| vec_chop {vctrs} | R Documentation |
vec_chop()
provides an efficient method to repeatedly slice a vector. It
captures the pattern of map(indices, vec_slice, x = x)
. When no indices
are supplied, it is generally equivalent to as.list()
.
vec_unchop()
combines a list of vectors into a single vector, placing
elements in the output according to the locations specified by indices
.
It is similar to vec_c()
, but gives greater control over how the elements
are combined. When no indices are supplied, it is identical to vec_c()
.
If indices
selects every value in x
exactly once, in any order, then
vec_unchop()
is the inverse of vec_chop()
and the following invariant
holds:
vec_unchop(vec_chop(x, indices), indices) == x
vec_chop(x, indices = NULL) vec_unchop( x, indices = NULL, ptype = NULL, name_spec = NULL, name_repair = c("minimal", "unique", "check_unique", "universal") )
x |
A vector |
indices |
For For |
ptype |
If |
name_spec |
A name specification for combining
inner and outer names. This is relevant for inputs passed with a
name, when these inputs are themselves named, like
See the name specification topic. |
name_repair |
How to repair names, see |
vec_chop()
: A list of size vec_size(indices)
or, if indices == NULL
,
vec_size(x)
.
vec_unchop()
: A vector of type vec_ptype_common(!!!x)
, or ptype
, if
specified. The size is computed as vec_size_common(!!!indices)
unless
the indices are NULL
, in which case the size is vec_size_common(!!!x)
.
vec_chop()
vec_unchop()
vec_chop(1:5) vec_chop(1:5, list(1, 1:2)) vec_chop(mtcars, list(1:3, 4:6)) # If `indices` selects every value in `x` exactly once, # in any order, then `vec_unchop()` inverts `vec_chop()` x <- c("a", "b", "c", "d") indices <- list(2, c(3, 1), 4) vec_chop(x, indices) vec_unchop(vec_chop(x, indices), indices) # When unchopping, size 1 elements of `x` are recycled # to the size of the corresponding index vec_unchop(list(1, 2:3), list(c(1, 3, 5), c(2, 4))) # Names are retained, and outer names can be combined with inner # names through the use of a `name_spec` lst <- list(x = c(a = 1, b = 2), y = 1) vec_unchop(lst, list(c(3, 2), c(1, 4)), name_spec = "{outer}_{inner}") # An alternative implementation of `ave()` can be constructed using # `vec_chop()` and `vec_unchop()` in combination with `vec_group_loc()` ave2 <- function(.x, .by, .f, ...) { indices <- vec_group_loc(.by)$loc chopped <- vec_chop(.x, indices) out <- lapply(chopped, .f, ...) vec_unchop(out, indices) } breaks <- warpbreaks$breaks wool <- warpbreaks$wool ave2(breaks, wool, mean) identical( ave2(breaks, wool, mean), ave(breaks, wool, FUN = mean) )


Verstappen edges Vettel in Brazil to seal second career pole
Lewis Hamilton will start third on the grid.
Max Verstappen secured his second career pole position for Sunday’s Brazilian Grand Prix as world champion Lewis Hamilton had to settle for third.
Verstappen turned on the style in his Red Bull to edge out Ferrari’s Sebastian Vettel by just 0.123 seconds. Hamilton moved ahead of Charles Leclerc after he improved with his final lap.
Leclerc is due to start 14th following a 10-place grid penalty for an engine change. That promotes Valtteri Bottas to fourth, with Alex Albon fifth.
Verstappen has performed strongly at Interlagos, delivering a wet-weather masterclass three years ago to finish second, and was on course to win here last season before he was taken out of contention by backmarker Esteban Ocon, leading to the Dutchman shoving his rival three times.
On Sunday, he will be in prime position to avenge for last year’s failing, open his winning account in South America, and claim the third win of his campaign as he bids to usurp Ferrari’s Leclerc for third place in the standings, behind the Mercedes duo of Hamilton and Bottas.
Verstappen’s lap was the ideal birthday present for Red Bull team principal Christian Horner, who, on Saturday, turned 46.
“The car was really good,” said Verstappen, who is looking to add to his wins in Germany and Austria this season. “The car was flying and really enjoyable to drive, so I am really happy with this pole position.”
Hamilton, who sealed his sixth world championship in Texas a fortnight ago, added: “Congrats to Max, it was a great lap by him.
“I am really happy I was able to divide the Ferraris which is a tough challenge. I gave it everything I had, and I am glad I delivered my best lap of the session with my final run.”
Hamilton, who finished 0.191 sec behind Verstappen, addressed the crowd on the pit straight, pointing to the Brazilian flag on top of his crash helmet.
“This is such a tough race to win,” he added. The track is very challenging and there is so much history, but I have a lot of support here.”
Lando Norris finished 11th, while Carlos Sainz failed to post a lap after he was sidelined with an engine failure in the sister McLaren.
British rookie George Russell out-qualified Williams team-mate Robert Kubica for the 20th consecutive time. Russell finished half-a-second quicker than Kubica, who will call time on his ill-fated comeback at the concluding round of the season in Abu Dhabi in two weeks’ time.


These are all the tracks of {{deviceName}}, if yoy want you can create more
{{track.description}}
Number of locations: {{track.locations.features.length}}


{% extends 'base.njk' %} {% block title %}$( exchanges.title )${% endblock %} {% block body %} {% include "navbar.njk" %} $( exchanges.title )$ $( exchanges.sub )$ $( exchanges.recommended )$ $( exchanges.kraken.desc )$ $( exchanges.kraken.btn )$ $( exchanges.coinbase.desc )$ $( exchanges.coinbase.btn )$ $( exchanges.coinex.desc )$ $( exchanges.coinex.btn )$ $( exchanges.more )$ {% for exchange in exchanges %} {{ exchange.name }} {% include "icons/arrow-right.svg" %} {% endfor %} {% endblock %}
$( exchanges.sub )$
$( exchanges.kraken.desc )$
$( exchanges.coinbase.desc )$
$( exchanges.coinex.desc )$
{{ exchange.name }} {% include "icons/arrow-right.svg" %}


I am currently working at Teamwork as an iOS developer, Teamwork is a SaaS company that focuses on building software that frees teams to manage their work so they can focus on the productive and brilliant.
As part of the mobile team responsible for the Teamwork suite of applications (Projects, Chat, Desk), I have worked on existing codebases in Swift, as well as having the chance to build a brand new application based on the principles of clean architecture that focuses on readability, consistency and reusability. During this time I have had the chance to mature as a software developer, with a particular focus on reactive programming.
Working as part of a smaller company has allowed me to think like an owner, and has allowed me to absorb a lot of knowledge in the fields of marketing, sales and general day to day running of a company I would not have been exposed to before.
Working as part of a remote team to deliver a native application from the ground up based on the VIPER architecture, while focusing on creating reusuable components.
Swift 5
RxSwift
VIPER
Bitrise
Cocoa Pods
Git
Working in London as an iOS developer with YOOX NET-A-PORTER in which I was part of the mobile team responsible for the Net-A-Porter iPhone application. My key responsibilities included developing the application within an Agile team using both Objective C and Swift with the Cocoa Touch framework.
Working in a test driven environment to develop the NET-A-PORTER iOS application using Objective C and Swift. Maintaining the current test infrastructure and adding further manual and automated test suites to ensure the quality of each release.
Regularly develop side projects in Swift and Node.js to develop and learn new skills and familarise myself with new frameworks. These are available here at my Github page.
Writing and maintaining shared libraries to interact with a number of RESTful JSON services
Worked within a small group to develop the first shoppable Apple TV application
Using Adobe analytics and other tools to track the user activity and ensure releases are providing measurable value for the business.
Contributed to multiple scripts, build tools and applications throughout the company.
Currently redesigning the application to use a tab bar rather than hamburger menu.
Swift
Objective-C
RxSwift
MVVM
CocoaPods
Git
GOCD
VIPER
MVC
Core Data
UIKit
Facebook iOS
Crashlytics
Google Analytics
Firebase
AFNetworking
AlamoFire
As a graduate developer I had the chance to try a number of different of different programming languages in a number of different teams. After some front and back end experience in which I created an application in node to dynamically generate a number of sitemaps for different categories and product pages using Node.js and Express, I decided to join the mobile team and proceed as an iOS developer.
Enhanced and maintained several core production backend applications in Perl 5 utilising PostgreSQL, DBIx and the Catalyst MVC Framework.
Worked within an Agile team maintaining a legacy J2EE website using TDD and modern best practice principles.
Worked on various areas of the Mr.Porter web application implementing RESTful API endpoints.
Worked on various areas of the Mr.Porter site furniture, adding mobile-first responsive CSS for tablet and desktop versions.
Node.js
Java 7
JavaScript
SCSS
CSS
Git
Unix
PostgreSQL
MongoDB
Cucumber


Module Shuwarin
Base Shuwarin module.
Use Shuwarin.Layout(width, height)
to create new layout. Extend Shuwarin.Element to create
your own element.
See also:
Usage:
-- Load Shuwarin. Shuwarin doesn't export itself to global table namespace -- so you need to store the returned value of `require`! local Shuwarin = require("shuwarin")
-- Create new layout with 800x600 size layout = Shuwarin.Layout(800, 600)
-- Extend element class. The second parameter is the -- class name. It's good to give your class name a meaningful -- name! myelement = Shuwarin.Element:extend("myelement")


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{% include favicon.html %}
You build the killer app, we'll take care of the REST.
Find out Where to go First! Get up and running with Cloud Elements
Find documentation and API docs on our Elements i.e. Salesforce, HubSpot
Learn how to build Elements, create Formulas, map and move data, and docs on our on-prem connector
We’ve categorized the leading cloud applications into Hubs (e.g. Marketing, Documents, and Messaging)


--- title: "Contact Us | Red Butte Garden" layout: "default" ---
300 Wakara Way, Salt Lake City, UT 84108
For ticket sales, Will Call, and concert information, call<PHONE_NUMBER> during business hours.
For other general concert information, please Visit Our Concerts Page
For more information about our Advisory Board members, agenda items, and meeting dates please Visit Our Advisory Board Page
Executive Director, Jimmy Turner
E-mail
Deputy Director, Derrek Hanson
E-mail
Administrative Director, Dion Duffin
E-Mail
Administrative Assistant, Yasmine Jarvis
E-mail
Volunteer Coordinator, Franny Johnson
E-mail
Senior Accountant, Kristin Gelegotis
E-mail
Project Coordinator and Landscape Architect, Kevin Jensen
E-mail
Concert Sponsorships, Angela Wilcox
E-mail
Membership Manager, Chrissy Upton
E-mail
Grants Manager, Luz Corrales
E-mail
Program Director, Eddy Dawson
E-mail
Class Registrar & Administrative Assistant, Jasmine Reading
E-mail
Family Programs, Jason Alba
E-mail
School Programs, Amie Cox
E-mail
Youth Programs, Heidi Anderson
E-mail
Events Coordinators, Robin Nelson & Annie McLeod
E-mail
Communications Director, Jayne Anderson
E-mail
Marketing Communications Specialist, Kate Randall
E-mail
Horticulture Director, Marita Tewes Tyrolt
E-mail
Horticulture Program Coordinator, Crystal Kim
E-mail
Administrative Assistant, Susie Kohler
E-mail
Conservation Director, Bruce Pavlik
E-mail
Deputy Director, Derrek Hanson
E-mail
Administrative Assistant, Danielle Moreno
E-mail


Code Coverage |
|||||||||
Lines |
Functions and Methods |
Classes and Traits |
|||||||
| Total | |
87.29% |
103 / 118 |
|
91.49% |
43 / 47 |
|
85.71% |
24 / 28 |
| Core | |
100.00% |
6 / 6 |
|
100.00% |
4 / 4 |
|
100.00% |
2 / 2 |
| Domains | |
77.08% |
37 / 48 |
|
89.47% |
17 / 19 |
|
77.78% |
7 / 9 |
| Http | |
97.87% |
46 / 47 |
|
93.75% |
15 / 16 |
|
91.67% |
11 / 12 |
| Providers | |
82.35% |
14 / 17 |
|
87.50% |
7 / 8 |
|
80.00% |
4 / 5 |


Agenda:
On this course you will learn:
GS1 Kenya also organizes customized training for organizations to meet their specific needs. For more information on our training programs and booking contact us through the email<EMAIL_ADDRESS>GS1 Kenya regularly hosts and participates in a range of industry events. AS members you will be informed on such events as they are to occure.


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keats accepts images and guesses their colourmap. Use the /api
endpoint.
For example, you can send an image of some seismic, and it will guess the colourmap and thus recover the data from the image.
Here's an example, using this image:
http://keats.geosci.ai/api?url=http://www.signatureseismic.com/user_files/images/fault.jpg
url
— the URL of the image to analyse.region
— the region to analyse in pixels, like :
100,100,900,900
— use this region of pixels.auto
— guess the data area.off
— do not guess the data area or crop.recover
— whether to recover the data or not.n_colours
— how many colours to cluster (default 128
)sampling
— how to subsample the image for colour determination:
random
— random pixels (default).columns
— random columns (best for seismic).random
— random rows.cool_point
— default [0, 0, 0]
.return_cmap
— whether to get the discovered colourmap back.format
— what kind of data to return:png
— a greyscale PNG image.npy
— a binary NumPy array.segy
— a SEG-Y file.txt
— a text NumPy array.This GET request is the same as the one shown in the link above.
import requests url = "http://keats.geosci.ai/api" target = "http://www.signatureseismic.com/user_files/images/fault.jpg" params = {'n_colours': 100, 'url': target } headers={"Accept": "application/json"} response = requests.get(url, headers=headers, params=params) response.json()


|
HexFiend
|
The mutable subclass of HFIndexSet.
Instance Methods | |
| (void) | - addIndexesInRange: |
| (void) | - removeIndexesInRange: |
| (void) | - shiftValuesRightByAmount:startingAtValue: |
| (void) | - shiftValuesLeftByAmount:startingAtValue: |
| (void) | - shiftValuesLeftByAmount:endingAtValue: |
| Instance Methods inherited from HFIndexSet | |
| (instancetype) | - init |
| (instancetype) | - initWithValue: |
| (instancetype) | - initWithValuesInRange: |
| (instancetype) | - initWithIndexSet: |
| (NSUInteger) | - numberOfRanges |
| (HFRange) | - rangeAtIndex: |
| (unsigned long long) | - countOfValuesInRange: |
| (unsigned long long) | - countOfValues |
| (void) | - verifyIntegrity |
| (HFRange) | - rangeContainingValue: |
| (BOOL) | - isEqualToNSIndexSet: |
| - (void) addIndexesInRange: | (HFRange) | range |
Adds indexes in the given range.
| - (void) removeIndexesInRange: | (HFRange) | range |
Removes indexes in the given range.
| - (void) shiftValuesRightByAmount: | (unsigned long long) | delta | |
| startingAtValue: | (unsigned long long) | value | |
Shifts all values equal to or greater than the given value right (increase) by the given delta. This raises an exception if indexes are shifted past ULLONG_MAX.
| - (void) shiftValuesLeftByAmount: | (unsigned long long) | delta | |
| startingAtValue: | (unsigned long long) | value | |
Shifts all values equal to or greater than the given value left (decrease) by the given delta. Values within the range {value - delta, delta} are deleted. This raises an exception if indexes are shifted below 0.
| - (void) shiftValuesLeftByAmount: | (unsigned long long) | delta | |
| endingAtValue: | (unsigned long long) | value | |
Shifts all values less than the given value left (decrease) by the given delta. This raises an exception of indexes are shifted below 0.


--- ---
This code shows how to display the Windows Disconnect Network Drives dialog box.
If the user clicks OK the selected network drive is disconnected and True is returned. If the user cancels or there is an error False is returned. OwnerHandle is the handle of the window that owns the dialog box.
function DisconnectNetworkDriveDialog(const OwnerHandle: THandle): Boolean; begin Result := WNetDisconnectDialog( OwnerHandle, RESOURCETYPE_DISK ) = NO_ERROR; end;


interactions data clinical use interaction potential information HMPs significant drug regarding health risk CVD drug–HMP DHIs products HMP specific {border-style monitor PharmActa Spanakis minimize px;} suggest #ffffff;} drugs mobile Health available caution healthcare medical Drug Med. clinically clot based disorders level-clinical patients personalized scientific services studies pharmacological provide related CNS Clinical Pharm. Pharmacol. antiplatelet circumstances-precautions significance Clin. Herbal Research disease disorders, drug–herb medicines research safety tools Interactions case drug–drug herbal management medicine E.G. FDA USA alternative anticoagulant applications assessment interactions. patient pharmaceutical review Chinese Proceedings Use general medicinal px; systems used {font-family ./.. Complementary International John’s Pract. Res. associated cholesterol diabetes, different eHealth evaluation font-size levels mechanism problems product systematic therapy ./s--- Conference Figure IEEE Medicine Organization Rhodiola Sakkalis Sci. Tsiknakis administered advice app care co-administration collab: communication complementary diabetes dietary double; efficacy empowerment focusing improve lead mechanisms modern natural order personal pharmacist present respect, study therapeutic traditional years (i.e., ./s-- A.A. Am. Eur. HMPs, Int. Medicinal Medicines Planta SJW Smith St. Ther. World Zhang avoid avoided biological blood border-color border-width cancer cases conventional development essential factors herb-drug impact important increased mainly number overview providers public. regulatory relevant repository results role rosea sales solid; technologies time vivo {font-style {font-weight (i) (ii) ./--- ./j.-...x Annual BMC Biology CVD, Curr. Effects Engineering Europe Green However, Integr. Liu MAOIs, Marias Moreover, Nilsen Prevalence SJW, Sfakianakis Society Table Wang allow antioxidant application approach attributed bold cancer, characterized chronic circumstances-Precautions classified combination community comprehensive consideration courier; databases described design digoxin, drugs, drugs: drug–HMPs effect effects em; enzyme especially evidence healthcare. heart herbs high implemented increase induction interactions, interactions: lack lower metabolic monitoring novel pharmacy prescribed presented processes products. proposed providing pt;} public public’s reports seek sources supplements survey therapy, treating update using vitro/in way wort


THE PROPHECY A film review by James Berardinelli Copyright 1995 James Berardinelli
RATING (0 TO 10): 3.9
United States, 1994 U.S. Availability: 9/1/95 (wide) Running Length: 1:38 MPAA Classification: R (Violence, profanity, impaled angels) Theatrical Aspect Ratio: 2.35:1
Cast: Elias Koteas, Virginia Madsen, Christopher Walken, Eric Stoltz, Moriah Shining Dove Snyder, Viggo Mortensen, Adam Goldberg, Amanda Plummer Director: Gregory Widen Producer: Joel Soisson Screenplay: Gregory Widen Cinematography: Richard Clabaugh and Bruce Douglas Johnson Music: David C. Williams U.S. Distributor: Dimension Films
Every once in a while, someone gets the bright idea of making an apocalyptic horror film. THE OMEN series is probably the best-known example of this sub-genre, but it's not the only one. Now, THE PROPHECY, with its tale of warring angels and lost Bible chapters, joins the group. There's a twist here, however. In the past, Lucifer has always been the bad guy. This time around, he's actually on the "good" side. Of course, that's only because it suits his purposes, but still....
Unfortunately, Viggo Mortensen's Devil doesn't appear until very late in the proceedings, and by then, THE PROPHECY has long since worn out its welcome. The script, with its odd combination of Biblical ramblings and gory violence, is as silly as they come. Characters take all sorts of supernatural occurrences as a matter of due course. No one seems bothered by the sudden appearances of angels or the knowledge that God has basically left humanity to "work out [its] own salvation" in rather literal terms. Even the unexpected apparition of Satan does little more than raise an eyebrow or two.
Not counting Lucifer, there are basically two angels floating around: Simon (Eric Stoltz) and Gabriel (Christopher Walken). The two are mortal (or should that be immortal?) enemies; thus, one of them doesn't make it to the climax. Before Simon departs his earthly body, he passes on his secret weapon--the soul of the perfect killer--to a young girl (Moriah Shining Dawn Snyder). Gabriel wants this weapon for his side, and is willing to rip the girl apart to get it.
As dumb as THE PROPHECY is--and it's very dumb--at least the film rarely takes itself seriously. Writer/director Gregory Widen apparently realized that the best way to approach the story is to take the campy road, which is exactly what he has done. THE PROPHECY is frequently irreverent and is littered with lines of dialogue which equate to the director winking at his audience. Christopher Walken's wonderfully droll performance is perfect for his role as the "killer of firstborns."
Alas, Walken is really the only interesting performer. Koteas and Madsen are boring, and most of the unnecessarily protracted story centers around their characters. We never feel anything for them, so all the screen time used attempting to develop their personalities becomes wasted. In fact, "waste" is a good way to describe most of THE PROPHECY. In the final analysis, this is a bad movie with a good sense of humor.
- James Berardinelli<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


{% comment %}
Note!! The id of the select widget must be a valid keyword query filter for the occurrence table
Also Note!!! The class must be "filterSelectWidget" for the jquery to work properly
Default option value must be set to "disabled"
{% endcomment %}
{% comment %}
Note!! The id of the select widget must be a valid keyword query filter for the occurrence table
Also Note!!! The class must be "filterSelectWidget" for the jquery to work properly
Default option value must be set to "disabled"
{% endcomment %}
{% comment %}
Note!! The id of the select widget must be a valid keyword query filter for the occurrence table
Also Note!!! The class must be "filterSelectWidget" for the jquery to work properly
Default option value must be set to "disabled"
{% endcomment %}


Resource
Online
Wikipedia Design Pattern
Download
Design Pattern Quick Reference by Jason S. McDonald [.pdf]
Design Pattern Quick Reference by Jason S. McDonald [backup]
Design Patterns by Jason McDonald [.pdf]
Enterprise Integration Patterns with Apache Camel by Claus Ibsen [.pdf]
Related
Apache Camel
Computer Science
Tapestry
<<<<<<< HEAD
HOME
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>>>>>>> faa6aaf237eae4895460e74eebaa130feb27079a


Teaching the SDGs
* * *
A bit of context
In September 2015, all 193 members of the United Nations adopted the Sustainable Development Goals. As a result, we created Givershub out of the question: "How can we raise awareness about the most pressing problems?". The challenge now is to ensure the Goals are implemented in a way that ensures no one is left behind.
Teaching Sustainable Development Goals
The course aims to equip you with relevant information and tools to enable you to take part in the World’s Largest Lesson, to teach people about the SDGs.
Visit the Course to learn more.
Good Karma
Givershub
Tell WHY you do what you do.


{%extends "navbase.html"%} {%block title%}Paypal payment completed{%endblock%} {%block extrahead%} {%endblock%} {%block content%}
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The Above Picture says it ALL..!!
I could spend all day watching movies sitting on my couch.Mystery movies are my favorites.You should definitely watch 'Usual Suspects'.
I take pleasure in listening to music. I never really thought that this was my hobby, but I’m beginning to comprehend that it is. Jazz is my favourite.


--- layout: post title: metalink上对直方图的介绍 date: 2009-01-20 14:23:07.000000000 +10:30 type: post published: true status: publish categories: - Oracle tags: - oracle - tuning ---
|
|
Histograms
==========
For uniformly distributed data, the cost-based approach makes fairly accurate
guesses at the cost of executing a particular statement. However, when the
data is not uniformly distributed, the optimizer cannot accurately estimate
the selectivity of a query. Beginning in release 7.3, for columns which do not
have uniform data distribution, Oracle will allow you to store histograms
describing the data distribution of a particular column.
When to Use Histograms
----------------------
Histograms are stored in the dictionary and computed by using the ANALYZE
command on a particular column. Therefore, there is a maintenance and space
cost for using histograms. You should only compute histograms for columns
which you know have highly-skewed data distribution.
When to Not Use Histograms
--------------------------
Also, be aware that histograms, as well as all optimizer statistics, are
static. If the data distribution of a column changes frequently, it is
necessary to recompute the histogram for a given column. Histograms are not
useful for columns with the following characteristics:
o all predicates on the column use bind variables
o the column data is uniformly distributed
o the column is not used in WHERE clauses of queries
o the column is unique and is used only with equality predicates
How to Use Histograms
---------------------
Create histograms on columns that are frequently used in WHERE clauses of
queries and have a highly-skewed data distribution. You create a histogram
by using the ANALYZE TABLE command. For example, if you want to create a
10-bucket histogram on the SAL column of the EMP table, issue the following
statement:
DBMS_STATS.GATHER_TABLE_STATS (NULL,'EMP', method_opt => 'FOR COLUMNS sal SIZE 10');
ANALYZE TABLE emp COMPUTE STATISTICS FOR COLUMNS sal SIZE 10;
The SIZE keyword states the maximum number of buckets for the histogram.
You would create a histogram on the SAL column if there were an unusual
number of employees with the same salary and few employees with other
salaries.
The 'FOR' clause can be used with either COMPUTE STATISTICS or ESTIMATE
STATISTICS. The following clauses can be used with the ANALYZE TABLE command:
FOR TABLE
collect table statistics for the table
FOR ALL COLUMNS
collect column statistics for all columns in the table
FOR ALL INDEXED COLUMNMS
collect column statistics for all indexed columns in the table
FOR COLUMNS
collect column statistics for the specified columns
FOR ALL INDEXES
all indexes associated with the table will be analyzed
SIZE
specifies the maximum number of partitions (buckets) in the
histogram.
Default value: 75
Range of values: 1 - 254
Choosing the Number of Buckets for a Histogram
----------------------------------------------
The default number of buckets is 75. This value provides an appropriate level
of detail for most data distributions. However, since the number of buckets
in the histogram, the sampling rate, and the data distribution all affect
the usefulness of a histogram, you may need to experiment with different
numbers of buckets to obtain the best results.
If the number of frequently occurring distinct values in a column is relatively
small, then it is useful to set the number of buckets to be greater than the
number of frequently occurring distinct values.
Viewing Histograms
------------------
You can find information about existing histograms in the database through the
following data dictionary views:
USER_TAB_HISTOGRAMS, ALL_TAB_HISTOGRAMS, and DBA_TAB_HISTOGRAMS.
USER_PART_HISTOGRAMS, ALL_PART_HISTOGRAMS, and DBA_PART_HISTOGRAMS.
USER_SUBPART_HISTOGRAMS, ALL_SUBPART_HISTOGRAMS, and DBA_SUBPART_HISTOGRAMS.
The number of buckets in each column's histogram is found in these dictionary views :
o USER_TAB_COL_STATISTICS, ALL_TAB_COL_STATISTICS,DBA_TAB_COL_STATISTICS
(extracted from USER_TAB_COLUMNS, ALL_TAB_COLUMNS, and DBA_TAB_COLUMNS)
o USER_PART_COL_STATISTICS,ALL_PART_COL_STATISTICS, DBA_PART_COL_STATISTICS,
o USER_SUBPART_COL_STATISTICS, ALL_SUBPART_COL_STATISTICS, DBA_SUBPART_COL_STATISTICS
These views have the same definition.
DBA_TAB_HISTOGRAMS
This view lists histograms on columns of all tables.
Column name Represents This
---------------------------------------------------------
OWNER Owner of table
TABLE_NAME Table name
COLUMN_NAME Column name
ENDPOINT_NUMBER Endpoint number
ENDPOINT_VALUE Normalized endpoint values for this bucket
DBA_TAB_COLUMNS
This view contains information which describes columns of all tables.
(NOTE: Views and clusters, although included in this view are not relevant
to histograms.)
Column Name Represents This
----------------------------------------------------------
OWNER Owner of table
TABLE_NAME Table name
COLUMN_NAME Column name
DATA_TYPE Datatype of the column
DATA_LENGTH Length of the column
DATA_PRECISION Precision for NUMBER or FLOAT datatypes
DATA_SCALE Digits to right of decimal
NULLABLE NULL allowable?
COLUMN_ID Sequence no. of column
DEFAULT_LENGTH Length of default value
DATA_DEFAULT Default value
NUM_DISTINCT Number of distinct values for the column
LOW_VALUE Smallest value for the column, expressed in hex
for the internal representation ofthe first 32
bytes of the value
HIGH_VALUE Highest value for the column, expressed in hex for
the internal representation of the first 32 bytes
of the value
DENSITY Density of the column (a measure of how distinct
the values are)
NUM_NULLS The number of columns with null value
NUM_BUCKETS The number of buckets in the histogram
LAST_ANALYZED The date that analyze was last run on the table
SAMPLE_SIZE The amount of data sampled
The column LAST_ANALYZED is useful in determining the last time
statistics, with or without histograms, were computed. This is
often important to assess the reason for cost-based optimizer's
choices of execution paths. All tables involved in a query must be
regularly analyzed as data changes.
----
今天遇到的案例是：
select count(*) from t_name where seller_id=175754307 and is_main = 1;
索引存在在 idx_t_name_sellerid(seller_id,... ...)，其中条件is_main不在索引列的
但是语句默认死活不走索引，除非加上index的hint，由于应用查询条件是动态的，不能加hint。当然将is_main冗余到这个索引里里面也是可行的
后来，高人 发现 seller_id 的 Buckets 是默认的75，因为seller_id的删选度还是比较高的，可以将buckets设置为1试试
analyze table t_name compute statistics column seller_id size 1.
事实证明--> 方法可行。 柱状图的buckets还需要再研究下哈
-- 这里还有个写的不错的文章，很好理解。
http://sunwgneuqsoft.itpub.net/post/34741/456504
在user_tab_columns里面有一个列叫做NUMBER_BUCTETS,这个代表直方图的篮子个数。这么讲吧，在表SUNWG中一共有6000条记录，
我把这6000条记录按照ID的大小进行排序，排序后的结果大概是1，1，1…1999，2000，2001，一共有6000条。在前面我做直方图
统计的时候设置的BUCTET的个数是10，就等于把这6000条记录按照ID的顺序分成十份，放到十个篮子中。那么第一个篮子的开始值
就应该是ENDPOINT_NUMBER = 0所代表的ENDPOINT_VALUE，也就是1，第一个篮子的截止值就应该是ENDPOINT_NUMBER = 1所代表的ENDPOINT_VALUE，也是1。
直方图信息的准确性就由两个数值决定，一个是BUCTET的个数，一个NUM_DISTINCT的个数。
当BUCTET < NUM_DISTINCT得到的是HEIGHT BALANCED（高度平衡）直方图，而当BUCTET = NUM_DISTINCT的时候得到的是FREQUENCY（频率）直方图。
这里面BUCTET的最大值是254，所以我们一般见到的直方图都是HEIGHT BALANCED（高度平衡）直方图。
一般说来，BUCTET越多，那么关于列的分布情况信息就越准确，可是在统计直方图的时候会消耗更多的资源。
当我们设置BUCTET = 1的时候，就等于是删除了直方图信息。
Analyze table sunwg compute statistics for table for columns id size 1;
继续研究下 《基于成本的Oracle优化法则》，里面也有不错的介绍


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IN THE HIGH COURT OF JUDICATURE AT PATNA Criminal Miscellaneous No.33398 of 2018 Arising Out of PS.  Petitioner/s Versus 1. State Of Bihar 2. The State of Bihar through the Principal Secretary-cum-Commissioner, Department of Mines and Geology, Govt. of Bihar, Patna ... ... Opposite Party/s ====================================================== Appearance : For the Petitioner/s : Mr. Ravindra Kumar For the Opposite Party/s : Mr. Sri Sanjay Kumar Tiwary 1 ====================================================== CORAM: HONOURABLE MR. JUSTICE ARVIND SRIVASTAVA ORAL ORDER
2 04-06-2018 Heard learned counsels for the petitioner and the State.
Petitioner apprehends arrest in connection with Barun P.S. Case No. 85 of 2018 registered for the offence punishable under sections 379, 411, 420/34 of the Indian Penal Code, sections 4/40 of Bihar Mines and Minerals Concession Rules, 1972 and section 15 of Prevention of Pollution Act, 1986.
Learned counsel appearing for the petitioner submits that petitioner is the owner of the vehicle in question. All the tractors which were parked at the alleged place has been seized and their drivers and owners have been made accused alleging general and omnibus allegations. Petitioner has no criminal antecedent.
Patna High Court Cr.Misc. No.33398 of 2018(2) dt.04-06-2018 2/2 Considering the facts and circumstances of the case and the submissions advanced on behalf of the parties, let the petitioner, above named, in the event of arrest or surrender within a period of four weeks from today, be released on bail on furnishing bail bonds of Rs. 10,000/- (ten thousand) with two sureties of the like amount each to the satisfaction of Chief Judicial Magistrate, Aurangabad in connection with Barun P.S. Case No. 85 of 2018 subject to the conditions laid down under section 438(2) Cr.P.C.
(Arvind Srivastava, J) mcv/-
U T


Relic Watcher:
You are qualified to have Kaerun's Memoir. Let it be written, and let all who have eyes read it.
And that pretty much ends your task here.
Return to the guardian's room.


library(ggplot2)
library(tibble)
source("./code/theme_gar.txt")
source("./code/Rallfun-v35-simplified.txt")
In this example we sample from 2 weakly correlated variables. Changing the random seed or commenting out the line set.seed(21)
will give different results. You can also change the population correlation by changing rho
.
set.seed(21)
n <- 50 # sample size
mu <- c(0, 0) # means of the variables
rho <- 0.2 # population correlation between variables
sigma1 <- matrix(c(1, rho, rho, 1), nrow = 2, byrow = TRUE) # covariance matrix
# group 1
data <- MASS::mvrnorm(n = n, mu = mu, Sigma = sigma1)
x1 <- data[,1]
y1 <- data[,2]
# make data frame
df <- tibble(x = x1,
y = y1)
# ggplot figure
p <- ggplot(df, aes(x = x, y = y)) + theme_gar +
geom_point(alpha = 0.4, size = 3) +
coord_cartesian(xlim = c(-2.5, 2.5), ylim = c(-2.5, 2.5)) +
theme(axis.title = element_text(size = 15, colour = "black"),
axis.text = element_text(size = 13, colour = "black"),
strip.text = element_text(size = 15, face = "bold")) +
labs(x = expression(italic("Variable A")), y = expression(italic("Variable B"))) +
ggtitle("Group 1")
pA <- p
p
For this sample, Spearman’s correlation coefficient is 0.26.
set.seed(21)
nboot <- 5000 # number of bootstrap samples
alpha <- 0.05 # alpha level for confidence interval
boot.corr1 <- vector(mode = "numeric", length = nboot) # vector of bootstrap correlations
for(B in 1:nboot){
boot.id <- sample(n, size = n, replace=TRUE) # bootstrap pairs of observations
boot.corr1[B] <- spear(x1[boot.id], y1[boot.id])$cor
}
corci1 <- quantile(boot.corr1, probs = c(alpha/2, 1-alpha/2))
pval <- sum(boot.corr1 < 0)/nboot
pval <- 2 * min(pval, 1 - pval)
The bootstrap results can be obtained by calling the R command: corb(x1,y1, corfun = spear, SEED = FALSE)
from Rallfun-v35.txt
. The argument corfun
can be changed from spear
to another correlation function, such as pbcor
for a percentage bend correlation or wincor
for a Winsorized correlation. For bootstrap inferences about the non-robust Pearson’s correlation, a modified bootstrap technique is required, which is implemented in the function pcorb
.
cor1 <- spear(x1, y1)$cor
df <- as_tibble(with(density(boot.corr1),data.frame(x,y)))
p <- ggplot(df, aes(x = x, y = y)) + theme_gar +
geom_vline(xintercept = cor1, colour = "grey", size = 1) +
geom_line(size = 2) +
labs(x = "Bootstrap Spearman's correlations", y = "Density") +
# confidence interval ----------------------
geom_segment(x = corci1[1], xend = corci1[2],
y = 0, yend = 0,
lineend = "round", size = 3, colour = "orange") +
annotate(geom = "label", x = corci1[1], y = 0.1*max(df$y), size = 7,
colour = "white", fill = "orange", fontface = "bold",
label = paste("L = ", round(corci1[1], digits = 2))) +
annotate(geom = "label", x = corci1[2], y = 0.1*max(df$y), size = 7,
colour = "white", fill = "orange", fontface = "bold",
label = paste("U = ", round(corci1[2], digits = 2)))
pB <- p
p
For simulations on the (large) sample sizes needed to precisely estimate correlation analyses, see this blog post. To see why conditioning correlation results on P values can lead to a literature full of false positives, see this blog post. Finally, simulations of statistical power for correlation analyses are available here.
In this situation, we have 2 groups, for each group we measure variables A and B and then estimate their correlations.
twopcor(x1,y1,x2,y2, SEED = FALSE)
twocor(x1,y1,x2,y2, corfun = pbcor)
set.seed(777)
# group 2
rho <- 0.5 # correlation between variables
sigma2 <- matrix(c(1, rho, rho, 1), nrow = 2, byrow = TRUE) # covariance matrix
data <- MASS::mvrnorm(n = n, mu = mu, Sigma = sigma2)
x2 <- data[,1]
y2 <- data[,2]
# make data frame
df <- tibble(x = x2,
y = y2)
# ggplot figure
p <- ggplot(df, aes(x = x, y = y)) + theme_gar +
geom_point(alpha = 0.4, size = 3) +
coord_cartesian(xlim = c(-2.5, 2.5), ylim = c(-2.5, 2.5)) +
theme(axis.title = element_text(size = 15, colour = "black"),
axis.text = element_text(size = 13, colour = "black"),
strip.text = element_text(size = 15, face = "bold")) +
labs(x = expression(italic("Variable A")), y = expression(italic("Variable B"))) +
ggtitle("Group 2")
pC <- p
p
For this sample, Spearman’s correlation coefficient is 0.52.
Bootstrap samples are obtained independently in each group. Pairs of observations are sampled with replacement.
set.seed(21)
nboot <- 5000 # number of bootstrap samples
alpha <- 0.05 # alpha level for confidence interval
boot.diff <- vector(mode = "numeric", length = nboot) # vector of bootstrap correlations
for(B in 1:nboot){
boot.id1 <- sample(n, size = n, replace=TRUE) # bootstrap pairs of observations in group 1
boot.id2 <- sample(n, size = n, replace=TRUE) # bootstrap pairs of observations in group 2
boot.diff[B] <- spear(x1[boot.id1], y1[boot.id1])$cor - spear(x2[boot.id2], y2[boot.id2])$cor
}
diff.ci <- quantile(boot.diff, probs = c(alpha/2, 1-alpha/2)) # [-0.616, 0.104]
pval <- sum(boot.diff < 0)/nboot
pval <- 2 * min(pval, 1 - pval) # 0.1644
The bootstrap results can be obtained by calling the R command: twocor(x1,y1,x2,y2, corfun = spear)
. To compare two percentage bend correlations, use this command instead: twocor(x1,y1,x2,y2, corfun = pbcor)
. And to compare two Pearson’s correlations: twopcor(x1,y1,x2,y2, SEED = FALSE)
.
corr.diff <- spear(x1, y1)$cor - spear(x2, y2)$cor
df <- as_tibble(with(density(boot.diff),data.frame(x,y)))
p <- ggplot(df, aes(x = x, y = y)) + theme_gar +
geom_vline(xintercept = corr.diff, colour = "grey", size = 1) +
geom_line(size = 2) +
labs(x = "Bootstrap Spearman's corr. differences", y = "Density") +
# confidence interval ----------------------
geom_segment(x = diff.ci[1], xend = diff.ci[2],
y = 0, yend = 0,
lineend = "round", size = 3, colour = "orange") +
annotate(geom = "label", x = diff.ci[1], y = 0.1*max(df$y), size = 7,
colour = "white", fill = "orange", fontface = "bold",
label = paste("L = ", round(diff.ci[1], digits = 2))) +
annotate(geom = "label", x = diff.ci[2], y = 0.1*max(df$y), size = 7,
colour = "white", fill = "orange", fontface = "bold",
label = paste("U = ", round(diff.ci[2], digits = 2)))
pD <- p
p
cowplot::plot_grid(pA, pC, pB, pD,
labels = c("A", "C", "B", "D"),
ncol = 2,
nrow = 2,
label_size = 20,
hjust = -0.5,
scale=.95)
# save figure
# ggsave(filename=('./figures/nb6_fig_corr.pdf'),width=12,height=10)
Simulations of false positives and power for correlation comparisons are reported in this blog post. Bottom-line: the Fisher’s r-to-z transform is not robust, and very large sample sizes are required to detect differences between correlation coefficients.
For instance, if we have 3 dependent variables, we want to compare the correlation between 1 and 3 to the correlation between 2 and 3.
Generate data
set.seed(21)
n <- 50 # sample size
mu <- c(0, 0, 0) # means of the variables
rho12 <- 0.8 # correlation between variables 1 and 2
rho13 <- 0.2 # correlation between variables 1 and 3
rho23 <- 0.6 # correlation between variables 2 and 3
# define covariance matrix
sigma <- matrix(c(1, rho12, rho13,
rho12, 1, rho23,
rho13, rho23, 1),
nrow = 3, byrow = TRUE)
data <- MASS::mvrnorm(n = n, mu = mu, Sigma = sigma)
x <- data[,1:2]
y <- data[,3]
# make data frame
df <- tibble(x = x[,1],
y = y)
# ggplot figure
ggplot(df, aes(x = x, y = y)) + theme_classic() +
geom_point(alpha = 0.6, size = 3) +
geom_smooth(method='lm',formula=y~x) +
theme(axis.title = element_text(size = 15, colour = "black"),
axis.text = element_text(size = 13, colour = "black"),
strip.text = element_text(size = 15, face = "bold")) +
# scale_x_continuous(limits = c(-4, 4),
# breaks = seq(-4, 4, 1)) +
labs(x = expression(italic("Variable A")), y = expression(italic("Variable C")))
# ggsave(filename = "./corr_samp_dep_ov1.pdf")
# make data frame
df <- tibble(x = x[,2],
y = y)
# ggplot figure
ggplot(df, aes(x = x, y = y)) + theme_classic() +
geom_point(alpha = 0.6, size = 3) +
geom_smooth(method='lm',formula=y~x) +
theme(axis.title = element_text(size = 15, colour = "black"),
axis.text = element_text(size = 13, colour = "black"),
strip.text = element_text(size = 15, face = "bold")) +
# scale_x_continuous(limits = c(-4, 4),
# breaks = seq(-4, 4, 1)) +
labs(x = expression(italic("Variable B")), y = expression(italic("Variable C")))
# ggsave(filename = "./corr_samp_dep_ov2.pdf")
For Pearson correlation:
# x = matrix with 2 columns
# y = vector
TWOpov(x,y)
#TWOpovPV to get a p-value
For a robust correlation:
# x = matrix with 2 columns
# y = vector
twoDcorR(x,y, corfun=wincor, SEED=FALSE)
For instance, if we have 4 dependent variables, we want to compare the correlation between 1 and 2 to the correlation between 3 and 4.
For Pearson correlation:
# x = matrix with 2 columns
# y = matrix with 2 columns
TWOpNOV(x,y)
For a robust correlation:
# x = matrix with 2 columns
# y = matrix with 2 columns
twoDNOV(x,y, corfun=wincor, SEED=FALSE)
Guillaume A. Rousselet, 2019, University of Glasgow.


We buy platforms!
- synthesize out-of-the-box eyeballs
- visualize integrated initiatives
- integrate end-to-end e-markets
- integrate cutting-edge communities
- recontextualize bleeding-edge bandwidth
- redefine bleeding-edge supply-chains
- deliver extensible solutions
- deliver viral schemas
- cultivate next-generation functionalities
- visualize sexy e-markets
- maximize bricks-and-clicks synergies
- matrix integrated e-tailers
- innovate bricks-and-clicks functionalities
- extend granular web-readiness


Ktu mundemi me ndnu naj thenje apo najsen tjeter ose hiq haha!
Find Out MoreWith supporting text below as a natural lead-in to additional content.
With supporting text below as a natural lead-in to additional content.
With supporting text below as a natural lead-in to additional content.
With supporting text below as a natural lead-in to additional content.
Ne jemi 24/7 ne cdo koh blla blla blaa!


The Weeknd Conjured Dark Disco On "House Of Balloons/Glass Table Girls"
The Canadian falsetto king laid down a classic.
by Mitch FindlayHouse Of Balloons/Glass Table Girls
The Weeknd
Editor Rating: THROWBACK
User Rating: n/a - 0% (0)
When The Weeknd first slid into the fold, he became an instant critical darling. The enigmatic Canadian, seemingly fueled by heartbreak and drug-fueled casual sex, was among the first to pioneer a darker brand of afterparty RnB. Now, his Trilogy is considered essential listening, with the first chapter in particular housing some of his best material to date. One need only look to the first chapter House Of Balloons for proof, with the title track standing the test of time as an arguable magnum opus. Somehow, The Weeknd manages to capture a party simultaneously going wrong and right, sloppy yet oddly sophisticated.
The "House Of Balloons" movement floats appropriately, with Weeknd's signature falsetto gliding over the dark disco beat. Conversely, "Glass Table Girls" finds our hero back down to Earth, damn near rapping his way through an inebriated tryst; there's something here for everyone, and it's no wonder the man's career skyrocketed from that moment onward. One day removed from Trilogy's seven-year birthday, be sure to show some love to one of Canada's national treasures.
Quotable Lyrics
Two puffs for the lady who be down for that
Whatever, together
Bring your own stash of the greatest, trade it
Roll a dub, burn a dub, cough a dub, taste it
Then watch us chase it
With a handful of pills, no chasers
Jaw clenching on some super-sized papers


SEE YOU APRIL 19th
We have the speakers confirmed for Momentum 2018, checkout the great lineup!
A special session focused on Women in IT is being held to kick-off Momentum. The event features a keynote by Holly Chessman discussing "Don't Let Fear Rule your Life" followed by a panel of women in tech. Additionally, we are inviting 100 high school students to attend this event to get a glimpse of the awesome opportunities in the tech field. We invite everyone attending Momentum to this great event to kick-off the day!


Welcome to the dojo, {{capsuler.characterName}}.
May your time here be violent.
Offline
You must be online in EVE to crush opponents. Please log in first.
Location
You are currently in {{location.solarSystem.name}}.
Your ship class
Chose the ship class you will be fighting in.
Enemy ship classes
Chose which enemy ship classes you are willing to fight.
{{type}}


| Created: | 6/27/2020 7:02:31 AM |
| Modified: | 6/27/2020 7:02:31 AM |
| Project: | |
| Advanced: |
| Attribute |
|
Public serial id |
|
Details:
|
|
Public varchar(255) bank_account |
|
Details:
|
|
Public varchar(255) created_by |
|
Details:
|
|
Public varchar(255) updated_by |
|
Details:
|
|
Public timestamp with time zone(0) created_at |
|
Details:
|
|
Public timestamp with time zone(0) updated_at |
|
Details:
|
|
Public timestamp with time zone(0) start_date |
|
Details:
|
|
Public timestamp with time zone(0) end_date |
|
Details:
|
| Operation | ||
|
Public bank_accounts_pkey( id: serial, |
||
|
Details:
<<PK>>
|
| Element | Source Role | Target Role |
|
«table» bank_account_versions Table «FK» |
Name: bank_account_versions_bank_account_id_foreign |
Name: bank_accounts_pkey |
|
Details:
|
| Tag | Value |
| Owner | public |
|
Details:
|
| Property | Value |
| isFinalSpecialization: | 0 |


vop.groep7.vop7backend.factories
Classes
AddressFactory
AlertEventFactory
CoordinateFactory
EventFactory
EventTypeFactory
Factory
JamEventFactory
JamFactory
POIFactory
RouteFactory
SourceFactory
TransportFactory
TravelFactory
UserCredentialsFactory
UserFactory


if this originatd from an encoded string we'll need a place to store the bit length; it can be set and got from here
maxId
clone - returns a copy of the classes with new values and not references
forEach - to traverse from id=1 to id=maxId in a sequential non-sparse manner
callback to execute
get
key for value to check
set - sets an id assumed to be a truthy value by its presence
id to set a value for or array of ids to include
unset
id or ids to unset
Generated using TypeDoc
Vector class is like a Set except it keeps track of a max id


Preparation time
30 mins to 1 hour
Cooking time
30 mins to 1 hour
Serves
Serves 6-8
Dietary
This tart looks like something you might have bought from a French patisserie, but it is so easy to make. Serve it gently warmed with a dusting of icing sugar, or top it with chocolate curls, drizzle with melted chocolate or top it with a little whipped cream and fresh summer berries. Or all three.
Equipment and preparation: you will need a 23cm/9in fluted flan tin, 3cm/1¼in deep and baking beans.
For the pastry, put flour and icing sugar in a large bowl and rub in the butter with your fingertips until the mixture resembles fine breadcrumbs.
Add the egg yolk and water and mix until it comes together to form a firm dough. Wrap in cling film and leave to rest in the fridge for about 30 minutes.
Preheat the oven to 200C/400F/Gas 6 (180C fan). Dust the work surface with flour and then roll out the pastry as thinly as you can to a circle - about 5cm/2in larger than your flan tin. Line the tin with pastry. Don’t worry if the pastry breaks a little, it is easy to patch up. Leave to chill in the fridge for 15 minutes.
Prick the base of the pastry with a fork and line the pastry case with a circle of baking parchment or foil and fill with baking beans. Bake the pastry blind for 10 minutes, or until just lightly golden-brown, then remove the paper and beans and return the tart to the oven to cook for a further 5-7 minutes, or until pale golden-brown and dried out.
For the filling, melt the butter in a medium pan over a low heat, then add the chocolate and stir until melted and smooth. Remove from the heat and stir in the sugar and flour. Beat in the eggs, one at a time.
Place the pastry case on a baking tray. Pour the mixture into the pastry case, filling it right to the top and place in the oven. Bake for 10-12 minutes, or until just set around the edges - but it should still be slightly wobbly in the centre.
Remove from the oven and allow to cool. Dust with icing sugar - if liked, you can cut a heart shape out of baking parchment and place on top of the tart before dusting with icing sugar, then remove the paper to make a stenciled effect. Or serve warm with strawberries, a dusting of icing sugar and cream.
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How to blind bake pastry


Erick Rowan posts heartwarming response to Luke Harper's recent WWE release
by Phillipa MarieLuke Harper and Erick Rowan have been partnered together for most of their on-screen WWE career, from The Wyatt Family to The Bludgeon Brothers, but last night this all came to an end when it was revealed that Luke Harper had been released from his current WWE contract.
Harper requested his release back in April but WWE had been dragging their feet since they were assessing what kind of competition AEW would later become since the likes of Chris Jericho, Cody Rhodes, Jon Moxley, and Pac are all steadying the ship over there.
Last night, WWE released Sin Cara, Luke Harper, Konnor and Viktor from The Ascension in the first release spree that the company has gone on in a while. Harper and Cara had asked for their releases publically, whilst The Ascension had been underutilized ever since their promotion to the main roster.
Many current WWE stars have responded to these releases, but many were waiting to see what Harper's on-screen best friend had to say about his release. Judging by the following Instagram post, the former Wyatt Family member has been forced to say goodbye to more than one best friend over the past 24 hours.


368_Sakamono.py
class Solution(object):
def cherryPickup(self, grid):
"""
:type grid: List[List[int]]
:rtype: int
"""
m = len(grid)
n = len(grid[0])
dp = [[float('-inf') for _ in range(n)] for _ in range(n)]
dp[0][n - 1] = grid[0][0] + grid[0][n - 1]
for i in range(1, m):
next_dp = [[float('-inf') for _ in range(n)] for _ in range(n)]
for j in range(n):
for k in range(n):
if dp[j][k] == float('-inf'):
continue
for next_j in (j - 1, j, j + 1):
for next_k in (k - 1, k, k + 1):
if 0 <= next_j < n and 0 <= next_k < n:
next_dp[next_j][next_k] = max(next_dp[next_j][next_k], dp[j][k] + grid[i][next_j]) \
if next_j == next_k else \
max(next_dp[next_j][next_k], dp[j][k] + grid[i][next_j] + grid[i][next_k])
dp = next_dp
ans = 0
for i in range(n):
ans = max(ans, max(dp[i]))
return ans


HTML5 is a markup language used for structuring and presenting content for the World Wide Web and a core technology of the Internet. It is the fifth revision of the HTML standard (created in 1990 and standardized as HTML 4 as of 1997) and, as of December 2012, is a candidate recommendation of the World Wide Web Consortium (W3C). Its core aims have been to improve the language with support for the latest multimedia while keeping it easily readable by humans and consistently understood by computers and devices (web browsers, parsers, etc.).
JavaScript (JS) is an interpreted computer programming language. As part of web browsers, implementations allow client-side scripts to interact with the user, control the browser, communicate asynchronously, and alter the document content that is displayed. It has also become common in server-side programming, game development and the creation of desktop applications.
Unlike CSS 2, which is a large single specification defining various features, CSS 3 is divided into several separate documents called "modules". Each module adds new capabilities or extends features defined in CSS 2, preserving backward compatibility. Work on CSS level 3 started around the time of publication of the original CSS 2 recommendation. The earliest CSS 3 drafts were published in June 1999
Java is a general-purpose, concurrent, class-based, object-oriented computer programming language that is specifically designed to have as few implementation dependencies as possible. It is intended to let application developers "write once, run anywhere" (WORA), meaning that code that runs on one platform does not need to be recompiled to run on another. Java applications are typically compiled to byte code (class file) that can run on any Java virtual machine (JVM) regardless of computer architecture.


A visualization of Elections PEI data set:
"lists of contributions in excess of $250 for year 2017"
Contains information licenced under the
Elections PEI Open Data Licence
.
Cross-reference of $250+ donations to multiple parties, where names are similar
Columns are sortable. Sheet is scrollable.
Other visualizations here
by
PEI Developers
extracted data
here
& source
here
uses
Google Charts


Class Puli
The Puli service locator.
Use this class to access the managers provided by this package:
$puli = new Puli(getcwd()); $puli->start(); $packageManager = $puli->getPackageManager();
The Puli
class either operates in the global or a project context:
* The "global context" is not tied to a specific root package. A global context only loads the settings of the "config.json" file in the home directory. The Puli
class operates in the global context if no project root directory is passed to the constructor. In the global context, only the global config file manager is available. * The "project context" is tied to a specific Puli project. You need to pass the path to the project's root directory to the constructor or to Puli\Manager\Api\Puli::setRootDirectory()
. The configuration of the "puli.json" file in the root directory is used to configure the managers.
The Puli
class creates four kinds of managers:
* The "config file manager" allows you to modify entries of the "config.json" file in the home directory. * The "package file manager" manages modifications to the "puli.json" file of a Puli project. * The "package manager" manages the package repository of a Puli project. * The "repository manager" manages the resource repository of a Puli project. * The "discovery manager" manages the resource discovery of a Puli project.
The home directory is read from the context variable "PULI_HOME".
If this variable is not set, the home directory defaults to:
* $HOME/.puli
on Linux, where $HOME
is the context variable "HOME". * $APPDATA/Puli
on Windows, where $APPDATA
is the context variable "APPDATA".
If none of these variables can be found, an exception is thrown.
A .htaccess file is put into the home directory to protect it from web access.
Author: Bernhard Schussek<EMAIL_ADDRESS>Since: 1.0
Located at Api/Puli.php


10 March 2011 - The Ministry of Health of Egypt has announced two new confirmed case of human infection with avian influenza A (H5N1) virus and the death of a previously announced case.
The first case is a 17 year old female from Behira Governorate. She developed symptoms on 27 February and was hospitalized on 1 March. She is in a stable condition.
The second case is a 17 year old female from Dakahlia Governorate. She developed symptoms on 24 February and was hospitalized on 26 February. She died on 28 February.
Investigations into the source of infection indicate that the both cases had exposure to sick and dead poultry. Both cases received oseltamivir treatment.
The cases were confirmed by the Egyptian Central Public Health Laboratory, a National Influenza Center of the WHO Global Influenza Surveillance Network.
The previously reported case, a 32 year old female from Sharkia Governorate (see update 45), died on 3 March.
Of the 129 cases confirmed to date in Egypt, 43 have been fatal.


Web torrent downloader and cloud storage.
Share file via WebTorrent and WEBRTC
Chat with your friends.
Check your web applications status.
Shortcut your links.
Todolist for your projects
Interractive Solar System
C / C++
Java
HTML / CSS
JavaScript
contact@lunik.xyz


On 5 January 2018, 5 new articles were published in 5 journals.
From Solitary to the Streets: the Effect of Restrictive Housing on Recidivism
Security Needs of Citizens and Community Policing in Serbia – Is There A Link?
Measuring the Effect Heterogeneity of Police Enforcement Actions Across Spatial Contexts
Advocate Safety Planning Training, Feedback, and Personal Challenges
The Securitization and Policing of Art Theft: the Case of London


@(theme: String) @main(theme){
The game proceeds in three phases
The players alternating place their men once per turn. If one player is able to place three men in a straigt line, he formed a mill and may remove one of an opponent's man, which is not in a mill. When all men are placed, phase 1 is finished.
Players now move their pieces to adjacent points and try to form mills like in phase 1. This is done until one player only remains 3 pieces.
When a player only has left 3 pieces, his movements are not longer limited to adjacent points. He may now set his pieces to any point of the field. The game ends, when one player has no pieces left
This game is developed by Thomas Amann and Lars Knaack, see the source code at GitHub https://github.com/LarsKnaack/de.htwg.wt.muehle


Created by: {{recipe.creator.firstName}} {{recipe.creator.lastName}}
Ingredients:
Tags:
Instructions:


Legendword Vocabulary
Welcome to Legendword Vocabulary! This is a vocabulary memorization platform. You can add, view, and review your saved vocabularies here.
LVocab is meant to be used with Google Translate. Read "Getting Started" to get information on how to start using this platform.
Right now, LVocab is still in development stage. Some have not been added yet. You may also experience some bugs or errors. Contact<EMAIL_ADDRESS>for bug report and suggestions. With all
Vocabulary List
Review
Function still in development...
Settings
* Some settings may only take effect after reloading the page.
Settings are in development...


most prevalent non-injury related cause of long-term neurological disability in younger adult
autoimmune disease
not directly fatal
life expectancy = 6-11yrs
epidemiology
-
onset 20-40years
-
2.5x females compared to males
- males more severely affected
-
more common increasing distance from equator
- ?vit d
-
1/5 wil have affected relative
-
smoking and EBV exposure
Patterns
-
inflammatory demyelination
-
white and grey matter of CNS
-
peripheral nervous system unaffected
-
loss of myelin and neuronal/axonal injury
-
over time remyelination can repiar some of this damage
-
Plaques formed from dense clusters
-
-
location, frequncy and balance of injury vs. repair determines pattern and associated diability
-
unpredictable course and prognosis
-
Typically begins with sudden episode
-
blurring one eye (optic neuritis)
-
double vision (diplopia)
-
unsteadiness
-
sensory impairment
- upwards from legs
-
dysfunction in limbs
-
-
increasing severity over days - weeks then gradual resolution - patiral/complete over several weeks
-
first episode = clnically isolated syndrome
-
if 2nd episode or subclinical changes on MRI -> relapsing-remitting MS
Relapsing-remitting MS
-
most common pattern (85%)
-
recurrent acute neruo episodes = relapses
- residual symptoms suddenly increase or new symptoms developing
-
recovery gradual
- residual symptoms often remain
-
1 relapse q2yrs
- frequency and severity vary widely
-
high frequency during first 2 years
- linked to increase likelihood of people devleoping secondary progressive MS and long term disability
Secondary progressive MS
-
2/3 with R-R MS
- within 15yrs of diagnosis
-
progressive worsening of neruological function
-
independent of acute relapses
-
low-grade degenerative process
-
long-term disability in peopel with MS
-
-
acute attacks may still occur
-
Benign MS
-
1/10
-
period of 10-20yrs without significant disability
Primary progressive MS
-
1/10 with MS
-
develop insidiously and progressively from outset
diagnosis
McDonald criteria:
2 or more episodes of inflammatory demyelination to cocur on seperate occasions at lease 30d apart in differnet locations
spearation in time and spacea can be estabilshed with single MRI
-
may report positive or negative symptoms
-
positive
-
muscle spasm
-
1/5 will various forms of neuralgia
- tingling/burning/pruritic sensations in skin of limbs, trunk, face
-
electric shock sensation down spine when neck is flexed (Lhermitte’s symptom)
- characteristic but not specific
-
-
Negative
-
visual disturbance
-
decrease VA
-
diploplia
-
-
numbness
-
motor dysfucntion
-
weakness
-
gait disturbances
-
ataxia
-
-
bladder dysfunction
-
bowel/erectile dysfuction
-
-
may experience transient worsening of symptoms due to heat
- Uhthoff’s symptom
-
optic neuritis
-
1/4 of people with MS present with this
-
more prevalent among younger women
-
impairment of VA
-
reduced colour vision
-
red desaturation
-
ishihara plates detect reduction in colour perception in 88%
-
-
eye pain - especially on movement
-
swelling may be observed
-
RAPD present
-
after attck disc may appear pale
- optic atrophy
-
-
Internuclear opthalmoplegia
-
damage to neuronal connections
-
abducens (VI) and oculomotor (III) cranial nerve nuclei
-
affects horizontal gaze
-
when looks laterally to unaffected side
-
impairment of adduction in affected eye
-
lags behind unaffected eye
-
‘adduction’ lag
-
-
-
extensor plantar response
- can be seen
Differential diagnosis
-
psychiatric
-
sciatica/msk injury
-
carpal tunnel syndrome nad other peripheral nerve disorders
Investigations
-
MRI = sensitivity of 95%
-
history often underestimates disease severity
-
other asympomatic cns lesions will be detected on MRI \~80% of people
-
-
Visual evoked potentioals
-
subclincal demyelination of optic nerve
-
non specific and may be N in half MS
-
-
Oligoclonal bands in CSF
-
present 95% iwth well established MS
-
rarely required where characteristic history and MRI changes
-
Treatment
-
3x disease modifing agents
-
require SA from neurologist
-
complex actions on immune function and mechanism uncertain
-
similar, limited effects on course
-
1/3 reduction in frequnce of relapses
-
non appear effective with secondary progressive MS
-
-
Acute relapses managed with methylprednisolone
- relapses more common who have viral infections or major life events
-
influenza vaccination may reduce likelihood of MS relapses and minimise risk of respiratory complciations
Other sympotms
-
Fatigue
-
85% of those with MS
-
muscle weakenss and ataxia also present
-
consider
-
depression
-
poor sleep
-
pain
-
inadequate nutrition
-
-
exercise
-
pysiotherapy
-
aspirin and amantadine = some benefit
-
-
pain affects 2/3
-
restricted movmeent or muscle spasms
-
peripheral pain can be managed with analgesics and physiotherapy
-
CNS lesions also cause pain
-
-
bowel dysufcntion
-
constipation/faecal incontinence 60%
-
fuild/fibre increased before laxative considered
-
overflow incontinence
-
may have difficulty initiating bowel movmeent
-
digital rotatoy stinulation
-
or suppositories
- bisacodyl or glycerl
-
-
loperamide = first line
-
-
bladder dysfunction
-
anticholinergics
-
may cause incomplete bladder emptying
-
urinary self-catherisation
-
-
nocturia
-
desmopressin
- subsidised by neurologist
-
-
-
Spasticity and spasm
-
physiotherapy
-
cannabidiol plus tetrahydrocannabinol oromuscoal spray = adjunctive treatment for moderate to severe spasitiicyt
-
not subs
-
erquires ministerial approval
-
contraindicated in severe psychiatric disorders/psychosis
-
-
baclofen 5mg tds increase 3 day intervals - maintence = 30-80mg/day
-
orphenadrine 100mg bd
-
Dantrolene 25mg od increase to 25mg 2-4 times/day every 4-7 days
-
diazepam
-
Fampridine
-
19mg orally bd
Managing disability
-
cognitive impairment
- 1/2


Awareness of your brand ecosystem will help you to be more effective and use your time more wisely as you build your personal brand. For some, completing their ecosystem model is enough. For others, using more detailed and systematic management tools is preferred. If you work better with greater granularity, you can put your ecosystem influencers into a spreadsheet or a customer relationship management (CRM) tool, or use the templates that are shared in this section.
If an influencer relationship is important to you, it deserves to be nurtured and managed over time. Ecosystem relationship management helps to establish priorities, goals, actions and measurement for important relationships. This optional management tool (Figure 5.9) is especially helpful when mapping out how to launch a new initiative or idea.
* Tier ratings are for understanding how important each influencer is. For instance, Tier Ones might always get your important news from you in person or with a personal call, and Tier Twos might get a personalized email.
** Friend or Foe Ratings should be on a scale of 1 to 5, with 1 being hostile to you and with 5 being an enthusiastic advocate. The goal will be to improve these ratings over time.
Ecosystem Relationship Management


Index
Sales
Add a New Store
Name:
Minimum Customer Per Hour:
Maximum Customer Per Hour:
Average Cookies Sales:
Add
Reset


{% load static %}
Инструмент: Карандаш Продвинутый Ластик
Unfortunately, your browser is currently unsupported by our web application. We are sorry for the inconvenience. Please use one of the supported browsers listed below, or draw the image you want using an offline tool.
Supported browsers: Opera, Firefox, Safari, and Konqueror.


Zoogle
Like googling, but with animals and stuff.
Enter an Animal to search:
Search by scientific name:
Search


Basic Info:
My name is Yanan Dong and I study computer science at Northeastern University for master degree. I have worked as a data engineer in Lenovo for 1 year and did an internship in PayPal for no more than two months. I am actively looking for 2022 software engineer summer internship.


Faculty of Business and Economics , Mendel University , Brno, Czech Republic.
Economics & Management MSC (2024 Expected)
Faculty of Engineering, Alexandria University, Alexandria, Egypt.
B.S Mechanical Engineering, June 2014
El Abasyia High school, Alexandria, Egypt
Science & Math department


This is your personal and collaborative “work space,” where you can keep track of your (re)search projects, store your bookmarks (selected corpus) or codes (tags), and other annotations you performed while searching and browsing. You can also perform analytical tasks with your corpus and annotations, and export your data. THIS SPACE IS UNDER CONSTRUCTION.
{{user.attributes}}


UC Berkeley Extending and Structuring User Queries for Efficient Free-form Code Search
March 19, 2018 - Berkeley, California, USA / Talk for team of Koushik Sen
Facebook FaCOY: a Code-to-Code Search Engine
March 21, 2018 - Palo Alto, California, USA / Talk for team of Satish Chandra
KTH Royal Institute of Technology Free-form and Code-to-Code Search: Leveraging Q&A Data for Translating between Query Terms and Code Tokens
September 26, 2018 - Stockholm, Sweden / Invitation by Martin Monperrus
|
Interview on the Open Source irreversible revolution - LaRevue |


This taking a flower image as input and outputting the most likely type/class of the flower. The goal of this project is to build an web application that will be trained on set of labeled flower images to make predictions on the given input.
Find Us on Github


|
数字式立体视觉刺激系统
1.0.1
Digital Stereo System
|
#include <nvapi.h>
Public 属性 | |
| NvU32 | version |
| NV_ROTATE | rotation |
| (IN) rotation setting. 更多... | |
| NV_SCALING | scaling |
| (IN) scaling setting. 更多... | |
| NvU32 | refreshRate1K |
| NvU32 | interlaced:1 |
| (IN) Interlaced mode flag, ignored if refreshRate == 0 更多... | |
| NvU32 | primary:1 |
| NvU32 | reserved:30 |
| (IN) Reserved, must be 0 更多... | |
| NV_GPU_CONNECTOR_TYPE | connector |
| Specify connector type. For TV only, ignored if tvFormat == NV_DISPLAY_TV_FORMAT_NONE. 更多... | |
| NV_DISPLAY_TV_FORMAT | tvFormat |
| (IN) to choose the last TV format set this value to NV_DISPLAY_TV_FORMAT_NONE 更多... | |
| NV_TIMING_OVERRIDE | timingOverride |
| Ignored if timingOverride == NV_TIMING_OVERRIDE_CURRENT. 更多... | |
| NV_TIMING | timing |
| NV_GPU_CONNECTOR_TYPE _NV_DISPLAYCONFIG_PATH_ADVANCED_TARGET_INFO_V1::connector |
Specify connector type. For TV only, ignored if tvFormat == NV_DISPLAY_TV_FORMAT_NONE.
| NvU32 _NV_DISPLAYCONFIG_PATH_ADVANCED_TARGET_INFO_V1::interlaced |
(IN) Interlaced mode flag, ignored if refreshRate == 0
| NvU32 _NV_DISPLAYCONFIG_PATH_ADVANCED_TARGET_INFO_V1::primary |
(IN) Declares primary display in clone configuration. This is NOT GDI Primary. Only one target can be primary per source. If no primary is specified, the first target will automatically be primary.
| NvU32 _NV_DISPLAYCONFIG_PATH_ADVANCED_TARGET_INFO_V1::refreshRate1K |
(IN) Non-interlaced Refresh Rate of the mode, multiplied by 1000, 0 = ignored This is the value which driver reports to the OS.
| NvU32 _NV_DISPLAYCONFIG_PATH_ADVANCED_TARGET_INFO_V1::reserved |
(IN) Reserved, must be 0
| NV_ROTATE _NV_DISPLAYCONFIG_PATH_ADVANCED_TARGET_INFO_V1::rotation |
(IN) rotation setting.
| NV_SCALING _NV_DISPLAYCONFIG_PATH_ADVANCED_TARGET_INFO_V1::scaling |
(IN) scaling setting.
| NV_TIMING _NV_DISPLAYCONFIG_PATH_ADVANCED_TARGET_INFO_V1::timing |
Scan out timing, valid only if timingOverride == NV_TIMING_OVERRIDE_CUST The value NV_TIMING::NV_TIMINGEXT::rrx1k is obtained from the EDID. The driver may tweak this value for HDTV, stereo, etc., before reporting it to the OS.
| NV_TIMING_OVERRIDE _NV_DISPLAYCONFIG_PATH_ADVANCED_TARGET_INFO_V1::timingOverride |
Ignored if timingOverride == NV_TIMING_OVERRIDE_CURRENT.
| NV_DISPLAY_TV_FORMAT _NV_DISPLAYCONFIG_PATH_ADVANCED_TARGET_INFO_V1::tvFormat |
(IN) to choose the last TV format set this value to NV_DISPLAY_TV_FORMAT_NONE
| NvU32 _NV_DISPLAYCONFIG_PATH_ADVANCED_TARGET_INFO_V1::version |


Just press connect and pick the chromecast to start
Waiting for URLs
(c) Rob Charlton 2014. Source is at https://github.com/charltones/dashboard-cast


Before Change
neighbours = [None] * n_size[cur_depth]
else:
// sample with replacement
neighbours = [
rs.choice(neighbours) for _ in range(n_size[cur_depth])
]
// add them to the back of the queue
q.extend((sampled_node, depth) for sampled_node in neighbours)
After Change
neighbours = [None] * n_size[cur_depth]
else:
// sample with replacement
neighbours = rs.choices(neighbours, k=n_size[cur_depth])
// add them to the back of the queue
q.extend((sampled_node, depth) for sampled_node in neighbours)


CSLBlePacket
@interface CSLBlePacket : NSObject
{
Byte prefix;
CONNECTION connection;
Byte payloadLength;
DEVICEID deviceId;
Byte Reserve;
DIRECTION direction;
Byte crc1;
Byte crc2;
NSData* payload;
}
@property (assign) Byte prefix;
@property (assign) CONNECTION connection;
@property (assign) Byte payloadLength;
@property (assign) DEVICEID deviceId;
@property (assign) Byte Reserve;
@property (assign) DIRECTION direction;
@property (assign) Byte crc1;
@property (assign) Byte crc2;
@property NSData* payload;
- (NSData*) getPacketInHexString;
- (NSString*) getPacketPayloadInHexString;
@end
Undocumented
-
Undocumented
Declaration
Objective-C
Byte prefix
-
Undocumented
Declaration
Objective-C
CONNECTION connection
-
Undocumented
Declaration
Objective-C
Byte payloadLength
-
Undocumented
Declaration
Objective-C
DEVICEID deviceId
-
Undocumented
Declaration
Objective-C
Byte Reserve
-
Undocumented
Declaration
Objective-C
DIRECTION direction
-
Undocumented
Declaration
Objective-C
Byte crc1
-
Undocumented
Declaration
Objective-C
Byte crc2
-
Undocumented
Declaration
Objective-C
NSData* payload
-
Undocumented
-
Undocumented
Declaration
Objective-C
@property (assign) CONNECTION connection
Swift
var connection: CONNECTION { get set }
-
Undocumented
Declaration
Objective-C
@property (assign) Byte payloadLength
Swift
var payloadLength: UInt8 { get set }
-
Undocumented
-
Undocumented
-
Undocumented
-
Undocumented
-
Undocumented
Declaration
Objective-C
- (NSData*) getPacketInHexString;
Swift
func getInHexString() -> Data!
-
Undocumented
Declaration
Objective-C
- (NSString*) getPacketPayloadInHexString;
Swift
func getPayloadInHexString() -> String!


Bypass Some Origin Policy clean script
Pretty things that makes your victim stays for few minutes...


India is an agrarian country because almost 55% of its population
depends on agriculture & related activities for their livelihoods.
Project Aahar aims to uplift the living standards of the small
scale farmers (by land area) in the metropolitan cities. The huge
cost of irrigation, toxic chemical fertilizers, less yield, and
labor-intensive farming methods are some of their problems. We
tackle these problems, primarily with introducing modern farming
methods and secondly our ‘Farm to Fork’ model for the sales side
In order to achieve our objective of uplifting the small scale farmer in the metropolitan cities, we have a sustainable business model: Primarily, we introduce the modern farming technique of ‘Coco Peat Farming’ to the farmer. To build trust and reduce the risk for our farmers, we invest in the raw materials and train the farmers by the on-ground implementation for the first crop cycle. Secondly, we focus on removing the exploitative middlemen from the supply chain. Hence, our farmers directly reach their customers through our ‘Farm To Fork’ model. We help them set up stalls inside the Delhi societies to sell their produce. This increases their monthly profits by two folds.
- 12 farmers in Dwarka, Delhi.
- 2 farmers in Yamuna Khadar region.
- Average 30% increase in the monthly profits of our
farmers.
- 4 society stalls for Farm to Fork model.
- 1000 happy society residents eating farm-fresh produce.


JWT Authentication in React App (Web)
Some thoughts on JWT authentication in a React App.
I am relatively new to React, I started writing React in September 2019. Having watched a 1-hour crash course on YouTube, I found React to be one of the easiest frontend framework to pick up, especially when compared to Angular.
I have gotten used to using React for every personal project I build for web. However, recently, I have a project that grew a bit bigger and I was using JWT for authentication. I must admit, I have very little implementational experience regarding JWT and JWT in React. My inital approach was simple, and works for small-scale apps. I would simply check for the presence of a JWT token in every protected component, via a lifecycle method, e.g. useEffect() or componentDidMount().
This very quickly revealed its flaws, each additional component that requires authentication will require the same (repeated) code, which violates the DRY principle. A bit of reading and I found that the authentication logic can be abstracted by simply wrapping a Route in a closure and performing the auth logic within, with a redirect to a landing page if user is not authenticated.
I have yet to try it out, but it is quite straightforward and clear that it will work.
It works.
When user is authenticated (true), protected route is accessible. When false, a redirect to “/” is returned instead.
What’s eating me though, is that for a SPA, regardless of what authentication method is being used. The user can actually bypass the mechanism being used, as the logic is completely client-sided. Of course, this might not be an issue, since each endpoint should authenticate the JWT as well. My thoughts are:
- Is there a way around it, to prevent user from being able to view protected pages by spoofing a JWT.
- Is it a problem that a user could potentially do the above.
Just to be clear, by spoofing, I really mean an invalid JWT which would fail in server-sided verification but should pass in client-sided checks since the only way the client can check would be to have the secret as well, but it will defeat the purpose of JWT.


Google seems like a very user friendly website to me. It has very little distractions with only two things that draw your eye, the logo and the search bar.
The beauty of google is it's simplicity but it seems in order to have that simplicity it needs to have a lot of power. By that I mean, that other websites give you options, places that they can take you. Where as Google asks you, what are you looking for? Now that's the nature of the site but also the nature of the ideas behind it.
YouTube is just a great concept and the creation of a whole new way to move knowledge and entertainment around. You literally can learn anything on YouTube and do it without picking up a book. It is changing future generations for better and for worse. Visually it seems like over the years its getting more and more cluttered like so many sites. They are trying to give you the customer everything you could ever want and everything you didn't know you wanted but unconsciously do. Ads seem like they are the downside to many good designs.
Yahoo is another site that is the starting point for so much information but it does it in the exact opposite way as Google. Ii is littered with links to other yahoo sites and scolling pictures and the works. But I do think it works as a site. Easily navigated and nicely laid out. I tend to bounce around in there multiple sites for a while everyday not because they are the best finance or sports sites but because they are easy to find.


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Please provide your email id so that we can update you when we launch this service


{% extends "base.html" %} {% block body %}
This is the homepage for the client! The routes for this URL are controlled in app/routes/client.py


AddressBook sample source
Sample code use in various examples throughout the Javadocs. Represents a simple POJO model consisting
of a collection (LinkedList
), beans (Address
, Person
), and a type 4a transformed type (Calendar
).
Public fields are used for bean properties in-leu of getters and setters to reduce the size of the example.
Bean properties defined using getters and setters would work identically.
// A collection of people
public class AddressBook extends LinkedList<Person> {
// Extra method for adding a person to this address book.
// Used in PojoIntrospector usage examples.
public void addPerson(String name, String birthDate, List<Address> addresses) {
add(new Person(name, birthdate, addresses));
}
}
public class Address {
// Bean properties
public String street, city, state;
public int zip;
public boolean isCurrent;
// Bean constructor
public Address() {}
// Other constructor
public Address(String street, String city, String state, int zip, boolean isCurrent) {
this.street = street;
this.city = city;
this.state = state;
this.zip = zip;
this.isCurrent = isCurrent;
}
}
public class Person {
// Bean properties
public String name;
public int age;
public Calendar birthDate;
public LinkedList<Address> addresses = new LinkedList<Address>();
// Bean constructor
public Person() {}
// Other constructor
public Person(String name, String birthDate, Address...addresses) {
this.name = name;
this.birthDate = getBirthDate(birthDate);
this.age = calcAge(birthDate);
this.addresses.addAll(Arrays.asList(addresses));
}
// Other method
// Calculates a persons age based on the birthdate
public static int calcAge(String birthDate) {
return new GregorianCalendar().get(Calendar.YEAR) - getBirthDate(birthDate).get(Calendar.YEAR);
}
// Utility method
// Converts a birthdate string to a Calendar
private static Calendar getBirthDate(String birthDate) {
try {
Calendar c = new GregorianCalendar();
c.setTime(DateFormat.getDateInstance(DateFormat.MEDIUM).parse(birthDate));
return c;
} catch (ParseException e) {
throw new RuntimeException(e);
}
}
}


Last updated: 2018-03-07
Code version: 18c3071
In this paper, using a novel HMM-based approach, researchers compare sequences of var gene DBL\(\alpha\) domains from two divergent isolates in P.falciparum: 3D7 and HB3, and its sister taxa P.reichenowi. Results demonstrate this two species share similar gene size, and gene structure. To more specifically, they both have more than 51 var genes in a genome, and they both have a series of “homology blocks”. In addition, through simulation, they found recombination occurs almost every residue in DBL\(\alpha\) domain which appears to be unusual, and no hotspot structure for this type of intradomain recombination even though interdomain hotspot structure could be considered in previous Rask’s study.
High level of sequence diversity in the PfEMP1 proteins, encoded by 50 to 60 var genes, express on the surface of infected red cells, provoking an immune response, and are known virulence factors. Previous studies show that recombination, combined with point mutation, is the mechanism of var gene revolution. Another noteworthy point is that since host’s immune system is more effective against antigens, as a result, parasites expressing less-common proteins avoid detection more effectively.
DBL\(\alpha\) domain, averaging 1.8kb in length, is the only functional domain in P.falciparum and P.reichenowi, and it has stable location as well.
Using the tBLASTx algorithm and the prototype DBL\(\alpha\) domain, researchers are able to extract reads, then using Clean Data assembly algorithm in Sequencer(GeneCodes), 51 unique DBL\(\alpha\) regions are recovered, which is within the range of P.falciparum, thus, demonstrating that the family is equally large in both species. In further phylogenetic analysis of the var gene DBL\(\alpha\) domains, P.reichenowi are not clustered together, indicating var genes likely arose as an entire family before the P.falciparum-P.reichenowi speciation event 2.5-6 million years ago.
Before introduction of method part, there are two pictures showing us the difference of gene conversion and crossover.
This graphic is adapted from Chen et al.(2007)
This graphic is adapted from webbook
Obviously, gene conversion in second figure belongs to the third type (Interallelic gene conversion)shown in first figure.
New method is necessary to identify the relationship between species. In this paper, Tesserae program written in C language is used and implemented. HMM is employed to find homology, global alignment algorithm (Needleman-Wunsch) is to detect mosaic recombination, product of approximately conditionals (PAC) likelihood is utilized to estimate the recombination parameter. This paper aim to reconstruct each sequence in dataset as a mosaic of one or more donor sequences, allowing substitution, indel and recombination. The precise steps are as follows:
Initially, recombination parameter set to zero, transition and emission probabilities , indels and mutations are estimated using Baum-Welch algorithm, then fix these parameters, a likelihood surface is constructed for the recombination parameter. Once MLE for recombination parameter is found, Viterbi path is computed for each sequence, this path provide the mosaic alignments.
For simulated sequences, they construct 10 gene families, each family has 60 genes, each gene is composed of 150 amino acid residues in length, and a table of input parameters are needed. Each gene family is used for 8 different sets of parameters, namely, different levels of recombination and conversion. Each gene family’s simulation could viewed as two groups, one is indels without recombination, another one is coalescent with recombination, since there is currently no available program for joint estimation about recombination and indel.
Here we have to note that the coalescent is likely to be an inaccurate description of the true var gene evolution. However, basic coalescent processes of coancestry and allelic recombination may represent var gene duplication and non-allelic recombination, thus, making aspects of coalescent represent several features of var gene family evolution. Here authors employ calibration method, allowing them to make comparisons between the \(\rho\) and recombination parameter in coalescent models.
Firstly, comparisons of the exact values show a high level of accuracy in the estimated recombination rates, through computing the difference of likelihood with and without recombination for each sequence, the statistical significance of the improvement as p less than \(1*10^{-32}\).
In order to test false positives further, they uses a non-recombining data from P.falciparum, the program is able to recover all known recombination event, while finding no recombination history, as expected.
In further examination of the P.falciparum and P.reichenowi DBL\(\alpha\) domain homology regions, researchers find multiple regions pf particular high homology, classified into two groups: core motifs and conserved peptides. The former one is between 18 and 28 residues, corresponding to HB1-5(termed ``homology blocks" by Rask et al.), among them, HB2 motif are he most frequently ones, then is HB3 and HB5. The latter one is between 24 and 140 residues, and between 80% and 100% similarity.
Lastly, the most important analysis result is recombination is uniform throughout the DBL\(\alpha\) domain and does not show a hot- or coldspot structure, that means recombination breaks at almost every residue. From the following table, high variance of block length indicate the lack of hot - or coldspots of recombination.
This table is adapted from original paper
One basic introduction I would add is the programs generating sequences , they are Seq-Gen, ms and Rose, which are all shown in the first column of Table S2.
Seq-Gen simulates sequences given a substitution model along a phylogeny. Ms, is an all-time classic engine for coalescent sequence simulations under a Wright-Fisher neutral model. and Rose, simulates sequences given a substitution model along a phylogeny incorporating indels. This introduction is originally from a web link. My own practice is here
Here is a summary of talk between Heejung and me on Monday and Tuesday afternoon in the first week of March 2018. Our talk focus on the explanation of the following transition matrix allowing insertion and deletion events.
This table is adapted from original paper
Firstly we have \(n\) destination sequences and one target sequence, assuming this target sequence as a mosaic of segments from those destination sequences. In this matrix, subscript “x” represents the target sequence and “k” represents any other destination sequence in the dataset.
Firstly, this matrix is not a symmetric matrix. For example, elements in the first column are not the same with elements in the first row.
The meaning of \(M_{x},I_{x},D_{x},M_{k},I_{k},D_{k}\) are shown in the following picture:
If we look at the two destination sequences in this figure, there are \(6+3=9\) positions, at first, the start position uniformly come from this nine positions, therefore, every position has the same probability 1/9, representing \(1/|Y|\) in the transition matrix. Next, let’s look at this matrix row by row, I will use \(T_{ij}\) represent the element in row i and column j.
In the first row, \(T_{11}\) and $T_{18} $are both 0, which are sensible, the target sequence’s start point come from and only come from Match or insert, not Delete. So \(T_{14}\)and \(T_{17}\) are 0, moreover, the start point may come from one of sequence x or sequence k , so my understanding is the first four elements are independent of the last four elements. Lastly, if start position comes from sequence x, it is match or insertion, so I suppose:
In the second row, gap open probability is both \(\delta\)m if we jump to another sequence k, then will add recombination probability. Here we should notice that \(T_{27}\) is 0, which means the start point in target sequence should not be a gap when we jump to another sequence.
In the third row, \(T_{34}=T_{37}=0\), here we can suppose there is no allowance to transition from insertion to deletion, gap extension is allowed, of course.
In the fourth row, There is no allowance to transition from deletion to insertion. gap extension is allowed, of course.
The last four rows also show the same rule with the first four rows. Therefore, we should notice that the last two positions shown in picture should not happen in practice!
Last point I would add is gap also exist in target sequence. In the simulation, these gaps is sampled from the statonary distribution of the emission matrix.
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YAZEED AL-OUFEE
YAZEED AL-OUFEE
I am an electronics engineer. I have worked in education. I am building my software development career at the moment.
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MANSOUR AL-BATRAN
MANSOUR AL-BATRAN
I am an civil engineer. I have worked in education. I am building my software development career at the moment.
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AREEJ ELAYYAN
AREEJ ELAYYAN
A graduate in software engineering with a passion for all things related to technology and programming. Worked in IT teaching and supervision, and looking for new opportunities to share my skills and passion while expanding my horizons.
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ABDULLAH NAZZAL
ABDULLAH NAZZAL
This is Abdullah Nazzal I'm 25 years old, I'm graduated from the Jadara University, majored Software Engineering.
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ALA ABU AL-RISH
ALA ABU AL-RISH
A visionary that handles global data systems and building enterprises infrastructure. Has a total of 7+ years experience as a CTO in the IT and multimedia industry.
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SARA BASAM
SARA BASAM
sara basem al khtibeh studied civil engineering 27 years old worked as site engineer for two years but now looking forward to find new career in programing.
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$interpolate
ng
Compiles a string with markup into an interpolation function. This service is used by the
HTML $compile
service for data binding. See
$interpolateProvider
for configuring the
interpolation markup.
var $interpolate = ...; // injected
var exp = $interpolate('Hello {{name | uppercase}}!');
expect(exp({name:'Angular'}).toEqual('Hello ANGULAR!');
$interpolate(text[, mustHaveExpression][, trustedContext]);
| Param | Type | Details |
|---|---|---|
| text | string | The text with markup to interpolate. |
| mustHaveExpression (optional) | boolean | if set to true then the interpolation string must have embedded expression in order to return an interpolation function. Strings with no embedded expression will return null for the interpolation function. |
| trustedContext (optional) | string | when provided, the returned function passes the interpolated
result through |
| function(context) | an interpolation function which is used to compute the interpolated string. The function has these parameters:
|
Symbol to denote the end of expression in the interpolated string. Defaults to }}
.
Use $interpolateProvider#endSymbol
to change
the symbol.
| string | start symbol. |
Symbol to denote the start of expression in the interpolated string. Defaults to {{
.
Use $interpolateProvider#startSymbol
to change
the symbol.
| string | start symbol. |


Karim Roswell
1234 Example Drive, Sample, WA 54321
425-123-1234
Email<EMAIL_ADDRESS>Objective
My goal is to become a Computer Software Engineer in AI for Triple-A video games.
Experience
- High School Yearbook Committee - Part of the team that created a yearbook for the high school. The person who designed, organized and finalized the student art page. Introduced the idea of an art page with pictures of different types of art created by the students in the high school and created it.
- Wind Ensemble Band- Played - Part of a wind ensemble band that preformed at school and played at a concert in Canada and one of the only three trumpet players.
- Volunteer Camp Counselor- A camp counselor for a group of elementary school children. Was responsible for the safety, well-being and skill achievement of these group of kids. Made the children feel like they were able to have a bit of fun and freedom even though they had a strict set of rules that they had to follow.
Education
Student of Bellevue College
Insight School of Washington High School – Graduated June 2017


|
On the applicability of the Hadamard as an input modulator for problems of classification.
v2.2
Documentation for lg.
|
#include <Partial.hpp>
Public Member Functions | |
| Partial (const int size, const double connectivity) | |
| Partial (lg::IOData &data) | |
| void | initialize (std::vector< Operation * > &inputs) |
| void | save (lg::IOData &data) |
| void | run (std::vector< Operation * > &inputs, const bool training) |
| void | backprop (std::vector< Operation * > &inputs) |
| void | accumulate_deltas (std::vector< Operation * > &inputs) |
| void | update_parameters (const float learningrate) |
| void | reset_deltas (const double momentum) |
| void | pruning (float alpha) |
| float | pruned_percent () |
| const Operation::Type | get_type () const |
| void | print () |
| Public Member Functions inherited from lg::Operation | |
| virtual | ~Operation () |
| virtual void | reset_errors () |
Static Public Member Functions | |
| static std::shared_ptr< Operation > | make (const int size, const double connectivity) |
| Static Public Member Functions inherited from lg::Operation | |
| static std::shared_ptr< Operation > | loadFromFile (lg::IOData &data) |
| static void | saveToFile (std::shared_ptr< Operation > &operation, lg::IOData &data) |
Public Attributes | |
| Tensor_float | _weights |
| Tensor_float | _bias |
| Tensor_float | _deltas |
| std::vector< std::vector< int > > | _forward_map |
| int | _input_size |
| double | _connectivity |
| Public Attributes inherited from lg::Operation | |
| int | _size |
| Tensor_float | _outputs |
| Tensor_float | _errors |
Additional Inherited Members | |
| Public Types inherited from lg::Operation | |
| enum | Type { Unknown, Variable, Linear, Linear_Freeze, Sigmoid, Tanh, Relu, Softmax, Recurrent, Partial, Dropout, Convolution, Normalization, Addition, Concatenate, Maxpooling, Averagepooling, Selu, Autoencoder, ResidualBlock, CapsulesDense, Types_Count } |
| OPERATIONS TYPES AVAILABLE. More... | |
Definition at line 19 of file Partial.hpp.
| lg::Partial::Partial | ( | const int | size, |
| const double | connectivity | ||
| ) |
Definition at line 25 of file Partial.cpp.
| lg::Partial::Partial | ( | lg::IOData & | data | ) |
Definition at line 32 of file Partial.cpp.
|
virtual |
Reimplemented from lg::Operation.
Definition at line 168 of file Partial.cpp.
|
virtual |
Reimplemented from lg::Operation.
Definition at line 147 of file Partial.cpp.
|
virtual |
Reimplemented from lg::Operation.
Definition at line 268 of file Partial.cpp.
|
virtual |
Reimplemented from lg::Operation.
Definition at line 82 of file Partial.cpp.
|
static |
Definition at line 19 of file Partial.cpp.
|
virtual |
Reimplemented from lg::Operation.
Definition at line 274 of file Partial.cpp.
| float lg::Partial::pruned_percent | ( | ) |
Definition at line 259 of file Partial.cpp.
| void lg::Partial::pruning | ( | float | alpha | ) |
|
virtual |
Reimplemented from lg::Operation.
Definition at line 244 of file Partial.cpp.
|
virtual |
Reimplemented from lg::Operation.
Definition at line 122 of file Partial.cpp.
|
virtual |
Reimplemented from lg::Operation.
Definition at line 62 of file Partial.cpp.
|
virtual |
Reimplemented from lg::Operation.
Definition at line 196 of file Partial.cpp.
| Tensor_float lg::Partial::_bias |
Definition at line 43 of file Partial.hpp.
| double lg::Partial::_connectivity |
Definition at line 48 of file Partial.hpp.
| Tensor_float lg::Partial::_deltas |
Definition at line 44 of file Partial.hpp.
| std::vector< std::vector< int > > lg::Partial::_forward_map |
Definition at line 46 of file Partial.hpp.
| int lg::Partial::_input_size |
Definition at line 47 of file Partial.hpp.
| Tensor_float lg::Partial::_weights |
Definition at line 42 of file Partial.hpp.


1 override of DoFinish
System.Management.Automation (1)
engine\remoting\client\Job.cs (1)
4379
protected override void
DoFinish
()
1 reference to DoFinish
System.Management.Automation (1)
engine\remoting\client\Job.cs (1)
3213
DoFinish
();