SIFT feature detector and descriptor extractor.
More...
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| AFAPI void | sift (features &feat, array &desc, const array &in, const unsigned n_layers=3, const float contrast_thr=0.04f, const float edge_thr=10.f, const float init_sigma=1.6f, const bool double_input=true, const float intensity_scale=0.00390625f, const float feature_ratio=0.05f) |
| | C++ Interface for SIFT feature detector and descriptor.
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| AFAPI void | gloh (features &feat, array &desc, const array &in, const unsigned n_layers=3, const float contrast_thr=0.04f, const float edge_thr=10.f, const float init_sigma=1.6f, const bool double_input=true, const float intensity_scale=0.00390625f, const float feature_ratio=0.05f) |
| | C++ Interface for SIFT feature detector and GLOH descriptor.
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| AFAPI af_err | af_sift (af_features *feat, af_array *desc, const af_array in, const unsigned n_layers, const float contrast_thr, const float edge_thr, const float init_sigma, const bool double_input, const float intensity_scale, const float feature_ratio) |
| | C++ Interface for SIFT feature detector and descriptor.
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| AFAPI af_err | af_gloh (af_features *feat, af_array *desc, const af_array in, const unsigned n_layers, const float contrast_thr, const float edge_thr, const float init_sigma, const bool double_input, const float intensity_scale, const float feature_ratio) |
| | C++ Interface for SIFT feature detector and GLOH descriptor.
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SIFT feature detector and descriptor extractor.
Detects features and extract descriptors using the Scale Invariant Feature Transform (SIFT), by David Lowe.
Lowe, D. G., "Distinctive Image Features from Scale-Invariant Keypoints", International Journal of Computer Vision, 60, 2, pp. 91-110, 2004.
◆ af_gloh()
| AFAPI af_err af_gloh |
( |
af_features * |
feat, |
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af_array * |
desc, |
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const af_array |
in, |
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const unsigned |
n_layers, |
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const float |
contrast_thr, |
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const float |
edge_thr, |
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const float |
init_sigma, |
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const bool |
double_input, |
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const float |
intensity_scale, |
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const float |
feature_ratio |
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) |
| |
C++ Interface for SIFT feature detector and GLOH descriptor.
- Parameters
-
| [out] | feat | af_features object composed of arrays for x and y coordinates, score, orientation and size of selected features |
| [out] | desc | Nx272 array containing extracted GLOH descriptors, where N is the number of features found by SIFT |
| [in] | in | array containing a grayscale image (color images are not supported) |
| [in] | n_layers | number of layers per octave, the number of octaves is computed automatically according to the input image dimensions, the original SIFT paper suggests 3 |
| [in] | contrast_thr | threshold used to filter out features that have low contrast, the original SIFT paper suggests 0.04 |
| [in] | edge_thr | threshold used to filter out features that are too edge-like, the original SIFT paper suggests 10.0 |
| [in] | init_sigma | the sigma value used to filter the input image at the first octave, the original SIFT paper suggests 1.6 |
| [in] | double_input | if true, the input image dimensions will be doubled and the doubled image will be used for the first octave |
| [in] | intensity_scale | the inverse of the difference between the minimum and maximum grayscale intensity value, e.g.: if the ranges are 0-256, the proper intensity_scale value is 1/256, if the ranges are 0-1, the proper intensity-scale value is 1/1 |
| [in] | feature_ratio | maximum ratio of features to detect, the maximum number of features is calculated by feature_ratio * in.elements(). The maximum number of features is not based on the score, instead, features detected after the limit is reached are discarded |
◆ af_sift()
| AFAPI af_err af_sift |
( |
af_features * |
feat, |
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af_array * |
desc, |
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const af_array |
in, |
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const unsigned |
n_layers, |
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const float |
contrast_thr, |
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|
const float |
edge_thr, |
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const float |
init_sigma, |
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const bool |
double_input, |
|
|
const float |
intensity_scale, |
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const float |
feature_ratio |
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) |
| |
C++ Interface for SIFT feature detector and descriptor.
- Parameters
-
| [out] | feat | af_features object composed of arrays for x and y coordinates, score, orientation and size of selected features |
| [out] | desc | Nx128 array containing extracted descriptors, where N is the number of features found by SIFT |
| [in] | in | array containing a grayscale image (color images are not supported) |
| [in] | n_layers | number of layers per octave, the number of octaves is computed automatically according to the input image dimensions, the original SIFT paper suggests 3 |
| [in] | contrast_thr | threshold used to filter out features that have low contrast, the original SIFT paper suggests 0.04 |
| [in] | edge_thr | threshold used to filter out features that are too edge-like, the original SIFT paper suggests 10.0 |
| [in] | init_sigma | the sigma value used to filter the input image at the first octave, the original SIFT paper suggests 1.6 |
| [in] | double_input | if true, the input image dimensions will be doubled and the doubled image will be used for the first octave |
| [in] | intensity_scale | the inverse of the difference between the minimum and maximum grayscale intensity value, e.g.: if the ranges are 0-256, the proper intensity_scale value is 1/256, if the ranges are 0-1, the proper intensity-scale value is 1/1 |
| [in] | feature_ratio | maximum ratio of features to detect, the maximum number of features is calculated by feature_ratio * in.elements(). The maximum number of features is not based on the score, instead, features detected after the limit is reached are discarded |
◆ gloh()
| AFAPI void gloh |
( |
features & |
feat, |
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|
array & |
desc, |
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const array & |
in, |
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const unsigned |
n_layers = 3 , |
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const float |
contrast_thr = 0.04f , |
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const float |
edge_thr = 10.f , |
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const float |
init_sigma = 1.6f , |
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const bool |
double_input = true , |
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const float |
intensity_scale = 0.00390625f , |
|
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const float |
feature_ratio = 0.05f |
|
) |
| |
C++ Interface for SIFT feature detector and GLOH descriptor.
- Parameters
-
| [out] | feat | features object composed of arrays for x and y coordinates, score, orientation and size of selected features |
| [out] | desc | Nx272 array containing extracted GLOH descriptors, where N is the number of features found by SIFT |
| [in] | in | array containing a grayscale image (color images are not supported) |
| [in] | n_layers | number of layers per octave, the number of octaves is computed automatically according to the input image dimensions, the original SIFT paper suggests 3 |
| [in] | contrast_thr | threshold used to filter out features that have low contrast, the original SIFT paper suggests 0.04 |
| [in] | edge_thr | threshold used to filter out features that are too edge-like, the original SIFT paper suggests 10.0 |
| [in] | init_sigma | the sigma value used to filter the input image at the first octave, the original SIFT paper suggests 1.6 |
| [in] | double_input | if true, the input image dimensions will be doubled and the doubled image will be used for the first octave |
| [in] | intensity_scale | the inverse of the difference between the minimum and maximum grayscale intensity value, e.g.: if the ranges are 0-256, the proper intensity_scale value is 1/256, if the ranges are 0-1, the proper intensity-scale value is 1/1 |
| [in] | feature_ratio | maximum ratio of features to detect, the maximum number of features is calculated by feature_ratio * in.elements(). The maximum number of features is not based on the score, instead, features detected after the limit is reached are discarded |
◆ sift()
| AFAPI void sift |
( |
features & |
feat, |
|
|
array & |
desc, |
|
|
const array & |
in, |
|
|
const unsigned |
n_layers = 3 , |
|
|
const float |
contrast_thr = 0.04f , |
|
|
const float |
edge_thr = 10.f , |
|
|
const float |
init_sigma = 1.6f , |
|
|
const bool |
double_input = true , |
|
|
const float |
intensity_scale = 0.00390625f , |
|
|
const float |
feature_ratio = 0.05f |
|
) |
| |
C++ Interface for SIFT feature detector and descriptor.
- Parameters
-
| [out] | feat | features object composed of arrays for x and y coordinates, score, orientation and size of selected features |
| [out] | desc | Nx128 array containing extracted descriptors, where N is the number of features found by SIFT |
| [in] | in | array containing a grayscale image (color images are not supported) |
| [in] | n_layers | number of layers per octave, the number of octaves is computed automatically according to the input image dimensions, the original SIFT paper suggests 3 |
| [in] | contrast_thr | threshold used to filter out features that have low contrast, the original SIFT paper suggests 0.04 |
| [in] | edge_thr | threshold used to filter out features that are too edge-like, the original SIFT paper suggests 10.0 |
| [in] | init_sigma | the sigma value used to filter the input image at the first octave, the original SIFT paper suggests 1.6 |
| [in] | double_input | if true, the input image dimensions will be doubled and the doubled image will be used for the first octave |
| [in] | intensity_scale | the inverse of the difference between the minimum and maximum grayscale intensity value, e.g.: if the ranges are 0-256, the proper intensity_scale value is 1/256, if the ranges are 0-1, the proper intensity-scale value is 1/1 |
| [in] | feature_ratio | maximum ratio of features to detect, the maximum number of features is calculated by feature_ratio * in.elements(). The maximum number of features is not based on the score, instead, features detected after the limit is reached are discarded |


Sports
A list of all the sports with a picture of each
They can see all the different sports and each one can bring up details about the sport itself and the sport at Roncalli
Fine Arts
Mention all the aspects from band, choir, theater, etc
Could make cool video collage from all the video we have of these events on booth computer
Main thing is we need to clutter this whole page with pictures and videos since we have really nice quality content and it helps breaks that language barrior


[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 document describes the formal schema of the Evaluation and Report Language (EARL) 1.0. EARL is a vocabulary, the terms of which are defined across a set of specifications and technical notes, and that is used to describe test results. The primary motivation for developing this vocabulary is to facilitate the exchange of test results between Web accessibility evaluation tools in a vendor-neutral and platform-independent format. It also provides reusable terms for generic quality assurance and validation purposes.
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 Last Call Working Draft of the Evaluation and Report Language (EARL) 1.0 Schema is an update of the previous EARL 1.0 Last Call Working Draft of 29 October 2009. It meets the requirements specified in the Requirements for the Evaluation and Report Language (EARL) 1.0, and incorporates all comments received. This document is intended to be published and maintained as a W3C Recommendation 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, Evaluation and Report Language (EARL) 1.0 Schema, 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, the Working Group is looking for feedback on the following items which are also highlighted within the document:
foaf:Document
as a further refinement for earl:TestSubject
(see Editor Note 1)earl:Software
as a subclass of doap:Project
(see Editor Note 2)Please send comments on this Evaluation and Report Language (EARL) 1.0 Schema 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. 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.
The Evaluation and Report Language (EARL) defines a vocabulary for expressing test results. It enables any person, software application, or organization to assert test results for any test subject tested against any set of criteria. The test subject might be a website, an authoring tool, a user agent, or some other entity. The set of criteria may be accessibility guidelines, formal grammars, or other types of quality assurance requirements. Thus, EARL is flexible with regard to the contexts in which it can be applied.
This document provides the core schema of EARL. Other parts of the EARL suite of specifications include:
The Developer Guide for Evaluation and Report Language (EARL) 1.0 explains how to implement and use EARL, including conformance requirements for software tools. An Evaluation and Report Language (EARL) Overview is also available.
EARL is not a comprehensive vocabulary for describing test procedures, test criteria, or test requirements but, rather, for describing the outcomes from such testing. EARL can be supplemented by test description vocabularies or other vocabularies for different aspects of the testing cycle.
The assumed audience of this specification is developers of software tools and processes who want to express test results in a machine readable format that is semantically rich. More introductory background about EARL as well as specific guidance for quality assurance tool developers, in particular for web accessibility evaluation tool developers, is provided in the Evaluation and Report Language (EARL) 1.0 Guide.
This document assumes that the reader is familiar with the Resource Description Framework (RDF) and can read its XML serialization. Readers who wish to understand more about RDF should read a general introduction or the RDF Primer [RDF-PRIMER].
The RDF representation of the vocabulary defined by this document uses the namespace http://www.w3.org/ns/earl#
. The prefix earl
is used throughout this document to denote this namespace. Other prefixes used throughout this document include:
cnt
- Representing Content in RDF namespace http://www.w3.org/2011/content#
(defined by [Content])dct
- Dublin Core (DC) namespace http://purl.org/dc/terms/
(defined by [DC])doap
- Description of a Project (DOAP) namespace http://usefulinc.com/ns/doap#
(defined by [DOAP])foaf
- Friend of a Friend (FOAF) namespace http://xmlns.com/foaf/0.1/#
(defined by [FOAF])http
- HTTP Vocabulary in RDF namespace http://www.w3.org/2011/http#
(defined by [HTTP])ptr
- Pointer Methods in RDF namespace http://www.w3.org/2009/pointers#
(defined by [Pointers])rdf
- RDF namespace http://www.w3.org/1999/02/22-rdf-syntax-ns#
(defined by [RDF])rdfs
- RDF Schema namespace http://www.w3.org/2000/01/rdf-schema#
(defined by [RDFS])xsd
- XMLS namespace http://www.w3.org/2001/XMLSchema#
(defined by [XMLS])This section describes the classes defined by this document. Every test result in EARL is expressed as an assertion. An EARL Assertion contains the following information:
EARL provides flexibility to describe different types of assertions, such as those carried out by automated testing tools or by human evaluators, or those made about generic testing requirements or specific test cases.
Example 1: A person carries out a manual evaluation of a web page to an accessibility requirement.
http://www.example.org/page.html
Example 2: A software application carries out automated validation of a web page to a technical specification.
http://validator.w3.org/
http://www.example.org/page.html
at 2004-04-14T14:00:04+1000

element on line 53, char 7 was not closed.Assertion - a statement that embodies the results of a test.
Example 3: Instance of an assertion expressed as an RDF/XML fragment.
<earl:Assertion rdf:about="#assertion">
<earl:assertedBy rdf:resource="#assertor"/>
<earl:subject rdf:resource="http://www.example.org/"/>
<earl:test rdf:resource="http://www.w3.org/TR/WCAG20-TECHS/H36"/>
<earl:result rdf:resource="#result"/>
</earl:Assertion>
Assertor - an entity such as a person, a software tool, an organization, or any other grouping that carries out a test collectively.
Rather than specifying only an earl:Assertor
type, it is recommended that one of the following types be employed in addition:
earl:Software
foaf:Agent
foaf:Person
foaf:Organization
foaf:Group
It is recommended to provide additional information about the Assertor by using the following properties from external vocabularies:
dct:title
dct:description
foaf:name
foaf:firstName
or foaf:surname
if the assertor is a person.foaf:nick
foaf:mbox
foaf:mbox_sha1sum
property.foaf:homepage
foaf:member
Example 4: An Assertor that is a person called Bob B. Bobbington.
<foaf:Person rdf:about="http://www.example.org/people/#bob">
<foaf:name>Bob B. Bobbington</foaf:name>
<foaf:mbox<EMAIL_ADDRESS><foaf:mbox_sha1sum>1a9daad476f0158b81bc66b7b27b438b4b4c19c0</foaf:mbox_sha1sum>
</foaf:Person>
Example 5: An Assertor that is a piece of software called Cool Tool.
<earl:Software rdf:about="http://www.example.org/tools/#cooltool">
<doap:name xml:lang="en">Cool Tool</doap:name>
<doap:description xml:lang="en">My favorite tool!</doap:description>
<doap:created>2011-04-27</doap:created>
<doap:homepage rdf:resource="http://example.org/tools/cool/"/>
<doap:release>
<doap:revision>1.0.3</doap:revision>
</doap:release>
</earl:Software>
Example 6: An Assertor that is the person from example 4 using the software tool from example 5.
<foaf:Group rdf:about="#assertor">
<dct:title xml:lang="en">Bob using Cool Tool</dct:title>
<dct:description xml:lang="en">Bob doing semi-automated testing</dct:description>
<earl:mainAssertor rdf:resource="http://www.example.org/people/#bob"/>
<foaf:member rdf:resource="http://www.example.org/tool/#cooltool"/>
</foaf:Group>
Note: According to this example, "Cool Tool" is a resource of type foaf:Agent
. According to example 5, it is also a resource of type earl:Software
. These are not contradictory statements and are valid RDF representations.
Test Subject - the class of things that have been tested against some test criterion.
Rather than specifying only an earl:TestSubject
type, it is recommended that one of the following types be employed in addition:
earl:Software
cnt:Content
http:Response
foaf:Document
foaf:Document
unless compelling use-cases can be presented; feedback on this consideration is welcome.]It is recommended to provide additional information about the Test Subject by using the following properties from external vocabularies:
dct:title
dct:description
dct:date
dct:hasPart
dct:isPartOf
Example 7: A group of resources that have been tested together as a single test subject.
<earl:TestSubject rdf:about="http://www.example.org/">
<dct:title xml:lang="en">example.org Web site</dct:title>
<dct:description xml:lang="en">Each page on the example.org Web site</dct:description>
<dct:hasPart rdf:resource="http://www.example.org/style.css"/>
<dct:hasPart rdf:resource="http://www.example.org/page1.html"/>
<dct:hasPart rdf:resource="http://www.example.org/page2.html"/>
<dct:hasPart rdf:resource="http://www.example.org/image1.png"/>
<dct:hasPart rdf:resource="http://www.example.org/image2.png"/>
</earl:TestSubject>
Test Criterion - a testable statement, usually one that can be passed or failed. It is a super class for all types of tests including things such as validation requirements, code test cases, checkpoints from guidelines such as Web Content Accessibility Guidelines [WCAG], or others.
Rather than specifying only an earl:TestCriterion
type, it is recommended that one of the following types be employed in addition:
earl:TestRequirement
earl:TestCase
It is recommended to provide additional information about the Test Subject by using the following properties from external vocabularies:
dct:title
dct:description
dct:hasPart
dct:isPartOf
Example 8: Instance of a test case that is described with a title and its relationship to a test suite.
<earl:TestCase rdf:about="http://www.w3.org/TR/WCAG20-TECHS/H36">
<dct:title xml:lang="en">H36</dct:title>
<dct:description xml:lang="en">Technique H36 - Using alt attributes
on images used as submit buttons </dct:description>
<dct:isPartOf rdf:resource="http://www.w3.org/TR/WCAG20-TECHS/"/>
<dct:hasPart rdf:resource="http://www.w3.org/TR/WCAG20-TECHS/H36#H36-tests"/>
</earl:TestCase>
Test Result - the actual result of performing the test. It includes both machine-readable values as well as human-readable description of the results (typically error messages).
It is recommended to provide additional information about the Test Result by using the following properties from external vocabularies:
dct:title
dct:description
dct:date
Example 9: A test result with a validity of fail and a description of the problem in English, and encoded in XHTML format.
<earl:TestResult rdf:about="#result">
<earl:outcome rdf:resource="http://www.w3.org/ns/earl#failed"/>
<dct:title xml:lang="en">Invalid Markup (code #353)</dct:title>
<dct:description rdf:parseType="Literal" xml:lang="en">

The table element is not allowed to appear
inside a p element

</dct:description>
<earl:pointer rdf:resource="#pointer"/>
<earl:info rdf:parseType="Literal" xml:lang="en">

It seems the p element has not been closed

</earl:info>
</earl:TestResult>
Test Mode - describes how a test was carried out. It reflects the information provided by the Assertor and is used to simplify some commonly used queries.
Where applicable it is recommended to use one of the following instances of earl:TestMode
, to categorize the mode in which the test was carried out:
earl:automatic
earl:manual
earl:semiAuto
earl:undisclosed
earl:unknownMode
It is recommended to provide additional information about the Test Mode by using the following properties from external vocabularies:
dct:title
dct:description
Example 10: The assertion from example 3 was carried out in semi-automatic mode.
<earl:Assertion rdf:about="#assertion">
<earl:mode rdf:resource="http://www.w3.org/ns/earl#semiAuto"/>
</earl:Assertion>
Outcome Value - a value or expression that describes a resulting condition from carrying out the test.
Where applicable it is recommended to use one of the following instances of earl:OutcomeValue
, to categorize the outcome of carrying out the test:
earl:passed
earl:failed
earl:cantTell
earl:inapplicable
earl:untested
In cases where it is necessary to create further instances of earl:OutcomeValue
, it is recommended that one of the following types be employed in addition:
earl:Pass
earl:Fail
earl:CannotTell
earl:NotApplicable
earl:NotTested
It is recommended to provide additional information about the Outcome Value by using the following properties from external vocabularies:
dct:title
dct:description
Example 11: A test result with an outcome of "Passed", using the corresponding instance of earl:OutcomeValue
.
<earl:TestResult rdf:about="#result">
<earl:outcome rdf:resource="http://www.w3.org/ns/earl#passed"/>
</earl:TestResult>
Example 12: A test result with a non-standard outcome of "Warning", which is a type earl:Pass
.
<rdf:Description rdf:about="http://example.org/my/warning#warning">
<rdf:type rdf:resource="http://www.w3.org/ns/earl#Pass"/>
<dc:title xml:lang="en">Warning</dc:title>
<dc:description xml:lang="en">the subject passed the test but there are warnings</dc:description>
</rdf:Description>
<earl:TestResult rdf:about="#result">
<earl:outcome rdf:resource="http://example.org/my/terms#warning"/>
</earl:TestResult>
[Editor's note 2: ERT WG is looking for feedback on the use of DOAP Project to describe Software; feedback on this issue is welcome.]
A Software is any piece of software such as an authoring tool, browser, or evaluation tool. It can be used to describe an Assertor, such as a validation or other quality assurance tool, and it can be used to describe a Test Subject (for example to test compliance of an authoring tool to Authoring Tool Accessibility Guidelines [ATAG] or of a browser to User Agent Accessibility Guidelines [UAAG]).
Note: earl:Software
is a sublass of doap:Project
to denote the narrower meaning of executable "Software", that is an outcome of a "Project".
It is recommended to provide information about the Software by using the following properties from external vocabularies:
doap:name
doap:description
doap:homepage
doap:created
doap:release
Example 13: Description of a software tool.
<earl:Software rdf:about="#cooltool">
<doap:name xml:lang="en">Cool Tool</doap:name>
<doap:description xml:lang="en">My favorite tool!</doap:description>
<doap:created>2011-04-27</doap:created>
<doap:homepage rdf:resource="http://example.org/tools/cool/"/>
<doap:release>
<doap:revision>1.0.3</doap:revision>
</doap:release>
</earl:Software>
This section describes the properties defined by this document. EARL also uses properties from external vocabularies to provide additional information where necessary.
Asserted By - the assertor of an assertion.
earl:Assertion
earl:Assertor
Subject - the test subject of an assertion.
earl:Assertion
earl:TestSubject
Test - the test criterion of an assertion.
earl:Assertion
earl:TestCriterion
Result - the result of an assertion.
earl:Assertion
earl:TestResult
Mode - the mode in which the test was performed.
earl:Assertion
earl:TestMode
Main Assertor - the assertor that is primarily responsible for performing the test. It is a refinement of the term foaf:member
defined by [FOAF].
earl:Assertor
earl:Assertor
Outcome - the outcome of performing the test.
earl:TestResult
earl:OutcomeValue
Pointer - the location within a test subject that are most relevant to a test result.
earl:TestResult
ptr:Pointer
Info - additional warnings or error messages in a human-readable form.
earl:TestResult
This section summarizes the terms defined and used by this EARL 1.0 Schema specification.
| Class Name | Label | Comment | Refinements | Related Properties |
|---|---|---|---|---|
earl:Assertion |
Assertion | a statement that embodies the results of a test | - | |
earl:Assertor |
Assertor | an entity such as a person, a software tool, an organization, or any other grouping that carries out a test collectively | ||
earl:TestSubject |
Test Subject | the class of things that have been tested against some test criterion | ||
earl:TestCriterion |
Test Criterion | a testable statement, usually one that can be passed or failed | ||
earl:TestRequirement (subclass of earl:TestCriterion ) |
Test Requirement | a higher-level requirement that is tested by executing one or more sub-tests | - | |
earl:TestCase (subclass of earl:TestCriterion ) |
Test Case | an atomic test, usually one that is a partial test for a requirement | - | |
earl:TestResult |
Test Result | the actual result of performing the test | - | |
earl:TestMode |
Test Mode | describes how a test was carried out | - | |
earl:OutcomeValue |
Outcome Value | a discrete value that describes a resulting condition from carrying out the test | ||
earl:Pass (subclass of earl:OutcomeValue ) |
Pass | the class of outcomes to denote passing a test | - | earl:outcome |
earl:Fail (subclass of earl:OutcomeValue ) |
Fail | the class of outcomes to denote failing a test | - | earl:outcome |
earl:CannotTell (subclass of earl:OutcomeValue ) |
Undetermined | the class of outcomes to denote an undetermined outcome | - | earl:outcome |
earl:NotApplicable (subclass of earl:OutcomeValue ) |
Not applicable | the class of outcomes to denote the test is not applicable | - | earl:outcome |
earl:NotTested (subclass of earl:OutcomeValue ) |
Not tested | the class of outcomes to denote the test has not been carried out | - | earl:outcome |
earl:Software |
Software | any piece of software such as an authoring tool, browser, or evaluation tool | - |
| Property Name | Label | Comment | Domain | Range |
|---|---|---|---|---|
earl:assertedBy |
Asserted By | assertor of an assertion | earl:Assertion |
earl:Assertor |
earl:subject |
Subject | test subject of an assertion | earl:Assertion |
earl:TestSubject |
earl:test |
Test | test criterion of an assertion | earl:Assertion |
earl:TestCriterion |
earl:result |
Result | result of an assertion | earl:Assertion |
earl:TestResult |
earl:mode |
Mode | mode in which the test was performed | earl:Assertion |
earl:TestMode |
earl:mainAssertor (subproperty of foaf:member ) |
Main Assertor | assertor that is primarily responsible for performing the test | earl:Assertor |
earl:Assertor |
earl:outcome |
Outcome | outcome of performing the test | earl:TestResult |
earl:OutcomeValue |
earl:pointer |
Pointer | location within a test subject that are most relevant to a test result | earl:TestResult |
ptr:Pointer |
earl:info |
Info | additional warnings or error messages in a human-readable form | earl:TestResult |
Literal |
| Instance Name | Title | Description |
|---|---|---|
earl:automatic (instance of earl:TestMode ) |
Automatic | where the test was carried out automatically by the software tool and without any human intervention |
earl:manual (instance of earl:TestMode ) |
Manual | where the test was carried out by human evaluators |
earl:semiAuto (instance of earl:TestMode ) |
Semi-Automatic | where the test was partially carried out by software tools, but where human input or judgment was still required to decide or help decide the outcome of the test |
earl:undisclosed (instance of earl:TestMode ) |
Undisclosed | where the exact testing process is undisclosed |
earl:unknownMode (instance of earl:TestMode ) |
Unknown | where the testing process is unknown or undetermined |
earl:passed (instance of earl:Pass ) |
Passed | the subject passed the test |
earl:failed (instance of earl:Fail ) |
Failed | the subject failed the test |
earl:cantTell (instance of earl:CannotTell ) |
Cannot tell | it is unclear if the subject passed or failed the test |
earl:inapplicable (instance of earl:NotApplicable ) |
Inapplicable | the test is not applicable to the subject |
earl:untested (instance of earl:NotTested ) |
Untested | the test has not been carried out |
This section provides references to related documents and specifications.
EARL is the result of the work of many people over the past. The editors would particularly like to thank Wendy Chisholm, Sean B Palmer, and Daniel Dardailler, whose contributions have included editing the first versions of the EARL specifications, and Leonard Kasday who set the work in motion to develop EARL. The editors apologise for any names left out of this list, and will endeavour to rectify any errors noted in comments.
Shadi Abou-Zahra, Philip Ackermann, Chrisoula Alexandraki, Shane Anderson, Myriam Arrue, Gabriele Bartolini, Giorgio Brajnik, Dan Brickley, Dan Connolly, Karl Dubost, Nick Gibbins, Al Gilman, Emmanuelle Gutiérrez y Restrepo, Dominique Hazaël-Massieux, Nadia Heninger, Sandor Herramhof, Ian Hickson, Björn Höhrmann, Carlos Iglesias, Nick Kew, Johannes Koch, Jim Ley, William Loughborough, Rui Lopes, John Lutts, Charles McCathieNevile, Libby Miller, Tom Martin, Yehya Mohamed, Daniela Ortner, Dave Pawson, Eric Prud'hommeaux, Pierre Queinnec, Chris Ridpath, Romain Roure, Christophe Strobbe, Michael Squillace, Aaron Swartz, Olivier Thoreaux, Carlos Velasco, Konstantinos Votis, and Rob Yonaitis.
Besides several minor editorial changes, the most significant changes from the 29 October, 2009 Working Draft include:
earl:Software
a subclass of doap:Project
, and reused several terms from DOAPA detailed listing of the comments, resolutions, and changes made is provided in the Open Issues for EARL 1.0 Schema listing.


Welcome to wklchris' Website!¶
My name is Kanglong Wu, or you can call me by my internet nickname wklchris
.
I got my BS degree in Transportation Engineering (with a Mathematics minor) in Tongji University in Shanghai, China. Now I am a PhD student in Transportation Engineering program of the Department of Civil and Environmental Engineering at University of California, Davis (UC Davis).
Although I don't major in computer science, I have been learning programming since I was 10 years old and have always loved it. This is the reason why I am active on Github and have created this personal site.
Sub-sites¶
Here is the list of sub-sites of this personal Github.io domain:
Blog: You may visit the Blogs page (from the sidebar/topbar, or via this link: Blogs) for my blog details.
Simrofy: I developed a website theme/template in my spare time called "Simrofy". The site you are viewing now is also built based on Simrofy. You can visit its documentation by visiting my Simrofy main documentation sub-site. The source code (Github repo) is available at wklchris/sphinx-simrofy-theme.
Contact Me¶
You can contact me via my usuall e-mail, which is my internet nickname wklchris followed by the hotmail dot com suffix.
If you would like to contact me academically, please send messages to klowu followed by the ucdavis.edu suffix.
Last build of the website: 2023-01-03


adobephotoshop
hike
games
Master the language of the web :html,css and javascript.This path will prepare you to build basic websites and then build interactive web apps. read more
Mobile app development indeed nowadays necessary for all the business. Our main focus, as a mobile app development company is to provide strategic planning and industry’s best product to compete in the market.read more
The Digital Marketing Master Program is an intensive, 100+ hours program designed and delivered by industry veterans for high-performing individuals who want to get practical and experiential learning, along with soft skills and placement assistance. read more
APIU Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with.
read moreGET FREE DOMAIN NAME AND WEB HOSTING
CONTACT USLearn your way Make it happen -anytime, anywhere.
Great course for beginners and for intermediate. From this course I have learned overall Spring framework with Hibernate, JPA, REST and Spring Boot.
I learned some new ways to use Views and the instructor gave me some great ideas on how to improve website pages. I'm glad I took this course.
The course really worked in establishing a strong basics in Spring framework which I had found much difficult to achieve.


class SortingContext : TableContext
A proxy on the df
that exposes just parts of the DataFrame api that are relevant for sorting
df
- A krangl.DataFrame instance
<init> |
A proxy on the SortingContext(df: DataFrame) |
desc |
Creates a sorting attribute that inverts the order of the argument fun desc(dataCol: DataCol): IntCol fun desc(columnName: String): IntCol |


This dish made me feel...
UPS: list three good things about this dish, separate each using a common
DOWNS: list three bad things about this dish, separate each using a common


guess which hero is using this ability
choose the name for the hero
pick all skills which can be used by this hero
choose if hero is using melee or ranged type of base attack
pick the right price which is needed to buy this item
remind yourself all components which needs to be used to craft the item


The African continent is undergoing a rapid transformation, as global forces (such as globalization, technological advances, climate change, global economic or health crises) and local forces (such as demographics, diet changes, political transitions, and social aspirations) interact in complex ways, at and between the different scales of territorial development. This generates a myriad of complex, wicked, interconnected problems (natural resources depletion, conflicts, migrations, food and nutrition insecurity, global health issues, etc.) with contrasted social, economic and environmental consequences.
A better understanding of the processes at work is required to explore:
Multidisciplinary research communities will be needed to effectively document and understand how governance and public policies can help solving these new challenges.
The purpose of this online meeting is to brainstorm with already research institutions in Africa about the construction of a partnership platform that would focus on how governance and policy innovation processes can foster the inclusiveness and sustainability of the following inter-related systems in Africa: (a) rural and territorial systems, (b) agricultural and food systems, (c) managed and natural ecosystems; and (d) how to manage sanitary and systemic risks affecting them
Collaborative activities of the partnership could include:
What do we want to be achieved by the end of the meeting?
We target at least these three objectives:


{% extends "./templates/base.tpl.html" %} {% block title %}Questions{% endblock %} {% block content %}
Please answer some questions about upload slides.
Q1. Which user's slide is the best?
Q2. What is the good point of selected slide?
Q3. Please write other opinion.


{%extends "master.html"%} {%block title%}Votes{%endblock%} {%block header%}Votes{%endblock%} {%block headextra%} {%endblock%} {%block content%}
Vote on whether you feel that each game is entertaining to watch on-stream. The host may heed this or ignore it at their choice. Probably ignore it.
{%for show in shows%}| {%if game['display'] != game['name']%}{%endif%} {{game['display']|e}} {%if game['display'] != game['name']%}{%endif%} |
|


This work performs a principled analysis of the class decision functions learned by classification CNNs by contextualising an existing geometrical framework for network decision boundary analysis.
Our research uncovers some very intriguing yet simplistic facets of the class score functions learned by these networks that explain their adversarial vulnerability.
We identify the fact that specific input image space directions tend to be associated with fixed class identities.
This means that simply increasing the magnitude of correlation between the input image and a single image space direction causes the nets to believe that more (or less) of the class is present.
This allows us to provide a new perspective on the existence of universal adversarial perturbations.
Further, the input image space directions which the networks use to achieve their classification performance are the same along which the networks are most vulnerable to attack; the vulnerability arises from the rather simplistic non-linear use of the directions. Thus, as it stands, the performance and vulnerability of these nets are closely entwined.
Various notable observations emerge from this, one of which is that any attempt to make a trained network more robust to an adversarial attack by suppressing input data dimensions or intermediate network features would always be accompanied by a corresponding loss in network accuracy.
Moreover, if the attack is optimised to take into account the suppression of dimensions, it regains its effectiveness.
These results present key implications for future efforts to construct neural nets that are both accurate and robust to adversarial attack.
[paper]
[code]
[video]
[recommendation]


The robustness of many of the statistical techniques, such as factor analysis, applied in the social sciences rests upon the assumption of item-level normality. However, when dealing with real data, these assumptions are often not met. The Box-Cox transformation (Box & Cox, 1964) <http://www.jstor.org/stable/2984418> provides an optimal transformation for non-normal variables. Yet, for large datasets of continuous variables, its application in current software programs is cumbersome with analysts having to take several steps to normalise each variable. We present an R package 'normalr' that enables researchers to make convenient optimal transformations of multiple variables in datasets. This R package enables users to quickly and accurately: (1) anchor all of their variables at 1.00, (2) select the desired precision with which the optimal lambda is estimated, (3) apply each unique exponent to its variable, (4) rescale resultant values to within their original X1 and X(n) ranges, and (5) provide original and transformed estimates of skewness, kurtosis, and other inferential assessments of normality.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.3.0) |
| Imports: | MASS, parallel, purrr, magrittr, rlang, shiny |
| Suggests: | testthat, covr |
| Published: | 2018-03-30 |
| Author: | Kevin Chang [aut, cre], Matthew Courtney [aut] |
| Maintainer: | Kevin Chang <k.chang at auckland.ac.nz> |
| BugReports: | https://github.com/kcha193/normalr/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| URL: | https://github.com/kcha193/normalr |
| NeedsCompilation: | no |
| Citation: | normalr citation info |
| Materials: | README NEWS |
| CRAN checks: | normalr results |
| Reference manual: | normalr.pdf |
| Package source: | normalr_1.0.0.tar.gz |
| Windows binaries: | r-devel: normalr_1.0.0.zip, r-release: normalr_1.0.0.zip, r-oldrel: normalr_1.0.0.zip |
| OS X binaries: | r-release: normalr_1.0.0.tgz, r-oldrel: normalr_1.0.0.tgz |
| Old sources: | normalr archive |
Please use the canonical form https://CRAN.R-project.org/package=normalr to link to this page.


- Fast access to Historical data - Time condionated searchs - Arrange graphics and Customize work-oriented Dashboard


In this universe, little imps have built a society on a remote island. The humans. after discoveng their world have tried to wage war or ally themselves with the imps, for their advanced technology and rare resources. However the little creatures felt about the humans, the imps could not go near them, or their physical and mental state would be altered by their pressence. The humans still tried to fight their own kind and the imps, until the creatures, out of fear for themselves and the humans, cut off all contact. 100 years later, there was a collosal boom and the world outside of the lone island became covered in a dark mist and was no more. Now the peacful little island, is surrounded by an errie world that wishes to consume them. And out of that strange, human world, comes problems that the little imps don't know how to solve. Seven little imps wish to cast aside fear, leave their villages and discover more about their world and what happened to the humans, while trying to appease the wishes of their villages.
The characters are all imps ( tiny mischievous devils) who were all representing the vices of mankind also known as the seven deadly sins. With no humans to torment and influence, they decide to find positive attributes to their vices. The build villages that cater to their ideas and realized that the other villages not only have different views, but resources that can help them. No other vice is stronger than the other, so they begin to trade with one another to avoid conflict and learn from one another. They begin to diversify their own villages, realizing that with different influences and points of views, their own lives can grow. However, some richer imps like greed and gluttony tend to take advantage of the poorer imps, like sloth and wrath imps, who live in less ideal places, by pushing them out by hoarding resources and isolating the imps in certain villages, like pride, who do not wish to interact with the other imps due to their drastic cultural difference and beliefs.
What are Imps?
What are the Seven Deadly Sins?
(The videos are for educational purposes and are not owned by me, nor do they reflect my religious beliefs or are here to influence yours. The videos are to help demonstrate the imp's personalities.)
How much do you like the subject matter of this website? The content is confusing. Not at all. A little. Kind of. A lot. I am unsure.


Bus Mall Market Research
Please enter your name, session number and product display:
Your name:
Focus Group Session
Session 1
Session 2
Session 3
Session 4
Please choose number of products to display
3
6
9
Submit


My name is Freya and I live around the south coast of Wellington.
Sprint 1's Core work has provoked thought about what things I actually value in myself and others such as honesty, integrity, listening, order and fun. Reflecting on these things has grown a new appreciation and given me something else to notice and enjoy everyday.
It has also highlighted things I struggle with which is useful to acknowledge and keep in mind when coming up against a challenge so I can respond in an understanding way towards myself.
I am attending Dev Academy because I love learning, want to up-skill myself so I can have more flexibility and freedom in my life, allow me to be involved in awesome projects and to be able to further my passion for environmental sustainability via funds. I am also taking this course because I know it will be a huge challenge for me for multiple reasons and I want to prove to myself that I can do it.


Industries Minister Nurul Majid Mahmud Humayun says that the fire incident in Chawkbazar area of Old Dhaka last night was caused by explosion of LP gas cylinder and not by chemicals stored in the warehouse.


Located between the major cities of Osaka and Kobe, the city stretches from the stores of the Inland Sea to the steep slopes of Mount Rokko.


WordPress theme. Without WordPress.
PressLess is a WordPress theme without WordPress. It uses Vue.js to load posts and pages via the WordPress REST API. When the theme is finished you can find the latest posts below or view some pages via the menu at the top of this page.


A ConflictingPersistentDataException
while starting up persistent members indicates that you have multiple copies of some persistent data, and Geode cannot determine which copy to use.
Normally Geode uses metadata to determine automatically which copy of persistent data to use. Along with the region data, each member persists a list of other members that are hosting the region and whether their data is up to date. A ConflictingPersistentDataException
happens when two members compare their metadata and find that it is inconsistent. The members either don’t know about each other, or they both think the other member has stale data.
The following sections describe scenarios that can cause ConflictingPersistentDataException
s in Geode and how to resolve the conflict.
Independently Created Copies
Trying to merge two independently created distributed systems into a single distributed system will cause a ConflictingPersistentDataException
.

- Create two different distributed systems by having members connect to different locators that are not aware of each other.
- Shut down all persistent members and then start up a different set of brand new persistent members.
Geode will not automatically merge independently created data for the same region. Instead, you need to export the data from one of the systems and import it into the other system. See the section Cache and Region Snapshots for instructions on how to export data from one system and import it into another.
Starting New Members First
Starting a brand new member that has no persistent data before starting older members with persistent data can cause a ConflictingPersistentDataException
.
One accidental way this can happen is to shut the system down, add a new member to the startup scripts, and start all members in parallel. By chance, the new member may start first. The issue is that the new member will create an empty, independent copy of the data before the older members start up. Geode will be treat this situation like the Independently Created Copies case.
In this case the fix is simply to move aside or delete the persistent files for the new member, shut down the new member and then restart the older members. When the older members have fully recovered, then restart the new member.
A Network Failure Occurs and Network Partitioning Detection is Disabled
When enable-network-partition-detection
is set to the default value of true, Geode will detect a network partition and shut down unreachable members to prevent a network partition (“split brain”) from occurring. No conflicts should occur when the system is healed.
However if enable-network-partition-detection
is false, Geode will not detect the network partition. Instead, each side of the network partition will end up recording that the other side of the partition has stale data. When the partition is healed and persistent members are restarted, the members will report a conflict because both sides of the partition think the other members are stale.
In some cases it may be possible to choose between sides of the network partition and just keep the data from one side of the partition. Otherwise you may need to salvage data and import it into a fresh system.
Salvaging Data
If you receive a ConflictingPersistentDataException
, you will not be able to start all of your members and have them join the same distributed system. You have some members with conflicting data.
First, see if there is part of the system that you can recover. For example if you just added some new members to the system, try to start up without including those members.
For the remaining members you can extract data from the persistent files on those members and import the data.
To extract data from the persistent files, use the gfsh export offline-disk-store
command.
gfsh> export offline-disk-store --name=MyDiskStore --disk-dirs=./mydir --dir=./outputdir
This will produce a set of snapshot files. Those snapshot files can be imported into a running system using:
gfsh> import data --region=/myregion --file=./outputdir/snapshot-snapshotTest-test0.gfd --member=server1


View the full library of solutions.
Minimum Requirements for Closed Captioning Text
Colors
The minimum colors required are white, black, red, green, blue, yellow, magenta, and cyan.
Color Choices
- font color
- font highlight color
- background color
- window color
Opacity Choices
- font opacity
- font highlight opacity
- background opacity
- window opacity


Here you can find solutions for various
coding/algorithmic problems and many useful
resources for learning algorithms and
data structures.
Also, this repo will be updated with new solutions and resources from time
to time.
Note that this repo is meant to be used for learning and researching purposes only and it is not meant to be used for production.
Algorithms and data structures are not language-specific (it’s true that
some languages are faster, and some are easier to use), but if you are
good with the logic and pseudocode, any language would be good.
So I’ve decided to use
Python
because I think it’s very close to pseudocode and it’s easily readable (so
it’ll be easy for someone from another environment to implement the same
solutions).
As I said previously, all solutions are written in
Python (more precisely,
Python 3), using the
Built-in Functions
(print, len, range, sorted, sum, min, max, etc…) and a few modules from
the
Python Standard Library
like:
So, to execute these solutions there is no need from installing any
external packages.
Coding style and name conventions are described in the official
PEP8 page.
Note that I’m not the author of these problems, they are from sites like LeetCode (you can find more than 40 sites like this in the Training Sites section). Only the solutions and explanations are mine. If you find any bug or incorrect implementation (or faster/better implementation) in this repo, please let me know by opening an issue or pull request.
For easier navigation into the solutions, each file with a solution in this repo will have the following template:
'''
Problem Name
Problem explanation.
Input: XXX
Output: XXX
Output explanation: XXX
=========================================
Solution 1 explanation.
Time Complexity: O(X)
Space Complexity: O(X)
Solution 2 explanation.
(some of the problems are solved in more than one way)
Time Complexity: O(X)
Space Complexity: O(X)
'''
##############
# Solution 1 #
##############
def name_of_solution_1(params):
# description of code
pass
##############
# Solution 2 #
##############
def name_of_solution_2(params):
# description of code
pass
###########
# Testing #
###########
# Test 1
# Correct result => 'result1'
test_val = 'example1'
print(name_of_solution_1(test_val))
print(name_of_solution_2(test_val))
# Test 2
# Correct result => 'result2'
test_val = 'example2'
print(name_of_solution_1(test_val))
print(name_of_solution_2(test_val))
Note that here I’m using the simplest way of testing, printing the results using the print method. Why? Because I think that the bigger part of the users of this repo isn’t familiar with unit testing and I wanted this part to be intuitive. Btw, I strongly recommend using some unit testing framework for this kind of testing. The Python Standard Library contains a great framework for unit testing called unittest, or you can install some third-party unit testing framework like pytest.
Each solution/problem in this repo belongs to one of these categories:
The learning resources are divided into 4 categories: Courses, Books, Training Sites, Other Resources.
Collection of free courses from one of the best CS universities.
Standford University
Princeton University
UC San Diego
MIT University
Harvard University
Several books that have made an impression on me:
If the problems from LeetCode are not enough and you need more problems like those, you can find much more on these platforms:


#include <Nano1X2Series.h>
@addtogroup LCD LCD Controller(LCD)
Memory Mapped Structure for LCD Controller
Definition at line 5241 of file Nano1X2Series.h.
◆ CTL
CTL
Offset: 0x00 LCD Control Register
| Bits | Field | Descriptions |
| [0] | EN | LCD Enable |
| | 0 = LCD controller operation Disabled. |
| | 1 = LCD controller operation Enabled. |
| [3:1] | MUX | Mux Select |
| | 000 = Static. |
| | 001 = 1/2 duty. |
| | 010 = 1/3 duty. |
| | 011 = 1/4 duty. |
| | 100 = 1/5 duty. |
| | 101 = 1/6 duty. |
| | 110 = Reserved. |
| | 111 = Reserved. |
| | Note: User does not need to set PD_H_MFP bit field, but only to set the MUX bit field to switch LCD_SEG0 and LCD_SEG1 to LCD_COM4 and LCD_COM5. |
| [6:4] | FREQ | LCD Frequency Selection |
| | 000 = LCD_CLK Divided by 32. |
| | 001 = LCD_CLK Divided by 64. |
| | 010 = LCD_CLK Divided by 96. |
| | 011 = LCD_CLK Divided by 128. |
| | 100 = LCD_CLK Divided by 192. |
| | 101 = LCD_CLK Divided by 256. |
| | 110 = LCD_CLK Divided by 384. |
| | 111 = LCD_CLK Divided by 512. |
| [7] | BLINK | LCD Blinking Enable |
| | 0 = Blinking Disabled. |
| | 1 = Blinking Enabled. |
| [8] | PDDISP_EN | Power Down Display Enable |
| | The LCD can be programmed to be displayed or not be displayed at power down state by PDDISP_EN setting. |
| | 0 = LCD display Disabled ( LCD is put out) at power down state. |
| | 1 = LCD display Enabled (LCD keeps the display) at power down state. |
| [9] | PDINT_EN | Power Down Interrupt Enable |
| | If the power down request is triggered from system management, LCD controller will execute the frame completely to avoid the DC component. |
| | When the frame is executed completely, the LCD power down interrupt signal is generated to inform system management that LCD controller is ready to enter power down state, if PDINT_EN is set to 1. |
| | Otherwise, if PDINT_EN is set to 0, the LCD power down interrupt signal is blocked and the interrupt is disabled to send to system management. |
| | 0 = Power Down Interrupt Disabled. |
| | 1 = Power Down Interrupt Enabled. |
Definition at line 5288 of file Nano1X2Series.h.
◆ DISPCTL
| __IO uint32_t LCD_T::DISPCTL |
DISPCTL
Offset: 0x04 LCD Display Control Register
| Bits | Field | Descriptions |
| [0] | CPUMP_EN | Charge Pump Enable |
| | 0 = Disabled. |
| | 1 = Enabled. |
| [2:1] | BIAS_SEL | Bias Selection |
| | 00 = Static. |
| | 01 = 1/2 Bias. |
| | 10 = 1/3 Bias. |
| | 11 = Reserved. |
| [4] | IBRL_EN | Internal Bias Reference Ladder Enable |
| | 0 = Bias reference ladder Disabled. |
| | 1 = Bias reference ladder Enabled. |
| [6] | BV_SEL | Bias Voltage Type Selection |
| | 0 = C-Type bias mode. Bias voltage source from internal bias generator. |
| | 1 = R-Type bias mode. Bias voltage source from external bias generator. |
| | Note: The external resistor ladder should be connected to the V1 pin, V2 pin, V3 pin and VSS. |
| | The VLCD pin should also be connected to VDD. |
| [10:8] | CPUMP_VOL_SET | Charge Pump Voltage Selection |
| | 000 = 2.7V. |
| | 001 = 2.8V. |
| | 010 = 2.9V. |
| | 011 = 3.0V. |
| | 100 = 3.1V. |
| | 101 = 3.2V. |
| | 110 = 3.3V. |
| | 111 = 3.4V. |
| [13:11] | CPUMP_FREQ | Charge Pump Frequency Selection |
| | 000 = LCD_CLK. |
| | 001 = LCD_CLK/2. |
| | 010 = LCD_CLK/4. |
| | 011 = LCD_CLK/8. |
| | 100 = LCD_CLK/16. |
| | 101 = LCD_CLK/32. |
| | 110 = LCD_CLK/64. |
| | 111 = LCD_CLK/128. |
| [16] | Ext_C | Ext_C Mode Selection |
| | This mode is similar to C-type LCD mode, but the operation current is lower than C-type mode. |
| | The control register setting is same with C-type mode except this bit is set to "1". |
| | 0 = Disable. |
| | 1 = Enable. |
| [18:17] | Res_Sel | R-Type Resistor Value Selection |
| | The LCD operation current will be different when we select different R-type resistor value. |
| | 00 = 200K Ohm. |
| | 01 = 300K Ohm. |
| | 10 = Reserved. |
| | 11 = 400K Ohm. |
Definition at line 5343 of file Nano1X2Series.h.
◆ FCR
FCR
Offset: 0x30 LCD frame counter control register
| Bits | Field | Descriptions |
| [0] | FCEN | LCD Frame Counter Enable |
| | 0 = Disabled. |
| | 1 = Enabled. |
| [1] | FCINTEN | LCD Frame Counter Interrupt Enable |
| | 0 = Frame counter interrupt Disabled. |
| | 1 = Frame counter interrupt Enabled. |
| [3:2] | PRESCL | Frame Counter Pre-Scaler Value |
| | 00 = CLKframe/1. |
| | 01 = CLKframe/2. |
| | 10 = CLKframe/4. |
| | 11 = CLKframe/8. |
| [9:4] | FCV | Frame Counter Top Value |
| | These 6 bits contain the top value of the Frame counter. |
Definition at line 5530 of file Nano1X2Series.h.
◆ FCSTS
| __IO uint32_t LCD_T::FCSTS |
FCSTS
Offset: 0x34 LCD frame counter status
| Bits | Field | Descriptions |
| [0] | FCSTS | LCD Frame Counter Status |
| | 0 = Frame counter value does not reach FCV (Frame Count TOP value). |
| | 1 = Frame counter value reaches FCV (Frame Count TOP value). |
| | If the FCINTEN is s enabled, the frame counter overflow Interrupt is generated. |
| [1] | PDSTS | Power-Down Interrupt Status |
| | 0 = Inform system manager that LCD controller is not ready to enter power-down state until this bit becomes 1 if power down is set and one frame is not executed completely. |
| | 1 = Inform system manager that LCD controller is ready to enter power-down state if power down is set and one frame is executed completely |
Definition at line 5547 of file Nano1X2Series.h.
◆ MEM_0
| __IO uint32_t LCD_T::MEM_0 |
MEM_0
Offset: 0x08 LCD SEG3 ~ SEG0 data
| Bits | Field | Descriptions |
| [5:0] | SEG_0_4x | SEG_0_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [14:8] | SEG_1_4x | SEG_1_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [21:16] | SEG_2_4x | SEG_2_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [29:24] | SEG_3_4x | SEG_3_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
Definition at line 5361 of file Nano1X2Series.h.
◆ MEM_1
| __IO uint32_t LCD_T::MEM_1 |
MEM_1
Offset: 0x0C LCD SEG7 ~ SEG4 data
| Bits | Field | Descriptions |
| [5:0] | SEG_0_4x | SEG_0_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [14:8] | SEG_1_4x | SEG_1_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [21:16] | SEG_2_4x | SEG_2_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [29:24] | SEG_3_4x | SEG_3_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
Definition at line 5379 of file Nano1X2Series.h.
◆ MEM_2
| __IO uint32_t LCD_T::MEM_2 |
MEM_2
Offset: 0x10 LCD SEG11 ~ SEG8 data
| Bits | Field | Descriptions |
| [5:0] | SEG_0_4x | SEG_0_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [14:8] | SEG_1_4x | SEG_1_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [21:16] | SEG_2_4x | SEG_2_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [29:24] | SEG_3_4x | SEG_3_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
Definition at line 5397 of file Nano1X2Series.h.
◆ MEM_3
| __IO uint32_t LCD_T::MEM_3 |
MEM_3
Offset: 0x14 LCD SEG15 ~ SEG12 data
| Bits | Field | Descriptions |
| [5:0] | SEG_0_4x | SEG_0_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [14:8] | SEG_1_4x | SEG_1_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [21:16] | SEG_2_4x | SEG_2_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [29:24] | SEG_3_4x | SEG_3_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
Definition at line 5415 of file Nano1X2Series.h.
◆ MEM_4
| __IO uint32_t LCD_T::MEM_4 |
MEM_4
Offset: 0x18 LCD SEG19 ~ SEG16 data
| Bits | Field | Descriptions |
| [5:0] | SEG_0_4x | SEG_0_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [14:8] | SEG_1_4x | SEG_1_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [21:16] | SEG_2_4x | SEG_2_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [29:24] | SEG_3_4x | SEG_3_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
Definition at line 5433 of file Nano1X2Series.h.
◆ MEM_5
| __IO uint32_t LCD_T::MEM_5 |
MEM_5
Offset: 0x1C LCD SEG23 ~ SEG20 data
| Bits | Field | Descriptions |
| [5:0] | SEG_0_4x | SEG_0_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [14:8] | SEG_1_4x | SEG_1_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [21:16] | SEG_2_4x | SEG_2_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [29:24] | SEG_3_4x | SEG_3_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
Definition at line 5451 of file Nano1X2Series.h.
◆ MEM_6
| __IO uint32_t LCD_T::MEM_6 |
MEM_6
Offset: 0x20 LCD SEG27 ~ SEG24 data
| Bits | Field | Descriptions |
| [5:0] | SEG_0_4x | SEG_0_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [14:8] | SEG_1_4x | SEG_1_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [21:16] | SEG_2_4x | SEG_2_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [29:24] | SEG_3_4x | SEG_3_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
Definition at line 5469 of file Nano1X2Series.h.
◆ MEM_7
| __IO uint32_t LCD_T::MEM_7 |
MEM_7
Offset: 0x24 LCD SEG31 ~ SEG28 data
| Bits | Field | Descriptions |
| [5:0] | SEG_0_4x | SEG_0_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [14:8] | SEG_1_4x | SEG_1_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [21:16] | SEG_2_4x | SEG_2_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [29:24] | SEG_3_4x | SEG_3_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
Definition at line 5487 of file Nano1X2Series.h.
◆ MEM_8
| __IO uint32_t LCD_T::MEM_8 |
MEM_8
Offset: 0x28 LCD SEG35 ~ SEG32 data
| Bits | Field | Descriptions |
| [5:0] | SEG_0_4x | SEG_0_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [14:8] | SEG_1_4x | SEG_1_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [21:16] | SEG_2_4x | SEG_2_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
| [29:24] | SEG_3_4x | SEG_3_4x DATA for COM0 ~ COM5 (x= 0 ~ 8) |
| | LCD display data |
Definition at line 5505 of file Nano1X2Series.h.
◆ RESERVE0
| uint32_t LCD_T::RESERVE0[1] |
The documentation for this struct was generated from the following file:


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|
TTK
|
Classes | |
| struct | ArcData |
| class | ArcRegion |
| class | AtomicUF |
| struct | Comparison |
| struct | CurrentState |
| class | FTMTree |
| class | FTMTree_CT |
| class | FTMTree_MT |
| class | FTMTreePP |
| struct | LocalFTM |
| class | Node |
| struct | NodeData |
| struct | Params |
| struct | Region |
| struct | Scalars |
| class | Segment |
| class | Segments |
| struct | SharedData |
| class | SuperArc |
| struct | TreeData |
| struct | VertData |
| struct | WrapperData |
Typedefs | |
| using | idSuperArc = long unsigned int |
| SuperArc index in vect_superArcs_. More... | |
| using | idNode = unsigned int |
| Node index in vect_nodes_. More... | |
| using | idCorresp = long long int |
| type used to recover Node/Arc in vert2tree SIGNED ONLY More... | |
| using | idSegment = idSuperArc |
| for the segmentation, we have an array of segment containing area of the mesh More... | |
| using | numThread = ThreadId |
| type use to store threads related numbers More... | |
| using | ufDataType = long int |
| type stored by UnionFind More... | |
| using | idThread = ThreadId |
| manage number of threads More... | |
| using | idTask = TaskId |
| for task identifiers More... | |
| using | valence = SimplexId |
| for vertex up/down valence More... | |
| using | VertCompFN = std::function< bool(SimplexId, SimplexId)> |
| using | SetPropagation = std::set< SimplexId, VertCompFN > |
| using | SetCompFN = std::function< bool(const SetPropagation &, const SetPropagation &)> |
| using | segm_it = std::vector< SimplexId >::iterator |
| using | segm_rev_it = std::vector< SimplexId >::reverse_iterator |
| using | segm_const_it = std::vector< SimplexId >::const_iterator |
| using | segm_const_rev_it = std::vector< SimplexId >::const_reverse_iterator |
| using | UF = AtomicUF * |
Enumerations | |
| enum | TreeType : char { Join = 0, Split = 1, Contour = 2, Join_Split = 3 } |
| enum | SimplifMethod : char { Persist = 0, Span = 1, NbVert = 2, NbArc = 3 } |
| enum | ComponentState : char { Visible, Hidden, Pruned, Merged } |
| enum | TreeComponent { TreeComponent::Arc = -1, TreeComponent::Local_minimum, TreeComponent::Saddle1, TreeComponent::Saddle2, TreeComponent::Local_maximum } |
| enum | ArcType : char { ArcType::Min_arc = 0, ArcType::Max_arc, ArcType::Saddle1_arc, ArcType::Saddle2_arc, ArcType::Saddle1_saddle2_arc } |
Functions | |
| std::ostream & | operator<< (std::ostream &o, Node const &n) |
| std::ostream & | operator<< (std::ostream &o, SuperArc const &a) |
| using ttk::ftm::idCorresp = typedef long long int |
type used to recover Node/Arc in vert2tree SIGNED ONLY
Definition at line 37 of file FTMDataTypes.h.
| using ttk::ftm::idNode = typedef unsigned int |
Node index in vect_nodes_.
Definition at line 32 of file FTMDataTypes.h.
| using ttk::ftm::idSegment = typedef idSuperArc |
for the segmentation, we have an array of segment containing area of the mesh
Definition at line 41 of file FTMDataTypes.h.
| using ttk::ftm::idSuperArc = typedef long unsigned int |
SuperArc index in vect_superArcs_.
Definition at line 30 of file FTMDataTypes.h.
| using ttk::ftm::idTask = typedef TaskId |
for task identifiers
Definition at line 53 of file FTMDataTypes.h.
| using ttk::ftm::idThread = typedef ThreadId |
manage number of threads
Definition at line 50 of file FTMDataTypes.h.
| using ttk::ftm::numThread = typedef ThreadId |
type use to store threads related numbers
Definition at line 44 of file FTMDataTypes.h.
| using ttk::ftm::segm_const_it = typedef std::vector<SimplexId>::const_iterator |
Definition at line 147 of file FTMStructures.h.
| using ttk::ftm::segm_const_rev_it = typedef std::vector<SimplexId>::const_reverse_iterator |
Definition at line 148 of file FTMStructures.h.
| using ttk::ftm::segm_it = typedef std::vector<SimplexId>::iterator |
Definition at line 145 of file FTMStructures.h.
| using ttk::ftm::segm_rev_it = typedef std::vector<SimplexId>::reverse_iterator |
Definition at line 146 of file FTMStructures.h.
| using ttk::ftm::SetCompFN = typedef std::function<bool(const SetPropagation &, const SetPropagation &)> |
Definition at line 63 of file FTMDataTypes.h.
| using ttk::ftm::SetPropagation = typedef std::set<SimplexId, VertCompFN> |
Definition at line 61 of file FTMDataTypes.h.
| using ttk::ftm::UF = typedef AtomicUF * |
Definition at line 56 of file FTMTree_MT.h.
| using ttk::ftm::ufDataType = typedef long int |
type stored by UnionFind
Definition at line 47 of file FTMDataTypes.h.
| using ttk::ftm::valence = typedef SimplexId |
for vertex up/down valence
Definition at line 56 of file FTMDataTypes.h.
| using ttk::ftm::VertCompFN = typedef std::function<bool(SimplexId, SimplexId)> |
Definition at line 60 of file FTMDataTypes.h.
|
strong |
| Enumerator | |
|---|---|
| Min_arc | |
| Max_arc | |
| Saddle1_arc | |
| Saddle2_arc | |
| Saddle1_saddle2_arc |
Definition at line 99 of file FTMDataTypes.h.
| enum ttk::ftm::ComponentState : char |
| Enumerator | |
|---|---|
| Visible | |
| Hidden | |
| Pruned | |
| Merged |
Definition at line 89 of file FTMDataTypes.h.
| enum ttk::ftm::SimplifMethod : char |
| Enumerator | |
|---|---|
| Persist | |
| Span | |
| NbVert | |
| NbArc |
Definition at line 87 of file FTMDataTypes.h.
|
strong |
| Enumerator | |
|---|---|
| Arc | |
| Local_minimum | |
| Saddle1 | |
| Saddle2 | |
| Local_maximum |
Definition at line 91 of file FTMDataTypes.h.
| enum ttk::ftm::TreeType : char |
| Enumerator | |
|---|---|
| Join | |
| Split | |
| Contour | |
| Join_Split |
Definition at line 85 of file FTMDataTypes.h.
| std::ostream& ttk::ftm::operator<< | ( | std::ostream & | o, |
| Node const & | n | ||
| ) |
| std::ostream& ttk::ftm::operator<< | ( | std::ostream & | o, |
| SuperArc const & | a | ||
| ) |


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DONALD TRUMP
Résumé
— I'm the most successful person ever to run for the presidency, by far. Nobody's ever been more successful than me. I'm the most successful person ever to run. Ross Perot isn't successful like me. Romney — I have a Gucci store that's worth more than Romney. Sorry losers and haters, but my IQ is one of the highest — and you all know it!

Experience
-
2017
Became 45th president of the United States
-
2016
Won election race with 77 votes more than Clinton, yippe-kee-yay!
-
2015
Became the most important Republican nominating for presidential election run
-
2009
Changed my mind and became Republican again
-
2005
Started Trump University with Michael Sexton and Jonathan Spitalny: no grades, certificates and other bureaucracy, but program offered courses in real estate, asset management entrepreneurship, and wealth creation. 98% students gave positive reviews while studying, however some of these ungrateful gringos filed lawsuits against Trump University after the course. I had to pay $25 million to settle that...
-
2002
Acquired the former Hotel Delmonico, which was renovated and reopened in 2004 as the Trump Park Avenue. I sure do like my surname!
-
2001
Completed Trump World Tower and joined the Democratic Party
-
1999
Started buying golf courses. They are very cheap, I have 18 of them now in USA and abroad! Also switched to the Reform Party that year
-
1997
Began a multi-building development along the Hudson River, which was named Trump Place. Me and the other investors sold our shares for $1.8 billion and that was then the biggest residential sale in the history of New York City
-
1996
Acquired the Bank of Manhattan Trust Building, which was a vacant seventy-one story skyscraper on Wall Street. After an extensive renovation, the high-rise was renamed the Trump Building
-
1994
Refurbished the Gulf and Western Building on Columbus Circle, turned it into a 44-story luxury property known as Trump International Hotel and Tower
-
1989 ― 1992
Ran a second hand airline called Trump Shuttle. What a waste of money, I had to sell it, now it is American Airlines Shuttle, btw
-
1988
Acquired the Plaza Hotel in Manhattan for $407 million, invested $50 million to restore the building and boosted it from three-star to four-star hotel (I called it "the Mona Lisa" and sold it for a lot of cash in 1995)
-
1987
Registered as a Republican
-
1985
I bought the Mar-a-Lago estate in Palm Beach and turned it into a private club.You can join if you pay $200K initiation fee (and there is another annual fee for members, it is very expensive to be rich). That year I also opened a casino called Trump Castle. My wife Ivana became a manager, because she is another Trump!
-
1983
I purchased New Jersey Generals, an American football team. I changed team's play schedule and filed an antitrust lawsuit against United States Football League, so NJG are not doing so good now, but back then they were great!
-
- Payed a crew of undocumented Polish workers to demolish the historic Bonwit Teller store, including art deco features.
- Architectural critic Paul Goldberger say in 1983 that the tower's atrium is "the most pleasant interior public space to be completed in New York in some years".
- Started a repair job on the Wollman Rink in Central Park.
- Completed the work in three months for $1.95 million, which was $775,000 less than the initial budget!
-
1978
Purchased a 50 percent stake in the derelict Commodore Hotel and turned it into the Grand Hyatt Hotel in 2 years of remodeling
-
1971
Was promoted to president of the company and renamed it The Trump Organization
-
1968
Began my career at my father's real estate development company, Elizabeth Trump & Son
Personal Life
-
2006
Melania gained United States citizenship and gave birth to our son, Barron
-
2005
Married her
-
1998
Met my future third wife, Melania Knauss
-
1999
Divorced Marla ¯\_(ツ)_/¯
-
1993
Married Marla and had a daughter, we named her Tiffany, because I like Tiffany & Company jewellery as well
-
1992
Started affair with Marla Maples, so I had to divorce. God knows I love nice women, I love them all!
-
1984
Birth of Eric Trump
-
1982
Birth of Ivanka Trump
-
1977
My first son is born. I called him Donald, of course!
-
1977
Married Czech model Ivana Zelníčková
Languages
Skills
Character
Likes😃
Dislikes😞
Wants🥺
Education
-
1959
-
1st September 1966 – 30th June 1968


| DOSE: |
50 mg |
smoked |
DMT |
(powder / crystals) |
After hearing and reading so much about N,N-DMT, I decided to move forward on a quest to aquire some. As for why I sought it out? I felt that my previous psychedelic experiences fell short of the kind of insight that I had been seeking. I had always had a glimpse through the proverbial 'doorway' while on psychedelics, but I never quite got through to really learn something spiritually - even at higher doses. Therefore, DMT was my obvious next step. As a side note, I would say, as many others have said before me, that this substance is absolutely not to be used for recreational purposes.
I had decided to have my experience over at my girlfriend's house, in the late afternoon. I had spent some time earlier in the day meditating on what was to come, what I might glean from it and trying to reduce my anxiety. Another friend of mine who had experienced DMT before (we'll call him 'Z') had agreed to be my 'sitter' and pipe holder. My girlfriend (we'll call her 'D') was also present.
I thought it would be good to do it in D's bedroom, on her bed. We lit some candles and dimmed the lights. My nervousness had come back a bit, I was about to smoke one of the world's most powerful psychedelics and was a bit scared by it!  It definately tased like burning plastic, as I had suspected, but it wasn't so bad that I couldn't deal with it.
T+ 5 seconds:
I'm still holding the smoke, but can already feel it coming on, it's so fast! The world around me begins to pixilate, kind of like a computer graphic dropping in resolution. I begin to here a popping sound, a bit like someone stepping on packing peanuts or something.
T+ 10 seconds:
I exhale the smoke from my first hit and motion for Z to move the pipe back to my mouth for a second. I'm barely conscious by now but desperately trying to hold on for my second toke. I weakly take a second toke, probably half the size of the first one. Looking at Z, I see that he's losing more resolution fast, but becoming VERY colorful and almost geometric, like stained glass. I don't remember exhaling at this point, but obviously I must have.
T+ 20 seconds:
I fell back onto the bed at this point, my head in D's lap. The world is gone. The popping sound abruptly stops and I am launched through the multiplex at the speed of light, shooting through a vortex, a bit like a black hole, except the vortex is brilliantly colored, again in a pulsing geometric type way. It seems that I'm in this vortex for an eternity. Also, I feel a LOT of fear right now, like my soul is leaving my body and is being taken somewhere else. I remember having a vague notion of 'Oh no, I've overdosed, I certainly can't live through this now.' Time has become meaningless, quick but forever. This is the most alien thing I have ever felt.
All of a sudden the travel stops, or maybe I just got through the vortex and was travelling through open blackness, but there was no point of reference to guage movement. I'm in the middle of something... Or maybe something is in the middle of me? Throughout the blackness I hear giggling, like children giggling. I can't see them though. But for some reason my fear subsides at this point, I become curious. I want to find the source of the giggling, but can't even imagine how to begin searching. Just then I feel the presence of something, or several somethings. The gigglers?
Then I move... I'm in my bathroom at my parent's old house accross the country. I'm on the floor, and I've become a baby again, just a few months old. Silence. Again I feel the presence. I look around at the shower, toilet, floor, and see all these things for the first time again. That is to say, I didn't recognize WHAT a bathtub really was, I was just amazed that it was there. I didn't even realize I was in my old bathroom until after the experience.
The bathroom melts away to blackness again, and I feel myself rapidly aging. Soon I was 100's of years old, and my soul had the feeling it was dead. No fear, just sad resignation at this realization. So this is death. I feel like I'm under warm water or something, or maybe in the middle of a bunch of soft furry animals or beings. Still silence.
T+ 8 minutes:
Suddenly I return to my body, and I'm cold. Shivering, in fact. I found that Z had put a blanket over me, thankfully. Looking up at D, her face is very wierd and distorted, like a caricature of herself, composed of pulsing colored glass. Kind of feels like 5 hits of LSD or something. I want to start talking now, but I know I can't. I also started crying a little, I was so overwhelmed by it all. I'm also feeling a kind of pure erotic love at this point, I'm not sure how to describe it, but it really feels good!
T+ 9 minutes:
I'm back, and start relating my experience to Z and D. My speech is a bit broken, but I'm getting it out. I still feel like I'm on like 2 hits of LSD.
T+ 13 minutes:
I'm almost back to baseline, walking around and eating a burrito.
T+ 45 minutes:
I'm completely sober and have fully returned to baseline.
Summary:
I have a LOT of respect for this drug. It seems like although fear wasn't necessarily a large part of my experience, it could have been. I feel like I have learned to appreciate life and have a newfound lust for life! Perhaps something was silently showing me the truth of beauty, the amazing simpleness of things that have been over-intellectualized. What this something was, I can't really know, but it was present for most of my experience, whatever it was.
I don't think I'll be smoking DMT again for a long while, if ever again. I'm VERY glad I did, but I think I got what I needed from it, and it will remain my ally in this way still. We may just never meet again. I recommend the experience to all those who think they are ready and might benefit from it. It is NOT recreational, and NOT something to do on a whim. If you need it, you will find it or it you. If you finally get to the point where you're ready for it, have a close 1 or 2 friends/lovers/family memebrs with you to be there when you re-integrate and to hold the pipe, it really helped me.
Peace, and be careful.
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.


Hexobfusc v0.1a
decode from hex
decode from binary hex file
decode from image file
encode to hex
encode to binary hex file
encode to image file


Cooking measurement conversions
Pounds
Convert to Kgs!
The weight is :
kgs
Litres
Convert to Gallons!
The volume is :
Gallons
Ounces
Convert to Grams!
The weight is :
grams


This is a demo site for Spark, the dual-themed static blog site generator. The pages for this site are included in the GitHub repo, which can be found here.


Minimal River.js Feed Reader
Load a sample river and enter a one word search term to filter the results
Credits:
Dave Winer sample code
| Blog post:
Minimal River.js Reader
| Code:
Github
Enter a search term and click Submit:
Submit
Wating for feeds to load....


Art Curator
Abstract art or child's drawing?
×
take screenshot
Predict Akira's answer
Child's art
Abstarct art
Please wait... I need to find my glasses...
I'm 99.53252% sure you are right
It's an abstract art
- Akimoto Akira
Upload image
or
Use camera


Five arrested for crimes in Oman
by Times News ServiceMuscat: The Royal Oman Police (ROP) have arrested four expatriates for theft and another for sabotage and theft of a number of shops.
The ROP said in a statement: "Muscat Police Command arrested a person for vandalism and theft from several shops and the Department of Crime Prevention seized four persons of Arab nationality who are accused of theft."
ROP added: "Legal procedures against the accused are underway."


Time
Type
Amount
10:22
Purchase
$10.00
12:12
Purchase
$12.50
14:11
Expense
$9.70
16:11
Expense
$10.70


dials.image_viewer¶
Introduction¶
This program can be used for viewing diffraction images, optionally overlayed with the results of spot finding, indexing or integration.
Examples:
dials.image_viewer image.cbf
dials.image_viewer datablock.json
dials.image_viewer datablock.json strong.pickle
dials.image_viewer datablock.json integrated.pickle
dials.image_viewer experiments.json
Basic parameters¶
brightness = 100 color_scheme = *grayscale rainbow heatmap invert show_beam_center = True show_resolution_rings = False show_ice_rings = False show_ctr_mass = True show_max_pix = True show_all_pix = True show_shoebox = True show_predictions = True show_miller_indices = False show_indexed = False show_integrated = False show_mask = False show_mask2 = False show_basis_vectors = True display = *image mean variance dispersion sigma_b sigma_s threshold global_threshold nsigma_b = 6 nsigma_s = 3 global_threshold = 0 kernel_size = 3,3 min_local = 2 gain = 1 d_min = None mask = None powder_arcs { show = False code = None } calibrate_silver = False calibrate_pdb { code = None d_min = 20. } calibrate_unitcell { unitcell = None d_min = 20. spacegroup = None } masking { border = 0 use_trusted_range = True d_min = None d_max = None resolution_range = None untrusted { panel = 0 circle = None rectangle = None polygon = None pixel = None } ice_rings { filter = False d_min = None } } output { mask = mask.pickle mask_params = mask.phil } predict_reflections = False profile { algorithm = gaussian_rs *gaussian_rs gaussian_rs { scan_varying = False min_spots { overall = 50 per_degree = 20 } sigma_m_algorithm = basic *extended parameters { sigma_b = None sigma_m = None } filter { min_zeta = 0.05 } fitting { scan_step = 5 grid_size = 5 threshold = 0.02 grid_method = single *regular_grid circular_grid spherical_grid fit_method = *reciprocal_space detector_space estimator = *least_squares maximum_likelihood } } gaussian_rs { scan_varying = False min_spots { overall = 50 per_degree = 20 } sigma_m_algorithm = basic *extended parameters { sigma_b = None sigma_m = None } filter { min_zeta = 0.05 } fitting { scan_step = 5 grid_size = 5 threshold = 0.02 grid_method = single *regular_grid circular_grid spherical_grid fit_method = *reciprocal_space detector_space estimator = *least_squares maximum_likelihood } } } prediction { d_min = None d_max = None margin = 1 force_static = False padding = 1.0 }
Full parameter definitions¶
brightness = 100 .type = int color_scheme = *grayscale rainbow heatmap invert .type = choice show_beam_center = True .type = bool show_resolution_rings = False .type = bool show_ice_rings = False .type = bool show_ctr_mass = True .type = bool show_max_pix = True .type = bool show_all_pix = True .type = bool show_shoebox = True .type = bool show_predictions = True .type = bool show_miller_indices = False .type = bool show_indexed = False .type = bool show_integrated = False .type = bool show_mask = False .type = bool show_mask2 = False .type = bool show_basis_vectors = True .type = bool display = *image mean variance dispersion sigma_b sigma_s threshold global_threshold .type = choice nsigma_b = 6 .type = float(value_min=0) nsigma_s = 3 .type = float(value_min=0) global_threshold = 0 .type = float(value_min=0) kernel_size = 3,3 .type = ints(size=2, value_min=1) min_local = 2 .type = int gain = 1 .help = "Set gain for the thresholding algorithm. This does not override the" "detector's panel gain, but acts as a multiplier for it." .type = float(value_min=0) sum_images = 1 .type = int(value_min=1) .expert_level = 2 d_min = None .type = float(value_min=0) mask = None .help = "path to mask pickle file" .type = path powder_arcs { show = False .help = "show powder arcs calculated from PDB file." .type = bool code = None .help = "PDB code (4 characters) for file; fetch it from the Internet." .type = str } calibrate_silver = False .help = "Open special GUI for distance/metrology from silver behenate." .type = bool calibrate_pdb { code = None .help = "Option is mutually exclusive with calibrate silver, unit cell and" "powder arcs options." .type = str d_min = 20. .help = "Limiting resolution to calculate powder rings" .type = float } calibrate_unitcell { unitcell = None .help = "Option is mutually exclusive with calibrate silver, pdb and" "powder arcs options." .type = unit_cell d_min = 20. .help = "Limiting resolution to calculate powder rings" .type = float spacegroup = None .help = "Specify spacegroup for the unit cell" .type = str } masking { border = 0 .help = "The border around the edge of the image." .type = int use_trusted_range = True .help = "Use the trusted range to mask bad pixels." .type = bool d_min = None .help = "The high resolution limit in Angstrom for a pixel to be accepted" "by the filtering algorithm." .type = float(value_min=0) d_max = None .help = "The low resolution limit in Angstrom for a pixel to be accepted" "by the filtering algorithm." .type = float(value_min=0) resolution_range = None .help = "an untrusted resolution range" .type = floats(size=2) .multiple = True untrusted .multiple = True { panel = 0 .help = "The panel number" .type = int circle = None .help = "An untrusted circle (xc, yc, r)" .type = ints(size=3) rectangle = None .help = "An untrusted rectangle (x0, x1, y0, y1)" .type = ints(size=4) polygon = None .help = "The pixel coordinates (fast, slow) that define the corners of" "the untrusted polygon. Spots whose centroids fall within the" "bounds of the untrusted polygon will be rejected." .type = ints(value_min=0) pixel = None .help = "An untrusted pixel (x, y)" .type = ints(size=2, value_min=0) } ice_rings { filter = False .type = bool unit_cell = 4.498,4.498,7.338,90,90,120 .help = "The unit cell to generate d_spacings for powder rings." .type = unit_cell .expert_level = 1 space_group = 194 .help = "The space group used to generate d_spacings for powder rings." .type = space_group .expert_level = 1 width = 0.002 .help = "The width of an ice ring (in 1/d^2)."  { scan_varying = False .help = "Calculate a scan varying model" .type = bool min_spots .help = "if (total_reflections > overall or reflections_per_degree >" "per_degree) then do the profile modelling." { overall = 50 .help = "The minimum number of spots needed to do the profile" "modelling" .type = int(value_min=0) per_degree = 20 .help = "The minimum number of spots needed to do the profile" "modelling" .type = int(value_min=0) } sigma_m_algorithm = basic *extended .help = "The algorithm to compute mosaicity" .type = choice parameters { sigma_b = None .help = "Override the sigma_b value (degrees)" .type = float(value_min=0) sigma_m = None .help = "Override the sigma_m value (degrees)" .type = float(value_min=0) } filter { min_zeta = 0.05 .help = "Filter reflections by min zeta" .type = float } fitting { scan_step = 5 .help = "Space between profiles in degrees" .type = float grid_size = 5 .help = "The size of the profile grid." .type = int threshold = 0.02 .help = "The threshold to use in reference profile" .type = float grid_method = single *regular_grid circular_grid spherical_grid .help = "Select the profile grid method" .type = choice fit_method = *reciprocal_space detector_space .help = "The fitting method" .type = choice estimator = *least_squares maximum_likelihood .help = "Least squares or maximum likelihood" .type = choice } } gaussian_rs .help = "An extension class implementing a reciprocal space gaussian" "profile model." { scan_varying = False .help = "Calculate a scan varying model" .type = bool min_spots .help = "if (total_reflections > overall or reflections_per_degree >" "per_degree) then do the profile modelling." { overall = 50 .help = "The minimum number of spots needed to do the profile" "modelling" .type = int(value_min=0) per_degree = 20 .help = "The minimum number of spots needed to do the profile" "modelling" .type = int(value_min=0) } sigma_m_algorithm = basic *extended .help = "The algorithm to compute mosaicity" .type = choice parameters { sigma_b = None .help = "Override the sigma_b value (degrees)" .type = float(value_min=0) sigma_m = None .help = "Override the sigma_m value (degrees)" .type = float(value_min=0) } filter { min_zeta = 0.05 .help = "Filter reflections by min zeta" .type = float } fitting { scan_step = 5 .help = "Space between profiles in degrees" .type = float grid_size = 5 .help = "The size of the profile grid." .type = int threshold = 0.02 .help = "The threshold to use in reference profile" .type = float grid_method = single *regular_grid circular_grid spherical_grid .help = "Select the profile grid method" .type = choice fit_method = *reciprocal_space detector_space .help = "The fitting method" .type = choice estimator = *least_squares maximum_likelihood .help = "Least squares or maximum likelihood" .type = choice } } } prediction { d_min = None .help = "The maximum resolution limit" .type = float d_max = None .help = "The minimum resolution limit" .type = float margin = 1 .help = "The margin to use to scan varying prediction" .type = int force_static = False .help = "For scan-varying prediction force scan-static prediction" .type = bool padding = 1.0 .help = "The padding in degrees" .type = float(value_min=0) }


weakest link
meno abels
@fastandfearless
Why do you have trust
in your door keys and lock?
Are broken Locks less trustworthy?
Even Mozilla is not very confident about these locks
What happens to your trust
when you know that there
are copies of your door key?
Could we detect copies?
PHYSICAL World:
It's difficult.
There are never 100% equal objects.
like the signature of
the bullet and the gun.
DIGITAL World:
Has exclusively 100% PERFECT copies.
PHYSICAL copies
do not scale.
DIGITAL copies scale.
And every copy is perfect!
DIGITAL copies
are not distinguishable!
EVERY copy is a perfect copy!
Common Key Scenerio
the perfect "man in the middle" impersonation
DIGITAL UNIVERSE
it is impossible to identify any copied
private keys!
ARE
PRIVATE KEYS
COPIED?
How do we detect copies of private keys from the outside?
Measure:
TLS handshake time
If only one copy of a key
exists?
t should be proportional to the
physical distance between server (a) and client (b).
t = (a-b) * c
t = (a-b) * c
amazon.com(SEATTLE)
t < (a-b) * c
google.com and facebook.com
Q.E.D.
THERE ARE
COPIES OF
KEYS!
YES (assumption)
amazon.com >= 1
google.com > 100,000
facebook.com > 100,000
Assuming the risk of losing
one private key:
99.9999% (not losing) => 0.0001%(losing)
99.9999%^100,000 = 90.48% => 9.52%
99.9999%^1,000,000 = 36.78% => 63.22%
One copy private key model
CA Certificates PKI
are only built to maintain the integrity of the public keys!
The integrity of the private key
is untouched
by the CA/PKI infrastructure
Conclusion:
THE WEAKEST LINK
is
THE PRIVATE KEY
of our partner
QUESTION:
Is there a solution to maintain
the private keys integrity along
the PKI implementations?
a tiny yes:
We could store our private keys on smartcards
to protect them
from being copied.
THX
@cramforce
@cybertini
@ingben
@MylesBorins
@jsconfeu
AUDIENCE
SEEYA
@fastandfearless
https://gitcom.com/mabels