[contents]
The terms defined by this document are also provided in RDF Schema format.
Copyright © 2011 W3C® (MIT, ERCIM, Keio), All Rights Reserved. W3C liability, trademark and document use rules apply.
This specification contains a framework for representing pointers - entities that permit identifying a portion or segment of a piece of content - making use of the Resource Description Framework (RDF). It also describes a number of specific types of pointers that permit portions of a document to be referred to in different ways. When referring to a specific part of, say, a piece of web content, it is useful to be able to have a consistent manner by which to refer to a particular segment of a web document, to have a variety of ways by which to refer to that same segment, and to make the reference robust in the face of changes to that document. This specification is part of the Evaluation And Report Language (EARL) but can be reused in other contexts too.
This section describes the status of this document at the time of its publication. Other documents may supersede this document. A list of current W3C publications and the latest revision of this technical report can be found in the W3C technical reports index at http://www.w3.org/TR/.
This 10 May 2011 Working Draft of the Pointer Methods in RDF 1.0 vocabulary is an update of the previous Pointer Methods in RDF Working Draft of 29 October 2009, and it incorporates all comments received since. This document is part of the Evaluation And Report Language (EARL) but can be reused in other contexts too. This document is intended to be published and maintained as a W3C Working Group Note after review and refinement.
The Evaluation and Repair Tools Working Group (ERT WG) believes it has addressed all issues brought forth through previous Working Draft iterations. The Working Group encourages feedback about this document, Pointer Methods in RDF 1.0, by developers and researchers who have interest in software-supported evaluation and validation of websites, and by developers and researchers who have interest in Semantic Web technologies for content description, annotation, and adaptation. In particular, feedback from the groups involved in the W3C Semantic Web Activity, especially the Semantic Web Coordination Group, the Semantic Web Deployment Working Group, and the Semantic Web Interest Group would be greatly appreciated.
Please send comments on this Pointer Methods in RDF 1.0 document by 10 June 2011 to<EMAIL_ADDRESS>(publicly visible mailing list archive).
Publication as a Working Draft does not imply endorsement by the W3C Membership. This is a draft document and may be updated, replaced or obsoleted by other documents at any time. It is inappropriate to cite this document as other than work in progress.
This document has been produced by the Evaluation and Repair Tools Working Group (ERT WG) as part of the Web Accessibility Initiative (WAI) Technical Activity.
This document was produced by a group operating under the 5 February 2004 W3C Patent Policy. The group does not expect this document to become a W3C Recommendation. W3C maintains a public list of any patent disclosures made in connection with the deliverables of the group; that page also includes instructions for disclosing a patent. An individual who has actual knowledge of a patent which the individual believes contains Essential Claim(s) must disclose the information in accordance with section 6 of the W3C Patent Policy.
This specification introduces a vocabulary constructed using the Resource Description Framework (RDF), to enable certain parts within a document, particularly HTML and XML documents, to be pointed to in an accurate way. The document introduces a series of RDF classes and properties that can be used to point to parts of a document in different ways.
Note that some pointers may be more appropriated to operate on the character or byte serialization of the resources and others for structured documents, such as XML documents, where character or byte based pointing mechanisms may be considered a bad practice.
The terms defined by this document can be used as part of the W3C Evaluation And Report Language (EARL) and in other contexts too. Developer Guide for Evaluation and Report Language (EARL) 1.0 explains how to implement and use EARL, including conformance requirements for software tools.
The namespace for Pointer Methods in RDF as specified in this draft is
http://www.w3.org/2009/pointers#
and uses the ptr
prefix. Other namespaces typically used by Pointer Methods in RDF include the
following:
cnt
http://www.w3.org/2011/content#
described in [Content]rdf
http://www.w3.org/1999/02/22-rdf-syntax-ns#
described in [RDF]rdfs
http://www.w3.org/2000/01/rdf-schema#
described in [RDFS]The keywords must, required, recommended, should, may, and optional are used in accordance with [RFC 2119].
One motivation for this vocabulary stems from methods for reporting test results such as the Evaluation and Report Language (EARL) [EARL] but this need not be its only application. Other typical applications could include:
This list is not meant to be exhaustive. This vocabulary is extensible, providing for alternative or enhanced methods for referring to portions of content and for referring to a variety of content types.
Pointer Methods in RDF is defined as an RDF vocabulary. The Resource Description Framework (RDF) is a general-purpose language for describing information in a way that is machine-understandable. The examples will be serialized with the abbreviated RDF/XML notation.
This document assumes the following background knowledge:
Pointer - a method that could be used to point out different parts
of electronic documents. It is an abstract class which is intended to be
subclassed into more specific ones, and every other type of pointer must be a ptr:Pointer
subclass.
This abstract class can not be used directly, one of the more specific refinements contained in this document must be used instead.
Properties defined by this document:
Pointers Group - a generic container for a group of pointers without any specific relationship between them.
Properties defined by this document:
While the generic PointersGroup
class can be used directly, one
of the following more specific refinements should be used instead to provide more information
about the existing relationship between the group members:
ptr:RelatedPointers
ptr:EquivalentPointers
Related Pointers - a group of pointers to be grouped
together for some purpose, indicating that the group members
(presumably pointing to different parts of the document) have some relationship
because they have a meaning as a whole. This is a subclass of
the PointersGroup
class
Properties defined by this document:
Example 1: A group of related pointers.
<ptr:RelatedPointers rdf:about="#relatedGroup"> <ptr:groupPointer rdf:resource="#relatedPointer1"/> <ptr:groupPointer rdf:resource="#relatedPointer2"/> ... </ptr:RelatedPointers>
Equivalent Pointers - a group of pointers that point
simultaneously to the same part of the document, so that they
can be considered equivalent. Put another way, each pointer in
a set of pointers that are identified as equivalent must identify or pick out
the same piece of content. This is a subclass of the PointersGroup
class
In order to achieve the maximum level of flexibility and interoperability, it is recommended to provide as much equivalent pointers as possible for any case.
Properties defined by this document:
Example 2: A series of equivalent pointers.
<ptr:EquivalentPointers rdf:about="#equivalentGroup"> <ptr:groupPointer rdf:resource="#equivalentPointer1"/> <ptr:groupPointer rdf:resource="#equivalentPointer2"/> <ptr:groupPointer rdf:resource="#equivalentPointer3"/> ... </ptr:EquivalentPointers>
Single Pointer - a pointing method made up of a unique pointer. This is an abstract single pointer that provides the necessary framework, but it does not provide any kind of pointer, so more specific subclasses must be used.
Properties defined by this document:
This vocabulary already provides several subclasses that refine the
SinglePointer
class in relation to the way the pointer is
defined.
ptr:ExpressionPointer
ptr:OffsetPointer
ptr:LineCharPointer
Expression Pointer - a single pointer that makes use of expression languages to point out parts of a document. This is a generic expression pointer that could be subclassed for extensibility, more specific subclasses should be used where suitable.
Properties defined by this document:
This vocabulary already provides several subclasses of the
ExpressionPointer
class depending on the language that is used to
define the pointer expression.
ptr:XPathPointer
ptr:CSSSelectorPointer
XPath Pointer - An expression pointer that makes use of XPath [XPath] expressions to point out parts of a document.
Properties defined by this document:
ptr:NamespaceMapping
ptr:XPointerPointer
Example 3: An XPathPointer
resource with
namespace reference.
<ptr:XPathPointer rdf:about="#xPathPointer"> <ptr:version>2.0</ptr:version> <ptr:expression>/html/body/div[@id='header']/img[1]</ptr:expression> <ptr:reference rdf:resource="http://example.org/doc1.html"/> <ptr:namespace rdf:resource="#NamespaceMapping1"/> </ptr:XPathPointer>
Namespace Mapping - an XML namespace mapping as defined by [Namespaces].
Properties defined by this document:
Example 4: A NamespaceMapping
resource
indicating prefix and name.
<ptr:NamespaceMapping rdf:about="#NamespaceMapping1"> <ptr:prefix>eg</ptr:prefix> <ptr:namespaceName>http://example.org/ns/</ptr:namespaceName> </ptr:NamespaceMapping>
XPointer Pointer - an expression pointer that makes use of xpointer() scheme [XPointer-SCH] expressions to point out parts of a document.
Properties defined by this document:
Example 5: An XPointerPointer
resource.
<ptr:XPointerPointer rdf:about="#xPointerPointer"> <ptr:expression>string-range(//P,"Thomas Pynchon")[3],"P",1,0)</ptr:expression> <ptr:reference rdf:resource="http://example.org/doc1.html"/> </ptr:XPointerPointer>
CSS Selector Pointer - an expression pointer that points out parts of a document by means of a CSS expression.
Properties defined by this document:
Example 6: A CSSSelector
resource with version
information.
<ptr:CSSSelectorPointer rdf:about="#cssSelectorPointer"> <ptr:reference rdf:resource="http://example.org/doc1.html"/> <ptr:expression>body > p#important</ptr:expression> <ptr:version>2.1</ptr:version> </ptr:CSSSelectorPointer>
Offset Pointer - a single pointer that points out parts of a document by means of an offset number counting from the start of the reference.
Properties defined by this document:
While the generic OffsetPointer
class can be used directly, one
of the following more specific refinements should be used instead to provide more information
about the type of offset that is being used.
ptr:CharOffsetPointer
ptr:ByteOffsetPointer
Char Offset Pointer - a single pointer that points out parts of a document by means of a character offset from the start of the reference.
Properties defined by this document:
Example 7: A CharOffsetPointer
resource.
<ptr:CharOffsetPointer rdf:about="#charOffsetPointer"> <ptr:reference rdf:resource="http://example.org/doc1.html"/> <ptr:offset>335</ptr:offset> </ptr:CharOffsetPointer>
Byte Offset Pointer - a single pointer that points out parts of a document by means of a byte offset from the start of the reference.
Properties defined by this document:
Example 8: A ByteOffsetPointer
resource.
<ptr:ByteOffsetPointer rdf:about="#byteOffsetPointer"> <ptr:reference rdf:resource="http://example.org/doc1.html"/> <ptr:offset>52</ptr:offset> </ptr:ByteOffsetPointer>
Line Char Pointer - a single pointer that points out parts of a document by means of the line number and character position where the target is localized.
Properties defined by this document:
Example 9: A LineCharPointer
resource.
<ptr:LineCharPointer rdf:about="#lineCharPointer"> <ptr:reference rdf:resource="http://example.org/doc1.html"/> <ptr:lineNumber>5</ptr:lineNumber> <ptr:charNumber>18</ptr:charNumber> </ptr:LineCharPointer>
Compound Pointer - a pointing method made up of a pair of pointers that identify a well defined section within a document delimited by a begin and an end.
This is an abstract compound pointer that provides the necessary framework, but it does not constitute a complete compound pointer, as it only defines the start point of the section. One of the more specific subclasses must be used instead.
Properties defined by this document:
ptr:StartEndPointer
ptr:CharSnippetCompoundPointer
ptr:CharOffsetCompoundPointer
ptr:ByteSnippetCompoundPointer
ptr:ByteOffsetCompoundPointer
Start End Pointer - a compound pointer pointing out parts of a document by means of a range delimited by a pair of single pointers that define the start point and the end point.
Properties defined by this document:
Example 10: A StartEndPointer
resource making
use of the CharOffsetPointer from example 7
and the LineCharPointer from example 9.
<ptr:StartEndPointer rdf:about="#startEndPointer"> <ptr:startPointer rdf:resource="#lineCharPointer"/> <ptr:endPointer rdf:resource="#charOffsetPointer"/> </ptr:StartEndPointer>
Char Snippet Compound Pointer - a compound pointer pointing out parts of a document by means of a range delimited by a single pointer that defines the start point and a character snippet from there.
Properties defined by this document:
Properties not defined by this document:
cnt:chars
Example 11: A CharSnippetCompoundPointer
resource.
<ptr:CharSnippetCompoundPointer rdf:about="#charSnippetCompoundPointer"> <ptr:startPointer rdf:resource="#charOffsetPointer"/> <cnt:chars>
Some text.
</cnt:chars> </ptr:CharSnippetCompoundPointer>
Char Offset Compound Pointer - a compound pointer pointing out parts of a document by means of a range delimited by a single pointer that defines the start point and a character offset from there.
Properties defined by this document:
Example 12: A CharOffsetCompoundPointer
resource.
<ptr:CharOffsetCompoundPointer rdf:about="#charOffsetCompoundPointer"> <ptr:startPointer rdf:resource="#XPathPointer"/> <ptr:charOffset>55</ptr:charOffset> </ptr:CharOffsetCompoundPointer>
Byte Snippet Compound Pointer - a compound pointer pointing out parts of a document by means of a range delimited by a single pointer that defines the start point and a byte snippet from there.
Properties defined by this document:
Properties not defined by this document:
cnt:bytes
Example 13: A ByteSnippetCompoundPointer
resource.
<ptr:ByteSnippetCompoundPointer rdf:about="#byteSnippetCompoundPointer"> <ptr:startPointer rdf:resource="#byteOffsetPointer"/> <cnt:bytes>R0lGODlhtQAxAOYAAKynpv3t4v3j1P/59ZuXlveYZ/vDovvMsfBbGWRiYf7+{...}</cnt:bytes> </ptr:ByteSnippetCompoundPointer>
Byte Offset Compound Pointer - a compound pointer pointing out parts of a document by means of a range delimited by a single pointer that defines the start point and a byte offset from there.
Properties defined by this document:
Example 14: A ByteOffsetCompoundPointer
resource.
<ptr:ByteOffsetCompoundPointer rdf:about="#byteOffsetCompoundPointer"> <ptr:startPointer rdf:resource="#byteOffsetPointer"/> <ptr:byteOffset>255</ptr:byteOffset> </ptr:ByteOffsetCompoundPointer>
A reference to a specific pointer.
A PointersGroup
will have one groupPointer
property per each of the pointers it contains. As any group of pointers must have one or more pointers, instances of the
PointersGroup
class must have at
least one instance of the groupPointer
property.
ptr:PointersGroup
ptr:Pointer
The document within which the pointer is applicable or meaningful.
A SinglePointer
must have
exactly one reference
.
ptr:SinglePointer
The language expression used as pointer.
An ExpressionPointer
must have
exactly one expression
.
ptr:ExpressionPointer
The language version used in the pointer expression.
An ExpressionPointer
must have
at most one version
.
ptr:ExpressionPointer
The namespace within an XPath expression operates.
ptr:XPathPointer
ptr:NamespaceMapping
Associates element and attribute names with a namespace URI.
An NamespaceMapping
must have
exactly one prefix
ptr:NamespaceMapping
Identifies the namespace.
A NamespaceMapping
must have
exactly one namespace
ptr:NamespaceMapping
The target position counting from the start of the referenced document. The count will start at one in each document.
An OffsetPointer
must have
exactly one offset
ptr:OffsetPointer
The line number where the target is localized. The line count will start at one in each document.
A LineCharPointer
must have
exactly one lineNumber
.
ptr:LineCharPointer
The character number where the target is localized within a line. The character count will start at one in each line.
A LineCharPointer
must have at
most one charNumber
.
ptr:LineCharPointer
Reference to the pointer that defines the beginning point for a range.
A CompoundPointer
must have
exactly one startPointer
.
ptr:CompoundPointer
ptr:SinglePointer
Reference to the pointer that defines the end point for a range.
A StartEndPointer
must have
exactly one endPointer
.
ptr:StartEndPointer
ptr:SinglePointer
The position of the end of a range from a startPointer
,
expressed by the number of characters that conform the range, and being the
first character of the range that one indicated by the
startPointer
.
A CharOffsetCompoundPointer
must have exactly one charOffset
.
ptr:CharOffsetCompoundPointer
The position of the end of a range from a startPointer
,
expressed by the number of bytes that conform the range, and being the first
byte of the range that one indicated by the startPointer
.
A ByteOffsetCompoundPointer
must have exactly one byteOffset
.
ptr:ByteOffsetCompoundPointer
The following terms are defined by this specification:
| Class name | Label | Suggested types | Required properties | Optional properties |
|---|---|---|---|---|
ptr:ByteOffsetPointer |
Byte Offset Pointer | ptr:offset , ptr:reference |
||
ptr:ByteOffsetCompoundPointer |
Byte Offset Compound Pointer | ptr:byteOffset , ptr:startPointer |
||
ptr:ByteSnippetCompoundPointer |
Byte Snippet Compound Pointer | cnt:bytes
,
ptr:startPointer |
||
ptr:CharOffsetPointer |
Char Offset Pointer | ptr:offset , ptr:reference |
||
ptr:CharOffsetCompoundPointer |
Char Offset Compound Pointer | ptr:charOffset , ptr:startPointer |
||
ptr:CharSnippetCompoundPointer |
Char Snippet Compound Pointer | cnt:chars
,
ptr:startPointer |
||
ptr:CSSSelectorPointer |
CSS selector Pointer | ptr:expression , ptr:reference |
ptr:version |
|
ptr:EquivalentPointers |
Equivalent Pointers | ptr:groupPointer |
||
ptr:ExpressionPointer |
Expression Pointer | ptr:CSSSelectorPointer, ptr:XPathPointer, ptr:XPointerPointer | ptr:expression , ptr:reference |
ptr:version |
ptr:LineCharPointer |
Line-Char Pointer | ptr:lineNumber , ptr:reference |
ptr:charNumber |
|
ptr:NamespaceMapping |
NamespaceMapping | ptr:namespaceURI ,
ptr:prefix |
||
ptr:OffsetPointer |
Offset Pointer | ptr:ByteOffsetPointer, ptr:CharOffsetPointer | ptr:offset |
|
ptr:Pointer |
Pointer | ptr:PointersGroup, ptr:CompoundPointer, ptr:SinglePointer | ||
ptr:PointersGroup |
Pointers Group | ptr:EquivalentPointers, ptr:RelatedPointers | ptr:groupPointer |
|
ptr:CompoundPointer |
Compound Pointer | ptr:StartEndPointer, ptr:CharSnippetCompoundPointer, ptr:CharOffsetCompoundPointer, ptr:ByteSnippetCompoundPointer, ptr:ByteOffsetCompoundPointer | ptr:startPointer |
|
ptr:RelatedPointers |
Related Pointers | ptr:groupPointer |
||
ptr:SinglePointer |
Single Pointer | ptr:OffsetPointer, ptr:ExpressionPointer, ptr:LineCharPointer, | ptr:reference |
|
ptr:StartEndPointer |
Start-End Pointer | ptr:endPointer , ptr:startPointer |
||
ptr:XPathPointer |
XPath Pointer | ptr:expression , ptr:reference |
ptr:namespace ,
ptr:version |
|
ptr:XPointerPointer |
XPointer Pointer | ptr:expression , ptr:reference |
ptr:namespace ,
ptr:version |
| Property name | Label | Domain | Range | Restriction |
|---|---|---|---|---|
ptr:byteOffset |
byte offset | ptr:ByteOffsetCompoundPointer |
Positive Integer | Exactly one per ptr:ByteOffsetCompoundPointer |
ptr:charNumber |
char number | ptr:LineCharPointer |
Positive Integer | At most one per ptr:LineCharPointer |
ptr:charOffset |
char offset | ptr:CharOffsetCompoundPointer |
Positive Integer | Exactly one per ptr:CharOffsetCompoundPointer |
ptr:endPointer |
end pointer | ptr:StartEndPointer |
ptr:SinglePointer |
Exactly one per ptr:StartEndPointer |
ptr:expression |
expression | ptr:ExpressionPointer |
Literal | Exactly one per ptr:ExpressionPointer |
ptr:lineNumber |
line number | ptr:LineCharPointer |
Positive Integer | Exactly one per ptr:LineCharPointer |
ptr:namespace |
namespace | ptr:XPathPointer |
ptr:namespaceMapping |
|
ptr:namespaceURI |
namespace URI | ptr:NamespaceMapping |
Exactly one per ptr:NamespaceMapping |
|
ptr:offset |
offset | ptr:OffsetPointer |
Positive Integer | Exactly one per ptr:OffsetPointer and
ptr:StartOffsetPointer |
ptr:groupPointer |
group pointer | ptr:PointersGroup |
ptr:Pointer |
At least one per ptr:PointerGroup |
ptr:prefix |
prefix | ptr:NamespaceMapping |
Literal | Exactly one per ptr:NamespaceMapping |
ptr:reference |
reference | ptr:SinglePointer |
Exactly one per ptr:SinglePointer |
|
ptr:startPointer |
start pointer | ptr:CompoundPointer |
ptr:SinglePointer |
Exactly one per ptr:CompoundPointer |
ptr:version |
version | ptr:ExpressionPointer |
Literal | At most one per ptr:ExpressionPointer |
http://www.w3.org/TR/Content-in-RDF10/
http://www.w3.org/TR/EARL10-Schema/
http://www.w3.org/TR/2006/REC-xml-names11-20060816
http://www.w3.org/TR/2004/REC-rdf-syntax-grammar-20040210/
http://www.w3.org/TR/rdf-primer/
http://www.w3.org/TR/rdf-schema/
http://www.w3.org/DesignIssues/RDF-XML
http://www.ietf.org/rfc/rfc2119.txt
http://www.w3.org/TR/xml/
http://www.w3.org/TR/1999/REC-xpath-19991116
http://www.w3.org/TR/2002/WD-xptr-xpointer-20021219/
Contributors to this Working Draft: Shadi Abou-Zahra, Sandor Herramhof, Carlos Iglesias, Nick Kew, Johannes Koch, Jim Ley, Charles McCathieNevile, Chris Ridpath, Christophe Strobbe, Michael Squillace and Carlos Velasco.
There are only editorial changes and references updates since 29 October, 2009 Working Draft


I've taken xanax plenty of times in the past. It has and always will be the perfect 'chill pill'. But I wanted to mention what happens when I take it. First off I get the 2mg 'bars' and when I go to take it I split it into halfs. I eat the 1mg with at least 2 or 3 200mg Ibuprofens. (I Swear on my LIFE that the Ibuprofen kicks it up, or prolongs it...I'm not sure, but either way I always take together) So...I swallow 1mg and then like and hour later I take the other half. That's how I dose myself.
Now the effects I get seem to be different from the ones I've read. Actually when I'm on Alpra. it makes me want to clean my house, or rearrange things. I know if I just sit down and close my eyes I'm going to fall asleep, so I brew a nice pot of coffee. I put on some good music, nothing hard core, something to fit the mood. Last night I had the urge to scrub the kitchen floor. ON my hands and knees, and I loved it. I was vaccuming and it was like 1:00am. My boyfriend (who dosed too) was just doing his thing, and I'd pop in the room he was at, we'd talk for bits at a time, then I'd have the urge to do something, so I start looking for things to clean. OMG - I almost forgot...we started watching a movie, and after like 10 min. of it, we were like, I can't just sit here and watch this, so we turned it off and just bummbled around the house all nite and enjoyed ourselves.
Now the main point is, when I woke up the next day, I had FORGOTTEN all the cleaning I did, I thought a maid must of broke through the window and went to work. The house was spotless...I even had set up a vase with flowers that I stole from the neighbors garden. Which I had forgotten...and almost everything I did while I was on it, I had forgotten. That is the MAIN side effect I dislike, other than that it's great, but I think only for chillin out...not for driving around town or being somewhere I don't feel safe. And falling asleep is as easy as 1-2-3. I just lay down and close my eyes when I'm ready to sleep, it's like magic. But if I'm not used to taking it over days and days, then waking up will feel like a chore. Dazed and Confused like. But all in all I find it's safe to use, not addictive. Be smart and educated...it's the golden rule to stepping out of 'reality'.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Choose one of the templates below. Once you have chosen a template, it will be loaded on every page
in the documentation and demos until you come back and select a different template. The CSS files
for each template can be found in the Templates
directory.
If you'd like to create your own template, just copy one of the templates below into it's own
subdirectory under the Templates
directory and modify it. You can even integrate it
into the template switcher by adding your own row of templates below.
Skeleton is a bare-bones template that you can customize to suit your needs.
Sketch is a template stylized especially for prototypes and wireframes.
Polish is a refined template for creating production websites and interfaces.
Tip Even though each template has suggested use cases, feel free to use them however you'd like.
Warning If you select a template other than the default, each page will experience a slight flicker when the page first loads. The flicker only occurs because the stylesheet is being loaded dynamically and will not happen if the stylesheet is statically loaded as in most normal use cases.
VM UI Framework is created, owned, maintained by Virtuosi Media, Inc. © 2012-2013.


I am a Beaver
Ian's Email
Visit
Bason's
website
Check out
my first page
.
I love hanging with friends and family
I am a HUGE Boston sports fan. Go Pats!
test
My Work in WebTech


This page is really cool. Please don't change anything!


UFC 244: Jorge Masvidal vs. Nate Diaz win probabilities and possible bets
by Paul GiftThe win probabilities for UFC 244’s five qualifying matchups are in. Here are the estimated odds for each bout where both fighters have at least four fully-documented prior fights and aren’t heavyweights. For statistical overload on the distance, clinch, and ground alternative stats behind the inaugural BMF belt matchup, see Tuesday’s piece.
Kelvin Gastelum (57.8%) over Darren Till
Vicente Luque (52.6%) over Stephen Thompson
Gregor Gillespie (57.9%) over Kevin Lee
Makwan Amirkhani (55.4%) over Shane Burgos
Julio Arce (57.7%) over Hakeem Dawodu
An official prediction can’t be made for Masvidal-Diaz since complete data for Masvidal’s most recent destruction of Ben Askren is unavailable, even though it was only five seconds. And it might’ve made a difference since - just for fun - the model would’ve edged to Diaz at 58.8% in a world where Masvidal’s last fight didn’t exist.
Current Bankroll: $9,535.25
No-Human-Error Bankroll: $9,932.62
The fight computer had a bad week at UFC Tampa, but that happens. As of this writing (10:25am PT), it has one bet of $190.70 on Amirkhani at +195.
Enjoy the glorious fights!
Note: This is an experiment and entertainment. Do not bet on the fights using these numbers. Taking sensible gambles with an edge over time is similar to investing. But if I’ve made a mistake somewhere, those same gambles without an edge become sucker bets. Let me be the possible sucker. You’ve been warned.
Paul writes about MMA analytics and officiating at Bloody Elbow and MMA business at Forbes, and is an ABC-certified referee and judge. Follow him @MMAanalytics . Fight data provided by FightMetric .


Hey there! My name's Jason. Before I get down to business, how about some info about me? I currently live in central Connecticut with my wife, Nicki, and our dog, Walker (not to be confused with our dog-walker - that would be weird). We're expecting a little girl in April and we're very excited!
Except for Walker. He's a dog, and he has no clue what's about to hit this family.
When I'm not working or with my family, I can be found out in a field playing ultimate frisbee, or on the ice playing hockey.
And now for the business. I've gotten my feet wet in software, and I'm looking to dive in! I have a mechanical engineering degree from Purdue (translation: analytical, curious, problem-solver, detail-oriented, motivated by learning new skills, generally awesome, kind of a nerd). I've got experience on the manufacturing floor and with various manufacturing softwares (ERP, PDM, Workflow systems). And I'm very excited to learn and grow as a developer! So Whether you're looking for back-end application development, front-end web work, a full-stack developer, looking to network, looking to share some knowledge, or you just need a goalie, please scroll down and send me your thoughts!


- Download
- Download
- System requirements
- File size
- 36 MB
- Release notes
-
- Works with the HTC Vive only.
Instructions
- See this post about WebVR coming to Servo.
- Download the experimental WebVR Servo build.
- Unpack the RAR archive into an appropriate location.
- Open Windows command line by clicking on Windows Start menu, and typing
cmd.exe
to launch the command line. - Change to the directory where you unpacked the archive (e.g.,
cd c:\servo-webvr\
). - Run
servo.exe --resources-path resources webvr\room-scale.html
- Click the
Enter VR
button.
Latest version
Known issues
-
The Servo window might flicker while running WebGL because of issue #14235. But, don’t be scared; the headset won't flicker because it uses a different render path!


Looping in JavaScript vs. Looping in Ruby
January 24, 2016Date
In which these sandals look even better with socks on
Let's say you had a list of things. (Maybe you are still shearing those sheep from a few weeks ago.) You want to do some things to some of the items on the list, different things to other items on the list, maybe skip over some items on the list entirely.
To Ruby's credit, the language makes it very, very intuitive to do that. The .each method is the most basic way of going over every single item on a list individually, whether one is changing the values or extracting a larger pattern from them. And the each method has many cousins and well-wishers to make sure that you are slicing your list in exactly the way you want. There's each_slice, each_index, each_char(a string method, so I'm cheating), and so forth.
But if Ruby's loops are several pairs of low-key sneakers (one for running, one for crossfit, one that has really good tread for Chicago Januaries), JavaScript looping functionalities feel like wearing sandals with socks.
Maybe I want to return every number from 1 to 10, and in doing so print each on a different line. Here's how a Rubyist could get it done.
(1..10).each{|number| p number}
If you know how ranges work (I'm asking it to represent all numbers from 1 to 10), and the versatility of p, then you can see this loop and understand it straightaway. How about this poor thing, though?
for(var number= 0; number >= 10; number ++) { return number; console.log(number); };
Easily twice as many characters to accomplish the same function! (And that's squishing the return and print statements onto the same line.) So it's established that I like conscision and I hate repeating myself. (One might even say I dislike redundancy or enjoy not repeating myself.) What are the benefits of JavaScripts notation? For one thing, creating a variable (in the above case, called "number") within the body of the loop seems bold.
The long answer is that I'm still figuring it out. What I've learned so far is that JavaScript's for loops are more or less borrowed wholesale from C and C++. What I do know is that this is yet another another way of thinking about the same problem/functionality.
I can say, okay, do x to every value in the array until there's no more array left. The same words, only refined, would mean that I am beginning a loop at value X, that the loop will terminate itself until value Y, and that each time the function finishes work on the function being looped around and over and through. Same task, very different ways to make it happen. In JS's case, it is an older language and it lacks the special syntax that make it easier for the newbie programmer.
But, while I research bigger and more theoretical reasons, I return in my mind to the simple fact that I've gotten better at solving problems when I try to take different paths to the same answer. And best when I have the time to fail at every place where I could fail, until I got results.


MLlib - Decision Tree
Decision trees and their ensembles are popular methods for the machine learning tasks of classification and regression. Decision trees are widely used since they are easy to interpret, handle categorical variables, extend to the multiclass classification setting, do not require feature scaling and are able to capture nonlinearities and feature interactions. Tree ensemble algorithms such as decision forest and boosting are among the top performers for classification and regression tasks.
Basic algorithm
The decision tree is a greedy algorithm that performs a recursive binary partitioning of the feature
space by choosing a single element from the best split set where each element of the set maximizes
the information gain at a tree node. In other words, the split chosen at each tree node is chosen
from the set $\underset{s}{\operatorname{argmax}} IG(D,s)$
where $IG(D,s)$
is the information
gain when a split $s$
is applied to a dataset $D$
.
Node impurity and information gain
The node impurity is a measure of the homogeneity of the labels at the node. The current implementation provides two impurity measures for classification (Gini impurity and entropy) and one impurity measure for regression (variance).
| Impurity | Task | Formula | Description |
|---|---|---|---|
| Gini impurity | Classification | $\sum_{i=1}^{M} f_i(1-f_i)$ | $f_i$ is the frequency of label $i$ at a node and $M$ is the number of unique labels. |
| Entropy | Classification | $\sum_{i=1}^{M} -f_ilog(f_i)$ | $f_i$ is the frequency of label $i$ at a node and $M$ is the number of unique labels. |
| Variance | Regression | $\frac{1}{n} \sum_{i=1}^{N} (x_i - \mu)^2$ | $y_i$ is label for an instance, $N$ is the number of instances and $\mu$ is the mean given by $\frac{1}{N} \sum_{i=1}^n x_i$. |
The information gain is the difference in the parent node impurity and the weighted sum of the two
child node impurities. Assuming that a split $s$ partitions the dataset $D$
of size $N$
into two
datasets $D_{left}$
and $D_{right}$
of sizes $N_{left}$
and $N_{right}$
, respectively:
$IG(D,s) = Impurity(D) - \frac{N_{left}}{N} Impurity(D_{left}) - \frac{N_{right}}{N} Impurity(D_{right})$
Split candidates
Continuous features
For small datasets in single machine implementations, the split candidates for each continuous feature are typically the unique values for the feature. Some implementations sort the feature values and then use the ordered unique values as split candidates for faster tree calculations.
Finding ordered unique feature values is computationally intensive for large distributed
datasets. One can get an approximate set of split candidates by performing a quantile calculation
over a sampled fraction of the data. The ordered splits create “bins” and the maximum number of such
bins can be specified using the maxBins
parameters.
Note that the number of bins cannot be greater than the number of instances $N$
(a rare scenario
since the default maxBins
value is 100). The tree algorithm automatically reduces the number of
bins if the condition is not satisfied.
Categorical features
For $M$
categorical features, one could come up with $2^M-1$
split candidates. However, for
binary classification, the number of split candidates can be reduced to $M-1$
by ordering the
categorical feature values by the proportion of labels falling in one of the two classes (see
Section 9.2.4 in
Elements of Statistical Machine Learning for
details). For example, for a binary classification problem with one categorical feature with three
categories A, B and C with corresponding proportion of label 1 as 0.2, 0.6 and 0.4, the categorical
features are ordered as A followed by C followed B or A, B, C. The two split candidates are A | C, B
and A , B | C where | denotes the split.
Stopping rule
The recursive tree construction is stopped at a node when one of the two conditions is met:
- The node depth is equal to the
maxDepth
training parameter - No split candidate leads to an information gain at the node.
Max memory requirements
For faster processing, the decision tree algorithm performs simultaneous histogram computations for all nodes at each level of the tree. This could lead to high memory requirements at deeper levels of the tree leading to memory overflow errors. To alleviate this problem, a ‘maxMemoryInMB’ training parameter is provided which specifies the maximum amount of memory at the workers (twice as much at the master) to be allocated to the histogram computation. The default value is conservatively chosen to be 128 MB to allow the decision algorithm to work in most scenarios. Once the memory requirements for a level-wise computation crosses the maxMemoryInMB
threshold, the node training tasks at each subsequent level is split into smaller tasks.
Practical limitations
- The implemented algorithm reads both sparse and dense data. However, it is not optimized for sparse input.
- Python is not supported in this release.
Examples
Classification
The example below demonstrates how to load a CSV file, parse it as an RDD of LabeledPoint
and then
perform classification using a decision tree using Gini impurity as an impurity measure and a
maximum tree depth of 5. The training error is calculated to measure the algorithm accuracy.
import org.apache.spark.SparkContext
import org.apache.spark.mllib.tree.DecisionTree
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.tree.configuration.Algo._
import org.apache.spark.mllib.tree.impurity.Gini
// Load and parse the data file
val data = sc.textFile("mllib/data/sample_tree_data.csv")
val parsedData = data.map { line =>
val parts = line.split(',').map(_.toDouble)
LabeledPoint(parts(0), Vectors.dense(parts.tail))
}
// Run training algorithm to build the model
val maxDepth = 5
val model = DecisionTree.train(parsedData, Classification, Gini, maxDepth)
// Evaluate model on training examples and compute training error
val labelAndPreds = parsedData.map { point =>
val prediction = model.predict(point.features)
(point.label, prediction)
}
val trainErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / parsedData.count
println("Training Error = " + trainErr)
Regression
The example below demonstrates how to load a CSV file, parse it as an RDD of LabeledPoint
and then
perform regression using a decision tree using variance as an impurity measure and a maximum tree
depth of 5. The Mean Squared Error (MSE) is computed at the end to evaluate
goodness of fit.
import org.apache.spark.SparkContext
import org.apache.spark.mllib.tree.DecisionTree
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.tree.configuration.Algo._
import org.apache.spark.mllib.tree.impurity.Variance
// Load and parse the data file
val data = sc.textFile("mllib/data/sample_tree_data.csv")
val parsedData = data.map { line =>
val parts = line.split(',').map(_.toDouble)
LabeledPoint(parts(0), Vectors.dense(parts.tail))
}
// Run training algorithm to build the model
val maxDepth = 5
val model = DecisionTree.train(parsedData, Regression, Variance, maxDepth)
// Evaluate model on training examples and compute training error
val valuesAndPreds = parsedData.map { point =>
val prediction = model.predict(point.features)
(point.label, prediction)
}
val MSE = valuesAndPreds.map{ case(v, p) => math.pow((v - p), 2)}.mean()
println("training Mean Squared Error = " + MSE)


ngStyle
ng
The ngStyle
directive allows you to set CSS style on an HTML element conditionally.
<ANY ng-style="{expression}"> ... </ANY>as class
<ANY class="ng-style: {expression};"> ... </ANY>
| Param | Type | Details |
|---|---|---|
| ngStyle | expression | Expression which evals to an object whose keys are CSS style names and values are corresponding values for those CSS keys. |


Happiness is a choice and I choose to be as happy and positive as possible everyday. Find out where I find my inspiration and why I like choosing to be happy.
Check here to find things that I found eye opening or even mind blowing. Enjoy!
Find an open slot in my calendar. I would love to make your aquaintance and discuss any number of topics with you.


Statutory guidance
Pre-key stage 1: pupils working below the test standard in 2016
These interim pre-key stage standards are to be used by teachers to report a statutory assessment outcome for specified pupils at the end of key stage 1.
Documents
Ref: ISBN
978-1-78644-059-4
,
STA/16/7478/e
PDF
,
244KB
,
5 pages
Details
These interim pre-key stage standards reflect the attainment of pupils who have not yet completed the relevant programmes of study but have reached a chronological age that requires a statutory outcome to be reported.
The statements within the standards reflect those areas of knowledge and understanding of core developmental milestones for progression on to the standards defined by the
interim teacher assessment framework at key stage 1
.
The guidance is designed to enable schools to print out the information separately for English reading, English writing and mathematics. This is to allow the standards for each of the 3 subject areas to be considered in isolation, if required.


Lauren Michael
is a Research Computing Facilitator at the University of Wisconsin - Madison,
where she helps researchers navigate the world of large-scale computing resources and related training opportunities.
Lauren's own previous research examined the formation of RNA motifs and RNA-protein interactions using compute-intensive methods.
She also has interests and experience in science communication, and serves as a member of the NSF-funded
ACI-REF program.


Duplicate art or collectibles are available in the Marketplace. So always check owner before Buying NFT
Check if the NFT is minted in On-chain Minting Platform
Do not send Money Privately to get NFT with 3rd parties.
Risk is everywhere. Risk is caused by Ignorance. So please get educated on NFT before Buy/Sell NFT and educate your friends too. Check this blog for more tips
Next Question: Why Support Us?.


{% extends 'core/base.html' %} {% block content %}
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Jane Doe
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Name: Justin Parker Link: http://github.com/jparkerweb/htmldiff-example About: HTML diff example Description: front-end lib to show differences between HTML blocks Category: JavaScript
1st HTML Block
Name: justin Parker Link: http://github.com/jparkerweb/htmldiff-example About: html diff poc Description: front-end lib to show what has changed between html blocks Category: javascript
HTML Diff Example (diff between Block1 and Block2)


Google Updates Play Store Design On Wear OS For Easier Navigation
Google has recently sent out an update to the Play Store on Wear OS that changes the design a little.
It still looks mostly the same as before, but there are a few tweaks. The most notable is that it's now supposed to be easier to navigate.
Now when you're scrolling through the Play Store on your Wear OS watch, it should be simpler to get to apps and other options.
Advertisement
The Play Store on Wear OS loses the pull-down menu
One of the noticeable changes you'll see once you update is the lack of the pull-down menu.
This used to be at the top of the watch display. And, would allow you to access various play store features. You had access to your apps, settings, and quick shortcut to the homescreen.
Google has since moved these options to the bottom of the homescreen. So, if you scroll you'll end up seeing things like accounts, and settings.
Advertisement
You also no longer have the my apps section. As part of the new Play Store design, Google simplified this particular menu by separating the apps which are on your watch and on your phone into their own sections.
The my apps section is still there like before, it's just been moved to a new location. When you boot up the Play Store on your watch, you'll see the apps on your phone section first. Scroll the bottom and this is where you'll find the my apps section.
When you tap it, everything within is listed as apps on your watch, as these are all apps which are only installed on your watch and not on your phone too. This is also where you will see any available updates, and have the ability to update the apps as needed.
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The update seems to have started this week
The update may not have been rolling out to devices long. It seems in fact that it may have started hitting devices this week. More specifically within the last day or so.
That means you may still see the old Play Store app design on your own Wear OS watch until it updates on its own.
If you haven't yet received the update for the Play Store on your watch, try connecting it to your smartphone if it isn't already. It might just be that your watch needs to be connected to a data source before it can start the update.
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Profile
John Doe
UI/UX Design Team
Web Designer-
Phone:
-
Email:
-
Birthday:24th July
-
Address:1861 Bayonne Ave, Manchester Township, NJ, 08759
-
Gender:Male
-
Reports to:
Bank information
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Bank nameICICI Bank
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Bank account No.159843014641
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IFSC CodeICI24504
-
PAN NoTC000Y56
Experience
-
Web Designer at Zen Corporation Jan 2013 - Present (5 years 2 months)
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Web Designer at Ron-tech Jan 2013 - Present (5 years 2 months)
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Web Designer at Dalt Technology Jan 2013 - Present (5 years 2 months)
Office Management
1 open tasks, 9 tasks completedLorem Ipsum is simply dummy text of the printing and typesetting industry. When an unknown printer took a galley of type and scrambled it...
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Project Management
2 open tasks, 5 tasks completedLorem Ipsum is simply dummy text of the printing and typesetting industry. When an unknown printer took a galley of type and scrambled it...
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Video Calling App
3 open tasks, 3 tasks completedLorem Ipsum is simply dummy text of the printing and typesetting industry. When an unknown printer took a galley of type and scrambled it...
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Hospital Administration
12 open tasks, 4 tasks completedLorem Ipsum is simply dummy text of the printing and typesetting industry. When an unknown printer took a galley of type and scrambled it...
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Implementation of the Fast Covariance Estimation for Sparse Functional Data paper published in Statistics and Computing, Xiao et al. (2018) <doi:10.1007/s11222-017-9744-8>.
| Version: | 0.1-4 |
| Depends: | R (≥ 2.1.4) |
| Imports: | stats, splines, Matrix, matrixcalc, mgcv, refund |
| Published: | 2018-01-19 |
| Author: | Luo Xiao [aut,cre], Cai Li [aut], William Checkley [aut], Ciprian Crainiceanu [aut] |
| Maintainer: | Luo Xiao <lxiao5 at ncsu.edu> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | face results |
| Reference manual: | face.pdf |
| Package source: | face_0.1-4.tar.gz |
| Windows binaries: | r-devel: face_0.1-4.zip, r-release: face_0.1-4.zip, r-oldrel: face_0.1-4.zip |
| OS X binaries: | r-release: face_0.1-4.tgz, r-oldrel: face_0.1-4.tgz |
| Old sources: | face archive |
| Reverse depends: | fcr |
Please use the canonical form https://CRAN.R-project.org/package=face to link to this page.


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Inspired by this graphical resume design
Sorry, it's still lacking an input GUI, but if you know how to edit JSON, you can open this up and make your own graph quite easily.


#section("content")
You may solve the tasks in any order, but you will have to complete the first before you continue with the rest. Also, whenever you happen to restart the application server, you will have to repeat the first task.
The application server compiles your code automatically as you type, so you should not have to restart it.
Get to know the blog application
Before any X-R-cise, we need to warm up
To DELETE or ! ?delete is not a question(?)
Save time and bandwidth with conditional requests
You're either with us, or against the state
What to do when there's too much REST on one plate


{{ define "main" }}
Please confirm the below bookings
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Lost in Just the Translation
About
This tool is a web-based implementation of a natural language-based
steganographic system developed by
Christian Grothoff at UCLA Computer Science Department,
Krista Grothoff,
Ryan Stutsman,
and
Mikhail Atallah at
CERIAS. The key
idea behind the system is to hide a message in the noise inherent
in natural language translation. This prototype implements some
of the ideas presented in our research paper
(also, more information is available in our paper
about a previous version of our work).
Features
This tool can be used to steganographically encode or decode a message
in user-defined or system-provided cover texts. The tool provides a
variety of options for the encoding as described in the paper, including
a selection of translation engines, multiple languages and various
transformations. The engine uses keyed cryptographic hashes of translations
to encode information. For details about how
the system works please read our paper.
Limitations
The prototype has limitations. It uses various on-line translation
engines to obtain translations. These engines have a high latency, are sometimes
unavailable and limit the rate at which sentences can be translated. To alleviate
these problems, the prototype caches translations in a local database. However,
for cover texts supplied by users the cache is likely to not have any effect. Thus,
user-supplied covers are likely not to work if they are longer than a few sentences.
For longer hidden messages, the provided covertexts should be used. For those
texts we have ensured that all translations are cached already, thus sidestepping
problems with doing translation requests on-the-fly.
Note: Changes in the web interfaces for our online translation sources and small, idiosyncratic changes in the way the translations are encoded by the engines make using the encoder extremely difficult. You will likely need your own translation software or sources to achieve any meaningful results.
Download
Please note since our original sources for translations have changed this software won't provide meaningful results without substanial effort as noted above. The encoder/decoder portion of the code is isolated fairly well and still serves as a practical example of the protocol described in detail in the paper.
You can download the current release here.
LiJtT requires PHP 5 with libtidy support, a PEAR supported SQL database (i.e. mysql)
and a PHP-enabled webserver (i.e. apache). The machine running LiJtT must be able to
access the WWW in order to perform on-line queries to various translation engines.
The size of the initial database included in the distribution is currently
around 8 MB.


Add known node versions for v0.10.45, v0.12.14, v4.4.4, v5.11.1, and v6.1.0
0.6.28
Now more verbose when remote binaries are not available. This is needed since npm is increasingly more quiet by default
and users need to know why builds are falling back to source compiles that might then error out.
0.6.27
Add known node version for node v6
Stopped bundling dependencies
Documented method for module authors to avoid bundling node-pre-gyp
Updated known node v0.12.x versions and io.js 1.x versions.
0.6.4
Improved support for io.js (@fengmk2)
Test coverage improvements (@mikemorris)
Fixed support for --dist-url that regressed in 0.6.3
0.6.3
Added support for passing raw options to node-gyp using -- separator. Flags passed after
the -- to node-pre-gyp configure will be passed directly to gyp while flags passed
after the -- will be passed directly to make/visual studio.
Added node-pre-gyp configure command to be able to call node-gyp configure directly
Fix issue with require validation not working on windows 7 (@edgarsilva)
0.6.2
Support for io.js >= v1.0.2
Deferred require of request and tar to help speed up command line usage of node-pre-gyp.
0.6.1
Fixed bundled tar version
0.6.0
BREAKING: node odd releases like v0.11.x now use major.minor.patch for {node_abi} instead of NODE_MODULE_VERSION (#124)
Added support for toolset option in versioning. By default is an empty string but --toolset can be passed to publish or install to select alternative binaries that target a custom toolset like C++11. For example to target Visual Studio 2014 modules like node-sqlite3 use --toolset=v140.
Added support for --no-rollback option to request that a failed binary test does not remove the binary module leaves it in place.
Added support for --update-binary option to request an existing binary be re-installed and the check for a valid local module be skipped.
Added support for passing build options from npm through node-pre-gyp to node-gyp: --nodedir, --disturl, --python, and --msvs_version
0.5.31
Added support for deducing node_abi for node.js runtime from previous release if the series is even
Added support for --target=0.10.33
0.5.30
Repackaged with latest bundled deps
0.5.29
Added support for semver build.
Fixed support for downloading from urls that include +.
0.5.28
Now reporting unix style paths only in reveal command
0.5.27
Fixed support for auto-detecting s3 bucket name when it contains . - @taavo
Fixed support for installing when path contains a ' - @halfdan
Ported tests to mocha
0.5.26
Fix node-webkit support when --target option is not provided
0.5.25
Fix bundling of deps
0.5.24
Updated ABI crosswalk to incldue node v0.10.30 and v0.10.31
0.5.23
Added reveal command. Pass no options to get all versioning data as json. Pass a second arg to grab a single versioned property value
Added support for --silent (shortcut for --loglevel=silent)
0.5.22
Fixed node-webkit versioning name (NOTE: node-webkit support still experimental)
0.5.21
New package to fix shasum check failed error with v0.5.20
0.5.20
Now versioning node-webkit binaries based on major.minor.patch - assuming no compatible ABI across versions (#90)
Fixed installation of windows packages sub directories on unix systems (#84)
0.5.14
Finished support for cross building using --target_platform option (#82)
Now skipping binary validation on install if target arch/platform do not match the host.
Removed multi-arch validing for OS X since it required a FAT node.js binary
0.5.13
Fix problem in 0.5.12 whereby the wrong versions of mkdirp and semver where bundled.
0.5.12
Improved support for node-webkit (@Mithgol)
0.5.11
Updated target versions listing
0.5.10
Fixed handling of -debug flag passed directory to node-pre-gyp (#72)
Added optional second arg to node_pre_gyp.find to customize the default versioning options used to locate the runtime binary
Failed install due to testbinary check failure no longer leaves behind binary (#70)
0.5.9
Fixed regression in testbinary command causing installs to fail on windows with 0.5.7 (#60)
0.5.8
Started bundling deps
0.5.7
Fixed the testbinary check, which is used to determine whether to re-download or source compile, to work even in complex dependency situations (#63)
Exposed the internal testbinary command in node-pre-gyp command line tool
Fixed minor bug so that fallback_to_build option is always respected
0.5.6
Added support for versioning on the name value in package.json (#57).
Moved to using streams for reading tarball when publishing (#52)
0.5.5
Improved binary validation that also now works with node-webkit (@Mithgol)
Upgraded test apps to work with node v0.11.x
Improved test coverage
0.5.4
No longer depends on external install of node-gyp for compiling builds.
0.5.3
Reverted fix for debian/nodejs since it broke windows (#45)
0.5.2
Support for debian systems where the node binary is named nodejs (#45)
Added bin/node-pre-gyp.cmd to be able to run command on windows locally (npm creates an .npm automatically when globally installed)
Updated abi-crosswalk with node v0.10.26 entry.
0.5.1
Various minor bug fixes, several improving windows support for publishing.
0.5.0
Changed property names in binary object: now required are module_name, module_path, and host.
Now module_path supports versioning, which allows developers to opt-in to using a versioned install path (#18).
Added remote_path which also supports versioning.
Changed remote_uri to host.
0.4.2
Added support for --target flag to request cross-compile against a specific node/node-webkit version.
Added preliminary support for node-webkit
Fixed support for --target_arch option being respected in all cases.
0.4.1
Fixed exception when only stderr is available in binary test (@bendi / #31)
0.4.0
Enforce only https: based remote publishing access.
Added node-pre-gyp info command to display listing of published binaries
Added support for changing the directory node-pre-gyp should build in with the -C/--directory option.
Added support for S3 prefixes.
0.3.1
Added unpublish command.
Fixed module path construction in tests.
Added ability to disable falling back to build behavior via npm install --fallback-to-build=false which overrides setting in a depedencies package.json install target.
0.3.0
Support for packaging all files in module_path directory - see app4 for example
Added testpackage command.
Changed clean command to only delete .node not entire build directory since node-gyp will handle that.
.node modules must be in a folder of there own since tar-pack will remove everything when it unpacks.


class Minitest::Reporters::SpecReporter
Turn-like reporter that reads like a spec.
Based upon TwP's turn (MIT License) and paydro's monkey-patch.
@see github.com/TwP/turn turn @see gist.github.com/356945 paydro's monkey-patch
Public Instance Methods
record(test)
click to toggle source
Calls superclass method
Minitest::Reporters::BaseReporter#record
# File lib/minitest/reporters/spec_reporter.rb, line 29 def record(test) super record_print_status(test) record_print_failures_if_any(test) end
report()
click to toggle source
Calls superclass method
Minitest::Reporters::BaseReporter#report
# File lib/minitest/reporters/spec_reporter.rb, line 19 def report super puts('Finished in %.5fs' % total_time) print('%d tests, %d assertions, ' % [count, assertions]) color = failures.zero? && errors.zero? ? :green : :red print(send(color) { '%d failures, %d errors, ' } % [failures, errors]) print(yellow { '%d skips' } % skips) puts end
start()
click to toggle source
Calls superclass method
# File lib/minitest/reporters/spec_reporter.rb, line 13 def start super puts('Started with run options %s' % options[:args]) puts end
Protected Instance Methods
after_suite(suite)
click to toggle source
# File lib/minitest/reporters/spec_reporter.rb, line 41 def after_suite(suite) puts end
before_suite(suite)
click to toggle source
# File lib/minitest/reporters/spec_reporter.rb, line 37 def before_suite(suite) puts suite end
record_print_failures_if_any(test)
click to toggle source
# File lib/minitest/reporters/spec_reporter.rb, line 53 def record_print_failures_if_any(test) if !test.skipped? && test.failure print_info(test.failure) puts end end
record_print_status(test)
click to toggle source
# File lib/minitest/reporters/spec_reporter.rb, line 45 def record_print_status(test) test_name = test.name.gsub(/^test_: /, 'test:') print pad_test(test_name) print_colored_status(test) print(" (%.2fs)" % test.time) unless test.time.nil? puts end


What Does WWF Do?
WWF's mission is to stop the degradation of our planet's natural environment, and build a future in which humans live in harmony with nature.
WWF's mission:
The World Wide Fund for Nature (WWF) is....
WWF's mission is to stop the degradation of our planet's natural environment, and build a future in which humans live in harmony with nature.


i am the
<greg>
languages greg knows
social media is
the worst
cool
send me some text!
social media is
the worstcool


#include <Config.h>
|
| static constexpr std::string_view | pathsKey = "Paths"sv |
| |
| static constexpr std::string_view | expressionsKey = "Expressions"sv |
| |
Definition at line 67 of file Config.h.
◆ expressionsType
◆ pathsType
◆ expressions
◆ expressionsKey
| constexpr std::string_view AnyCollect::Config::file::expressionsKey = "Expressions"sv |
|
static |
◆ paths
◆ pathsKey
| constexpr std::string_view AnyCollect::Config::file::pathsKey = "Paths"sv |
|
static |
The documentation for this struct was generated from the following file:


Aggriculture is the process of producing food, feed, fiber ad many other desired producets byt the cultivation of certain plants and the raising of domesticated animals (livestock). The practice of aggriculture is also known as farming, while scientists, inventors and other devoted to improving farming methods and implements are also said to be engaged in agriculture. Subsistence farming, who farms a small area wiht limited resource inputs, and produces only enough food to meet the needs of his/her family. At the other end is commercial intensive agriculture, including industrial agriculture. Such farming involves large fields and / or numbers of animals, large resources inputs (pesticides, fertilizers, etc,), and a high level of mechanization. These operations generally attempt to maximize financial income from grain, produce, or livestock. Modern agriculture extends well beyond the traditional production of foood for humans and animals


--- layout: default title: Contact excerpt: "A blog about using the Ruby on Rails web application framework to get stuff done as a web developer. New content a few times a week." ---
If you've got a question or comment about something you read here, please use the form below to send me an email. Please fill in all the fields—I just need it to be able to reply, not to sell or market. I do my best to reply in a reasonable amount of time, but please understand that it can sometimes take me a little while to get back to you.
If you have a question about something from Everyday Rails Testing with RSpec, please direct it to the GitHub issues page for that project.
Heads up, I will probably not respond to the following:
Thanks!


Add new loan product or modify or inactivate loan product
Add new savings product or modify or inactivate savings product
Add new share product or modify or inactivate share product
Define charges/penalties for loan products, savings and deposit products
Defines rules for taking multiple rules
Add, modify or inactivate a Fixed deposit product
Add, modify or inactivate a Recurring Deposit product
Define Tax components and Tax groups
Define floating rates for loan products


Application Settings
Specify the application feature support for your NTL project.
Overview
Application Type
Application Settings
Runtime support library:
CR
T
(startup and termination)
E
xception handling
R
TTI
I
nput/output library (iostreams)
F
loating point support
C
standard library headers
Finish
Cancel
Help


To develop MedME the team utilized HTML, CSS, Bootstrap, JavaScript, JQuery, Firebase, Twilio, and various API's such as Google Maps, National Institue for Health and the FDA.
MedME GitHub RepoTo develop Friend Finder a full stack site, I utilized HTML, CSS, Bootstrap, JavaScript, JQuery, Node.js, Express-Node, Body-Parser-Node, Path-node, GitHub and Heroku.
This game was developed using HTML, CSS, Bootstrap, JavaScript and JQuery
News Scrape was developed using HTML, CSS, Bootstrap, JavaScript, MongoDB,


Welcome to my little corner of the internet, I'm Jilada! I'm from Christchurch, New Zealand. Here, you can find out a little more about me... although I'm really sure why you would need to 🙉
Education
I studied engineering at the University of Auckland where I majored in Computer Systems Engineering. I spent way too much of my time at uni but I enjoyed every second of it... well, most of it 💩
Check out IEEE Student Branch if you're studying at the University of Auckland, into technology and need to grow your network. As a club, they hold social and information events around emerging technologies and facilitating student-to-student and student-to-industry engagement.
Work
I'll be moving to Tokyo, Japan in February 2019 to work as a robotics engineer at Ascent Robotics! It is going to be a very exciting and rewarding experience for a young graduate like myself.
Over the summer of 2017/2018 I was working as a Software Engineering Intern at Orion Health in the Rhapsody team. The project I worked on is now open source on GitHub.  Designed for Rhapsody users.
Interests
Aside from glueing my eyes to the screen 24/7 I do have other more sociable hobbies. Spending time with friends and family will always be on the top of the list. You can always count on Ben to be stuck to my hip as we explore the world together!
Sonobe Origami is an amazing way to destress yourself for a few hours. Although I must say the last hour of putting it together can be stress inducing - particularly when watching others do it
As a youngling, I grew up learning three different languages; English (from my Dad), Thai (from my Mum) and Chinese/Mandarin (from school). Sadly, I lost most of my Thai when I moved to New Zealand. Now, I am picking up Thai again as well as learning Japanese.
Travel
As a kid I was fortunate enough to have lived and experienced different countries and cultures. I was born in Singapore and lived there for 8 years. Then we moved to Christchurch, New Zealand, where I spent the rest of my primary school and high school days.
Some countries still on the hit list include: Korea, Malaysia, Egypt, Greece, Italy and France.
Games
I don't play a lot due to other commitments but I do enjoy: Stardew Valley and Civilisation V. Occasionally, I will brush the dust off my 3DS and play some Animal Crossing: New Leaf or Professor Layton 🕵♀


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Alissa in Wonderland


The arrangement (also called layout) of nodes in a network is called network topology. There are broadly two types of topologies In broadcast topology, all nodes share the same physical link. When one node transmits, all nodes receive. simultaneously transmits, and there is a collision resolution mechanism for handling it. Broadcast topologies are mainly bus and ring. In point to point topology, every pair of nodes has a dedicated link.
In bus topology, there is a long cable, called backbone cable (or simply backbone), tha connects various nodes through a connector called tap In this, a message sent by one is received by all devices connected to backbone cable. This topology requires less cabling and is easy to install and extend the network laid u however, fault detection and isolation is difficult.
In ring topology, all the devices are attached through a cable in the form of ring. The message to be communicated is transmitted in one direction, thereby, relaying the message to the intended recipient. addition and deletion of devices, and fault detection and isolation is easy. However, the topology suffers from the limitation of single point failure leading to disruption of the entire network. sending a message from one node to another node may take more time.
In star topology, all the devices are connected to the central controller called hub communication between any two devices takes place through the hub responsible for relaying messages. configured. Also, fault detection and isolation is easy. However, it requires more cabling as compared to bus and ring topology. Also, hub failure will lead to network failure
In mesh topology, every node is connected with every other node in the network Because of dedicated point to point connection between every possible pair of nodes, the topology provides secure data t problem. it requires a large number of connections to establish the topology. This leads to difficulty in installation as the number of nodes grow as the network grows.
tree topology is a hybrid topology using a combination of star and bus topology. Backbone cable in a bus topology acts like the stem of the tree, and star networks (and even individual nodes) are connected to the main tree as shown in Figure 1.14 damage to a segment of a network laid using tree topology will not affect other segments.installation and configuration is difficult as compared to other topologies. also, if the backbone cab communication is disrupted


Animals at Risk
About Climate Change
The Animals
How to Help
African Elephant
Polar Bear
Monarch Butterfly
Giant Panda
Tiger
Green Sea Turtle
Mountain Gorilla
Cheetah
Koala
Emperor Penguin
Bumblebee
Manatee


HEAD
Bug Fixes
- Footer overlapping issue (Support Microsoft Explorer 11) (#248)
- Update multiline config comment. (#346)
Development Fixes
- remove deprecated
sudo: false
in .travis.yml (#347)
2.5.0
Bug Fixes
- Add
jekyll-feed
plugin in config (#228)
Minor Enhancements
- Stick footer for short posts (#223)
- Consolidate trigger SVG paths (#148)
2.4.1
Bug Fixes
- Reintroduce removed social includes for backwards compatibility (#217)
2.4.0
Minor Enhancements
- Add better system fonts (#205)
- Remove whitespace due to Liquid tags in generated HTML (#202)
- Adding Mastodon to the social networks (#198)
Bug Fixes
- social icons should resolve baseurl properly (#201)
- fix: styling main element in IE 11 (#199)
Documentation
- Improve thw wording in index.md (#175)
- Update config description comment (#203)
2.3.0
- Add option to show excerpts on the homepage (#90)
- Handle RSS feed with jekyll-feed (#183)
- Test build with Ruby 2.4 on CI (#184)
- Document how to customize navigation links (#192)
2.2.1
- Revert social_sites hash for retrocompatibility (#190)
2.2.0
Minor Enhancements
- handling content outside of posts (#88)
- Add default table styles (#144)
- Add
jekyll-seo-tag
dependency (#139)
- Add Microformats markup (#160)
- Add more social links (#141)
Documentation
- Docs: clarification about page title (#142)
2.1.1 / 2017-04-13
Minor Enhancements
- Close #55: Improve mobile user experience (#62)
- Remove incomplete status from spec summary (#63)
- Use date_format if set in configuration (#70)
- Corrected misspelled word (#72)
- Scale headings relative to base-font-size (#78)
- Remove 2.0 post-install message (#82)
- Fixes #115 - Only include the nav tag if there are pages to be displayed in the menu (#116)
- Fixes #80, #106. Make the hamburger work with pure CSS. (#111)
- add
header_pages
config to link only specific files in header (#52)
- More easily customizable CSS (#117)
- Use relative size for fonts and line-height (#103)
- Add 404 page from jekyll
master
(#121)
Development Fixes
- Remove duplicate .sass-cache (#71)
- Demo minima on GitHub Pages (#76)
- Remove the Rakefile (#118)
Documentation
- Update README with theme content description (#91)
- Update default local URL (#120)
2.1.0 / 2016-11-15
Minor Enhancements
- Update gemspec to require at least Jekyll 3.3 (#59)
- Use
absolute_url
filter for disqus (#66)
- replace
example/index.html
with example/index.md
(#58)
Development Fixes
- Example should require Jekyll 3.3 (#61)
2.0.0 / 2016-10-06
Minor Enhancements
- priority to site.url when site.github.url is available (#46)
- Move contents of the index.html page to the ‘home’ layout (#48)
- Use new
absolute_url
and relative_url
filters in minima (#57)
Development Fixes
- Use standardized tab size of 2 spaces (#44)
Major Enhancements
- move
css/
directory from jekyll into assets/
(#43)
Bug Fixes
- Fix html proofer errors (#49)
- Update gem regex to include new
assets
directory (#54)
1.2.0 / 2016-09-12
Minor Enhancements
- Look to site.url when available (#35)
Documentation
- Move instruction for CSS in install section (#37)
1.1.0 / 2016-09-07
Major Enhancements
- Add support to Disqus (#21)
- Google analytics support (#26)
- Look for
site.github.url
if it exists (#25)
Minor Enhancements
- Fixed Flash Of Unstyled SVG (#12)
- Added “author” in footer instead of double title (#22)
Documentation
- Document how to link to the theme styles (#18)
- Update Example to resemble Default site (#24)
Development Enhancements
- Use upstream gemspec file matcher (#9)
- Fix gemspec regex (#31)
- Add Travis and a basic smoke test (#33)
1.0.1 / 2016-07-28
- include
_layouts
, _includes
, and _sass
dir in gem (#6)
1.0.0 / 2016-07-27
- default layout: html
lang
attribute should allow user customization (#3)
- Use
normalize_whitespace
filter for meta description (#4)
- Fix repo’s URL in contributing (#5)


| Name | Interviewers work hours | |
|---|---|---|
| {{ each_detail.interviewers_name }} | {{ each_detail.interviewers_email }} |
{% for each_time in each_detail.time %}
{{each_time['starttime'] }}-{{ each_time['endtime']}} {% endfor %} |


WITNESS LIST Intergovernmental Relations Committee January 23, 2001 - 9:00A SB 202 FOR: Cooke, Alex Ty Mayor Pro Tem (Lubbock), Lubbock, TX Rocha, Susan Attorney (City of Lubbock), Lubbock, TX Registering, but not testifying: For: Allison, Jim General Counsel (County Judges and Commissioners Association of Texas), Austin, TX Igo, Shanna Director of Legislative Services (Texas Municipal League), Plainview, TX Lee, Donald (Texas Conference of Urban Couties), Austin, TX Wheeless, Skipper Runnels County Commissioner (County Judges and Commissioners Association of Texas), Ballinger, TX On: Ramsey, Mike Attorney (Texas Trial Lawyers Association), Beaumont, TX SB 229 Registering, but not testifying: For: Heinrich, Gary Mayor of Keene (City of Keene), Keene, TX


WITNESS LIST Intergovernmental Relations Committee May 5, 2003 - 9:00A SB 203 Registering, but not testifying: For: Delgadillo, Christina Flanagan Shipping, Real Estate Developin (Flanagan Shipping), Beaumont, TX Flanagan, Jim Flanagan Shipping /Galveston Water Front (Galveston Waterfront Ventures), Beaumont, TX Parker, Carl Lawyer (Tom Flanagan), Austin, TX Against: Schwartz, A.R. Attorney (Port of Galveston), Austin, TX SB 1019 FOR: Keilers, Lance Administrator (Ballinger Memorial Hospital), Ballinger, TX Registering, but not testifying: For: Claymore, Jennifer Attorney (Ballinger Hospital District), Austin, TX Goetz, Michael Grocery Retail Owner (Ballinger Memorial Hospital District), Ballinger, TX Martinez, Benny Banking (Ballinger Memorial Hospital District), Ballinger, TX SB 1166 FOR: Wilkison, Charley Political Legislative Director (Combined Law Enforcement Associations of Texas), Austin, TX AGAINST: Walls, Dale Detective, Travis County Sheriffs Office (Travis County Sheriffs Law Enforcement Association), Round Rock, TX Registering, but not testifying: For: Andersen, Lt. Cris Police Commander (San Antonio Police Officers Association), San Antonio, TX Delord, Ron President, CLEAT (Combined Law Enforcement Associations of Texas), Austin, TX Hutchinson, Lynda Travis County Sheriffs Officer (Travis County Sheriffs Association), Austin, TX Kamm, Bob Lawyer (Travis County Commissioners Court), Austin, TX Martinez, Lt. Greg Travis County Sheriff's Office (Travis County Sheriffs Officers Association), Austin, TX Renoon, Feliciano Patrolman (San Antonio Police Officers Association), San Antonio, TX Against: Sanders, Stephen Retired Police Officer (Texas Fraternal Order Police), Austin, TX Spicer, Brett Travis County Deputy Sheriff (Travis Co. Sheriff Law Enforcement Deputy Association, Texas Fraternal Order of Police and Tx Lab.), Austin, TX SB 1655 Registering, but not testifying: For: Dahill, John Attorney (Texas Conference of Urban Counties), Austin, TX Lee, Donald Executive Director (Texas Conference of Urban Counties), Austin, TX Mendez, Mark Assistant County Administrator (Tarrant County Commissioners Court), Fort Worth, TX Mitchell, Seth Bexar County Intergovernmental Ser. Mngr (Bexar County Commissioners Court), San Antonio, TX SB 1658 Registering, but not testifying: For: Higgins, Mike Chief of Staff (Texas State Association of Firefighters), Austin, TX On: May, Robert Actuary (Self), Austin, TX Sandefer, Morris Commissioner (Office of the Firefighters Pension Commissioner), Austin, TX SB 1659 FOR: Andro, Richard Programmer Assistant (Karyn A. Ward CPA PLLC), Parker, TX Registering, but not testifying: For: Winn, Buddy Tax Assessor Collector (Texas Association of Appraisal Districts and Tax Ass'r Coll. Assn. of TX), Bryan, TX SB 1731 Registering, but not testifying: For: Lueck, Lloyd Wichita County Clerk (Self), Wichita Falls, TX Mitchell, Cynthia County Clerk (Self), Denton, TX Against: Carr, Mindy Director of Government Affairs (Texas Land Title Association), Austin, TX SB 1777 Registering, but not testifying: For: Villarreal, Johnny Firefighter (Houston Firefighters Local 341), Houston, TX SB 1903 FOR: Draughn, Rex Consultant (Management Insights), Dallas, TX Registering, but not testifying: For: Mintz, David (Texas Apartment Assocation), Austin, TX SB 1907 FOR: Kessler, Ron Attorney (Texas Burglar and Fire Alarm Association), Austin, TX SB 1912 FOR: Lary, Trey Attorney (Vinson and Elkins), Houston, TX Sullivan, John Rancher and Marine Terminal Owner (Property Owners in District), Galveston, TX Registering, but not testifying: For: Ryan, Richard Marine Boat Operator (Property Owners in District), Galveston, TX Sullivan, James Attorney (Property Owners), Austin, TX SB 1936 Registering, but not testifying: For: Lary, Trey Attorney (Vinson and Elkins), Houston, TX SB 1937 FOR: Hargrave, Harry Real Estate Developer (Self), Dallas, TX AGAINST: Boulware-Wells, Barbara Attorney (City of Cedar Park), Cedar Park, TX Houston, Scott Attorney (Texas Municipal League), Austin, TX Turner, Frank Assistant City Manager (City of Plano), Plano, TX Registering, but not testifying: For: Brown, Gary Real Estate (Self), Austin, TX Mintz, David (Texas Apartment Association), Austin, TX Rice, Chuck Lobbyist (Texas Land Developers Association), Austin, TX


| Jersey Number | {{player.id}} |
|---|---|
| Name | {{player.name | prettierPlayerName}} |
| Country | {{player.flag}} |
| Position | {{player.position}} |
| Height and width | {{player.heigth}} {{player.width}} |
| Date of birth | {{player.birthDate}} ({{player.age}} years old) |
| Draft | {{player.draft}} |
| Experience | {{player.experience}} |
| Last season stats | {{player.summary}} |
| Salary | {{player.salary}} |


8.3.1. Spherical wavefront.
Download Python script: SphericalWavefront.py
In this experiment a spherical wavefront is made with a screen containing a small hole. If the diameter, \(d\), of the hole is small enough there will be a spherical wave at some distance behind the screen.
You can check for a spherical wave by measuring the irradiance at the optical axis as a function of the distance, \(z\).
Just behind the hole, the diameter of the spot is about equal to the diameter of the hole. When the distance, \(z\), increases the spot irradiance distribution will be a set of concentric rings with alternating maximum and minimum irradiance at the axis. At some distance, called the Rayleigh length, the pattern will change into a more uniform distribution. For distances shorter than the Rayleigh length the diffraction is called Fresnel or near-field diffraction and for larger distances Fraunhofer or far-field diffraction. When \(z\) is much larger than the Rayleigh length, the beam can be approximated near axis as a spherical wave.
The Rayleigh length is also defined as the distance at which the area of the spot doubles and depends on the hole diameter and the wavelength of the light:
with \(A\) the hole area. Another indicator of Fresnel- or Fraunhofer diffraction is the Fresnel number, given by:
The Fresnel number gives the number of so-called Fresnel half-wavelength zones ‘seen’ by the observer. If the number of zones is even, a minimum irradiance can be observed on axis. A maximum occurs if \(N_F\) is odd. (The Fresnel and Fraunhofer diffraction theory will be introduced in experiment 6, Fresnel Diffraction. Also see Pedrotti chapter 13). Depending on the required accuracy the minimum distance from the hole must be some factor times \(Z_R\). A factor 100-200 is often chosen, for this exercise a factor 10 will due.
<IP_ADDRESS>. Irradiance dependency of a spherical wavefront.
Download Python script: SphericalWavefront.py
Write down the expression for the irradiance distribution as a function of the propagation distance, \(z\), for a spherical electro-magnetic wave.
Measure the irradiance at the optical axis as a function of \(z\) for a small (\(d \approx 0.15 mm\)) and a large (\(d \approx 0.65 mm\)) hole diameter.
Plot the irradiance as a function of \(z\) for both hole diameters. Plot such that you obtain a straight line. (In general, the benefit of a straight line is that one can easily judge whether or not the theory is valid and also in which range of parameters the theory is valid. Furthermore, values of theoretical parameters can, in general, be obtained from the slope of the line and the axis-cut)
Determine, for both cases, the range of \(z\) for which the beam can be considered as spherical. Compare this range with the Rayleigh length.
<IP_ADDRESS>. Questions about the spherical wavefront.
You should be able to answer the following questions:
What happens if the diameter of the hole is too large and/or if the screen is too close to the hole?
What is the influence of the wavelength?
Why did you plot your results as a straight line?
You should be able to answer the questions below at the end of the course (Try it now if you can!)
What type of diffraction do you see just behind the hole and what type far away? What kind of diffraction are we dealing with for spherical waves?
What is meant with the “Fresnel number” and “Rayleigh length”. When is the Fresnel number equal to one?
What are Fresnel half-wavelength zones? What is the relation of these zones with the Fresnel number?
A diffraction pattern can be considered as an interference phenomena. Why?
Under some conditions the hole can be used as some kind of camera lens, the so-called pin-hole camera or “camera obscura”. What condition must be fulfilled and what are the advantages and disadvantages of such a camera compared to the common camera with a normal lens.


Hashes in Ruby and Objects in JavaScript are similar structures used to organize data. The main differences lie in some terminology and syntax. Both are essential for grouping one or more values to a corresponding object (piece of data). Think of a dictionary: each word is tied to its definition.
In Ruby Hashes, keys have corresponding values.
my_hash = {
apples: 2,
peaches: 5,
oranges: 3
}
In JavaScript Objects, properties have corresponding values.
var myObject = {
apples: 2,
peaches: 5,
oranges: 3
}
There are two small differences thus far. Ruby uses the lowercase_lowercase convention for multi-word variables. JavaScript uses the keyword var and the naming convention lowercaseUppercase for multi-word variables
Beyond naming the Hash/Object variables, you access values in these objects differently depending on the language you're working in.
In Ruby, you can look up a hash value like this:
p my_hash[:apples]
=> 2
p prints and returns a value in Ruby.
Similarily in JavaScript:
console.log(myObject["apples"])
=> 2
console.log() prints and returns a value in JavaScript.
There are other slightly different ways to look up values in Ruby Hashes and JavaScript Objects, but you can see these two examples are nearly identical beyond a few syntax tweaks.
Unlike in Ruby, when working in JavaScript you can look up the value(s) of a property of an Object by directly calling it using the .property notation:
myObject.apples
=> 2


Shreya Kamath
Computer Science Graduate Student
About Me
I am pursuing a masters degree in Computer Science from the University of Wisconsin Madison.I completed my bachelors degree in Computer Science in 2013 from R.V College of Engineering India. Prior to starting my masters I worked as a Software Development Engineer 2 at NetApp for 2 years.
I am interested in pursuing a career in research and development in the following fields: Distributed Systems ,Operating Systems and Data Science.
Resume
Please refer to my resume for an abridged version of my experiences and skills.
Contact
Skills - Languages
- Java
- C
- Perl
- Python
- Shell
- C++
- PHP
- SQL
- HTML
- CSS
Proficient
Working Knowledge
Skills - Framework/Technoligies
- Apache Tomcat
- Spring Framework
- JPA
- SOAP
- JSON
- Git
- P4
- Maven
- Make
- Ant
Source Control
Build Management
Education
-
University of Wisconsin Madison | USA
Subjects Introduction to Operating Systems (CS537) , Data Models and Languages/ Data Science(CS784) , Masters Research (CS799)
-
R. V. College of Engineering | India
GPA 3.66
Subjects Algorithms , Data Structures , Computer Networks , Database Systems , Compilers , Unix System Programming , Cryptography, Operating Systems
Work Experience
-
University of Wisconsin Madison
Research and development of the Software Assurance Marketplace(SWAMP). Developed scripts to determine trends and statistics about missing and incorrect results produced by the SWAMP parser.Analyzed 6000 software packages and 500GB of data across multiple virtual machines.Perl,Shell script,Make,REST APIs,Unix, XML
-
NetApp Inc.
Development of the NetApp 7-Mode Transition Tool.Worked on the modules to automate the process of migrating FlexVol volumes and configuration from Data ONTAP filers operating in non-clustered mode to filers operating in a clustered environment.
Worked on the transition of the following modules :primary and secondary volumes,SnapMirror data protection relationships,SAN ,DNS, LDAP, NIS , NFS, CIFS , IPv6, Unix Users and Groups.
Java, Apache Tomcat , Spring Framework,Hibernate, SOAP, JSON, P4, SnapMirror.
-
NetApp Inc
Developed a web tool for scheduling and deploying test suites.Features:manage hardware reservations,monitor storage controllers,monitor status of deployed test suites,team dashboard,resource statistics, graphs.Perl, CGI, PHP, JavaScript,MySQL
-
Indian Institute of Science - Medical Intelligence and Language Engineering Lab
Developed a voice recorder with an audio level meter to record,playback,manage and upload audio clippings to a central database. Users need to record 200 sentences in three languages,which are used as input to a text to speech engine.Java,JPA,Derby,Maven,SWT
Projects
-
- Implemented a Multi Level Feedback Queue and a Round Robin scheduler.Newly created processes have the highest priority, priority is decreased as time slices are consumed.IO bound processes have higher priority than batch mode processes
- Round robin scheduler is used to schedule processes at the same priority
Technology used : C
-
Stack Relocation Relocated the user stack form the top to the bottom of the user address space.Stack and heap can grow until the virtual address space is over-committed and do not over write each other.
Exception Handling Added a module to trap null pointers in the kernel and to produce a null pointer exception.Modified the user address space and page table inorder to trap null pointer accesses
Technology used : C
-
Shell Implemented a shell to execute input commands,execute built-in commands like history, input redirection ,execute batch scripts
Web ServerDeveloped a multithreaded web server which can handle multiple HTTP requests by using a pool of threads.
Technology used : C
-
- Implemented the end to end data science pipeline to compare and consolidate movie attributes from amazon.com and rottentomatoes.com
- Information ExtractionDeveloped web crawlers using the Scrapy framework to extract movie , cost and user review details from the web sites
- BlockingPerformed global and tuple level blocking in order to eliminate obvious non matching tuples . Used the following similarity measures to perform blocking : jaccard similarity, levenshtein distance
- MatchingUsed Precision/Recall/F-1 measure to find the best learner among the following : Decision tree,Random forest,Naive bayes,Support vector machines,Logistic regression,Linear regression.
- Added custom rules in addition to the selected learner to perform entity matching
-
- Developed a prototype search engine that leveraged people’s browsing history to replace web crawler indexing and used user similarity to rank search results
-Used only anonymously collected implicit and explicit user data.Ranked pages based on UserRank,TF/IDF score and user interests
-Data from 78 users over 3 weeks showed an 18\% percent improvement in precision of results as compared to a web crawled search engine. -
-Analyzed the performance of pre-copy and post-copy live migration algorithms with varying VM workloads and host resource allocation.
-Implemented a load balancing strategy to live migrate VMs using two Xen hosts and an iSCSI target when the host system resource utilization breached a threshold.
-Technology used: Perl,Xen,iSCSI. -
Features developed:monitor course enrollment by students , monitor staff allocated for courses,class room scheduling , student attendance monitoring, marks management and report generation.
Java,HTML,CSS,JavaScript,PHP,MySQL.
Awards
-
NetApp
Promoted from Member of Technical Staff-1 to Member of Technical Staff-2 within one year of joining NetApp -
Microsoft TechEd Windows 8 Camp
Developed a educational mathematics app targeted at high school students -
IEEE UVCE national student conference
Topic:Vulnerability of the TCP Protocol to Parasitic Computing. -
IEEE UVCE national student conference
Topic:Models of Quantum Computation in particular Quantum Dot Cellular Automata -
NetApp SnapCreator Innovation Challenge
Feature prototyped : backup and recovery of Data ONTAP FlexVols using Amazon S3
Volunteer
-
Organized a cycling event and a trekking event for the ‘Unlock Smiles’ campaign to fund the education of underprivileged children
-
Trained high school students from vulnerable backgrounds in basic computer science as part of the LearnIT program .


"Data is the new oil", "we need to be data driven", "we need to apply AI to keep being competitive" are some of the prashes I hear a lot. As I haven't seen yet a clear article pointing out what is done with the data ... here you are 🙂
Why it's complicated
Coorporations have a lot of data which is analyzed under different aspects all the time. Micro services are built which have their own data flows, applications and services are updated while they are running. Bugs get fixed. All of this makes the data very heterogenous.
Data quality problems include:
- Mixed attribute availability: Changes and bugs lead to NULL values.
- Semantic Changes: The applications which produce the data might change. Maybe you have "price" column somewhere. At one point it included tax, at another it doesn't. Time might be local, server and UTC. Durations might be seconds and milliseconds.
- Consistency: The data format might change. Maybe the user name is sometimes "FIRSTNAME LASTNAME" and in other cases "LASTNAME, FIRSTNAME".
- Duplicates: Some entries might be duplicated, e.g. multiple form submission.
Engineering problems include:
- Big Data: The amount of data might be bigger than what fits into memory.
- Fast Data: The incoming data might need to be processed in a few milliseconds.
- Privacy: How do we make the platform GDPR complient?
- Security: Authentifiction and Authorization
Data Warehouse
If you have many different data sources and you want to analyze them often, you want a Data Warehouse (DWH). A central place where the data lives. It is likely not completely recent, but should not be too old either. One Data Warehousing solution as a service is Snowflake. Amazon Redshift is another one (promo video). You can execute SQL queries in those warehouses. So you can already make use of that data to answer simple questions.
Reports and Dashboards
Sometimes a table as a result is not enough. You need graphics. The first step might be Excel, but very often this is cumbersome as the data load is too big, as one needs to download the data from the warehouse. Then Tableau (promo video) and QlikView (promo video) come into play. The Buzzword here are data driven and self-service BI. People using this software are often called Business Analysts and work in an BI department. They usually create dashboards and reports which drive business decisions.
If the company has a lot of very different data, a data dictionary might help. In the simplest case, it could be a word document. A bit more advanced would be to use the comment attribute for columns in databases. Then there are tools like dataedo as well.
POC: Model creation
Sometimes, analysis of the past behavior is not enough. You need a predictive model for future cases. Maybe even have it as a part of a customer-facing product. Now you need a Data Scientist. The work starts at the warehouse. As the warehouses and QlikView / Tableaus options are too limited, the data scientist gets a dump of the relevant parts. This might include some aggregtions, filtering and unions in the warehouse before, but at some point it will likely be imported in an environment where Python can be used. The Data Scientist analyzes the data, e.g. with a Jupyter Notebook. Usually the data contains many problems like unrealistic values or missing data (NULL values). Then the model creation part starts. This could still happen in the Notebook. To solve the prediction task, the data scientist might use models such as SVMs, neural networks or Random Forests.
The data scientists create a Proof of Concept (PoC). The model is not in a state to be used directly in production, because a lot of the cables might not be connected. But it should be clear from the PoC if the data allows building the kind of model you want to have. A valid outcome of the PoC is that it is (currently) not possible.
The two parts that can consume arbitrary much time are data cleaning and model optimization. Even after the PoC. If you're interested in that topic, ping me in the comments.
From POC to MVP
Once the model is ready and seems to work well enough for practice, a Data Engineer gets involved. The mentioned delay a Data Warehouse often has and the different kind of workload the model typically requires makes it necessary to get the data from a different source. If it should be event-based, it might be Apache Kafka for messaging or Apache Flink. The important point to notice here is that machine learning models typically contain two seperate phases: Model training and model inference. At training time, one needs to process a lot of data and needs a lot of computational power. At inference time, each requests data is tiny. The amount of computation necessary is small. So it is a mixed execution mode: Regularly re-train a model in a batch-way and have a continuously running service for inference. They might build things like Kappa Architectures or Lambda Architectures. To do so, many cloud technologies can be used (see "Cloud Service Overview").
If you wish to get more information about this part, ping me in the comments.
Cloud Service Overview
The following cloud services might be used to create services which run in the cloud:
- AWS Lambda Compute
- Running small and short scripts after a triggering event. Has a delay of a couple of seconds. Provides Computational Resources.
- AWS Cloudformation
- Infrastructure as Code (IaC) Tool
- AWS EC2 Compute (AWS)
- provides scalable computing capacity;
- AWS S3 Storage
- Store Files
- AWS InnoDB Storage
- Key-Value Store
- AWS SSM Storage
- Store Passwords and configuration
- AWS Athena
- run interactive queries directly against data in Amazon S3
- AWS Kinesis
- a real-time data processing platform
- AWS Redshift
- fully managed, petabyte-scale data warehouse to run complex queries on collections of structured data
- Amazon EMR
- deploy open source, big data frameworks like Apache Hadoop, Spark, Presto, HBase, and Flink
See also
There are a couple of follow-up topics which might result in:
- How to deploy machine learning models
Stuff I would like to share / look into for enhancing this article:
- Top 20 Free, Open Source and Premium Stream Analytics Platforms
- PyBay2018: How to Instantly Publish Data to the Internet with Datasette
- Reproducibility in Machine Learning
- Carl Anderson: Data Dictionary: a how to and best practices
- Connecting Scientific Models across Scales & Languages w/ Python, SciPy 2018.
- Mark Keinhörster: Production ready Data-Science with Python and Luigi, PyData 2018.
- Dr. Benjamin Weigel: Deploying a machine learning model to the cloud using AWS Lambda, PyData 2018.
- Dmitry Petrov: Data versioning in machine learning projects PyData 2018.
- Why Not Airflow?
- How to deploy a Docker app to Amazon ECS using AWS Fargate, 2018.
- Deploying on AWS Fargate using Cloudformation, 2018.
- Jen Underwood: Why You Need a Data Catalog and How to Select One
- Nick Schrock: Introducing Dagster, 2019.
- Github Issue: Dagster vs. Rest, 2019.


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