", …
## $ embarked <chr> "S", "C", "S", "S", "S", "Q", "S", "S", "S", "C", "S", "…
# Table of the first 10 entries
df.titanic %>%
head(10) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| passenger_id | survived | pclass | name | sex | age | sib_sp | parch | ticket | fare | cabin | embarked |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22 | 1 | 0 | A/5 21171 | 7.25 | S | |
| 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female | 38 | 1 | 0 | PC 17599 | 71.28 | C85 | C |
| 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26 | 0 | 0 | STON/O2. 3101282 | 7.92 | S | |
| 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35 | 1 | 0 | 113803 | 53.10 | C123 | S |
| 5 | 0 | 3 | Allen, Mr. William Henry | male | 35 | 0 | 0 | 373450 | 8.05 | S | |
| 6 | 0 | 3 | Moran, Mr. James | male | NA | 0 | 0 | 330877 | 8.46 | Q | |
| 7 | 0 | 1 | McCarthy, Mr. Timothy J | male | 54 | 0 | 0 | 17463 | 51.86 | E46 | S |
| 8 | 0 | 3 | Palsson, Master. Gosta Leonard | male | 2 | 3 | 1 | 349909 | 21.07 | S | |
| 9 | 1 | 3 | Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) | female | 27 | 0 | 2 | 347742 | 11.13 | S | |
| 10 | 1 | 2 | Nasser, Mrs. Nicholas (Adele Achem) | female | 14 | 1 | 0 | 237736 | 30.07 | C |
20.4 Logistic regression
Let’s see if we can predict whether or not a passenger survived based on the price of their ticket.
Let’s run a simple regression first:
# fit a linear model
fit.lm = lm(formula = survived ~ 1 + fare,
data = df.titanic)
# summarize the results
fit.lm %>% summary()
##
## Call:
## lm(formula = survived ~ 1 + fare, data = df.titanic)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9653 -0.3391 -0.3222 0.6044 0.6973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3026994 0.0187849 16.114 < 2e-16 ***
## fare 0.0025195 0.0003174 7.939 6.12e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4705 on 889 degrees of freedom
## Multiple R-squared: 0.06621, Adjusted R-squared: 0.06516
## F-statistic: 63.03 on 1 and 889 DF, p-value: 6.12e-15
Look’s like fare
is a significant predictor of whether or not a person survived. Let’s visualize the model’s predictions:
ggplot(data = df.titanic,
mapping = aes(x = fare,
y = survived)) +
geom_smooth(method = "lm") +
geom_point() +
labs(y = "survived")
## `geom_smooth()` using formula 'y ~ x'
This doesn’t look good! The model predicts intermediate values of survived
(which doesn’t make sense given that a person either survived or didn’t survive). Furthermore, the model predicts values greater than 1 for fares greather than ~ 300.
Let’s run a logistic regression instead.
# fit a logistic regression
fit.glm = glm(formula = survived ~ 1 + fare,
family = "binomial",
data = df.titanic)
fit.glm %>%
summary()
##
## Call:
## glm(formula = survived ~ 1 + fare, family = "binomial", data = df.titanic)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4906 -0.8878 -0.8531 1.3429 1.5942
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.941330 0.095129 -9.895 < 2e-16 ***
## fare 0.015197 0.002232 6.810 9.79e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1186.7 on 890 degrees of freedom
## Residual deviance: 1117.6 on 889 degrees of freedom
## AIC: 1121.6
##
## Number of Fisher Scoring iterations: 4
And let’s visualize the predictions of the logistic regression:
ggplot(data = df.titanic,
mapping = aes(x = fare,
y = survived)) +
geom_smooth(method = "glm",
method.args = list(family = "binomial")) +
geom_point() +
labs(y = "p(survived)")
## `geom_smooth()` using formula 'y ~ x'
Much better! Note that we’ve changed the interpretation of our dependent variable. We are now predicting the probability that a person survived based on their fare. The model now only predicts values between 0 and 1. To achieve this, we apply a logit transform to the outcome variable like so:
\[ \ln(\frac{\pi_i}{1-\pi_i}) = b_0 + b_1 \cdot X_i + e_i \] where \(\pi_i\) is the probability of passenger \(i\) having survived. Importantly, this affects our interpretation of the model parameters. They are now defined in log-odds, and can apply an inverse logit transformation to turn this back into a probability:
With
\[ \pi = P(Y = 1) \] and the logit transformation
\[ \ln(\frac{\pi}{1-\pi}) = V, \] where \(V\) is just a placeholder for our linear model formula, we can go back to \(\pi\) through the inverse logit transformation like so:
\[
\pi = \frac{e^V}{1 + e^V}
\]
In R, we can use log(x)
to calculate the natural logarithm \(\ln(x)\), and exp(x)
to calculate e^x
.
20.4.1 Interpreting the parameters
##
## Call:
## glm(formula = survived ~ 1 + fare, family = "binomial", data = df.titanic)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4906 -0.8878 -0.8531 1.3429 1.5942
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.941330 0.095129 -9.895 < 2e-16 ***
## fare 0.015197 0.002232 6.810 9.79e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1186.7 on 890 degrees of freedom
## Residual deviance: 1117.6 on 889 degrees of freedom
## AIC: 1121.6
##
## Number of Fisher Scoring iterations: 4
The estimate for the intercept and fare are in log-odds. We apply the inverse logit transformation to turn these into probabilities:
## (Intercept)
## 0.2806318
Here, we see that the intercept is \(p = 0.28\). That is, the predicted chance of survival for someone who didn’t pay any fare at all is 28% according to the model. Interpreting the slope is a little more tricky. Let’s look at a situation first where we have a binary predictor.
<IP_ADDRESS> Binary predictor
Let’s see whether the probability of survival differed between male and female passengers.
fit.glm2 = glm(formula = survived ~ sex,
family = "binomial",
data = df.titanic)
fit.glm2 %>%
summary()
##
## Call:
## glm(formula = survived ~ sex, family = "binomial", data = df.titanic)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6462 -0.6471 -0.6471 0.7725 1.8256
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.0566 0.1290 8.191 2.58e-16 ***
## sexmale -2.5137 0.1672 -15.036 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1186.7 on 890 degrees of freedom
## Residual deviance: 917.8 on 889 degrees of freedom
## AIC: 921.8
##
## Number of Fisher Scoring iterations: 4
It looks like it did! Let’s visualize:
df.titanic %>%
mutate(survived = factor(survived, labels = c("died", "survived"))) %>%
ggplot(data = .,
mapping = aes(x = sex,
fill = survived)) +
geom_bar(position = "fill",
color = "black") +
scale_fill_brewer(palette = "Set1") +
labs(x = "", fill = "", y = "probability")
And let’s interpret the parameters by applying the inverse logit transform. To get the prediction for female passengers we get
\[ \widehat{\ln(\frac{\pi_i}{1-\pi_i})} = b_0 + b_1 \cdot \text{sex}_i = b_0 + b_1 \cdot 0 = b_0 \]
since we dummy coded the predictor and female is our reference category. To get the predicted probability of surival for women we do the logit transform:
\[ \pi = \frac{e^{b_0}}{1 + e^{b_0}} \] The predicted probability is:
## (Intercept)
## 0.7420382
To get the prediction for male passengers we have:
\[ \widehat{\ln(\frac{\pi_i}{1-\pi_i})} = b_0 + b_1 \cdot \text{sex}_i = b_0 + b_1 \cdot 1 = b_0 + b_1 \] Applying the logit transform like so
\[ \pi = \frac{e^{b_0 + b_1}}{1 + e^{b_0 + b_1}} \]
The predicted probability of male passengers surviving is:
## [1] 0.1889081
Here is the same information in a table:
df.titanic %>%
count(sex, survived) %>%
mutate(p = n/sum(n)) %>%
group_by(sex) %>%
mutate(`p(survived|sex)` = p/sum(p)) %>%
head(10) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| sex | survived | n | p | p(survived|sex) |
|---|---|---|---|---|
| female | 0 | 81 | 0.09 | 0.26 |
| female | 1 | 233 | 0.26 | 0.74 |
| male | 0 | 468 | 0.53 | 0.81 |
| male | 1 | 109 | 0.12 | 0.19 |
<IP_ADDRESS> Continuous predictor
To interpret the predictions when a continuous predictor is invovled, it’s easiest to consider a few concrete cases. Here, I use the augment()
function from the “broom” package to get the model’s predictions for some values of interest:
fit.glm %>%
augment(newdata = tibble(fare = c(0, 10, 50, 100, 500))) %>%
clean_names() %>%
select(fare, prediction = fitted) %>%
mutate(`p(survival)` = prediction %>% inv.logit()) %>%
head(10) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| fare | prediction | p(survival) |
|---|---|---|
| 0 | -0.94 | 0.28 |
| 10 | -0.79 | 0.31 |
| 50 | -0.18 | 0.45 |
| 100 | 0.58 | 0.64 |
| 500 | 6.66 | 1.00 |
<IP_ADDRESS> Several predictors
Let’s fit a logistic regression that predicts the probability of survival based both on the passenger’s sex and what fare they paid (allowing for an interaction of the two predictors):
fit.glm = glm(formula = survived ~ 1 + sex + fare,
family = "binomial",
data = df.titanic)
fit.glm %>%
summary()
##
## Call:
## glm(formula = survived ~ 1 + sex + fare, family = "binomial",
## data = df.titanic)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2082 -0.6208 -0.5824 0.8126 1.9658
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.647100 0.148502 4.358 1.32e-05 ***
## sexmale -2.422760 0.170515 -14.208 < 2e-16 ***
## fare 0.011214 0.002295 4.886 1.03e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1186.66 on 890 degrees of freedom
## Residual deviance: 884.31 on 888 degrees of freedom
## AIC: 890.31
##
## Number of Fisher Scoring iterations: 5
And let’s visualize the result:
df.titanic %>%
mutate(sex = as.factor(sex)) %>%
ggplot(data = .,
mapping = aes(x = fare,
y = survived,
color = sex)) +
geom_point(alpha = 0.1, size = 2) +
geom_smooth(method = "glm",
method.args = list(family = "binomial"),
alpha = 0.2,
aes(fill = sex)) +
scale_color_brewer(palette = "Set1")
## `geom_smooth()` using formula 'y ~ x'
Let’s see how large the difference between genders is once we take into account how much each person paid for the fair:
##
## # Predicted values of survived
## # x = sex
##
## x | Predicted | SE | 95% CI
## ----------------------------------------
## female | 0.73 | 0.13 | [0.68, 0.78]
## male | 0.20 | 0.11 | [0.16, 0.23]
##
## Adjusted for:
## * fare = 32.20
We notice that there is one outlier who was male and paid a $500 fare (or maybe this is a mistake in the data entry?!). Let’s remove this outlier and see what happens:
fit.glm2 = glm(formula = survived ~ 1 + sex + fare,
family = "binomial",
data = df.titanic %>%
filter(fare < 500))
fit.glm2 %>%
summary()
##
## Call:
## glm(formula = survived ~ 1 + sex + fare, family = "binomial",
## data = df.titanic %>% filter(fare < 500))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2030 -0.6212 -0.5828 0.8124 1.9647
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.650213 0.149265 4.356 1.32e-05 ***
## sexmale -2.423381 0.170518 -14.212 < 2e-16 ***
## fare 0.011111 0.002345 4.739 2.15e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1180.89 on 887 degrees of freedom
## Residual deviance: 884.23 on 885 degrees of freedom
## AIC: 890.23
##
## Number of Fisher Scoring iterations: 4
df.titanic %>%
filter(fare < 500) %>%
mutate(sex = as.factor(sex)) %>%
ggplot(data = .,
mapping = aes(x = fare,
y = survived,
color = sex)) +
geom_point(alpha = 0.1, size = 2) +
stat_smooth(method = "glm",
method.args = list(family = "binomial"),
alpha = 0.2,
fullrange = T,
aes(fill = sex)) +
scale_color_brewer(palette = "Set1") +
scale_x_continuous(limits = c(0, 500))
## `geom_smooth()` using formula 'y ~ x'
There is still a clear difference between female and male passengers, but the prediction for male passengers has changed a bit. Let’s look at a concrete example:
# with the outlier:
# predicted probability of survival for a male passenger who paid $200 for their fare
inv.logit(fit.glm$coefficients[1] + fit.glm$coefficients[2] + fit.glm$coefficients[3] * 200 + fit.glm$coefficients[4] * 200)
## (Intercept)
## NA
# without the outlier:
# predicted probability of survival for a male passenger who paid $200 for their fare
fit.glm_no_outlier = glm(formula = survived ~ 1 + sex * fare,
family = "binomial",
data = df.titanic %>%
filter(fare < 500))
inv.logit(fit.glm_no_outlier$coefficients[1] + fit.glm_no_outlier$coefficients[2] + fit.glm_no_outlier$coefficients[3] * 200 + fit.glm_no_outlier$coefficients[4] * 200)
## (Intercept)
## 0.4658284
With the oulier removed, the predicted probability of survival for a male passenger who paid $200 decreases from 49% to 47%.
<IP_ADDRESS>.1 With interactions
fit.glm3 = glm(formula = survived ~ 1 + sex * fare,
family = "binomial",
data = df.titanic)
df.data = ggpredict(fit.glm3,
terms = c("fare [0:500]", "sex"))
ggplot(data = df.data,
mapping = aes(x = x,
y = predicted,
color = group)) +
geom_ribbon(mapping = aes(ymin = conf.low,
ymax = conf.high,
fill = group),
alpha = 0.2,
color = NA) +
geom_line(size = 1) +
geom_point(data = df.titanic,
mapping = aes(x = fare,
y = survived,
color = sex),
alpha = 0.2) +
scale_color_brewer(palette = "Set1")
<IP_ADDRESS> Using the “ggeffects” package
The “ggeffects” package helps with the interpretation of the results. It applies the inverse logit transform for us, and shows the predictions for a range of cases.
##
## # Predicted values of survived
## # x = fare
##
## x | Predicted | SE | 95% CI
## -------------------------------------
## 0 | 0.28 | 0.11 | [0.24, 0.33]
## 100 | 0.55 | 0.18 | [0.46, 0.64]
## 200 | 0.79 | 0.40 | [0.63, 0.89]
## 300 | 0.92 | 0.63 | [0.77, 0.98]
## 400 | 0.97 | 0.86 | [0.87, 0.99]
## 500 | 0.99 | 1.08 | [0.93, 1.00]
I’ve used the []
construction to specify for what values of the predictor fare
, I’d like get the predicted values.
20.5 Simulate a logistic regression
As always, to better understand a statistical modeling procedure, it’s helpful to simulate data from the assumed data-generating process, fit the model, and see whether we can reconstruct the parameters.
# make example reproducible
set.seed(1)
# set parameters
sample_size = 1000
b0 = 0
b1 = 1
# b1 = 8
# generate data
df.data = tibble(
x = rnorm(n = sample_size),
y = b0 + b1 * x,
p = inv.logit(y)) %>%
mutate(response = rbinom(n(), size = 1, p = p))
# fit model
fit = glm(formula = response ~ 1 + x,
family = "binomial",
data = df.data)
# model summary
fit %>% summary()
##
## Call:
## glm(formula = response ~ 1 + x, family = "binomial", data = df.data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1137 -1.0118 -0.4591 1.0287 2.2591
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.06214 0.06918 -0.898 0.369
## x 0.92905 0.07937 11.705 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1385.4 on 999 degrees of freedom
## Residual deviance: 1209.6 on 998 degrees of freedom
## AIC: 1213.6
##
## Number of Fisher Scoring iterations: 3
Nice! The inferred estimates are very close to the parameter values we used to simulate the data.
Let’s visualize the result:
ggplot(data = df.data,
mapping = aes(x = x,
y = response)) +
geom_smooth(method = "glm",
method.args = list(family = "binomial")) +
geom_point(alpha = 0.1) +
labs(y = "p(response)")
## `geom_smooth()` using formula 'y ~ x'
<IP_ADDRESS> Calculate the model’s likelihood
To calculate the likelihood of the data for a given logistic model, we look at the actual response, and the probability of the predicted response, and then determine the likelihood of the observation assuming a bernoulli process. To get the overall likelihood of the data, we then multiply the likelihood of each data point (or take the logs first and then the sum to get the log-likelihood).
This table illustrate the steps involved:
fit %>%
augment() %>%
clean_names() %>%
mutate(p = inv.logit(fitted)) %>%
select(response, p) %>%
mutate(p_response = ifelse(response == 1, p, 1-p),
log_p = log(p_response)) %>%
rename(`p(Y = 1)` = p, `p(Y = response)` = p_response,
`log(p(Y = response))` = log_p) %>%
head(10) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| response | p(Y = 1) | p(Y = response) | log(p(Y = response)) |
|---|---|---|---|
| 1 | 0.34 | 0.34 | -1.07 |
| 0 | 0.53 | 0.47 | -0.75 |
| 1 | 0.30 | 0.30 | -1.20 |
| 1 | 0.81 | 0.81 | -0.22 |
| 1 | 0.56 | 0.56 | -0.58 |
| 0 | 0.30 | 0.70 | -0.36 |
| 1 | 0.60 | 0.60 | -0.52 |
| 1 | 0.65 | 0.65 | -0.43 |
| 1 | 0.62 | 0.62 | -0.48 |
| 0 | 0.41 | 0.59 | -0.54 |
Let’s calculate the log-likelihood by hand:
fit %>%
augment() %>%
clean_names() %>%
mutate(p = inv.logit(fitted),
log_likelihood = response * log(p) + (1 - response) * log(1 - p)) %>%
summarize(log_likelihood = sum(log_likelihood))
## # A tibble: 1 x 1
## log_likelihood
## <dbl>
## 1 -605.
And compare it with the model summary
fit %>%
glance() %>%
select(logLik, AIC, BIC) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| logLik | AIC | BIC |
|---|---|---|
| -604.82 | 1213.64 | 1223.45 |
We’re getting the same result – neat! Now we know how the likelihood of the data is calculated for a logistic regression model.
20.6 Testing hypotheses
To test hypotheses, we use our gold old model comparison approach:
# fit compact model
fit.compact = glm(formula = survived ~ 1 + fare,
family = "binomial",
data = df.titanic)
# fit augmented model
fit.augmented = glm(formula = survived ~ 1 + sex + fare,
family = "binomial",
data = df.titanic)
# likelihood ratio test
anova(fit.compact, fit.augmented, test = "LRT")
## Analysis of Deviance Table
##
## Model 1: survived ~ 1 + fare
## Model 2: survived ~ 1 + sex + fare
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 889 1117.57
## 2 888 884.31 1 233.26 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Note that in order to get a p-value out of this, we need to specify what statistical test we’d like to run. In this case, we use the likelihood ratio test (“LRT”).
20.7 Logistic mixed effects model
Just like we can build linear mixed effects models using lmer()
instead of lm()
, we can also build a logistic mixed effects regression using glmer()
instead of glm()
.
Let’s read in some data:
# load bdf data set from nlme package
data(bdf, package = "nlme")
df.language = bdf %>%
clean_names() %>%
filter(repeatgr != 2) %>%
mutate(repeatgr = repeatgr %>% as.character() %>% as.numeric())
rm(bdf)
Fit the model, and print out the results:
fit = glmer(repeatgr ~ 1 + ses + minority + (1 | school_nr),
data = df.language,
family = "binomial")
fit %>%
summary()
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: repeatgr ~ 1 + ses + minority + (1 | school_nr)
## Data: df.language
##
## AIC BIC logLik deviance df.resid
## 1659.1 1682.1 -825.6 1651.1 2279
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.9235 -0.4045 -0.3150 -0.2249 5.8372
##
## Random effects:
## Groups Name Variance Std.Dev.
## school_nr (Intercept) 0.2489 0.4989
## Number of obs: 2283, groups: school_nr, 131
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.506291 0.197570 -2.563 0.01039 *
## ses -0.060086 0.007524 -7.986 1.39e-15 ***
## minorityY 0.673612 0.238660 2.822 0.00477 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ses
## ses -0.898
## minorityY -0.308 0.208
To visualize the results, we can use the ggeffects
package.
20.8 Logit transform
Here is a visualization of what the odds and log odds transformation look like.
20.9 Additional information
20.10 Session info
Information about this R session including which version of R was used, and what packages were loaded.
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] forcats_0.5.0 stringr_1.4.0 dplyr_0.8.4 purrr_0.3.3
## [5] readr_1.3.1 tidyr_1.0.2 tibble_2.1.3 ggplot2_3.3.0
## [9] tidyverse_1.3.0 ggeffects_0.14.1 boot_1.3-24 lme4_1.1-21
## [13] Matrix_1.2-18 broom_0.5.5 janitor_1.2.1 kableExtra_1.1.0
## [17] titanic_0.1.0 knitr_1.28
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-145 fs_1.3.2 lubridate_1.7.4 insight_0.8.1
## [5] webshot_0.5.2 RColorBrewer_1.1-2 httr_1.4.1 tools_3.6.2
## [9] backports_1.1.5 utf8_1.1.4 R6_2.4.1 sjlabelled_1.1.3
## [13] DBI_1.1.0 mgcv_1.8-31 colorspace_1.4-1 nnet_7.3-13
## [17] withr_2.1.2 tidyselect_1.0.0 compiler_3.6.2 cli_2.0.2
## [21] rvest_0.3.5 xml2_1.2.2 labeling_0.3 effects_4.1-4
## [25] bookdown_0.18 scales_1.1.0 digest_0.6.25 minqa_1.2.4
## [29] rmarkdown_2.1 pkgconfig_2.0.3 htmltools_0.4.0 dbplyr_1.4.2
## [33] highr_0.8 rlang_0.4.5 readxl_1.3.1 rstudioapi_0.11
## [37] generics_0.0.2 farver_2.0.3 jsonlite_1.6.1 magrittr_1.5
## [41] Rcpp_1.0.3 munsell_0.5.0 fansi_0.4.1 lifecycle_0.1.0
## [45] stringi_1.4.6 yaml_2.2.1 snakecase_0.11.0 carData_3.0-3
## [49] MASS_7.3-51.5 grid_3.6.2 crayon_1.3.4 lattice_0.20-40
## [53] haven_2.2.0 splines_3.6.2 hms_0.5.3 pillar_1.4.3
## [57] estimability_1.3 reprex_0.3.0 glue_1.3.1 evaluate_0.14
## [61] mitools_2.4 modelr_0.1.6 vctrs_0.2.3 nloptr_1.2.1
## [65] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 xfun_0.12
## [69] survey_3.37 survival_3.1-8 viridisLite_0.3.0


Common Name: Giant Panda
Scientific Name: Ailuropoda melanoleuca
Population: 1000 to 2000 remain in the wild
Common Name: Northern Hairy-nosed Wombat
Scientific Name: Lasiorhinus krefftii
Population: Only about 65 individuals remain
Common Name: Saiga Antelope
Scientific Name: Saiga tatarica
Population: Less than 1500
Common Name: Mediterranean Monk Seal
Scientific Name: Monachus monachus
Population: Less than 600
Common Name: Sand Cat
Scientific Name: Felis margarita
Population: Less than 200
Common Name: Black Rhinoceros
Scientific Name: Diceros bicornis
Population: Extinct
Common Name: Little Spotted Cat
Scientific Name: Leopardus tigrinus
Population: Less than 100
Common Name: Spider Monkey
Scientific Name: Ateles geoffroyi
Population: Less than 100
Common Name: American Bison
Scientific Name: Bison bison
Population: Approximately 9000
Common Name: Humpback Whale
Scientific Name: Megaptera novaeangliae
Population: Approximately 6000


Robert Talbert, Grand Valley State University / @RobertTalbert
American Society for Engineering Education, Indianapolis IN 2014-06-17
Pre-class: Instructor decides concepts and topics, devises learning objectives, plans direct instruction
In class:Direct instruction, presentation of information
After class:Students assimilate information through homework, projects, etc.
Pre-class:Direct instruction through reading, video, audio + simple examples
In class:Students assimilate information through directed work on challenging problems, with instructor supervision
After class:Extended work (additional HW, projects, etc.) and preparation for upcoming classes
Inverse relationship between difficulty of work and accessibility to help
Lack of student control over information flow
Lack of time for exploration of concepts/difficult problems
Lack of explicit instruction on lifelong learning skills
Closely related to lifelong learning
Did student do the pre-class work?
Median completion rates of Guided Practice were 92% for one section and 96% in the other section.
Did students learn through the pre-class work?
Entrance Quiz median values were 78% and 72%.
How much time did it take to make the videos?
With a collaborator, 60 of the 91 videos were completed in a 10-day period prior to the start of classes.
The setup for the class is perfect, i dont [sic] understand why all classes are not like this, like you said the hardest part of the class is the homework and other teachers expect us to just pick up from what they said in lectures which is out of date teaching. Like for my Engineering class we do all the hard stuff outside of class.
I really enjoy this style of class. I would like to take one similar to this for my calc 2 experience. Are there any other professors that run class this way? If so I would love to know as I would switch into a class like this in a heartbeat.
Large scale study on student gains in flipped vs. non-flipped instruction, using Calculus Concept Inventory and the Motivated Strategies for Learning Questionnaire
Creation of video materials for Calculus 2


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Comments
-
Greg Hurrell
A further note, to minimize the risk of the change I'll be making it to the admin issues interface first. If it works there then I'll generalize it elsewhere.
-
Greg Hurrell
Interesting viewpoint about implementation:
The argument there is "archive deleted records (somewhere else) rather than marking them as deleted (in-place in the table)".
-
Greg Hurrell
I was thinking about this earlier today in relation to the "spam" field that I currently have on a lot of models.
It turns out that this has been an unnecessary complication ever since it was implemented. Basically, I never mark stuff as spam; I always just delete it. So all of those queries where I have to add a
spam == false
condition could have been made simpler.If you want to do some sort of bayesian training on spain records then you don't really need to keep those records around in their original form anyway. In short: keep the "Spam" button, but get rid of the "spam" attribute in the database tables. Training as spam would analyse the message and update the corpus, and then the spam record would be deleted.
So the point is, if all those queries were made unnecessarily complicated by the
spam == false
check, then perhaps I should forget about using this "deleted_at" column in the database because it will complicate all those queries in exactly the same way.It would be better to have a separate table (even tables, one for each kind of record) to hold "deleted" records. Sure, it would be more complicated in some ways (more tables, more classes), but it would make the "99% code path", the one which gets used almost exclusively, a lot more simple. For basic, non-deleted records (ie. 99% of all use), you can just forget entirely about the notion of "deleted_at".
-
Greg Hurrell
I've made a new ticket, ticket #1264, to track the removal of the "spam" field.
-
Greg Hurrell
I currently have two models with a "deleted_at" field: "emails" and "users". So whatever I decide to do, will have to take those existing arrangements into account.
-
anonymous
(Posting anonymously from another computer seeing as I don't have my login details with me.)
I'm thinking that I do want to go ahead with this after all, for the reasons stated in ticket #1587. In short, it allows us to elegantly handle the no-JavaScript case, and is a nicer workflow anyway.
The basic idea is to show in the flash, "Record successfully deleted ([link to undo])".
The undo link would just be a post back to the
update
action with thedeleted_at
field set tonil
. The update action updates and then redirects to theshow
action, with a flash saying "Record successfully updated".Will need some kind of helper method to produce the "undo" links in a comfortable fashion.
Will still be tricky in terms of adapting the other queries to use
WHERE deleted_at IS NULL
, but I think it's most likely worth it in the overall scheme of things. -
anonymous
(Me again.)
One of the arguments against using this "mark as deleted" model is that the database can fill up with crud.
In reality, it's just a simple mechanism for providing a short-term "undo-ability".
There is nothing stopping us from allow the admin to really delete (as in destroy, remove from the database) records that are "in the Trash" (marked as deleted). Best place for this is probably in the admin-only interface.
-
Greg Hurrell
In terms of query complexity, the comment model will actually be the most complex because it already has two boolean fields that must be checked on pretty much every query (the
public
andawaiting_moderation
flags).For the initial implementation, then, will probably start with a more simple model, like the
Post
,Article
orTweet
models, seeing as they at least don't have theawaiting_moderation
flag (nor author/user fields either). It will therefore be simpler to modify those queries.As for the whole object graph question, most (all?) of the "commentable" models use
:dependent => :destroy
. So I guess if we mark a parent object like a port or an article as deleted, the simplest thing will be to leave the comments untouched. Any attempt to access an individual one will end up yielding a 404. When we actually proceed to really destroy the parent model (ie. "empty the recycle bin") then we'll destroy the dependent comments.
Add a comment
Comments are now closed for this issue.


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Renault Kwid Climber 2019 – Top 7 Exterior Highlights
by Sahil KukrejaWhat’s new on the Kwid compared to the pre-facelift model on the exterior? Take a read
Renault launched the 2019 Kwid facelift earlier this month at a starting price of Rs 2.84 lakh, which goes up to Rs 4.84 lakh (ex-showroom) for the top-end variant. The Kwid is available in five variants, namely STD, RXE, RXL, RXT and the Climber being the range-topping variant, that gets all the bells and whistles.
The pre-facelift model received mild updates and feature refreshes over the course of four years of its production. However, it was still in need of a mid-cycle refresh to fire things up in the entry-level segment. Here are the 7 exterior highlights from the new Renault Kwid –
1. Front Fascia
The 2019 Renault Kwid features a distinctive face with an all-new grille and larger air dams. Apart from the stylish front end, the high bonnet which gives the car a much larger appeal than its actual size, is what will please a younger audience.
2. DRLs
The new Kwid gets new horizontal LED DRLs, which are standard across the trim. The black front grille bearing the Renault logo spreads across the front, and the DRLs are positioned in such a way, that they continue the horizontal flow of the front grille to make it look even more aesthetic.
3. Headlamps
The headlamps on the 2019 Renault Kwid feature a split design, with the LED DRLs on the top, and the actual headlamps sitting at the bottom in a different unit. The headlights are bigger than the outgoing model and get covered in a chunky black housing with orange accents on the Climber variant.
4. LED Tail lamps
The entry-segment crossover now features first-in-class LED tail lamps along with new reflectors on the rear bumper.
5. Wheels
Renault offers the 2019 Kwid with bigger 14-inch wheels as standard, as compared to the 13-inchers on the previous model. The Climber variant sports smoked grey wheel covers, which make them look sporty.
6. Ground Clearance
The French carmaker has left no stone unturned, and has increased the ground clearance of the 2019 Kwid by 4mm, which takes it to 184mm.
7. Competitors
Compared to the pre-facelift model, the Kwid has a new direct rival in the entry-level segment in the form of Maruti Suzuki S-Presso. Apart from that, it also continues to put up against Datsun RediGO and Maruti Suzuki Alto.


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| Total net profit | -71.66 | Gross profit | 110.93 | Gross loss | -182.59 |
| Profit factor | 0.61 | Expected payoff | -0.34 | ||
| Absolute drawdown | -72.09 | Maximal drawdown (%) | 73.12 (7099.03%) | ||
| Total trades | 213 | Profit trades (% of total) | 100 (46.95%) | Loss trades (% of total) | 113 (53.05%) |
| Long positions (won %) | 111 (54.05%) | Short positions (won %) | 102 (39.22%) | ||
| Largest | profit trade | 4.72 | loss trade | -3.58 | |
| Average | profit trade | 1.11 | loss trade | -1.62 | |
| Minimum | profit trade | 0.0 | loss trade | -0.01 | |
| Maximum | take profit in pips | 85.0 | stop loss in pips | -50.0 | |
| Average | take profit in pips | 17.8 | stop loss in pips | -25.2 | |
| Minimum | take profit in pips | -7.3 | stop loss in pips | -6.3 | |
| Maximum | Reward-Risk | 3.9 | Duration in minutes | 3507 | |
| Average | Reward-Risk | 0.9 | Duration in minutes | 81.7 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0 | |
| Maximum | consecutive wins (profit in money) | 7.0 (4.16) | consecutive losses (loss in money) | 8.0 (-10.66) | |
| Average | consecutive wins | 1.0 | consecutive losses | 2.0 |
| Total net profit | -26.46 | Gross profit | 60.39 | Gross loss | -86.85 |
| Profit factor | 0.7 | Expected payoff | -0.24 | ||
| Absolute drawdown | -27.36 | Maximal drawdown (%) | 28.39 (2756.31%) | ||
| Total trades | 111 | Profit trades (% of total) | 60 (54.05%) | Loss trades (% of total) | 51 (45.95%) |
| Largest | profit trade | 4.22 | loss trade | -3.58 | |
| Average | profit trade | 1.01 | loss trade | -1.7 | |
| Minimum | profit trade | 0.05 | loss trade | -0.01 | |
| Maximum | take profit in pips | 85.0 | stop loss in pips | -50.0 | |
| Average | take profit in pips | 15.2 | stop loss in pips | -28.0 | |
| Minimum | take profit in pips | -7.3 | stop loss in pips | -10.8 | |
| Maximum | Reward-Risk | 3.3 | Duration in minutes | 3507.0 | |
| Average | Reward-Risk | 0.6 | Duration in minutes | 101.5 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0.0 | |
| Maximum | consecutive wins (profit in money) | 9.0 (5.89) | consecutive losses (loss in money) | 6.0 (-15.07) | |
| Average | consecutive wins | 1.0 | consecutive losses | 1.0 |
| Total net profit | -45.2 | Gross profit | 50.54 | Gross loss | -95.74 |
| Profit factor | 0.53 | Expected payoff | -0.44 | ||
| Absolute drawdown | -47.95 | Maximal drawdown (%) | 45.56 (-1906.28%) | ||
| Total trades | 102 | Profit trades (% of total) | 40 (39.22%) | Loss trades (% of total) | 62 (60.78%) |
| Largest | profit trade | 4.72 | loss trade | -3.08 | |
| Average | profit trade | 1.26 | loss trade | -1.54 | |
| Minimum | profit trade | 0.0 | loss trade | -0.06 | |
| Maximum | take profit in pips | 65.6 | stop loss in pips | -40.7 | |
| Average | take profit in pips | 20.6 | stop loss in pips | -22.1 | |
| Minimum | take profit in pips | -1.0 | stop loss in pips | -6.3 | |
| Maximum | Reward-Risk | 3.9 | Duration in minutes | 1212.0 | |
| Average | Reward-Risk | 1.1 | Duration in minutes | 60.1 | |
| Minimum | Reward-Risk | Duration in minutes | 0.0 | ||
| Maximum | consecutive wins (profit in money) | 4.0 (3.06) | consecutive losses (loss in money) | 8.0 (-10.66) | |
| Average | consecutive wins | 1.0 | consecutive losses | 2.0 |


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Curling is a sport played by two teams of four on a sheet of ice. The objective of the game is to score the most points by the games end.
The score is calculated by seeing which team has the stone closest to the middle (known as the button) and then counting how many stones that team has that are closer than the best stone of the other team. In the picture below red has the closet stone (called shot rock) and yellow has second shot. In this case if the round were to end red would score one point.
Here is a link to a popular curling website. I think this is a well-made site which presents a lot of information in an organized way. There are grouped sections (Championships, News, Stories) where in each section there is the most recent item featured on the left side of the page and then three older items floated to the right of the featured item.
For a sneak peak at a future Team USA curler working on her coordination, click here.


Merges the
else
branch of an
if...else
statement if this
else
branch only contains a nested
if...else
statement.


And I can see the validation message as "Mobile number is not in valid format. Please select a country from the dropdown list, then enter the rest of the number in the box without the initial zero"
|
done: AddNewClientSteps.GivenICanSeeTheValidationMessageAs("Mobile number is ...") (5.0s)
|
Succeeded in 5.523s |
And I remove the mobile no
|
done: ClientSummaryStep.GivenIRemoveTheMobileNo() (0.1s)
|
Succeeded in 0.587s |
And I enter the Primary Email as "Invalid"
|
done: ClientSummaryStep.GivenIEnterThePrimaryEmailAs("Invalid") (0.2s)
|
Succeeded in 0.678s |
And I save the client
|
done: AddNewClientSteps.GivenISaveTheClient() (5.1s)
|
Succeeded in 5.585s |
And I can see the validation message as "Please ensure the email entered is valid"
|
done: AddNewClientSteps.GivenICanSeeTheValidationMessageAs("Please ensure the...") (5.0s)
|
Succeeded in 5.518s |
And I remove the Primary Email
|
done: ClientSummaryStep.GivenIRemoveThePrimaryEmail() (0.1s)
|
Succeeded in 0.578s |
And I enter the Other Email as "Invalid"
|
done: ClientSummaryStep.GivenIEnterTheOtherEmailAs("Invalid") (0.2s)
|
Succeeded in 0.672s |
When I save the client
|
done: AddNewClientSteps.GivenISaveTheClient() (5.1s)
|
Succeeded in 5.58s |
Then I can see the validation message as "Please ensure the other email entered is valid"
|
done: AddNewClientSteps.GivenICanSeeTheValidationMessageAs("Please ensure the...") (5.0s)
|
Succeeded in 5.519s |
Scenario: Mandatory Validation for Client Fact Find (in Enable_UIAutomation, V2_ClientSearch)
tags: Smoke, validation, LoginEnableTaur
- Status: Succeeded
- Start time: 26/03/2019 19:57:51
- Execution time (sec): 39.8857618
- Thread: #0
| Steps |
Trace |
Result |
Given I am logged in to Enable using automation.user and CPWSRD0@
|
done: NavigationSteps.GivenIAmLoggedInToEnableUsingUsernameAndPasswordAndOnTheHomepage("automation.user", "CPWSRD0@") (18.9s)
|
Succeeded in 26.64s |
Given I am on Clients search page
|
done: ClientSearchSteps.GivenIAmOnClientsSearchPage() (2.0s)
|
Succeeded in 2.458s |
And I select the "Bobson, Mr. Bob" client
|
done: ClientSearchSteps.GivenISelectTheClient("Bobson, Mr. Bob") (1.0s)
|
Succeeded in 1.528s |
And I click on Fact Find button
|
done: ClientSearchSteps.GivenIClickOnFactFindButton() (5.8s)
|
Succeeded in 6.34s |
And I remove the Date
|
done: ClientSearchSteps.GivenIRemoveTheDate() (0.1s)
|
Succeeded in 0.617s |
When I update
|
done: ClientSearchSteps.WhenIUpdate() (0.1s)
|
Succeeded in 0.589s |
Then I can see the error message as "Error The Date field is required."
|
done: QuestionGroupingStep.ThenICanSeeTheErrorMessageAs("Error The Date fi...") (1.1s)
|
Succeeded in 1.554s |
Scenario: Mandatory Validation for Profile (in Enable_UIAutomation, V2_ClientSearch)
tags: Smoke, validation, LoginEnableTaur
- Status: Ignored
- Start time: 26/03/2019 19:58:31
- Execution time (sec): 0
- Thread: #-1
| Steps |
Trace |
Result |
|
|
Ignored
|
Ignored in 0s |
Scenario: Add exiting asset to client (in Enable_UIAutomation, V2_AddAssets)
tags: Smoke, LoginEnableTaur
- Status: Succeeded
- Start time: 26/03/2019 19:58:31
- Execution time (sec):<PHONE_NUMBER>
- Thread: #0
| Steps |
Trace |
Result |
Given I am logged in to Enable using automation.user and CPWSRD0@
|
done: NavigationSteps.GivenIAmLoggedInToEnableUsingUsernameAndPasswordAndOnTheHomepage("automation.user", "CPWSRD0@") (74.6s)
|
Succeeded in 83.273s |
Given I am on Clients search page
|
done: ClientSearchSteps.GivenIAmOnClientsSearchPage() (1.5s)
|
Succeeded in 2.015s |
And I select the "Bobson, Mr. Bob" client
|
done: ClientSearchSteps.GivenISelectTheClient("Bobson, Mr. Bob") (1.0s)
|
Succeeded in 1.526s |
And I click on Fact Find button
|
done: ClientSearchSteps.GivenIClickOnFactFindButton() (6.6s)
|
Succeeded in 7.121s |
And I select the existing "Full Fact Find"
|
done: ClientSearchSteps.WhenISelectTheExisting("Full Fact Find") (13.8s)
|
Succeeded in 14.258s |
And I click the "Assets" from left nav
|
done: ClientSearchSteps.WhenIClickTheFromLeftNav("Assets") (2.6s)
|
Succeeded in 3.118s |
And I choose to Add New Asset from the existing assets
|
done: ClientAssetsStep.GivenIChooseToAddNewAssetFromTheExistingAssets() (1.7s)
|
Succeeded in 2.209s |
When I select an asset
|
done: ClientAssetsStep.WhenISelectAnAsset() (0.1s)
|
Succeeded in 0.562s |
And I save the existing asset
|
done: ClientAssetsStep.WhenISaveTheExistingAsset() (0.1s)
|
Succeeded in 0.56s |
Then I can see "Saved Successfully saved."
|
done: QuestionGroupingStep.ThenICanSee("Saved Successfull...") (0.0s)
|
Succeeded in 0.54s |
Scenario: Add a new Asset to client, Asset_01 (in Enable_UIAutomation, V2_AddAssets)
tags: Smoke, LoginEnableTaur
- Status: Succeeded
- Start time: 26/03/2019 20:00:26
- Execution time (sec):<PHONE_NUMBER>
- Thread: #0
| Steps |
Trace |
Result |
Given I am logged in to Enable using automation.user and CPWSRD0@
|
done: NavigationSteps.GivenIAmLoggedInToEnableUsingUsernameAndPasswordAndOnTheHomepage("automation.user", "CPWSRD0@") (74.4s)
|
Succeeded in 81.901s |
Given I am on Clients search page
|
done: ClientSearchSteps.GivenIAmOnClientsSearchPage() (1.4s)
|
Succeeded in 1.854s |
And I select the "Bobson, Mr. Bob" client
|
done: ClientSearchSteps.GivenISelectTheClient("Bobson, Mr. Bob") (1.4s)
|
Succeeded in 1.922s |
And I click on Fact Find button
|
done: ClientSearchSteps.GivenIClickOnFactFindButton() (6.1s)
|
Succeeded in 6.6s |
And I select the existing "Full Fact Find"
|
done: ClientSearchSteps.WhenISelectTheExisting("Full Fact Find") (14.7s)
|
Succeeded in 15.201s |
And I click the "Assets" from left nav
|
done: ClientSearchSteps.WhenIClickTheFromLeftNav("Assets") (1.7s)
|
Succeeded in 2.181s |
And I choose to a Add New Asset with new details
|
done: ClientAssetsStep.GivenIChooseToAAddNewAssetWithNewDetails() (2.4s)
|
Succeeded in 2.891s |
And I enter asset details "Asset_01","Property","Residential Property - Investment Property","5000" and "20"
|
done: ClientAssetsStep.GivenIEnterAssetDetailsAnd("Asset_01", "Property", "Residential Prope...", "5000", "20") (5.6s)
|
Succeeded in 6.08s |
When I save the asset
|
done: ClientAssetsStep.WhenISaveTheAsset() (5.1s)
|
Succeeded in 5.565s |
Then I can see "Saved Asset has been successfully saved."
|
done: QuestionGroupingStep.ThenICanSee("Saved Asset has b...") (0.0s)
|
Succeeded in 0.538s |
Scenario: Mandatory Field Validation (in Enable_UIAutomation, V2_AddAssets)
tags: Smoke, validation, LoginEnableTaur
- Status: Succeeded
- Start time: 26/03/2019 20:02:31
- Execution time (sec):<PHONE_NUMBER>
- Thread: #0
| Steps |
Trace |
Result |
Given I am logged in to Enable using automation.user and CPWSRD0@
|
done: NavigationSteps.GivenIAmLoggedInToEnableUsingUsernameAndPasswordAndOnTheHomepage("automation.user", "CPWSRD0@") (75.9s)
|
Succeeded in 83.773s |
Given I am on Clients search page
|
done: ClientSearchSteps.GivenIAmOnClientsSearchPage() (1.4s)
|
Succeeded in 1.888s |
And I select the "Bobson, Mr. Bob" client
|
done: ClientSearchSteps.GivenISelectTheClient("Bobson, Mr. Bob") (1.0s)
|
Succeeded in 1.514s |
And I click on Fact Find button
|
done: ClientSearchSteps.GivenIClickOnFactFindButton() (5.0s)
|
Succeeded in 5.51s |
And I select the existing "Full Fact Find"
|
done: ClientSearchSteps.WhenISelectTheExisting("Full Fact Find") (13.5s)
|
Succeeded in 13.96s |
And I click the "Assets" from left nav
|
done: ClientSearchSteps.WhenIClickTheFromLeftNav("Assets") (1.7s)
|
Succeeded in 2.235s |
And I choose to a Add New Asset with new details
|
done: ClientAssetsStep.GivenIChooseToAAddNewAssetWithNewDetails() (3.0s)
|
Succeeded in 3.454s |
And I save the asset
|
done: ClientAssetsStep.WhenISaveTheAsset() (5.1s)
|
Succeeded in 5.57s |
And I can see the error message as "Cannot Save The Description field is required.; The Last Known Value field is required.; The GrowthRateValueAsPercentage field is required."
|
done: QuestionGroupingStep.ThenICanSeeTheErrorMessageAs("Cannot Save The D...") (0.0s)
|
Succeeded in 0.536s |
And I enter the asset Description
|
done: ClientAssetsStep.GivenIEnterTheAssetDescription() (0.1s)
|
Succeeded in 0.628s |
And I enter the Last Known Value as "1000"
|
done: ClientAssetsStep.GivenIEnterTheLastKnownValueAs("1000") (0.1s)
|
Succeeded in 0.604s |
And I enter the Growth Rate Value As Percentage value as "20"
|
done: ClientAssetsStep.GivenIEnterTheGrowthRateValueAsPercentageValueAs("20") (0.2s)
|
Succeeded in 0.7s |
And I remove the whose name
|
done: ClientAssetsStep.GivenIRemoveTheWhoseName() (0.1s)
|
Succeeded in 0.56s |
And I save the asset
|
done: ClientAssetsStep.WhenISaveTheAsset() (5.1s)
|
Succeeded in 5.565s |
And I can see the error message as "Cannot Save The Whose Name field is required."
|
done: QuestionGroupingStep.ThenICanSeeTheErrorMessageAs("Cannot Save The W...") (0.0s)
|
Succeeded in 0.537s |
And I select the whose name as "Client"
|
done: ClientAssetsStep.GivenISelectTheWhoseNameAs("Client") (0.0s)
|
Succeeded in 0.531s |
And I save the asset
|
done: ClientAssetsStep.WhenISaveTheAsset() (5.1s)
|
Succeeded in 5.555s |
And I can see the error message as "Cannot Save The Type field is required.; The Sub Classification field is required."
|
done: QuestionGroupingStep.ThenICanSeeTheErrorMessageAs("Cannot Save The T...") (0.0s)
|
Succeeded in 0.537s |
And I select the Type as "Property"
|
done: ClientAssetsStep.GivenISelectTheTypeAs("Property") (0.1s)
|
Succeeded in 0.555s |
And I save the asset
|
done: ClientAssetsStep.WhenISaveTheAsset() (5.1s)
|
Succeeded in 5.558s |
Then I can see "Saved Asset has been successfully saved."
|
done: QuestionGroupingStep.ThenICanSee("Saved Asset has b...") (0.0s)
|
Succeeded in 0.534s |
Scenario: Integrity Validation (in Enable_UIAutomation, V2_AddAssets)
tags: Smoke, validation, LoginEnableTaur
- Status: Succeeded
- Start time: 26/03/2019 20:04:52
- Execution time (sec): 75.6608181
- Thread: #0
| Steps |
Trace |
Result |
Given I am logged in to Enable using automation.user and CPWSRD0@
|
done: NavigationSteps.GivenIAmLoggedInToEnableUsingUsernameAndPasswordAndOnTheHomepage("automation.user", "CPWSRD0@") (13.7s)
|
Succeeded in 22.805s |
Given I am on Clients search page
|
done: ClientSearchSteps.GivenIAmOnClientsSearchPage() (2.4s)
|
Succeeded in 2.947s |
And I select the "Bobson, Mr. Bob" client
|
done: ClientSearchSteps.GivenISelectTheClient("Bobson, Mr. Bob") (0.9s)
|
Succeeded in 1.388s |
And I click on Fact Find button
|
done: ClientSearchSteps.GivenIClickOnFactFindButton() (5.7s)
|
Succeeded in 6.182s |
And I select the existing "Full Fact Find"
|
done: ClientSearchSteps.WhenISelectTheExisting("Full Fact Find") (14.1s)
|
Succeeded in 14.604s |
And I click the "Assets" from left nav
|
done: ClientSearchSteps.WhenIClickTheFromLeftNav("Assets") (2.2s)
|
Succeeded in 2.686s |
And I choose to a Add New Asset with new details
|
done: ClientAssetsStep.GivenIChooseToAAddNewAssetWithNewDetails() (1.6s)
|
Succeeded in 2.1s |
And I enter the asset Description
|
done: ClientAssetsStep.GivenIEnterTheAssetDescription() (0.1s)
|
Succeeded in 0.643s |
And I select the Type as "Property"
|
done: ClientAssetsStep.GivenISelectTheTypeAs("Property") (0.1s)
|
Succeeded in 0.565s |
And I enter the Last Known Value as "HUNDRED"
|
done: ClientAssetsStep.GivenIEnterTheLastKnownValueAs("HUNDRED") (0.1s)
|
Succeeded in 0.634s |
And I save the asset
|
done: ClientAssetsStep.WhenISaveTheAsset() (5.1s)
|
Succeeded in 5.581s |
And I can see the error message as "Cannot Save The Last Known Value field is not a valid number."
|
done: QuestionGroupingStep.ThenICanSeeTheErrorMessageAs("Cannot Save The L...") (0.0s)
|
Succeeded in 0.541s |
And I enter the Last Known Value as "1000"
|
done: ClientAssetsStep.GivenIEnterTheLastKnownValueAs("1000") (0.1s)
|
Succeeded in 0.604s |
And I enter the Growth Rate Value As Percentage value as "HUNDRED"
|
done: ClientAssetsStep.GivenIEnterTheGrowthRateValueAsPercentageValueAs("HUNDRED") (0.2s)
|
Succeeded in 0.724s |
And I save the asset
|
done: ClientAssetsStep.WhenISaveTheAsset() (5.1s)
|
Succeeded in 5.579s |
And I can see the error message as "Cannot Save The Estimated Annual Revaluation % field is not a valid number."
|
done: QuestionGroupingStep.ThenICanSeeTheErrorMessageAs("Cannot Save The E...") (0.0s)
|
Succeeded in 0.54s |
And I enter the Growth Rate Value As Percentage value as "200"
|
done: ClientAssetsStep.GivenIEnterTheGrowthRateValueAsPercentageValueAs("200") (0.1s)
|
Succeeded in 0.628s |
And I enter the start purchase value as "HUNDRED"
|
done: ClientAssetsStep.GivenIEnterTheStartPurchaseValueAs("HUNDRED") (0.1s)
|
Succeeded in 0.633s |
When I save the asset
|
done: ClientAssetsStep.WhenISaveTheAsset() (5.1s)
|
Succeeded in 5.557s |
Then I can see the error message as "Cannot Save The Start/Purchase Value field is not a valid number."
|
done: QuestionGroupingStep.ThenICanSeeTheErrorMessageAs("Cannot Save The S...") (0.0s)
|
Succeeded in 0.534s |
Scenario: View client assets, Bobson, Mr. Bob (in Enable_UIAutomation, V2_ViewAssets)
tags: Smoke, LoginEnableTaur
- Status: Succeeded
- Start time: 26/03/2019 20:06:08
- Execution time (sec): 60.4116228
- Thread: #0
| Steps |
Trace |
Result |
Given I am logged in to Enable using automation.user and CPWSRD0@
|
done: NavigationSteps.GivenIAmLoggedInToEnableUsingUsernameAndPasswordAndOnTheHomepage("automation.user", "CPWSRD0@") (15.2s)
|
Succeeded in 23.141s |
Given I am on Clients search page
|
done: ClientSearchSteps.GivenIAmOnClientsSearchPage() (1.3s)
|
Succeeded in 1.806s |
And I select the "Bobson, Mr. Bob" client
|
done: ClientSearchSteps.GivenISelectTheClient("Bobson, Mr. Bob") (0.9s)
|
Succeeded in 1.417s |
And I click on Fact Find button
|
done: ClientSearchSteps.GivenIClickOnFactFindButton() (5.4s)
|
Succeeded in 5.946s |
And I select the existing "Full Fact Find"
|
done: ClientSearchSteps.WhenISelectTheExisting("Full Fact Find") (15.1s)
|
Succeeded in 15.563s |
And I click the "Assets" from left nav
|
done: ClientSearchSteps.WhenIClickTheFromLeftNav("Assets") (1.7s)
|
Succeeded in 2.247s |
When I choose the "yes" option
|
done: ClientAssetsStep.WhenIChooseTheOption("yes") (5.1s)
|
Succeeded in 5.563s |
Then I see the assets listed for that client
|
done: ClientAssetsStep.ThenISeeTheAssetsListedForThatClient() (0.0s)
|
Succeeded in 0.513s |


S2 Table Distribution of SARSr-CoVs highly similar to SARS-CoV in the variable S, ORF3 and ORF8 genes in the single cave
|
Gene |
SARSr-CoV strains highly similar to SARS-CoV |
Sampling time (No. of strains) |
Bat species (No. of strains) |
|
S-RBD |
WIV1, Rs3367, Rs4079, Rs4087, Rs4090, Rs4105, Rs4230, Rs4829, Rs4832, WIV16, Rs4874, Rs4952, Rs7326, Rs7327, Rs9401, Rs9403 |
May 2012 (2) Sep 2012 (4) Apr 2013 (1) Jul 2013 (5) Oct 2014 (2) Oct 2015 (2) |
Rhinolophus sinicus (16) |
|
S-NTD |
Rs4231, WIV16, Rs4874, Rs4952 |
Apr 2013 (1) Jul 2013 (3) |
Rhinolophus sinicus (4) |
|
ORF8 |
Rs3261, Rf4075, Rs4084, Rs4091, Rf4092, Rs4110, Rf4122, Rs4832, Rs4943 |
Oct 2011 (1) Sep 2012 (6) Jul 2013 (2) |
Rhinolophus sinicus (6) Rhinolophus ferrumequinum (3) |
|
ORF3a |
RsSHC014, WIV1, Rs3367, Rs3369, Rs4084, Rs4087, Rs4090, Rs4105, Rs4231, Rs4829, WIV16, Rs4874, Rs4900, Rs4952, Rs7326, Rs7327, Rs9401 |
Apr 2011 (1) May 2012 (3) Sep 2012 (4) Apr 2013 (1) Jul 2013 (5) Oct 2014 (2) Oct 2015 (1) |
Rhinolophus sinicus (17) |
|
ORF3b |
Rs7326, Rs7327 |
Oct 2014 (2) |
Rhinolophus sinicus (2) |


As you can see the above AndroidManifest file, they filter the events by setting the action type values between intent-filter tags.
Because GCMBroadcastReceiver is the class for GCM so you do not need other actions apart from “com.google.android.c2dm.intent.RECEIVE”, “com.google.android.c2dm.intent.REGISTRATION”
publicclassGCMBroadcastReceiverextendsBroadcastReceiver{privatestaticfinalStringTAG="GCMBroadcastReceiver";@OverridepublicfinalvoidonReceive(Contextcontext,Intentintent){Log.v(TAG,"onReceive: "+intent.getAction());StringreceiverClassName=getClass().getName();if(!receiverClassName.equals(GCMBroadcastReceiver.class.getName())){GCMRegistrar.setRetryReceiverClassName(receiverClassName);}StringclassName=getGCMIntentServiceClassName(context);Log.v(TAG,"GCM IntentService class: "+className);// Delegates to the application-specific intent service.GCMBaseIntentService.runIntentInService(context,intent,className);setResult(Activity.RESULT_OK,null/* data */,null/* extra */);}...}
The other way is to customize the BroadcastReciever class by inheriting
In this method, you first need to set the customized java class name including package name for the tag android:name.The next step is to define the broadcast intents containing an action string.
There is two ways to filer the action type. One is to use intent-filter tag as you can see above. The other way is to write a code to distinguish the action type on GCMBroadcastReceiver class file. You can see the example for the following code.
onReceive function
12345678910111213
@OverridepublicvoidonReceive(Contextcontext,Intentintent){Log.d(TAG,"onReceive with action:"+intent.getAction()+"!");if(intent.getAction().equals("com.google.android.c2dm.intent.REGISTRATION")){handleRegistration(context,intent);}elseif(intent.getAction().equals(ACTION_SMS_RECEIVED)){}elseif(intent.getAction().equals(ACTION_NEW_OUTGOING_SMS)){}elseif(intent.getAction().equals("com.google.android.c2dm.intent.RECEIVE")){...}


Calculating speed with simple distance time method
Distance travelled : meter
Time Taken : Seconds
Spot speed is referred to as the instantaneous speed of a vehicle at a point or cross section; however there are two distinctly different methods of determination of spot speeds.
In the first method, the time, t (sec) taken by the vehicle to travel a short distance, d (m) is determined. Therefore the speed, v = (d/t) m/sec.
In the second method, the instantaneous speed is measured by a pre-calibrated radar equipment which displays or records the speed in desired units, such as kmph.
Calculating speed with simple distance time method
Distance travelled : meter
Time Taken : Seconds
Distance : Meter
Fill The Readings And Click on Submit.
Observation Tables
*Note=First Fill table1 after the table1 details completed fill the table 2 taking reference from table 1
| Cars | Speed |
|---|---|
| Car1 | |
| Car2 | |
| Car3 | |
| Car4 | |
| Car5 | |
| Car6 | |
| Car7 | |
| Car8 |
| Cars | Speed |
|---|---|
| Car9 | |
| Car10 | |
| Car11 | |
| Car12 | |
| Car13 | |
| Car14 | |
| Car15 | |
| Car16 |
| Speed range, kmph |
Mean speed, kmph |
Frequency, f |
Percent frequency |
|---|---|---|---|
| 0-10 | 5 | --- |
|
| 10-20 | 15 | --- |
|
| 20-30 | 25 | --- |
|
| 30-40 | 35 | --- |
|
| 40-50 | 45 | --- |
|
| 50-60 | 55 | --- |
|
| 60-70 | 65 | --- |
|
| 70-80 | 75 | --- |
Frequency Distribution Diagram Of spot speeds
The Modal Speed for the following observation is ---
Calculating speed with Radar Gun
Distance travelled : meter
Distance : Meter
Fill The Readings And Click on Submit.
Observation Tables
*Note=First Fill table1 after the table1 details completed fill the table 2 taking reference from table 1
| Cars | Speed |
|---|---|
| Car1 | |
| Car2 | |
| Car3 | |
| Car4 | |
| Car5 | |
| Car6 | |
| Car7 | |
| Car8 |
| Cars | Speed |
|---|---|
| Car9 | |
| Car10 | |
| Car11 | |
| Car12 | |
| Car13 | |
| Car14 | |
| Car15 | |
| Car16 |
| Speed range, kmph |
Mean speed, kmph |
Frequency, f |
Percent frequency |
|---|---|---|---|
| 0-10 | 5 | --- |
|
| 10-20 | 15 | --- |
|
| 20-30 | 25 | --- |
|
| 30-40 | 35 | --- |
|
| 40-50 | 45 | --- |
|
| 50-60 | 55 | --- |
|
| 60-70 | 65 | --- |
|
| 70-80 | 75 | --- |
Frequency Distribution Diagram Of spot speeds
The Modal Speed for the following observation is ______


uptime in days: {{ stat.uptime_in_days }}
bg save in progress: {{ stat.bgsave_in_progress }}
multiplexing api: {{ stat.multiplexing_api }}
number of connected slaves: {{ stat.connected_slaves }}
total connections received: {{stat.total_connections_received}}
redis version: {{stat.redis_version}}
process id: #{{stat.process_id}}
os: {{stat.process_id}}


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