{{departmentName}} Department Report
------------------------------------------------------
Employee Name ::::::: {{ employee.empName }}
Employee Contact ::::::{{ employee.empContact }}
Employee Email ::::::: {{ employee.empEmail }}
Department ::::::: {{ employee.empDept }}
Designation ::::::: {{ employee.empDesignation }}
Qualifications ::::::: {{ employee.empEdu }}
Gender ::::::: {{ employee.empGender }}
Starting Date ::::::: {{ employee.startDate }}


@(message: String) @base("empty") {
A prototype demonstrating the potentials of ontologies in the context of business process intelligence. Hello @{val name = message; name}!
Import event data from different sources
Export the event in different serialization formats
Get an overview about the data basis
Get detailed insight into the performce of the workflows and activities
Create, execute and save custom SPARQL queries


06 Jun 2015
Tbilisi II - Visa-troubles and David Gareja
The articles here are still almost a month behind my actual itinerary. Normally I would deal with the mounting feeling of guilt by taking a nap and then using the boost of energy to clean the kitchen or look up cat videos on Youtube.
Unfortunately the nap is out of the question: It’s impossible to find a sleeping position in which my lobstered skin doesn’t cause me to suffer dreams of hellfire, father-of-the-year King Stannis or the Vietnam war. Turns out, the colour of your skin can cause lots of problems. Who knew?
In the absence of decent excuses I’m now sitting in the living room, not-sipping my not-coffee (because Ramadan). As its rich, smokey flavour not-fills my mouth, I’m thinking that I might as well get on with the writing.
In Tbilisi I ended up spending little time on sightseeing, but rather more on socializing. My theory is that any person troubled by shyness or lack of friends should grab a bag, book a ticket and find themselves some nice hostels to stay in. Hostels are full of people with the strangest background and experiences! Most of them tend to actively look for other people to hang out with. That relative abundance of social interaction also puts any fears of blunders or awkwardness in perspective: So what, you’ll never see those people again - unless you want to.
In a way, that’s what the choice between hotels and hostels comes down to: Would you rather talk to waiters and room service or other travelers?
Airport? No.
Between the meals and nights out, I managed to clarify the situation regarding my visa for Iran. Clarify that I wasn’t going to get one, that is. My initial plan had been to fly into Teheran, spend two weeks roaming around Iran and then flying out to Bahrain. This actually sounded like a really good plan because of Iran’s Visa-on-arrival service, which allows citizens of some countries to just pick up a visa at the airport. Being lazy but cautious I decided to ask about that at the embass before booking my flight.
The first attempt, I walked to the embassy (for about 90 minutes). Unfortunately, the place had closed down 10 minutes before I reached it. The opening hours listed on the website were out of date, apparently.
The second attempt, I reached the embassy early and was admitted in, together with a couple from Poland. It was their second visit to the embassy, and the looks on their faces didn’t exactly inspire confidence. Sure enough, we were met by a almost-certainly-competent embassy official. I could elaborate on the specifics, but I fear that it might jeopardize my chances of getting a visa in the future. Suffice to say that he eloquently answered our initial question in one sentence: “… … … …airport? … … … no.” He really stuck to his guns and used the same sentence to answer all of our follow-up questions, too. Instead, he helpfully directed us towards a visa service agency, which would apply for a regular visa on our behalf, for around 200 USD. That would take around 2 weeks. For me, this meant I simply was not going to go to Iran: If I had to wait for two weeks to get a visa, I would only have around 5 days to spend in Iran before meeting my friend in Bahrain. 200 USD (visa) + ~200 USD (flight) / 5 days = 80 USD/day just to get into the country.
Now I had met other travelers (tourists!) who had gotten into Iran on those airport visas very recently, no problem. Plus, all Iranian government websites stated the Visa-on-Arrival was still an option. So technically, I could have just booked my ticket and hoped for the best. However, this meant running the risk of being rejected at the airport and then being forced to book a pricy flight out on the spot. I decided to trust the charming man from the embassy and leave my Iran visit for another journey.
David Gareja
With that sorted, I decided to explore the Tbilisi area some more. Luckily, I had met some interesting people at the hostel before (Hi, Rene!) and we decided to do a day-trip to the David Gareja monastery right on the Georgian-Azerbaijani border. The orthodox monastery was founded in the 6th century and consists of dozens of chapels, caves and buildings. Today, the area is the object of a border dispute between Georgia and Azerbaijan.
We picked a tour operator near Liberty Square and were directed to a waiting car.  He would drive us to the monastery, we would complete a short hike there and then be driven back. Luckily, our driver was very accommodating of our tourist-y desire to take pictures of…everything and stopped frequently along the way.
The monastery itself turned out to be a fairly low-key affair: It is still operating (monks are still monking) and thus much of it is off-limits.
Leaving the monastery behind, we started our hike up the cliff towards the Azerbaijani border. The temperature must have hovered around 35°C and the path was baked solid by the sun. The path was also quite steep, so after a short while we were all sweat-soaked.
(Is that humming in your head the sign of an oncoming headache?  And no aspirin on you. Yep, definitely a headache. One of those pulsing ones. Ka-CHOCK….Ka-CHOCK. Awesome.)
After a few hundred meters of steep ascent we reached the top of the ridge. There, sitting on a huge rock, we found two Azerbaijani soldiers on watch, who called us in on their radio. Interestingly enough, they did not check our passports or require any documents. And thus we sat foot into Azerbaijan. The view from up there was stunning:
We then followed a footpath along the ridge and found several small caves with wallpaintings and inscriptions. All of these were outlying parts of the monastery and still used as places of worship.
Eventually, we made our way up to the peak of the ridge, where we checked back in with two Georgian soldiers sitting in the shade of a chapel there. We ended up spending half an hour just admiring the landscape and trying to snap a few good shots of the enormous birds of prey that circled around us:
Afterwards, we climbed down the other side of the ridge, back towards the car.
On the way back, we had a lunch-stop in the town of Udabno. As these things go, the only restaurant there was operated by our driver’s parents. Their entire community had been relocated to this area after their hometown in Svaneti had been devastated by a rockslide. The Soviets had begun building a completely new town for them, when the USSR collapsed and the money ran out.
After a quick lunch there, we all slept through the entire drive back to Tbilisi.
1300 words! On a sunday, no less! Next time: Another episode of “Wanna come?” - “Sure, why not?” with mountains, sheep and chacha - nectar of the gods and terror of mankind.
Until next time,
Arne


The Document Object Model (DOM) is an application programming interface (API) for valid HTML and well-formed XML documents. It defines the logical structure of documents and the way a document is accessed and manipulated. In the DOM specification, the term "document" is used in the broad sense - increasingly, XML is being used as a way of representing many different kinds of information that may be stored in diverse systems, and much of this would traditionally be seen as data rather than as documents. Nevertheless, XML presents this data as documents, and the DOM may be used to manage this data.
With the Document Object Model, programmers can build documents, navigate their structure, and add, modify, or delete elements and content. Anything found in an HTML or XML document can be accessed, changed, deleted, or added using the Document Object Model, with a few exceptions - in particular, the DOM interfaces for the XML internal and external subsets have not yet been specified.
As a W3C specification, one important objective for the Document Object Model is to provide a standard programming interface that can be used in a wide variety of environments and applications. The DOM is designed to be used with any programming language. In order to provide a precise, language-independent specification of the DOM interfaces, we have chosen to define the specifications in Object Management Group (OMG) IDL [OMGIDL], as defined in the CORBA 2.3.1 specification [CORBA]. In addition to the OMG IDL specification, we provide language bindings for Java [Java] and ECMAScript [ECMAScript] (an industry-standard scripting language based on JavaScript [JavaScript] and JScript [JScript]).
Note: OMG IDL is used only as a language-independent and implementation-neutral way to specify interfaces. Various other IDLs could have been used ([COM], [JavaIDL], [MIDL], ...). In general, IDLs are designed for specific computing environments. The Document Object Model can be implemented in any computing environment, and does not require the object binding runtimes generally associated with such IDLs.
The DOM is a programming API for documents. It is based on an object structure that closely resembles the structure of the documents it models. For instance, consider this table, taken from an HTML document:

 Shady Grove	 Aeolian

 Over the River, Charlie	 Dorian

A graphical representation of the DOM of the example table
is:
In the DOM, documents have a logical structure which is very much like a tree; to be more precise, which is like a "forest" or "grove", which can contain more than one tree. Each document contains zero or one doctype nodes, one root element node, and zero or more comments or processing instructions; the root element serves as the root of the element tree for the document. However, the DOM does not specify that documents must be implemented as a tree or a grove, nor does it specify how the relationships among objects be implemented. The DOM is a logical model that may be implemented in any convenient manner. In this specification, we use the term structure model to describe the tree-like representation of a document. We also use the term "tree" when referring to the arrangement of those information items which can be reached by using "tree-walking" methods; (this does not include attributes). One important property of DOM structure models is structural isomorphism: if any two Document Object Model implementations are used to create a representation of the same document, they will create the same structure model, in accordance with the XML Information Set [Infoset].
Note: There may be some variations depending on the parser being used to build the DOM. For instance, the DOM may not contain whitespaces in element content if the parser discards them.
The name "Document Object Model" was chosen because it is an "object model" in the traditional object oriented design sense: documents are modeled using objects, and the model encompasses not only the structure of a document, but also the behavior of a document and the objects of which it is composed. In other words, the nodes in the above diagram do not represent a data structure, they represent objects, which have functions and identity. As an object model, the DOM identifies:
The structure of SGML documents has traditionally been represented by an abstract data model, not by an object model. In an abstract data model, the model is centered around the data. In object oriented programming languages, the data itself is encapsulated in objects that hide the data, protecting it from direct external manipulation. The functions associated with these objects determine how the objects may be manipulated, and they are part of the object model.
This section is designed to give a more precise understanding of the DOM by distinguishing it from other systems that may seem to be like it.
The DOM originated as a specification to allow JavaScript scripts and Java programs to be portable among Web browsers. "Dynamic HTML" was the immediate ancestor of the Document Object Model, and it was originally thought of largely in terms of browsers. However, when the DOM Working Group was formed at W3C, it was also joined by vendors in other domains, including HTML or XML editors and document repositories. Several of these vendors had worked with SGML before XML was developed; as a result, the DOM has been influenced by SGML Groves and the HyTime standard. Some of these vendors had also developed their own object models for documents in order to provide an API for SGML/XML editors or document repositories, and these object models have also influenced the DOM.
In the fundamental DOM interfaces, there are no objects representing entities. Numeric character references, and references to the pre-defined entities in HTML and XML, are replaced by the single character that makes up the entity's replacement. For example, in:

This is a dog & a cat

the "&" will be replaced by the character "&", and the text in the P element will form a single continuous sequence of characters. Since numeric character references and pre-defined entities are not recognized as such in CDATA sections, or in the SCRIPT and STYLE elements in HTML, they are not replaced by the single character they appear to refer to. If the example above were enclosed in a CDATA section, the "&" would not be replaced by "&"; neither would the
 be recognized as a start tag. The representation of general entities, both internal and external, are defined within the extended (XML) interfaces of DOM Level 1 [DOM Level 1].
Note: When a DOM representation of a document is serialized as XML or HTML text, applications will need to check each character in text data to see if it needs to be escaped using a numeric or pre-defined entity. Failing to do so could result in invalid HTML or XML. Also, implementations should be aware of the fact that serialization into a character encoding ("charset") that does not fully cover ISO 10646 may fail if there are characters in markup or CDATA sections that are not present in the encoding.
This section explains the different levels of conformance to DOM Level 2. DOM Level 2 consists of 14 modules. It is possible to conform to DOM Level 2, or to a DOM Level 2 module.
An implementation is DOM Level 2 conformant if it supports the Core module defined in this document (see Fundamental Interfaces). An implementation conforms to a DOM Level 2 module if it supports all the interfaces for that module and the associated semantics.
Here is the complete list of DOM Level 2.0 modules and the features used by them. Feature names are case-insensitive.
Note: At time of publication, this DOM Level 2 module is not yet a W3C Recommendation.
A DOM implementation must not return "true"
to the
hasFeature(feature, version)
method of the DOMImplementation
interface for that feature unless the implementation conforms to
that module. The version
number for all features used
in DOM Level 2.0 is "2.0".
The DOM specifies interfaces which may be used to manage XML or HTML documents. It is important to realize that these interfaces are an abstraction - much like "abstract base classes" in C++, they are a means of specifying a way to access and manipulate an application's internal representation of a document. Interfaces do not imply a particular concrete implementation. Each DOM application is free to maintain documents in any convenient representation, as long as the interfaces shown in this specification are supported. Some DOM implementations will be existing programs that use the DOM interfaces to access software written long before the DOM specification existed. Therefore, the DOM is designed to avoid implementation dependencies; in particular,
The Level 1 interfaces were extended to provide both Level 1 and Level 2 functionality.
DOM implementations in languages other than Java or ECMAScript may choose bindings that are appropriate and natural for their language and run time environment. For example, some systems may need to create a Document2 class which inherits from Document and contains the new methods and attributes.
DOM Level 2 does not specify multithreading mechanisms.


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@(title: String)(message: String)(firstname: String)(lastname: String)(error: String)(errorType: String) @style = {} @script = {} @main(title)(style){ Virtual coach Export chat }(script)(firstname)(lastname)(error)(errorType)


Setting Up A Pipeline Configuration¶
Overview¶
There are two ways of setting up a pipeline configuration for C-PAC:
- Using a text editor (useful for remote servers where using the C-PAC GUI is not possible or impractical)
- Using the pipeline configuration interface in the C-PAC GUI
Definitions¶
Workflow - A workflow accomplishes a particular processing task (e.g. functional preprocessing, scrubbing, nuisance correction). Each workflow can be turned on or off in the pipeline configuration. Sometimes a workflow can be set to both on and off, allowing for pipelines to branch.
Pipeline - A pipeline is a combination of workflows.
- Strategy - A strategy is a set of preprocessing options. Specifically, a strategy is defined by nuisance corrections and scrubbing settings. Strategies can branch depending on which of these workflows are turned on or off and how they are configured. Their names are constructed by concatenating the following parameters:
- Number of principle components calculated by CompCor (if enabled)
- Nuisance corrections selected.
- Scrubbing threshold (if enabled)
For instance, _compcor_ncomponents_5_linear1.motion1.compcor1.SCRUB_0.2 is a strategy with 5 principle components for compcor, linear drift, motion, and compcor corrections applied, and a scrubbing threshold of 0.2 mm.
- Derivative - Derivatives are the results of processing a participant’s raw data (i.e., connectivity measures).
Using a Text Editor¶
Pipeline configuration files, like the data configuration and subject list files discussed in the subject list builder section , are stored as YAML files. Similarly, each of the parameters used by C-PAC to assemble your pipeline can be specified as key-value pairs, so a pipeline configuration YAML would have multiple lines of the form:
key : value
An example of a pipeline configuration YAML file can be found here . Tables explaining the keys and their potential values can be found on the individual pages for each of the outputs C-PAC is capable of producing. All pipeline configuration files should have the keys in the Output Settings table defined.
Why a list?¶
You may notice as you learn about the settings for various outputs that many of the values for C-PAC’s configurable settings are stored in lists (i.e., multiple values are separated by commas and surrounded by square brackets). Such lists containing 1s and 0s (for ‘True’ and ‘False’ respectively) allow you to toggle on multiple options at the same time, and branch a pipeline into two different analysis strategies. See the developer documentation for more information about how lists are used in C-PAC.
Using the GUI¶
Opening the GUI¶
If the C-PAC GUI is not open already, type the command cpac_gui in a terminal window as you would with the subject list builder. Then, on the main screen click on New next to Pipelines. For each of the settings in the lefthand pane, refer to the pages linked to below in the Configurable Settings section.
When you have finished configuring your pipeline, click Save. You will be asked to specify a location to save a configuration file containing information about the pipeline, and to specify a name for the pipeline.
Configurable Settings¶
Data Management and Environment Settings¶
Pre- and post-processing¶
Derivatives¶
- Seed-based Correlation Analysis (SCA) - Analyze the connectivity between brain regions.
- Voxel-mirrored Homotopic Connectivity (VMHC) - Investigate connectivity between hemispheres.
- Amplitude of Low Frequency Fluctuations (ALFF) and fractional ALFF (fALFF) - Measure the power of slow fluctuations in brain activity.
- Regional Homogeneity (ReHo) - Measure the similarity of activity patterns across neighboring voxels.
- Network Centrality - Analyze the structure of functional networks.


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QGraph is designed for use with databases that permit set-valued attributes. Therefore if an attribute has more than one value for an individual object or link, we must consider all of its values when evaluating conditions. And because the set of values may be empty, we also need to understand how QGraph evaluates attribute value conditions when the object or link has no values for the given attribute.
A condition is satisfied when the value of the corresponding attribute satisfies the test established in the condition. When an object or link has more than one value for a particular attribute, a condition on that entity is satisfied if any member of the set of attribute values for that entity satisfies the condition.[2]
Consider a movie database that links actors to the movies they appear in. Each of these links includes a Role attribute that lists the characters the actor played in the movie. In Monty Python and the Holy Grail, Graham Chapman played several roles, so the edge connecting Graham Chapman to Monty Python and the Holy Grail has a Role attribute with multiple values: King Arthur, Voice of God, Middle Head and Hiccuping Guard. A relevant fragment of such a database is shown in Figure 3.8.
To find actors and the movies in which they played King
Arthur we create a query that requires an edge condition
Role = King Arthur
, as shown in
Figure 3.9.
The pair of Peter Cook and Jake’s Journey is
returned as a match because the link connecting them has a
Role attribute with a single value of
King Arthur. The link connecting
Graham Chapman to Monty Python and the Holy Grail
has multiple values for the Role attribute.
Because King Arthur is one of these values,
this link also matches the condition on the query edge. The results of
the query are shown in Figure 3.10.
As we have seen before, even though the condition only requires
matching a single
attribute value, the matching subgraph includes all values for the
Role attribute.
Because QGraph considers the condition to be satisfied if any attribute value for the corresponding database entity satisfies the condition, care must be taken in defining queries and interpreting results when objects or links have multiple values for that attribute. For example, books are often released in different formats over time: hard cover, trade paperback, mass market paperback, etc. As a result, a single book can have multiple publication dates, as shown in Figure 3.11.
Consider a condition that tests the publication date for a book, e.g.,
PubDate = 1981
Because 1981 is a member of the set of values for Carrie’s PubDate attribute, the condition is satisfied and Carrie matches a query vertex with this condition.
But because Carrie’s PubDate attribute contains other values, the condition
PubDate <> 1981
is also satisfied. The other values for this attribute (1974, 1993, and 2003) make the expression true. Similarly, the conditions
PubDate < 1990, PubDate <= 1990, PubDate > 1990,
andPubDate >= 1990
are also satisfied because at least one PubDate value satisfies each of these tests.
Because objects and links in a Proximity database can be structurally heterogeneous, some entities may not have a value for the attribute listed in a condition. In such cases, an attribute condition’s test always fails because there are no values to compare against the condition’s requirements. Consider the database fragment shown in Figure 3.12.
Two of the movies, Carrie and It, have a ReleaseType attribute that indicates whether the movie was released in theaters or made for TV. The third movie, Pet Sematary, does not have a value for this attribute. If we execute the query shown below
we get the results shown in Figure 3.14.
The query finds only a single matching movie, Carrie,
which was released theatrically. Both
It, which was made into a TV movie, and
Pet Sematary, which does not include a release
type, do not match the query’s condition.
Objects and links that do not have the specified attribute similarly fail to match inequality conditions. The query shown in Figure 3.15, below, also finds books made into theatrical movies. For the given database fragment, where the ReleaseType attribute can only take a single value drawn from the choices theater or TV, this query is equivalent to the query shown earlier in Figure 3.13.
This new query yields the same results as the original. In addition to excluding the movie It because its value for the ReleaseType attribute is TV, it also excludes the movie Pet Sematary because this object has no value for the ReleaseType attribute. It’s important to note that the equivalence of these two queries depends on the database enforcing the restriction of legal values for the ReleaseType attribute to those specified. If, for example, the database also lets ReleaseType assume values of video and DVD, this equivalence no longer holds.
[2] This reflects a change in the QGraph language since the publication of the technical report A Visual Language for Querying and Updating Graphs [Blau, Immerman, and Jensen, 2002]. The QGraph Guide provides the current and correct rules for how QGraph evaluates conditions on set-valued attributes.


Below are some links to get you started. Happy exploring :-)
Please see src/test/htmlunit/org/wyona/yanel/htmlunit/yanelwebsite/GlobalDataWebTest.java
The source of this page is located at src/webapp/htdocs/help.html or rather at local/apache-tomcat-5.5.33/webapps/yanel/htdocs/help.html


This is a multi-person 2D pose estimation network (based on the OpenPose approach) with tuned MobileNet v1 as a feature extractor. It finds a human pose: body skeleton, which consists of keypoints and connections between them, for every person inside image. The pose may contain up to 18 keypoints: ears, eyes, nose, neck, shoulders, elbows, wrists, hips, knees and ankles.
| Metric | Value |
|---|---|
| Average Precision (AP) | 42.8% |
| GFlops | 15.435 |
| MParams | 4.099 |
| Source framework | Caffe* |
Average Precision metric described in COCO Keypoint Evaluation site.
Tested on a COCO validation subset from the original paper: Cao et al. "Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields".
Link to performance table
[*] Other names and brands may be claimed as the property of others.


Only your organisation can see this level of detail. All members of your organisation can access and edit your project finances. We recommend assigning completion of your project finances to one member of your team.
Complete your project costs using the information in your funding conversion spreadsheet (opens in new window). This will convert your EU Horizon 2020 spend details to use for your project costs in the finance section. It is vital that you keep a copy of the spreadsheet. You will need it to finish setting up your project.


Black Friday: Beware these cyber scams
by Brooke CrothersIn the last few weeks, tens of thousands of potential scams involving brand names have been discovered, cybersecurity firms are warning.
ZeroFOX identified 61,305 potential scams between Nov. 1 and Nov. 20, according to a report that focused on 26 brands, including names like Samsung, Microsoft, Sony and Tiffany.
In a separate report, Check Point said that more than 1,700 domains, which look similar to the amazon.com domain have been registered in recent months. The sites, such as “amaz0n-jp[.]com,” are meant to mimic legitimate sites.
BEWARE OF BLACK FRIDAY EMAIL SCAMS, SAY EXPERTS
And in the first half of November, the use of e-commerce phishing websites has more than doubled since last November’s peak, up by 233 percent, Check Point said.
The goal, like all scams, is to trick shoppers to input personal information in order to steal credit card data.
The lure is fake gift cards or discounts and even charity-giving, ZeroFOX said.
'DONALD TRUMP SCREEN OF DEATH': PRESIDENT’S LIKENESS USED AS RANSOMWARE BAIT
Keywords like “coupon” or “deal” or “free” are used to get shoppers’ attention. Then there is a “call to action” to try to get the victim to log in or verify an account, purportedly to get the reward, according to ZeroFOX.
Scammers are active on social media with hashtags such as “#blackfriday” or “#cybermonday” or “#giveaway.”
“This makes these posts more likely to be shown to social media users, based on the social platform’s algorithms, and also makes them searchable,” ZeroFOX said.
HACKERS ARE DRAINING ATMS ACROSS THE US
“Similarly, scammers may leverage fake accounts to like and share or retweet these scam posts, giving them more legitimacy,” the cybersecurity firm added.
The bad guys also use Google Chrome extensions – small software programs that you install to customize how you browse – to spread malware.
One Chrome extension had over 60,000 installs with dozens of negative reviews complaining about data theft and malware, ZeroFOX said.
MASSIVE SEXTORTION ATTACK RUNNING WILD, SECURITY FIRM SAYS
Black Friday invariably means a big spike in scams.
“Legitimate giveaways rarely ask for anything more than an email address. A promotion requesting anything further is likely a scam,” ZeroFOX said.
Also, be sure the URL, or web address, is legitimate. A rule of thumb that all cybersecurity experts advise is never clicking on a link in an email or text. Rather, go directly to the retailer’s or brand's website via your browser. And use special caution when interacting with promotional sites. “If a promotion sounds too good to be true, it probably is,” ZeroFOX said.
CLICK HERE TO GET THE FOX NEWS APP
Finally, consider using another email address – not your primary email – for promotional entries.


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|
Leitor e Exibidor JVM
01/2019 - Software Básico - Professor Ladeira
|
A estrutura do cp_info ira compor a constant_pool do classFile. Mais...
#include <reader.h>
Campos de Dados | |
| uint8_t | tag |
| Valor de um byte que indica o tipo de entrada. | |
| union { | |
| struct { | |
| uint16_t name_index | |
| Detailed description after the member. | |
| } Class | |
| ALO. Mais... | |
| struct { | |
| uint16_t class_index | |
| uint16_t name_and_type_index | |
| } Fieldref | |
| ALO. | |
| struct { | |
| uint16_t class_index | |
| uint16_t name_and_type_index | |
| } Methodref | |
| ALO. Mais... | |
| InterfaceMethodref | |
| struct { | |
| uint16_t string_index | |
| } String | |
| struct { | |
| uint32_t bytes | |
| } Integer | |
| struct { | |
| uint32_t bytes | |
| } Float | |
| struct { | |
| } Long | |
| struct { | |
| uint32_t high_bytes | |
| uint32_t low_bytes | |
| } Double | |
| struct { | |
| uint16_t name_index | |
| uint16_t descriptor_index | |
| } NameAndType | |
| struct { | |
| uint16_t length | |
| uint8_t * bytes | |
| } Utf8 | |
| struct { | |
| uint8_t reference_kind | |
| uint16_t reference_index | |
| } MethodHandle | |
| struct { | |
| uint16_t descriptor_index | |
| } MethodType | |
| InvokeDynamic | |
| } | info |
| Tipo de dado retornado pela union. | |
| uint16_t | bootstrap_method_attr_index |
A estrutura do cp_info ira compor a constant_pool do classFile.
cp_info varia de acordo com o byte lido em sua tag
| uint16_t cp_info::bootstrap_method_attr_index |
Detailed description after the member
| uint32_t cp_info::bytes |
Detailed description after the member
| uint8_t* cp_info::bytes |
Detailed description after the member
| struct { ... } cp_info::Class |
ALO.
Class Detailed description after the member
| uint16_t cp_info::class_index |
Detailed description after the member1
Detailed description after the member
| uint16_t cp_info::descriptor_index |
Detailed description after the member
| uint32_t cp_info::high_bytes |
Detailed description after the member
| uint16_t cp_info::length |
Detailed description after the member
| uint32_t cp_info::low_bytes |
Detailed description after the member
| struct { ... } cp_info::Methodref |
ALO.
Methodref Detailed description after the member
| uint16_t cp_info::name_and_type_index |
Detailed description after the member2
Detailed description after the member
| uint16_t cp_info::name_index |
Detailed description after the member.
Detailed description after the member
| uint16_t cp_info::reference_index |
Detailed description after the member
| uint8_t cp_info::reference_kind |
Detailed description after the member
| uint16_t cp_info::string_index |
Detailed description after the member
Horsemade by The Amazing Suicidal Horsegroup| -- |™
Gerado por 1.8.16


--- layout: post title: Athletics Kenya Confirm Use of Banned Substances date: 2012-09-29 08:16:39.000000000 +01:00 tags: - link - athletics - drugs - Kenya type: post published: true ---
Renowned German sports journalist Hajo Seppelt claims he spent time in various parts of Kenya where he posed as an undercover sports agent and found out that doping is rife not only amongst Kenyan middle and long distance runners and but even with some of the foreign athletes who have been training in the famous high altitude areas of Iten.
— Athletics Kenya confirm use of banned substances
I guess it IS something about the region that produces some of the world's best endurance runners, pity it's more than just the altitude, hills and way of life and training for some of them.


| package ti.sysbios.knl |
|
Contains core threading modules for the SYS/BIOS kernel
Many real-time applications must perform a number of functions at the
same time, often in response to external events such as the availability of
data or the presence of a control signal. Both the functions and
when
they are performed are important.
[
more ... ]
package ti.sysbios.knl [2, 0, 0, 0] {
// System Clock Manager
// Event Manager
// Idle Thread Manager
// Mailbox Manager
// Queue manager
// Semaphore Manager
// Software Interrupt Manager
// Task Manager
}
DETAILS
Many real-time applications must perform a number of functions at the
same time, often in response to external events such as the availability of
data or the presence of a control signal. Both the functions and when
they are performed are important.
Such functions are called "threads". Within SYS/BIOS, the term is defined
broadly to include any independent stream of instructions executed by the
processor. A thread is a single point of control that can activate a
function call or an interrupt service routine (ISR).
SYS/BIOS enables your applications to be structured as a collection of
threads. Multithreaded programs allow higher-priority threads to preempt
lower-priority threads and allow various types of interaction between
threads, including blocking, communication, and synchronization.
SYS/BIOS provides support for several types of threads with different
priorities. Each thread type has different execution and preemption
characteristics. The thread types (from highest to lowest priority) are:
The ti.sysbios.knl package also provides several modules for
synchronizing threads.
- Semaphores (ti.sysbios.knl.Semaphore) are often used to
coordinate access to a shared resource
among a set of competing tasks. Semaphores can be used for task
synchronization and mutual exclusion. Semaphore objects can be declared
as either counting or binary semaphores. The same APIs are used for
both counting and binary semaphores.
- Events (ti.sysbios.knl.Event) provide a means for
communicating between and synchronizing
threads. They are similar to Semaphores, except that they allow you
to specify multiple conditions ("events") that must occur before the
waiting thread returns.
- Mailboxes (ti.sysbios.knl.Mailbox) can be used to pass buffers
from one thread to another.
A Mailbox instance can be used by multiple readers and writers.
In addition to Semaphores, Events, and Mailboxes, you can use
Gates to prevent concurrent accesses to critical regions of code.
The
xdc.runtime.Gate module is provided by XDCtools, but
SYS/BIOS
provides some additional implementations in
ti.sysbios.gates.
generated on Wed, 28 Aug 2019 17:00:29 GMT


Train Scheduler
Choo Choo!
Current Train Schedule
Train Name
Destination
Frequency (min)
Next Arrival
Minutes Away
Add Train
Train Name
Destination
First Train Time (HH:mm- military time)
Frequency (min)


RESULTS
Results of 2019 General Elections
| Office | Candidates | Number of votes | Result |
|---|---|---|---|
| PRESIDENT |
Muhammad Buhari
Atiku Abubakar
Festus Chiamaka
|
14,678,987
12,678,987
12,678,987
|
WON
LOST
LOST
|
| GOVERNOR |
Muhammad Buhari
Atiku Abubakar
Festus Chiamaka
|
14,678,987
12,678,987
12,678,987
|
WON
LOST
LOST
|


Symfony2 CMF Project
Why does the world need this?
Drupal is awesome!
No REALLY!
Drupal is awesome!

Drupal is CMS 1st, development framework 2nd
This is a legitimate prioritization for their users
Drupal developer nightmares ..
- .. no clean separation of configuration, logic and content
- .. no clean deployment and staging concept
- .. inconsistent cache layers
- .. function callback AOP is not every ones cup of tea
- .. lots of legacy baggage
- .. NIH (not invented here) syndrom
Note the Drupal guys agree and are actually collaborating with Symfony2 to improve
Do we also suffer from NIH?
- Collaboration with Drupal and others to share basic content management code
- Based ourselves as much on standard tools and specs
- Leverage existing bundles and technologies of the Symfony and web developer community
- Deliver value within a reasonable time
- Both Drupal 8 and ezPublish 5 use the CMF routing component
CMF = Content Management Framework
- In other words: its a toolbox to create your own custom CMS
- Not a one size fits all, but increase code sharing
- Imagine Diem, Sympal, Apostrophe all build on the same content foundation
The Symfony CMF project makes it easier for developers to add CMS functionality to
applications built with the Symfony2 PHP framework. Key development principles for the provided
set of bundles are scalability, usability,
documentation and testing
What is Content Management?
User controlled
- Content
- Content structure
- Navigation structure
- Routes (URLs)
The CMF solutions to these requirements are discussed in the "Big picture" slide set
Data in a CMS is mostly unstructured
RDBMS are not a good fit, hurray for NoSQL
CMS often organize content as a tree/graph
Most NoSQL not a good fit, hurray for Graph DBs
CMS should be able to store content versions
Complexity shouldn't overwhelm developers
Need a solution that can scale both from small to large projects and we want to get somewhere within 12 months!
Enter Doctrine PHPCR ODM
PHP Content Repository + Doctrine ODM
PHPCR (aka PHP-ified JCR specification)
Works like MongoDB or CouchDB ODM, but also includes a tree/graph, versioning API
PHPCR provides a standardized API that can be used by any PHP content management system to interface with any content repository.
PHPCR implementations
- Jackalope
- Jackrabbit (via Http WebDav/Davex) transport layer
- Doctrine DBAL transport layer
- Midgard2 PHPCR
- ..
PHPCR has been submitted to the JCR spec at the request of David Nüschler, JCR spec lead
Not all data fits well in PHPCR/JCR
- For example aggregation is better done in an RDBMS
- Store web store product description in PHPCR/JCR
- Store web store inventory and orders in RDBMS
Door swings both ways, so remember
Welcome to the real world
- Quiet Storm Solutions is utilizing the CMF and to drive a newly launched Saas CRM service
- ServerGrove is using the PHP content repository in their KnowledgeBase tool.
- Liip integrated the CMF as a simple editing solution into Symfony web applications like www.barometredesprix.ch.
- Eat your own dog food: the Liip website and VDW website are both built with the CMF
- Joiz uses it for their social media based TV station
- See our references for a more complete overview
Interest is huge!
- Over 350 people on the developer mailinglist
- About 5 more per month
- About half a dozen core contributors with weekly contributions
- Community is very willing to teach new comers
- All key decisions have been made in public forums
- Multiple people are working and committing to all parts of the code
Several companies and organisations are investing into the effort
Roadmap
- Follow about one month after Symfony2 core releases
- Respect deadline over cramming in more features
- But regular releases every half a year


{{define "main"}} {{ partial "navbar" . }}
Feel free to reach out to us with any questions, comments, or concerns. We will get back to you as soon as possible. If you are looking to collaborate with us in any way shape or form, you can do it here.


Full Stack Web Developer
Full Stack Web Developer, love developing apps and websites with a focus on mobile-first design & graduate of the UNC Chapel Hill Coding Boot Camp
Languages :
Applications :
Tools :
Team Profile Generator
It's back end project that's help users to create a web page for a team that are working together its stats by asking the leader or the manager the insert information about his employee and for each worker there are specific form of questions that have to be field to get full information about each person with each info.
technologies :
Employee Tracker
This is back end project that allows users to track their employees or add new employees or managers and set them right in specific tables in data base and show their position in the company and their salaries depends on their position and in case if you want to add new department to the actual table you the choice for that two.
technologies :
README Generator
A README file that helps developers to write their project files in a professional way.
technologies :
Tech Blog
This is a back end and front end project that allows users to post about whatever they wish. It supports account creation with secure password hashing and storage as well as the ability to login They can also comment on posts if they are logged in. If the user idles for too long they will be logged out of their account The application contained,
technologies :
Im Feeling (Project)
I'AM feeling app it's an app that helps users to get anywhere they like depends on how they feel.


gallons of water
A lot of people think that being vegan is about letting your hair grow long, never take a shower again and be a hippy animal lover. Well, it’s a little lot more than that. When you eat a plant based diet you’re not only saving millions of animals but also contributing to the environment. Wait what? Aren’t we supposed to take shorter showers, ride our bikes everywhere and recycle our trash to save the planet? Well, turns out you could be doing double the amount of impact just by eating a vegan diet.
What if I ate vegan 1 day a week for the rest of the year?
SELECT THE DAYS YOU COULD BE VEGAN A WEEK
You would save a year...
gallons of water
pounds of grain
sq. feet of Forested Land
pounds of CO2
animals lives
We know going 100% vegan is a big commitment, which is why we encourage people to select the days they think they could commit to a plant based diet. Of course we would love for everyone to become vegan, but we believe that by simply creating awareness on how our food choices can have an impact on the environment is a great starting point.
You can make a difference
today


This is an SPA made with
Mirage and
Vue. It has two routes to help
demonstrate how Mirage's in-memory database enables realistic data
fetching and persisting during a single application session.
Mirage's state is reset whenever the application is reloaded.


A wallpaper app of it's own kind. Comes with many diferent features, such as account specific sharing, high resolution, downloads and much much more
Yet another music app, but with a twist! This allows you to stream music and share it with friends, you can add your paradesigns account.


Congratulations, LNMP a successful installation!
LNMP stack Linux+Nginx/Tengine+MySQL/MariaDB/Percona+PHP+Pureftpd+phpMyAdmin+redis+memcached+jemalloc/tcmalloc.
Check environment: Proberv phpinfo xcache phpMyAdmin
Create FTP virtual running script: ./pureftpd_vhost.sh
For feedback, questions, and to follow the progress of the project: https://github.com/lj2007331/lnmp
Author email<EMAIL_ADDRESS>Donate:
PayPal<EMAIL_ADDRESS>AliPay: lj2007331@gmail.com


{% extends "admin/layout.html" %} {% block title %}{{ title|striptags }}{% endblock %} {% block contents %}
{{ _("The online documentation could not be found.") }}
{% trans %}Possibly you have to generate the docs first by running the build-documentation script from pyClanSphere's scripts folder.{% endtrans %}
{% endblock %}


Grain boundary effect on the morphology of cutting chips for different kinds of work material: ( a) raised line structure on the cutting chip of aluminium alloy Al6061; ( b) step structure on the cutting chip of polycrystalline copper. Reproduced with permission from [ 113, 114].


This page will tell you whether you are applicable for the process. Provide marks
Validate My Application


Implements classes and methods for modeling spatial point patterns using inhomogeneous Poisson point processes, where the intensity surface is assumed to be analogous to a finite additive mixture of normal components and the number of components is a finite, fixed or random integer. Extensions to the marked inhomogeneous Poisson point processes case are also presented. We provide an extensive suite of R functions that can be used to simulate, visualize and model point patterns, estimate the parameters of the models, assess convergence of the algorithms and perform model selection and checking in the proposed modeling context.
| Version: | 1.0.2 |
| Depends: | R (≥ 3.2.1), stats, graphics, grDevices, utils |
| Imports: | Rcpp, spatstat, rgl, fields, mvtnorm, ggplot2 (≥ 2.1.0) |
| LinkingTo: | Rcpp, RcppArmadillo, mvtnorm |
| Suggests: | knitr, rmarkdown |
| Published: | 2017-11-17 |
| Author: | Jiaxun Chen and Athanasios (Sakis) Christou Micheas. Significant contributions on the package skeleton creation, plotting functions and other code by Yuchen Wang |
| Maintainer: | Sakis Micheas <micheasa at missouri.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://faculty.missouri.edu/~micheasa/sppmix/index.html |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | sppmix results |
| Reference manual: | sppmix.pdf |
| Vignettes: |
Tutorials for the 'sppmix' package |
| Package source: | sppmix_1.0.2.tar.gz |
| Windows binaries: | r-devel: sppmix_1.0.2.zip, r-release: sppmix_1.0.2.zip, r-oldrel: sppmix_1.0.2.zip |
| OS X binaries: | r-release: sppmix_1.0.2.tgz, r-oldrel: sppmix_1.0.2.tgz |
| Old sources: | sppmix archive |
Please use the canonical form https://CRAN.R-project.org/package=sppmix to link to this page.


Welcome to Quizzy!
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Cloud Native - Serverless Microservice Example
Retrieve Current weather for a location:
Zip Code:
OpenWeatherMap App Id (API Key):


--- layout: page.hbt ---
Join our slack (online forum) community
Working on a way to make this easier but for now it is a manual activity where we need to invite people ourselves. https://wiredcraft.com/blog/using-slack-as-a-public-chat/
Attend one of our public meetings, details at http://www.meetup.com/The-Research-Triangle-BioCoders/
JimRose@tridiybio.org


Location: Provo, UT 84604
Phone:<PHONE_NUMBER>
Email<EMAIL_ADDRESS>Proficient in Excel
Proficient in Microsoft Word
Vast Experience with STATA Programming
Experienced with RStudio
Knowledge of HTML/CSS, Boostrap, C++, Python and JavaScript
Fluent in Spanish & Portuguese
Potandon / Accounting Intern
FEBRUARY 2012 - JANUARY 2015, IDAHO FALLS, IDAHO
Worked in account payables and receivables.
Edge Pest Control/ Initial Service Coordinator
JUNE 2017- DECEMBER 2017, OREM, UTAH
Managed routes for service technicians and managed client’s accounts.
Academy for Creating Enterprise/ Marketing
JANUARY 2018 -MAY 2018, PROVO, UTAH
Used Stata and R Studio to analyze current trends of offices throughout South America and the Philippines. Created a new incentivization program to motivate employees and volunteers globally. Designed new training materials, including presentations and pamphlets. Translated the program to Spanish.
Impact Evidence / Research Assistant
OCTOBER 2017 - CURRENT , PROVO, UTAH
Data mined South American government officials to send information about international development. Translated high quality impact evidence research for policy makers in South America in Portuguese and Spanish.
Brigham Young University / Research Assistant
OCTOBER 2017 - CURRENT , PROVO, UTAH
Used Stata and R Studio to analyze current trends of public land politics. Did qualitative research in Arizona, Colorado, Southern Utah, and the Navajo Nation.
ㅡ
Brigham Young University-Hawaii / Associates of Arts and Sciences
AUGUST 2014 - OCTOBER 2015, LAIE, HAWAII
Received high honors. Cluster emphasis in Social Science, including Disaster Management, Intermediate Spanish, and Pacific Island Studies.
Brigham Young University / Bachelors
JANUARY 2016 - PRESENT, PROVO, UTAH
Studying Political Science with an emphasis in Research Methodology.
ㅡ
2013 Beijing International Communications College Representative
2013-2014 Shelley High School Debate Captain
2014 BYU-Hawaii’s United Nations’ Panel Champion
2017 Spanish BYU Oral Proficiency Exam Advanced-Intermediate
2018 Portuguese BYU Oral Proficiency Exam Intermediate-Advanced
If you wish to view my resum as a PDF instead of HTML (as shown above) you can download it here!


{% extends "templates/partials/base.html" %}
{% block crp_block %}
{% endblock %}
{% block content %}
{% set data = cms.tags %}
{# This is how you set pagination. It's always important to do this after your sort. #}
List of tags
{# This forloop can use any data from http:localhost:2000/cms/#/form/tags/ #}


California sex offender steals car with grandfather, baby inside, then leads police on chase: report
by Brie StimsonA registered sex offender was arrested in Southern California on Tuesday for allegedly stealing a car with a grandfather and his 1-year-old grandson still inside, according to a report.
The suspect, identified as Brian Edward Hardy, allegedly stole the parked car in Covina in Los Angeles County around 9 a.m., but the grandfather was able to briefly put it into park so he could get out with his grandson before Hardy could drive off again, KABC-TV of Los Angeles reported.
 He then led officers on a chase through East Los Angeles before he crashed into another car. The other driver was hospitalized, KABC reported.
Hardy faces charges of carjacking, kidnapping, assault with a deadly weapon and evading police.


Home
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Contact
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Contact Us
Interested in knowing more, tracking products, or just have a question?
Send us an email
at<EMAIL_ADDRESS>specifying the following information:
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Best way to contact you:


February 12
Now thanks be unto God, which always causeth us to triumph in Christ, and maketh manifest the savour of his knowledge by us in every place. 2 Corinthians 2:14
As you fight the good fight of faith today, remember this: Your mind is where the battle will take place.  Will it be the Word of God or the lies of Satan?
The decision is yours.
If you want the Word to reign, make up your mind now to resist the devil when he comes to plant doubts within you. Refuse to surrender to the circumstantial pressure he brings your way. Decide at the onset that you will not succumb to this trial. Dig your heels in and stand immovable upon the promises of God.
When thoughts come that are contrary to God's will, cast them down. And above all, don't worry. Worrying is meditating the thoughts of Satan.
When you recognize you are worrying, stop it immediately. Replace worried, fearful, and doubtful thoughts with the Word.
Be assured, Satan will continually try to tell you that your situation is hopeless. He will persistently peddle doubt, defeat, and discouragement. But if you won't allow him to sell you his goods, his commission will be zero.
God has promised that He will always cause you to triumph in Christ. You are guaranteed victory over any adversity you face today. Make up your mind right now to be a winner and you'll spread the knowledge of victory in Christ everywhere you go.
Scripture Reading: Philippians 4:1-9


New event:
Add
First event:
Move
Rename
Scale:
Hours
Days
Weeks
Months
Selected events (click or Ctrl+click to select):
{{item.text()}} - {{item.start()}}


Afia Schwarzenegger puts her breasts on display in a see-through dress
by Nana YawShe loves to be spoken about in the media, There are no two ways about that. Her antics always get people talking and this photo of Afia Schwar will definitely set social media ablaze.
Afia Schwarzenegger after her atopa video got into the media space seems not to be shy of her nakedness anymore – After all, it has been seen by many already.
Well, Afia Schwar has shared a new photo with her social media followers and nothing about the photo got people talking other than her firm breasts.
In the photo, Afia wore a black see-through dress and her b00bs were just as firm as the Burj Khalifa. See the photo below:


This is an articel title 4th March 2013 - by John Doe Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor Read More
This is an articel title 4th March 2013 - by John Doe Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor Read More


Contributor Licenses
By affixing their names below, the listed source code contributors agree to the following statements about all their respective past and future contributions to this source code base:
- They are the owner of any copyrights on their respective contributions, and they have the right to license those contributions. If they are employed as a software developer, they are either issuing this license on behalf of their employer and are authorized to do so, or they have obtained a waiver from their employer disclaiming ownership rights in the contribution.
- All their respective contributions to the source code base, including but not limited to both any sort of change to existing files or code, and any added files or code, are hereby licensed under the [New] "BSD" open source license (described at http://www.opensource.org/licenses/bsd-license.php).
- Any patents that they might ever own or control that cover their respective contributions are licensed royalty-free for use in this source code.
- They are unaware of any other patents that might cover their respective contributions.
Contributors (include email address, and if contributing on behalf of an organization, organization name)
- David Griswold -<EMAIL_ADDRESS>- Andras Pahi -<EMAIL_ADDRESS>- Carlo Dapor -<EMAIL_ADDRESS>- Thiago Silva -<EMAIL_ADDRESS>- Dave Raymer -<EMAIL_ADDRESS>- Marc Fauconneau Dufresne -<EMAIL_ADDRESS>- Stephen Rees -<EMAIL_ADDRESS>- Jirka Daněk - dnk@mail.muni.cz


CHORDS: ------- EADGBE EADGBE A (x02220) D (xx0232) E (022100) C#m (x46654) G (320033) F (133211) F# (244322) Bb (x1333x) Bm (x24432) G# (466544) Eb (x6888x) INTRO: ------ A E A E E|---5-------5-9-7--7------------------------| B|---5-----5-----9--9---------------2--0--0--| G|-------6-------9--9-----2-------2----1--1--| D|---7--------------------2-----2------2--2--| A|---------------7--7-----0---4--------------| E|-------------------------------------0--0--| VERSE 1: -------- A E Making your way in the world today A E takes everything you've got A E Taking a break from all your worries A E sure would help a lot G F# Bm E Wouldn't you like to get away?


Trim steps
Displays the trim steps of one channel in a telemetry window.
Visit my
GitHub repository
for more details and source code downloads.
LICENSE


| Project News and Announcements |
|
||||||||||||||||
|
April
11 , 2020 The online translation has been improved significantly particularly in cases where there were some lines that were not recognized. This generally now still parses. Also you can set multiple options to get fine grained control over the conversion. April
4 , 2020 The live translation page now includes multiple examples that you can pull up to see how the conversion handles various kinds of code. Once you have pulled them up you can edit them also to see how that impacts the conversion. March
15 , 2019 Version 0.3 of the converter is released. This includes many parsing and conversion fixes. Based on converting a few projects from github the conversion has improved from ~10% to 90% of files first time round. March
2 , 2019 After many years the site is being revamped to allow you to convert code much more easily. The old dependencies were making it very hard to download the code and get this to work. The site is being revamped to make this easier for common use cases. Feburuary
1 , 2004 v0.2.1 is a bug fix and minor enhancement version. Quite a large number of Visual Basic functions and keywords were not supported in v0.2 and this is addressed to some extent by this release. The following additional functions were added for this release: Array, ChDir, Choose, Environ, EOF, Erase, FileCopy, FileLen, Filter, FreeFile, IIf, Input, InStrRev, IsArray, Join, Kill, Let, Lof, LSet, LTrim, RTrim, Trim, MkDir, Name, Randomize, Replace, Reset, RGB, RmDir, Rnd, Round, RSet, GetAllSettings, GetSetting, SaveSetting, DeleteSetting, Spc, Split, StrReverse, Switch, Timer. The coverage is still not 100% but if you whave an urgent need for a function it can easily be bumped up the queue! January
26 , 2004 The Wiki, which was previously a very simple system, is now running MoinMoin and so should be much easier for people to add comments and should be much more useable for sketching development ideas. Click here to take a look.
January
26 , 2004 Converting large VB applications can be daunting because there is so much code to test to confirm that the VB and Python version really do behave in the same way. To aid in this process there is now an experimental code tester available on the CVS version. This tester allows you to copy VB code into one pane and have the code converted and tested automatically. This works by using the Windows Scripting host so this only works on Windows and currently only supports VB Script (so some valid VB code will not work). Click here to see some screenshots.
October
13 , 2003 You can now try out vb2Py online without all the hassle of installing libraries etc. Follow this link to the demo page and type or paste in your VB code and the watch as it is converted to Python before your very eyes! The conversion is based on the v0.2 code base and currently only supports Code and Class module conversion for a single file. However, this is a good way to try things out or to convert small sections of VB code. Depending on the type of code you may need to down load the vb2py package to actually run the code as many of the VB intrinsic functions are only defined in the package.
September
9 , 2003 Version 0.2 is now available. The key development in v0.2 is the inclusion of the full VB code parser and converter. Whereas v0.1 was just a form layout converter, v0.2 parses all the VB code and translates it to the equivalent Python code. All major code constructs from VB are covered (Sub, Fn, If, While, For, Select, With, Classes, Properties, Types, etc) and most instrinsic functions and constants are implemented as Python equivalents. The other major addition to v0.2 is the inclusion of a GUI to enable the progress of the conversion to be monitored and tweaked using the options system. The GUI is a PythonCard application running on top of the main vb2py library. The GUI is not required to convert VB code, a fully functioning command line alternative is available. Linux is now supported (this was badly broken in v0.1!) The main download is available here. The GUI download
is here.
August
23, 2003 There are now some screenshots of the GUI available. Things are still at an early stage, but it is actually useable to browse projects and see the converted code. Using the Options dialog you can see the impact of changing global settings. The GUI pictures are available here.
August
19 , 2003 The online documentation for v0.2 is now online - this documentation covers the upcoming v0.2 release and also the CVS code. The docs cover various aspects of the code conversion and detail the options which are available to fine tune the translation process. The documentation is available here.
August
1 , 2003 This is a bug-fix release for v0.1 - there were a number of issues relating to forms which contained controls with unset properties. These would cause vb2py to fall over. Many of these issues have now been resolved and even if one form fails the conversion for other forms will continue. The download is available here.
July
20, 2003 There are now a series of Forums where people can discuss various issues regarding the software and its development. I will be using the forum to give detailed updates on the progress that is being made (rather than posting news items here). The aim of the forum is to complement the mailing list rather than replacing it. The mailing list is good for asking questions and getting quick answers. The forum is a good place to enter into more detailed discussions. We are using the phpBB system rather than the Sourceforge forums because the later get hard to use for longer discussions.
July
14, 2003 The mailing list for vb2Py users was launched today. This provides another way for people to discuss the use of the software. You can subscribe to the mailing list here.
July
9, 2003 This initial release contains code which converts simple forms with standard controls to PythonCard forms. A very simplistic attempt is made to convert some VB code and control event names but there is still a long way to go. The download is available here.
July
5, 2003 The project
itself has been in development for a while now but I finally got around
to putting some web pages up here. |
||||||||||||||||


BuddhaCoin
An ERC20 compliant cryptocurrency for Buddha Enthusiasts.
Purchase BuddhaCoin at this address:
0x123f681646d4a755815f9cb19e1acc8565a0c2ac
Get an Ether Wallet here


Here are key takeways from FM Nirmala Sitharaman's press conference
The Cabinet has given the in-principle approval for disinvestment in select CPSEs, and a two-year moratorium on spectrum payment for telecom companies, among other measures.
by Moneycontrol NewsFinance Minister Nirmala Sitharaman announced, on November 20, that the Cabinet has given the in-principle approval for disinvestment in select CPSEs, and a two-year moratorium on spectrum payment for telecom companies, among other measures.
Here are the key highlights of the meet:
- The government’s 53.2 percent stake in Bharat Petroleum Corporation Limited (BPCL) will be sold to a strategic buyer. This is excluding BPCL’s equity shareholding in Numalighar Refinery, based in Assam.
- In-principle approval has also been granted for the government's 63.75 percent in Shipping Corporation of India, 30.8 percent of Container Corporation of India, 74.23 percent of THDC and 100 percent in North Eastern Electric Power Coporation Limited (NEEPCO) will be sold to NTPC.
- Management control of these respective companies will be handed over to the strategic buyer.
-The Cabinet has approved deferring receipts due from Telecom Service Providers (TSPs) for spectrum payment for a period of two years.
-Cabinet has also approved monetising of NHAI projects that are operational and collecting toll for at least one year since construction through the TOT model.
-The concessional period for these projects has been brought down from 30 years to anywhere between 15-30 years
-The Cabinet has also authorised the NHAI to raise long term funds from banks through securitisation of the user fee it collects.
-A unified authority for regulating all activity in international financial services centre (IFFC) will be formed. Eight regulatory bodies will be brought together to constitute a board with due process. These bodies include SEBI, RBI, and PFRB.
-The Cabinet has also given approval for import of 1.2 lakh metric tonnes of onions.
-The Cabinet has also given a nod for the ordinance that implemented the corporate income tax cuts.


|
ESP DateTime
0.2.0
Date Time Functions and Classes for ESP8266 and ESP32
|
ESPDateTime header. More...
#include <Arduino.h>
#include <time.h>
#include <sys/time.h>
Go to the source code of this file.
Classes | |
| struct | DateTimeParts |
| DateTime Parts struct, similar to struct tm in <time.h>, containing a calendar date and time broken down into its components, but more readable, include some useful getter methods. More... | |
| struct | DateFormatter |
| DateTime Formatter constants and static format methods. More... | |
| class | DateTimeClass |
| DateTime Library Main Class, include time get/set/format methods. More... | |
Variables | |
| DateTimeClass | DateTime |
| Global DateTimeClass object. More... | |
ESPDateTime header.
| DateTimeClass DateTime |
Global DateTimeClass object.


---
layout: default
title: James + Alicia
class: middle, center
count: false
---
# Once there was a girl
---
---
# And a boy
---
---
# Who fell in love
---
---
# And got married!
---
---
# At a church
---
---
# We loved to dance
---
---
# With the people we love
---
---
# Now we have a wonderful family
---
---
# With a beautiful home
---
---
# And two weird dogs
---
---
# The best kid ever
---
---
# With so much love
---
---
# Happy Anniversary!
---


CSS elements for CSS Designer page
body element:
- set the font-family to Georgia, serif
- set the text color to black (#000000)
- set the line-height to 140% (em 1.4)
- set the margin to 30px (all-around)
-
set the background-image to page-bg.gif
- set the background-color to #F7F3DE
header element, h1 selector:
- set the font-size to 350%
- set the color to #7C1A00
- set the padding to 40px
- set the left-paddingto 120px
-
set the background-image to header-image-1.gif
- position it at the top left
- and it should not repeat
#container id (for section element):
- set the margin-bottom to 30px
nav element:
- set the float to left
- set the width to 150px
nav element, ul tag:
- set the paddingto 0px
- set the margin to 0px
- set the list-style-type to none
- set the color to #7C1A00
nav element, li tag:
- set the margin to 10px
- set the border width to 1px
- set border-style to solid
- set the border-color to #7C1A00
- set the background-color to #FFE597
nav element, a tag:
- set the color to white
- set the background-color to #8C9273
- set the padding to 3px
- set the right-padding to 60px
- set the text-decoration to none
set the background-color to #7C1A00
#content id (for article element):
- set the background-image to trans-bg.gif
- set the border to 1px solid #CCCCCC
- set the margin-left to 180px
- set the padding to 10px
- set the clear to both
- set the text-align to center
footer element, li tag:
- set the display to inline
- set the padding to 10px
global h1 element:
- set the color to #8C9273
global h2 element:
- set the border-left to 20px solid #8C9273
- set the border-bottom to 5px solid #8C9273
- set the padding-left to 5px
global h3 element:
- set the background-image to header-star-1.gif, the image should not repeat
- set the padding-left to 20px
global blockquote element:
- set the background-color to #FFE597
ul element:
- set the list-style-type to square
global a element:
- set the color to #7C1A00
- set the font-weight to bold
global a:hover pseudo-class:
- set the background-color to #FFE597


When you are looking for a new house, how many criteria do you consider? What's the process like? How do you know when you've found the right one?
Tradeoff Analytics found the best _ out of _ options.
Use the filters and comparison to find the car that is right for you.


Forrest Gump [1994]
A retro reveiw by: Richard Matich Somewhere out there is someone almost like Forrest Gump. They have a limited Iq. Thats about as close to being like Tom Hank's charecter as they are probably ever going to get.  Or perhaps they attend "classes" in the city where they watch Barney tapes, stare at the walls, or paint bad watercolor. When they are not busy with those "fun" activities, they are being cleaned because they wet themselves. Forrest Gump is a uniquely American screen figure. Money seems to be at the very heart of the story. The morel to this tale is that you will live in happiness and all problems are soon to fade as long as you are rich and tremendously famous. What a naive and twisted vision! My theory on why this film has been so widely populer is well [don't laugh] simple. Americans have an extreme guilt for the way they treat the "mentaly challenged". This film makes the average Joe sixpack feel better about himself and the American way of life all at the same time. He does not even have to think all that much about plot since the music that fills the movie gives a music video feeling. Take out the music and you have one dull film that would not "inspire" half as much as has been claimed by some reviewers. I do not totaly hate the movie. The end was done to great effect. It does tug at the heart strings, even though it takes the mind for granted. It is intresting as a look back at the times from the 1950s to the 1980s. But, I must stress, its no substatute for a great documentary on the era.  Hank's performance as Gump was exicuted in great ways. I liked the accent that he learned from the boy who plays Forrest as a youth. The score, by Alan Silvestri, was one of the few treats of the film. I bought the soundtrack on cd for that reason alone. If you like the film, more power to you. I just think it is overly simplistic to believe that this retard ends up winning the beutiful blond, meeting several Presidents and celebraties, and becomes a war hero. Want to explain how any University "gives" a degree to somebody of Forrest's low Iq? Want to explain how he graduated from High School [or even passed any classes]? I dod'nt think you can even begin to defend the "in your face" problems with the script. If you want to see a movie about a person of the Gump vein, I must recommend the Hal Ashby and Peter Sellers collaberation Being There [1979]. "Thats all I have to say about that!"-Forrest Gump.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


UHELNÝ MLÝN
Areál Šroubáren 860
Libčice nad Vltavou
Website
Strong industrial atmosphere in a unique event space that received many architectural awards.
Within 30 minutes from the center of Prague.
Excellent transport options: a train station and a big parking lot near the venue.


ultra.learning_algorithm package¶
Submodules¶
ultra.learning_algorithm.base_algorithm module¶
The basic class that contains all the API needed for the implementation of an unbiased learning to rank algorithm.
-
class
ultra.learning_algorithm.base_algorithm.
BaseAlgorithm
(data_set, exp_settings, forward_only=False)¶ Bases:
abc.ABC
The basic class that contains all the API needed for the implementation of an unbiased learning to rank algorithm.
-
PADDING_SCORE
= -100000¶
-
abstract
__init__
(data_set, exp_settings, forward_only=False)¶ Create the model.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
exp_settings – (dictionary) The dictionary containing the model settings.
forward_only – Set true to conduct prediction only, false to conduct training.
-
get_ranking_scores
(input_id_list, is_training=False, scope=None, **kwargs)¶ Compute ranking scores with the given inputs.
- Parameters
input_id_list – (list<tf.Tensor>) A list of tensors containing document ids. Each tensor must have a shape of [None].
is_training – (bool) A flag indicating whether the model is running in training mode.
scope – (string) The name of the variable scope.
- Returns
A tensor with the same shape of input_docids.
-
pairwise_cross_entropy_loss
(pos_scores, neg_scores, name=None)¶ Computes pairwise softmax loss without propensity weighting.
- Parameters
pos_scores – (tf.Tensor) A tensor with shape [batch_size, 1]. Each value is
ranking score of a positive example. (the) –
neg_scores – (tf.Tensor) A tensor with shape [batch_size, 1]. Each value is
ranking score of a negative example. (the) –
name – A string used as the name for this variable scope.
- Returns
(tf.Tensor) A single value tensor containing the loss.
-
ranking_model
(list_size, scope=None)¶ Construct ranking model with the given list size.
- Parameters
list_size – (int) The top number of documents to consider in the input docids.
scope – (string) The name of the variable scope.
- Returns
A tensor with the same shape of input_docids.
-
remove_padding_for_metric_eval
(input_id_list, model_output)¶
-
abstract
step
(session, input_feed, forward_only)¶ Run a step of the model feeding the given inputs.
- Parameters
session – (tf.Session) tensorflow session to use.
input_feed – (dictionary) A dictionary containing all the input feed data.
forward_only – whether to do the backward step (False) or only forward (True).
- Returns
A triple consisting of the loss, outputs (None if we do backward), and a tf.summary containing related information about the step.
-
ultra.learning_algorithm.dbgd module¶
Training and testing the Dueling Bandit Gradient Descent (DBGD) algorithm for unbiased learning to rank.
See the following paper for more information on the Dueling Bandit Gradient Descent (DBGD) algorithm.
Yisong Yue and Thorsten Joachims. 2009. Interactively optimizing information retrieval systems as a dueling bandits problem. In ICML. 1201–1208.
-
class
ultra.learning_algorithm.dbgd.
DBGD
(data_set, exp_settings, forward_only=False)¶ Bases:
ultra.learning_algorithm.base_algorithm.BaseAlgorithm
The Dueling Bandit Gradient Descent (DBGD) algorithm for unbiased learning to rank.
This class implements the Dueling Bandit Gradient Descent (DBGD) algorithm based on the input layer feed. See the following paper for more information on the algorithm.
Yisong Yue and Thorsten Joachims. 2009. Interactively optimizing information retrieval systems as a dueling bandits problem. In ICML. 1201–1208.
-
__init__
(data_set, exp_settings, forward_only=False)¶ Create the model.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
exp_settings – (dictionary) The dictionary containing the model settings.
forward_only – Set true to conduct prediction only, false to conduct training.
-
step
(session, input_feed, forward_only)¶ Run a step of the model feeding the given inputs.
- Parameters
session – (tf.Session) tensorflow session to use.
input_feed – (dictionary) A dictionary containing all the input feed data.
forward_only – whether to do the backward step (False) or only forward (True).
- Returns
A triple consisting of the loss, outputs (None if we do backward), and a tf.summary containing related information about the step.
ultra.learning_algorithm.dbgd_interleave module¶
ultra.learning_algorithm.dla module¶
Training and testing the dual learning algorithm for unbiased learning to rank.
See the following paper for more information on the dual learning algorithm.
Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft. 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. In Proceedings of SIGIR ‘18
-
class
ultra.learning_algorithm.dla.
DLA
(data_set, exp_settings, forward_only=False)¶ Bases:
ultra.learning_algorithm.base_algorithm.BaseAlgorithm
The Dual Learning Algorithm for unbiased learning to rank.
This class implements the Dual Learning Algorithm (DLA) based on the input layer feed. See the following paper for more information on the algorithm.
Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft. 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. In Proceedings of SIGIR ‘18
-
DenoisingNet
(list_size, forward_only=False, scope=None)¶
-
__init__
(data_set, exp_settings, forward_only=False)¶ Create the model.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
exp_settings – (dictionary) The dictionary containing the model settings.
forward_only – Set true to conduct prediction only, false to conduct training.
-
click_weighted_log_loss
(output, labels, propensity_weights, name=None)¶ Computes pointwise sigmoid loss with propensity weighting.
- Parameters
output – (tf.Tensor) A tensor with shape [batch_size, list_size]. Each value is
ranking score of the corresponding example. (the) –
labels – (tf.Tensor) A tensor of the same shape as output. A value >= 1 means a
example. (relevant) –
propensity_weights – (tf.Tensor) A tensor of the same shape as output containing the weight of each element.
name – A string used as the name for this variable scope.
- Returns
(tf.Tensor) A single value tensor containing the loss.
-
click_weighted_pairwise_loss
(output, labels, propensity_weights, name=None)¶ Computes pairwise entropy loss with propensity weighting.
- Parameters
output – (tf.Tensor) A tensor with shape [batch_size, list_size]. Each value is
ranking score of the corresponding example. (the) –
labels – (tf.Tensor) A tensor of the same shape as output. A value >= 1 means a relevant example.
propensity_weights – (tf.Tensor) A tensor of the same shape as output containing the weight of each element.
name – A string used as the name for this variable scope.

- Parameters
output – (tf.Tensor) A tensor with shape [batch_size, list_size]. Each value is
ranking score of the corresponding example. (the) –
labels – (tf.Tensor) A tensor of the same shape as output. A value >= 1 means a
example. (relevant) –
propensity_weights – (tf.Tensor) A tensor of the same shape as output containing the weight of each element.
name – A string used as the name for this variable scope.
- Returns
(tf.Tensor) A single value tensor containing the loss.
-
get_normalized_weights
(propensity)¶ Computes listwise softmax loss with propensity weighting.
- Parameters
propensity – (tf.Tensor) A tensor of the same shape as output containing the weight of each element.
- Returns
(tf.Tensor) A tensor containing the propensity weights.
-
separate_gradient_update
()¶
-
softmax_loss
(output, labels, propensity=None, name=None)¶ Computes listwise softmax loss without propensity weighting.
- Parameters
output – (tf.Tensor) A tensor with shape [batch_size, list_size]. Each value is
ranking score of the corresponding example. (the) –
labels – (tf.Tensor) A tensor of the same shape as output. A value >= 1 means a
example. (relevant) –
propensity – No use.
name – A string used as the name for this variable scope.
- Returns
(tf.Tensor) A single value tensor containing the loss.
-
step
(session, input_feed, forward_only)¶ Run a step of the model feeding the given inputs.
- Parameters
session – (tf.Session) tensorflow session to use.
input_feed – (dictionary) A dictionary containing all the input feed data.
forward_only – whether to do the backward step (False) or only forward (True).
- Returns
A triple consisting of the loss, outputs (None if we do backward), and a tf.summary containing related information about the step.
-
ultra.learning_algorithm.dla.
sigmoid_prob
(logits)¶
ultra.learning_algorithm.ipw_rank module¶
Training and testing the inverse propensity weighting algorithm for unbiased learning to rank.
See the following paper for more information on the inverse propensity weighting algorithm.
Xuanhui Wang, Michael Bendersky, Donald Metzler, Marc Najork. 2016. Learning to Rank with Selection Bias in Personal Search. In Proceedings of SIGIR ‘16
Thorsten Joachims, Adith Swaminathan, Tobias Schnahel. 2017. Unbiased Learning-to-Rank with Biased Feedback. In Proceedings of WSDM ‘17
-
class
ultra.learning_algorithm.ipw_rank.
IPWrank
(data_set, exp_settings, forward_only=False)¶ Bases:
ultra.learning_algorithm.base_algorithm.BaseAlgorithm
The Inverse Propensity Weighting algorithm for unbiased learning to rank.
This class implements the training and testing of the Inverse Propensity Weighting algorithm for unbiased learning to rank. See the following paper for more information on the algorithm.
Xuanhui Wang, Michael Bendersky, Donald Metzler, Marc Najork. 2016. Learning to Rank with Selection Bias in Personal Search. In Proceedings of SIGIR ‘16
Thorsten Joachims, Adith Swaminathan, Tobias Schnahel. 2017. Unbiased Learning-to-Rank with Biased Feedback. In Proceedings of WSDM ‘17
-
__init__
(data_set, exp_settings, forward_only=False)¶ Create the model.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
exp_settings – (dictionary) The dictionary containing the model settings.
forward_only – Set true to conduct prediction only, false to conduct training.
-
click_weighted_pairwise_loss
(output, labels, propensity, name=None)¶ Computes pairwise entropy loss with propensity weighting.
- Parameters
output – (tf.Tensor) A tensor with shape [batch_size, list_size]. Each value is
ranking score of the corresponding example. (the) –
labels – (tf.Tensor) A tensor of the same shape as output. A value >= 1 means a relevant example.
propensity – (tf.Tensor) A tensor of the same shape as output containing the weight of each element.
name – A string used as the name for this variable scope.
- Returns
(tf.Tensor) A single value tensor containing the loss.
-
click_weighted_softmax_loss
(output, labels, propensity, name=None)¶ Computes listwise softmax loss with propensity weighting.
- Parameters
output – (tf.Tensor) A tensor with shape [batch_size, list_size]. Each value is
ranking score of the corresponding example. (the) –
labels – (tf.Tensor) A tensor of the same shape as output. A value >= 1 means a
example. (relevant) –
propensity – (tf.Tensor) A tensor of the same shape as output containing the weight of each element.
name – A string used as the name for this variable scope.
- Returns
(tf.Tensor) A single value tensor containing the loss.
-
sigmoid_loss
(output, labels, propensity, name=None)¶ Computes pointwise sigmoid loss without propensity weighting.
- Parameters
output – (tf.Tensor) A tensor with shape [batch_size, list_size]. Each value is
ranking score of the corresponding example. (the) –
labels – (tf.Tensor) A tensor of the same shape as output. A value >= 1 means a relevant example.
propensity – No use.
name – A string used as the name for this variable scope.
- Returns
(tf.Tensor) A single value tensor containing the loss.
-
softmax_loss
(output, labels, propensity, name=None)¶ Computes listwise softmax loss without propensity weighting.
- Parameters
output – (tf.Tensor) A tensor with shape [batch_size, list_size]. Each value is
ranking score of the corresponding example. (the) –
labels – (tf.Tensor) A tensor of the same shape as output. A value >= 1 means a relevant example.
propensity – No use.
name – A string used as the name for this variable scope.
- Returns
(tf.Tensor) A single value tensor containing the loss.
-
step
(session, input_feed, forward_only)¶ Run a step of the model feeding the given inputs.
- Parameters
session – (tf.Session) tensorflow session to use.
input_feed – (dictionary) A dictionary containing all the input feed data.
forward_only – whether to do the backward step (False) or only forward (True).
- Returns
A triple consisting of the loss, outputs (None if we do backward), and a tf.summary containing related information about the step.
-
ultra.learning_algorithm.ipw_rank.
selu
(x)¶
ultra.learning_algorithm.pairwise_debias module¶
Training and testing the Pairwise Debiasing algorithm for unbiased learning to rank.
See the following paper for more information on the Pairwise Debiasing algorithm.
Hu, Ziniu, Yang Wang, Qu Peng, and Hang Li. “Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm.” In The World Wide Web Conference, pp. 2830-2836. ACM, 2019.
-
class
ultra.learning_algorithm.pairwise_debias.
PairDebias
(data_set, exp_settings, forward_only=False)¶ Bases:
ultra.learning_algorithm.base_algorithm.BaseAlgorithm
The Pairwise Debiasing algorithm for unbiased learning to rank.
This class implements the Pairwise Debiasing algorithm based on the input layer feed. See the following paper for more information on the algorithm.
Hu, Ziniu, Yang Wang, Qu Peng, and Hang Li. “Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm.” In The World Wide Web Conference, pp. 2830-2836. ACM, 2019.
-
__init__
(data_set, exp_settings, forward_only=False)¶ Create the model.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
exp_settings – (dictionary) The dictionary containing the model settings.
forward_only – Set true to conduct prediction only, false to conduct training.
-
step
(session, input_feed, forward_only)¶ Run a step of the model feeding the given inputs.
- Parameters
session – (tf.Session) tensorflow session to use.
input_feed – (dictionary) A dictionary containing all the input feed data.
forward_only – whether to do the backward step (False) or only forward (True).
- Returns
A triple consisting of the loss, outputs (None if we do backward), and a tf.summary containing related information about the step.
-
ultra.learning_algorithm.pairwise_debias.
get_bernoulli_sample
(probs)¶ Conduct Bernoulli sampling according to a specific probability distribution.
- Parameters
prob – (tf.Tensor) A tensor in which each element denotes a probability of 1 in a Bernoulli distribution.
- Returns
A Tensor of binary samples (0 or 1) with the same shape of probs.
ultra.learning_algorithm.pdgd module¶
Training and testing the Pairwise Differentiable Gradient Descent (PDGD) algorithm for unbiased learning to rank.
See the following paper for more information on the Pairwise Differentiable Gradient Descent (PDGD) algorithm.
Oosterhuis, Harrie, and Maarten de Rijke. “Differentiable unbiased online learning to rank.” In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1293-1302. ACM, 2018.
-
class
ultra.learning_algorithm.pdgd.
PDGD
(data_set, exp_settings, forward_only=False)¶ Bases:
ultra.learning_algorithm.base_algorithm.BaseAlgorithm
The Pairwise Differentiable Gradient Descent (PDGD) algorithm for unbiased learning to rank.
This class implements the Pairwise Differentiable Gradient Descent (PDGD) algorithm based on the input layer feed. See the following paper for more information on the algorithm.
Oosterhuis, Harrie, and Maarten de Rijke. “Differentiable unbiased online learning to rank.” In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1293-1302. ACM, 2018.
-
__init__
(data_set, exp_settings, forward_only=False)¶ Create the model.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
exp_settings – (dictionary) The dictionary containing the model settings.
forward_only – Set true to conduct prediction only, false to conduct training.
-
step
(session, input_feed, forward_only)¶ Run a step of the model feeding the given inputs.
- Parameters
session – (tf.Session) tensorflow session to use.
input_feed – (dictionary) A dictionary containing all the input feed data.
forward_only – whether to do the backward step (False) or only forward (True).
- Returns
A triple consisting of the loss, outputs (None if we do backward), and a tf.summary containing related information about the step.
ultra.learning_algorithm.regression_EM module¶
Training and testing the regression-based EM algorithm for unbiased learning to rank.
See the following paper for more information on the regression-based EM algorithm.
Wang, Xuanhui, Nadav Golbandi, Michael Bendersky, Donald Metzler, and Marc Najork. “Position bias estimation for unbiased learning to rank in personal search.” In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 610-618. ACM, 2018.
-
class
ultra.learning_algorithm.regression_EM.
RegressionEM
(data_set, exp_settings, forward_only=False)¶ Bases:
ultra.learning_algorithm.base_algorithm.BaseAlgorithm
The regression-based EM algorithm for unbiased learning to rank.
This class implements the regression-based EM algorithm based on the input layer feed. See the following paper for more information.
Wang, Xuanhui, Nadav Golbandi, Michael Bendersky, Donald Metzler, and Marc Najork. “Position bias estimation for unbiased learning to rank in personal search.” In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 610-618. ACM, 2018.
In particular, we use the online EM algorithm for the parameter estimations:
Cappé, Olivier, and Eric Moulines. “Online expectation–maximization algorithm for latent data models.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 71.3 (2009): 593-613.
-
__init__
(data_set, exp_settings, forward_only=False)¶ Create the model.
- Parameters
data_set – (Raw_data) The dataset used to build the input layer.
exp_settings – (dictionary) The dictionary containing the model settings.
forward_only – Set true to conduct prediction only, false to conduct training.
-
step
(session, input_feed, forward_only)¶ Run a step of the model feeding the given inputs.
- Parameters
session – (tf.Session) tensorflow session to use.
input_feed – (dictionary) A dictionary containing all the input feed data.
forward_only – whether to do the backward step (False) or only forward (True).
- Returns
A triple consisting of the loss, outputs (None if we do backward), and a tf.summary containing related information about the step.
-
ultra.learning_algorithm.regression_EM.
get_bernoulli_sample
(probs)¶ Conduct Bernoulli sampling according to a specific probability distribution.
- Parameters
prob – (tf.Tensor) A tensor in which each element denotes a probability of 1 in a Bernoulli distribution.
- Returns
A Tensor of binary samples (0 or 1) with the same shape of probs.


My first 15k: San Francisco Hot Chocolate Run
Yesterday, I ran further than I have ever run before: 15k (9.6mi). Today, my legs feel sore and stiff, as if I am walking around on knees made up of chipped pallasite.
[img “Miles vs. Date.png”]
I trained for two months, but as you can see from the above chart, I only ran past the 7-mile range once.
[img “femur.png”]
I can make all sorts of excuses as to why I didn’t train more, but a significant reason for it was pain. After about 5 miles, I began to feel significant pain where the (medial|lateral) collateral ligaments met the fibula. This pain would last for days after a run. Running the actual Hot Chocolate 15k was no exception.
[img “start.jpg” “The starting line”]
The day of the run was compatively smooth. The starting line was only a 20-minute walk away, so I went directly in without checking any gear. Once at the starting point, the line was divided up in groups based on the expected lap time we signed up with: 12:00/mile pace, in my case. 14-minutes after the first group left, ours did as well.
[img “windmill.jpg” “The famous windmills of Golden Gate Park”]
The actual run didn’t cover any new ground for me, as Golden Gate Park has been my stomping ground for cycling and running for the last 10 years. The weather was a perfect 55’F (13’C), and the sun was out. Roughly every 3 miles there was a rest stop with water, gummy bears, marshmellows, and best of all: bathrooms!
[img “beach.jpg” “Runners coming and going along Ocean Beach”]
The detour down the Great Highway toward the Zoo gave us a glimpse of the ocean, albeit buried behind the sand dunes:
[img “selfie.jpg” “Tom and I on the Great Highway”]
At the 7-mile mark, I began to feel tired - like I really had to push myself up the hill to go on. At the 8-mile mark, I was tired, but everything was autopilot.
[img “trio.jpg” “Tom, Little Bird, and myself”]
At 9-miles, some familiar folks were waving to me: my wife & kids! It wasn’t difficult to coax the youngest to run the final 0.6mi (1km) to the fininsh line with me.
So, why did I run? I blame my good friend and long-time cycling partner, Tom. He’s been running the Hot Chocolate for 7 years now. He had previously coaxed me into it 5 years ago, but unfortunately I broke my foot a few weeks ahead of the actual run.
Would I recommend the Hot Chocolate Run? Most definitely. The experience certainly has me thinking about the viability of running a Half Marathon later this year.
[img “finish.jpg” “Tom and I, as the adrenaline wears down”]
Special thanks to Tom Kocon & Peter van Vugt for all the peer pressure and training.


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