Frank Yiannas, VP of Food Safety at Walmart, and John Cohn, IBM Fellow & Chief Scientist for Internet of Things, share the opportunistic area of supply chain quality control in the food industry, powered by blockchain-based solution developed by IBM.
Genius of Things: Blockchain and Food Safety with IBM and Walmart


Erasing CIX File Directory Files
Selected from the Action button menu in the file list dialog.
This button allows you to delete the selected files from the file directory. Note that if any have entries in the file list, those entries will be removed.
Depending on how you have the file directory deletion confirmation options set in Settings Moderate..., you may be asked to confirm deletion of all the files you have selected, or for those files that have entries in the file list.


Is there anyway of getting 30d6 sneak with dev crit and high ac? if not is there a very high sneak attack with dev crit and high ac I am trying a build by mike1471 something like that and it has 30d6sneak but low hp and lowish ac but apart from that its a really good build. If it can be done could someone post me a lvl guide please please please thank you guys u r all da best next me lol
IMO you don't need sneak attack if you have a high dev crit DC.


Birmingham Airport named as one of most likely to lose passengers' luggage
MoneySupermarket did some research to find out which UK airports are the worst for this, and unsurprisngly all the London ones came in the top ten
by James Rodger, https://www.facebook.com/jamesrodgerjournalistBirmingham Airport has been named and shamed as one of the most likely in the country to lose your luggage.
The Civil Aviation Authority, responsible for handling disputes that are unresolved by the airline, received over 2,000 unresolved luggage complaints in the past five years.
MoneySupermarket did some research to find out which UK airports are the worst for this, and unsurprisngly all the London ones came in the top ten.
London Heathrow is the worst airport for lost baggage, with 397 per cent more complaints than the average UK airport and more than double that of London Gatwick, which came second in the list.
Birmingham Airport is fourth, with 100 unresolved lost luggage claims, with Manchester Airport slightly ahead, in third.
The research also found that on average, passengers wait nearly eight days for luggage to be returned by an airline, with eight per cent of people claiming they never saw their belongings again.
Flights between the UK and Spain have the highest number of lost or delayed luggage claims, and damaged baggage is also an issue, with 32 per cent experiencing broken suitcases or items within their suitcase when flying to or from the UK.
Anna Sant, travel insuranceexpert at MoneySuperMarket, advised: "Preparing for your trip with items such as luggage tags can help any lost items find their way back to you.
"Many airlines have information sheets for anyone whose luggage goes missing, which details next steps and acts as written confirmation. Noting the contents and value of your cases will also assist with any subsequent insurance claims.
"It's therefore vital you take out travel insurance with the right level of cover as soon as you book your trip. Most policies will cover the full cost of your belongings but it's worth double checking before proceeding with a policy."
Birmingham Airport
Birmingham Airport is the UK's third largest airport outside London and the seventh largest overall.
By 2033, Birmingham Airport is forecast to handle around 18 million passengers a year, an increase of 40% compared to current passengers numbers.
Size: Seventh largest in UK. Third largest outside London.
Location: Bickenhill, Solihull
Runways: 3,052 length, 10,013 ft.
Passengers: 12,445,295 (as of 2018)
Busiest domestic routes: Belfast City (275,000 passengers), Edinburgh (267,000), Belfast International (252,000), Glasgow International (234,000), Aberdeen (112,560).
Busiest international routes: Dublin (923,000 passengers), Dubai (710,517), Amsterdam (703,047), Paris-Charles de Gaulle (412,080), Palma de Mallorca (383,348).
Operating base for: Flybe, Jet2.com, Ryanair, Thomas Cook Airlines and TUI Airways.
The top 10 UK airports for unresolved lost luggage claims
London Heathrow - 636
London Gatwick - 280
Manchester - 186
Birmingham - 100
London Stansted - 61
Edinburgh - 47
London Luton - 27
Glasgow - 26
Newcastle - 23
London City Airport - 15


Id: 256
Name: Pat Gueterman
Status: Dead
Species: Robot
Type:
Gender: Male
OriginName: unknown
OriginUrl:
LocationName: Planet Squanch
LocationUrl: https://rickandmortyapi.com/api/location/35
Image: https://rickandmortyapi.com/api/character/avatar/256.jpeg
Episode: ['https://rickandmortyapi.com/api/episode/21']
Url: https://rickandmortyapi.com/api/character/256
Created: 2017-12-31T13:23:08.567Z


Rose Coconut | sh.2960
Becca lip tint | sh.1460
Skin perfecting loose powder | sh.2450
Blue shadow tint | sh.1450
Lush spring time 2 color eyshadow | sh.1300
Caramel poreless Foundation | sh.2650
Makeup forever skin perfecting matte finish | sh.1500
Stunna matte elusive lip paint | sh.1300
Fawn Stilla concealer | sh.1250


Imagine the middle between mailing and messaging. Imagine Smalltalk. The place to chat and communicate as simple as instant messaging, with the benefits of mail.
Centralized. Open Source.
Smalltalk is open to everyone. It's open source, meaning everyone can suggest ideas or read what Smalltalk does.
Secure. Ready to use.
Smalltalk is more secure as mailing: messages are getting sent via SSL-Encryption right in your browser and no one can fake mail addresses.
Ready? Go!
Smalltalk is immediately ready to use. Just sign in with your AXOME account. Done.


---
layout: page
header:
image_fullwidth: "head.png"
title: 'Solving your way through languages & codes: the International Linguistics
Olympiad'
date: '2015-07-13T18:10:00.001+01:00'
author: Hedvig Skirgård
tags:
- linguistic olympiad
- "#ioling"
- ioling
- linguistics
- bulgaria
- IOL
modified_time: '2017-05-27T01:48:51.097+01:00'
blogger_id: tag:blogger.com,1999:blog-6439072690783735486.post-4135436214391217151
blogger_orig_url: http://international-linguistics-olympiad.blogspot.com/2015/07/solving-your-way-through-languages.html
---
New post at the Facebook page 13-th International Linguistics Olympiad:
We want to recommend this: https://www.youtube.com/watch?v=-Fu44oJqDjI https://www.youtube.com/watch?v=-Fu44oJqDjI As the IOL draws nearer, here is a neat video from last year about the competition and linguistics as a whole! What kind of problems do you expect on the Olympiad this year?
Best,
Local Organising Committee in Bulgaria of the 13th International Linguistics Olympiad


I am a full stack web developer with a passion for creating highly performant web apps with cool UIs. I have honed my programming skills through my studies at Seneca College in their Computer Programmer Diploma Program and through my Co-op as a web developer at the Ontario's Ministry of Health. I enjoy tackling complex problems and I'm always looking for the next one.
When I'm not coding, I am a big sports guy, watching and playing. I also like to eat BBQ, go on nature walks and watch movies. Thank you for visiting my website.


Astronaut the cleaner is a PC game built using Unity. In this game, players are required to destroy trash on earth by shooting lasers to achieve the highest score, besides that there is a reduction in the score if the laser hits humans.
Team :


The greatest of all time
Damn (stylized as DAMN.) is the fourth studio album by American rapper Kendrick Lamar. It was released on April 14, 2017, through Top Dawg Entertainment, distributed by Aftermath Entertainment and Interscope Records.
The album features production from a variety of record producers, including executive producer and Top Dawg Entertainment label-head Anthony "Top Dawg" Tiffith, Sounwave, DJ Dahi, Mike Will Made It, and Ricci Riera; as well as further production contributions from James Blake, Steve Lacy, BadBadNotGood, Greg Kurstin, The Alchemist, and 9th Wonder, among others. Damn features appearances from singers Rihanna and Top Dawg signee Zacari, along with Irish rock band U2.
Untitled Unmastered (stylized as untitled unmastered.) is a compilation album by American rapper Kendrick Lamar. It was released on March 4, 2016, by Top Dawg Entertainment, Aftermath Entertainment and Interscope Records.
It consists of previously unreleased demos that originated during the recording of Lamar's album To Pimp a Butterfly (2015),2 continuing that work's exploration of politically-charged and philosophical themes, as well as its experimentation with free jazz, soul, avant-garde music, and funk styles. The compilation album received widespread acclaim from critics, and it debuted atop the US Billboard 200.
To Pimp a Butterfly is the third studio album by American rapper Kendrick Lamar. It was released on March 15, 2015, by Top Dawg Entertainment, distributed by Aftermath Entertainment and Interscope Records.
The album was recorded in studios throughout the United States, with production from Sounwave, Terrace Martin, Taz "Tisa" Arnold, Thundercat, Rahki, LoveDragon, Flying Lotus, Pharrell Williams, Boi-1da, Knxwledge, and several other high-profile hip hop producers, as well as executive production from Dr. Dre and Anthony "Top Dawg" Tiffith. Guest appearances include Thundercat, George Clinton, Bilal, Anna Wise, Snoop Dogg, James Fauntleroy, Ronald Isley, and Rapsody.
Good Kid, M.A.A.D City (stylized as good kid, m.A.A.d city) is the second studio album by American rapper Kendrick Lamar.
It was released on October 22, 2012, through Top Dawg Entertainment, distributed by Aftermath Entertainment and Interscope Records. The album is Lamar's major label debut, after his independently released first album Section.80 in 2011 and his signing to Aftermath and Interscope the following year.
Section.80 is the debut studio album by American rapper Kendrick Lamar. It was released on July 2, 2011, through Top Dawg Entertainment.
The album features guest appearances from GLC, Colin Munroe, Ashtrobot, BJ the Chicago Kid, Schoolboy Q, Ab-Soul and vocals from late singer-songwriter Alori Joh. The production was mainly handled by Top Dawg in-house producers from production group Digi+Phonics, along with THC, Tommy Black, Wyldfyer, Terrace Martin and J. Cole. The concept album features lyrical themes delivered by Lamar such as the 1980s crack epidemic, racism and medication tolerance. The album's lead single, "HiiiPoWeR" was released on April 12, 2011.


CoDesign: International Journal of CoCreation in Design and the Arts
Authors
Klas Karlgren & Robert Ramberg
Notes
Concepts
Quotations
References


@LabsAssistant
Time to try this. No idea if it will work, which is why I am changing port on iguana to 9998. Instructions:
1) sudo ufw allow 9998
2) ./stop.sh
3) ./start.sh -reindex
This is a major update to how MoMoM and dPoW work, the daemon especally for KMD, should use a fair bit less ram now. I dont know if this will fix the migrate problem, but it certainly can't hurt it and remvoes a variable allowing us to test further.
(edited)


@import conf.Configuration @import model.{Competition, Group, TeamUrl} @import common.LinkTo @import views.support.RenderClasses @import views.support.`package`.Seq2zipWithRowInfo @import model.CompetitionDisplayHelpers.cleanTeamName @import football.model.GuTeamCodes @(competition: Competition, group: Group, heading: Option[String] = None, headingLink: Option[String] = None, highlightTeamId: Option[String] = None, striped: Boolean = false, responsiveFont: Boolean = false, linkToCompetition: Boolean = false, withCrests: Boolean = false)(implicit request: RequestHeader)
| P | Team | GP | W | D | L | F | A | GD | Pts | Form |
|---|---|---|---|---|---|---|---|---|---|---|
| @entry.team.rank | @TeamUrl(entry.team).map{ url => @cleanTeamName(entry.team.name) }.getOrElse{ @cleanTeamName(entry.team.name) } | @entry.team.total.played | @entry.team.total.won | @entry.team.total.drawn | @entry.team.total.lost | @entry.team.total.goalsFor | @entry.team.total.goalsAgainst | @entry.team.goalDifference | @entry.team.points | @football.views.html.fragments.teamForm(entry.team.id, competition) |
| View full @competition.fullName table |


Arguments: crt_test_name : test name where this attempt was found sva_state : SVA state to be added sva_time_start : SVA start time to be added sva_time_end : SVA end time to be added
virtual function uvm_object_wrapper
get_object_type
()
function string
get_sva_details
( string
crt_test_name)
Print the SVA details for a given test name
Returns: a string with all SVA details
Arguments: crt_test_name : test name from where the user want's to print SVA info


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CSS has been lacking proper layout mechanisms for far too long. Transitions, animations, filters, all of these are great and useful additions to the language, but they don't address the major problems that Web developers have been complaining about for what seems like an eternity.
Finally, thanks to Flexbox, we have a solution.
This site is not another CSS framework. Instead, its purpose is to showcase problems once hard or impossible to solve with CSS alone, now made trivially easy with Flexbox. And with the recent release of Internet Explorer 11 and Safari 6.1, the latest Flexbox spec is now supported in every modern browser.
Check out the demos below. View the styles in the Web inspector or dive into the source to see just how easy CSS layout will become once Flexbox becomes mainstream.
{{ demo.excerpt }}
display:flexbox
).display:box
).All of the code samples on this site show how to solve a particular design problem with Flexbox. They show just the code that's needed to make the demos work in a spec-compliant browser. Some browsers do not fully comply with the latest version of the spec, so sadly, a few workarounds were necessary.
Workarounds for non-compliant browsers are not shown in the code samples, but if you're curious about those implementation details, you can check out the source files. Each demo links to its source, and all browser-specific workarounds are well-documented, so don't be afraid to take a look.
The vendor prefixing and translating of current flex properties to their legacy equivalents is all handled by autoprefixer. If you're writing Flexbox code and not using autoprefixer, well, you're making a horrible mistake.
The class naming convention used in the code samples and source files is taken from SUIT CSS, which is based on BEM methodologies. Each example includes one or more reusable CSS components allowing you to adapt or copy these patterns in to your own projects. Links are provided to their respective components on each example page.
If you find a mistake or would like to suggest an additional example, feel free to open an issue or submit a pull request on Github.
The following translations have been graciously provided by the community: Chinese.
Please note that translations are unofficial and may be inaccurate or out of date. To submit your own translation, please submit a pull request or open an issue on Github and link to your translated content.


# UoPeople
Quiz Emphasise negative words Tool
This tool emphisise the words like
not, rarely and hardly in the quiz questions. It helpes to choose the negative answers.
Please test it in the review quiz before use in graded quiz or final exam.
# How to use
- Drag this ->
Emphasise
<- link into the bookmark bar of your browser.
- Open any quiz in moodle
- Click the bookmarklet that you dragged into your bookmark bar
- If there is a negative word in the questions, it has been changed the color. If there's no negative words, nothing happened.


--- date: 2010-06-08T00:00:00Z published: true title: The Freegan Establishment - NYTimes.com url: /2010/06/08/the-freegan-establishment-nytimes-com/ ---
Kit is a freegan. He maintains that our society wastes far too much. Freeganism is a bubbling stew of various ideologies, drawing on elements of communism, radical environmentalism, a zealous do-it-yourself work ethic and an old-fashioned frugality of the sock-darning sort. Freegans are not revolutionaries. Rather, they aim to challenge the status quo by their lifestyle choices. Above all, freegans are dedicated to salvaging what others waste and — when possible — living without the use of currency. “I really dislike spending money,” Kit told me. “It doesn’t feel natural.


| read.fwf {utils} | R Documentation |
Read a table of fixed width formatted
data into a data.frame
.
read.fwf(file, widths, header = FALSE, sep = "\t", skip = 0, row.names, col.names, n = -1, buffersize = 2000, fileEncoding = "", ...)
file |
the name of the file which the data are to be read from. Alternatively, |
widths |
integer vector, giving the widths of the fixed-width fields (of one line), or list of integer vectors giving widths for multiline records. |
header |
a logical value indicating whether the file contains the
names of the variables as its first line. If present, the names
must be delimited by |
sep |
character; the separator used internally; should be a character that does not occur in the file (except in the header). |
skip |
number of initial lines to skip; see
|
row.names |
see |
col.names |
see |
n |
the maximum number of records (lines) to be read, defaulting to no limit. |
buffersize |
Maximum number of lines to read at one time |
fileEncoding |
character string: if non-empty declares the
encoding used on a file (not a connection) so the character data can
be re-encoded. See the ‘Encoding’ section of the help for
|
... |
further arguments to be passed to
|
Multiline records are concatenated to a single line before processing.
Fields that are of zero-width or are wholly beyond the end of the line
in file
are replaced by NA
.
Negative-width fields are used to indicate columns to be skipped, e.g.,
-5
to skip 5 columns. These fields are not seen by
read.table
and so should not be included in a col.names
or colClasses
argument (nor in the header line, if present).
Reducing the buffersize
argument may reduce memory use when
reading large files with long lines. Increasing buffersize
may
result in faster processing when enough memory is available.
Note that read.fwf
(not read.table
) reads the supplied
file, so the latter's argument encoding
will not be useful.
A data.frame
as produced by read.table
which is called internally.
Brian Ripley for R version: originally in Perl
by Kurt Hornik.
scan
and read.table
.
read.fortran
for another style of fixed-format files.
ff <- tempfile() cat(file = ff, "123456", "987654", sep = "\n") read.fwf(ff, widths = c(1,2,3)) #> 1 23 456 \ 9 87 654 read.fwf(ff, widths = c(1,-2,3)) #> 1 456 \ 9 654 unlink(ff) cat(file = ff, "123", "987654", sep = "\n") read.fwf(ff, widths = c(1,0, 2,3)) #> 1 NA 23 NA \ 9 NA 87 654 unlink(ff) cat(file = ff, "123456", "987654", sep = "\n") read.fwf(ff, widths = list(c(1,0, 2,3), c(2,2,2))) #> 1 NA 23 456 98 76 54 unlink(ff)


@(title: String, user: User)(implicit flash: play.api.mvc.Flash) @styles = {} @scripts = {} @main(title, styles, scripts, user) { @flash.get("success").map { message =>
Welcome, @{user.email}!
} else {Welcome to SHINE, our prototype historical search engine.
}This tool has been developed as part of the Big UK Data Arts and Humanities project funded by the AHRC. Read more about the project on our blog.
The data was acquired by JISC from the Internet Archive (IA) and includes all .uk websites in the IA web collection crawled between around 1996 until April 2013 (you can read more details about this dataset here). The collection comprises over 3.5 billion items (urls, images and other documents) and has been full-text indexed by the UK Web Archive. Every word of every website in the collection can be searched for and analysed.
Use ‘trends’ to analyse the number of pages a word or phrase appears in the collection over a given period (within 1996-2013). Comparisons can be drawn by adding several words or phrases separated by a comma. E.g. cat, dog, goldfish
Clicking on a graph point gives a sample of websites that use that word or phrase and give the context it was used.
On the Search page, you can enter search terms and get the results back as a list of pages, presenting more detailed information per page. The result set may be a VERY large number of webpages (possibly millions) and they are NOT ordered by relevance which you may expect with a search engine such as Google.
You can, however, narrow down the search with faceting on such things as ‘content type’, ‘year of capture’, domain and even Post Code amongst others.
See Search Tips for help with search terms and query syntax.
This is a prototype service and we need YOUR feedback so that we can improve it and extend it to our other web archive collections. Please get in touch with your experiences (good and bad), issues and questions through our contact page or on Twitter.
}


IJSE WEB POS
Sign out
Dashboard
Customers
Items
Place Order
Search Orders
Place Order
Customer Select
C001
C002
C003
C004
C005
Customer Name
Address
Phone
Item Select
I001
I002
I003
I004
I005
Item description
Price
Quantity available
Enter order quantity:
Add to cart
Current items on cart :
Item ID
Description
Price
Quantity ordered
I001
Sugar
120
2
Remove
I002
Butter
350
1
Remove
I003
Noodles
325
1
Remove


Thane: Mentally-challenged man raped, 1 booked
“Over the weekend, the accused found the victim near his shop while he was closing it.  The accused, Monu Sharma (30), a salon owner, has been booked under section 377 (unnatural sexual act) of the IPC.
“Over the weekend, the accused found the victim near his shop while he was closing it. He lured the victim by promising him food and raped him inside his shop,” said an officer. He allegedly also threatened to kill the victim.
“When his parents spoke to the salon owner, he denied the incident and threatened them too,” the officer said. The victim’s medical tests have confirmed rape, said police. “The accused has fled. We are trying to get CCTV camera footage of him taking the victim inside his shop,” the officer added.


GitHub Activiewer
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Methods for generating Markov Chain Monte Carlo (MCMC) posterior samples of Bayesian linear regression model parameters that require only summary statistics of data as input. Summary statistics are useful for systems with very limited amounts of physical memory. The package provides two functions: one function that computes summary statistics of data and one function that carries out the MCMC posterior sampling for Bayesian linear regression models where summary statistics are used as input. The function read.regress.data.ff utilizes the R package 'ff' to handle data sets that are too large to fit into a user's physical memory, by reading in data in chunks.
| Version: | 1.0-1 |
| Depends: | R (≥ 3.1.1), mvnfast, ff |
| Published: | 2015-03-03 |
| Author: | Evgeny Savel'ev, Alexey Miroshnikov, Erin Conlon |
| Maintainer: | Evgeny Savel'ev <savelev at vt.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| In views: | Bayesian |
| CRAN checks: | BayesSummaryStatLM results |
| Reference manual: | BayesSummaryStatLM.pdf |
| Package source: | BayesSummaryStatLM_1.0-1.tar.gz |
| Windows binaries: | r-devel: BayesSummaryStatLM_1.0-1.zip, r-release: BayesSummaryStatLM_1.0-1.zip, r-oldrel: BayesSummaryStatLM_1.0-1.zip |
| OS X binaries: | r-release: BayesSummaryStatLM_1.0-1.tgz, r-oldrel: BayesSummaryStatLM_1.0-1.tgz |
| Old sources: | BayesSummaryStatLM archive |
Please use the canonical form https://CRAN.R-project.org/package=BayesSummaryStatLM to link to this page.


--- layout: home title: This is the home page title ---
This is a simple example of a minimal website with shared layouts. Take a look at this guide on using Jekyll on GitHub Pages to learn more.


CLUObserveModule
@interface CLUObserveModule : NSObject <CLURecordableModule>
CLUObserveModule
is a base class for all modules which needs to observe their data (except CLUVideoModule
module) and record them only if new data has arrived (like CLUNetworkModule
or CLUUserInteractionModule
modules) or something changed with next frame iteration (like CLUViewStructureModule
module). This class is responsible for thread safety of new data writing via Writers and Data buffer management.
-
BOOL property which indicates whether video recording has started or not
Declaration
Objective-C
@property (readonly, nonatomic) BOOL isRecording;
Swift
var isRecording: Bool { get }
-
Current available timestamp. This property updating constantly with
addNewFrameWithTimestamp:
fromCLURecordableModule
protocolDeclaration
Objective-C
@property (readonly, nonatomic) CFTimeInterval currentTimeStamp;
Swift
var currentTimeStamp: CFTimeInterval { get }
-
Indicate whether data buffer empty or not
Declaration
Objective-C
- (BOOL)isBufferEmpty;
Swift
func isBufferEmpty() -> Bool
Return Value
BOOL value which indicates whether data buffer empty or not
-
Add new
NSData
object to buffer, so it could be saved to file via Writer on next iteration ofaddNewFrameWithTimestamp:
fromCLURecordableModule
protocolDeclaration
Objective-C
- (void)addData:(NSData *)bufferItem;
Swift
func add(_ bufferItem: Data!)
Parameters
bufferItem
NSData
object which you need to save to buffer -
Remove all
NSData
entities from data bufferDeclaration
Objective-C
- (void)clearBuffer;
Swift
func clearBuffer()


{% if page.description %}{% endif %} {% if page.css %}{% endif %} {% if site.google_ad_client %} {% endif %}
{{ content }}Do you like this freebie? Want to get more stuff like this?
Subscribe to designmodo news and updates to stay tuned on great designs.
Go to: designmodo.com/flat-free


| full.score {mgcv} | R Documentation |
Evaluates GCV/UBRE score for a GAM, given smoothing
parameters. The routine calls gam.fit
to fit the model, and is
usually called by nlm
to optimize the smoothing parameters.
This is basically a service routine for gam
, and is not usually
called directly by users. It is only used in this context for GAMs fitted by
outer iteration (see gam.outer
) when the the outer method is
"nlm.fd"
(see gam
argument optimizer
).
full.score(sp,G,family,control,gamma,...)
sp |
The logs of the smoothing parameters |
G |
a list returned by |
family |
The family object for the GAM. |
control |
a list returned be |
gamma |
the degrees of freedom inflation factor (usually 1). |
... |
other arguments, typically for passing on to |
The value of the GCV/UBRE score, with attribute "full.gam.object"
which is the full object returned by gam.fit
.
Simon N. Wood simon.wood@r-project.org


--- layout: backedpost title: Skeleton War subtitle: A 4-Player Bone-Chilling Brawl! carousel-title: Skeleton War carousel-subtitle: A 4-Player Bone-Chilling Brawl! back-img: /assets/img/skeletonWar.png cover-img: /assets/img/frame.png carousel-img: /assets/img/skeletonwarCaro.png tags: [game, project] front-page: false comments: true full-width: false ---
An experimental brawl-it-up with skeletons and bones! Grow a spine and hurl it at your opponents to crush their skulls under their own feet!
This was made as a part of the virtual Game Jam, Hype Jam! In Hype Jam, instead of making a game in a limited time, you make a game pitch to test your ideas. You can make a trailer about any game idea under the sun, and the final product literally hypes itself! It was a really fun project, even if Skeleton Wars doesn't exist.
You can learn more about it here
Available in stores never. :P


Hello! I'm Jennifer, a software engineer based in the Bay Area of California. I love coding and solving problems. I'm always tempted to solve difficult problems, and it feels so rewarding after solving it.
I'm experienced in Ruby, Ruby on Rails, React, Redux, JavaScript, Node, and other technologies, shown in my projects below. And here's a like to my resume!
When I’m not coding, I enjoy spending time with family and friends. Other times, I enjoy a ride out doing longboard dancing. All these help me de-stress and stay healthy.
A YouTube clone, with a twist of a dark theme, built with React, Redux, Rails, PostgreSQL, and AWS S3 for video storage. It uses bcrypt for authentication measures, which requires users to login in order to upload videos, but allows them to watch videos without having to login.


The Filter Scheduler supports filtering and weighting to make informed decisions on where a new instance should be created. This Scheduler supports only working with Compute Nodes.
During its work Filter Scheduler firstly makes dictionary of unfiltered hosts, then filters them using filter properties and finally chooses hosts for the requested number of instances (each time it chooses the least costed host and appends it to the list of selected costs).
If it turns up, that it can’t find candidates for the next instance, it means that there are no more appropriate instances locally.
If we speak about filtering and weighting, their work is quite flexible in the Filter Scheduler. There are a lot of filtering strategies for the Scheduler to support. Also you can even implement your own algorithm of filtering.
There are some standard filter classes to use (nova.scheduler.filters):
AllHostsFilter - frankly speaking, this filter does no operation. It passes all the available hosts.
ImagePropertiesFilter - filters hosts based on properties defined on the instance’s image. It passes hosts that can support the specified image properties contained in the instance.
AvailabilityZoneFilter - filters hosts by availability zone. It passes hosts matching the availability zone specified in the instance properties.
|ComputeCapabilityFilter| - checks that the capabilities provided by the host compute service satisfy any extra specifications associated with the instance type (that have no scope, see TrustedFilter for details). It passes hosts that can create the specified instance type.
The extra specifications can have an operator at the beginning of the value string of a key/value pair. If there is no operator specified, then a default operator of ‘s==’ is used. Valid operators are:
* = (equal to or greater than as a number; same as vcpus case)
* == (equal to as a number)
* != (not equal to as a number)
* >= (greater than or equal to as a number)
* <= (less than or equal to as a number)
* s== (equal to as a string)
* s!= (not equal to as a string)
* s>= (greater than or equal to as a string)
* s> (greater than as a string)
* s<= (less than or equal to as a string)
* s< (less than as a string)
* <in> (substring)
* <or> (find one of these)
Examples are: ">= 5", "s== 2.1.0", "<in> gcc", and "<or> fpu <or> gpu"
Now we can focus on these standard filter classes in details. I will pass the simplest ones, such as AllHostsFilter, CoreFilter and RamFilter are, because their functionality is quite simple and can be understood just from the code. For example class RamFilter has the next realization:
class RamFilter(filters.BaseHostFilter):
"""Ram Filter with over subscription flag"""
def host_passes(self, host_state, filter_properties):
"""Only return hosts with sufficient available RAM."""
instance_type = filter_properties.get('instance_type')
requested_ram = instance_type['memory_mb']
free_ram_mb = host_state.free_ram_mb
total_usable_ram_mb = host_state.total_usable_ram_mb
used_ram_mb = total_usable_ram_mb - free_ram_mb
return total_usable_ram_mb * FLAGS.ram_allocation_ratio - used_ram_mb >= requested_ram
Here ram_allocation_ratio means the virtual RAM to physical RAM allocation ratio (it is 1.5 by default). Really, nice and simple.
Next standard filter to describe is AvailabilityZoneFilter and it isn’t difficult too. This filter just looks at the availability zone of compute node and availability zone from the properties of the request. Each compute service has its own availability zone. So deployment engineers have an option to run scheduler with availability zones support and can configure availability zones on each compute host. This classes method host_passes returns True if availability zone mentioned in request is the same on the current compute host.
The ImagePropertiesFilter filters hosts based on the architecture, hypervisor type, and virtual machine mode specified in the instance. E.g., an instance might require a host that supports the arm architecture on a qemu compute host. The ImagePropertiesFilter will only pass hosts that can satisfy this request. These instance properties are populated from properties define on the instance’s image. E.g. an image can be decorated with these properties using glance image-update img-uuid –property architecture=arm –property hypervisor_type=qemu Only hosts that satisfy these requirements will pass the ImagePropertiesFilter.
|ComputeCapabilitesFilter| checks if the host satisfies any ‘extra specs’ specified on the instance type. The ‘extra specs’ can contain key/value pairs, and the ComputeCapabilitiesFilter will only pass hosts whose capabilities satisfy the requested specifications. All hosts are passed if no ‘extra specs’ are specified.
ComputeFilter is quite simple and passes any host whose compute service is enabled and operational.
Now we are going to IsolatedHostsFilter. There can be some special hosts reserved for specific images. These hosts are called isolated. So the images to run on the isolated hosts are also called isolated. This Scheduler checks if image_isolated flag named in instance specifications is the same that the host has.
DifferentHostFilter - its method host_passes returns True if host to place instance on is different from all the hosts used by set of instances.
SameHostFilter does the opposite to what DifferentHostFilter does. So its host_passes returns True if the host we want to place instance on is one of the set of instances uses.
SimpleCIDRAffinityFilter looks at the subnet mask and investigates if the network address of the current host is in the same sub network as it was defined in the request.
JsonFilter - this filter provides the opportunity to write complicated queries for the hosts capabilities filtering, based on simple JSON-like syntax. There can be used the following operations for the host states properties: ‘=’, ‘<’, ‘>’, ‘in’, ‘<=’, ‘>=’, that can be combined with the following logical operations: ‘not’, ‘or’, ‘and’. For example, there is the query you can find in tests:
['and',
['>=', '$free_ram_mb', 1024],
['>=', '$free_disk_mb', 200 * 1024]
]
This query will filter all hosts with free RAM greater or equal than 1024 MB and at the same time with free disk space greater or equal than 200 GB.
Many filters use data from scheduler_hints, that is defined in the moment of creation of the new server for the user. The only exception for this rule is JsonFilter, that takes data in some strange difficult to understand way.
The RetryFilter filters hosts that have already been attempted for scheduling. It only passes hosts that have not been previously attempted.
The TrustedFilter filters hosts based on their trust. Only passes hosts that match the trust requested in the `extra_specs’ for the flavor. The key for this filter is `trust:trusted_host’, where `trust’ is the scope of the key and `trusted_host’ is the actual key value’. The value of this pair (`trusted’/`untrusted’) must match the integrity of a host (obtained from the Attestation service) before it is passed by the TrustedFilter.
To use filters you specify next two settings:
scheduler. This setting can be used multiple times.
scheduler_default_filters - Of the available filters, defines those that the scheduler uses by default.
The default values for these settings in nova.conf are:
--scheduler_available_filters=nova.scheduler.filters.standard_filters
--scheduler_default_filters=RamFilter,ComputeFilter,AvailabilityZoneFilter,ComputeCapabilityFilter,ImagePropertiesFilter
With this configuration, all filters in nova.scheduler.filters would be available, and by default the RamFilter, ComputeFilter, AvailabilityZoneFilter, |ComputeCapabilityFilter|, and ImagePropertiesFilter would be used.
If you want to create your own filter you just need to inherit from BaseHostFilter and implement one method: host_passes. This method should return True if host passes the filter. It takes host_state (describes host) and filter_properties dictionary as the parameters.
As an example, nova.conf could contain the following scheduler-related settings:
--scheduler_driver=nova.scheduler.FilterScheduler
--scheduler_available_filters=nova.scheduler.filters.standard_filters
--scheduler_available_filters=myfilter.MyFilter
--scheduler_default_filters=RamFilter,ComputeFilter,MyFilter
With these settings, nova will use the FilterScheduler for the scheduler driver. The standard nova filters and MyFilter are available to the FilterScheduler. The RamFilter, ComputeFilter, and MyFilter are used by default when no filters are specified in the request.
Filter Scheduler uses so-called weights and costs during its work.
Costs are the computed integers, expressing hosts measure of fitness to be chosen as a result of the request. Of course, costs are computed due to hosts characteristics compared with characteristics from the request. So trying to put instance on a not appropriate host (for example, trying to put really simple and plain instance on a high performance host) would have high cost, and putting instance on an appropriate host would have low.
So let’s find out, how does all this computing work happen.
Before weighting Filter Scheduler creates the list of tuples containing weights and cost functions to use for weighing hosts. These functions can be got from cache, if this operation had been done before (this cache depends on topic of node, Filter Scheduler works with only the Compute Nodes, so the topic would be “compute” here). If there is no cost functions in cache associated with “compute”, Filter Scheduler tries to get these cost functions from nova.conf. Weight in tuple means weight of cost function matching with it. It also can be got from nova.conf. After that Scheduler weights host, using selected cost functions. It does this using weighted_sum method, which parameters are:
This method firstly creates a grid of function results (it just counts value of each function using host_state and weighing_properties) - scores, where it would be one row per host and one function per column. The next step is to multiply value from the each cell of the grid by the weight of appropriate cost function. And the final step is to sum values in the each row - it would be the weight of host, described in this line. This method returns the host with the lowest weight - the best one.
If we concentrate on cost functions, it would be important to say that we use compute_fill_first_cost_fn function by default, which simply returns hosts free RAM:
def compute_fill_first_cost_fn(host_state, weighing_properties):
"""More free ram = higher weight. So servers will less free ram will be
preferred."""
return host_state.free_ram_mb
You can implement your own variant of cost function for the hosts capabilities you would like to mention. Using different cost functions (as you understand, there can be a lot of ones used in the same time) can make the chose of next host for the creating of the new instance flexible.
These cost functions should be set up in the nova.conf with the flag least_cost_functions (there can be more than one functions separated by commas). By default this line would look like this:
--least_cost_functions=nova.scheduler.least_cost.compute_fill_first_cost_fn
As for weights of cost functions, they also should be described in nova.conf. The line with this description looks the following way: function_name_weight.
As for default cost function, it would be: compute_fill_first_cost_fn_weight, and by default it is -1.0.
--compute_fill_first_cost_fn_weight=-1.0
Negative function’s weight means that the more free RAM Compute Node has, the better it is. Nova tries to spread instances as much as possible over the Compute Nodes. Positive weight here would mean that Nova would fill up a single Compute Node first.
Filter Scheduler finds local list of acceptable hosts by repeated filtering and weighing. Each time it chooses a host, it virtually consumes resources on it, so subsequent selections can adjust accordingly. It is useful if the customer asks for the some large amount of instances, because weight is computed for each instance requested.
In the end Filter Scheduler sorts selected hosts by their weight and provisions instances on them.
P.S.: you can find more examples of using Filter Scheduler and standard filters in nova.tests.scheduler.


Nisanth Velluva
President
<EMAIL_ADDRESS>Vinod Mohan
Vice President
<EMAIL_ADDRESS>Lekshmi Gopinathan
Secretary
<EMAIL_ADDRESS>Bibin Kuruvilla
Joint Secretary
<EMAIL_ADDRESS>Nandakumar Gopalakrishnan
Treasurer
<EMAIL_ADDRESS>Babitha Bipin
Cultural Secretary
<EMAIL_ADDRESS>Priya Pandalay
Cultural Secretary
<EMAIL_ADDRESS>Sreejith Nair
Sports Secretary
<EMAIL_ADDRESS>Biju Somarajan
Sports Secretary
<EMAIL_ADDRESS>Reshma Ranjan
Yuva & Literature Secretary
<EMAIL_ADDRESS>Jikku Joyce
Communications Secretary
webmaster@colorkerala.org


Create & Control Semantic UI Checkboxes & Radio Buttons with Angular Directives
optional
required
model
=
the variable to place the value of the checkbox.label
@
a string for the label of the checkbox.settings
=
reference to a settings object to pass to the initialization function.enabled
=
a value which can enable/disable the checkbox when it changes.checked-value
=
the value of the model when the checkbox is in a checked state (default is true).unchecked-value
=
the value of the model when the checkbox is in an unchecked state (default is false).indeterminat-value
=
the value of the model when the checkbox is in an indeterminate state (default is undefined).children
@
a selector string of checkboxes that are the 'children' of this checkbox. I.E. the state of this checkbox is determined by the collective state of it's children. When all children are checked - this checkbox is checked. When all children are unchecked - this checkbox is unchecked. When the children have mixed states - this checkbox is indeterminate. When this checkbox is checked it checks all children. When this checkbox is unchecked it unchecks all children.on-init
=
Callback once the module is initialized, passing the initialized element as the first argument.on-change
=
Callback after a checkbox is either checked or unchecked.on-checked
=
Callback after a checkbox is checked.on-indeterminate
=
Callback after a checkbox is set to undeterminate.on-determinate
=
Callback after a checkbox is set to determinate.on-unchecked
=
Callback after a checkbox is unchecked.on-enable
=
Callback after a checkbox is enabled.on-disable
=
Callback after a checkbox is disabled.model
=
the variable to place the value of the radio.label
@
a string for the label of the radio.name
@
the name which groups radio buttons together.value
=
the value of the model if this radio button is selected.settings
=
reference to a settings object to pass to the initialization function.enabled
=
a value which can enable/disable the checkbox when it changes.on-init
=
Callback once the module is initialized, passing the initialized element as the first argument.on-change
=
Callback after a checkbox is either checked or unchecked.on-checked
=
Callback after a checkbox is checked.on-unchecked
=
Callback after a checkbox is unchecked.on-enable
=
Callback after a checkbox is enabled.on-disable
=
Callback after a checkbox is disabled.These attribute directives invoke a behavior on the component where the attribute value can be one of the following formats:
$
@
A selector string.evt
@
The event string which triggers the behavior ('click' by default).enabled
=
A boolean value which determines whether or not the behavior should be called when the event occurs.value
=
The first argument to pass to the behavior if it requires one.


'br}
'br}
{\
podchecker - check the syntax of POD format documentation files
podchecker [-help] [-man] [-(no)warnings] [file ...]
Print a brief help message and exit.
Print the manual page and exit.
\fB-warnings\fR \fB-nowarnings\fR 8
Turn on/off printing of warnings. Repeating -warnings increases the warning level, i.e. more warnings are printed. Currently increasing to level two causes flagging of unescaped *(L"<,>*(R" characters.
The pathname of a s-1PODs0 file to syntax-check (defaults to standard input).
podchecker will read the given input files looking for s-1PODs0 syntax errors in the s-1PODs0 documentation and will print any errors it find to s-1STDERR.s0 At the end, it will print a status message indicating the number of errors found.
Directories are ignored, an appropriate warning message is printed.
podchecker invokes the f(BIpodchecker() function exported by Pod::Checker Please see *(L"podchecker()*(R" in Pod::Checker for more details.
podchecker returns a 0 (zero) exit status if all specified s-1PODs0 files are ok.
podchecker returns the exit status 1 if at least one of the given s-1PODs0 files has syntax errors.
The status 2 indicates that at least one of the specified files does not contain any s-1PODs0 commands.
Status 1 overrides status 2. If you want unambiguous results, call podchecker with one single argument only.
Please report bugs using <http://rt.cpan.org>.
Brad Appleton<EMAIL_ADDRESS>Marek Rouchal<EMAIL_ADDRESS>Based on code for f(BIPod::Text::pod2text|(1) written by Tom Christiansen<EMAIL_ADDRESS>RETURN VALUE
ERRORS
SEE ALSO
AUTHORS


WITNESS LIST Transportation Committee March 23, 2000-9:00A Interim Charge #3 On: Bass, James (Texas Department of Transportation) Behrens, Mike (Texas Department of Transportation) Bohuslav, Ken (Texas Department of Transportation) Jackson, Lee (Dallas Regional Mobility Coalition) Lanham, Bob (Associated General Contractors of Texas) Luedecke, Alvin (Texas Department of Transportation) Ullman, Gerald (Texas Transportation Institute)


Constituency profile: Stirling
A key test of the Tories’ popularity in Scotland.
The constituency of Stirling, which Nicola Sturgeon visited on Tuesday, is the SNP’s top target at this election.
Conservative candidate Stephen Kerr is defending a majority of just 148.
A swing to the SNP of only 0.2% would see the seat change hands.
It is one of the most marginal constituencies in Scotland – and one of a number of seats the SNP are determined to take from the Conservatives, having lost them at the 2017 election.
Stephen Kerr won the seat in 2017 on a swing to the Tories of 11.4%.
The result was so close that a recount was necessary.
This time the SNP will be hoping for the sort of swing that will see them take Stirling easily, along with the likes of Gordon (2.5%), Angus (3.3%) and Moray (4.4%).
For the Conservatives, holding seats like Stirling will be a vital test of the party’s popularity in Scotland.
It would also help to offset potential losses elsewhere in the country.
Labour held Stirling continuously from 1997 to 2015, but finished third in 2017.
The party isn’t really in contention this time, as Stirling is now very much a two-horse race.
Along with the SNP, Conservatives and Labour, the Liberal Democrats and Greens are also standing in the seat.
Stirling voted 67.7% Remain in the EU referendum – the fourth highest Remain vote in Scotland, and the 20th highest Remain vote in the UK.


QUIZ
Do you know Kat?! The Ultimate Test.
Katherine Beame is a magical person and your knowledge of her should illustrate your
deference to her beauty.
Does Kat love pumpkins more than pumpkin spice lattes? Please answer Yes or No.
CURRENT SCORE
Is Kat a Canadian Citizen. Yes or No?
CURRENT SCORE
Is Kat an American Citizen. Yes or No?
CURRENT SCORE
How many countries is Kat a citizen of?
CURRENT SCORE
How many teeth has Kat had pulled? Please answer with an integer.
BONUS QUESTION!
Kat loves the West Wing more than anything else.


To move selector to Release 2016Q1
To select Release 2016Q2 and move into Iteration Selector
To move selector to Release 2016Q3
To move selector to Sprint 1
To select Sprint 2 and move into Board Selector


Drones have become a hot topic in today's press and media. From aerial photography to Amazon and Google's new quicker air delivery systems, they offer numerous useful and creative opportunities; however, with those opportunities come new concerns for personal privacy and safety.
What is a drone?
Drones, technically referred to as Unmanned Aerial Vehicles (or UAVs), are aircrafts without humans aboard. They were originally used primarily in the military, however, they have more recently been used for other commercial and personal purposes, such as new aerial shipping systems including Amazon Prime Air and Google's Project Wing.
Noncommercially, drones have been used for numerous purposes, including photography/videography.
Limitations
Despite all the advantages drones present, some citizens are skeptical of this new technology. Specific compliants include that drones, with their common camera capabilities, may be used to violate personal privacies; and that they may be used as dangerous weapons.
There are legal restrictions on the use of drones, specifically in the Washington DC area. Flying drones above 200 feet within 15 miles of Washington DC is against the law, as well as flying any drones inside of DC itself. There are numerous other no-fly zones, including in and around military bases, national parks, and major airports. It is also required to register a “small unmanned aircraft”, as drones are called according to the Federal Aviation Administration (FAA), which was an interim final rule effective December 21, 2015.


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Skully pioneers advanced situational awareness technology. The company was founded in 2013 with the goal of revolutionizing the most frequent, ubiquitous, and dangerous aspect of our lives: transportation. Skully’s human-centered approach to design and technology has led to the development of the Skully AR-1, the first intelligent HUD motorcycle helmet for consumers. Skully is a privately held company with strategic partnerships spanning the world’s leading automotive technology companies.
A visionary savant, Marcus Weller, Co-Founder & Chairman of Skully Inc., is a motorcycle fanatic with an obsession for human-centered technology and design. Marcus spent many years working in the automotive and semiconductor industries and was quite content to keep doing so until a motorcycle accident inspired him to form Skully in 2013. His innovations have been lauded by the likes of CNN, Popular Science, USA Today, and Popular Mechanics.
As as co-founder and Vice President of Business Operations at Skully, Mitchell (Mitch) Weller is also the Chief of Staff..He’s a people person! Not only that, like Marcus, the CEO, he has an eye for spotting talent which stems from his time as Supervisor in Human Resources for the U.S. Army. With a product management and strategic partnership background as well, when it comes to branching out into new markets, evangelizing the Skully message to new and existing markets, and overall being the eyes and ears of the company, with his ear to the ground watching
Josh Bloom is currently the Vice President of Product Design at Skully. He spent his career working for the likes of Samsung and Frogdesign focused on mobile phones, tablets, wearables, and room scale computing. Josh joined Skully to make the motorsports industry safer for everyone, including designing heads up display motorcycle helmets that look awesome while keeping riders safe.


When we manipulate data from a SQL database, we often need to compute operations that
depend on values scattered across several rows. For instance, it is important to be able to compute
sums or averages. In this post we will see how the SQL clauses GROUP BY
and
PARTITION BY
enable us to do that.
To clarify the usage of these functions, let's consider the table covid
containing the number of new COVID-19 cases by day in a few countries.
mysql> SELECT *
FROM covid
ORDER BY date, country;
+---------+------------+-----------+
| country | date | new_cases |
+---------+------------+-----------+
| Germany | 2020-03-01 | 51 |
| Italy | 2020-03-01 | 566 |
| USA | 2020-03-01 | NULL |
| Germany | 2020-03-02 | 33 |
| Italy | 2020-03-02 | 342 |
| USA | 2020-03-02 | 16 |
| Germany | 2020-03-03 | 38 |
| Italy | 2020-03-03 | 466 |
| USA | 2020-03-03 | 21 |
| Germany | 2020-03-04 | 52 |
| Italy | 2020-03-04 | 587 |
| USA | 2020-03-04 | 36 |
| Germany | 2020-03-05 | 109 |
| Italy | 2020-03-05 | 769 |
| USA | 2020-03-05 | 67 |
+---------+------------+-----------+
In this blog we will see how to use GROUP BY
and PARTITION BY
to solve three
different use cases.
GROUP BY
: Aggregate Functions In SQL, aggregate functions operate on sets of values, returning a single value. Examples of commonly used aggregate functions are the following.
COUNT(*)
: number of rows retrieved.AVG(col)
: average of values in col
.SUM(col)
: sum of values in col
.MAX(col), MIN(col)
: max / min value in col
.
The clause GROUP BY
is used together with aggregate functions to define on which subset
of rows the operation is to be computed. For Use Case 1 we want to know the
average number of new cases—which can be computed using AVG(new_cases)
—for each
country—which can be achieved using GROUP BY country
—so the query we need is
the following.
mysql> SELECT country, AVG(new_cases) avg_new_cases
FROM covid GROUP BY country
ORDER BY country;
+---------+---------------+
| country | avg_new_cases |
+---------+---------------+
| Germany | 56.6000 |
| Italy | 546.0000 |
| USA | 35.0000 |
+---------+---------------+
In graphical terms, this is how this query operates.
Sometimes we just want to compute an operation on the whole table, in which case we have to omit the
GROUP BY
clause. For instance, to compute the total number of new cases in any country
in this period of five days, we can use the following query.
mysql> SELECT SUM(new_cases) tot_cases
FROM covid;
+-----------+
| tot_cases |
+-----------+
| 3153 |
+-----------+
Finally, it may be useful to have at the same time the results aggregated by country—what you
get by
GROUP BY
—as well as the results aggregated across all rows. This can be achieved
using
the clause WITH ROLLUP
. As an example, the following query computes the total number of
new cases per country and overall (this latter goes under the special label NULL
).
mysql> SELECT country, SUM(new_cases) tot_cases
FROM covid
GROUP BY country
WITH ROLLUP
ORDER BY country;
+---------+-----------+
| country | tot_cases |
+---------+-----------+
| NULL | 3153 |
| Germany | 283 |
| Italy | 2730 |
| USA | 140 |
+---------+-----------+
PARTITION BY
: Window Functions In SQL, window functions operate by considering, for each row of a given query, a set of rows related to that row. With a few exceptions, virtually all aggregate functions can also be used as window functions. On top of those, SQL supports window functions that are nonaggregate functions, of which the commonly used are the following.
ROW_NUMBER()
: index of current row within a partition.RANK()
: same as ROW_NUMBER(col)
, but if an entry is repeated in a
partition their index is different while their rank is the same.
FIRST_VALUE(col), LAST_VALUE(col), NTH_VALUE(col)
: first / last / n-th element in
a partition.
LEAD(col), LAG(col)
: value of the previous / next row in a partition.
If GROUP BY
is used for aggregate functions to specify the set of rows to be aggregated,
for window function we have to use the OVER (window_spec)
clause to specify how to
partition rows into groups to be processed by the window function.
The structure of window_spec
can be broken down into three components, all of which are
optional. If you don't specify any of the components, the trivial OVER()
clause will
take all the rows in the table.
window_spec:
[PARTITION BY col] [ORDER BY col] frame_spec
The first two components are easy to understand.
PARTITION BY col
works exactly in the same way of GROUP BY
, i.e. splits
the
table into various subsets of rows.
ORDER BY col
is also nothing new, just a regular clause to sort the rows in each
subset of rows.
window_spec
is the third component frame_spec
. A
frame specification defines, for each row of a partition, an interval of rows of the partition that should
be considered to apply the window function. The frame specification must be given in the format
frame_spec:
ROWS BETWEEN frame_begin AND frame_end
where frame_begin
and frame_end
are to be chosen among the following options.
CURRENT ROW
: the current row.UNBOUNDED PRECEDING
: the first row in the partition.UNBOUNDED FOLLOWING
: the last row in the partition.n PRECEDING
: the n-th preceding row in the partition.n FOLLOWING
: the n-th following row in the partition.frame_end
is just the current row, SQL allows to use
the shorter equivalent clause
ROWS frame_start
instead of
ROWS BETWEEN frame_start AND CURRENT ROW
Here are a couple of examples of frame_spec
, showing how different subset of rows in
the partition are used to compute AVG(new_cases)
.
So now, after this long discussion on PARTITION BY
and window functions, we are finally able
to solve Use Case 2 and Use Case 3!
Let's start with Use Case 2, i.e. computing the cumulative number of new cases per
country on each day. To compute sums of new cases we can use SUM(new_cases)
. To specify
that we want sums to be computed by country we will use PARTITION BY country
. Finally, a
cumulative sum means that our frame must start with the first row of the partition and end with the
current row, we will use ROWS UNBOUNDED PRECEDING
—or equivalently ROWS
BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
.
mysql> SELECT country,
date,
new_cases,
SUM(new_cases) OVER (PARTITION BY country
ORDER BY date
ROWS UNBOUNDED PRECEDING) my_avg
FROM covid,
ORDER BY country, date;
+---------+------------+-----------+---------+
| country | date | new_cases | cum_sum |
+---------+------------+-----------+---------+
| Germany | 2020-03-01 | 51 | 51 |
| Germany | 2020-03-02 | 33 | 84 |
| Germany | 2020-03-03 | 38 | 122 |
| Germany | 2020-03-04 | 52 | 174 |
| Germany | 2020-03-05 | 109 | 283 |
| Italy | 2020-03-01 | 566 | 566 |
| Italy | 2020-03-02 | 342 | 908 |
| Italy | 2020-03-03 | 466 | 1374 |
| Italy | 2020-03-04 | 587 | 1961 |
| Italy | 2020-03-05 | 769 | 2730 |
| USA | 2020-03-01 | NULL | NULL |
| USA | 2020-03-02 | 16 | 16 |
| USA | 2020-03-03 | 21 | 37 |
| USA | 2020-03-04 | 36 | 73 |
| USA | 2020-03-05 | 67 | 140 |
+---------+------------+-----------+---------+
Now, as for Use Case 3, we want to compute a moving average centered over a period of 3
days. This
means that we want to use the function AVG(new_cases)
and specify the frame to include rows
from previous day to next day so ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING
.
mysql> SELECT country,
date,
new_cases,
AVG(cases) OVER (PARTITION BY country
ORDER BY DATE
ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) mov_avg
FROM covid
ORDER BY country;
+---------+------------+-----------+----------+
| country | date | new_cases | mov_avg |
+---------+------------+-----------+----------+
| Germany | 2020-03-01 | 51 | 42.0000 |
| Germany | 2020-03-02 | 33 | 40.6667 |
| Germany | 2020-03-03 | 38 | 41.0000 |
| Germany | 2020-03-04 | 52 | 66.3333 |
| Germany | 2020-03-05 | 109 | 80.5000 |
| Italy | 2020-03-01 | 566 | 454.0000 |
| Italy | 2020-03-02 | 342 | 458.0000 |
| Italy | 2020-03-03 | 466 | 465.0000 |
| Italy | 2020-03-04 | 587 | 607.3333 |
| Italy | 2020-03-05 | 769 | 678.0000 |
| USA | 2020-03-01 | NULL | 16.0000 |
| USA | 2020-03-02 | 16 | 18.5000 |
| USA | 2020-03-03 | 21 | 24.3333 |
| USA | 2020-03-04 | 36 | 41.3333 |
| USA | 2020-03-05 | 67 | 51.5000 |
+---------+------------+-----------+----------+
GROUP BY
to compute a single aggregate value (e.g. a
sum, mean, etc.) from each specified set of rows.
WITH ROLLUP
to compute also meta-aggregate values.OVER (window_spec)
to compute, for each row, a value that depends on a subset of rows
within a
given partition.
window_spec
defines how to partition the table with
PARTITION BY
, how to sort the rows within each partition with ORDER BY
,
and
the frame of rows within the partition to be considered to compute the value of each rows with
ROWS BETWEEN frame_begin AND frame_end
.


{{ partial "header.html" . }} {{ .Title }} Whatever it is you're looking for, you're looking in the wrong place. {{ partial "footer.html" . }}


| Struts 2 > Home > Tutorials > Step by Step > Lesson 4 - An html form with data, without getters or setters |
For the form field named "yourName" in the previous lesson, we also had to create the getters and setters getYourName() and setYourName() in the action, as well as the private variable yourName. With dozens of forms and hundreds of form fields, you'll be typing thousands of getters and setters. That can get old fast. In this lesson, we'll repeat the last lesson, but without any of that extra typing.
Use the same JSP form from the previous lesson, but change the form action to page04.action:
  <title>A simple form with data</title>
What is your name?
 <form action="form04.action" method="post">
<input type="text" name="yourName">

<input type="submit" value="Submit your name." />
 </form>
Paste this code into [src]/lessons/Form04Action.java:
package lessons; import com.opensymphony.xwork.ActionSupport; import com.opensymphony.webwork.interceptor.ParameterAware; import java.util.Map; public class Form04Action extends ActionSupport implements ParameterAware { Map parameters; public Map getParameters() { return parameters; } public void setParameters(Map parameters) { this.parameters = parameters; } public String execute() { String[] yourName = (String[]) parameters.get("yourName"); if(yourName == null || yourName[0] == null || yourName[0].length() == 0) return ERROR; else return SUCCESS; } }
Edit [webapp]/WEB-INF/classes/xwork.xml:
<!DOCTYPE xwork PUBLIC "-//OpenSymphony Group//XWork 1.0//EN" "http://www.opensymphony.com/xwork/xwork-1.0.dtd"> <xwork>  <include file="webwork-default.xml" />  <package name="default" extends="webwork-default">  <default-interceptor-ref name="defaultStack" />  <action name="form02" class="lessons.Form02Action"> <result name="success" type="dispatcher">page02-success.jsp</result> </action>  <action name="form03" class="lessons.Form03Action"> <result name="success" type="dispatcher">page03-success.jsp</result> <result name="error" type="dispatcher">page03-error.jsp</result> </action>  <action name="form04" class="lessons.Form04Action"> <result name="success" type="dispatcher">page04-success.jsp</result> <result name="error" type="dispatcher">page03-error.jsp</result> <interceptor-ref name="servlet-config"/> </action> </package> </xwork>
We'll use the same error page, but create a slightly different success page page04-success.jsp. The only difference is the <ww:property> tag.
<%@ taglib uri="webwork" prefix="ww" %>   <title>Success page for form with data</title>   Hello, <ww:property value="parameters.yourName" />!
Don't forget to compile your action to [webapp]/WEB-INF/classes, and to restart your web application if necessary.
Go ahead and try it now. Load page04.jsp, enter "Bob" in the text field, and click the form submit button. You should see page04-success.jsp saying "Hello, Bob!"
You've probably figured out what is going on just from looking at the code.
Instead of a setter setYourName() setting a private variable yourName in the action, setParameters() magically extracts everything from the JSP request object and puts into a private local Map parameters. Then execute(), instead of looking for a yourName variable, is able to get the value of the "yourName" field from parameters. So far so good .
Back on the page04-success.jsp page, <ww:property value="yourName" /> isn't going to work any more, because there is no getYourName() getter in the action. Instead, <ww:property value="parameters.yourName" /> calls the getParameters() getter, and is able to get the value of the "yourName" field. Pretty neat!
We haven't covered how to handle radio buttons, checkboxes, and other strange html form fields. That involves dealing with the fact that every entry in the parameters Map is a String[]. We'll cover this in a later lesson.


The introduction of the 'broom' package has made converting model objects into data frames as simple as a single function. While the 'broom' package focuses on providing tidy data frames that can be used in advanced analysis, it deliberately stops short of providing functionality for reporting models in publication-ready tables. 'pixiedust' provides this functionality with a programming interface intended to be similar to 'ggplot2's system of layers with fine tuned control over each cell of the table. Options for output include printing to the console and to the common markdown formats (markdown, HTML, and LaTeX). With a little 'pixiedust' (and happy thoughts) tables can really fly.
| Version: | 0.8.5 |
| Depends: | R (≥ 3.1.2) |
| Imports: | broom, checkmate (≥ 1.8.0), dplyr, htmltools, knitr, labelVector, magrittr, scales, tidyr |
| Suggests: | rmarkdown, testthat |
| Published: | 2018-07-16 |
| Author: | Benjamin Nutter [aut, cre], David Kretch [ctb] |
| Maintainer: | Benjamin Nutter <benjamin.nutter at gmail.com> |
| BugReports: | https://github.com/nutterb/pixiedust/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/nutterb/pixiedust |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | pixiedust results |
| Reference manual: | pixiedust.pdf |
| Vignettes: |
Advanced Magic with pixiedust Creating Magic with pixiedust Sprinkles |
| Package source: | pixiedust_0.8.5.tar.gz |
| Windows binaries: | r-devel: pixiedust_0.8.5.zip, r-release: pixiedust_0.8.5.zip, r-oldrel: pixiedust_0.8.5.zip |
| OS X binaries: | r-release: pixiedust_0.8.5.tgz, r-oldrel: pixiedust_0.8.5.tgz |
| Old sources: | pixiedust archive |
| Reverse imports: | HydeNet, tadaatoolbox |
Please use the canonical form https://CRAN.R-project.org/package=pixiedust to link to this page.


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Intrusion Detection by Scikit Decision Tree
Using Scikit decision tree classification algorithm to detect network intrusions
I did this project as a part of the Data Mining course. It is an implementation in Python 2 to detect intrusions of the connections, which was completed by Zhaoqing Peng, Junyi Dai, Dastan, and Lingbin Ni. In this blog post I have described the pipeline to achieve this task. The complete code for the project is available here.
Overview of network intrusion detection
The purpose of network intrusion detection is to prevent computer networks from avoiding non -authorized users' infringement. Using machine learning methods, network invasion detectors can build prediction models through learning, which can distinguish harmful connections (invasion or attack) and normal connections.
Generally speaking, invasion is divided into four main types:
1) DOS (Denial-OF-Service): Request rejection, such as flood attacks.
2) R2L: Unauthorized access to remote machines, such as guessing passwords.
3) U2R: Unauthorized access to local super user privileges, such as overflow attacks of multiple buffers.
4) Probing: monitoring or other monitoring, such as port scanning.
Some characteristics can be used to distinguish ordinary connections and invasion, such as:
Same Host: Check the connection within two seconds of the previous connection as the current connection has the same destination host, and calculate and protocol -related behaviors, services, and so on.
Same Service: Just check that the connection within two seconds has the same service as the current connection.
Same Host and Same Service are also known as Time-based Traffic Features of the Connection Records.
However, some probing attacks use more than two seconds time interval scanning hosts or ports, such as once a minute. Therefore, the connection record is also sorted by the destination host, and the feature is constructed by the window of 100 connected to the same host instead of using a real -time window. This produces a data set called Host-based Traffic Features.
Different from the invasion of most DOS and Probing, there is no frequent sequence mode in R2L and U2R invasion. This is because DOS and Probing invasion contains many connections with the host in a short period of time. However, R2L and U2R invasion are embedded in data ports and packets, and they generally include only one connection.
KDD99 dataset
KDD CUP is a game of data mining analysis sponsored by SIGKDD. It is held once a year. KDD99 is a data set of invasion detection in the 1999 competition, but now the academic world has abandoned this data set. Using this datasets, we only first glimpse two classification algorithms.
The purpose of the task is to detect network connections to distinguish normal and non-normal connections. There are about four categories of non-normal connection: DOS, R2L, U2R, and Probing, and there are several sub categories in each category.
It is worth noting that the probability distribution of the data in the training set and the test set is different. The test set has 14 more attack subcategories than the training set. The official explanation is that this can simulate the real situation well, and the new attack category is generally a variation of the known attack category. Therefore, the algorithm model needs to be able to capture the features of each attack category well, and the dataset can be obtained on the official website.
Data Preprocessing
1. Original Data Preprocessing
The classification results of the original training set and test set are both specific classes of attacks, with a total of 24+14=38 types. Here, we do not consider such a detailed classification first, but only classify all connections into four attack categories. Therefore, based on the mapping relationship provided on the official website, we reduce the type attribute to 5 categories and replace it with the numbers 0, 1, 2, 3, and 4, representing the main categories and normal situations of the four attacks.
2. Data Storage
In order to facilitate subsequent operations such as reading and sorting, and reduce runtime, we read and write the file data in the data directory to the MongoDB database. During the reading and writing process, pay attention to converting some variables into ints and floats before storing them, and add an index to the type attribute of the training dataset.
Scikit Decision Tree Classification
1. Loading data
We extract all the training and testing sets from MongoDB. Since the decision tree can accelerate training with ordered inputs, we use the sort command to sort the type attributes in the training set. Then, all the inputs in the training and testing sets are stored in the dataset, and all the output targets are stored in the datatarget. To distinguish between the training and testing sets, we use T_ Len records the size of the training set.
2. Category variable recoding
For categorical variables, such as male and female, high and low, these string variables cannot be directly input into the algorithm model and need to be recoded as numbers 1,2,3,4 or binary bitmaps.
However, for scalars with ordered sizes such as 1,2,3,4, some algorithms understand that 2 is greater than 1, meaning that men are greater than women. In fact, men and women only represent men or women without any relationship between order and size. Therefore, binary encoding is generally used: men are represented by 01, and women are represented by 10. And this increases the number of variables, from one-dimensional variable sex to two-dimensional variables sex=male and sex=female. Therefore, if there are many types of categorical variables, the final encoded variable dimension will greatly increase, which is not conducive to calculation and is called a dimension disaster.
For the decision tree algorithm, it utilizes information purity for classification, so encoding as 1,2,3 will not have any impact. So we will recode the three category variables (protocol_type, service, flag) in the dataset into a set of numbers.
3. Feature selection
The following situations may occur between variables in a general dataset:
1) Variables with strong correlation and severe interdependence can lead to redundant variables
2) The data in variables is extremely sparse, with a large portion being either zero or meaningless
3) The variable dimension is too high, and after recoding, the variable dimension has reached hundreds
The above three situations are all very unfavorable for the training of the model, and the second situation can filter some sparse variables by setting a threshold; Scenario 1 and 3 can extract the main factors for dimensionality reduction through principal component analysis (PCA). In addition, there are also attribute subset selection methods: reducing the amount of data by deleting irrelevant or redundant attributes. There are three options: gradual forward selection, gradual backward selection, and decision tree induction.
After the recoding of the previous category variables, dataset variables have 42 dimensions in total. The training of high-dimensional datasets in the decision tree is prone to overfitting, so we need to filter variables, that is, feature selection. A good set of features can directly affect the accuracy of the algorithm. Scikit has provided a tree based attribute subset selection method, We directly input the data into the ExtraTreesClassifier for training here, then use SelectFromModel to extract the trained model, and finally use the transform method to filter out important features.
Due to the fact that variables in the test set also need to be filtered, but the filtering principle should be consistent with the training set, otherwise the input of the training set and the test set will be different. Therefore, we will store the index of this set of features in fea_index array.
4. Cross validation 1
Before training the model, we use 1/10 of the training set for cross validation and the remaining data for training. Therefore, we divide the training set into two parts:
1) training_set、training_target: used to train the model
2) test_set、test_ target: used for cross validation
5. Training decision tree model
Decision tree is a non parametric supervised learning algorithm, commonly used are ID3, C4.5 and CART. ID3 and C4.5 are based on information entropy, CART is based on gini impurity. Scikit provides an optimized version of CART algorithm, which is encapsulated in DecisionTree Classifier, and has some main parameters:
• criterion: The criterion for node splitting, gini or entropy
• max_depth: the maximum depth of the tree. If the value is too large, the algorithm will overfitting the training set, while if the value is too small, the algorithm will be hindered from learning the data. The initial value is recommended to be 5, and then slowly increase it.
• min_samples_split: The minimum number of samples that need to be split into child nodes. When the number of samples is less than this value, it is directly marked as a leaf node without continuing to generate child nodes. The larger the value, the fewer branches the tree has.
• class_weight: When the number of categories in the input sample set varies greatly, the final shape of the tree tends to be generated in the direction of big data samples, resulting in imbalanced trees. Therefore, the weights of each category can be set. Classes with less data have greater weights, while those with more data have smaller weights, which can effectively balance the tree.
Through repeated attempts, we found a good set of training parameters: using information entropy to split each node; min_ samples_ split＝30； Ensure that all training samples are fully balanced, that is, the number of input tuples is equal.
6. Cross validation 2
We predict 1/10 of the training set (test_set), and compare the obtained predict value (trained_target) and actual value (test_target), and output confusion matrix and classification results statistics to verify the effectiveness of the trained model for data sets with the same probability distribution.
7. Testing
Finally, we test whether the trained model can predict test data well. We use T_len to retrieve the test_data_test and test_ data_target from the dataset, then use fea_index to extract sub attributes (features) from the test input, and finally compares the predicted value (test_ trained_ target) and actual value (test_ data_ target).
Repository
https://github.com/LingbinNi/Intrusion-Detection


[list]
[*]Entry A
[*]Entry B
[/list]
[list=1]
[*]Entry 1
[*]Entry 2
[/list]
[style color="fuchsia"]Text in fuchsia[/style] or
[style color=#FF00FF]Text in fuchsia[/style] or
[color=#FF00FF]Text in fuchsia[/color]
[quote]quoted text[/quote]
[quote="author"]quoted text[/quote]
[:-)]
[url]https://en.wikipedia.org[/url]
[url=https://en.wikipedia.org]English Wikipedia[/url]


Val
abstract class Val (View source)
Utility class for converting variables.
Methods
Can the given value be coerced into a string.
Can the given value be coerced into a number?
Can value be traversed (is it iterable) ?
Can value be accessed as an array?
Can we count the elements in $value?
Can the value be converted to an integer without loss of information?
Can the value be thrown via the throw keyword?
Coerce a value to string.
Coerce a value to to an array.
Convert a variable to an integer.
Convert a variable to a float.
Convert a variable to a bool.
Count the elements in an array, a Countable or a Traversable.
Format a given value into a string.
Format a value for being displayed as a string in an error message.
Get the callback as a string.
Ensure that a value has a given type or class.
Normalize a string of separated words into an array.
Check if a value has a given type or class.
Check if $value is an 0-indexed continuous array of the given type.
Map an iterable variable.
Return the first argument that is not null.
Return the first array element that is not null.
Details
at line 35
static bool
stringable(mixed $value)
Can the given value be coerced into a string.
at line 50
static bool
numeric(mixed $value)
Can the given value be coerced into a number?
at line 66
static bool
iterable(mixed $value)
Can value be traversed (is it iterable) ?
at line 79
static bool
arrayable(mixed $value)
Can value be accessed as an array?
at line 92
static bool
countable(mixed $value)
Can we count the elements in $value?
at line 105
static bool
intable(mixed $value)
Can the value be converted to an integer without loss of information?
at line 118
static bool
throwable(mixed $value)
Can the value be thrown via the throw keyword?
at line 138
static string
toString(mixed $value, string|null $error = null)
Coerce a value to string.
Throw an exception if not possible.
Strings, integers, floats and objects with a __toString method can be coerced.
at line 161
static array
toArray(mixed $value, string|null $error = null)
Coerce a value to to an array.
Throw an exception if not possible.
Arrays and instances of Traversable can be converted to array.
at line 182
static int
toInt(mixed $value, string|null $error = null)
Convert a variable to an integer.
at line 205
static float
toFloat(mixed $value, string $error = null)
Convert a variable to a float.
at line 232
static bool
toBool(mixed $value, mixed $error = null)
Convert a variable to a bool.
at line 270
static Closure
toClosure(callable $callable, string|Exception|null $error = null)
Create a closure from a callable.
at line 295
static int
count(mixed $value, string $error = null)
Count the elements in an array, a Countable or a Traversable.
at line 318
static string
format(mixed $value, string $format)
Format a given value into a string.
at line 374
static string
escape(mixed $value)
Format a value for being displayed as a string in an error message.
at line 419
static string
formatCallback(mixed $callback)
Get the callback as a string.
at line 458
static mixed
mustBe(mixed $value, string|string[] $types, mixed $error = null)
Ensure that a value has a given type or class.
at line 499
static string[]
explodeAndTrim(string $separator, string|string[] $types)
Normalize a string of separated words into an array.
If $types is already an array, it will merely be trimmed.
at line 539
static bool
is(mixed $value, string|string[] $types)
Check if a value has a given type or class.
$types can be a string with a type name such as: 'int', 'float', 'bool', 'array' or even pseudy types such as 'callable', 'iterable', 'countable', 'stringable' and 'arrayable'.
It can also be a fully qualified class name such as 'Valit\Check'
It can also be an array of the above types. If it is an array, then $value can be any of the given values. Example: ['int', 'DateTimeInterface']
It can also be a string with many types separated by a pipe |
characer.
Example:
'string|DateTimeInterface', 'int | float'
at line 649
static bool
isArrayOf(mixed $value, string $type)
Check if $value is an 0-indexed continuous array of the given type.
All keys from 0 to count($value) must exist. All values must be of type $type.
at line 693
static array
map(mixed $iterable, mixed $callable)
Map an iterable variable.
Example:
// the two lines below are equivalent:
$escaped = Val::map($array, '::escape');
$escaped = Val::map($array, 'Valit\Util\Val::escape');
$integers = Val::map($array, 'intval'); // php version
$integers = Val::map($array, '::toInt'); // robust version
at line 720
static mixed
firstNotNull()
Return the first argument that is not null.
Similar to the php 7 null-coalesce operator.
at line 732
static mixed
firstElementNotNull(iterable $array)
Return the first array element that is not null.


treedb is a simple in-memory database for hierarchical keys. This means, your keys are structured like a path in a file system or a system regstry.
treedb comes with an ASDF system, so it can be loaded via ASDF or as a local project via quicklisp.
treedb lives in the package treedb
.
Here are some examples:
;;create a new instance
(defparameter *db* (treedb:make-alist-treedb))
;;set values:
(setf (treedb:node *db* :a :1) "Hello" ; /:a/:1 <- "Hello"
(treedb:node *db* :a :2) :world ; /:a/:2 <- :world
(treedb:node *db* :b) 1 ; /:b <- 1
(treedb:node *db* :c) '((1 . 2) (3 . 4))) ; /:c <- '((1 . 2) (3 . 4))
;;get values
(treedb:node *db* :a :1) ; => "Hello"
;;delete nodes
(treedb:del-node *db* :b)
;;list keys
(treedb:children *db*) ; => (:A :C)
(treedb:children *db* :a) ; => (:|1| :|2|)
;;take a subtree
(treedb:subtree *db* :a) ; => subtree from :a
;;convert to alists or json (via cl-json)
(treedb:to-alist (treedb:subtree *db* :a))
; => ((:1 . "Hello") (:2 . :world))
(treedb:to-json (treedb:subtree *db* :a))
; => "{\"2\":\"Hello\",\"2\":\"world\"}"
/k/e/y/s
Returns a list of keys, which are the children of the given node.
Deletes the value at /k/e/y/s
.
t
or nil
Tests, if /k/e/y/s
is a leaf in db
.
Returns an empty tree of type <alist-treedb>
.
/k/e/y/s
, t
/ nil
)
Returns the value of a leaf node as the first
and t
or nil
as the second value,
depending on if the node was found or not.
Can be used with setf
.
t
or nil
Tests, if /k/e/y/s
is a node in db
.
/k/e/y/s
Returns a new tree object with the contents of the subtree.
The new object has the same type as db
.
Returns the tree in alist form
nil
Serializes the tree into json objects.
If stream
is supplied, the json object is written to the corresponding stream.
Otherwise it is returned as a string.
/k/e/y/s or
default`
Returns the value at keys or, if it is not found,
default`.
Run the test specified by TEST-SPEC.
TEST-SPEC can be either a symbol naming a test or test suite, or a testable-object object. This function changes the operations performed by the !, !! and !!! functions.
Equivalent to (explain (run TEST-SPEC)).
Creates the documentation (doc/treedb.html) using cl-gendoc.


The default component
selected value :
initial Date
i18n
to get locale code refer to :
Locale Codes
minimum date
maximum date
A stylished component
Example of linked calendars


Gardaí appeal for witnesses after woman is hit by truck in Louth
by By Digital Desk staffA 24-year-old woman is in a critical condition after being hit by a truck.
The woman was struck as she crossed the road in Mullary, Dunleer, Co Louth, at approximately 3.30pm on Monday.
She was taken to Our Lady of Lourdes Hospital in Drogheda and later transferred to Beaumont Hospital in Dublin where her condition is described as critical.
The truck driver was uninjured.
Gardaí in Drogheda are appealing for witnesses.
Anyone who may have travelled on the R132 – the old Drogheda to Dunleer road – or any motorist who may have dashcam footage of the incident can contact Drogheda Garda station on<PHONE_NUMBER>, or the Garda Confidential Line on 1800 666 111.
A spokesman for the force said investigations are continuing.
READ MORE
Govt planning to build Direct Provision centres in 13 counties


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Snookes1516.github.io
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