Layout HTML Document
Websites often display content in multiple columns (like a magazine or newspaper).
HTML offers several semantic elements that define the different parts of a web page
header - Defines a header for a document or a section
nav - Defines a container for navigation links
section - Defines a section in a document
article - Defines an independent self-contained article
aside - Defines content aside from the content (like a sidebar)
footer - Defines a footer for a document or a section
details - Defines additional details
summary - Defines a heading for the details element
There are following HTML Layout techniques:
1) Tables (HTML)
2) CSS Float
3) CSS FlexBox
4) CSS Framework
5) CSS Grid


Classes | |
| struct | NativeFunction |
| struct | NativeFunction< void > |
| struct | Pushable |
| struct | Reference |
| struct | StateWrapper |
| struct | Table |
| struct | Value |
| struct | Value< bool > |
| struct | Value< CFunction > |
| struct | Value< char[n]> |
| struct | Value< const char * > |
| struct | Value< const char[n]> |
| struct | Value< const T > |
| struct | Value< double > |
| struct | Value< FieldVector > |
| struct | Value< float > |
| struct | Value< long double > |
| struct | Value< NativeFunction< R > > |
| struct | Value< Pushable > |
| struct | Value< Reference > |
| struct | Value< signedchar > |
| struct | Value< signedint > |
| struct | Value< signedlong int > |
| struct | Value< signedlong long int > |
| struct | Value< signedshort > |
| struct | Value< std::nullptr_t > |
| struct | Value< std::string > |
| struct | Value< std::tuple< A... > > |
| struct | Value< Table > |
| struct | Value< U & > |
| struct | Value< U * > |
| struct | Value< unsigned char > |
| struct | Value< unsigned int > |
| struct | Value< unsigned long int > |
| struct | Value< unsigned long long int > |
| struct | Value< unsigned short > |
| struct | Value< volatile T > |
Typedefs | |
| using | FieldVector = std::vector< std::pair< Pushable, Pushable >> |
| using | Integer = lua_Integer |
| using | Number = lua_Number |
| using | State = lua_State |
| using | CFunction = lua_CFunction |
Functions | |
| static bool | equal (State *state, int index1, int index2) |
| static void | setMetatable (State *state, const char *name) |
| template<typename V > | |
| static void | setGlobal (State *state, const std::string &name, V value) |
| template<typename V > | |
| static V | getGlobal (State *state, const std::string &name) |
| template<typename... R> | |
| static void | setFields (State *state, int index, R &&...args) |
| static void | setFields (State *state, int index, const FieldVector &fields) |
| template<typename V , typename K > | |
| static V | getField (State *state, int index, K key) |
| template<typename S , typename F , typename... X> | |
| static internal::Layout< S >::ReturnType | direct (State *state, int pos, F &&hook, X &&...args) |
| template<typename S , typename F , typename... A> | |
| static internal::Layout< S >::ReturnType | direct (State *state, F &&hook, A &&...args) |
| template<typename R , typename... A> | |
| static R | apply (State *state, int pos, R(*fun)(A...)) |
| template<typename R , typename... A> | |
| static R | apply (State *state, R(*fun)(A...)) |
| template<typename R , typename... A> | |
| static R | apply (State *state, int pos, const std::function< R(A...)> &fun) |
| template<typename R , typename... A> | |
| static R | apply (State *state, const std::function< R(A...)> &fun) |
| template<typename S , typename F , typename... A> | |
| static size_t | map (State *state, int pos, F &&hook, A &&...args) |
| template<typename S , typename F , typename... A> | |
| static size_t | map (State *state, F &&hook, A &&...args) |
| static Table | getGlobalsTable (State *state) |
| template<typename T > | |
| static size_t | push (State *state, T value) |
| template<typename T1 , typename T2 , typename... TR> | |
| size_t | push (State *state, T1 value, T2 &&head, TR &&...rest) |
| template<typename T > | |
| static T | read (State *state, int index) |
| template<typename U , typename... A> | |
| static internal::StripUserType< U > & | construct (State *state, A &&...args) |
| template<typename U > | |
| static void | registerUserType (State *state, const FieldVector &methods=FieldVector(), const FieldVector &meta_methods=FieldVector()) |
| template<typename T > | |
| static void | registerUserType (State *state, const std::string &ctor_name, const FieldVector &methods=FieldVector(), const FieldVector &meta_methods=FieldVector()) |
| using luwra::CFunction = typedef lua_CFunction |
| using luwra::FieldVector = typedef std::vector<std::pair<Pushable, Pushable>> |
A collection of key-value pairs.
| using luwra::Integer = typedef lua_Integer |
| using luwra::Number = typedef lua_Number |
| using luwra::State = typedef lua_State |
|
inlinestatic |
Synonym for direct with a function pointer which lets you omit all template parameters. The stack layout will be inferred using the signature of the given function pointer.
|
inlinestatic |
Same as apply(state, 1, fun)
.
|
inlinestatic |
Synonym for direct with a function object which lets you omit all template parameters. The stack layout will be inferred using the template parameter to your std::function
object.
|
inlinestatic |
Same as apply(state, 1, fun)
.
|
inlinestatic |
Construct a user type value on the stack.
| state | Lua state |
| args | Constructor arguments |
|
inlinestatic |
Retrieve values from the stack and invoke a Callable
with them.
| S | Signature in the form of R(A...) where A is a sequence of types, which shall be retrieved from the stack, and R the return type of hook |
| F | An instance of Callable which accepts parameters X..., A... and returns R (this parameter should be inferable and can be omitted) |
| X | Extra argument types |
| state | Lua state instance |
| pos | Index of the first value |
| hook | Callable value |
| args | Extra arguments which shall be be passed to hook before the stack values |
hook
|
inlinestatic |
Same as direct(state, 1, hook)
.
|
inlinestatic |
Check if two values are equal.
| state | Lua state |
| index1 | Index of left-hand side value |
| index2 | Index of right-hand side value |
|
inlinestatic |
Retrieve a field from a table.
|
inlinestatic |
Retrieve a global value.
| state | Lua state |
| name | Global name |
Retrieve the table containing all global values.
| state | Lua state |
|
inlinestatic |
Similiar to direct but pushes the result of the given Callable
onto the stack.
|
inlinestatic |
Same as map(state, 1, hook)
.
|
inlinestatic |
Convenient wrapped for Value<T>::push.
| size_t luwra::push | ( | State * | state, |
| T1 | value, | ||
| T2 && | head, | ||
| TR &&... | rest | ||
| ) |
Allows you to push multiple values at once.
|
inlinestatic |
Convenient wrapper for Value<T>::read.
|
inlinestatic |
Register the metatable for user type T
. This function allows you to register methods which are shared across all instances of this type.
By default a garbage-collector hook and string representation function are added as meta methods. Both can be overwritten.
| U | User type struct or class |
| state | Lua state |
| methods | Map of methods |
| meta_methods | Map of meta methods |
|
inlinestatic |
Same as the other registerUserType
but registers the construtor as well. The template parameter is a signature U(A...)
where U
is the user type and A...
its constructor parameters.
|
inlinestatic |
Set multiple fields at once. Allows you to provide multiple key-value pairs.
| state | Lua state |
| index | Table index |
| args | Key-value pairs |
|
inlinestatic |
|
inlinestatic |
Register a value as a global.
| state | Lua state |
| name | Global name |
| value | Global value |
|
inlinestatic |
Set a registered metatable for the value on top of the stack.
| state | Lua state |
| name | Metatable name |


Use this interface to send remote commands to the sonic PI
Sam will tell you what to do
Styled with w3css


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Event Location:
Sydney University - New Law Annex
New Law Annex, Eastern Ave, Camperdown NSW
Sydney, New South Wales, Australia
Abstract:
DBA always faces challenges in troubleshooting live performance issues. In SQL Server 2016, the two new features introduced to help a DBA to be more productive. The Query Store is a feature that will help in visualizing what is happening with your executing queries, which otherwise is hard to collect with DMVs. The UI rich interface makes it easier to troubleshoot live scenarios as well has collect data for troubleshooting. Have you ever felt a need to see the execution plan when executing a query while troubleshooting? Live Query Statistics helps you view the live execution plan rather than waiting till end of the execution of query to see the actual execution plan. This session is recommended for advanced users as you will need basic understanding of troubleshooting using execution plans.
Speaker(s): Manohar Punna,
Abstract:
With Azure data centres located round the globe, will your company be looking more at cloud options? The Azure VM approach doesn't change your day to day role much, but Azure SQL Database certainly does. In this session we will compare the core features of Azure SQL Database against SQL Server in an Azure VM, including the limitations and how to create, access and configure it. We'll then compare scaling-up and scaling-out and the change in perspective that a DBA needs to manage a mixed environment. Finally, we'll look at some of the DBA activities that are still needed on Azure SQL Database and how these can be achieved easily.
Speaker(s): Martin Cairney,
Abstract:
Although Availability Groups are relatively easy to implement, there are many complexities when it comes to administering and optimizing them--especially if you are familiar with Failover Clustering. In some cases, your existing database solutions perform slower. In this session, we examine the various potential problems you can have when implementing Availability Groups and the potential solutions to these problems. This session covers areas such as performance, administration, and security management. You learn how to manage complex jobs, backup strategies, and logins within an Availability Group environment. You also learn how Availability Groups might dramatically affect your application performance, how to recognise such performance problems, and how to resolve them. With a number of real-world examples, this practical session enables you to maximize the benefits of Availability Groups.
Speaker(s): Victor Isakov,
Abstract:
Testing software is hard work. Testing BI solutions is even harder;there's lots of moving parts, the tools are thin and it takes a lot of time and effort. Software development uses unit testing and while this can be applied to the different parts of a BI solution, most people are more interested in the numbers the solution produces and proving that they are right. So can you validate the numbers in your BI solution? And can you do it quickly and easily? In this session we will cover an approach for automating the validation of your BI solution covering multiple scenarios with a hand full of demos. We will be using nUnit as a framework and some free codeplex tools, so we can run a suite of tests every time we load data into our BI platform. This is not continuous integration, and it is not unit testing in the true sense either. It is, instead, "continuous validation", and it can provide you with an awful lot of assurance for not very much extra effort.
Speaker(s): Dan de Sousa,
Abstract:
Big Data is a phrase used to describe large, possibly complex datasets. With today's data explosion of devices and sensors, technology is responding with new ways to practically and economically store and process the resulting Big Data. This session describes the fundamentals of Big Data and Hadoop, leading to the introduction of Microsoft’s cloud-based Big Data service: Azure HDInsight. The session includes numerous demonstrations to show how files can be archived in the Azure Blob Store and then efficiently processed with a cluster of servers. The demonstrations will introduce Hive, a query language designed to ensure that Big Data remains approachable to database developers, and the integration of Big Data into a conventional Business Intelligence (BI) solution. This session will be of interest to those new to the concept of Big Data and interested to understand the challenges that it is designed to solve.
Speaker(s): Peter Myers,
Abstract:
Within every Data Warehouse project there is always ETL development that is very repetitive in nature, for example, you need to import 20 different flat file exports from the ERP system, you need to load 15 dimensions into the data warehouse and so on. With Microsoft SSIS out-of-the-box, you are forced to create multiple individual packages which is very time consuming due to the lack of easy code reuse. In a typical BI project, the ETL implementation can easily take up 60% of the project time, so it is beneficial if the development time of similar SSIS packages can be cut down. Business Intelligence Markup Language (Biml) is an XML dialect for defining Business Intelligence assets across the Microsoft SQL Server platform In this session Craig will introduce you to Biml BimlScript, providing those new to Biml with a foundational understanding of what BIML is and what the benefits of using Biml are.
Speaker(s): Craig Bryden,
Abstract:
How do you optimize a DAX expression? This session will introduce you to the useful tools to measure performance, gathering data to find the bottlenecks and helping you in writing new optimized versions of DAX. Starting from SQL Profiler, you will learn which events are relevant for DAX, and how to collect them in different environments (Analysis Services, Power Pivot, Power BI). We will show DAX Studio, which simplify and speed-up the data collection process, and makes it easy to find bottlenecks in storage engine and formula engine. VertiPaq Analyzer and other small tools will be also used to collect other useful information. The goal of the session is to provide you a methodology to analyze performance of your DAX measures, to find the bottleneck and to identify the main reason of a performance issue.
Speaker(s): Marco Russo,
Abstract:
In this session James will use his experience building large cubes for large organisations and show how you can optimise all aspects of the process - preparing data for load, processing tips and how to get the thing to perform as users expect.
Speaker(s): James Beresford,
Abstract:
Maintaining a solid set of information about our servers and their performance is critical when issues arise, and often help us see a problem before it occurs. Building a baseline of performance metrics allows us to know when something is wrong and help us to track it down and fix the problem. This session will walk you through a series of PowerShell scripts you can schedule which will capture the most important data and a set of reports to show you how to use that data to keep your server running smoothly.
Speaker(s): Allen White,
Abstract:
Martin has seen life from a few different sides of the database environment. Often the world of TSQL and DBA are seen from vastly different viewpoints – but it doesn’t have to be this way. Join Martin to find out some ways that these two groups can enhance each other’s life and work as efficiently as possible. We’ll examine a range of tip during this presentation that most SQL professionals should get value from.
Speaker(s): Martin Catherall,
Abstract:
In this session you will see how to solve a number of business scenarios using DAX and Power Pivot, including: #183; Taking real time user input to change the calculations in a pivot table/report - changing the results of your DAX calculations using slicers #183; Changing the visualisation experience of charts and tables using user input from slicers #183; Grouping customers into age groups for reporting and analysis using 2 different Banding techniques #183; Displaying the "latest survey result" in a Pivot Table for any date selected.
Speaker(s): Matt Allington,
Abstract:
Get a sneak preview of my USA 2016 PASS Analytics Conference session - creating an Activity-Based Costing System with Power BI. Having the final report model in Power BI gives you the ultimate control over reporting, including adding other information possibly not even held with Information Technology. We will use a real-world health care model to show how we replaced an expensive, difficult to understand and maintain SAS system, with a simple, elegant, and auditable solution based on the Microsoft stack. The rules are table-driven with allocated costs handling built in.
Speaker(s): Grant Paisley,
Abstract:
SQL Server Integration Services (SSIS) is a mature on-premise ETL and data-transformation tool. Azure Data Factory is a cloud ETL and data-consolidation tool that was recently released as part of Microsoft Azure services. In this session, learn themain differences between these two tools and learn how to determine which works best in specific scenarios. We compare Azure Data Factory and SSIS on many factors; demos will help illustrate the differences.
Speaker(s): Reza Rad,
Abstract:
Do you want to test hardware upgrades, index tuning or multi-client applications? Would you like to do this with data specific to your organization? Distributed Replay, introduced in SQL Server 2012, is a tool that will allow you to accomplish exactly that. In this session, we will cover why you should use it, how to set it up, and what types of data to collect for testing and replay. This session will demonstrate tangible methods and undocumented tidbits, that you can take back to your environment to create immediate, and measurable tests.
Speaker(s): Melody Zacharias,
Abstract:
The R language is one of the key programming languages used in Azure Machine Learning. During this session we will introduce the basic programming constructs of the language and get you started on the road to detailed data analysis. We describe the language, data structures and libraries used in R development and show how your completed code can be deployed to a Azure Machine Language environment and executed through the web API. At the completion of this session, you may not be an R hotshot, but you will hopefully have accelerated your journey toward becoming one.
Speaker(s): Glyn LLewellyn,
Abstract:
Tempdb is an integral part of SQL Server, and is often overlooked when trying to get the most out of your system. In this session we will go over what tempdb is used for, why it is a special type of database and how to use it to tune the performance of your overall system. We will start with determining the best configuration, number of database files to use and the common performance issues that tempdb can indicate. We will then use these best practices to optimise our instance and performance tune our queries to make your system faster.
Speaker(s): Hamish Watson,
Abstract:
Mark will be flying over from the states to give the latest updates from the engineering team with respect to the Microsoft data platform. With the upcoming release of SQL Server 2016 around the corner you will get a drill in on the product including the latest on performance and customer experiences. Mark will also give you an update on the other important pieces of the platform including Azure SQL DB, Azure SQL DW, Data Lake, Hybrid capabilities, etc.
Speaker(s): Grant Paisley,
Abstract:
Internet of Things (IoT) is now even more practicable with the power of the cloud analytics. Now with Cortana Analytics which have included components across a broad set of Azure Services, Power BI and APIs, you can connect your sensors and devices to the cloud to do more intelligent stuff and “Connect the Dots”. In this session you will get an overview of the capabilities in Cortana Analytics, and Iman will show you an outlook of the future.
Speaker(s): Iman Eftekhari,
Abstract:
A look at the options for providing High Availability and Disaster Recovery using the native features available in SQL Server. This session will cover the basis of how the technologies work, what is(and isn't) protected and a list of the advantages and disadvantages of each technology. We'll also look at the why of High Availability, Disaster recovery and how to get the business to commit to a design to provide Business Continuity when disaster strikes.
Speaker(s): Rob Douglas,
Abstract:
Presenter: Kale Temple, Iman Eftekhari, Jixin (Gin) Jia Abstract: in this half-an-hour session we will show you real-life dashboards for 4 scenarios, no Adventure Works! BIO: Agile BI is a Sydney based consulting firm that specialises in Microsoft Data Analytics. As a Microsoft Gold partner, we have built up a reputation as one of Australia’s most respected service providers in the Microsoft Business Intelligence arena especially Power BI.
Speaker(s): Iman Eftekhari,
Abstract:
Having your SQL Server databases not available for business to be making money is not a good way to stay employed. Do you understand what features and options can assist you with keeping SQL Server Databases Available? In this session we will look at the options available to you currently to meet your Availability requirements using features being released in SQL Server 2016 to better provide high availability
Speaker(s): Warwick Rudd,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
LinkedIn: http://au.linkedin.com/in/martincairney
Martin has 20 years’ experience in SQL Server technologies. His experience spans both the Database Engine and infrastructure requirements, and Data Warehousing and BI implementations. He has worked across many industry sectors including London’s global financial institutions and both State and Federal Government Departments. He is a SQL Server MVP, MCT and an experienced conference presenter, having presented at PASS Summit, SQLBits, and SQL Saturday events and User Groups round the world
LinkedIn: http://www.linkedin.com/in/victorisakov
Contact: http://www.victorisakov.com
Victor Isakov (MCA, MCM, MCSM, MCT, MVP) is a Database Architect and trainer who provides consulting and training services to various organizations in the public, private, and NGO sectors globally. He regularly speaks at international conferences such as Microsoft TechEd/Ignite, PASS Summit, IT/Dev Connections, and SQLSaturdays. Victor has written a number of books on SQL Server and has worked with Microsoft to develop the SQL Server exams and certification. In 2017 he authored "Exam Ref 70-764 Administering a SQL Database Infrastructure (1st Edition)". He is one of only three non-Microsoft, Microsoft Certified Architects in the world.
LinkedIn: http://au.linkedin.com/pub/warwick-rudd/0/956/718
Contact: http://www.sqlmastersconsulting.com.au/Blog
Warwick Rudd is a Microsoft Certified Master – SQL Server 2008 and Principal Consultant at SQL Master Consulting. He is a frequent speaker at local SQL Server User Groups and SQLSaturday events in Australia. When not playing with the SQL stack, Warwick likes to get away to the cold and spend time snowboarding.
LinkedIn: http://www.linkedin.com/pub/allen-white-sql-server-mvp/5/784/b08/
Contact: http://dataperfpro.com/blog/
Allen White is a Data Platform consultant. He has been both a developer and an administrator, so he understands both perspectives towards database technology. He loves sharing his experiences and helping people learn how to use SQL Server. Allen has spent over 40 years in IT, using SQL Server since 1992, and has been awarded Microsoft’s MVP Award for the last 13 years. Allen was a PASS Director from 2016 - 2018.
Grant Paisley is a Data Platform MVP and founder of Angry Koala, a Microsoft Business Intelligence partner based in Sydney, Australia. Grant is president of the SQL Server Usergroup Sydney and created http://reportsurfer.com, a community built around reporting, where you can also run the reports. He was contributing author for quot;SQL Server 2008 Analysis Services with MDXquot; and quot;Microsoft SQL Server Reporting Services Recipesquot;.
LinkedIn: https://www.linkedin.com/pub/manohar-punna/15/153/508
Contact: http://www.sqlservergeeks.com/author/manoharpunna/
Manohar Punna is an Data Platform Geek and Vice President of DataPlatformGeeks by passion and a Data Platform Consultant by profession. He is a speaker at various conferences like PASS Summit, SQLBits, DPS. He has authored over 150 blogs and has written a One DMV a Day series which is a longest one day series on any topic on SQL Server till date. Manohar is a product of this community and strongly believes in giving back to the community. So, ask him anything you would like to know and he is happy to learn it with you.
LinkedIn: http://www.linkedin.com/in/sqlbi
Contact: https://www.sqlbi.com/blog/marco
Marco is a Business Intelligence consultant and mentor. He wrote several books about Power BI, Analysis Service, and Power Pivot. He also regularly writes articles and white papers that are available on sqlbi.com. Marco is a Microsoft MVP and an SSAS Maestro, the highest level of certification on Microsoft Analysis Services. Today, Marco focuses his time with SQLBI customers, traveling extensively to train and consult on DAX and data modeling for Power BI and Analysis Services. Marco also teaches public classes worldwide. Marco is a regular speaker at international conferences like Microsoft Ignite, PASS Summit, and SQLBits. He also enjoys delivering evening sessions at local user groups during his trips.
LinkedIn: http://ca.linkedin.com/in/melodyzacharias
Contact: http://sqlmelody.com
Melody is the Sr. Microsoft Solutions Manager at Pure and has been in love with data since 1991. She has been sharing her passion with the community in technical sessions and blogs since 2014. She has been a Microsoft MVP since 2016, including winning Rookie of the year for Canada that year. This last year, she was elected to the board of directors for PASS.org, the professional association for SQL Server and was awarded Regional Director by Microsoft. She has co-authored 3 books, including, SQL Server 2019 Administration inside out by Microsoft Press.
LinkedIn: https://au.linkedin.com/in/ieftekhari
Contact: http://www.agilebi.com.au
Iman Eftekhari (MCSE BI, MCITP, CSP) has more than 15 years of experience predominantly in Microsoft BI space. He has been involved in many successful BI projects across the country mainly with financial services, health and the public sectors. He is the co-founder of Agile BI and is passionate about delivering BI projects using Agile methods.
Contact: http://www.gumption.co.nz
Robs SQL history started as a remote DBA, branched into consultancy and now includes providing SQL Solutions to a variety of businesses, government departments and banks throughout Australia and New Zealand using a variety of native SQL solutions to meet their performance, higher availability and Disaster Recovery needs.
Glyn has been an SQL Server database administrator since the days of 4.2k11 an is currently working as a senior consultant for Angry Koala. He specialises in SQL sever performance tuning and machine learning.
LinkedIn: https://www.linkedin.com/in/craigbryden/
Craig has been working with SQL Server for over 20 years and currently holds several SQL Server based certifications. He has also previously held an MCT certification. Craig's contribution to the SQL Server community was recognised in 2018 with a Microsoft MVP award. He was involved with Microsoft Learning to set some of the current SQL server Exams. Craig is the Chief Data Strategist at BiTQ. Craig is an organiser of the QLD Power BI User Group
LinkedIn: http://linkedin.com/in/peterjsmyers
Peter Myers is a consultant, trainer and presenter, and has worked with Microsoft database and development products since 1997. Today he specializes in all Microsoft Business Intelligence products and also authors training course content for Microsoft products and services. He has a broad business background supported by a bachelor’s degree in applied economics and accounting, and he extends this with extensive experience backed by current MCSE certifications. He has been a Data Platform MVP since 2007.
LinkedIn: https://au.linkedin.com/in/ieftekhari
Contact: http://www.agilebi.com.au
Iman Eftekhari (MCSE BI, MCITP, CSP) has been involved in many successful BI implementations mainly with financial services, health and the public sectors. He has presented at number of events such as PASS SQL Saturday Sydney, Brisbane, Melbourne, Copenhagen,Sydney BI User Group, SQL Server User Group, etc. Iman is the co-founder of the Agile BI consulting, co-organiser of the Sydney BI Social group (SYBIS) and Sydney Power BI User Group (PUG).
LinkedIn: https://au.linkedin.com/pub/daniel-de-sousa/28/198/b4
Contact: https://sqlblog2017.wordpress.com/
Dan de Sousa is a Senior Data Consultant for DSI in Mississauga, Canada. He has been working with SQL Server for the last 10 years and has been involved in the development of datawarehouses as well as creating reports and dashboards. Dan is currently working on a data security compliance project for Europe which involves making sure any personal information is secured/encrypted before leaving its country of origin.
LinkedIn: https://au.linkedin.com/in/mattallington
Contact: http://exceleratorbi.com.au/exceleratorblog/
Matt is a BI Professional with over 30 years’ experience in the Consumer Packaged Goods industry. He has held senior job commercial and IT roles, always leveraging data to drive success. Matt was the BI Director for Asia Pacific at The Coca-Cola Company before leaving to set up his own consulting business in 2014. Matt is now the Principal Consultant at Excelerator BI and specialises in helping small, medium and large companies leverage Microsoft Excel, Power Pivot, Power BI and SharePoint to deliver rapid business value through Self Service Business Intelligence.
LinkedIn: http://nz.linkedin.com/in/rezarad/
Contact: http://radacad.com/blog
Reza Rad is a Microsoft Regional Director, MVP, author, trainer, speaker and consultant. He is a Microsoft Data Platform MVP for nine years from 2011. Reza wrote many books on Power BI subjects including the Power BI from Rookie to Rock Star, and Pro Power BI Architecture. Reza is the invited speaker at the world's well-known conferences such as Microsoft Ignite, Microsoft Business Applications Summit, Ignite Tour, PASS Summit, and many other conferences. Reza is an active blogger and writes regularly about Power BI at https://radacad.com.
LinkedIn: http://nz.linkedin.com/in/rezarad/
Contact: http://radacad.com/blog
Reza Rad is a Microsoft Regional Director, MVP, author, trainer, speaker and consultant. He is a Microsoft Data Platform MVP for nine years from 2011. Reza wrote many books on Power BI subjects including the Power BI from Rookie to Rock Star, and Pro Power BI Architecture. Reza is the invited speaker at the world's well-known conferences such as Microsoft Ignite, Microsoft Business Applications Summit, Ignite Tour, PASS Summit, and many other conferences. Reza is an active blogger and writes regularly about Power BI at https://radacad.com.
LinkedIn: http://au.linkedin.com/in/jamesberesford
Contact: http://www.bimonkey.com
James - aka the BI Monkey - has been delivering Microsoft BI Solutions for over a Decade. He#39;s an unashamed SSIS geek and is responsible for the heavily downloaded BI Monkey ETL Framework on Codeplex. Recently he#39;s been digging in to Data Quality Services in SQL2012 and also developing on the HDInsight Big Data platform. In his spare time he cultivates two small BI Monkeys - so really has no spare time!
LinkedIn: https://www.linkedin.com/in/martincatherall
Contact: http://martincatherall.com
Martin is a data consultant in Melbourne, Australia, a Microsoft Data Platform MVP, and PASS Regional Mentor for the Asia Pacific (APAC) region. Martin founded the Christchurch, New Zealand PASS Local Group and lead the group for a number of years before relocating to Melbourne. He has been using SQL Server in various roles since 2000. In addition to anything data related, he enjoys playing the guitar and having fun with his family.
LinkedIn: http://www.linkedin.com/in/sqlbi
Contact: https://www.sqlbi.com/blog/marco
Marco is a Business Intelligence consultant and mentor. He wrote several books about Power BI, Analysis Service, and Power Pivot. He also regularly writes articles and white papers that are available on sqlbi.com. Marco is a Microsoft MVP and an SSAS Maestro, the highest level of certification on Microsoft Analysis Services. Today, Marco focuses his time with SQLBI customers, traveling extensively to train and consult on DAX and data modeling for Power BI and Analysis Services. Marco also teaches public classes worldwide. Marco is a regular speaker at international conferences like Microsoft Ignite, PASS Summit, and SQLBits. He also enjoys delivering evening sessions at local user groups during his trips.
LinkedIn: https://nz.linkedin.com/in/hamishwatson8
Contact: http://www.sqlservercentral.com/blogs/the-hybrid-dbas-blog/
Hamish Watson is a Microsoft Data Platform MVP and VMware Expert with a passion for efficient database and application deployment using DevOps methodologies. He has 20+ years of IT experience in managing and deploying large scale databases on SQL Server technologies. He has been managing SQL Server since SQL Server 2000, and pragmatic approaches to delivering business value to clients is his career passion. Educating and helping others learn is a driver for Hamish. He is a Director-At-Large on the PASS Board, international speaker, and a repeat guest lecturer at a local university.
Grant Paisley is a Data Platform MVP and founder of Angry Koala, a Microsoft Business Intelligence partner based in Sydney, Australia. Grant is president of the SQL Server Usergroup Sydney and created http://reportsurfer.com, a community built around reporting, where you can also run the reports. He was contributing author for quot;SQL Server 2008 Analysis Services with MDXquot; and quot;Microsoft SQL Server Reporting Services Recipesquot;.
The following is a list of sponsors that helped fund the event.


ShopClient
Constructor
ShopClient
-
config
Parameters:
-
[config]
Config optionalConfig data to be used throughout all API interaction
Methods
Create an instance of type
, optionally including modelAttrs
.
client.create('carts', { line_items: [ ... ] }).then(cart => {
// do things with the cart.
});
Parameters:
Creates a CartModel instance, optionally including attrs
.
client.createCart().then(cart => {
// do something with cart
});
Parameters:
-
[attrs={}]
Object optionalattributes representing the internal state of the cart to be persisted to localStorage.
deserialize
-
type
-
payload
-
adapter
-
existingSerializer
-
opts
Proxy to serializer's deserialize.
Parameters:
-
type
StringThe pluralized name of the type, in lower case.
-
payload
ObjectThe raw payload returned by the adapter.
-
adapter
BaseAdapterThe adapter that yielded the payload.
-
existingSerializer
BaseSerializerThe serializer to attach. If none is passed, then
this.deserialize
will create one for the type. -
opts
ObjectOptions that determine which deserialization method to use.
Returns:
an instance of type
reified into a model.
Fetch one of a type
, returning a promise.
client.fetch('products', 123).then(product => {
// do things with the product
});
Parameters:
Returns:
a promise resolving with a single instance of
type
expressed as a BaseModel
.
Fetch all of a type
, returning a promise.
client.fetchAll('products').then(products => {
// do things with products
});
Parameters:
-
type
StringThe pluralized name of the type, in lower case.
Convenience wrapper for fetchAll. Equivalent to:
client.fetchAll('collections');
Convenience wrapper for fetchAll. Equivalent to:
client.fetchAll('products');
Retrieve a previously created cart by its key.
client.fetchCart('shopify-buy.1459804699118.2').then(cart => {
console.log(cart); // The retrieved cart
});
Parameters:
-
id
StringThe cart's unique identifier
Fetch one collection by its ID.
client.fetchCollection(123).then(collection => {
console.log(collection); // The collection with an ID of 123
});
Returns:
The collection model with the specified ID.
Fetch one product by its ID.
client.fetchProduct(123).then(product => {
console.log(product); // The product with an ID of 123
});
Returns:
The product model with the specified ID.
Fetch many of a type
, that match query
client.fetchQuery('products', { collection_id: 456 }).then(products => {
// do things with the products
});
Parameters:
Fetches a list of collections matching a specified query.
client.fetchQueryCollections({page: 2, limit: 20}).then(collections => {
console.log(collections); // An array of collection resources
});
Parameters:
Fetches a list of products matching a specified query.
client.fetchQueryProducts({ collection_id: 123}).then(products => {
console.log(products); // An array of products in collection 123
});
Parameters:
-
query
Objecta query sent to the api server containing one or more of:
-
[collection_id]
String | Number optionalthe ID of a collection to retrieve products from
-
[product_ids]
Array optionala list of product IDs to retrieve
-
[page=1]
String | Number optionalthe page offset number of the current lookup (based on the
limit
) -
[limit=50]
String | Number optionalthe number of products to retrieve per page
-
Create an instance of type
, optionally including attrs
.
client.create('carts', { line_items: [ ... ] }).then(cart => {
// do things with the cart.
});
Parameters:
-
type
StringThe pluralized name of the type, in lower case.
-
updatedModel
BaseModelThe model that represents new state to to persist.
Attributes
Default: { products: ListingsAdapter, collections: ListingsAdapter, carts: CartAdapter }


Mathematical Background
Sets
A set is a collection of well defined and distinct objects.
A relation (x,y) in a set A is a collection of ordered pairs of elements in A.
Functions
A function is a relation between a set of inputs and a set of potential outputs with the property that each input is related to exactly one output.
Formal description of a function typically involves the function's name, its domain, its codomain, and a rule of correspondence.
- e.g. ƒ : ℜ → ℜ, ƒ(x) = x2
The floor and ceiling functions map a real number to the largest previous or the smallest following integer, respectively.
- ⌊x⌋ (floor of x): the largest integer not greater than x (i.e. round-down / truncate)
- ⌈x⌉ (ceiling of x): the smallest integer not less than x (i.e. round-up)
- ⌊x⌋ ≤ x ≤ ⌈x⌉
Power Function (Exponentiation)
Exponentiation is a mathematical operation, written as bn, involving two numbers, the base b and the exponent (a.k.a. index or power) n.
When n is a positive integer, exponentiation corresponds to repeated multiplication.
- b0 = 1
- b1 = b
- b2 = b(b)
- ...
- bn = b(bn-1)
Logarithm Function
The logarithm of a number to the base b is the exponent by which the base b has to be raised to produce that number.
- y = logb x ⇔ x = by
formulas:
- logb 1 = 0 (since b0 = 1)
- logb 0 is undefined (since b? = 0)
- logb xn = n logb x
- logb xy = logb x + logb y
- logb x = logc x / logc b
Summations and Series
A series is, informally speaking, the sum of the terms of a sequence.
- finite series: S = 1 + 2 + 3 + ... + n = n(n + 1) / 2
- infinite series: S = 1 + ½ + ¼ + ⅛ + ... = ∑n→∞1/2n
Arithmetic Series
Each successive term is produced by adding a constant number to the previous term.
n-1∑i=0 (a +
id)
= a + (a + d) + (a + 2d) + ... +
(a + id) + ... + [a + (n-1)d]
= n/2[2a +
(n-1)d]
= n/2(a + L)
where:
- n = number of terms
- a = first term
- d = common difference
- L = last term
Geometric Series
Each successive term is produced by multiplying the previous term by a constant number.
n-1∑i=0
ari
= a + ar + ar2 + ... +
ari + ... + arn-1
= a(1 - rn / 1 -
r)
= a(rn - 1 / r -
1)
where:
- n = number of terms
- a = first term
- r = common ratio
Limits
A limit is the value that a function or sequence "approaches" as the input or index approaches some value.
TODO: plot chart
Differentiation
Differentiating a function is to find the derivative or rate of change of the function.
formulas:
- d/dx cxn = cnxn-1
- d/dx bf(x) = bf(x) ln b f'(x)
- d/dx logb f(x) = f'(x) / f(x) ln b
Proof by Mathematical Induction
summary - to prove that a theorem is true:
- prove that the base case is true
- prove inductive step or strong induction


| Company Name | Dimension Engineers |
| Class of Activity | Design |
| Business Category | Medium |
| Business Industry | Service Provider |
| Sales Figure | ₹ 10,000,000,000 |
| No. of Employees | 50 |
| Date of Incorporation | 26 Jul 2007 |
| PAN Number | AABBC4567A |
| Registration Number | 22AAAA0000A125 |
| CIN | U67190TN2014PTC09697 |
| TIN | 22AAAA0000A125 |
| Location Type | Address | First Reported Date | Last Reported Date |
|---|---|---|---|
Location Type: Registered Office |
Address: Abhishek, 17-A, Gandharva, Near Hotel, Erandwane, Pune, Maharashtra 415520 |
First Reported Date: 24 Nov 2018 |
Last Reported Date: 24 Nov 2018 |
Location Type: Branch / Regional Office |
Address: Abhishek, 17-A, Gandharva, Near Hotel, Erandwane, Pune, Maharashtra 411004 |
First Reported Date: 24 Nov 2018 |
Last Reported Date: 24 Nov 2018 |
Location Type: Others |
Address: Abhishek, 17-A, Gandharva, Near Hotel, Erandwane, Pune, Maharashtra 415520 |
First Reported Date: 24 Nov 2018 |
Last Reported Date: 24 Nov 2018 |
Location Type: Warehouse |
Address: Abhishek, 17-A, Gandharva, Near Hotel, Erandwane, Pune, Maharashtra 411004 |
First Reported Date: 24 Nov 2018 |
Last Reported Date: 24 Nov 2018 |
Location Type: Branch / Regional Office |
Address: Abhishek, 17-A, Gandharva, Near Hotel, Erandwane, Pune, Maharashtra 415520 |
First Reported Date: 24 Nov 2018 |
Last Reported Date: 24 Nov 2018 |
Location Type: Others |
Address: Abhishek, 17-A, Gandharva, Near Hotel, Erandwane, Pune, Maharashtra 415520 |
First Reported Date: 24 Nov 2018 |
Last Reported Date: 24 Nov 2018 |
Location Type: Registered Office |
Address: Abhishek, 17-A, Gandharva, Near Hotel, Erandwane, Pune, Maharashtra 411004 |
First Reported Date: 24 Nov 2018 |
Last Reported Date: 24 Nov 2018 |
Location Type: Branch / Regional Office |
Address: Abhishek, 17-A, Gandharva, Near Hotel, Erandwane, Pune, Maharashtra 415520 |
First Reported Date: 24 Nov 2018 |
Last Reported Date: 24 Nov 2018 |
Location Type: Registered Office |
Address: Abhishek, 17-A, Gandharva, Near Hotel, Erandwane, Pune, Maharashtra 411004 |
First Reported Date: 24 Nov 2018 |
Last Reported Date: 24 Nov 2018 |


Road-rager ‘shoots victim, takes him to hospital, hands himself over to cops’
The victim is said to be in critical condition in Sandton Mediclinic.
by News24 WireBullet casings crime scene. Picture: SAPS
A man has handed himself over to Randburg police after allegedly shooting a man in what seems to be a road-rage incident on Monday afternoon.
Eyewitnesses said the two had an altercation in Bryanston, Johannesburg, at which point the man allegedly shot his victim.
In a strange turn of events, after the shooting, the man allegedly loaded his victim into his car and drove him to the hospital before handing himself over at the Randburg police station.
The victim is said to be in critical condition in Sandton Mediclinic.
The police are still gathering information about the incident.


|
Ipass project Natan Houwaart
|
Implementation of the Abstract NFC interface for the pn532. More...
Go to the source code of this file.
Classes | |
| class | nfc::PN532_chip |
| Implementation of the NFC class specificly for the pn532. More... | |
Implementation of the Abstract NFC interface for the pn532.
This file provides an implementation of the Abstract NFC interface class that can be used by a pn532.
This file contains detailled doxygen lines, but if a particulair function of thepn532 is not clear, plese refer to the user manual of this chip: https://www.nxp.com/docs/en/user-guide/141520.pdf
You can also refer to the application note in order to see how the pn532 is used to read cards/ etc: https://www.nxp.com/docs/en/nxp/application-notes/AN133910.pdf
The pn532 is manufactured by NXP®. All rights reserved
PN532 NFC RFID Module is a highly integrated transmission module for Near Field Communication at 13.56MHz. With the mode switch on board, you can change easily between I2C, SPI, and UART modes. The integrated level shifter provides 3.3V or 5V working voltage for your choice. In addition, it supports RFID reading and writing, and NFC function with Android phone, which makes it quite convenient for wireless connection. This module is equipped with two 3mm mounting holes, of which the small dimension makes it easy for using in your project! (source: http://wiki.sunfounder.cc/index.php?title=PN532_NFC_RFID_Module )
Note: When your wiring is correct (no short circuit), the module may be a little heated, which is just normal for use.


Using a sliding lookback duration defined by an expression viewable in the pipeline JSON editor.
The report frequency is defined via an expression in the pipeline JSON editor.


Press release
Persistent pupil absence falls by a third
This was published under the 2010 to 2015 Conservative and Liberal Democrat coalition government
The number of schoolchildren regularly missing school has fallen by almost 140,000 over the past 2 years.
The figures for the first two terms of the 2012-to-2013 academic year show:
-
139,750 fewer children missed 15% or more of school - equivalent to missing one-and-a-half years of a whole school career - compared to two years ago
-
the number missing 15% or more of school is down from 450,330 in the 2010-to-2011 academic year to 310,580 in the 2012-to-2013 academic year
-
65,540 fewer children missed 20% or more of school - equivalent to missing two years of a whole school career - compared to two years ago
-
the number missing 20% or more of school is down from 199,370 in the 2010-to-2011 academic year to 133,830 in the 2012-to-2013 academic year
The government has taken the following action to reduce persistent absence:
-
in October 2011 the government reduced the definition of ‘persistent absence’ used to hold schools to account from 20% to 15%, in order to encourage schools to address the problem at an earlier stage
-
in September 2012 fines for truancy were increased from £50 to £60, and from £100 to £120 if not paid within 28 days
-
from last month the time limit for paying the penalties was reduced from 42 to 28 days
Education Minister Elizabeth Truss said:
It is vital all children attend as much school as possible. That is why we have increased fines for truancy and encouraged schools to tackle persistent absence at an earlier stage.
We know that poor attendance can have a hugely damaging effect on a child’s education.
Children who attend school regularly are four times more likely to achieve 5 or more good
GCSEs
than those who are persistently absent.
Notes to editors
-
The
figures for pupil absence in schools in England: autumn 2012 and spring 2013
are available to download
-
Figures on overall and persistent absence in the 2011-to-2012 academic year were affected by unusually low levels of flu-like illness in schools along with the religious festival Eid falling out of term time. However, today’s figures confirm that the reductions in persistent absence shown in the 2011-to-2012 two-term figures have been maintained in the 2012-to-2013 academic year.
-
Of pupils who miss between 10% and 20% of school, only 39% manage to achieve five A* to C
GCSEs
including English and maths. Of pupils who miss less than five% of school, 73% achieve five A* to Cs including English and maths.


2 Samuel 13:23
And it came to pass after two full years, that Absalom had sheepshearers in Baalhazor, which is beside Ephraim: and Absalom invited all the king's sons.


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Replay¶
Various versions of experience replays.
Memory Efficient Experience Replay¶
-
class
wintermute.replay.
MemoryEfficientExperienceReplay
(capacity: int = 100000, batch_size: int = 32, hist_len: int = 4, async_memory: bool = True, mask_dtype=torch.uint8, bootstrap_args=None)[source]¶ Bases:
object
Experience Replay Buffer which stores states in order and samples concatenated states of a given history length.
- Parameters
capacity (int, optional) – Defaults to 100_000. ER size.
batch_size (int, optional) – Defaults to 32.
hist_len (int, optional) – Defaults to 4. Size of the state.
async_memory (bool, optional) – Defaults to True. If enabled it will try to take advantage of the time it takes to do a policy improvement step and sample asyncronously the next batch.
mask_dtype (torch.type, optional) – Defaults to torch.uint8.
bootstrap_args (list, optional) – Defaults to None.
-
is_async
¶ If memory uses threads.
Naive Experience Replay¶
Naive Experience Replay and helper functions.
-
class
wintermute.replay.naive_experience_replay.
NaiveExperienceReplay
(capacity=100000, batch_size=32, collate=None, full_transition=False)[source]¶ Bases:
object
Stores in a circular buffer transitions containing observations formed by concatenating several (usually four, in DQN) frames as opposed to FlatExperienceReplay which stores transitions containing the current frame.
This makes Naive Experience Replay faster at the expense of RAM. The only memory optimiation is that it can store either full transitions (_s, _a, r, s, d) or half transitions (_s, _a, r, d).
-
push
(transition)[source]¶ - Add a transition tuple to the buffer. Several things happen:
1. Keep the last state for the corner case in which we sample the last transition in the buffer. 2. If we don’t store full transitions we strip the tuple 3. Add to the cyclic buffer
- Parameters
transition (tuple) – Contains an (_s, _a, r, [s], d) experience.
-
Pinned Experience Replay¶
This file contains a version of MemoryEfficientExperienceReplay that allocates from the start full-capacity tensors
-
class
wintermute.replay.pinned_er.
PinnedExperienceReplay
(capacity: int = 100000, batch_size: int = 32, hist_len: int = 4, async_memory: bool = True, scren_dtype=torch.uint8, mask_dtype=torch.uint8, screen_size: tuple = (84, 84), bootstrap_args=None, device='cuda')[source]¶ Bases:
wintermute.replay.mem_efficient_experience_replay.MemoryEfficientExperienceReplay
docstring not found.
-
is_async
¶ If memory uses threads.
-
Prioritized Replay¶
Prioritized Experience Replay implementations.
1. ProportionalSampler implements the proportional-based prioritization using the SumTree in data_structures.py.
2. RankSampler implements the rank-based prioritization using the PriorityQueue in data_structures.py.
-
class
wintermute.replay.prioritized_replay.
ProportionalSampler
(er=None, is_async: bool = True, optim_steps=None, **kwargs)[source]¶ Bases:
object
Implements the proportional-based sampling in [Prioritized Experience Replay](https://arxiv.org/pdf/1511.05952.pdf).
-
batch_size
¶ Batch size, duh!
-
Data Structures¶
Data structures used in Prioritized Experience Replay implementations.
-
class
wintermute.replay.data_structures.
PriorityQueue
(data: Optional[list] = None)[source]¶ Bases:
object
A priority queue which can update the priority the items it holds.
-
push
(item: tuple)[source]¶ Inserts the (priority, content) element and percolates it up in the binary heap.
- Parameters
item (tuple(priority, content)) – The item to be stored in the heap.
comprises of a priority term that is used for ordering the item (It) –
the data structure and the actual content of the item. (in) –
-
wintermute.replay.transitions module¶
Transition containers.
-
class
wintermute.replay.transitions.
ComparableTransition
[source]¶ Bases:
tuple
Attributes of a Comparable
-
priority
¶ Alias for field number 0
-
transition
¶ Alias for field number 1
-


GAS ranking models¶
Introduction¶
GAS ranking models can be used for pairwise comparisons or competitive activities. The \(GASRank\) model of Taylor, R. (2016) models a point difference between two competitors. The point difference is assumed to follow a particular distribution. For example, suppose that the point difference \(\mu_{t}\) is normally distributed, then we can model the point difference as the location parameter \(\mu\):
Where \(\delta_{t}\) is a ‘home advantage’ latent variable, \(i\) and \(j\) refer to home and away competitors, and \(\alpha\) contains the team power rankings. The power rankings are modelled as random walk processes between each match:
Where \(k\) is the game index, \(\eta\) is a learning rate or scaling parameter to be estimated.
The model can be extended to a two component model where each competitor has two aspects to their ‘team’. For example, we might model an NFL team along with the Quarterback power rankings in the same game:
Here \(\gamma\) represents the power ranking of the second component. The secondary component power rankings are modelled as random walk processes between each match:
Developer Note¶
- This model type has yet to be cythonized, so performance can be slow.
Example¶
We will model the point difference in NFL games with a simple model. Here is the data:
import pyflux as pf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv("nfl_data_new.csv")
data["PointsDiff"] = data["HomeScore"] - data["AwayScore"]
data.columns
Index(['Unnamed: 0', 'AFirstDowns', 'AFumbles0', 'AFumbles1', 'AIntReturns0',
'AIntReturns1', 'AKickoffReturns0', 'AKickoffReturns1', 'ANetPassYards',
'APassesAttempted', 'APassesCompleted', 'APassesIntercepted',
'APenalties0', 'APenalties1', 'APossession', 'APuntReturns0',
'APuntReturns1', 'APunts0', 'APunts1', 'AQB', 'ARushes0', 'ARushes1',
'ASacked0', 'ASacked1', 'AwayScore', 'AwayTeam', 'Date', 'HFirstDowns',
'HFumbles0', 'HFumbles1', 'HIntReturns0', 'HIntReturns1',
'HKickoffReturns0', 'HKickoffReturns1', 'HNetPassYards',
'HPassesAttempted', 'HPassesCompleted', 'HPassesIntercepted',
'HPenalties0', 'HPenalties1', 'HPossession', 'HPuntReturns0',
'HPuntReturns1', 'HPunts0', 'HPunts1', 'HQB', 'HRushes0', 'HRushes1',
'HSacked0', 'HSacked1', 'HomeScore', 'HomeTeam', 'Postseason',
'PointsDiff'],
dtype='object')
We can plot the point difference to get an idea of potentially suitable distributions:
data = pd.read_csv("nfl_data_new.csv")
data["PointsDiff"] = data["HomeScore"] - data["AwayScore"]
plt.figure(figsize=(15,7))
plt.ylabel("Frequency")
plt.xlabel("Points Difference")
plt.hist(data["PointsDiff"],bins=20);
We will use a pf.Normal()
families, although we could try a family with heavier tails also. We setup the \(GASRank\) model, referring to the appropriate columns in our DataFrame:
model = pf.GASRank(data=data,team_1="HomeTeam", team_2="AwayTeam",
score_diff="PointsDiff", family=pf.Normal())
Next we estimate the latent variables. For this example we will use a maximum likelihood point mass estimate \(z^{MLE}\):
x = model.fit()
x.summary()
NormalGAS Rank
======================================== ==================================================
Dependent Variable: PointsDiff Method: MLE
Start Date: 0 Log Likelihood: -10825.1703
End Date: 2667 AIC: 21656.3406
Number of observations: 2668 BIC: 21674.0079
===========================================================================================
Latent Variable Estimate Std Error z P>|z| 95% C.I.
========================= ========== ========== ======== ======== =========================
Constant 2.2405 0.2547 8.795 0.0 (1.7412 | 2.7398)
Ability Scale 0.0637 0.0058 10.9582 0.0 (0.0523 | 0.0751)
Normal Scale 13.9918
===========================================================================================
Once we have fit the model we can plot the power rankings of the teams in our DataFrame over their competitive history using plot_abilities()
:
model.plot_abilities(["Denver Broncos", "Green Bay Packers", "New England Patriots",
"Carolina Panthers"],figsize=(15,8))
model.plot_abilities(["San Francisco 49ers", "Oakland Raiders", "San Diego Chargers"],
figsize=(15,8))
We can predict the point difference between two competitors in the future using predict()
:
model.predict("Denver Broncos","Carolina Panthers",neutral=True)
array(-4.886816685966575)
Our DataFrame also has information on quarterbacks. Let’s extend our model with a second component by including quarterbacks in the model:
model.add_second_component("HQB","AQB")
x = model.fit()
x.summary()
NormalGAS Rank
======================================== ==================================================
Dependent Variable: PointsDiff Method: MLE
Start Date: 0 Log Likelihood: -10799.4544
End Date: 2667 AIC: 21606.9087
Number of observations: 2668 BIC: 21630.4651
===========================================================================================
Latent Variable Estimate Std Error z P>|z| 95% C.I.
========================= ========== ========== ======== ======== =========================
Constant 2.2419 0.2516 8.9118 0.0 (1.7488 | 2.735)
Ability Scale 1 0.0186 0.0062 2.9904 0.0028 (0.0064 | 0.0307)
Ability Scale 2 0.0523 0.0076 6.8492 0.0 (0.0373 | 0.0673)
Normal Scale 13.8576
==========================================================================================================
We can plot the power rankings of the QBs in our DataFrame over their competitive history using plot_abilities()
:
model.plot_abilities(["Cam Newton", "Peyton Manning"],1,figsize=(15,8))
We can predict the point difference between two competitors in the future using predict()
:
model.predict("Denver Broncos","Carolina Panthers","Peyton Manning","Cam Newton",neutral=True)
array(-7.33759714587138)
And some more power rankings for fan interest...
model.plot_abilities(["Aaron Rodgers", "Tom Brady", "Russell Wilson"],1,figsize=(15,8))
model.plot_abilities(["Peyton Manning","Michael Vick", "David Carr", "Carson Palmer"
,"Eli Manning","Alex Smith","JaMarcus Russell","Matthew Stafford"
,"Sam Bradford","Cam Newton","Andrew Luck","Jameis Winston"],1,
figsize=(15,8))
Class Description¶
-
class
GASRank
(data, team_1, team_2, family, score_diff)¶ Generalized Autoregressive Score Ranking Models (GASRank).
Parameter Type Description data pd.dataframe Containing the competitive data team_1 string Column name for home team names team_2 string Column name for away team names family pf.Family instance The distribution for the time series, e.g pf.Normal()
score_diff string Column name for the point difference Attributes
-
latent_variables
¶ A pf.LatentVariables() object containing information on the model latent variables, prior settings. any fitted values, starting values, and other latent variable information. When a model is fitted, this is where the latent variables are updated/stored. Please see the documentation on Latent Variables for information on attributes within this object, as well as methods for accessing the latent variable information.
Methods
-
add_second_component
(team_1, team_2)¶ Adds a second component to the model
Parameter Type Description team_1 string Column name for team 1 second component team_2 string Column name for team 2 second component Returns : void - changes model to a second component model
-
adjust_prior
(index, prior)¶ Adjusts the priors for the model latent variables. The latent variables and their indices can be viewed by printing the
latent_variables
attribute attached to the model instance.Parameter Type Description index int Index of the latent variable to change prior pf.Family instance Prior distribution, e.g. pf.Normal()
Returns: void - changes the model
latent_variables
attribute
-
fit
(method, **kwargs)¶ Estimates latent variables for the model. User chooses an inference option and the method returns a results object, as well as updating the model’s
latent_variables
attribute.Parameter Type Description method str Inference option: e.g. ‘M-H’ or ‘MLE’ See Bayesian Inference and Classical Inference sections of the documentation for the full list of inference options. Optional parameters can be entered that are relevant to the particular mode of inference chosen.
Returns: pf.Results instance with information for the estimated latent variables
-
plot_abilities
(team_ids)¶ Plots power rankings of the model components. Optional arguments include figsize, the dimensions of the figure to plot.
Parameter Type Description team_ids list Of strings (team names) or indices For a two component model, arguments are:
Parameter Type Description team_ids list Of strings (team names) or indices component_id int 0 for component 1, 1 for component 2 Returns : void - shows a matplotlib plot
-
plot_fit
(**kwargs)¶ Plots the fit of the model against the data. Optional arguments include figsize, the dimensions of the figure to plot.
Returns : void - shows a matplotlib plot
-
plot_z
(indices, figsize)¶ Returns a plot of the latent variables and their associated uncertainty.
Parameter Type Description indices int or list Which latent variable indices to plot figsize tuple Size of the matplotlib figure Returns : void - shows a matplotlib plot
-
predict
(team_1, team_2, neutral=False)¶ Returns predicted point differences. For a one component model, arguments are:
Parameter Type Description team_1 string or int If string, team name, else team index team_2 string or int If string, team name, else team index neutral boolean If True, disables home advantage For a two component model, arguments are:
Parameter Type Description team_1 string or int If string, team name, else team index team_2 string or int If string, team name, else team index team1b string or int If string, team 1, player 2 name team2b string or int If string, team 2, player 2 name neutral boolean If True, disables home advantage Returns : np.ndarray - point difference predictions
-
References¶
Creal, D; Koopman, S.J.; Lucas, A. (2013). Generalized Autoregressive Score Models with Applications. Journal of Applied Econometrics, 28(5), 777–795. doi:10.1002/jae.1279.
Harvey, A.C. (2013). Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series. Cambridge University Press.
Taylor, R. (2016). A Tour of Time Series Analysis (and a model for predicting NFL games). https://github.com/RJT1990/PyData2016-SanFrancisco


This dataset is from the American National Election Studies (ANES), a survey that aims to provide insights into voter behavior. It has been conducted since 1948 before and after each presidential election, and combines questions about voters' political attitudes with extensive biographical information. Current as of 07/29/2020.
nes
A tibble with 38,558 observations and 10 variables:
integer variable for year of study
character variable for the 2-letter abbreviation of the state of the interview of a respondent
character variable with values "Male", "Female", and "Other"
factor variable for respondents' income percentile group. Has 5 levels: 0-16th percentile, 17-33rd, 34-67th, 68-95th, 96-100th
factor variable of respondents' age ranges, 75+ is accurate except for year 1954
factor variable with a 7 levels measuring educational achievement
character variable for respondants' race / ethnicity identification
factor variable for party identification with 7 levels
character variable of respondents' self-reported approval of the sitting president. Question was not asked before 1972
character variable indicating whether the respondent voted in the national elections
factor variable indicating the region corresponding to the voter's survey state
https://electionstudies.org/data-center/anes-time-series-cumulative-data-file/
Some of the questions aksed in the survey have changed slightly over time. The ANES codebook provides further information on this issue.
Table: Data summary
| Name | nes |
| Number of rows | 38558 |
| Number of columns | 11 |
| _______________________ | |
| Column type frequency: | |
| character | 5 |
| factor | 5 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
| state | 110 | 1.00 | 2 | 2 | 0 | 50 | 0 |
| gender | 141 | 1.00 | 4 | 6 | 0 | 3 | 0 |
| race | 287 | 0.99 | 5 | 15 | 0 | 6 | 0 |
| pres_appr | 9646 | 0.75 | 6 | 10 | 0 | 3 | 0 |
| voted | 4078 | 0.89 | 2 | 3 | 0 | 2 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
| income | 2517 | 0.93 | FALSE | 5 | 34 : 11740, 68 : 9974, 0 -: 6300, 17 : 6213 |
| age | 450 | 0.99 | FALSE | 7 | 25 : 7669, 35 : 7342, 45 : 6545, 55 : 6021 |
| education | 397 | 0.99 | FALSE | 7 | Hig: 9269, Som: 8540, Col: 5540, Som: 4688 |
| ideology | 619 | 0.98 | FALSE | 7 | Str: 7784, Wea: 7330, Wea: 4894, Str: 4807 |
| region | 0 | 1.00 | FALSE | 4 | Sou: 13680, Mid: 10072, Wes: 7428, Nor: 7378 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
| year | 0 | 1 | 1989.4 | 20.5 | 1952 | 1972 | 1992 | 2012 | 2016 | ▃▃▃▃▇ |
David Kane


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timeR
package creates a R6 class, which allows you to create a timer object to easily time your codes. Meanwhile, all records are saved to a data frame, so it’s easy to retrieve all the records for later use.
Timing codes is not difficult but can be very tedious. With timeR
, you can save your energy on timing and put more effort on your analysis. You can use timeR
to timing training time for machine learning models, record speed for requests hen running web-scraping codes or other situations that you need to keep records of time.
library(timeR)
# Create a timer object(precision default to s)
my_timer <- createTimer()
# start timing for an event
my_timer$start("event one")
#start timing for another event
my_timer$start("event two")
# stop timing for the events
my_timer$stop("event one")
#> Event: 'event one' already has a record. Overwriting previous one.
#>
#> For event: 'event one', 0 seconds elapsed.
my_timer$stop("event two", comment = "my comment") # comment is optional
#> Event: 'event two' already has a record. Overwriting previous one.
#>
#> For event: 'event two', 0 seconds elapsed.
# retrieve the table for all recordings
getTimer(my_timer)
#> event start end timeElapsed comment
#> 1 event one 2020-06-22 10:19:35 2020-06-22 10:19:35 0 <NA>
#> 2 event two 2020-06-22 10:19:35 2020-06-22 10:19:35 0 my comment
# or create a timer object and setting verbose to false
my_timer2 <- createTimer(verbose = F)
# toggle on/off verbose
my_timer$toggleVerbose()
#> Verbose set to: FALSE.
# warnings will still be shown when verbose is turned off
my_timer$stop("event one")
Let’s compare the workflow with and without timeR
in the following scenario.
Our goal is to keep records of two events: event 1 and event 2.
We need to keep records of starting and stopping time for both events as well as calculate the time difference between them. Also, we want to save those information to a data frame for later analysis.
timeR
#initialize a timer object named mytimer: s, ms, us can be used as precision
mytimer <- createTimer(precision = "us")
# event 1
mytimer$start("event 1")
# do something here
Sys.sleep(1)
mytimer$stop("event 1")
#> Event: 'event 1' already has a record. Overwriting previous one.
#>
#> For event: 'event 1', 1.01 seconds elapsed.
#event 2
mytimer$start("event 2")
# do something here
Sys.sleep(1)
mytimer$stop("event 2",comment = "custom comment")
#> Event: 'event 2' already has a record. Overwriting previous one.
#>
#> For event: 'event 2', 1.01 seconds elapsed.
# print records
getTimer(mytimer)
#> event start end
#> 1 event 1 2020-06-22 10:19:35.112322 2020-06-22 10:19:36.120058
#> 2 event 2 2020-06-22 10:19:36.145055 2020-06-22 10:19:37.151870
#> timeElapsed comment
#> 1 1.007736 <NA>
#> 2 1.006815 custom comment
# get attributes for selected events
mytimer$getStartTime("event 1")
#> [1] "2020-06-22 10:19:35.112322"
mytimer$getStopTime("event 1")
#> NULL
mytimer$getTimeElapsed("event 1")
#> [1] 1.007736
mytimer$getComment("event 1")
#> [1] NA
mytimer$getEvent("event 1")
#> event start end
#> 1 event 1 2020-06-22 10:19:35.112322 2020-06-22 10:19:36.120058
#> timeElapsed comment
#> 1 1.007736 <NA>
#initalize a dataframe to store the information
timer_df = data.frame(matrix(ncol = 5, nrow = 0))
colnames(timer_df) <- c("event","start","end","timeElapsed","comment")
#event 1
t1 <- Sys.time()
# do something here
Sys.sleep(1)
t2 <- Sys.time()
timer_df <- rbind(timer_df,
data.frame(start = t1,
end = t2,
event = "event 1",
timeElapsed = t2-t1,
comment=NA))
#event 2
t1 <- Sys.time()
# do something here
Sys.sleep(1)
t2 <- Sys.time()
timer_df <- rbind(timer_df,
data.frame(start = t1,
end = t2,
event = "event 2",
timeElapsed = t2-t1,
comment = "custom comment"))
# print records
timer_df
#> start end event timeElapsed
#> 1 2020-06-22 10:19:37 2020-06-22 10:19:38 event 1 1.005554 secs
#> 2 2020-06-22 10:19:38 2020-06-22 10:19:39 event 2 1.005216 secs
#> comment
#> 1 <NA>
#> 2 custom comment


Defined in generated file: python/ops/gen_linalg_ops.py
Computes the sign and the log of the absolute value of the determinant of
tf.compat.v1.linalg.slogdet
tf.compat.v2.linalg.slogdet
tf.linalg.slogdet( input, name=None )
one or more square matrices.
The input is a tensor of shape [N, M, M]
whose inner-most 2 dimensions
form square matrices. The outputs are two tensors containing the signs and
absolute values of the log determinants for all N input submatrices
[..., :, :]
such that the determinant = signexp(log_abs_determinant).
The log_abs_determinant is computed as det(P)sum(log(diag(LU))) where LU
is the LU decomposition of the input and P is the corresponding
permutation matrix.
input
: A Tensor
. Must be one of the following types: half
, float32
, float64
, complex64
, complex128
.
Shape is [N, M, M]
.name
: A name for the operation (optional).A tuple of Tensor
objects (sign, log_abs_determinant).
sign
: A Tensor
. Has the same type as input
.log_abs_determinant
: A Tensor
. Has the same type as input
.


A toolset for data reduction, visualization and analysis in skyscape archaeology, archaeoastronomy and cultural astronomy.
| Version: | 0.2.2 |
| Depends: | R (≥ 2.10), astrolibR |
| Imports: | pracma, png, plotrix, oce, utils, stats, graphics, grDevices, MESS, RColorBrewer, numDeriv, parallel, foreach, doParallel, rootSolve |
| Suggests: | testthat, knitr, rmarkdown |
| Published: | 2017-10-21 |
| Author: | Fabio Silva |
| Maintainer: | Fabio Silva <f.silva at uwtsd.ac.uk> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | skyscapeR results |
| Reference manual: | skyscapeR.pdf |
| Vignettes: |
"How to use _skyscapeR_" |
| Package source: | skyscapeR_0.2.2.tar.gz |
| Windows binaries: | r-devel: skyscapeR_0.2.2.zip, r-release: skyscapeR_0.2.2.zip, r-oldrel: skyscapeR_0.2.2.zip |
| OS X binaries: | r-release: skyscapeR_0.2.2.tgz, r-oldrel: skyscapeR_0.2.2.tgz |
Please use the canonical form https://CRAN.R-project.org/package=skyscapeR to link to this page.


My name is Angus Pollmann, 26, born and raised in Chile. My mother language is Spanish, but all my work is in english so i'll just stick with it for consistency.
Also here's my resume, in a Character Sheet.
My studies are a universitary and self-taught. There are no "bachelor degree" in Chile, but an equivalent of my university career would be a bachelor of business information systems (science in information technology + business study).
On my own i've studied as much as i can, programming, game design theory, writting, psychology, philosophy, systems, mechanics, some level and graphic design, and will keep learning as long as i have time.
I want to dedicate myself to make games, because i love it. I can't stop thinking about it, i don't ever get tired of it. Doesn't matter if it is a mechanic, some balance that needs to be done, a new idea for the story or about the player experience, i love the game as a whole experience. If i'm to do something for the rest of my life, i'll do something i love, doesn't matter how much i have to work for it.
I understand that to be a great game designer means to learn about everything (communication with people, programming, computers, math, systems, mechanics, levels, psychology, philosophy, history, literature, myths, religions, graphic design, architecture, music, art, design, anthropology, phisics..), and i'm not afraid, but thrilled.
I don't really discriminate videogames by genre or where they come from, as long as they're good.
I specially like videogames that are immersive, that allow you to experience like you're living in another place, another time, another reality. That kind of game that teaches you something about yourself, about the world, about people, that once you've finished it, you're something more. For me, great games will make the players feel like they've lived again.
And i also like movies, series (don't care if they're occidental or not), books (specially sci-fi), comics (Invincible <3) and of course, Board Games.
Email<EMAIL_ADDRESS>Facebook: facebook.com/wesker.beoulve
I like to keep a list of games i've played, because i believe they're a good source of references and/or ideas. These are all i can remember by name at last. And if i have it i might as well share it.
| Games Played |
|---|
| Infamous 1/2/Second Son/First Light |
| Mass effect 1/2/3/Andromeda |
| Dishonored / Dishonored 2 |
| Unreal Tournament 2000/2004/3 |
| Life is Strange |
| Zeno Clash 1 |
| The witcher 1/2/3 |
| Elder Scrolls Skyrim |
| Red Faction Guerrilla/Armageddon |
| Prototype 1/2 |
| Dragon Age Origins/2/Inquisition |
| Far Cry 3/4 |
| Dying Light |
| GTA Vice City/V |
| Legend Of Korra |
| Tales of Symphonia 2 |
| Fable 1/3/Remastered |
| Dead Rising 2/3 |
| Digimon World 1/3/Re: Digitize/Next Order |
| Ragnarok Online |
| Amazing Spiderman 1/2 |
| Duke Nukem Forever |
| From Dust |
| Game of Thrones game (RPG) |
| The Walking Dead (telltale) 1/2 |
| the Wolf among Us |
| Hard Reset |
| Inversion |
| Dead Space 1/2/3 |
| Dead Island 1 |
| Civilization V/Beyond Earth |
| Bioshock 1/Infinite |
| Binary Domain |
| Alice Madness Return |
| Back to the Future (telltale) |
| Batman Arkham Asylum/City/Origins/Knight |
| Crysis 1/2/3 |
| Darkness 2 |
| Bulletstorm |
| Borderlands 1/2 |
| Metal Gear Rising |
| Sims 1/2/3/Medieval |
| Lego Star wars 1/2, the clone wars, indiana jhones |
| Jade Empire |
| Saint’s Row 4/Gat out of hell |
| Starcraft 1/2 wings of liberty |
| Warhammer 40k Space Marine |
| XCOM: Enemy Unknown/ XCOM 2 |
| Red Dead Redemption |
| Vanquish |
| Plants vs Zombies 1/2/Garden Warfare/GW2/Heroes |
| Pokemon (black/emerald/red leaf?) |
| Pepsiman |
| Final Fantasy Tactics (Lion War) |
| Legend of the Dragoon |
| Final Fantasy VII |
| Age of Empires I/II |
| Disciples III |
| Heroes of Might and Magic VI |
| Rock of Ages 1/2 |
| The Bureau: XCOM Declassified |
| Katamari Damacy/Touch my Katamari |
| Phoenix Wright 1/2/3 |
| Strike Suit Zero |
| Digimon Masters Online (korean version) |
| Omega Boost |
| Orochi Warriors 1/2 |
| Hokuto Musou |
| Gundam Musou |
| Tales of Vesperia |
| Spore |
| Black & White 1 and 2 |
| Metal Gear Solid: Ground Zeroes/the twin snakes |
| Journey |
| Super Smash Bros/melee/brawl |
| Rayman |
| Thief |
| Saint Seiya Musou/soldier soul/soul of gold |
| Game of Thrones (Telltale) |
| Valkyria Chronicles 1/2 |
| Marvel Avengers Alliance 1/2 |
| XMEN Alliance |
| Marvel Heroes |
| Tribes: Ascend |
| Scribblenauts / Scribblenauts Unlimited |
| The elder scrolls V Skyrim |
| Exteel |
| Front Mission Evolved |
| Lost Planet 2/3 |
| Firewatch |
| God of War 3 |
| DMC Devil May Cry |
| Darksiders 1/2 |
| Resident Evil 4/5/6/7 |
| Deus Ex/Deus Ex: Human Revolution/Mankind Divided |
| Doom 1/2/DOOM (2016) |
| Overwatch |
| Chrono Cross |
| Chrono Trigger |
| Diablo 2/3 |
| Star Wars: Force Arena |
| Deadpool |
| Command & Conquer: Red Alert/Red Alert 3 |
| Star Wars: Force Unleashed (wii version) |
| Trauma Team |
| Trauma Center: Under the Knife/2/New Blood |
| No More Heroes 1/2 |
| Eternal Darkness |
| Harry Potter (EA series) 1/2/3/7/8 |
| Digimon Story Cyber Sleuth |
| The Technomancer |
| Uncharted 1 / 2 |
| Horizon: Zero Dawn |
| Middle-earth: Shadow of Mordor / Shadow of War |
| Wolfenstein: The New Order / The New Colossus |
| Heavy Rain |
| Deadly tower of monsters |
| Watch Dogs 1 / 2 |
| Persona 5 |
| Prey |


|   |
|
||||
|
Numerous new internship opportunities are available. Recently, our students have held internships with NASA, Sun Microsystems, IBM, Mircosoft, and the Department of Commerce, CNAV Systems, and Dynamicsoft. For additional information about internships, contact the department or the Internship office.
|
||||


My Education Skills
- Able to write a clean code.
- Good in SQL and ER diagram
- I get some practice on problem solving
- Good in OOP and Data Structure
- I can work in team member
- Good idea in the files proccess
- Front ender
- I learned some programming language like Python,Dart and C++


Find Us Here!
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Tokyo Tokyo・Marunouchi・Otemachi
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Gate Village No, 8, Dubai International Financial Center - Dubai - United Arab Emirates
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44 Rue Damrémont, 75018 Paris, France
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2339 Elida Rd, Lima, OH 45805, United States


Report: Germany to Completely Outlaw Hezbollah Next Week
by JNSYou who dwell in Lebanon, Nestled among the cedars, How much grace will you have When pains come upon you, Travail as in childbirth!Jeremiah 22:23 (The Israel Bible™)
he German government is planning to ban all activity of Lebanese terrorist group Hezbollah in the country, German daily Der Spiegel reported on Thursday.
Sources in the German foreign, interior and justice ministries told the news outlet that the ban could happen as early as next week. Such a move would put Hezbollah on the same level as terrorist groups such as Islamic State and the PKK Kurdish militia.
Following the ban, all activities of Hezbollah would be forbidden in the country, including the public display of the group’s flag.
The European Union has to date outlawed only the so-called military branch of Hezbollah, but not the entire group.
German lawmakers had previously considered outlawing the Lebanese terrorist group during the summer.
E.U. members the Netherlands and the United Kingdom consider all of Hezbollah a terrorist entity, as do the United States, Canada, Israel, and even the Arab League.


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Some unique question description
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© 2016 John Wiley & Sons, Performance SQA Team


Created with Dhanya Mahesh and Reva Kulkarni
Information withheld until patent processed.
Information withheld until patent processed.
Affordability: Pulse Headphones are $30, at least 1/5 the price of comparable products by Bose, Beats, and SkullCandy
Marketability: Pulse is the sole competitor in the field, as there is no other type of "headphone-notification" on the market today. The many applications also increase potential profitability
Quality: Our product was thoroughly tested by over 30 users, all of whom were highly impressed by and interested in Pulse


Menu
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Sides
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Curiosity
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General Pod Status
Pod Name: Curiosity
Time Start:
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Battery Life:
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Getting Started with Windows Phone
Video Tutorials:
1. Requirements
- Windows 7 or Windows Vista with SP2
Note: Running in VM has issues, if you are on a Mac, you will need to setup a bootcamp partition with Windows 7 or Vista
Necessary for Installing on Device and Submitting to Market Place:
- Become an App Hub member.
2. Install SDK + PhoneGap
- Download and install Windows Phone SDK
- Donwload the latest copy of PhoneGap and extract its contents. We will be working with the Android directory.
3. Setup New Project
- Open Visual Studio Express for Windows Phone and choose New Project.
- Select PhoneGapStarter.
-
Give your project a name, and select OK.
4. Review the project structure
- The 'www' folder contains your PhoneGap html/js/css and any other resources included in your app.
-
Any content that you add here needs to be a part of the Visual Studio project, and it must be set as content.
5. Build and Deploy to Emulator
- Make sure to have Windows Phone Emulator selected in the top drop-down menu.
-
Hit the green play button beside the Windows Phone Emulator drop-down menu to start debugging or press F5.
6. Build your project for the device
In order to test your application on a device, the device must be registered. Click here.aspx) to read documentation on deploying and testing on your Windows Phone.
- Make sure your phone is connected, and the screen is unlocked
- In Visual Studio, select 'Windows Phone Device' from the top drop-down menu.
-
Hit the green play button beside the drop-down menu to start debugging or press F5.
Done!
You can also checkout more detailed version of this guide here.


All layers in CURIO map gallery are using public data source. Most data are published by City of Columbus, Mid-Ohio Regional Planning Commission (MORPC) and Center for Urban and Regional Analysis (CURA).
Based on layers' nature, all layers are divided into Transportation (with orange edge), Environment (with green edge) and Social (with blue edge).
CURIO map gallery supports most mainstream browsers (Chrome, Firefox, safari, etc.) for widescreen OS such as Windows, MacOS and Android.
Basemaps are provided by Esri and hosted on ArcGIS online. Basemaps can be integrated into webmaps for free.
This map layout is adapted by Luyu Liu based on a bootleaf template created by Brian McBride


JLaso Redis tools
This app is an experiment combining access to Redis calls with a cool UI.
Features
Combine in a single tool access to several Redis servers where the app can access to.
It can be run locally or in a server, maybe just for the time you need to debug keys.
Easy to use
The scripts combines libraries to access Redis and an HTTP server to serve the UI through your preferred broswer.
Independence
Don't need to install anything else, so you can scp the tool to your server and run it when you need it, independently of libraries or the OS you have installed.


At the time of the experience, I was perhaps four months into my relationship with Wuzzle, and was quite happy in that relationship at the time. There wasn't too terribly much going on in my life, but I was feeling quite removed from my environment. Nothing seemed to be quite getting through to me; nothing was evoking any serious emotional response. Feeling the need to knock myself out of this daze, I went about acquiring 1/4 oz of P. Cubensis, which weighed out to be just over 6 grams. Go figure. Originally, there were to be three of us participating, but an unfortunate series of events left the third participant in a terrible state of mind, and she decided to back out. Given the circumstances, I can't say that I blame her.
I weighed out 2 grams of mushrooms for myself, and 2 grams for Wuzzle. At about 10:00 or 11:00 PM, we started eating them. Ritz crackers and peanut butter. Delicious. Flesh of the gods. By midnight, things were starting to get interesting, but certainly not serious by any means.  Wuzzle's roommate, Guzzle, wanted to smoke some pot. A joint was rolled, and then smoked between the three of us.
That's about when things started getting serious. The visuals suddenly shot up perhaps 3-fold. Waving my arm left trails that remained suspended in the air for subjective minutes. The walls of the apartment were dripppppiiinnnng down to the floor, and there appeared to be faces poking out through the plaster.
'You Okay?'
'Yeah. It's just... it's a bit much is all,' I tell Wuzzle.
I continue sitting on the couch, staring straight ahead.
I move from the couch to the floor, and start looking through my CD collection for something happy.
I flip through the pages, then close the book, and flip through them again. Then again. Then again. My eyes started tearing up. None of my CD's looked appealing. Every single one was a depressing mess.  FUCK. Eventually, I found some up-beat house music and put it on, but my mood didn't really change. I knew things were going to start going downhill if something didn't change, so I went outside on the landing for a cigarette. I made it to the door, and suddenly had this terrible feeling of despair wash over me... I looked down at the floor and a few tears fell to the ground while I tried to maintain my composure long enough to get the door open and get a change of setting.
Which I did.
But it didn't help.
The floor swirled and climbed and swarmed and slithered around, the walls melted down the floor, the cigarette in my hand twisted around and around. I walked around the landing, just pacing back and forth for a bit, eventually feeling good enough to go back inside again. When I came in, I found that Wuzzle was in her bedroom, comfortably sprawled out on her mattress. We looked through her CD's a bit, and finally stumbled upon The Beatles - Let It Be. Huh. Sounds good to me.
We throw in the CD, hit play...
bingo.
Houston, we have enlightenment.
I quite literally found myself sitting in full lotus with my eyes closed, breathing in deep, full breaths of air that have never felt so beautiful. I opened my eyes and listened to the music. I looked at Wuzzle, sitting across from me.
I ask her: 'Are you ...
OH MY GOD
Yes. What is this? What have we found!!!??? This is important! Everyone needs to do this! Old people, dying people, my parents, teachers, world leaders, everyone needs to experience this!!! My god this is incredible! What is it? What is this feeling that we've both so suddenly stumbled upon. What is this om that's escaping my lips between beats? What is this crying out of sheer joy!? I'm crying! I'm crying out of the beauty of the world! Everything is perfect! Everything is as it should be! I desire nothing! I see everything as it is!! As it is, it is perfect! This is not euphoria! Insulting! Utterly completely different! Infintely more complex! This isn't sadness, this isn't joy, this isn't anything at all, but at the same time it's everything rolled up into one. This is the pure white light! This is the color of nothing, completely transparent contains everything!
Think the world’s imperfect? Wrong. It's not the world that's imperfect, people. It's you. More specifically, it's your ego. It tarnishes everything you sense. It filters this perfect world and tells you that not everything is as it should be. It fuels desire and hatred and everything ugly. It brings you suffering and sadness and disappointment and expectations. It crushes you
Every second of every day and you don't even realize it until the weight has suddenly been lifted.
THIS IS TOTAL SURRENDER.

THIS IS FINALLY LIVING!!
This is the Buddha, this is Jesus. This is giving without wanting. This is the poetic genius. This is any number of names under any number of religions anywhere in the world.
Ego is desire. Ego IS SEPERATION!
That's what it is, isn't it!? Seperation from the outside world!? The completely shit-filled illusion that you are seperate! Everything has an inside and an outside and the outside of you is everything!
Adam and Eve, separating themselves from the sacred!
IF YOU HAVE NOT BEEN THERE
YOU DO NOT UNDERSTAND
~-~-~-~
In total, the ++++ portion of the experience lasted perhaps an hour.  Two grams of mushrooms.
The experience isn't dose-dependant. It isn't a trip you can chase. It will come to you when you least expect it, and I hope that it is as beautiful for you as it was for me. The drugs serve only as a catalyst. The drugs, however, can only show you the peak of the mountain; it’s up to you to figure out how to walk the path.
Peace.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


| Class and Description |
|---|
| BaseInput
BaseInput represents a way to map input to a HashMap.
|
| BaseOutput
BaseOutput represents a way to map input to a HashMap.
|
| Deserializer
The Deserializer class reads data input and handles the data according to the core data types
|
| Input
Interface for Input which defines the contract methods which are
to be implemented.
|
| Output
Output interface which defines contract methods to be implemented
|
| RecordSet
Read only RecordSet object that might be received through remoting calls.
|
| Serializer
The Serializer class writes data output and handles the data according to the
core data types
|
| Class and Description |
|---|
| Deserializer
The Deserializer class reads data input and handles the data according to the core data types
|
| Serializer
The Serializer class writes data output and handles the data according to the
core data types
|
| Class and Description |
|---|
| Deserializer
The Deserializer class reads data input and handles the data according to the core data types
|
| Serializer
The Serializer class writes data output and handles the data according to the
core data types
|
| Class and Description |
|---|
| Deserializer
The Deserializer class reads data input and handles the data according to the core data types
|
| Serializer
The Serializer class writes data output and handles the data according to the
core data types
|
| Class and Description |
|---|
| Deserializer
The Deserializer class reads data input and handles the data according to the core data types
|
| Serializer
The Serializer class writes data output and handles the data according to the
core data types
|
| Class and Description |
|---|
| Deserializer
The Deserializer class reads data input and handles the data according to the core data types
|
| Serializer
The Serializer class writes data output and handles the data according to the
core data types
|
| Class and Description |
|---|
| Deserializer
The Deserializer class reads data input and handles the data according to the core data types
|
| Serializer
The Serializer class writes data output and handles the data according to the
core data types
|
| Class and Description |
|---|
| Deserializer
The Deserializer class reads data input and handles the data according to the core data types
|
| Serializer
The Serializer class writes data output and handles the data according to the
core data types
|
| Class and Description |
|---|
| Deserializer
The Deserializer class reads data input and handles the data according to the core data types
|
| Serializer
The Serializer class writes data output and handles the data according to the
core data types
|
| Class and Description |
|---|
| Flag
Serial flag options.
|
| Input
Interface for Input which defines the contract methods which are
to be implemented.
|
| Output
Output interface which defines contract methods to be implemented
|
| Serializer
The Serializer class writes data output and handles the data according to the
core data types
|
| UnsignedByte
The UnsignedByte class wraps a value of and unsigned 8 bits number.
|
| UnsignedInt
The UnsignedInt class wraps a value of an unsigned 32 bits number.
|
| UnsignedLong
The UnsignedLong class wraps a value of an unsigned 64 bits number.
|
| UnsignedNumber |
| UnsignedShort
The UnsignedByte class wraps a value of an unsigned 16 bits number.
|
| Class and Description |
|---|
| Input
Interface for Input which defines the contract methods which are
to be implemented.
|
| Output
Output interface which defines contract methods to be implemented
|
| Class and Description |
|---|
| Input
Interface for Input which defines the contract methods which are
to be implemented.
|
| Output
Output interface which defines contract methods to be implemented
|
| Class and Description |
|---|
| Deserializer
The Deserializer class reads data input and handles the data according to the core data types
|
| Serializer
The Serializer class writes data output and handles the data according to the
core data types
|
| Class and Description |
|---|
| Deserializer
The Deserializer class reads data input and handles the data according to the core data types
|
| Input
Interface for Input which defines the contract methods which are
to be implemented.
|
| Serializer
The Serializer class writes data output and handles the data according to the
core data types
|
| Class and Description |
|---|
| ICustomSerializable
Interface for objects that know how to serialize their contents.
|
| Output
Output interface which defines contract methods to be implemented
|
| Serializer
The Serializer class writes data output and handles the data according to the
core data types
|
| Class and Description |
|---|
| Deserializer
The Deserializer class reads data input and handles the data according to the core data types
|
| Serializer
The Serializer class writes data output and handles the data according to the
core data types
|
| Class and Description |
|---|
| Input
Interface for Input which defines the contract methods which are
to be implemented.
|
| Output
Output interface which defines contract methods to be implemented
|
Copyright © 2005-2013. All Rights Reserved.


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nips nips2008 nips2008-88 nips2008-88-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Ofer Dekel
Abstract: We present cutoff averaging, a technique for converting any conservative online learning algorithm into a batch learning algorithm. Most online-to-batch conversion techniques work well with certain types of online learning algorithms and not with others, whereas cutoff averaging explicitly tries to adapt to the characteristics of the online algorithm being converted. An attractive property of our technique is that it preserves the efﬁciency of the original online algorithm, making it appropriate for large-scale learning problems. We provide a statistical analysis of our technique and back our theoretical claims with experimental results. 1
[1] Anonimous. Technical appendix submitted with this manuscript, 2008.
[2] N. Cesa-Bianchi, A. Conconi, and C. Gentile. On the generalization ability of online learning algorithms. IEEE Transactions on Information Theory, 50(9):2050–2057, September 2004.
[3] N. Cesa-Bianchi and C. Gentile. Improved risk bounds for online algorithms. NIPS 19, 2006.
[4] O. Dekel, S. Shalev-Shwartz, and Y. Singer. The Forgetron: A kernel-based perceptron on a budget. SIAM Journal on Computing, 37:1342–1372, 2008.
[5] O. Dekel and Y. Singer. Data-driven online to batch conversions. NIPS 18, 2006.
[6] D. A. Freedman. On tail probabilities for martingales. Annals of Prob., 3(1):100–118, 1975.
[7] Y. Freund and R. E. Schapire. Large margin classiﬁcation using the perceptron algorithm. Machine Learning, 37(3):277–296, 1999.
[8] S. I. Gallant. Optimal linear discriminants. Proc. of ICPR 8, pages 849–852. IEEE, 1986.
[9] R. Khardon and G. Wachman. Noise tolerant variants of the perceptron algorithm. Journal of Machine Learning Research, 8:227–248, 2007.
[10] Y. Li. Selective voting for perceptron-like learning. Proc. of ICML 17, pages 559–566, 2000.
[11] Y. Li, H. Zaragoza, R. He, J. ShaweTaylor, and J. Kandola. The perceptron algorithm with uneven margins. Proc. of ICML 19, pages 379–386, 2002.
[12] N. Littlestone. From online to batch learning. Proc. of COLT 2, pages 269–284, 1989.
[13] F. Rosenblatt. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65:386–407, 1958.
[14] T. Zhang. Solving large scale linear prediction problems using stochastic gradient descent algorithms. Proc. of ICML 21, 2004. 8


EFCC to arraign Mompha, Monday
by Ifeanyi NgeremFollowing Mompha’s action against EFCC, The antigraft agency is set to arraign Ismaila Mustapha popularly known as Mompha on Monday, November 25, before a Federal High Court, Lagos.
According to reports, the anti-graft agency said on Friday that it had filed a 14-count criminal charge on the suspect.
EFCC’s response comes after Mompha filed a suit against it seeking N5 million damages for alleged unlawful detention and violation of his rights.
Mompha is to be arraigned alongside his company, Ismalob Investments Limited, the firm through which he allegedly laundered N14 billion.
Recall, Mompha was arrested on October 19 at Nnamdi Azikiwe International Airport, Abuja on allegations of cybercrime and money laundering.