EventType
SmartInsiderEventType
Description of what has taken place in an execution. Possible values are:
Authorization
- Notification that the board has gained the authority to repurchaseIntention
- Notification to the board that shares will be repurchasedTransaction
- Repurchase transactions that have been actionedUpwardsRevision
- Increase in the scope of the existing plan (extended date, increased value, etc.)DownwardsRevision
- Decrease in the scope of the existing plan (shortened date, reduced value, etc.)RevisedDetails
- General change of details of the plan (max/min price alteration, etc.)Cancellation
- Total cancellation of the plantransaction.EventType
LastUpdate
DateTime
The date when a transaction is updated after it has been reported
transaction.LastUpdate
LastIDsUpdate
DateTime
Date that company identifiers were changed. Can be a name, Ticker Symbol, or ISIN change.
transaction.LastIDsUpdate
CompanyName
string
Company name. PLC is always excluded.
transaction.CompanyName
PreviousResultsAnnouncementDate
DateTime
Announcement date of last results, this will be the end date of the last "Close Period"
transaction.PreviousResultsAnnouncementDate
NextResultsAnnouncementsDate
DateTime
Announcement date of next results, this will be the end date of the next "Close Period"
transaction.NextResultsAnnouncementsDate
NextCloseBegin
DateTime
Start date of next trading embargo ahead of scheduled results announcement
transaction.NextCloseBegin
LastCloseEnded
DateTime
Date trading embargo (Close Period) is lifted as results are made public
transaction.LastCloseEnded
SecurityDescription
string
Type of security. Does not contain nominal value.
transaction.SecurityDescription
AnnouncementDate
DateTime
Date Transaction was entered into Smart Insider systems. Where a transaction is after the London market close (usually 4:30 P.M.) this will be stated as the next day.
transaction.AnnouncementDate
TimeReleased
DateTime
Time the announcement first appeared on a Regulatory News Service or other disclosure system and became available to the market, time stated is local market time
transaction.TimeReleased
TimeProcessed
DateTime
Time the transaction was entered into Smart Insider systems and appeared on their website, time stated is local to London, UK
transaction.TimeProcessed
TimeReleasedUtc
DateTime
Time the announcement first appeared on a Regulatory News Service or other disclosure system and became available to the market. Time stated is GMT standard (UTC).
transaction.TimeReleasedUtc
TimeProcessedUtc
DateTime
Time the transaction was entered into Smart Insider systems and appeared on their website. Time stated is GMT standard (UTC).
transaction.TimeProcessedUtc
BuybackDate
DateTime
Date traded through the market
transaction.BuybackDate
Execution
SmartInsiderExecution
Describes how transaction was executed. Possible values are:
Market
- Took place via the open market placeTenderOffer
- Via a companywide tender offer to all shareholdersOffMarket
- Under a specific agreement between the issuer and shareholdertransaction.Execution
ExecutionEntity
SmartInsiderExecutionEntity
Describes which entity carried out the transaction. Possible values are:
Issuer
- Issuer carried out the transactionSubsidiary
- A subsidiary of the issuer makes the transactionBroker
- Brokers are commonly used to repurchase shares under mandate to avoid
insider information rules and to allow repurchases to carry on through close periodsEmployerBenefitTrust
- UnknownEmployeeBenefitTrust
- To cater for shares which will need to be transferred to
employees as part of remunerative plansThirdParty
- An undisclosed 3rd party, althrough this is likely to be a brokertransaction.ExecutionEntity
ExecutionHolding
SmartInsiderExecutionHolding
Describes what will be done with those shares following repurchase. Possible values are:
Treasury
- Held in treasury until they are sold back to the marketCancellation
- Immediately canceledTrust
- Held in trust, generally to cover employee remunerative plansSatisfyEmployeeTax
- Shares will be used to satisfy employee tax liabilitiesNotReported
- Not disclosed by the issuer in the announcementsSatisfyStockVesting
- Shares will be used to satisfy vesting of employee stocktransaction.ExecutionHolding
Price
decimal
Denominated in currency of transaction
transaction.Price
Amount
decimal
Number of shares traded
transaction.Amount
NoteText
string
Free text which explains further details about the trade
transaction.NoteText
BuybackPercentage
decimal
Percentage of value of the trade as part of the issuers total market cap
transaction.BuybackPercentage
VolumePercentage
decimal
Percentage of the volume traded on the day of the buyback
transaction.VolumePercentage
TreasuryHolding
int
Post trade holding of the treasury or trust in the security traded
transaction.TreasuryHolding


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| Packages that use LoadFunc | |
|---|---|
| org.apache.hadoop.zebra.pig | Implementation of PIG Storer/Loader Interfaces |
| org.apache.pig | Public interfaces and classes for Pig. |
| org.apache.pig.backend.hadoop.executionengine.mapReduceLayer | |
| org.apache.pig.backend.hadoop.executionengine.physicalLayer.relationalOperators | |
| org.apache.pig.backend.hadoop.hbase | |
| org.apache.pig.builtin | This package contains builtin Pig UDFs. |
| org.apache.pig.impl.builtin | |
| org.apache.pig.impl.io | |
| org.apache.pig.impl.logicalLayer | The logical operators that represent a pig script and tools for manipulating those operators. |
| org.apache.pig.impl.util | |
| org.apache.pig.newplan.logical.relational | |
| org.apache.pig.piggybank.storage | |
| org.apache.pig.piggybank.storage.apachelog | |
| org.apache.pig.piggybank.storage.partition |
| Uses of LoadFunc in org.apache.hadoop.zebra.pig |
|---|
| Subclasses of LoadFunc in org.apache.hadoop.zebra.pig | |
|---|---|
class |
TableLoader
Pig IndexableLoadFunc and Slicer for Zebra Table |
| Uses of LoadFunc in org.apache.pig |
|---|
| Subclasses of LoadFunc in org.apache.pig | |
|---|---|
class |
FileInputLoadFunc
This class provides an implementation of OrderedLoadFunc interface which can be optionally re-used by LoadFuncs that use FileInputFormat, by having this as a super class |
| Uses of LoadFunc in org.apache.pig.backend.hadoop.executionengine.mapReduceLayer |
|---|
| Subclasses of LoadFunc in org.apache.pig.backend.hadoop.executionengine.mapReduceLayer | |
|---|---|
class |
MergeJoinIndexer
Merge Join indexer is used to generate on the fly index for doing Merge Join efficiently. |
| Constructors in org.apache.pig.backend.hadoop.executionengine.mapReduceLayer with parameters of type LoadFunc | |
|---|---|
PigRecordReader(org.apache.hadoop.mapreduce.InputFormat inputformat,
PigSplit pigSplit,
LoadFunc loadFunc,
org.apache.hadoop.mapreduce.TaskAttemptContext context)
|
| Uses of LoadFunc in org.apache.pig.backend.hadoop.executionengine.physicalLayer.relationalOperators |
|---|
| Methods in org.apache.pig.backend.hadoop.executionengine.physicalLayer.relationalOperators that return LoadFunc | |
|---|---|
LoadFunc |
POLoad.getLoadFunc()
|
| Constructors in org.apache.pig.backend.hadoop.executionengine.physicalLayer.relationalOperators with parameters of type LoadFunc | |
|---|---|
POLoad(OperatorKey k,
LoadFunc lf)
|
| Uses of LoadFunc in org.apache.pig.backend.hadoop.hbase |
|---|
| Subclasses of LoadFunc in org.apache.pig.backend.hadoop.hbase | |
|---|---|
class |
HBaseStorage
A HBase implementation of LoadFunc and StoreFunc TODO(dmitriy) test that all this stuff works TODO(dmitriy) documentation |
| Uses of LoadFunc in org.apache.pig.builtin |
|---|
| Subclasses of LoadFunc in org.apache.pig.builtin | |
|---|---|
class |
BinStorage
Load and store data in a binary format. |
class |
PigStorage
A load function that parses a line of input into fields using a delimiter to set the fields. |
class |
TextLoader
This load function simply creates a tuple for each line of text that has a single chararray field that contains the line of text. |
| Uses of LoadFunc in org.apache.pig.impl.builtin |
|---|
| Subclasses of LoadFunc in org.apache.pig.impl.builtin | |
|---|---|
class |
DefaultIndexableLoader
Used by MergeJoin . |
class |
PoissonSampleLoader
See "Skewed Join sampler" in http://wiki.apache.org/pig/PigSampler |
class |
RandomSampleLoader
A loader that samples the data. |
class |
SampleLoader
Abstract class that specifies the interface for sample loaders |
| Fields in org.apache.pig.impl.builtin declared as LoadFunc | |
|---|---|
protected LoadFunc |
SampleLoader.loader
|
| Uses of LoadFunc in org.apache.pig.impl.io |
|---|
| Subclasses of LoadFunc in org.apache.pig.impl.io | |
|---|---|
class |
InterStorage
LOAD FUNCTION FOR PIG INTERNAL USE ONLY! This load function is used for storing intermediate data between MR jobs of a pig query. |
class |
ReadToEndLoader
This is wrapper Loader which wraps a real LoadFunc underneath and allows to read a file completely starting a given split (indicated by a split index which is used to look in the List |
class |
TFileStorage
LOAD FUNCTION FOR PIG INTERNAL USE ONLY! This load function is used for storing intermediate data between MR jobs of a pig query. |
| Methods in org.apache.pig.impl.io with parameters of type LoadFunc | |
|---|---|
DataBag |
PigFile.load(LoadFunc lfunc,
PigContext pigContext)
|
| Constructors in org.apache.pig.impl.io with parameters of type LoadFunc | |
|---|---|
ReadToEndLoader(LoadFunc wrappedLoadFunc,
org.apache.hadoop.conf.Configuration conf,
String inputLocation,
int splitIndex)
|
|
ReadToEndLoader(LoadFunc wrappedLoadFunc,
org.apache.hadoop.conf.Configuration conf,
String inputLocation,
int[] toReadSplitIdxs)
This constructor takes an array of split indexes (toReadSplitIdxs) of the splits to be read. |
| Uses of LoadFunc in org.apache.pig.impl.logicalLayer |
|---|
| Methods in org.apache.pig.impl.logicalLayer that return LoadFunc | |
|---|---|
LoadFunc |
LOLoad.getLoadFunc()
|
| Uses of LoadFunc in org.apache.pig.impl.util |
|---|
| Methods in org.apache.pig.impl.util with parameters of type LoadFunc | |
|---|---|
static ResourceSchema |
Utils.getSchema(LoadFunc wrappedLoadFunc,
String location,
boolean checkExistence,
org.apache.hadoop.mapreduce.Job job)
|
| Uses of LoadFunc in org.apache.pig.newplan.logical.relational |
|---|
| Methods in org.apache.pig.newplan.logical.relational that return LoadFunc | |
|---|---|
LoadFunc |
LOLoad.getLoadFunc()
|
| Uses of LoadFunc in org.apache.pig.piggybank.storage |
|---|
| Subclasses of LoadFunc in org.apache.pig.piggybank.storage | |
|---|---|
class |
CSVLoader
A load function based on PigStorage that implements part of the CSV "standard" This loader properly supports double-quoted fields that contain commas and other double-quotes escaped with backslashes. |
class |
HadoopJobHistoryLoader
|
class |
HiveColumnarLoader
Loader for Hive RC Columnar files. Supports the following types: * Hive Type Pig Type from DataType string CHARARRAY int INTEGER bigint or long LONG float float double DOUBLE boolean BOOLEAN byte BYTE array TUPLE map MAP Partitions The input paths are scanned by the loader for [partition name]=[value] patterns in the subdirectories. If detected these partitions are appended to the table schema. For example if you have the directory structure: |
class |
MyRegExLoader
|
class |
PigStorageSchema
This Load/Store Func reads/writes metafiles that allow the schema and aliases to be determined at load time, saving one from having to manually enter schemas for pig-generated datasets. |
class |
RegExLoader
RegExLoader is an abstract class used to parse logs based on a regular expression. |
class |
SequenceFileLoader
A Loader for Hadoop-Standard SequenceFiles. |
class |
XMLLoader
The load function to load the XML file This implements the LoadFunc interface which is used to parse records from a dataset. |
| Uses of LoadFunc in org.apache.pig.piggybank.storage.apachelog |
|---|
| Subclasses of LoadFunc in org.apache.pig.piggybank.storage.apachelog | |
|---|---|
class |
CombinedLogLoader
CombinedLogLoader is used to load logs based on Apache's combined log format, based on a format like LogFormat "%h %l %u %t \"%r\" %>s %b \"%{Referer}i\" \"%{User-Agent}i\"" combined The log filename ends up being access_log from a line like CustomLog logs/combined_log combined Example: raw = LOAD 'combined_log' USING org.apache.pig.piggybank.storage.apachelog.CombinedLogLoader AS (remoteAddr, remoteLogname, user, time, method, uri, proto, status, bytes, referer, userAgent); |
class |
CommonLogLoader
CommonLogLoader is used to load logs based on Apache's common log format, based on a format like LogFormat "%h %l %u %t \"%r\" %>s %b" common The log filename ends up being access_log from a line like CustomLog logs/access_log common Example: raw = LOAD 'access_log' USING org.apache.pig.piggybank.storage.apachelog.CommongLogLoader AS (remoteAddr, remoteLogname, user, time, method, uri, proto, bytes); |
| Uses of LoadFunc in org.apache.pig.piggybank.storage.partition |
|---|
| Method parameters in org.apache.pig.piggybank.storage.partition with type arguments of type LoadFunc | |
|---|---|
List<org.apache.hadoop.fs.FileStatus> |
PathPartitionHelper.listStatus(org.apache.hadoop.mapreduce.JobContext ctx,
Class<? extends LoadFunc> loaderClass,
String signature)
This method is called by the FileInputFormat to find the input paths for which splits should be calculated. If applyDateRanges == true: Then the HiveRCDateSplitter is used to apply filtering on the input files. Else the default FileInputFormat listStatus method is used. |
void |
PathPartitionHelper.setPartitionFilterExpression(String partitionFilterExpression,
Class<? extends LoadFunc> loaderClass,
String signature)
Sets the PARITITION_FILTER_EXPRESSION property in the UDFContext identified by the loaderClass. |
void |
PathPartitionHelper.setPartitionKeys(String location,
org.apache.hadoop.conf.Configuration conf,
Class<? extends LoadFunc> loaderClass,
String signature)
Reads the partition keys from the location i.e the base directory |
|
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@(currentUser: Option[services.user.User])(implicit request: RequestHeader, messages: Messages) @if(currentUser.isDefined) {


Deep learning IndabaXMorocco is launching a Data Challenge to offer an opportunity to learn and test ML skills on a real-case problem. One of the goals is to involve anyone interested in ML (especially undergraduate students) in this event.
For this edition, the organizing committee chose DrivenData’s warm up challenge: Machine Learning with a Heart. This challenge is a simple ML challenge with scarce data that pushes competitors to test several ML methods, not only deep learning methods, we hope that this will encourage all ML practitioners to participate despite of their level.
Rules of participation
You can compete individually or form a team. Each team is to be composed of at most three people. You must name your team by concatenating the chosen name + IXM, example: TeamName-IXM. At least one of the members should be able to attend IndabaXMorocco (29-30 April) in case they won.
How to Participate?
- Register on Drivendata plateform : go to DrivenData
- Join the competition : go to Machine Learning with a Heart
- Register on IndabaX Morocco: by clicking on the button on the top-right of this page. You will need informations from your DrivenData account.
- Submit your result: At the end of the competition we will allow you to submit your best score.
Results and prizes
At the end of the competition, the IndabaXMorocco website will show a form where the teams must send:
- Their rank in the leaderboard and their score
- The code used to generate the .csv submission file
During the IndabaXMorocco main event, a special session will be organized where the three winning solutions to the Data Challenge will be presented. All the winners will be granted a special invitation to IndabaXMorocco’s main conference, where they can learn much more about the state of AI research and industry in Morocco and hopefully make new connections that will boost their careers. A special prize will be offered to the winner team.


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My name is Nick Steinmetz and I'm a Web Developer specializing in in front-end web development. I graduated from the University of toledo in 2015 with a Bachelors degree in Information Systems and Marketing. I began programming and designing in college and have been doing it ever since!
I began my first professional programming job in 2017 for Tesco Bus in Oregon, Ohio. I have experience in both back and front end web development along with design software. I am currently learning Javascript frameworks including View, React, and Angular. If you would like to contact me, please see my contact info below.


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Praveen Laws, Jaskaran Singh Saini, Ajit Kumar
With increasing visible effects of fossil fuel on our climate, the world is desperately looking for alternative sources of energy. The two most promising alternatives are solar energy and wind energy. The wind energy harvesting basically happens through wind turbines. Wind-generated rotary motion in turbines, which through appropriate circuitry and gear system, is converted into electric power. Different turbine designs function well in different wind pattern. We are always in pursuit of best wind turbines shapes design. It would be very helpful to have a computer framework where one can input any conceivable turbine design and be able to see its efficiency. Our group is working on developing such a design testing framework. One particular example is presented here. Design of a typical savonius wind turbine is placed in a virtual wind tunnel and power output is measured. The results are compared to published experimental works and validations are very satisfactory. Flow around the turbines is solved using OpenFOAM.
Velocity Field
Pressure Field
Overset Mesh Motion
Mesh Motion and Velocity Field
We are expert in solving Fluid-Structure-Interaction (FSI) problems. All our work are built over open source codes like OpenFOAM, Gmsh. If you need our help in FSI simulations for your applications, contact me at ajit.kumar@snu.edu.in.


Mkoolima is an app that brings modern farming techniques to you from anywhere in Kenya.
Don’t have an account? Sign Up
Get latest news on pest and disease outbreaks,
best farming practices and
insights from fellow
farmers across the world.
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Consumers Increasingly In The ‘Buy Now, Pay Later’ Mood
by PYMNTSThe holidays are here, and that means wallets and purses spring open. Debt loads have been getting heavier too. And among those who may a bit “credit shy,” especially younger consumers, buy now and pay later options are gaining traction. The Buy Now, Pay Later Tracker shows that flexible retail solutions can unlock the purchasing power of Generation Z and millennial shoppers.
Data:
$1T: Amount U.S. consumers are expected to spend during the 2019 holiday season.
52 percent: Share of millennials who believe it is acceptable to take on debt during the holiday season.
$2,225: Average individual retail spending per year for U.S. bridge millennials.
30 percent: U.S. consumers who have “buy now, pay later” accounts.
48M: Number of Americans still paying off debt from the 2018 holiday season.


Our team of 4 proposed a restructuring of the public transport, private transport and connected public space axes throughout the Barcelona city center.
The new public transport system replaces the present convulated bus routes and proposes a simple horizontal and thoroughfare of buses that has larger coverage, better frequency and minimises confusion for new travellers. This would increase the efficiency of public transport
Based on the current urban planning pattern of the city, we proposed a super-block that would combine 4 blocks. Private transportation and parking is moved outside the super-block, and the interior is freed up to be open public spaces.
We mapped the redistribution of the traffic flows based on the current traffic flow data
This project was presented at U.P.C, Barcelona, as part of the inter-university program - European Masters in Urbanism


{% extends 'admin/master.html' %} {% block body %} {{ super() }}
Customize the layout
This example shows how you can customize the look & feel of the admin interface.
This is done by overriding some of the built-in templates.


City of Angels (PG-13) ** 1/2 (out of ****)
Brad Silberling directed the 1995 live-action version of Casper; Dana Stevens wrote the 1994 Madeleine Stowe thriller Blink. Both of those films are competent entertainments, but it takes something more than mere competence to successfully pull off a redo of Wim Wenders's 1988 German classic Wings of Desire. In translating Wings into the film known as City of Angels, Silberling and Stevens have turned a fresh, unique cinematic work into a standard-issue Hollywood romantic fantasy.
Although Wings told the story of Damiel, an angel who decides becomes human after falling in love with trapeze artist Marion, Wenders was more interested with the day-to-day work of these celestial beings. His angels were unseen, trenchcoated observers of the human race, wandering about, listening to unsuspecting people's thoughts, but mostly just watching the earth from above, below, and beyond. These angels did not perform miracles, become visible to people in need, or engage in any of the actions that one typically associates with them; the angels simply watched and took notes.
In the dismaying opening scene of City of Angels, it becomes clear that Stevens and Silberling's vision is far more ordinary than Wenders's. When we first meet this version's Damiel, Seth (Nicolas Cage), he is on the job--he's by the side of a dying child to lead her spirit into the great beyond. No more simple observation; in this City dwell Americanized angels that act upon the human world, collect spirits, sometimes shape events, and somehow make themselves visible to people dying or in despair, such as surgeon Maggie Rice (Meg Ryan), City's Marion. Seth falls in love with her at first sight, and he is further drawn to her after she loses a patient on the operating table. One night, without any explanation (reasonable or otherwise), the distraught Maggie is able to see Seth, and she falls instantly in love. All this _before_ Seth makes his big fall into humanity's ranks. Long live Hollywood.
Even though Stevens and Silberling maintain a few of Wenders's touches (the black trenchcoats, the angels' library hangout, the cacophony of human "thought voices") and touchstone lines (such as "I can't see you, but I know you're there"), City eventually veers from the source material and settles into familiar Tinseltown convention. Observation and thought-reading, which took up a lot of Wings's running time, falls by the wayside in favor of a more prominent romance. Not necessarily a bad move, but Stevens inexplicably shoehorns the love story into the Meg Ryan romantic comedy formula. As usual, Ryan's character has a boring, barely-exists-as-a-character boyfriend (Colm Feore) from whom the more exciting love interest (in this case, Seth) must "rescue" her. Shouldn't the celestial divide between Seth and Maggie create enough dramatic tension in itself? Dennis Franz has the Peter Falk role as an angel-turned-human (Franz doesn't play himself, however), but unlike the original, Franz's heavenly past is explicitly spelled out rather than implied, and his character becomes nothing more than a walking vessel of angelic exposition.
Cage and Ryan turn in admirable performances that are matched by the rest of the cast, which includes an effective Andre Braugher playing the only character to survive the translation with his name intact, Seth's angel pal Cassiel. But Cage and Ryan's chemistry, while not completely frigid, does not ignite as it should, making the finale oddly lacking in emotional punch. But that is not quite as odd as the general direction of the film's final act, which is entirely different from that of the original. In taking such a radical turn, City goes against the very spirit of its poetic source material; the true magic and profound inspirational uplift of Wings is sacrificed in favor of sap and weepy manipulation. To be fair, on stand-alone terms, City of Angels is watchable, but even from this standpoint, it is still just a conventional and only moderately involving love story wearing angel's wings.
Michael Dequina Chat Forum Host, The Official Michael Jordan Web Site http://jordan.sportsline.com
<EMAIL_ADDRESS><EMAIL_ADDRESS>|<EMAIL_ADDRESS>>My personal WWW sites< Mr. Brown's Movie Site: http://welcome.to/mrbrown Michael Jordan Beyond the Court: http://fly.to/michaeljordan A Michael Jordan Fan's Heartbreak: http://fly.to/mj23 Personal Page: http://welcome.to/w3md >Other WWW sites I work on< CompuServe Hollywood Hotline: http://www.HollywoodHotline.com Albany Online: http://www.AlbanyOnline.com Eyepiece Network: http://www.eyepiece.com
"I didn't know what to expect. It's like something you chase for so long, but then you don't know how to react when you get it. I still don't know how to react." --Michael Jordan, on winning his first NBA championship in 1991 ...or, my thoughts after meeting him on November 21, 1997
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France's 5G spectrum to be sold at floor price of 2.17 billion euros: Minister
PARIS: Frances's 5G spectrum will be sold at a floor price of 2.17 billion euros (US$2.4 billion), a minister said on Sunday (Nov 24), significantly more than the amount previously recommended by the country's telecoms operator Arcep.
Wary of the heavy investments needed for the deployment of the new mobile technology, Arcep's president Sébastien Soriano said earlier this week that the minimum price for the sale of the spectrum should not be higher than 1.5 billion euros.
France's economy ministry has the final word on the floor price for sale.
"The floor (price) that we're proposing seems reasonable to us," Junior Economy Minister Agnès Pannier-Runacher told French newspaper Les Echos. "It's up to the government to assume its responsabilities and decide."
Arcep kicked off the long-awaited sale of 5G spectrum on Thursday, ending months of intense debate between the country's four telecoms operators and authorities on how to best deploy the new mobile technology.
Disagreements between the finance ministry and the telecoms authority over the spectrum to be auctioned and the minimum price for the 5G frequency blocs postponed the process, two sources close to the matter told Reuters this week.
For French authorities, the key issue is to find the right balance between raising cash from the sale and avoiding putting an excessive financial burden on telecoms operators so that the deploy the pricy technology fast.
Germany and Italy have raised about 6.5 billion euros each through 5G spectrum auctions, an amount that shocked the industry and raised concerns among the four French telecoms operators - Orange , Altice Europe's SFR , Iliad and Bouygues Telecoms - whose margins are already suffering from a protracted price war.


{% extends "layout.html" %} {% block page_title %} Blank page {% endblock %} {% block content %} {% block phase_banner %} {% include "includes/phase_banner_alpha.html" %} {% endblock %} Home Companies House Forms Statement of company's objects (CC04) Company's objects What changes are you making to the company objects? I'm adding to the objects I'm removing some of the objects I'm altering some of the objects Confirmation You must agree to this statement to proceed. I confirm that the information above is correct {% endblock %}
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October 27, 2002
Eminem impersonator causes panic
BBC | Submitted by: Bob_McBob
A chainsaw wielding Eminem impersonator caused panic when he decided to
make a dramatic entrance to a fancy dress party, but got the wrong address...
A Lincolnshire Police spokesman said: "The poor occupant was clearly
frightened out of his skin."
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Read article...
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Thirteen year old 's classroom cock-up
South Africa Sunday Times | Submitted by: mahiTMler uber alles
Not content with rape per se, this teen has five young classmates hold down
his 9 year old victims as he rapes them in front of other pupils. And a new
victim every day. He also took their lunch money. Bonus heart-wrenching
pic of one of the tiny tots who told.
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Read article...
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Getting jiggy with jargon
Wichita Eagle | Submitted by: deathstar461
"What's funnier than someone who casually drops the phrase "bling bling"
into his everyday vocabulary? What's more hilarious than a speaker who
believes that "bootylicious" is a word worthy of frequent, all-purpose usage?
People with influence and important jobs are beginning to take our
generation's wacky, playful lingo a little too seriously."
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Read article...
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We kill our birds.  He is passionate about the scenery, the
people, and the wildlife. Blasting pigeons from the sky over Fife last week
with a Benellie semi-automatic shotgun, the 31-year-old Roman was in a state
of bliss... Nearly 2,000 fly in to shoot the birds every season, which lasts
from August to January. The reason? From Sicily to Verona, the skies over
Italy have largely been emptied of pretty much everything with
wings and feathers."
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Read article...
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Perverts with Camcorders Month continues
Courier Times | Submitted by: Son of Benway
"A 28-year-old Falls mother of two was arrested Thursday after she
allegedly confessed to sexually assaulting her 8 year old son while her
boyfriend allegedly videotaped it... Falls police on Thursday searched
the trailer where the woman and her boyfriend were living and found an
8mm videotape that showed her having sex with her son, the affidavit
said. Falls police said the woman subsequently confessed."
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Art turns exhibit into shooting gallery
New York Post | Submitted by: wingnut
"A video installation in the basement of a SoHo art gallery gives
New Yorkers a sniper's-eye view, by letting them take aim and fire at
videos of mothers with their children and tongue-wagging porn stars.
Some people are freaked out by it, but most of them understand that
it's not intended to provoke violence... You've got to be able to
separate fantasy from reality." Not to mention art from shit.
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Read article...
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Fun at job interviews
Arizona Republic | Submitted by: wingnut
We were interviewing for a pool attendant, and she decided it was getting
too hot, so she took off her pantyhose in the middle of the interview...
Another candidate filled out an application coming straight from the pool,
dressed only in a bikini. There was the candidate that appeared on a Monday
as "Randy" and on Tuesday as "Rhonda," and completed an identical
resume. Neither got the job.
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Read article...
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Demonstrators nearly lose their heads
San Francisco Chronicle | Submitted by: Elton Tom
"Two demonstrators heading to Saturday's anti-war march in San Francisco
were critically injured when their heads, sticking through the roof of a
former school bus, hit the top of the Broadway Tunnel... "It sounded like
a gunshot," said Warren Deming, 47, of Guerneville, one of about 25
people on the bus. "It was a very loud impact."
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Wasm++
|
#include <src/ir.h>
#include <third_party/wabt/src/stream.h>
#include <src/wasmpp/wasm-instructions.h>
#include <src/wasmpp/common.h>
Go to the source code of this file.
Classes | |
| class | wasmpp::Memory |
| Block of bytes in the linear memory. More... | |
| class | wasmpp::MemoryManager |
| Linear memory manager. More... | |
| class | wasmpp::FirstFit |
| First fit memory manager. More... | |
| class | wasmpp::ContentManager |
Manage the content of a Wasm instruction block e.g. block , loop , if , etc ... More... | |
| struct | wasmpp::DataEntry |
| A data entry or unit in the linear memory. More... | |
| class | wasmpp::LabelManager |
Manager for Wasm instruction labels e.g. loop , locals, params, etc ... More... | |
| class | wasmpp::ModuleManager |
| WebAssembly module manager. More... | |
Typedefs | |
| typedef ContentManager | wasmpp::FuncBody |
| Function body block. | |


Angular 2 is still in beta. On top of any normal beta issues, another issue is plain JavaScript documentation and tutorials are extremely limited. A new user of Angular 2 is highly encouraged to go the TypeScript route rather than follow this project's pure JS route.


Modern Python Coding Guidelines
- Runs under Python3
- 3-space indents
Modern C++ Coding Guidelines
We are using Modern C++11. Smart pointers, Lambdas, and C++11 multithreading primitives are your friend.
Quick Note
The great thing about "standards" is that there are many to chose from: ISO, Sutter & Stroustrup, ROS, LINUX, Google's, Microsoft's, CERN's, GCC's, ARM's, LLVM's and probably thousands of others. Unfortunately most of these can't even agree on something as basic as how to name a class or a constant. This is probably due to the fact that these standards often carry lots of legacy issues due to supporting existing code bases. The intention behind this document is to create guidance that remains as close to ISO, Sutter & Stroustrup and ROS while resolving as many conflicts, disadvantages and inconsistencies as possible among them.
Naming Conventions
Avoid using any sort of Hungarian notation on names and "_ptr" on pointers.
| Code Element | Style | Comment |
|---|---|---|
| Namespace | under_scored | Differentiate from class names |
| Class name | CamelCase | To differentiate from STL types which ISO recommends (do not use "C" or "T" prefixes) |
| Function name | camelCase | Lower case start is almost universal except for .Net world |
| Parameters/Locals | under_scored | Vast majority of standards recommends this because _ is more readable to C++ crowd (although not much to Java/.Net crowd) |
| Member variables | under_scored_with_ | The prefix _ is heavily discouraged as ISO has rules around reserving _identifiers, so we recommend suffix instead |
| Enums and its members | CamelCase | Most except very old standards agree with this one |
| Globals | g_under_scored | You shouldn't have these in first place! |
| Constants | UPPER_CASE | Very contentious and we just have to pick one here, unless if is a private constant in class or method, then use naming for Members or Locals |
| File names | Match case of class name in file | Lot of pro and cons either way but this removes inconsistency in auto generated code (important for ROS) |
Header Files
Use a namespace qualified #ifdef to protect against multiple inclusion:
#ifndef msr_airsim_MyHeader_hpp
#define msr_airsim_MyHeader_hpp
//--your code
#endif
The reason we don't use #pragma once is because it's not supported if same header file exists at multiple places (which might be possible under ROS build system!).
Bracketing
Inside function or method body place curly bracket on same line. Outside that the Namespace, Class and methods levels use separate line. This is called K&R style and its variants are widely used in C++ vs other styles which are more popular in other languages. Notice that curlies are not required if you have single statement, but complex statements are easier to keep correct with the braces.
int main(int argc, char* argv[])
{
while (x == y) {
f0();
if (cont()) {
f1();
} else {
f2();
f3();
}
if (x > 100)
break;
}
}
Const and References
Religiously review all non-scalar parameters you declare to be candidate for const and references. If you are coming from languages such as C#/Java/Python,
the most often mistake you would make is to pass parameters by value instead of const T&;
Especially most of the strings, vectors and maps you want to
pass as const T&;
(if they are readonly) or T&
(if they are writable). Also add const
suffix to methods as much as possible.
Overriding
When overriding virtual method, use override suffix.
Pointers
This is really about memory management. A simulator has much performance critical code, so we try and avoid overloading the memory manager with lots of calls to new/delete. We also want to avoid too much copying of things on the stack, so we pass things by reference when ever possible. But when the object really needs to live longer than the call stack you often need to allocate that object on the heap, and so you have a pointer. Now, if management of the lifetime of that object is going to be tricky we recommend using C++ 11 smart pointers. But smart pointers do have a cost, so don’t use them blindly everywhere. For private code where performance is paramount, raw pointers can be used. Raw pointers are also often needed when interfacing with legacy systems that only accept pointer types, for example, sockets API. But we try to wrap those legacy interfaces as much as possible and avoid that style of programming from leaking into the larger code base.
Religiously check if you can use const everywhere, for example, const float * const xP
. Avoid using prefix or suffix to indicate pointer types in variable names, i.e. use my_obj
instead of myobj_ptr
except in cases where it might make sense to differentiate variables better, for example, int mynum = 5; int* mynum_ptr = mynum;
This is Too Short, ye?
Yes, and it's on purpose because no one likes to read 200 page coding guidelines. The goal here is to cover only most significant things which are already not covered by strict mode compilation in GCC and Level 4 warnings-as-errors in VC++. If you had like to know about how to write better code in C++, please see GotW and Effective Modern C++ book.


Download Whitepaper
pevo - decentralized open access and evaluation
Pevo will be a decentral long-term digital archive for scholarly publishing through the open access paradigm and a web platform facilitating continuous open evaluation. The invention is based on blockchain technology powered by Steem.


Customization¶
Sphinx Options¶
In addition to theme options (below), some common Sphinx options (set in
conf.py
) are also respected by the theme. See the full link of Sphinx
configuration options.
html_logo — Path to a logo image which will appear in the sidebar.
project — The name of your project. It appears in the sidebar and elsewhere in the docs.
copyright — String containing date and/or owner of the project.
html_last_updated_fmt — If not
None
, adds a timestamp in the footer.html_show_copyright — Show the
copyright
field in the footer.html_show_sourcelink — Show the
.rst
source link in the footer.html_show_sphinx — Show “Created using Sphinx” in the footer.
Theme Options¶
Options are set in the html_theme_options
variable in conf.py
:
# conf.py
html_theme_options = {
"show_breadcrumbs": True,
}
The following theme options are available:
description — Short text blurb about your project, to appear under the logo or project name.
extra_links — Dictionary mapping link names to link targets; these will be added in the sidebar (if you’ve set
extralinks.html
via thehtml_sidebars
option). This is useful for adding GitHub links, donate links, etc. For example:# conf.py html_theme_options: { "extra_links": { "Source Code": "https://github.com/", } }
globaltoc_collapse — Boolean to only expand subsections of the current document in
globaltoc.html
. Defaults to True.globaltoc_includehidden — Boolean to show even those subsections in
globaltoc.html
which have been included with the:hidden:
flag of the toctree directive. Defaults to True.globaltoc_maxdepth — Integer of the maximum depth of the toctree in
globaltoc.html
. Set it to -1 to allow unlimited depth. Defaults to the max depth selected in the toctree directive.reading_mode — Set the default reading mode color scheme. One of
light
,sepia
, ordark
. Set toNone
to use the browser’s default styles. Defaults to light.show_breadcrumbs — Boolean to show breadcrumb links of ancestors to the current document at the top of the page. Defaults to False.
show_project_name — Set to
False
to hide the project name in the sidebar.show_version — Shows the project version below the logo/name in the sidebar.
show_relbar_bottom — Boolean to show Previous/Next links at the bottom of the page. Defaults to True.
show_relbar_top — Boolean to show Previous/Next links at the top of the page. Defaults to False.
sponsors — List of dictionaries containing sponsors or other highlighted links in the sidebar. Dictionary contents are all optional and include:
# conf.py html_theme_options = { "sponsors": [ { "href": "http://example.com", # URL to link to. "image": "logo.png", # URL or path to image. "large": True, # Use large image format. "title": "Sponsor Us", # Shown beside/below image. "note": "Become a member.", # Shown beside/below image. }, ], }
sponsors_heading — Heading above the sponsors section in the sidebar, if
sponsors
is provided. Defaults to “Sponsors”.typography — The typeface stack to use throughout the docs. One of the following values:
academy
academy-native
book
book-native
engineer
engineer-native
humanist
humanist-native
swiss
swiss-native
For example:
# conf.py html_theme_options = { "typography": "humanist", }
See typography section below for more details.
Typography¶
Several hand-tuned typography stacks are included to change the feel of your documentation. Each features carefully selected typeface pairings which have been balanced with the theme layout to create a book-like reading experience.
Book — Highly legible type inspired by printed books. The default, and good for general purpose use.
Humanist — Another highly legible type, with a fresh and more casual look.
Academy — An elegant and formal early 20th-century type favored by academic publications and legal documents.
Engineer — A semi-slab-serif type; formal but much cleaner and more modern than “academy”.
Swiss — Sans-serif type providing an austere but clean look.
These stacks utilize the Google Fonts and jsDelivr CDN to load web fonts (and
include appropriate fallbacks). Each stack also includes a -native
version,
which does not load any web fonts but instead uses fonts available from the
user’s computer. Use this if you seek absolute minimal resource usage or plan to
primarily distribute the HTML offline.
For more details, see Design Notes.
To implement your own custom type stack, set the typography
option to
None
and then add your own styles following the Custom CSS instructions
below.
Custom CSS¶
Library’s CSS is designed to be easily overridden. This means it avoids complex
queries, and does not specify attributes more than once where possible (e.g.
color, font, font-size, etc. are only set on the body
element, so changing
it once here will affect the whole document).
If you need to modify Library’s default CSS styles, you may provide a custom stylesheet as follows:
Create a file named
custom.css
in your static folder (typically named_static/
).Set the Sphinx option html_static_path to either that file’s path, or the directory it lives within (i.e.
"_static"
).


IMGS
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A bunch of beautiful images that I didn't take(except for one of em)


This document describes the use of the NTP Project's summary
program.
This document applies to version 4.2.8p8 of summary
.
This section was generated by AutoGen,
using the agtexi-cmd
template and the option descriptions for the summary
program.
This is the automatically generated usage text for summary.
The text printed is the same whether selected with the help
option
(--help) or the more-help
option (--more-help). more-help
will print
the usage text by passing it through a pager program.
more-help
is disabled on platforms without a working
fork(2)
function. The PAGER
environment variable is
used to select the program, defaulting to more. Both will exit
with a status code of 0.
summary - compute various stastics from NTP stat files - Ver. 4.2.8p8 USAGE: summary [ -<flag> [<val>] | --<name>[{=| }<val>] ]... --directory=str Directory containing stat files --end-date=num End date --output-directory=str Output directory --peer-dist-limit=float Peer dist limit --skip-time-steps=float Ignore time offsets larger that this --start-date=num Start date -?, --help Display usage information and exit --more-help Pass the extended usage text through a pager Options are specified by doubled hyphens and their name or by a single hyphen and the flag character.
This is the “directory containing stat files” option.
This option takes a string argument.
The directory where ntpd
will search for .stat files generated
by ntpd
.
This is the “end date” option.
This option takes a number argument.
Process all files with the date suffix less or equal to value of this
option. Defaults to today minus one day (Use date -u +%Y%m%d
)
to get the timestamp.
This is the “output directory” option.
This option takes a str argument.
The output directory summary
will write all output files to.
This is the “start date” option. This option takes a num argument. Process all files with the date suffix more or equal to value of this option. Defaults to 197000101.
One of the following exit values will be returned:


Salazar feels the rusty sliver of paradox from his magickal feat slide under his thumbnail as he watches cargo shorts bro hug a couple more dudes but then their forms start to wobble and bleed colors as the they move.
The Bag Lady begains some kind of crazy prayer and John feels a strange elation that smells like lilacs while the words of the prayer march towards like wave after wave of domino soliders.
As the finally ghostly echoes of word soldiers fade away you can both hear the voice of the Bag Lady, now magnified into a roar. “Ignore all that sweeties. That’s the just the acid.” And then she blows a puff of some kind of ash into the wind that carries away the rest of the reality you were standing in.
how to approach this? maybe from the outside?
do they find themselves together or not as they start?
could be an individual discussion Could be salazar gets a little lost.
movie style establishing shot?
I think it would be better to ease into it from salazar’s point of view. he’s about to get a drop of spirit magick.
Bag Lady is working magick
she’s going to give all three of them the ability to see avatars. Seeing avatars is Mind 3/Prime 2/Spirit 1
adding the sharing of that as Life 2.
That’s a complex effect.
Alba has:
Mental d10
Tradition d8
Occult d10
Arete d12
Mind d8
Prime d6
Spirit d10
[going to remove the casting time because that’s more or less dependent on the practice. and it’s extra dice we don’t need to use. ]
Reality gets:
Effect d8
Mind d8
Prime d6
Spirit d4
Duration Prime d8, long enough to have a conversation
Plus whatever paradox I feel like using.


--- title: "Kevoree Tutorial #2 Video is available !!" layout: post published: true ---
Yes, The second tutorial video of Kevoree is now available.
This new tutorial shows the compilation chain, and the continuous design ability offered by Kevoree.
Have a great time ;-)
... and stay tuned !


Meet a partner for your best holiday
Lucas Marsha
I get my inspiration from nature and objects around me. I have a passion to colors,typography and skateboards.
I get my inspiration from nature and objects around me. I have a passion to colors,typography and skateboards.


Mind that you can move method parameters in both declaration and invocation. To do that, use &shortcut:MoveElementLeft;/&shortcut:MoveElementRight;.
Moreover, it's possible to propagate such a move in a method declaration to the method invocations - just immediately after move press &shortcut:ShowIntentionActions;, and choose Apply signature change.


The Emerald City of Oz
by L. Frank Baum
Chapter 4: “How the Nome King Planned Revenge”
Additional Information
- Year Published: 1910
- Language: English
- Country of Origin: United States of America
- Source: Baum, L. F. (1910). The Emerald City of Oz. Chicago, IL: Reilly and Britton.
-
Readability:
- Flesch–Kincaid Level: 6.0
- Word Count: 1,795
- Genre: Fantasy
- ✎ Cite This
Downloads
The reason most people are bad is because they do not try to be good. Now, the Nome King had never tried to be good, so he was very bad indeed. Having decided to conquer the Land of Oz and to destroy the Emerald City and enslave all its people, King Roquat the Red kept planning ways to do this dreadful thing, and the more he planned the more he believed he would be able to accomplish it.
About the time Dorothy went to Ozma the Nome King called his Chief Steward to him and said:
“Kaliko, I think I shall make you the General of my armies.”
“I think you won’t,” replied Kaliko, positively.
“Why not?” inquired the King, reaching for his scepter with the big sapphire.
“Because I’m your Chief Steward and know nothing of warfare,” said Kaliko, preparing to dodge if anything were thrown at him. “I manage all the affairs of your kingdom better than you could yourself, and you’ll never find another Steward as good as I am.  “Summon my army to assemble in the Great Cavern.”
Kaliko bowed and retired, and in a few minutes returned to say that the army was assembled. So the King went out upon a balcony that overlooked the Great Cavern, where fifty thousand Nomes, all armed with swords and pikes, stood marshaled in military array.
When they were not required as soldiers all these Nomes were metal workers and miners, and they had hammered so much at the forges and dug so hard with pick and shovel that they had acquired great muscular strength. They were strangely formed creatures, rather round and not very tall. Their toes were curly and their ears broad and flat.
In time of war every Nome left his forge or mine and became part of the great army of King Roquat. The soldiers wore rock-colored uniforms and were excellently drilled.
The King looked upon this tremendous army, which stood silently arrayed before him, and a cruel smile curled the corners of his mouth, for he saw that his legions were very powerful. Then he addressed them from the balcony, saying:
“I have thrown away General Blug, because he did not please me. So I want another General to command this army. Who is next in command?”
“I am,” replied Colonel Crinkle, a dapper-looking Nome, as he stepped forward to salute his monarch.
The King looked at him carefully and said:
“I want you to march this army through an underground tunnel, which I am going to bore, to the Emerald City of Oz. When you get there I want you to conquer the Oz people, destroy them and their city, and bring all their gold and silver and precious stones back to my cavern. Also you are to recapture my Magic Belt and return it to me. Will you do this, General Crinkle?”
“No, your Majesty,” replied the Nome; “for it can’t be done.”
“Oh indeed!” exclaimed the King. Then he turned to his servants and said: “Please take General Crinkle to the torture chamber. There you will kindly slice him into thin slices. Afterward you may feed him to the seven-headed dogs.”
“Anything to oblige your Majesty,” replied the servants, politely, and led the condemned man away.
When they had gone, the King addressed the army again.
“Listen!” said he. “The General who is to command my armies must promise to carry out my orders. If he fails he will share the fate of poor Crinkle. Now, then, who will volunteer to lead my hosts to the Emerald City?”
For a time no one moved and all were silent. Then an old Nome with white whiskers so long that they were tied around his waist to prevent their tripping him up, stepped out of the ranks and saluted the King.
“I’d like to ask a few questions, your Majesty,” he said.
“Go ahead,” replied the King.
“These Oz people are quite good, are they not?”
“As good as apple pie,” said the King.
“And they are happy, I suppose?” continued the old Nome.
“Happy as the day is long,” said the King.
“And contented and prosperous?” inquired the Nome.
“Very much so,” said the King.
  That is why I am so fond of your Majesty. Make me your General and I’ll promise to conquer and destroy the Oz people.  Very good, indeed! That’s the way to talk!” cried Roquat the Red, who was greatly pleased. “What is your name, General?”
“I’m called Guph, your Majesty.”
“Well, Guph, come with me to my private cave, and we’ll talk it over.” Then he turned to the army. “Nomes and soldiers,” said he, “you are to obey the commands of General Guph until he becomes dog-feed. Any man who fails to obey his new General will be promptly thrown away. You are now dismissed.”
Guph went to the King’s private cave and sat down upon an amethyst chair and put his feet on the arm of the King’s ruby throne. Then he lighted his pipe and threw the live coal he had taken from his pocket upon the King’s left foot and puffed the smoke into the King’s eyes and made himself comfortable. For he was a wise old Nome, and he knew that the best way to get along with Roquat the Red was to show that he was not afraid of him.
“I’m ready for the talk, your Majesty,” he said.
The King coughed and looked at his new General fiercely.
“Do you not tremble to take such liberties with your monarch?” he asked.
“Oh no,” replied Guph, calmly, and he blew a wreath of smoke that curled around the King’s nose and made him sneeze. “You want to conquer the Emerald City, and I’m the only Nome in all your dominions who can conquer it. So you will be very careful not to hurt me until I have carried out your wishes. After that—”
“Well, what then?” inquired the King.
“Then you will be so grateful to me that you won’t care to hurt me,” replied the General.
“That is a very good argument,” said Roquat. “But suppose you fail?”
“Then it’s the slicing machine. I agree to that,” announced Guph. “But if you do as I tell you there will be no failure. The trouble with you, Roquat, is that you don’t think carefully enough. I do. You would go ahead and march through your tunnel into Oz, and get defeated and driven back. I won’t. And the reason I won’t is because when I march I’ll have all my plans made, and a host of allies to assist my Nomes.”
“What do you mean by that?” asked the King.
“I’ll explain, King Roquat. You’re going to attack a fairy country, and a mighty fairy country, too. They haven’t much of an army in Oz, but the Princess who rules them has a fairy wand; and the little girl Dorothy has your Magic Belt; and at the North of the Emerald City lives a clever sorceress called Glinda the Good, who commands the spirits of the air. Also I have heard that there is a wonderful Wizard in Ozma’s palace, who is so skillful that people used to pay him money in America to see him perform. So you see it will be no easy thing to overcome all this magic.”
“We have fifty thousand soldiers!” cried the King proudly.
“Yes; but they are Nomes,” remarked Guph, taking a silk handkerchief from the King’s pocket and wiping his own pointed shoes with it. “Nomes are immortals, but they are not strong on magic. When you lost your famous Belt the greater part of your own power was gone from you. Against Ozma you and your Nomes would have no show at all.”
Roquat’s eyes flashed angrily.

“Not yet,” said the General, filling his pipe from the King’s private tobacco pouch.
“What do you propose to do?” asked the monarch.
“I propose to obtain the power we need,” answered Guph.  We will get them on our side, band them all together, and then take Ozma and her people by surprise. It’s all very simple and easy when you know how. Alone, we should be helpless to injure the Ruler of Oz, but with the aid of the evil powers we can summon we shall easily succeed.”
King Roquat was delighted with this idea, for he realized how clever it was.
 “You must go at once and make arrangements with the evil powers to assist us, and meantime I’ll begin to dig the tunnel.”
“I thought you’d agree with me, Roquat,” replied the new General. “I’ll start this very afternoon to visit the Chief of the Whimsies.”


WITNESS LIST Subcommittee on Agriculture Committee September 16, 2004 - 2:30P Charge 1 ON: Avant, Bob Executive Director (Texas Food and Fiber Commission), Austin, TX Hellman, Ed Associate Prof/Viticulture Specialist (Texas Tech and Texas A&M Universities), Lubbock, TX Hillman, Bob Executive Director (Texas Animal Health Commission), Austin, TX Hubert, Martin Deputy Commissioner (Texas Department of Agriculture), Austin, TX Shotwell, Thomas Chairman of the Board (Shotwell & Carr, Inc.), Runaway Bay, TX Woody, Tracy General Manager (Jeri's Seafood Oyster Industry), Anahuac, TX Registering, but not testifying: On: Lucas, Angela Director of Financial Services (Texas Animal Health Commission), Austin, TX Charge 2 ON: Holtzapple, Mark Professor (Texas A&M University), College Station, TX Mark, McLellan Director (Institue of Food Science and Engineering, Texas A&M University), College Station, TX Mullican, Bill Deputy Executive Admin. for Planning (Texas Water Development Board), Austin, TX Storment, Scott Senior Project Development Officer (North American Development Bank), San Antonio, TX


Lost in a Forest
Click on the choice you want to continue the story. Good luck!
Click here to begin.
Oh no, you're stuck in the middle of a forest! What to do?
Go north.
You go north. It leads into two different areas: a dark cave and a bridge above a lake. Now what?
Go in the cave.
You enter the cave. It sure is dark, and you don't have a flashlight.
The end.
The end.
Go across the bridge.
You head to the bridge. Youch, there are alligators!
The end.
The end.
Go west.
You go west. It leads to a small abandoned village. Interesting, isn't it?
Walk across the village.
As you traverse the village, you find a hidden chamber that heads downward. Any thoughts?
Head on in.
As you enter the chamber, the entrance immediately locks shut, leaving you no chance to escape.
The end.
The end.
Keep going.
You continue your journey, eventually making it to the end of the village.  Now what?
Search the village for a key.
Your attempt to find a key is unsuccessful, and to make matters worse, you fall into a trapdoor during the search.
The end.
The end.
Climb the fence.
You successfully climb the fence. After all, the fence was quite low and didn't have any spikes or electrical wires. From there, you keep following the path. The more you travel, the more you see the sun. Eventually, you see civilization again. You have escaped the forest!
The end.
The end.
Search the village for treasure.
Where do you start searching?
The nearest cottage you see.
You head on inside, and begin searching.  You find yourself doomed.
The end.
The end.
In a lush digging spot.
You grab the nearest shovel you see and start digging. Look, it's a small treasure chest! Your reaction?
Open it.
You open the chest, and there's a jewel inside! But little do you know that the jewel really is as sticky as hot glue, designed as a nasty trap. Yeowzers!
The end.
The end.
Discard it and keep digging.
You keep digging, only to eventually find yourself so low to the ground that you can't get back to the surface. You're doomed!
The end.
The end.
Go east.
You go east. There is another fork in the road. Which way should you go?
Go left.
You go left. You arrive at a wide open area with trees surrounding it. In other words, it's a dead end.
Traverse through the trees.
You enter unmarked territory. As you progress, you get tangled up from twigs and leaves, leaving you stuck and without a way to escape.
The end.
The end.

What message should you carve?
HELP!
You successfully carve the message. At some point, someone with a helicopter arrives at dead end as a result of reading your message. They ask if you want a ride back to safety. You say yes. Then, the person takes you to the helicopter and flies you back to safety. You have successfully escaped the forest!
The end.
The end.
I LIKE PIE.
You successfully carve the message. At some point, someone with a helicopter arrives at dead end as a result of reading your message. They ask if you like pie. You say yes. Then, you get pied to the face, and before you can protest, you get kidnapped.
The end.
The end.
Go right.
You go right. It leads to a bridge above a ledge. As you cross the bridge, the bridge snaps.
The end.
The end.
Hack and slash your way in between.
As dangerous as it sounds, you go for it anyway. You're doing fine, until you reach animal territory. Then something bad happens.
The end
The end
Yell for help.
You yell for help. Nothing happens.
Continue yelling while waving and dancing rapidly.
As crazy as it sounds, you proceed with the action. Luckily, someone with a helicopter notices and rescues you! You may be out of the forest, but instead of at home, you're stuck in a mental institution.
The end.
The end.
Climb up the nearest tree and try again.
Exalted on the tree, you proudly stand up, raise your hands, and howl for help again. Unfortunately, that's when you begin to slip, and suddenly, you start to fall...
The end.
The end.
Whack yourself in agony.
Whack, whack, whack! Ouch, that must hurt!
Continue whacking yourself, this time with a stick.
The stick you find is a giant one. Whacka, whacka, whacka!
Let's find some coconuts, shall we?
Yeah, this ain't looking good.
The end.
The end.
Suddenly collapse to the floor.
When you do that, you coincidentally pass out as well. Oh well.
The end.
The end.

The nearest object is a poison berry bush. You can probably tell what happens next.
The end.
The end.
©2013-2016 Timothy Hsu


$> git clone git@github.com:unity26org/wheel.git
Wheel is based on Package Templates of the Go language. If you want to contribute we recommend follow thw steps:
$> git clone git@github.com:unity26org/wheel-doc.git
$> cd wheel-doc
Wheel-doc is a Node JS application, build with Gulp. First. we need to install dependences:
$> npm install
Second, install the server:
$> npm start
Now, every change on the Wheel-doc your browser will refresh so you can see the changes is real-time.


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YOUR GRAPHIC DESIGNER
Erastus Kimani
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Illustrator
ART
WEAVING
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Kenya
Nairobi
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--- layout: post status: publish published: true title: "距离" author: Leask author_login: leask author_email<EMAIL_ADDRESS>author_url: https://leaskh.com wordpress_id: 500 wordpress_url: http://leaskh.wordpress.com/2006/07/11/%e8%b7%9d%e7%a6%bb-9 date: '2006-07-11 14:40:22 +0800' date_gmt: '2006-07-11 14:40:22 +0800' categories: - Life tags: [] comments: [] ---


This Is Ground is handmade leather goods that started as creative solutions to everyday problems. Their first products, the Cord Taco and the Cordito, gained attention and love because of their simple design and utility.
The goal of this project was to work as a group with a client on a feature that would give users ability to search for a product, customize it with an artwork (ex. logo) and place a bulk order.
Create user interface for bulk orders and order customizations.
As the research is one of the most important step in creating a persona, for this project I have conducted a user interview to better understand the targeted audience.
As a user, I want to be able to search for a product, customize it and place a bulk order.


Build options
Most non-trivial builds require user-settable options. As an example a program may have two different data backends that are selectable at build time. Meson provides for this by having a option definition file. Its name is meson_options.txt
and it is placed at the root of your source tree.
Here is a simple option file.
option('someoption', type : 'string', value : 'optval', description : 'An option')
option('other_one', type : 'boolean', value : false)
option('combo_opt', type : 'combo', choices : ['one', 'two', 'three'], value : 'three')
This demonstrates the three basic option types and their usage. String option is just a free form string and a boolean option is, unsurprisingly, true or false. The combo option can have any value from the strings listed in argument choices
. If value
is not set, it defaults to empty string for strings, true
for booleans or the first element in a combo. You can specify description
, which is a free form piece of text describing the option. It defaults to option name.
These options are accessed in Meson code with the get_option
function.
optval = get_option('opt_name')
This function also allows you to query the value of Meson's built-in project options. For example, to get the installation prefix you would issue the following command:
prefix = get_option('prefix')
It should be noted that you can not set option values in your Meson scripts. They have to be set externally with the mesonconf
command line tool. Running mesonconf
without arguments in a build dir shows you all options you can set. To change their values use the -D
option:
$ mesonconf -Doption=newvalue
The results of the search are


I provide administrative and marketing support, project management, technology integration, and organizing services to small business owners and their teams. With experience and insight across a broad range of business functions, I am a go-to resource for my clients.
As an Administrative Consultant, I partner with small business owners to mend the fray—freeing them to do their important work.
At work, I am an office ninja—equipped to prioritize, organize, and solve problems. I aim to harness order out of chaos.
At home, I am a houseplant waterer, chihuahua enthusiast, and aspiring minimalist. Currently residing in sunny San Diego.


--- title: 2018 Schedule layout: default permalink: seasons/2018/schedule ---
| Event | Date | Location |
|---|---|---|
| Club Centex (Preseason) | May 12 - 13 | Onion Creek Soccer Complex Austin, TX |
| Riverside Tryouts | May 19 | Zilker Metropolitan Park Austin, TX |
| Texas Two Finger | June 30 - July 1 | 5 Star Soccer Complex The Colony, TX |
| PB & J | Aug 4 - 5 | Bryan Regional Athletic Complex Bryan, TX |
| Riverside Classic | Aug 18 - 19 | Round Rock Multisports Complex Austin, TX |
| Texas Sectionals | Sept 8 - 9 | Austin, TX |
| South Central Regionals | Sept 22 - 23 | Dick's Sporting Goods Park Denver, CO |
| Date | Time | Opponent | Score |
|---|---|---|---|
| Sept 8 | 8:00AM | Gamble | 6 - 11 |
| 9:10AM | Shrike Cutters | 11 - 3 | |
| 10:20AM | DUCS | 9 - 7 | |
| 11:30AM | Prime | 5 - 6 | |
| Sept 9 | 9:50AM | Clutch | 7 - 9 |
| 11:40AM | Foxtrot | 11 - 5 | |
| 1:30PM | DUPlex | 11 - 5 | |
| 3:20PM | Prime | 11 - 5 |
| Date | Time | Opponent | Score | |
|---|---|---|---|---|
| Aug 18 | 10:00AM | Prime | 15 - 8 | |
| 11:30AM | DUCS | 15 - 3 | ||
| 1:00PM | Gaucho | 9 - 15 | ||
| Aug 19 | 9:00AM | The Bayou Boys | 15 - 4 | |
| 10:45AM | Gamble | 9 - 13 | ||
| 12:30PM | Papa Bear | 11 - 12 |
| Date | Time | Opponent | Score |
|---|---|---|---|
| Sept 8 | 8:00AM | DUPlex | 8 - 11 |
| 10:20AM | Gaucho | 5 - 11 | |
| 11:30AM | Rock Steady | 5 - 11 | |
| 1:50PM | The Bayou Boys | 9 - 11 | |
| Sept 9 | 9:50AM | Papa Bear | 8 - 11 |
| 11:40AM | Nitro | 1 - 11 | |
| 1:30PM | DUCS | 6 - 11 | |
| 3:20PM | Shrike Cutters | W - F |
| Date | Time | Opponent | Score |
|---|---|---|---|
| Aug 18 | 8:30AM | The Bayou Boys | 10 - 13 |
| 9:45AM | Papa Bear | 7 - 11 | |
| 11:00AM | Gamble | 2 - 13 | |
| 1:30PM | Surrillic Audovice | 13 - 7 | |
| Aug 19 | 9:00AM | Gaucho | 1 - 15 |
| 12:30PM | Prime | W - F | |
| 2:15PM | DUCS | 12 - 11 |
| Date | Time | Opponent | Score |
|---|---|---|---|
| Aug 4 | 8:30AM | Rougaroux | 7 - 13 |
| 10:15AM | DUCS | 13 - 10 | |
| 12:00PM | Gamble | 4 - 13 | |
| 1:45PM | The Bayou Boys | 13 - 9 | |
| Aug 5 | 8:45AM | Riverside | 5 - 13 |
| 10:30AM | Gaucho | 8 - 15 | |
| 12:30PM | Dallas United: Bazooka | 15 - 13 |


This is an homage to Color64 BBS by Greg Pfountz This is very much a work in progress! Any help is appreciated. It is my goal to get this functioning exactly like the original Color64 BBS. It is written entirely using jQuery, HTML and PHP with the awesome jQuery Terminal plugin by Jakub Jankiewicz Thanks to Greg Pfountz (Daily Exchange) and Sam Lewit (Terrapin Transit BBS) for inspiring me to waste a good portion of my youth and now a good portion of middle age (re)-writing this fabulous piece of software If you would like to contribute to this project please contact me at<EMAIL_ADDRESS>Yes, refresh it. All of the colors will change ;)
What will it do? Not much for now. You can register, login, get a membership list, get the main menu. That's about it.
I write this in my spare time when I'm bored and the code is sloppy.
I'm using a copy of Color64 running on an old MacBook running Power64.
I would love some help. Please shoot me a line if you are interested.


--- title: Mislav Balunović position: PhD Student image: mislav.jpg email<EMAIL_ADDRESS>office: CAB H 66 ---
I am Mislav Balunović, a PhD student at the Department of Computer Science, ETH Zürich. I am part of the Secure, Reliable, and Intelligent Systems Lab, supervised by Martin Vechev, since April 2019.
ETH Medal for Outstanding Master Thesis 2020
| 2017 | ACM ICPC World Finals | USA | 27/133 |
| 2016 | Central European Regional Contest (CERC) | Croatia | 2/67 |
| 2014 | Central European Regional Contest (CERC) | Poland | 4/79 |
| 2014 | International Olympiad in Mathematics (IMO) | South Africa | Gold medal |
| 2014 | International Olympiad in Informatics (IOI) | Taiwan | Silver medal |
| 2014 | Central European Olympiad in Informatics (CEOI) | Germany | Silver medal |
| 2014 | Croatian Olympiad in Mathematics (HMO) | Croatia | Gold medal |
| 2013 | International Olympiad in Mathematics (IMO) | Colombia | Bronze medal |
| 2013 | International Olympiad in Informatics (IOI) | Australia | Silver medal |
| 2013 | Croatian Olympiad in Informatics (HIO) | Croatia | Gold medal |


Full stack engineer intent on building engaging apps that revolutionize how people consume media. I complimented my Computer Science education with web development training in Rails, Javascript and React at the Flatiron School. I have a proven track record as a team player throughout my tenure as an IT professional. My qualities as a collaborator and computational thinker will prove valuable to an engineering team bent on revolutionizing the mobile app space.


Plain_Sight Rule File Validator
Directions:
This form allows you to validate a custom rule file.
In "Rule file" select or enter a candidate plain_sight rule file.
Hit Validate to have the file tested and receive feedback.
Rule File
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The Student Performance Dataset is from a study of secondary education of two Portuguese schools. The data was collected via school reports and questionnaires. G3 or the final grade is the main outcome variable for the dataset. The data was obtained from the UCI Machine Learning Repository and can be found at: http://archive.ics.uci.edu/ml/datasets/Student+Performance. The following table lists each variable and their descriptions.
Type in the variable search term below:
| Variable | Scale | Predictor/Output | Measurement |
|---|---|---|---|
| School | GP or MS | Predictor | Qualitative |
| Sex(Male or Female) | F or M | Predictor | Qualitative |
| Age | 15 to 22 | Predictor | Quantitative |
| Address(Urban or Rural) | U or R | Predictor | Qualitative |
| Famesize(Family Size) | LE3 or GT3 | Predictor | Quantitative |
| Pstatus(Parent Cohabitation) | T-living together / A-Apart | Predictor | Qualitative |
| Medu(Mothers education) | 0 to 4 | Predictor | Qualitative |
| Fedu (Fathers Education) | 0 to 4 | Predictor | Qualitative |
| Mjob(Mothers job) | Nominal | Predictor | Qualitative |
| Fjob(Fathers job) | Nominal | Predictor | Qualitative |
| Reason(reason school chose) | Nominal | Predictor/Output | Qualitative |
| Guardian(student’s guardian) | Mother/father/Other | Predictor | Qualitative |
| Traveltime(home, to school, traveltime) | 1 to 4 | Predictor | Quantitative |
| Studytime(Weekly study time) | 1 to 4 | Predictor/Output | Quantitative |
| Failures(number of past class failures) | 1 to 4 | Predictor | Quantitative |
| Schoolsup(extra educational support) | Yes or No | Predictor | Qualitative |
| Famsup(family educational support) | Yes or No | Predictor/Output | Qualitative |
| Paid(extra paid classes within the coursesubject) | Yes or No | Predictor | Qualitative |
| Activities(exra-curricular activities) | Yes or No | Predictor | Qualitative |
| Nursery(attended nursery school) | Yes or No | Predictor | Qualitative |
| Higher(wants to take higher education) | Yes or No | Predictor/Output | Qualitative |
| Internet(internet access at home) | Yes or No | Predictor/Output | Qualitative |
| Romantic(in a romantic relationship) | Yes or No | Predictor | Qualitative |
| Famrel(Quality of family relationships) | 1 to 5 | Predictor | Qualitative |
| Freetime(free time after school) | 1 to 5 | Predictor/Output | Quantitative |
| Goout(going out with friends) | 1 to 5 | Predictor | Qualitative |
| Dalc(workday alcohol consumption) | 1 to 5 | Predictor | Qualitative |
| Walc(weekend alcohol consumption) | 1 to 5 | Predictor/Output | Qualitative |
| Health(current health status) | 1 to 5 | Predictor | Qualitative |
| Absences(number of school absences) | 0 to 93 | Predictor | Quantitative |
| G1(First Period Grade) | 0 to 20 | Predictor/Output | Quantitative |
| G2(Second Period Grade) | 0 to 20 | Predictor/Output | Quantitative |
| G3(Final Grade) | 0 to 20 | Output | Quantitative |
> summary(Student_Performance_Ben_Gonzalez)
school sex age address famsize Pstatus Medu Fedu Mjob
GP:349 F:208 Min. :15.0 R: 88 GT3:281 A: 41 Min. :0.000 Min. :0.000 at_home : 59
MS: 46 M:187 1st Qu.:16.0 U:307 LE3:114 T:354 1st Qu.:2.000 1st Qu.:2.000 health : 34
Median :17.0 Median :3.000 Median :2.000 other :141
Mean :16.7 Mean :2.749 Mean :2.522 services:103
3rd Qu.:18.0 3rd Qu.:4.000 3rd Qu.:3.000 teacher : 58
Max. :22.0 Max. :4.000 Max. :4.000
Fjob reason guardian traveltime studytime failures schoolsup famsup
at_home : 20 course :145 father: 90 Min. :1.000 Min. :1.000 Min. :0.0000 no :344 no :153
health : 18 home :109 mother:273 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:0.0000 yes: 51 yes:242
other :217 other : 36 other : 32 Median :1.000 Median :2.000 Median :0.0000
services:111 reputation:105 Mean :1.448 Mean :2.035 Mean :0.3342
teacher : 29 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:0.0000
Max. :4.000 Max. :4.000 Max. :3.0000
paid activities nursery higher internet romantic famrel freetime goout
no :214 no :194 no : 81 no : 20 no : 66 no :263 Min. :1.000 Min. :1.000 Min. :1.000
yes:181 yes:201 yes:314 yes:375 yes:329 yes:132 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:2.000
Median :4.000 Median :3.000 Median :3.000
Mean :3.944 Mean :3.235 Mean :3.109
3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :5.000 Max. :5.000 Max. :5.000
Dalc Walc health absences G1 G2 G3
Min. :1.000 Min. :1.000 Min. :1.000 Min. : 0.000 Min. : 3.00 Min. : 0.00 Min. : 0.00
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:3.000 1st Qu.: 0.000 1st Qu.: 8.00 1st Qu.: 9.00 1st Qu.: 8.00
Median :1.000 Median :2.000 Median :4.000 Median : 4.000 Median :11.00 Median :11.00 Median :11.00
Mean :1.481 Mean :2.291 Mean :3.554 Mean : 5.709 Mean :10.91 Mean :10.71 Mean :10.42
3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:5.000 3rd Qu.: 8.000 3rd Qu.:13.00 3rd Qu.:13.00 3rd Qu.:14.00
Max. :5.000 Max. :5.000 Max. :5.000 Max. :75.000 Max. :19.00 Max. :19.00 Max. :20.00
Overview: For the Student Performance Dataset I utilized several modeling techniques (Simple Linear Regression, Multiple Linear Regression, Subset Selection, and Generalized Additive Models). Correlation models were not utilized as many of the predictors are qualitative while the outcome variable is quantitative. The utilization of the plot function helped to determine what variables in the dataset would be significant and also of those which would not be significant. While classification models (LDA, QDA, and KNN) are useful for this dataset they were only able to be utilized sparingly due to the continuous nature of the outcome variable. For several variables the outcome and predictor variables were switched with the outcome variable of G3 to get some sense of the predictive capabilities of the Logisitic Regression, QDA, and LDA modeling techniques. The classification models also did not allow for the interpretability of all of the variables for this particular dataset. KNN was ignored due to the fact it would over fit the data due to the small nature of the dataset itself
Type in the variable search term below:
| Model | Learning Technique | Variables Used | Coefficient(s) |
|---|---|---|---|
| Model 1 | Simple Linear Regression | G3~SchoolMS | -0.6421 |
| Model 2 | Simple Linear Regression | G3~Sex | 0.9481 |
| Model 3 | Simple Linear Regression | G3~addressU | 1.163 |
| Model 4 | Simple Linear Regression | G3~famsize | 0.8221 |
| Model 5 | Simple Linear Regression | G3~PstatusT | -0.8703 |
| Model 6 | Simple Linear Regression | G3~Medu | 0.9088 |
| Model 7 | Simple Linear Regression | G3~Fedu | 0.6419 |
| Model 8 | Simple Linear Regression | G3~Mjob | MjobHealth=2.9945 Mjobother=0.6702 Mjobservices=1.8669 Mjobteacher=1.8992 |
| Model 9 | Simple Linear Regression | G3~Fjob | Fjobhealth=1.46111 Fjobother=0.04355 Fjobservices=0.14730 Fjobteacher=1.81552 |
| Model 10 | Simple Linear Regression | G3~reason | Reasonhome=0.4362 Reasonother=1.3460 Reasonreputation=1.3222 |
| Model 11 | Simple Linear Regression | G3~guardian | Guardianmother=-0.2504 Guardianother=-1.6264 |
| Model 12 | Simple Linear Regression | G3~traveltime | -0.7694 |
| Model 13 | Simple Linear Regression | G3~studytime | 0.534 |
| Model 14 | Simple Linear Regression | G3~failures | -2.22 |
| Model 15 | Simple Linear Regression | G3~schoolsup | Schoolsupyes=-1.13 |
| Model 16 | Simple Linear Regression | G3~famsup | Famsupyes=-0.3678 |
| Model 17 | Simple Linear Regression | G3~paid | Paidyes=0.9367 |
| Model 18 | Simple Linear Regression | G3~activities | Activitiesyes=0.1474 |
| Model 19 | Simple Linear Regression | G3~nursery | Nurseryyes=0.5844 |
| Model 20 | Simple Linear Regression | G3~higher | Higheryes=3.808 |
| Model 21 | Simple Linear Regression | G3~internet | Internetyes=1.208 |
| Model 22 | Simple Linear Regression | G3~romantic | Romanticsyes=-1.261 |
| Model 23 | Simple Linear Regression | G3~famrel | 0.2624 |
| Model 24 | Simple Linear Regression | G3~freetime | 0.05186 |
| Model 25 | Simple Linear Regression | G3~goout | -0.5465 |
| Model 26 | Simple Linear Regression | G3~Dalc | -0.2811 |
| Model 27 | Simple Linear Regression | G3~Walc | -0.1848 |
| Model 28 | Simple Linear Regression | G3~health | -0.2021 |
| Model 29 | Simple Linear Regression | G3~absences | 0.01961 |
| Model 30 | Simple Linear Regression | G3~G1 | 1.106 |
| Model 31 | Simple Linear Regression | G3~G2 | 1.102 |
| Model 32 | Simple Linear Regression | G3~age | -0.5801 |
Best Model Simple Linear Regression: The best model for simple linear regression is Model 8 for mjob. The coefficient of MjobHealth has the highest level 2.9945 that will increase the outcome variable of G3.
> schoolmodel.8
Call:
lm(formula = G3 ~ Mjob, data = Student_Performance_Ben_Gonzalez)
Coefficients:
(Intercept) Mjobhealth Mjobother Mjobservices Mjobteacher
9.1525 2.9945 0.6702 1.8669 1.8992
Type in the variable search term below:
| Model | Learning Technique | Variables Used | Adjusted R2 | P-Value |
|---|---|---|---|---|
| Model 33 | Multiple Linear Regression | Outcome=G3Predictors=All othervariables | 0.827 | 2.20E-16 |
| Model 34 | Multiple Linear Regression | Outcome=G3Predictors=Fjob,Mjob,G2G1,absences,famrel,Activities,age | 0.83 | 2.20E-16 |
| Model 35 | Multiple Linear Regression | Outcome=G3Predictors= G2, G1,Absences, age,Activities, mjob | 0.8262 | 2.20E-16 |
Best Model Multiple Linear Regression: The best model for multiple linear regression is Model 34. The model has the highest adjusted r-squared 0.83 and has a p-value of 2.2e-16.
> summary(studenmodel.2)
Call:
lm(formula = G3 ~ Fjob + Mjob + G2 + G1 + absences + famrel +
activities + age, data = Student_Performance_Ben_Gonzalez)
Residuals:
Min 1Q Median 3Q Max
-8.7249 -0.4813 0.3266 1.0366 4.1175
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.119993 1.508999 -0.080 0.936662
Fjobhealth 0.409542 0.633288 0.647 0.518222
Fjobother 0.191078 0.450705 0.424 0.671840
Fjobservices -0.218778 0.468459 -0.467 0.640756
Fjobteacher 0.039987 0.561466 0.071 0.943261
Mjobhealth 0.007434 0.421358 0.018 0.985933
Mjobother 0.035604 0.299174 0.119 0.905331
Mjobservices 0.135151 0.315477 0.428 0.668601
Mjobteacher 0.166370 0.362091 0.459 0.646159
G2 0.981361 0.050065 19.602 < 2e-16 ***
G1 0.156834 0.057144 2.745 0.006347 **
absences 0.043203 0.012128 3.562 0.000415 ***
famrel 0.368475 0.107646 3.423 0.000687 ***
activitiesyes -0.343471 0.193128 -1.778 0.076127 .
age -0.200841 0.078544 -2.557 0.010944 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.884 on 380 degrees of freedom
Multiple R-squared: 0.8368, Adjusted R-squared: 0.8308
F-statistic: 139.2 on 14 and 380 DF, p-value: < 2.2e-16
Type in the variable search term below:
| Model | Learning Technique | Variables Used | Mean of Squared Residuals | Minimum MSE |
|---|---|---|---|---|
| Model 35 | Cross ValidationApproach | Outcome=G3 Predictors=All other variables | MSE= 4.248499 | 4.248499 |
| Model 36 | K-Fold Cross Validation | Outcome=G3 Predictor=G2 | 3.735935 | |
| Model 37 | LOOCV Outcome=G3 Predictors= All other variables | 4.074051 |
Best Model Validation Set Approach: The best model for the validation set approach is Model 36 K-Fold Cross Validation. The model has the lowest MSE of 3.735935.
> cv.error
[1] 3.828677 3.817115 3.742704 3.735935 3.751337
Type in the variable search term below:
| Model | Learning Technique | Variables Used | Mean of Squared Residuals | Minimum MSE | % Variance Explained | Optimal Configuration | OptimalTrees/Interaction F-Statistic |
|---|---|---|---|---|---|---|---|
| Model 38 | Random Forest | Outcome=G3 Predictors=All other variables | MSE=3.397405 | 24.23154 | 83.39 | 10 Predictors | 1 Trees |
| Model 40 | Random Forest K-Fold CV Approach | Outcome=G3 Predictors=All other Variables | 3.397405 | 1.951061 | 83.39 | 20 Predictors | 9 Trees |
| Model 41 | Random Forest Boosting K-Fold CVApproach | Outcome= G3 Predictor= All other Variables | 16.27524 | 9 predictors had a nonzeroinfluence | Optimal number of Trees 7 | Optimal Interaction Depth 3 | |
| Model 42 | Linear Model K-Fold CV Approach | Outcome= G3 Predictor= All other Variables | P-value=2.2e-16 | 4132.434 | Adjusted R2=0.8194 | Multiple R2=0.8572 | F-Statistic= 22.69 |
Best Model Tree-Based Methods: The best model is Model 40 it has the lowest MSE of all of the approaches. The model also has one of the largest variances explained at 83.39%. While it has 20 predictors and 9 trees this is minimized due to the low MSE of the model.
> model.bagged.2
Call:
randomForest(formula = G3 ~ ., data = Student_Performance_Ben_Gonzalez,
mtry = numpreds, ntree = numtrees, importance = TRUE, subset = train)
Type of random forest: regression
Number of trees: 10
No. of variables tried at each split: 32
Mean of squared residuals: 3.397405
% Var explained: 83.39
> min(lstMSEs)
[1] 1.951061
Type in the variable search term below:
| Model | Learning Technique | Variables Used | RSQ | Adjusted R2 | Min BIC |
|---|---|---|---|---|---|
| Model 43 | Subset Model Selection One | Outcome=G3 Predictors=All other Variables | 0.837 | 0.834 | 5 |
| Model 44 | Subset Model Selection Two-Forward | Outcome=G3 Predictors=All other Variables | 0.845 | 0.831 | 5 |
| Model 45 | Subset Model Selection Three-Backward | Outcome=G3 Predictors=Allother Variables | 0.845 | 0.8365 | 5 |
Best Model Subset Selection: The best model is Model 45 it has a BIC of 5 and the highest adjusted r-squared 0.8365.
> student.summary3$adjr2
[1] 0.8183250 0.8224978 0.8264537 0.8289151 0.8314632 0.8326033 0.8337533 0.
8346263 0.8352697 0.8358128
[11] 0.8363263 0.8365028 0.8365416 0.8363004 0.8362920 0.8362409 0.8361633 0.
8360635 0.8359323 0.8357703
[21] 0.8355427 0.8353416 0.8351001 0.8348176 0.8344913 0.8341480 0.8338058 0.
8334593 0.8331049 0.8327369
[31] 0.8323729 0.8319962
> which.min(student.summary3$bic)
[1] 5
> which.max(student.summary3$adjr2)
[1] 13
> which.min(student.summary3$rss)
[1] 32
Type in the variable search term below:
| Model | LearningTechnique | Variables Used | Multiple R2 | Adjusted R2 | P-value |
|---|---|---|---|---|---|
| Model 46 | Generalized Additive Model Spline Based Regression | Outcome=G3 Predictors=Activities Age Famrel Absences G1 G2 | 0.8351 | 0.8326 | 2.2e-16 |
| Model 47 | Generalized Additive Model Polynomial Regression to 2nd Power | Outcome= G3 Predictors=Activities Age^2 Famrel^2 Absences^2 G1^2 G2 | 0.8433 | 0.8392 | 2.2e-16 |
| Model 48 | Generalized Additive Model Polynomial Regression to 3rd Power | Outcome=G3 Predictors=Activities Age^3 Famrel^3 Absences^3 G1^3 G2 | 0.8514 | 0.8459 | 2.2e=16 |
| Model 49 | Generalized Additive Model Polynomial Regression | Outcome=G3Predictors=ActivitiesAge^7 Famrel Absences^4 G1 G2 | 0.8541 | 0.8483 | 2.2e-16 |
Best Model: The best model for logistic regression predictions is Model 50. The model is able to accurately predict schoolsup from G3 0.87 percent of the time
Best Model Generalized Additive Models: The best model for the generalized additive models is Model 49 and is a GAM that utilizes polynomial regression. Model 49 has an adjusted r-squared of 0.8483 and a p-value of 2.2e-16.
Type in the variable search term below:
| Model | Learning Technique | Variables Used | Correct Predictions | Test Error |
|---|---|---|---|---|
| Model 50 | Logistic RegressionGLM | Outcome=schoolsup Predictor=G3 | 0.87 | 0.13 |
| Model 51 | Logistic RegressionGLM | Outcome=famsup Predictor=G3 | 0.39 | 0.61 |
| Model 52 | Logistic RegressionGLM | Outcome=Paid Predictor=G3 | 0.54 | 0.46 |
| Model 53 | Logistic RegressionGLM | Outcome=Activities Predictor=G3 | 0.49 | 0.51 |
| Model 54 | Logistic RegressionGLM | Outcome=Nursery Predictor=G3 | 0.21 | 0.79 |
| Model 55 | Logistic RegressionGLM | Outcome=Higher Predictor=G3 | 0.05 | 0.95 |
| Model 56 | Logistic RegressionGLM | Outcome=Internet Predictor=G3 | 0.16 | 0.83 |
| Model 57 | Logistic RegressionGLM | Outcome=Romantic Predictor=G3 | 0.67 | 0.33 |
Type in the variable search term below:
| Model | Learning Technique | Variables Used | QDA Prediction % |
|---|---|---|---|
| Model 58 | QDA | Outcome=schoolsup Predictor=G3 | 0.87 |
| Model 59 | QDA | Outcome=famsup Predictor=G3 | 0.38 |
| Model 60 | QDA | Outcome=Paid Predictor=G3 | 0.55 |
| Model 61 | QDA | Outcome=Activities Predictor=G3 | 0.51 |
| Model 62 | QDA | Outcome=Nursery Predictor=G3 | 0.79 |
| Model 63 | QDA | Outcome=Higher Predictor=G3 | 0.67 |
| Model 64 | QDA | Outcome=Internet Predictor=G3 | 0.83 |
| Model 65 | QDA | Outcome=Romantic Predictor=G3 | 0.67 |
Best Model QDA: The best model for QDA is Model 58. QDA is able to correctly predict schoolsup from G3 87% of the time.
> mean(qda.class1==schoolsup)
[1] 0.8708861
Type in the variable search term below:
| Model | Learning Technique | Variables Used | LDA Prediction % |
|---|---|---|---|
| Model 66 | LDA | Outcome=schoolsup Predictor=G3 | 0.87 |
| Model 67 | LDA | Outcome=famsup Predictor=G3 | 0.61 |
| Model 68 | LDA | Outcome=Paid Predictor=G3 | 0.53 |
| Model 69 | LDA | Outcome=Activities Predictor=G3 | 0.51 |
| Model 70 | LDA | Outcome=Nursery Predictor=G3 | 0.79 |
| Model 71 | LDA | Outcome=Higher Predictor=G3 | 0.95 |
| Model 72 | LDA | Outcome=Internet Predictor=G3 | 0.83 |
| Model 73 | LDA | Outcome=Romantic Predictor=G3 | 0.67 |
Best Model: The best model for LDA is Model 71. LDA is able to predict the outcome variable Higher 95% utilizing the predictor variable of G3.
> mean(lda.class6==higher)
[1] 0.9493671
Type in the variable search term below:
| Model | Learning Technique | Variables Used | Coefficient(s) MSE RSQ Adjusted R2 % VarianceExplained | AIC BIC CP | F-Statistic p-value TestError |
|---|---|---|---|---|---|
| Model 1 | Simple Linear Regression | Outcome=G3 Predictor=Mjob | MjobHealth=2.9945 | ||
| Model 2 | Multiple Linear Regression | Outcome=G3 Predictor= All other Variables | Multiple RSquared=0.845 Adjusted R2=0.827 | pvalue=2.2e-16 | |
| Model 3 | Validation Set Approach LOOCV | Outcome=G3 Predictor=All other Variables | Minimum MSE=4.074051 | ||
| Model 4 | Subset Model Selection Method=Backward | Outcome=G3 Predictors=All other Variables | RSQ=0.845 Adjusted R2=0.8365 | BIC=5 | |
| Model 5 | Tree Based Methods-Random Forest K-Fold CV Approach | Outcome=G3 Predictors=All other Variables | Minimum MSE=1.951061 %Variance Explained=83.37 | ||
| Model 6 | Generalized Additive Models Polynomial Regression | Outcome=G3 Predictors=Activities Age^7 Famrel Absences^4 G1 G2 | Multiple RSquared=0.8541 Adjusted R2=0.8483 | pvalue=2.2e-16 | |
| Model 7 | Logistic Regression | Outcome=schoolsup Predictor=G3 | Logistic %=0.87 Test | Error=0.13 | |
| Model 8 | QDA | Outcome=schoolsup Predictor=G3 | QDA%=0.87 Test | Error=0.13 | |
| Model 9 | LDA | Outcome=Higher Predictor=G3 | LDA %=0.95 | Test Error=0.05 |
Best Model Chosen: The best model from all of the models utilized is Model 6 in the Best Model Selection Table the GAM model that utilized polynomial regression. The model utilized the least amount of significant predictors for a non-linear model. The model also has the highest adjusted r-squared value of 0.8483. The p-value is similar to other models, but the variance explained via the r-squared shows this model is the best fitting model. The model also allows the best interprability of any of the models via charts and graphs that can be understood by the lay person as well. The anova(s) ran helped to show which predictors were significant and at what polymonial degree for the GAM polynomial regression had the best fit. The charts and graphs that follow are for Model 6 from the Best Model Selection Table.
> summary(gam.studentperformance5)
Call: gam(formula = G3 ~ activities + poly(age, 7) + famrel + poly(absences,
4) + G1 + G2, data = Student_Performance_Ben_Gonzalez)
Deviance Residuals:
Min 1Q Median 3Q Max
-8.0215 -0.6436 0.3041 1.0051 3.9303
(Dispersion Parameter for gaussian family taken to be 3.1839)
Null Deviance: 8269.909 on 394 degrees of freedom
Residual Deviance: 1206.708 on 379 degrees of freedom
AIC: 1596.084
Number of Local Scoring Iterations: 2
Anova for Parametric Effects
Df Sum Sq Mean Sq F value Pr(>F)
activities 1 2.1 2.1 0.6732 0.412438
poly(age, 7) 7 331.4 47.3 14.8709 < 2.2e-16 ***
famrel 1 22.2 22.2 6.9691 0.008635 **
poly(absences, 4) 4 425.1 106.3 33.3750 < 2.2e-16 ***
G1 1 5044.9 5044.9 1584.4894 < 2.2e-16 ***
G2 1 1237.5 1237.5 388.6663 < 2.2e-16 ***
Residuals 379 1206.7 3.2
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The most significant predictors for the Student Performance dataset when regressed on G3(final grade) are activitesyes, age, famrel, absences, G1, G2. The adjusted r-squared is 0.85 and is the highest adjusted r-squared out of any model. The p-value for this model is 2.2e-16. The age predictor shows that as age increases the final grade of the individual’s decreases. As absences decrease then G3 increases. As famrel increase then G3 increases. As G1 increases then G3 increases. Finally as G2 increases G3 increases.


---
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