|
pf
|
Another class template for Gamma filtering, but this time. More...
#include <cf_filters.h>
Public Types | |
| using | psv = Eigen::Matrix< float_t, dim_pred, 1 > |
| "predictor size vector" | |
| using | bsm = Eigen::Matrix< float_t, dim_obs, dim_pred > |
| "beta size matrix" | |
| using | tsv = Eigen::Matrix< float_t, 2, 1 > |
| "two by 1 vector" to store size and shapes of gamma distributions | |
| using | osv = Eigen::Matrix< float_t, dim_obs, 1 > |
| "observation size vector" | |
| using | osm = Eigen::Matrix< float_t, dim_obs, dim_obs > |
| "observation size matrix" | |
| Public Types inherited from pf::bases::cf_filter< 1, dim_obs, float_t > | |
| using | obs_sized_vec = Eigen::Matrix< float_t, dimobs, 1 > |
| using | state_sized_vec = Eigen::Matrix< float_t, dimstate, 1 > |
Public Member Functions | |
| multivGamFilter (const float_t &nOneTilde, const float_t &dOneTilde) | |
| Constructor. More... | |
| virtual | ~multivGamFilter () |
| The (virtual) desuctor. | |
| float_t | getLogCondLike () const |
| Get the latest conditional likelihood. More... | |
| tsv | getFilterVec () const |
| Get the current filter vector. More... | |
| void | update (const osv &yt, const psv &xt, const bsm &B, const osm &Sigma, const float_t &delta) |
| Perform a filtering update. More... | |
| osv | getFcastMean (const psv &xtp1, const bsm &B, const osm &Sigma, const float_t &delta) |
| Get the forecast mean (assuming filtering has been performed already) More... | |
| osm | getFcastCov (const psv &xtp1, const bsm &B, const osm &Sigma, const float_t &delta) |
| Get the forecast covariance matrix (assuming filtering has been performed already) More... | |
| Public Member Functions inherited from pf::bases::cf_filter< 1, dim_obs, float_t > | |
| virtual | ~cf_filter () |
| The (virtual) destructor. | |
| virtual float_t | getLogCondLike () const=0 |
| returns the log of the most recent conditional likelihood More... | |
Private Attributes | |
| tsv | m_filtVec |
| filter vector (shape and rate) | |
| float_t | m_lastLogCondLike |
| last log of the conditional likelihood | |
| bool | m_fresh |
| has data been observed? | |
Another class template for Gamma filtering, but this time.
| pf::filters::multivGamFilter< dim_obs, dim_pred, float_t >::multivGamFilter | ( | const float_t & | nOneTilde, |
| const float_t & | dOneTilde | ||
| ) |
Constructor.
| nOneTilde | degrees of freedom for time 1 prior. |
| dOneTilde | rate parameter for time 1 prior. |
| auto pf::filters::multivGamFilter< dim_obs, dim_pred, float_t >::getFcastCov | ( | const psv & | xtp1, |
| const bsm & | B, | ||
| const osm & | Sigma, | ||
| const float_t & | delta | ||
| ) |
Get the forecast covariance matrix (assuming filtering has been performed already)
gets the forecast covariance matrix!
| xtp1 | the next time period's predictor vector |
| B | the loadings matrix |
| Sigma | the observation "shape" matrix |
| delta | between 0 and 1 the discount parameter |
| auto pf::filters::multivGamFilter< dim_obs, dim_pred, float_t >::getFcastMean | ( | const psv & | xtp1, |
| const bsm & | B, | ||
| const osm & | Sigma, | ||
| const float_t & | delta | ||
| ) |
Get the forecast mean (assuming filtering has been performed already)
gets the forecast mean!
| xtp1 | the next time period's predictor vector |
| B | the loadings matrix |
| Sigma | the observation "shape" matrix |
| delta | between 0 and 1 the discount parameter |
| auto pf::filters::multivGamFilter< dim_obs, dim_pred, float_t >::getFilterVec |
Get the current filter vector.
get the current filtering distribution. First element is the shape, second is the rate.
| auto pf::filters::multivGamFilter< dim_obs, dim_pred, float_t >::getLogCondLike |
Get the latest conditional likelihood.
| void pf::filters::multivGamFilter< dim_obs, dim_pred, float_t >::update | ( | const osv & | yt, |
| const psv & | xt, | ||
| const bsm & | B, | ||
| const osm & | Sigma, | ||
| const float_t & | delta | ||
| ) |
Perform a filtering update.
Perform a Gamma filter update.
| yt | the most recent dependent random variable |
| xt | the most recent predictor vector |
| B | the loadings matrix |
| Sigma | the observation "shape" matrix. |
| delta | between 0 and 1 the discount parameter |


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*** User 2017-05-05 20:09:58.337 ***
20:09:58.337 [info] Disabling blocking sync...
*** User 2017-05-05 20:09:58.343 ***
20:09:58.343 [info] Configured peer_service_manager partisan_default_peer_service_manager on node 'node-1@MacBook-Pro-de-Anne-France'
*** User 2017-05-05 20:09:59.925 ***
20:09:59.925 [info] Enabling membership...
*** User 2017-05-05 20:09:59.927 ***
20:09:59.927 [info] Disabling blocking sync...
*** User 2017-05-05 20:09:59.933 ***
20:09:59.933 [info] Configured peer_service_manager partisan_default_peer_service_manager on node 'node-2@MacBook-Pro-de-Anne-France'
*** User 2017-05-05 20:10:01.490 ***
20:10:01.490 [info] Enabling membership...
*** User 2017-05-05 20:10:01.493 ***
20:10:01.492 [info] Disabling blocking sync...
*** User 2017-05-05 20:10:01.498 ***
20:10:01.498 [info] Configured peer_service_manager partisan_default_peer_service_manager on node 'node-3@MacBook-Pro-de-Anne-France'
*** User 2017-05-05 20:10:01.751 ***
Joining node: 'node-3@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 57720
*** User 2017-05-05 20:10:01.752 ***
Joining issued: 'node-3@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 57720
*** User 2017-05-05 20:10:01.752 ***
Joining node: 'node-2@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 57704
*** User 2017-05-05 20:10:01.753 ***
Joining issued: 'node-2@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 57704
*** User 2017-05-05 20:10:01.753 ***
Joining node: 'node-1@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 57691
*** User 2017-05-05 20:10:01.754 ***
Joining issued: 'node-1@MacBook-Pro-de-Anne-France' to 'runner@MacBook-Pro-de-Anne-France' at port 57691
*** User 2017-05-05 20:10:01.815 ***
Waiting for join to complete with manager: partisan_default_peer_service_manager
*** User 2017-05-05 20:10:01.816 ***
Finished.
*** CT 2017-05-05 20:10:01.816 *** COVER INFO
Adding nodes to cover test: ['runner@MacBook-Pro-de-Anne-France']
*** CT 2017-05-05 20:10:01.826 *** COVER INFO
Successfully added nodes to cover test: []
*** User 2017-05-05 20:10:09.865 ***
Case finished: orset_test
*** User 2017-05-05 20:10:16.943 ***
20:10:16.942 [info] Application lasp exited with reason: stopped
*** User 2017-05-05 20:10:16.943 ***
20:10:16.943 [info] Application partisan exited with reason: stopped
=== Ended at 2017-05-05 20:10:16
=== successfully completed test case
=== Returned value: ok
Test run history
| Top level test index


read_term/[2,3]
[ISO]read_term(
-Term,
+Options)
read_term(
+Stream,
-Term,
+Options)
Read a term from Stream, optionally returning extra information about the term.
A valid Prolog input stream, defaults to the current input stream.
The term that is read.
A list of zero or more of the following:
syntax_errors(
Val)
syntax_errors
Prolog flag. The
default is set by that flag. See ref-lps-flg.
variables(
Vars)
variable_names(
Names)
=
Var
pairs, where each Name is an atom indicating the name of a
non-anonymous variable in the term, and Var is the
corresponding variable.
singletons(
Names)
=
Var
pairs, one for each variable appearing only once in the term
and whose name does not begin with `_'.
cycles(
Boolean)
true
or false
. If selected, any
occurrences of @/2
in the term read in are replaced by the
potentially cyclic terms they denote as described above.
Otherwise (the default), Term is just unified with the
term read in.
layout(
Layout)
consume_layout(
Boolean)
true
or false
. If this option is
true
, read_term/[2,3]
will consume the
layout-text-item that follows the terminating `.' (this
layout-text-item can either be a layout-char or a
comment starting with a `%'). If the option is false
, the
layout-text-item will remain in the input stream, so that
subsequent character input predicates will see it. The default of
the consume_layout
option is false
.
The characters read are subject to character-conversion.
Stream errors (see ref-iou-sfh-est), plus:
syntax_error
instantiation_error
domain_error
domain_error
| ?- read_term(T, [variable_names(L)]). |: append([U|X],Y,[U|Z]) :- append(X,Y,Z). L = ['U'=_A,'X'=_B,'Y'=_C,'Z'=_D], T = (append([_A|_B],_C,[_A|_D]):-append(_B,_C,_D))
| ?- read_term(T, [layout(L), variable_names(Va), singletons(S)]). |: [ foo(X), X = Y ]. L = [35,[36,36],[36,[37,37,37],38]], S = ['Y'=_A], T = [foo(_B),_B=_A], Va = ['X'=_B,'Y'=_A] | ?- read_term(T, [consume_layout(false)]), get_code(C). |: 1. C = 10, T = 1 | ?- read_term(T, [consume_layout(true)]), get_code(C). |: 1. |: a C = 97, T = 1
read/[1,2]
, char_conversion/2
, ref-iou-tin-trm.


Chronicle List
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Marquez:
You know, now that I think of it, I remember that he kept a journal...
Perhaps it will contain some clue.
"Where is the journal?"


White Pine Lake via White Pine Lake trailhead
Directions:
The White Pine trailhead sits on the south side of the road 5.6 miles up Little
Cottonwood Canyon Road, past Tanner’s Campground.
After the road bends from traveling northeast to southeast, watch for a road closure gate on the south side of the road. Directly after this gate (which should be standing 10+ ft in the air if the road is open), there is a right turn that is easy to miss.
Trail Description:
The trail begins on the south side of the trailhead parking lot behind the restrooms.
Begin by crossing the bridge over Little Cottonwood Creek and following the trail approximately 1
mile to the “Y” where the trail to White Pine Lake takes a sharp
left and heads east, and the trails to
Red Pine and Maybird head west. A sign points the way, and because these trails are heavily used
in the winter, they will be easily distinguishable. Head west at the "Y" where the White Pine Lake Trail and Red Pine Lake Trail diverge.
The trail to White Pine winds up the canyon to the east and then turning south
beneath many a steep slope, through evergreens, and toward the end of the trail
over huge
granite boulders that may
be comp
letely covered in snow. If the trail becomes hard to pinpoint
after the ‘Y’ remember that you are heading south to nearly the top of White Pine.
The Lake itself
sits up and over a ridge near the top of the canyon. It does not become visible until you have
climbed right to it, and even then may not be visible due to the snow. Sitting at the
base of steep,
sheer granite cliffs and rock
beds, it snuggles into the top of the canyon, but you can snowshoe right
to it. At times the view on the way up opens to the other side of Little Cottonwood Canyon with its
sheer cliffs and the colorful strata near the top of the walls. Enjoy the journey. White Baldy and
Pfeifferhorn sit above the Lake.
Return on the same path.
Distance:
About 8 miles roundtrip
Elevation Gain:
Elevation at trailhead: 7700 ft
Elevation at White Pine Lake: 10137 ft
Gain: 2437 ft (way up there!)
Trail Rating:
Difficult.
There are switch-backs, which indicates the mountain is steep.
Toward the end of the climb, there is a pass that is narrow and steep. Use extreme caution.
We hiked this in late June and there was still snow to traverse.
Known Avalanche Locations:
High. There is a high awareness for avalanches in this area and beacons are generally worn. Personally, I would not attempt this trail unless avalanche conditions are stable.
Parking Spaces:
Yes
Restroom Facilities:
Yes
Source: www.snowshoeutah.com and www.hikesandlakes.com/littlecottonwoodcanyon.html


The project is to build a tool that can recognize the breed of a dog from an image. This idea originated from a Kaggle challenge which provides the data and specifications for the tool. Although the competition has since ended, it is still a worthwhile challenge with practical applications. Our goal was to compare and contrast machine learning models for use in this problem. We tried many approaches to classifying the 120 breeds available in the 10,000 images from the Kaggle challenge. In the Analysis and Visualizations tab of this website, you can find our results.
{#For more information, click Read Me
#}For this project we looked at Kaggle's Dog Breed Dataset.
Below is the dog breeds distribution in our dataset. There are 120 total breeds spread out over about 10,000 images, making for less than 100 images per breed on average. This is a pretty small dataset considering the size of the problem.


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Monsanto pleads guilty to using banned pesticide on research crop
LOS ANGELES: Biotech giant Monsanto on Thursday (Nov 21) agreed to plead guilty to illegally using a banned and highly toxic pesticide on research crops at one of its facilities on the Hawaiian island of Maui and to pay US$10 million in fines.
The company admitted in court documents filed in US District Court in Honolulu that it sprayed the pesticide known as Penncap-M on corn seed and other crops at its Valley Farm facility in 2014, even though it knew the chemical had been banned by the Environmental Protection Agency the year before.
"The illegal conduct in this case posed a threat to the environment, surrounding communities and Monsanto workers," said Nick Hanna, the United States Attorney for the Central District of California, whose office handled the case. "Federal laws and regulations impose a clear duty on every user of regulated and dangerous chemicals to ensure the products are safely stored, transported and used."
The case against Monsanto was brought as the agriculture giant faced a slew of lawsuits arguing that its Roundup weed killer causes cancer.
Federal prosecutors had initially sought to file felony charges against the company for illegally spraying Penncap-M, a nerve agent. But they reportedly agreed to let the company plead to a lesser misdemeanor offense after Monsanto's lawyers intervened at the highest levels of the Department of Justice.
In its guilty plea, Monsanto admitted that after the 2014 spraying, it told employees to enter the sprayed fields a week later even though it knew the workers should have been kept from entering the area for 31 days.
The plea calls for Monsanto to pay a US$6 million criminal fine and US$4 million in community service payments to Hawaiian government entities.
Hanna's office said the government agreed to dismiss felony charges in two years if Monsanto abides by the plea agreement.


Vulnerable REST API in Docker
Do not host this on production!!
Vulnerabilities
SQL injection
Weak session management
Cross Site Scripting
CORS misconfiguration
Vulnerable API Test function
Shoutouts
Sambhav Sharma
sprintcube
w3schools


Lunch with Jennifer Williams
On Wednesday, December 16, invited speaker Jennifer Williams, of MIT Lincoln Labs, gave a talk on her work on cross-language summarization and her experience as a first generation college student.
Finding Good Enough: A Task-Based Evaluation of Query Biased Summarization for Cross Language Information Retrieval
A large portion of Internet content is not in English. In fact, 70% of all Internet users are non-English speakers. News, blogs, tons of data in foreign languages. How do we understand what is happening around the web in languages other than English? How do we help users exlore all this data if they don’t know the language it is in? The problem at hand is the following: given a large data set in a foreign language and a query in English, we want to produce a summary of the query result in English. This task requires a number of techniques used in natural language processing: machine translation, cross-language search, and summarization.
The algorithm often used to summarize the results of a query is LexRank, which is a modified version of PageRank algorithm adapted for NLP purposes. Sentences in the documents of the given data set are represented as vertices in a graph. Edges represent similarity between sentenses. From that the algorithm ranks vertices by how similar they are to the query and how cental they are to the documents. Top-ranked sentenses are then returned as the summary.
There are several approaches for closing the language gap between the query and the data set. Jennifer Williams tested three of them on a large data set in Farsi:
- Translate the query from English to Farsi, search the data and summarize search results in Farsi, then translate the summary back to English.
- Translate the documents from Farsi to English, search and summarize the results in English.
- Use the two methods above and combine the results.
To evaluate the results, human relevance prediction was used: showing people a query and a summary and asking if the summary is relevant. As it turned out, the joint approach was superior, followed by translating the entire data set to English, followed by translating the quesry and searching in Farsi. One possible explanation might be the loss of accuracy when translating the query. Since queries are generally short, every word matters, and translating even one incorrectly can lead to huge accuracy losses. However, the problem with the other two approaches is that they require translation of the entire data set (which can be massive) to English.
As the main future work direction Jennifer Williams names developing golden standards for full-scale evaluation. If such standards were set, we could then apply intrinsic metrics, potentially improving evaluation process.
Personal Talk: First Jen-eration
Jennifer Williams comes from a small town in Oregon called Central Point.  Thus, she had to carve her own way, getting out of her home town, seeing the world, and having a career despite all the roadblocks she had on her way.
When she was younger, Jennifer’s dream was to become an astronaut. She enjoyed welding, construction, and working on cars. In high school, she worked as an assistant electrical engineer and even gave a presentation at NASA headquarters during her junior year.
In 2003, Jennifer went to college majoring in engineering and physics. However, soon she had to drop out due to a major car accident. While recovering, she opened herself to subjects that she didn’t have much interest in before, such as history, politics, philosophy, and social issues. She read a lot and wanted to study how human brain worked. This got her interested in artificial intelligence and natural language processing. And after returning back to college, she graduated with a degree in applied linguistics.
Graduate school brought a number of stories, both fortunate and unfortunate. She spent eight days driving from Oregon to DC and there had to rent a closet with no windows for 895 dollars a month plus utilities. During her second year, she lost two grandparents one month apart. She moved to Virginia and rented a living room for two years, where she was separated from the rest of the house by just curtains. She missed her best friend’s wedding despite being friends with her for 23 years. At the age of 27, she left United States for the first time in her life, got to publish six top-tier papers in two years, and traveled the world.
Some of her friends’ and colleagues’ advice and encouragement stuck with her during those rocky times:
- Don’t try to fit a mold.
- Live in a space where the problems are hard.
- Doing what makes you happy is the most rewarding direction to pursue.
- Truth flourishes best in places of light and cooperation.
Bio
Jennifer Williams joined the Human Language Technology Group at MIT Lincoln Laboratory in 2012 as an associate technical staff member. Prior to joining the Laboratory, she held a position as visiting researcher in the Human Language Technology Department at the Institute for Infocomm Research in Singapore, where she developed a novel approach to discourse processing for real time speech-to-speech machine translation. Before she began her research in HLT and natural language processing, she worked across several of the engineering disciplines including electrical engineering, mechanical engineering, and physics. She holds degrees in Applied Linguistics (B.A., 2009, magna cum laude) from Portland State University, and Computational Linguistics (M.S., 2012) from Georgetown University.


by
iv. a
"CLEMENT DASSA .
Department of Educational Theory
'
A Thesis Submitted in congonmity with’ the nequirements fon 4 the Degree of Doctor of Philosophy in the University of Toronto. {
» ‘ . -
© Clément Dassa 1979 2
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


WSDL generation in WSO2 WSF/Ruby is done using Ruby reflection and an annotation parser. To generate a WSDL from a given WSO2 WSF/Ruby service, a ?wsdl request should be sent to the server. For example, if you want to generate the WSDL for the service echoService.php, a request should be sent as,
http://localhost/services/echoService.php?wsdl
Upon receiving the request, the relevant WSDL is generated. If the Ruby script that implements the service contains annotations provided by the programmer, they will be used for WSDL generation. If the annotations are missing or if there is a syntax error, the WSDL will be generated using reflection and XSD:Any as the type for the parameters.
Parameter types and return types should be annotated for a Ruby function using the following syntax.
Syntax:
@param PHP_parameter_type $parameterName ( xs:XSDType )
Example:
/** * @param string $name your first name * (maps to the xs:string XML schema type) */
You can add your own comments only after the $parameterName
Syntax:
@return PHP_return_type $returnValueName ( xs:XSDType )
Example:
/** * @return int $t your lucky number * (maps to the xs:int XML schema type ) */
You can add your own comments only after the $returnValueName
/** * The purchaseOrder function * @param int $itemNo ID of the item to be purchased * (maps to the xs:int XML schema type ) * @param string $date Date that the purchase order was done * (maps to the xs:gDate XML schema type) * @return int $price total price * (maps to the xs:nonNegativeInteger schema type ) */ function purchaseOrder ($itemNo, $date) { // some logic return $Price; }
Syntax:
@param array of PHP_parameter_type $paramterName ( xs:XSDType )
You can add your own comments only after the $parameterName similar to the early cases
Example:
/** * @param array of string $items item list * (maps to the xs:string) */
Syntax:
@param object PHP_class_name $paramterName
Example:
/** * @param object Purchase $purchase the purchase object */In order to work this, you need to put doc comments in the class and the class variables it self. Following two section describes how you can do that.
Syntax:
@namespace Schema_type_custom_namespace
If the @namespace is not given, the schema type will have the default namespace http://www.wso2.org/php/xsd
Example:
/** * @namespace http://custom.host/custom/location */
Syntax:
@var PHP_parameter_type $parameterName ( xs:XSDType )
Here PHP_parameter_type may contain "object" or "array of" phrases similar to @param and @return
Example:
/** * @var String $name */
/** * The function to register students for a course * @param string $course the course to register * (maps to xs:string) * @param array of object Student $students students list * @return boolean $success * (maps to xs:boolean) */ function registerStudents($course, $students) { } /** * @namespace http://my.university/my/faculty */ class Student { /** * @var string $name student name */ public $name; /** * @var int $age student age */ public $age; }
If you use the same scenario in the Example 2 to use with the classmap API, you need to declare both the request and response types as Ruby Classes like shown in the following code segment
NOTE: You are using classmap API, if you provide the "classmap" option in the WSService constructor.
/** * The function to register students for a course * @param object RegisterStudents $RegisterStudents * @return object RegisterStudentsResponse $RegisterStudentsResponse */ function registerStudents($RegisterStudents) { } /** * @namespace http://my.university/my/faculty */ class RegisterStudents { /** * @var string $course the course to register * (maps to xs:string) */ public $course; /** * @var array of object Student $students students list */ public $students; } /** * @namespace http://my.university/my/faculty */ class RegisterStudentsResponse { /** * @var boolean $success whether the operation successful or not * (maps to xs:boolean) */ public $success; } /** * @namespace http://my.university/my/faculty */ class Student { /** * @var string $name student name */ public $name; /** * @var int $age student age */ public $age; }
Since Ruby is a weakly-typed language in nature, the following type mapping is required to map the Ruby types to the XML Schema types.
| Schema Type | Ruby Type |
| string | string |
| boolean | boolean |
| double | float |
| float | float |
| int | int |
| integer | int |
| byte | int |
| decimal | string |
| base64Binary | string |
| hexBinary | string |
| anyType | soap var object |
| any | soap var object |
| QName | string |
| dateTime | string |
| date | string |
| time | string |
| unsignedLong | int |
| unsignedInt | int |
| unsignedShort | int |
| unsignedByte | int |
| positiveInteger | int |
| negativeInteger | int |
| nonNegativeInteger | int |
| nonPositiveInteger | int |
| gYearMonth | string |
| gMonthDay | string |
| gYear | string |
| gMonth | string |
| gDay | string |
| duration | string |
| Name | string |
| NCName | string |
| NMTOKEN | string |
| NOTATION | string |
| NMTOKENS | string |
| ENTITY | string |
| ENTITIES | string |
| IDREF | string |
| IDREFS | string |
| anyURI | string |
| language | string |
| ID | string |
| normalizedString | string |
| token | string |
Both WSDL 1.1 as well as WSDL 2.0 are supported.
To get WSDL 1.1 use '?wsdl'. To get WSDL 2.0 use '?wsdl2'
Example,
For WSDL 1.1 - http://localhost/services/echoService.php?wsdl
For WSDL 2.0 - http://localhost/services/echoService.php?wsdl2
The SOAP binding style should be set in a Ruby service option array. If the SOAP style is not set, then the document style is assumed as the default.
Example,
$service = new WSService(array("operations"=>$operations, "bindingstyle"=>"rpc"))
| Binding Style | Parameter |
|---|---|
| Document literal | doclit |
| RPC encoded | rpc |
Example: Generating Message elements and a schema for the document literal style .
/** The LuckyNo function * @param string $yr year of your birthday * (maps to the xs:gYear XML schema type ) * @param string $name your first name * (maps to the xs:string XML schema type) * @return int $t your lucky number * (maps to the xs:int XML schema type ) */ function luckyNoFunction($yr,$name) { // some logic return $t; }
In the generated WSDL, the operation named "luckyfunction" has two messages.
<operation name="luckyNoFunction"> <input message="s0:luckyNoFunctionInput"/> <output message="s0:luckyNoFunctionOutput"/> </operation>
In message elements,
<message name="luckyNoFunctionInput"> <part name="parameters" element="luckyNoFunction"/> </message> <message name="luckyNoFunctionOutput"> <part name="parameters" element="luckyNoFunctionResponse"/> </message>
Therefore, the generated schema that can be accessible on the server side is,
<element name="luckyNoFunction"> <complexType> <sequence> <element name="yr" type="xsd:gYear"/> <element name="name" type="xsd:string"/> </sequence> </complexType> </element> <element name="luckyNoFunctionResponse"> <complexType> <sequence> <element name="t" type="xsd:int"/> </sequence> </complexType> </element>
Example, for RPC style:
The message elements are,
<message name="luckyNoFunction"> <part name="yr" type="xsd:gYear"/> <part name="name" type="xsd:string"/> </message> <message name="luckyNoFunctionResponse"> <part name="t" type="xsd:int"/> </message>
You can specify a name for the service for generating WSDL. If you have not provided the service name then will generate a service name based on the address location
Example,
$service = new WSService(array("operations"=>$operations, "serviceName"=>"sampleService"))


| Trees | Indices | Toggle frames |
|---|
Manage related vertex attributes within a single vertex domain.
A vertex "domain" consists of a set of attribute descriptions that together describe the layout of one or more vertex buffers which are used together to specify the vertices in a primitive. Additionally, the domain manages the buffers used to store the data and will resize them as necessary to accomodate new vertices.
Domains can optionally be indexed, in which case they also manage a buffer containing vertex indices. This buffer is grown separately and has no size relation to the attribute buffers.
Applications can create vertices (and optionally, indices) within a domain with the VertexDomain.create method. This returns a VertexList representing the list of vertices created. The vertex attribute data within the group can be modified, and the changes will be made to the underlying buffers automatically.
The entire domain can be efficiently drawn in one step with the VertexDomain.draw method, assuming all the vertices comprise primitives of the same OpenGL primitive mode.
|
VertexDomain
Management of a set of vertex lists.
|
|
|
VertexList
A list of vertices within a VertexDomain.
|
|
|
IndexedVertexDomain
Management of a set of indexed vertex lists.
|
|
|
IndexedVertexList
A list of vertices within an IndexedVertexDomain that are indexed.
|
|
create_attribute_usage(format)
Create an attribute and usage pair from a format string.
|
|
| VertexDomain |
create_domain(*attribute_usage_formats)
Create a vertex domain covering the given attribute usage formats.
|
| VertexDomain |
create_indexed_domain(*attribute_usage_formats)
Create an indexed vertex domain covering the given attribute usage
formats.
|
Create an attribute and usage pair from a format string. The format string is as documented in pyglet.graphics.vertexattribute, with the addition of an optional usage component:
usage ::= attribute ( '/' ('static' | 'dynamic' | 'stream' | 'none') )?
If the usage is not given it defaults to 'dynamic'. The usage corresponds to the OpenGL VBO usage hint, and for static also indicates a preference for interleaved arrays. If none is specified a buffer object is not created, and vertex data is stored in system memory.
Some examples:
| Trees | Indices | Toggle frames |
|---|
| Generated by Epydoc 3.0beta1 on Sun Mar 22 20:15:38 2009 | http://epydoc.sourceforge.net |


${userInfo.user_name}
${userInfo.user_phone}
- Name: ${userInfo.user_name}
- Email: ${userInfo.user_email}
- Phone: ${userInfo.user_phone}
- NID Number: ${userInfo.user_national_id_card}
- Date of Birth: ${ dateTimeFormat(userInfo.date_of_birth, "fullDate") }
- Gender: Male Female Other
- State/City: ${userInfo.city.state.state_name}/${userInfo.city.city_name}
- Country: ${userInfo.city.state.country.country_name}
- Address: ${userInfo.user_short_address}
Update Profile Information
Change Password
Booking List
| SL No. | Booking No. | Booking Status | Total Amount | Booking Date | Action | |
|---|---|---|---|---|---|---|
| ${ index + 1 } | PORZ${bookingIdDateFormat(booking.created_at, "fullDate")}${booking.booking_id} | Completed | Pending | TK ${ booking.total_amount } | ${ dateTimeFormat(booking.created_at, "fullDate") } |


---
title: "View Embroidered Logo Hats No Minimum Images"
description: "I just went to a thrift store and got an old hat.i have a."
date: "2017-05-15"
categories:
- "template"
images:
- "https://cdn2.shopify.com/s/files/1/1489/7608/products/VC-WHITE_large.jpg?v=1537759300"
featuredImage: "http://cdn.shopify.com/s/files/1/0860/0364/products/0007_C812_SilverBlack_hat_GA12.jpg1_large.jpg?v=1456328170"
featured_image: "https://mallpatches.com/wp-content/uploads/2018/06/Denim-embroidered-patch-for-hats.jpg"
image: "https://mallpatches.com/wp-content/uploads/2018/06/red-embroidered-patch-for-hats.jpg"
---
View Embroidered Logo Hats No Minimum Images. On your first order of 6+ items. We have no minimums, online design, and fast delivery.
Alibaba.com offers 1,633 embroidered hats no minimum products. Check out our deals on custom new era, flexfit, yupoong and more. I ordered this one because it has the mesh back for the summer and it's lighter weight than the other i bought, but this is.
See more ideas about corporate outfits, logo embroidered, apparel.
There is no minimum quantity requirement. Some hats can not be embroidered. Fast turnaround, design your own hat online today. Any size, any color, any design.


{{top_article.intro}}
{{article1.intro}}
{{article2.intro}}
{{article3.intro}}
{{article4.intro}}
{{article5.intro}}
Built with django+python powed by picard picardxie@foxmail.com


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Redmi Note 4Rs 12000/-Best PhoneA good Security system has a flexible and optimized surveillance system that is light and easily accessible. Strantin ip camera software uses the latest technology ...
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Redmi Note 4Rs 12000/-Best PhoneA good Security system has a flexible and optimized surveillance system that is light and easily accessible. Strantin ip camera software uses the latest technology ...
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Redmi Note 4Rs 12000/-Best PhoneA good Security system has a flexible and optimized surveillance system that is light and easily accessible. Strantin ip camera software uses the latest technology ...
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Redmi Note 4Rs 12000/-Best PhoneA good Security system has a flexible and optimized surveillance system that is light and easily accessible. Strantin ip camera software uses the latest technology ...
-
Redmi Note 4Rs 12000/-Best PhoneA good Security system has a flexible and optimized surveillance system that is light and easily accessible. Strantin ip camera software uses the latest technology ...
-
Redmi Note 4Rs 12000/-Best PhoneA good Security system has a flexible and optimized surveillance system that is light and easily accessible. Strantin ip camera software uses the latest technology ...
-
Redmi Note 4Rs 12000/-Best PhoneA good Security system has a flexible and optimized surveillance system that is light and easily accessible. Strantin ip camera software uses the latest technology ...
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Redmi Note 4Rs 12000/-Best PhoneA good Security system has a flexible and optimized surveillance system that is light and easily accessible. Strantin ip camera software uses the latest technology ...
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Redmi Note 4Rs 12000/-Best PhoneA good Security system has a flexible and optimized surveillance system that is light and easily accessible. Strantin ip camera software uses the latest technology ...


This Privacy Policy helps you understand what Personal data we collect, why we collect it, what we do with it and the choices you have, including how to access and update information. Our policy applies to you if you use our products or services in store, over the phone, online, through our mobile applications or if you use any of our websites or interact with us on social media (our “Services”).
The Carphone Warehouse Limited (referred to using “We”, “Us”, “Our” or the “Company”) is a company registered in England and Wales (Company registration number: 2142673) and is part of the Dixons Carphone group of companies. Dixons Carphone is Europe's leading specialist electrical and telecommunications retailer and services company, employing over 42,000 people in nine countries. We pride ourselves on giving our customers simple and independent advice on which electronic and technology products are right for them.
Our primary brands are [Currys PC World, Dixons Travel and Team Knowhow]
When you shop with us or use our services, you trust us with your data. We’re a company that puts you, our customer, first, respecting you as an individual but also as a member of our community. We listen to our customers to understand your expectations and make sure these are reflected in our business decisions. We’re committed to maintaining your trust and confidence. In this Privacy Policy, we’ve provided details on when and why we collect your personal information, how we use it, the very limited conditions under which we may disclose it to others (where direct marketing is concerned only with your explicit permission) and how we keep it secure.
We collect personal information about you when you visit one of our stores, use our Websites (“Websites”), or use our web or mobile device applications (“Mobile Apps”) or if you communicate with us by phone, e-mail and social media. We refer to our Websites and Apps collectively as “Online Services”.
The types of personal information we collect includes:
· Personal details such as your name, address, date of birth, email address, phone number and other contact information Transaction information, such as the product you purchased, its price, your method of payment and your payment details.
· Information about you like your employment details, financial position and information taken from identification documents like your passport or driving licence when we review your application for insurance or loans offered by selected third parties partners
· Your account information – such as dates of payments owed and received, the subscription services you use or any other information related to your account
· The phone numbers that you call/send messages to or the phone numbers that you recieve calls/messages from.
· The date and time of the calls and messages you send or receive through our network, and your location at the time these communications take place.
When you’re online the information we collect includes:
· Account information, like your username, password, and other identifiers or credentials you use to access our online services or to buy our products and services, details of your shopping preferences, such as your favourite brands and products, as well as which of our stores you prefer to shop in
· Details of your visits to the Website, in-store WIFI or App and the resources that you access. Examples include ads that you click, device information and your location
· Information that you provide in your dealings with us. This includes when you register to use our online services, or when you subscribe to our services or request further services and/or information from us.
The situations when you provide personal information could include when you:
· Purchase products at our stores or through our contact centre
· Register or use our Online Services
· Request to receive marketing or other communications
· Use our Wi-Fi networks or other in-store tech
· Enter one of our competitions or when you complete one of our customer surveys
· Submit information when you’re providing feedback
· Use interactive features of our Online Services.
The law on data protection sets out a number of different reasons for which a company may collect and process your personal data. These are set out below.
When we’re required to enter into a contract with you
· We use your personal information to process your orders and payments or to give you a refund.
Where you have provided consent
We use email and text messages to communicate with you about our products and services, competitions, offers, promotions or special events where you tick a box.
For example
· To provide a free mobile phone upgrade reminder service to inform you when a product is back in stock
· To send you a renewal notice
· If you give us permission, to communicate with you about our third party partners that we believe may interest you
Where there is a Legitimate Interest
As a Company we are often required to process your personal data in order to carry out certain tasks relating to our business activities. In such cases, processing of personal data can be justified on grounds of legitimate interest.
Communications
Personalise & Improve our Service
Company Interest
When we’re required to comply with our Legal Obligation
We’ll use your personal information to comply with our legal obligations including:
· To identify you when you contact us
· To verify the accuracy of data that we hold about you
· To assist HMRC and/or the Police and/or other regulatory bodies in relation to an investigation by a public authority.
Where it is in your Vital Interest
In certain circumstances it is in your vital interests for us to process your personal information. We may need to contact you if there are any urgent safety or product recall notices or where we otherwise reasonably believe that the processing of your personal information will prevent or reduce any potential harm to you.
We share personal information within the Dixons Carphone PLC Group. Members of the Dixons Carphone Group that receive this information are not authorised to use or disclose the information except as provided in this privacy policy.
Our service providers
We work with partners, suppliers, insurers and agencies so they can process your personal information on our behalf and only where they meet our standards on the processing of data and security. We only share information that helps them provide their services to us or to help them provide their services to you. For example, some of our service providers place advertising for us online, about our products and services and those of our retail partners, suppliers and third parties. As a result, where you have indicated you are happy to receive marketing from us, you might see online advertising that we have placed on the web sites you visit, or the interactive services you use.
Other organisations and individuals
We may transfer your personal information to other organisations in certain scenarios. For example:
Credit Reference Agencies
In order to process your application we’ll supply your personal information to credit reference agencies (CRAs) and they will give us information about you.
This will include information from your credit application and about your financial situation and financial history. CRAs will supply to us both public (including the electoral register) and shared credit, financial situation and financial history information and fraud prevention information. We will use this information to:
· Assess your creditworthiness and whether you can afford to take the product
· Verify the accuracy of the data you have provided to us
· Prevent criminal activity, fraud and money laundering
· Trace and recover debts
· To make sure any offers provided to you are appropriate to your circumstances.
We’ll also continue to exchange information about you with CRAs on an on-going basis, including about your settled accounts and any debts not fully repaid on time. CRAs will share your information with other organisations. Your data will also be linked to the data of your spouse, any joint applicants or other financial associates.
The identities of the CRAs, and the ways in which they use and share personal information, are explained in more detail at www.equifax.co.uk/crain
From time to time we may transfer your personal information to our suppliers, service providers and other company offices based outside of the European Economic Area (EEA) for the purposes described in this privacy policy. If we do this your personal information will continue to be subject to one or more appropriate safeguards set out in the law.
If we do transfer information to our agents or advisers outside of the EEA, we will make sure that it is protected in the same way as if it was being used in the EEA. We’ll use one of these safeguards:
We sometimes use systems to make automated decisions based on your personal information or the information we are allowed to collect from others about you or your business (for example where we perform credit checks). This helps us to make sure our decisions are quick, fair, efficient and correct, based on what we know. These automated decisions can affect the products, services or features we may offer you now or in the future, or the price that we charge you for them.
Your rights
You have rights over automated decisions.
If you want to know more about these rights, please contact us.
We will keep your personal information for as long as you’re a customer. If you haven’t made a purchase or engaged with us for 3 years or more, then we’ll remove you from our marketing mailing lists.  This could include one of the ways specified in ‘How we use your personal information’ and for one of these reasons:
We may keep your data for longer than 7 years if we cannot delete it for legal, regulatory or technical reasons. We may also keep it in order to help support product recalls or safety notices. If we do, we will make sure that your privacy is protected and only use it for those purposes.
We do not retain personal information in an identifiable format for longer than is necessary.
Access to Information held about you
You have the right to request what personal information we hold about you. This is sometimes called a 'Data Subject Access Request'. If we agree that we are obliged to provide personal information to you (or someone else on your behalf), we’ll provide it to you free of charge. Before providing personal information to you or another person on your behalf, we may ask for proof of identity and sufficient information about your interactions with us that we can locate your personal information. Except in rare cases, we’ll respond to you within 30 days after we’ve received this information or, where no such information is required, after we’ve received your request.
If any of the personal information we hold about you is inaccurate or out of date, you may ask us to correct it.
In certain circumstances you have the right to request a copy of your personal information from us or to have that information passed to an organisation of your choice in a format that can be easily re-used.
Right to stop or limit our processing of your data
You have the right to object to us processing your personal information if we’re not entitled to use it any more, to have your information deleted if we are keeping it too long or have its processing restricted in certain circumstances.
Where we rely on our legitimate interests, as set out under ‘How we use your personal information’, you may object to us using it for these purposes. If we agree that your objection is justified in accordance with your rights under data protection laws, we’ll permanently stop using your data for those purposes. Otherwise we’ll provide you with our justification as to why we need to continue using your data.
You can ask us to restrict the use of your personal information if:
Please note that we may be required by law to retain certain information. Before we are able to provide you with any information or correct any inaccuracies, we may ask you provide other details to help us respond to your request.
If you would like to exercise these rights, please contact us
Please make sure you provide the following identification documents as part of your application:
We won't send you marketing messages if you tell us not to.
· You can click onto the "unsubscribe" link in any communication that we send to you by email which will automatically unsubscribe you from that type of communication. Each "unsubscribe" link only relates to that specific type of communication. Please also note that you may continue to receive Service communications in relation to the product and services we have sold to you
· In relation to our App(s), you can manage your preferences in the App(s)
· Alternatively, you can unsubscribe by submitting your request here.
Please note that it may take up to 28 days to process your request.
You can also find further information within our Direct Marketing Charter
The purpose of our Cookies Policy is to help explain what they are, why you are collecting them, and what information cookies store.
Please refer to our Cookies Policy
All companies within the Dixons Carphone Group use technical and organisational security measures to protect the personal information supplied by you against loss, destruction, and any unauthorised access by third parties.
Our websites (which includes this Privacy Policy) contain links to other websites run by other organisations which we do not control. This policy does not apply to those other websites and Apps‚ so we encourage you to read their privacy statements.
We use so-called social plugins (buttons) of social networks such as Facebook, Google+, You Tube, Instragram and Twitter.
After activation of a button, the social network can retrieve data, independently of whether you interact with the button or not. If you are logged on to a social network, the network can assign your visit to the website to your user account. A social network cannot assign a visit to websites operated by our other group companies unless you activate the respective button there as well.
If you’re a member of a social network and don’t want that network to combine data retrieved from your visit to our websites with data they hold on you, you must log out from the social network concerned before activating the buttons.
If you have a question or a complaint about this policy, the way your personal information is handled, please contact us by one of the following means:
By email<EMAIL_ADDRESS>By post:
Data Protection Officer
PO Box 375
Southampton
SO30 2PU
You have the right to complain to the Information Commissioners Office (ICO) if you are concerned about the way we have processed your personal information. Please visit the ICO’s website for further details at: https://ico.org.uk/
We may review this policy from time to time and any changes will be notified to you by posting an updated version on our website and/or by contacting you by email.
*If you find an upgrade, pay monthly SIM only or pay monthly deal for less at O2, EE or Vodafone, we'll match it and pay the equivalent of your first month's standard line rental via cheque. Applies to published prices only where the handset is in stock and available for immediate purchase or dispatch. Proof of competitor price(s) required. Applies to deals on a like for like basis, offered in the same sales channel and may be claimed at the time of purchase or up to 14 days after. Cannot be used in conjunction with other deals, including cash back, personalised variations offered directly to individual customers, offers via third parties or pre-order incentives. Applies to the UK and Ireland only. For full Terms and conditions click here or ask staff for full details.
* Calls to these numbers are free from a BT landline (depending on your calling plan). Call costs from mobiles and other networks vary.


Sub /sub/index.html
My contents are here.
My contents are here.
My contents are here.
My contents are here.


Barefoot Investor: Scott Pape’s script for getting a better home loan
Not sure how to ask your bank for a better home loan? Barefoot Investor Scott Pape has done the hard work for you, creating a script for you to use to get a better home loan from your bank.
You: Hello, my account number is ______. I’ve been with you for ___ years, but I’ve applied to refinance with UBank. Their rate is ____ per cent, which is a full ___ per cent cheaper than you’re charging me. Given our longstanding relationship, I’d like you to match the offer — or send me the forms I need to switch to UBank.
Bank rep: One moment, please.
(You’re bluffing, of course. However, the bank’s sales team have strict targets, backed by incentives, that they have to meet — one of which is giving profitable customers discounts to stop them leaving.)
Bank rep: We can’t match the rate you have quoted. However, we understand you are a valuable customer, so we would like to offer you a 0.15 per cent discount.
You: That’s not good enough. I’ve already got conditional approval … so in order to stay I need at least a 0.5 per cent discount. Could you please speak to your supervisor? I’m happy to wait.
Bank rep (a full six minutes later): On reviewing your case, we can offer you that 0.5 per cent discount on your current rate.
You: Brilliant! Please send me an email confirming the new rate and confirming that it will be applied as of start of business tomorrow.


Sample Code - Quick Sort
Feb 04, 2019
Sample
Quick sort in python
# Python program for implementation of Quicksort Sort
# This function takes last element as pivot, places
# the pivot element at its correct position in sorted
# array, and places all smaller (smaller than pivot)
# to left of pivot and all greater elements to right
# of pivot
def partition(arr,low,high):
i = ( low-1 ) # index of smaller element
pivot = arr[high] # pivot
for j in range(low , high):
# If current element is smaller than or
# equal to pivot
if arr[j] <= pivot:
# increment index of smaller element
i = i+1
arr[i],arr[j] = arr[j],arr[i]
arr[i+1],arr[high] = arr[high],arr[i+1]
return ( i+1 )
# The main function that implements QuickSort
# arr[] --> Array to be sorted,
# low --> Starting index,
# high --> Ending index
# Function to do Quick sort
def quickSort(arr,low,high):
if low < high:
# pi is partitioning index, arr[p] is now
# at right place
pi = partition(arr,low,high)
# Separately sort elements before
# partition and after partition
quickSort(arr, low, pi-1)
quickSort(arr, pi+1, high)
# Driver code to test above
arr = [10, 7, 8, 9, 1, 5]
n = len(arr)
quickSort(arr,0,n-1)
print ("Sorted array is:")
for i in range(n):
print ("%d" %arr[i]),
# This code is contributed by Mohit Kumra
Quick sort in haskell
quicksort :: ( Ord a ) = > [ a ] -> [ a ]
quicksort [] = []
quicksort ( x : xs ) =
let smallerSorted = quicksort [ a | a <- xs , a <= x ]
biggerSorted = quicksort [ a | a <- xs , a > x ]
in smallerSorted ++ [ x ] ++ biggerSorted
Quick sort in scheme
(define (quicksort lst)
(cond
((or (null? lst) ; empty list is sorted
(null? (cdr lst))) ; single-element list is sorted
lst)
(else
(let ((pivot (car lst)) ; Select the first element as the pivot
(rest (cdr lst)))
(append
(quicksort ; Recursively sort the list of smaller values
(filter (lambda (x) (< x pivot)) rest)) ; Select the smaller values
(list pivot) ; Add the pivot in the middle
(quicksort ; Recursively sort the list of larger values
(filter (lambda (x) (>= x pivot)) rest))))))) ; Select the larger and equal values
Quick sort in java
package com.theme.rouge;
public class MyQuickSort {
private int array[];
private int length;
public void sort(int[] inputArr) {
if (inputArr == null || inputArr.length == 0) {
return;
}
this.array = inputArr;
length = inputArr.length;
quickSort(0, length - 1);
}
private void quickSort(int lowerIndex, int higherIndex) {
int i = lowerIndex;
int j = higherIndex;
// calculate pivot number, I am taking pivot as middle index number
int pivot = array[lowerIndex+(higherIndex-lowerIndex)/2];
// Divide into two arrays
while (i <= j) {
/**
* In each iteration, we will identify a number from left side which
* is greater then the pivot value, and also we will identify a number
* from right side which is less then the pivot value. Once the search
* is done, then we exchange both numbers.
*/
while (array[i] < pivot) {
i++;
}
while (array[j] > pivot) {
j--;
}
if (i <= j) {
exchangeNumbers(i, j);
//move index to next position on both sides
i++;
j--;
}
}
// call quickSort() method recursively
if (lowerIndex < j)
quickSort(lowerIndex, j);
if (i < higherIndex)
quickSort(i, higherIndex);
}
private void exchangeNumbers(int i, int j) {
int temp = array[i];
array[i] = array[j];
array[j] = temp;
}
public static void main(String a[]){
MyQuickSort sorter = new MyQuickSort();
int[] input = {24,2,45,20,56,75,2,56,99,53,12};
sorter.sort(input);
for(int i:input){
System.out.print(i);
System.out.print(" ");
}
}
}


- rayPlaneIntersectTest(Position, Position, Position, Position, Position, double, DenseMatrix64F, DenseMatrix64F, DenseMatrix64F) - Method in class application.PhysicsSimulation
-
Tests for intersection between a ray and a plane (triangle) and returns the time of collision.
- readableFPS - Variable in class application.FPSSystem
-
The current fps in readable form (less decimal places)
- readableFrameTime - Variable in class application.FPSSystem
-
The time it took to render the current frame in readable form (less decimal places)
- readConfig(String) - Static method in class application.MainSimulation
-
Reads a config file and returns the information as a config object
- readFileIn(String, ArrayList<Character>) - Static method in class application.ObjImporter
-
Reads the contents of a file into a given ArrayList of characters.
- relativeCenterOfMass - Variable in class application.CustomObject
-
The center of mass of the object relative to its position
- relativeCenterOfMass() - Method in class application.CustomObject
-
Calculates the relative center of mass of the object assuming mass only comes from vertices
and each vertex has equal mass
- removeObject(int) - Method in class application.ConfigCreatorSim
-
Removes an object from the running simulation
- removeObject - Variable in class application.ConfigSimulation
-
Is there an object waiting to be removed? If so this will be the position of the object to remove, otherwise -1 suggests no object waiting to be removed
- removeObject(int) - Method in class application.ConfigSimulation
-
Removes an object from the simulation.
- removeObjectBox - Variable in class application.ConfigController
-
- removeObjectButton - Variable in class application.ConfigController
-
- render - Variable in class application.PhysicsSimulation
-
The renderer for displaying to the screen
- renderer - Variable in class application.MainSimulation
-
The renderer displaying the simulation to the screen
- Renderer - Class in application
-
Custom renderer for displaying objects of the type CustomObject to the screen in 3d using OpenGL.
- Renderer() - Constructor for class application.Renderer
-
Creates a renderer and prepares it for rendering.
- requestVBOUpdate() - Method in class application.Renderer
-
Requests that the renderer redo its VBO.
- reshape(GLAutoDrawable, int, int, int, int) - Method in class application.Renderer
-
Reshapes the canvas.
- resolveCollision(ArrayList<AABB>, double, int, int) - Method in class application.PhysicsSimulation
-
Resolves a collision between two objects that have intersecting AABBs and updates them if a full collision is present
- rInc - Variable in class application.ConfigController
-
The amount the rotation increments without fine control selected
- rot - Variable in class application.Camera
-
The rotation of the camera about x,y and z axis
- rot - Variable in class application.ObjectInit
-
The rotation of the object
- rotateAboutPoint(Position, double, double, double) - Method in class application.CustomObject
-
Rotates the object about a point given an x axis rotation, y axis rotation, z axis rotation
and the position of the point relative to the object.
- rotation - Variable in class application.CustomObject
-
The rotation of the object about x,y,z axis
- Rotation - Class in application
-
Stores a rotation in 3d space, made up of an X, Y, and Z axis rotation
- Rotation() - Constructor for class application.Rotation
-
Creates a rotation object with no rotation.
- Rotation(double, double, double) - Constructor for class application.Rotation
-
Creates a rotation object with a custom rotation in each axis
- Rotation(Rotation) - Constructor for class application.Rotation
-
Creates a rotation object as a copy of an existing rotation
- rotationMatrix - Variable in class application.CustomObject
-
The matrix representing rotation of the object around x,y,z axis (4x4 matrix)
- run() - Method in class application.ConfigCreatorSim
-
Runs the the renderer and animator
- run() - Method in class application.ConfigSimulation
-
Runs the config simulation until the renderer it is using closes.
- run() - Method in class application.PhysicsSimulation
-
Runs the simulation until the attached renderer is closed.
- runButton - Variable in class application.MainGUIController
-
- runSim(String) - Method in class application.MainSimulation
-
Reads in the config at the given filepath, imports all the objects needed and starts the physics simulation and renderer.
- runSimInConfigMode(String) - Method in class application.ConfigCreatorSim
-
Runs a sim in config mode (things won't move around unless moved by the gui buttons)


Want Ivan Chai for free? Take part in our
monthly giveaways
.
@{body}
We love real mail. Our mailing address is:
Ivan's Herbs
Vaartstraat 8-1
Amsterdam 1075RN
Netherlands
Copyright © 2019 Ivan's Herbs, All rights reserved.


|
Bullet Collision Detection & Physics Library
|
Public Member Functions | |
| virtual const btCollisionShape * | getChildShape (int index) |
| virtual | ~ChildShapeRetriever () |
Public Attributes | |
| GIM_ShapeRetriever * | m_parent |
Definition at line 93 of file btGImpactCollisionAlgorithm.cpp.
|
inlinevirtual |
Definition at line 101 of file btGImpactCollisionAlgorithm.cpp.
|
inlinevirtual |
Reimplemented in GIM_ShapeRetriever::TetraShapeRetriever, and GIM_ShapeRetriever::TriangleShapeRetriever.
Definition at line 97 of file btGImpactCollisionAlgorithm.cpp.
| GIM_ShapeRetriever* GIM_ShapeRetriever::ChildShapeRetriever::m_parent |
Definition at line 96 of file btGImpactCollisionAlgorithm.cpp.


Daily sales of salmon cookies per hour in 5 locations
here you can add another city branch:
Location:
The minimum number of customers per hour:
The maximum number of customers per hour:
The average number of cookies purchased per customer:
Take me to home page


| Project Name | # of Buildings | Snapshot Timestamp | Type of Submittal | Year Ending | Deadline | Last Changed | Changed By |
|---|---|---|---|---|---|---|---|
| {$ p.name $} | {$ p.number_of_buildings $} | 12/31/2014 1:32pm | {$ p.compliance_type || "None" $} | {$ p.end_date | date:'MM/dd/yyyy' $} | {$ p.deadline_date | date:'MM/dd/yyyy' $} | {$ p.last_modified | fromNow $} | {$ p.last_modified_by.email $} |


*Search is in early alpha, please report bugs to the scylla github repo
Queries use Lucene query syntax. You will get a 500 error if your query is incorrect. Use the fields listed above to guide you. Full query syntax (including wildcards) are supported
The search API can be accessed by sending a GET request to this page with the Accept header set
to Accept: application/json
GET https://scylla.sh/search?q=your_query&num=1000&from=200
The above GET request grabs 1000 results of your_query starting at the 200th record. Pagination
can be done using the "from" parameter. Queries return the first 10,000 hits.
Data is returned in JSON format like the following:
[
{
"_id":"hKW8WWsBBGJneKoTbFS-",
"_index":"pw_data",
"_score":19.8656,
"_source":{
"Domain":"exploit.in",
<EMAIL_ADDRESS>"Password":"freeshit"
},
"_type":"_doc"
},
{
"_id":"_3PgVGsBBGJneKoTof0e",
"_index":"pw_data",
"_score":16.46111,
"_source":{
"Domain":"linkedin.com",
<EMAIL_ADDRESS>"PassHash":"a3f5b3b7c790e397b2d77eca31cd96bcd57c8700"
},
"_type":"_doc"
},
{
"_id":"dBmbWmsBBGJneKoTZ5Wk",
"_index":"pw_data",
"_score":16.46111,
"_source":{
"Domain":"exploit.in",
<EMAIL_ADDRESS>"Password":"vili2005"
},
"_type":"_doc"
}
]
Note you may use the fields below to guide you, but not all fields are returned with each query,
only available fields. If fields are added, they will be appended to the list of available fields for
query.
The following are the databases available for search.
{% for db in dbs %} {% if "all" in db %} {% endif %} The {{ db.split(".")[0] }} DatabasePlease see the Home page for the terms of service before using this.


Souvenir
Shop
Admin
Login
Logout
Basket({{basketLength}})
Basket
WOMAN
MAN
CHILD
HOME & LIFE
COSMETIC
SHOE & BAG
WATCHES & ACCESSORIES
ELECTRONIC


TDI helps
match with
The emphasis of this “quant security pricing” (QSP) module will be on the use of stochastic models to price derivative securities in various asset classes including equities, fixed income. Particular attention will be paid to how banks use these tools in practice to price derivative products. Students who have completed this module will gain a solid understanding of the "rocket science" behind financial engineering both in theory and practice.
This week first introduces the fundamental concepts of present value and principle of no-arbitrage. Pricing of swaps, forward rate agreements and floating rate bonds will then be discussed using present value and no-arbitrage principle. Concepts of short rate, forward rate and discount factor and their relationship will then be introduced followed by swap curve construction using stripping of market instruments and interpolation.
This week first introduces the notions of derivatives such as futures and options. We will then discuss the mechanics of various vanilla option markets including equity options, foreign currency options, interest rate options and commodity options. We will then introduce the basic options trading strategies such as spread, straddle and butterfly spread, and illustrate the motivation of the investors behind each trading strategy. Finally, we will introduce some of the more popular exotic equity and foreign currency derivatives that are traded in the market and the motivation of the investors who are buying such products.
This week introduces the famous Black Scholes Equation of pricing vanilla options. Fischer Black, Myron Scholes and Robert Merton made a breakthrough in the pricing of stock options in early 1970’s and Myron Scholes and Robert Merton were awarded the Nobel Prize in Economics in 1997 for this work (Fischer Black unfortunately died in 1995). We will derive the Black Scholes Equation using the technique of replication. This technique turns out to be more important than the equation itself and is the cornerstone of derivatives pricing. We will introduce a very useful and very popular technique for pricing derivatives known as binomial trees using the technique of replication. Monte Carlos simulation, which is a popular technique to price path independent options will also be introduced. This week will continue with the introduction of Greeks such as delta, gamma and vega. These are the key parameters for derivative sellers to manage and hedge their market risks of transacting in derivatives.
This week will discuss how to price vanilla European and American equity and foreign currency options using Black Scholes and binomial trees. The volatility surface will then be introduced to illustrate the assumption of constant volatility is violated in the market. The week will end with discussion of pricing exotic equity and FX options using constant and stochastic volatility models, and dynamics and statics hedging of such exotics.
This course discusses an overview of different interest rate
models and pricing of vanilla and interest rate options.
We will first introduce a version of Hull-White model known as LGM (Linear Gauss
Markov) of pricing caps and swaptions will then be introduced, followed by
discussion of other interest rate models and their strengths and weaknesses.
We will first introduce some of the more popular exotic interest rate derivatives that are traded in the market and the motivation of the investors who are buying such products. We will go through the pricing and hedging of one kind of interest rates exotics known as Bermudan swaptions with early termination in full detail using LGM model. We will use the swap curve and market prices of swaptions and caps to calibrate our LGM model. Which market instruments should be chosen for the calibration will also be discussed? The calibrated LGM model will then be used to build a binomial for pricing the Bermudan option. Finally we will use the bumping method to calculate the delta and vega for hedging followed by the trick of internal adjusters for constructing more stable hedges.
In this “quant portfolio management” (QPM) module, you will gain an understanding of the theory underlying optimal portfolio construction, the different ways portfolios are actually built in practice and how to measure and manage the risk of such portfolios. You will start by studying how imperfect correlation between assets leads to diversified and optimal portfolios as well as the consequences in terms of asset pricing. Then, you will learn how to shape an investor's profile and build an adequate portfolio by combining strategic and tactical asset allocations. Finally, you will have a more in-depth look at risk: its different facets and the appropriate tools and techniques to measure it, manage it and hedge it.
In this introductory week, you will first be presented with a
few mistakes you will no longer make after following this course. In order
to avoid making these mistakes, you will start by gaining a foundation and
understanding of the three main types of information we need in order to
build optimal portfolios: expected returns, risk and dependence.
The focus of this section is on Modern Portfolio Theory. By
understanding how imperfect correlations between asset returns can lead to
superior risk-adjusted portfolio returns, we will soon be looking for ways
to maximize the effect of diversification, which is at the heart of Modern
Portfolio Theory. But we won’t stop there: we will also explore the
implications of Modern Portfolio Theory on real-world investment decisions
and whether or not these implications are followed by investors. Finally, we
will see how Modern Portfolio Theory can be built upon to derive the most
popular asset pricing model: the Capital Asset Pricing Model.
This section is dedicated to asset allocation. We will
introduce the Markowitz mean-variance asset allocation that is foundational
to modern portfolios and then points out the challenges of implementing the
mean-variance model in practice. Finally, we will examine the
Black-Litterman Model developed at Goldman Sachs in 1990 that is designed to
overcome problems that institutional investors have encountered in applying
the mean-variance model in practice.
We will start by looking in more depth at different sources
of risk and different tools available to investors to perform risk
management. But how should we measure risk? We will see that it may be
valuable to go a step beyond standard deviation, the risk measure we used so
far, and look at the Value-at-Risk and Expected Shortfall which focus on
potential large losses. Finally, we will use the financial instruments at
our disposal to hedge market and currency risk.
This “quant algo trading” (QAT) module is for students to gain familiarity with the broad area of algorithmic trading strategies and learn about the underlying theory and mechanics behind the most common trading strategies. Students will also acquire an understanding of the mechanics of standard implementations of the single asset and portfolio based risk-premia trading strategies, recognize pros and cons of various approaches to designing strategies and the common pitfalls encountered by algorithmic traders and be able to devise new and improved algorithmic strategies. Finally, students will learn about the statistical properties of strategies and discern the mathematically proven from the empirical so that they can acquire and improve methods to prevent overfitting.
Overview, Math Background, Trend Following, Mean-Reversion.
Topics covered: Quant Trading – Definitions and Motivation; Quant Trading as
an Industry; The Basics: Quick Overview of Background
Mathematics/Statistics; Emphasis on modern fitting techniques, methods of
solution, properties of solutions; Fads, Fancies and Trends; Momentum;
Impact on design – option value vs reactivity, skewness and Sharpe;
Mean-Reversion Indecisive Markets?
More Mean-Reversion: Pairs/RV trading, Carry and Value.
Topics covered: Mean-Reversion Indecisive markets (continued); Pair Trading;
Statistical Arbitrage; Catching falling knives; When trades go bad; Seeing
things more simply; Carry – When things stay the same; What should it really
be worth?
How to combine different trading strategies for a trading model? Topics covered: Motivation; Bootstrap Aggregation; Random Forest; Boosting.
Overfitting, Multiple testing, Covariance Penalties, Robustness and Rehash. Data Snooping, P-Hacking and Bad Science. Topics covered: Data snooping; P-hacking and non-reproducible results; Standard fixes – Train/Test/Holdout; Multiple testing methods; Backtest Statistics – Drawdown/ Shortfall; Data snooping and (Robust) Machine Learning; Preventing data-snooping in practice; Summary.


This is an application where you can manage Marvel heroes, enjoy! Newly added Avengers can be found on List Avengers page.
{{hero.description}}


COVID RESTRICTIONS
Government Regulations
Due to covid measures set by he govt. we allow a maximum of 100 attendees per sessions. Book for your spot and session below
ReserveDue to covid measures set by he govt. we allow a maximum of 100 attendees per sessions. Book for your spot and session below
Reserve


A short typeId that's better for network message size but requires a lookup in the ClassRegistry 16-bit sequential id
A long type id that is computable from the typename 64-bit hash
The human readable name of the type, must be globally unique.
Scheduled a callback repeatedly at periodMS
milliseconds after the start of each callback call.
Avoids the period being dependent on execution time. This is not guaranteed if the callback takes longer than periodMS
or if the executor is hogged by other code.
Name of the callback used in errors and warnings.
Generated using TypeDoc
Utility to get nominally typed type-aliases See Approach 4 in https://michalzalecki.com/nominal-typing-in-typescript/


CAD SNPs
As in the exercise from Dr. Euan Ashley, various genetic variants have been associated with coronary artery disease (CAD).
In this exercise, we will examine a different set of alleles associated with coronary artery disease.
Again, note that this is not an exhaustive list of variants,
nor is the total number of risk alleles an official indicator of developing CAD.
These variants are purely for educational and research purposes only.


{% extends 'base.html' %} {% block page_title %}Log in | {% endblock %} {% block content %}
Please log in to your VK account to show and save last 10 posts from your VK wall.
Log in to VK and save my posts


Getting Started
So it’s a new year and I’ve decided that I’ll get back into blogging - it’s been a while since I’ve gotten anything down so I thought I’d give it a go - New Year, New Start and all that.
Anyhow, so as this year I’m looking to do some serious cycling, what better option to write on then?
So this is the start of a new blog that will be focussing on my riding for the year.
With the new year, it also sees the addition of a new bike - however, this isn’t likely to arrive until the end of January thanks to the ordering process.
The end of last year saw me decline in the number of rides that I did, compared to the start - overall I managed 2,500Km last year, up 500Km on the year before (tracked Km’s that is - the actual distance is probably longer, due to commuting to the station and lost GPS files when the Garmin played silly buggers etc).
The graph (taken from the excellent Veloviewer) shows distance against time on my rides on Strava. The last few months are fairly flat. That’s something I’m looking to change this year.
In addition to getting out and keeping the riding fairly constant, I’m looking at taking one photo (at least) on each ride I do. This is because sometimes I feel I just put my head down and cycle and don’t keep an eye on things around me - there’s some beautiful countryside around where I live and cycle and very often I can just blaze right by it. I’m looking to change that. After all, the view from Box Hill last year was stunning (after I made it to the top) but I couldn’t describe it fully now as I didn’t stop for a photo.
The aim for the year is to match last years distance - 2,500Km in a year. I’m hoping to beat that but that can be a starting point! I doubt that will include my commutes (it doesn’t seem worthwhile as I could probably get to the station before the GPS achieved a GPS lock!) but I’ve a few trips I want to do this year that should see that mileage tick up.
Anyhow, this blog is designed to help me document my cycling this year.
In short:-
-
Ride more
-
Take my photos


Before Change
]
// Again, not sure max_workers == number of diffs to avg
diffs = random.sample(diffs, server_config.get("max_workers"))
raw_diffs = [
[diff[model_param] for diff in diffs]
for model_param in range(len(model_params))
]
logging.info("raw diffs lengths: %s" % str([len(row) for row in raw_diffs]))
sums = [reduce(th.add, param) for param in raw_diffs]
logging.info("sums shapes: %s" % str([sum.shape for sum in sums]))
diff_avg = [th.div(param, len(diffs)) for param in sums]
logging.info("diff_avg shapes: %s" % str([d.shape for d in diff_avg]))
After Change
if iterative_plan:
diff_avg = raw_diffs[0]
for i, diff in enumerate(raw_diffs[1:]):
diff_avg = avg_plan(diff_avg, diff, i + 1)
else:
diff_avg = avg_plan(raw_diffs)
logging.info("diff_avg shapes: %s" % str([d.shape for d in diff_avg]))


when you like/reblog/steal things, which blog should it post to?
{% for blog in user_blogs %}
{% endfor %}