Conversions from cubic inches More...
Static Public Member Functions | |
| static float | CubicMillimeters (float cubicInches) |
| Convert from cubic inches to cubic millimeters. More... | |
| static float | CubicCentimeters (float cubicInches) |
| Convert from cubic inches to cubic centimeters. More... | |
| static float | CubicFeet (float cubicInches) |
| Convert from cubic inches to cubic feet. More... | |
| static float | CubicMeters (float cubicInches) |
| Convert from cubic inches to cubic meters. More... | |
| static float | CubicKilometers (float cubicInches) |
| Convert from cubic inches to cubic kilometers. More... | |
| static float | CubicMiles (float cubicInches) |
| Convert from cubic inches to cubic miles. More... | |
Public Attributes | |
| const float | PER_CUBIC_MILLIMETER = 1 / Units.CubicMillimeters.PER_CUBIC_INCH |
| Conversion factor from cubic millimeters to cubic inches. More... | |
| const float | PER_CUBIC_CENTIMETER = 1 / Units.CubicCentimeters.PER_CUBIC_INCH |
| Conversion factor from cubic centimeters to cubic inches. More... | |
| const float | PER_CUBIC_FOOT = Inches.PER_FOOT * Inches.PER_FOOT * Inches.PER_FOOT |
| Conversion factor from cubic feet to cubic inches. More... | |
| const float | PER_CUBIC_METER = PER_CUBIC_CENTIMETER * Units.CubicCentimeters.PER_CUBIC_METER |
| Conversion factor from cubic meters to cubic inches. More... | |
| const float | PER_CUBIC_KILOMETER = PER_CUBIC_METER * Units.CubicMeters.PER_CUBIC_KILOMETER |
| Conversion factor from cubic kilometers to cubic inches. More... | |
| const float | PER_CUBIC_MILE = PER_CUBIC_FOOT * Units.CubicFeet.PER_CUBIC_MILE |
| Conversion factor from cubic miles to cubic inches. More... | |
Conversions from cubic inches
Definition at line 6 of file CubicInches.cs.
|
inlinestatic |
Convert from cubic inches to cubic centimeters.
| cubicInches | The number of cubic inches |
Definition at line 53 of file CubicInches.cs.
References Juniper.Units.Units.
|
inlinestatic |
Convert from cubic inches to cubic feet.
| cubicInches | The number of cubic inches |
Definition at line 63 of file CubicInches.cs.
References Juniper.Units.Units.
|
inlinestatic |
Convert from cubic inches to cubic kilometers.
| cubicInches | The number of cubic inches |
Definition at line 83 of file CubicInches.cs.
References Juniper.Units.Units.
|
inlinestatic |
Convert from cubic inches to cubic meters.
| cubicInches | The number of cubic inches |
Definition at line 73 of file CubicInches.cs.
References Juniper.Units.Units.
|
inlinestatic |
Convert from cubic inches to cubic miles.
| cubicInches | The number of cubic inches |
Definition at line 93 of file CubicInches.cs.
References Juniper.Units.Units.
|
inlinestatic |
Convert from cubic inches to cubic millimeters.
| cubicInches | The number of cubic inches |
Definition at line 43 of file CubicInches.cs.
References Juniper.Units.Units.
| const float Juniper.Units.CubicInches.PER_CUBIC_CENTIMETER = 1 / Units.CubicCentimeters.PER_CUBIC_INCH |
Conversion factor from cubic centimeters to cubic inches.
Definition at line 16 of file CubicInches.cs.
| const float Juniper.Units.CubicInches.PER_CUBIC_FOOT = Inches.PER_FOOT * Inches.PER_FOOT * Inches.PER_FOOT |
Conversion factor from cubic feet to cubic inches.
Definition at line 21 of file CubicInches.cs.
| const float Juniper.Units.CubicInches.PER_CUBIC_KILOMETER = PER_CUBIC_METER * Units.CubicMeters.PER_CUBIC_KILOMETER |
Conversion factor from cubic kilometers to cubic inches.
Definition at line 31 of file CubicInches.cs.
| const float Juniper.Units.CubicInches.PER_CUBIC_METER = PER_CUBIC_CENTIMETER * Units.CubicCentimeters.PER_CUBIC_METER |
Conversion factor from cubic meters to cubic inches.
Definition at line 26 of file CubicInches.cs.
| const float Juniper.Units.CubicInches.PER_CUBIC_MILE = PER_CUBIC_FOOT * Units.CubicFeet.PER_CUBIC_MILE |
Conversion factor from cubic miles to cubic inches.
Definition at line 36 of file CubicInches.cs.
| const float Juniper.Units.CubicInches.PER_CUBIC_MILLIMETER = 1 / Units.CubicMillimeters.PER_CUBIC_INCH |
Conversion factor from cubic millimeters to cubic inches.
Definition at line 11 of file CubicInches.cs.


HTML5 Presentation Layout
A nice subtitle
Firstname Lastname
<EMAIL_ADDRESS>January 25th, 2018
# Audio research in the Zenith team 1. The KAMoulox ANR project 2. Two research focuses - Denoising with $\underbrace{\alpha}$-stable distributions - Deep learning for music demixing 3. The Signal Separation Evaluation campaign
Website without the header
# Archives of the CNRS - Musée de l'Homme * French ethnologists to record world audio heritage * Since 1900: 42k documents, 32k digitized * From 199 countries, 1200 ethnies * Users are __scientists__ in digital humanities (2600/month) __Challenges__ * Most recordings are __old, degraded, noisy__, but _precious_ * Users are __not__ sound engineers * Impossible to automatically restore __all__ entries __Key idea__:
embed online audio restoration tools in the archives
(ANR JCJC KAMoulox grant, 300k€)
# The KAMoulox project _Cutting-edge signal processing research for the audio heritage_ 1. __Restoration of immaterial audio heritage__ denoising, declipping, enhancement 2. __Repurposing of musical heritage__ demixing, remastering, resampling 3. __Online tools__ for scientists in digital humanities ### Challenges * __Scale__: each of the $3.10^5$ signals is a $10^7$ samples time-series, with highly structured content * __Noise__: recordings are very old, noisy and bandpass-limited * __Users__: are scientists, but __non experts__ ### Research activity * __Probabilistic models__: $\alpha$-stable distributions, multichannel filtering, interpolation, extrapolation, regression * __Data analysis__: deep neural networks, scalability, online processing * __Community service__: international evaluation campaigns, open data & software
# Denoising old recordings * Noise is modelled with $\beta$-stable stochastic processes * Robust and fast inference based on moment matching and sketching * A new light on some audio best practices
# Music demixing __Many applications__ * Wide audience: active listening, karaoke * Sound engineers: remastering, resampling... * Humanities: linguistics and ethnomusicology __Challenges__ * Inverse ill-posed problem * Diversity of music styles > Rafii, Liutkus, Stöter, Mimilakis, FitzGerald, Pardo, "A Review of the research on the separation of Lead and accompaniment in music" IEEE TASLP (2018)..


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{% include "pages/services/google-analytics.html" %}
{% include "pages/services/facebook-script.html" %}
{% import 'pages/includes/get-theme-value.html' as theme %}
{% set script_top = theme.get('script_top',site,page) %}
{% if script_top %}
{% endif %}


|
MoveToCode
|
Enumerations | |
| enum class | FlattenModeType { DoNotFlatten = 0 , FlattenX , FlattenY , FlattenZ , FlattenAuto } |
| Enum which describes how an object's bounds control is to be flattened. More... | |
| enum class | HandleType { None = 0 , Rotation , Scale , Translation } |
| Enum describing the type of handle grabbed; can be a rotation (edge-mounted) handle, a scaling (corner-mounted) handle, or a translation (face-mounted) handle. More... | |
| enum class | WireframeType { Cubic = 0 , Cylindrical } |
| This enum describes which primitive type the wireframe portion of the bounds control consists of. More... | |
| enum class | BoundsCalculationMethod { RendererOverCollider = 0 , ColliderOverRenderer , ColliderOnly , RendererOnly } |
| This enum defines what volume type the bound calculation depends on and its priority for it. More... | |
| enum class | BoundsControlActivationType { ActivateOnStart = 0 , ActivateByProximity , ActivateByPointer , ActivateByProximityAndPointer , ActivateManually } |
| This enum defines how the bounds control gets activated More... | |
| enum class | HandlePrefabCollider { Sphere , Box } |
| This enum defines the type of collider in use when no handle prefab is provided. More... | |
| enum class | CardinalAxisType { X = 0 , Y , Z } |
| This enum defines which of the axes a given rotation handle revolves about. More... | |
| enum class | HandleScaleMode { Uniform , NonUniform } |
| Scale mode that is used for scaling behavior of bounds control. More... | |
This enum defines what volume type the bound calculation depends on and its priority for it.


ANIMAL HOUSE A Film Review Copyright Dragan Antulov 1999
One of the things that differentiates the author of this review from most of the world's film critics is the opinion of John Landis. Many of Landis' films, often lauded by critics, were disappointments for me. That is especially the case in the latter part of his career, and it is hard to imagine why and how this author reached a cult status. But, earlier, while he was starting his cinema career in 1970s, he used to make quite nice films. One of the most legendary ones is ANIMAL HOUSE, 1978 comedy that was overshadowed by his greatest success - BLUES BROTHERS, shot two years later.
This film actually doesn't have a lot of plot. It is set in fictional Faber college in 1962 and chronicles the struggle between two student fraternities - Omegas, nice, by-the-book, rich, arrogant and powerful students, and Deltas, nonconformist losers who, instead of studying, spend all their time for alcohol, drugs, sex and different kinds of orgies. Their activity become the thorn in the side of the college administration who decide to kick them all out. But the brave Deltas would refuse to give up without a fight, that would culminate in the apocalyptic parade in the end.
Some may think that ANIMAL HOUSE represents sorts of autobiography for its writer Chris Miller, who based the plot on his own experiences as Darthmouth College student in 1962. Others may argue that Landis in this film, by portraying the rivalry between Omegas and Deltas, gives his own commentary about the great struggle between the forces of Old and New in 1960s America. But the real motive for ANIMAL HOUSE was most likely nothing more but the excuse for entire series of jokes, many of them so tasteless that they really questioned the standards of its, rather liberal, times. Yes, some of these jokes are rather tasteless, many of them including Bluto (brilliantly played by late John Belushi) and his repetitive use of bodily fluids becomes boring after a while. Some other jokes also aren't so funny. But guess what? Film as a whole works as an excellent guilty pleasure - made without any redeeming values nor high artistic standards, but film that makes other, more noble and ambitious works, to pale in comparison simply by entertaining viewers. Most of the jokes are funny, characters are well- played by excellent actors (Donald Sutherland, John Vernon, young Kevin Bacon among others), and the soundtrack is very good. The only real reason to bash ANIMAL HOUSE is the fact that it spawned entire sub-genre of its imitations in the decade to come - movies with the words "soft", "hard" and "body" in title, all of them sharing its low standards, but not the talent of its creators. But we shouldn't be too harsh towards ANIMAL HOUSE - today, same as two decades ago, it serves its purpose by entertaining us.
RATING: 7/10 (+++)
Review written on June 9th 1999
Dragan Antulov a.k.a. Drax Fido: 2:381/100 E-mail<EMAIL_ADDRESS>E-mail<EMAIL_ADDRESS>E-mail<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


• 1 Stock Image
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Drink-driving arrests by gardaí have fallen by just over 1,800 in the past year after reduced alcohol levels for drivers were introduced.
Figures show the number of arrests for the offence have halved in the past five years.
Releasing official figures today, gardaí said incidents are gradually reducing and are based on a time period after the alcohol permitted in a driver's system was reduced from 80 to 50mg per 100ml of blood last November.
Gardaí mounted 66,000 checkpoints last year, an increase of 3%, with 450,000 people being breathalysed.
Figures have now more than halved since 2007 when 19,848 drink-driving arrests were made by gardaí, who say young male drivers still form a majority of the problem.
The number of arrests for drink-driving incidents to November 2012, a full year after lower alcohol driving limits were introduced, was 8,747, compared to 10,575 for 2011.
Gardaí say there has been a change in driving culture generally, with people now more compliant with speed limits for instance.
However, they are concerned about the number of repeat offenders, with one individual linked to 26 drink-driving offences since 2008. Five others had committed ten offences since 2008.
Irish, Latvian and Lithuanian nationals were among the main repeat offenders.
User contributions and/or comments do not, unless specifically stated, represent the views of RTÉ.ie or RTÉ. Click here for Terms of use.
Add your own comment


--- layout: default comments: true title: Thank You! ---
Thank you for buying The Honest Guide to Cooking. Honestly. It's a swell thing for you to do. Your contribution will help me keep the site up, and more importantly - I hope you enjoy the ebook.
If you have any questions about your purchase, don't hesitate to get in touch at info@honestguidetocooking. Apologies if I'm a bit slow with replying - this is a side hobby project and I won't always get the time to keep up with it.


Initial Coin Offering Example
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NOTE: Due to a bug in MetaMask, events may not work after switching networks (e.g. from the main network to the Ropsten test network). If you experience this, you need to restart the browser after switching to the desired network.


Output File Excel
Output File Excel as the name suggests writes data to Microsoft Excel. It is present under the Output category in the component palette.
Output File Excel component will enable writing data in the data flow pipeline to a Microsoft Excel spreadsheet with extension as .xls or .xlsx . Multiple worksheets can be created.
The Output File Excel component can be easily distinguished in the component palette.
An enlarged version of the same is depicted when dragged on the canvas.
For further reference click on the links below:


Authoring a Skill for DIY.org is fun, difficult, and most of all, rewarding. As soon as you see a Maker complete one of the Challenges you’ve made, you’ll feel a warm, fuzzy feeling – like a proud parent. But it’s tricky to get a Skill to be just right. That’s why we’ve compiled this guide for new Skillmakers.
Before you can author your first Skill, you’ll need to send us an email requesting to become a skillmaker. Once setup, you can login at admin.diy.org and get started.


Home Assistant
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--- layout: page title: Learning OData on Postman date: 2014-12-12 07:45:00.000000000 +08:00 permalink: /getting-started/learning-odata-on-postman/ ---
There are various ways to learn about OData and starting with the native HTTP request may be the most direct one. We have a series of Basic Tutorials and Advanced Tutorials already, but these still ask you to copy&paste the requests. We now provide an easier way to help you understand OData and contribute your own scenarios. We create a collection of OData HTTP requests using Postman - an efficient way to test, develop, and document APIs.
It's quite easy for Chrome users to install the Postman tool and run the collections. Just follow the steps below :
You can find more details of Postman collection in Postman collection docs. After you successfully import the collection, you can see the query collection as below :
And the resources behind this is on OData tutorial Guidelines.
As you can see from the picture above, we have created several requests into different categories in the collection with description, HTTP method, request URL, request headers and response payload (if no response, just click the Send button).
Currently, Postman collection doesn't support collaborating on one specific collection URL, so you may not directly contribute to the collection. BUT, you can contribute to the GitHub repositories which servers as a central OData tutorials guidelines and we will update the content regularly.


Some text...
More text...
The theory goes that if it's at the bottom, everything is already loaded when the HTML5 interpreter gets to this line.
HTML5 has a new attribute 'async' but I'll leave the details out for this class.


Sun has always been a company that prided itself on technical and business innovation. The company pioneered the Unix workstation, the Unix business server, the Java programming language, the RISC microprocessor, the “database server,” and in fact really popularized the whole concept of computers as “servers.” One could argue that much of the architecture of the internet (thin clients with highly scalable servers) was really invented by Sun. “The Network is the Computer,” one of Sun's most memorable taglines, was way ahead of its time.
But as our “enduring organization” research discusses, corporate cultures must change over time. As Microsoft and others started to dominate the corporate computing landscape, Sun failed to make the transition from a “pioneering” culture to a purely “market driven” culture. Rather than try to build more “complete products” for its existing customers, the company moved continuously into new, technology-driven markets. During my days at Sybase, Sun virtually owned the server and desktop business for Wall Street traders. But by not focusing on building complete solutions for this market, companies like IBM slowly entered and today this market is a far more heterogenous environment than Sun would like.
Oracle had very similar beginnings. The company revolutionized corporate computing by commercializing the standard SQL database (which was actually invented by IBM, much to IBM's chagrin). Like Sun, Oracle is an engineering-driven company. In its early days the company focused on building the fastest, most feature-rich database in the market. Back in the days of the “database wars”, Oracle tenaciously and aggressively defended its technology leadership and made up for some of its technical flaws with a highly aggressive sales and marketing culture.
Oracle learned, as perhaps Sun did not, that as the company becomes a market leader, it must integrate its operations and behave more like an end-to-end solutions company, not only a technology leader. Hence the company moved aggressively from IT infrastructure to applications, with the acquisition of a wide range of applications companies, including PeopleSoft.
One cannot argue with Oracle's astounding success. The company's relentless focus on database allowed Oracle to force Sybase, Ingres, Informix, and even IBM out of the mid-market database industry. Today its only real competitors are Microsoft and OpenSQL (which the company now owns through the Sun acquisition).
From a cultural perspective, Oracle and Sun have always aligned well. The companies jointly engineered one of the highest-performing combinations of hardware and software in the world of corporate applications – and have continued to work closely together over the years. (Oracle in fact works with all major hardware providers, just as Sun used to work closely with competing database providers.)
But unlike Sun, Oracle evolved into a serious, solutions-driven enterprise software company. While we still see tremendous engineering focus in the company, Oracle now focuses on application software, vertical markets, and end-to-end business integration. Where Sun continues to define itself as an innovative provider of internet-based products and solutions, Oracle now defines itself as the “safe and reliable market leader” in enterprise software. This has not reduced Oracle's fierce focus on sales and marketing – but has given the company a real culture of “business planning” and “market leadership.


| CONTENTS | PREV | NEXT | Java Remote Method Invocation |
When a remote object class is created that extendsUnicastRemoteObject
, the object is exported, meaning it can receive calls from external Java virtual machines and can be passed in an RMI call as either a parameter or return value. An object can either be exported on an anonymous port or on a specified port. For objects not extended fromUnicastRemoteObject
, thejava.rmi.server.UnicastRemoteObject.exportObject
method is used to explicitly export the object.
Exception Context
java.rmi.StubNotFoundException
Class of stub not found.
Name collision with class of same name as stub causes one of these errors:
Stub can't be instantiated
Stub not of correct class
Bad URL due to wrong codebase.
Stub not of correct class.
java.rmi.server.SkeletonNotFoundException
note: this exception is deprecated as of
Java 2 SDK, Standard Edition, v1.2
Class of skeleton not found.
Name collision with class of same name as skeleton causes one of these errors:
Skeleton can't be instantiated
Skeleton not of correct class
Bad URL due to wrong codebase.
Skeleton not of correct class.
java.rmi.server.ExportException
The port is in use by another VM.


Ooops, we could not display the BPMN 2.0 diagram.
cause of the problem
Work Flow History
BPMN diagram
SVG image


{% include head.html %}
Books 2 Read
{% include navigation.html %}
{{ content }}
Source Repo
|
email
|
github.com/arossmann
{% include footer.html %}


Event Location:
Consorzio Universitario di Pordenone
Via Prasecco
Pordenone, , Italy
Abstract:
Confrontato con altri linguaggi di programmazione, T-SQL non è un linguaggio difficile da imparare, e al netto di eventuali errori sintattici può essere molto tollerante. Nel comparto IT è utilizzato da molte persone, che però, tendono ad imparare soltanto gli statement fondamentali del linguaggio ovvero: SELECT, INSERT, UPDATE e DELETE. Solo chi investe più tempo scopre la natura dichiarativa di questo linguaggio e può ottenere il massimo delle prestazioni e della potenza dalle proprie queries. In questa sessione vedremo come migliorare la scrittura del codice T-SQL sfruttando gli statement Set-Based presenti da tempo in SQL Server, nonché quelli implementati con SQL Server 2012 con particolare riferimento alle "Window Functions".
Speaker(s): Sergio Govoni,
Abstract:
In questa sessione introduttiva vedremo all’opera il motore OLAP di Microsoft (Analysis Services) sia nella fase di creazione di un cubo OLAP che in quella di interrogazione dello stesso attraverso il linguaggio MDX. Dopo una breve introduzione teorica sulla tecnologia OLAP, vedremo come creare un semplice progetto formato da un cubo e da un paio di dimensioni partendo da zero. A seguire, iniziando da query molto semplici sulla base dati appena creata, illustreremo alcuni dei concetti base del linguaggio MDX, con l’obiettivo di arrivare a scrivere query più complesse per soddisfare alcune delle più comuni richieste di analisi quali Previous Period Growth e Percentage Share.
Speaker(s): Francesco De Chirico,
Abstract:
ORM technologies can assist the developers’ work with a simplified data access layer and nobody questions their usefulness any more: ORMs are here to stay. On the other hand, DBAs often get frustrated when ORMs are used to access data, because many tools in their belt turn out being ineffective. Developers and DBAs are focused on different goals and finding a balance between ease of development and performance can be an overwhelming challenge, or even turn into a continuous and unproductive blamestorm. In this session we will walk through some common performance issues in ORM technologies and we will try to address them, hoping to bridge the notorious gap between developers and DBAs.
Speaker(s): Gianluca Sartori,
Abstract:
Have you ever had the need to access documents in your database as if they were files in the file system? SQL Server 2012 introduces a brand new method for managing large data objects (BLOBs) in a database. FILETABLEs provide access to data using Transact-SQL - just like any other table inside the database, while at the same time also provide access to the data using the operating system File I/O API - i.e. just like any other folder in the file system. In this session you will learn how to upgrade your document management solutions by migrating your large data to FILETABLEs. The session covers two most typical migration scenarios: migrating from a distributed data store, where files are stored outside the database, and from a homogeneous d
Speaker(s): Matija Lah,
Abstract:
Nessuno può negare che ignorare i blocchi in lettura dei dati porti, solitamente, a performance migliori. Un po' come se tutti i semafori nelle strade avessero sempre luce verde: le code calerebbero drasticamente. Nel caso dei semafori, però, i rischi di una scelta del genere sono facilmente intuibili. NOLOCK e READ UNCOMMITED sono molto diffusi in viste e SP ma raramente vengono usati con cognizione di causa e con piena consapevolezza dei pericoli a cui ci si espone. La comprensione dei meccanismi di lock e dei livelli di isolamento è fondamentale per compiere scelte oculate nella scrittura del codice e nel pianificare le strategie di accesso ai dati. In questa sessione si cercherà di fornire uno sguardo di insieme su tali meccanismi.
Speaker(s): Francesco Milano,
Abstract:
Nella creazione di applicazioni, non abbiamo mai avuto a disposizione un ambiente integrato, dedicato allo sviluppatore per la progettazione e il deployment di un Database, per la scrittura di codice SQL. Oggi questa lacuna è stata colmata dall'introduzione degli SQL Server Data Tools: grazie ad un apposito template per Visual Studio 2010 e 2012, è possibile gestire un progetto SQL, come un progetto di una applicazione desktop o di una applicazione web. Vediamo insieme cosa possiamo fare con questi strumenti.
Speaker(s): Marco Parenzan,
Abstract:
BISM is the acronym for Business Intelligence Semantic Model and in SQL Server Analysis Service 2012 you have two options for creating a data model in this environment: Multidimensional and Tabular. In this introduction to the Tabular models in Analysis Services you will understand the capabilities of this type of data modeling for Data Analysis and you will learn the basics of DAX, the language used to define the measures for Tabular models.
Speaker(s): Marco Russo,
Abstract:
In questa sessione verrà introdotto il Data Warehouse, punto cardine di ogni soluzione di Business Intelligence. Dopo aver definito il ruolo del DWH e le sue motivazioni di base, durante la sessione saranno mostrate le tecniche di modellazione, i principi guida da seguire, al terminologia ed i design pattern più noti e comuni.
Speaker(s): Davide Mauri,
Abstract:
Come popolare un DWH con sql server integration services 2012(SSIS) .
Speaker(s): Marco Pozzan,
Abstract:
Con l'aumentare del numero di documenti creati, ricevuti e archiviati informaticamente, aumenta di conseguenza la difficoltà e i tempi per reperire tali documenti in base al loro contenuto e significato. Grazie a SQL Server 2012, alle sue nuove funzionalità Filestream e caretteristiche del motore FullText, è possibile andare oltre alla "classica" gestione di parole e frasi chiave collegate al singolo documento. Grazie alla ricerca semantica si è in grado di ricercare i documenti secondo il loro significato, trovare documenti correlati e reperire documenti simili, in modo semplice ed efficace.
Speaker(s): Emanuele Zanchettin,
Abstract:
Illustrate how multipurpose queries can lead to issues with scalability and performance. Show a few alternatives that will work for smaller and simpler queries while highlighting their limitations for more complex queries. Then work through building a dynamic search query to resolve the identified performance issues. Touching on: • Testing. • Tuning each part of the query in isolation. • Plan cache re-use • Security – SQL injection attacks and authorisation / code signing. • Possibly, mentioning greater re-use and extendibility.
Speaker(s): Ian Meade,
Abstract:
Business continuity is becoming more and more a central topic for many companies. AlwaysOn is the new solution for high availability and disaster recovery introduced with SQL Server 2012. With the help of live demos, in this session we will cover in details this new technology that extends on the familiar concepts of database mirroring and failover clustering. We will also introduce some other engine enhancements that will help raising the overall service availability.
Speaker(s): Gianluca Hotz,
Abstract:
Cross-selling is one of the best ways how to increase your sales. In order to know what products to offer together, you need to do a market basket analysis. Many solutions still use all kinds of distinct count queries, including T-SQL queries from SQL Server data, MDX queries from Analysis Services Dimensional models, or DAX queries from PowerPivot and Analysis Services Tabular data. However, once you know how to perform market basket analysis with data mining, you will forget on all other techniques. DMX queries will blow away other language queries. In this session, you are going to learn which data mining algorithms you should use for market basket analysis, how they work, and how you can efficiently use them in production.
Speaker(s): Dejan Sarka,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
LinkedIn: https://it.linkedin.com/in/fmilano
I've been working with Microsoft technologies since 2000, and I'm specialized in .NET Framework and SQL Server platform. I focus primarily on back-end development, integration solutions, relational model design and implementation. Starting from 2011 I'm also member of UGISS (Italian SQL Server User Group) and speaker at main italian SQL Server conferences and workshops.
LinkedIn: https://it.linkedin.com/in/marcoparenzan
Contact: https://www.linkedin.com/in/marcoparenzan/
Marco Parenzan is an independent Cloud and IoT Architect and .NET Developer. He has been awarded as a MVP on Microsoft Azure since 2014. He is a speaker in major community events in Italy about Azure and .NET development. He is a community lead for 1nn0va, an official Microsoft community in Pordenone, Italy. He has written a book on Azure in 2016. He loves developing retrogames and reading comix. Marco Parenzan è professionista per le architetture Cloud e IoT e lo sviluppo in .NET. Ha ricevuto il titolo di MVP su Microsoft Azure dal 2014. È uno speaker in Italia su Azure e lo sviluppo di .NET. E' un community lead per 1nn0va, una community ufficiale Microsoft a Pordenone, in Italia. Ha scritto un libro su Azure nel 2016.
LinkedIn: http://www.linkedin.com/in/sgovoni
Contact: https://mvp.microsoft.com/it-it/PublicProfile/4029181?fullName=Sergio%20Govoni
Since 1999 Sergio Govoni has been a software developer; in 2000 he got a degree in Computer Science at "Università degli Studi" in Ferrara (Italy). He has worked for over 18 years in Centro Software, a software house that produces the best ERP for manufacturing companies that are export oriented. Now, he manages the development product team and he is constantly involved on several team projects, where he focues his attention on the architecture and the mission-critical technical details. Since 2016 he is the vice president of the UGISS (www.ugiss.org). For the provided help to technical communities and for sharing his own experience, since 2010 he has received the Microsoft Data Platform MVP award.
LinkedIn: http://it.linkedin.com/in/ghotz
Contact: http://www.ghotz.com
Gianluca Hotz is an independent consultant, trainer, speaker and Mentor specialized in architecture, database design, high availability, capacity planning, performance tuning, system integration and migrations for Microsoft SQL Server. He has been working as a consultant in the IT field since 1993 and with SQL Server since 1996. He is among the original founders of ugiss.org, where he served as vice-president from 2001 to 2016 and he is now serving as president, for his contribution to the community he has been a SQL Server MVP since 1998.
LinkedIn: http://www.linkedin.com/in/davidemauri
Contact: https://medium.com/@mauridb
Data Platform MVP for almost 12 years, I love Data and Database in all their forms. Developer for passion (C# and Python), I've been working in the IT field since 1997. My focus has been databases and performance tuning since the start, focusing both on transactional and analytical workloads. I worked for more than 10 years on Business Intelligence and Data Warehousing, then moved to IoT and Big Data for a while. Now back to database space, injecting passion and innovation into Azure SQL as Microsoft Program Manager for Azure SQL Hyperscale, helping developers to re-discover SQL, which is now almost everywhere, and take advantage of all its amazing power!
LinkedIn: http://it.linkedin.com/pub/emanuele-zanchettin/18/921/34/
Emanuele Zanchettin, after studying in Computer Engineering, began his career with DB2 in the banking sector in 1998. After a few years and a change of scope of application, he encountered Access and Oracle with which he subsequently worked for several years in data access tuning and database design for new application features. Since 2007 he has also launched into the SQLServer world 2K, 2005, 2008* and 201* and more importantly, since 2011, into Azure SQL Database (SQL Azure). In order to have a greater capacity for assessment and familiarity with the various technologies that the market offers, he has deepened his knowledge to include MySQL and DocumentDB. Today Emanuele is mainly involved in IT projects and database management.
LinkedIn: https://it.linkedin.com/in/marcopozzan
Contact: http://www.marcopozzan.it
Consultant and trainer in business intelligence, analytics and data mining on Methode . Teacher for ITS courses of data warehouse at the University of Pordenone.
LinkedIn: http://www.linkedin.com/in/fdechirico
Contact: http://francescodechirico.wordpress.com/
Francesco De Chirico is a consultant, trainer and speaker specialized in modeling and developing complex BI solutions with the Microsoft BI stack. He has been working with SSAS since 2001 and has a strong knowledge of the MDX, DAX and M. He focused on all the Microsoft Data Platform especially on Power BI. Co-leader of the PASS Global Italian Virtual Group and Milan Power BI User Group, regular speaker at events like PASS SQLSaturday, Microsoft Certified Trainer for many years, Francesco holds several Microsoft certifications including the prestigious SSAS Maestro for BI certification (2012) and the Microsoft Professional Program Data Science certification (2017). He is the project creator of the Analysis Services Query Analyzer tool.
LinkedIn: http://it.linkedin.com/pub/gianluca-sartori/23/50b/a12
Contact: http://spaghettidba.com
Gianluca Sartori is a Data Platform MVP, independent consultant and performance tuning specialist. He has been working in the software industry since 1999 and has been working with SQL Server ever since. He also works as a SQL Server trainer and in his spare time he writes technical articles and participates the SQL Server forums. Gianluca enjoys presenting SQL Server topics at conferences in Europe and in Italy in particular. He is currently working as lead DBA at a famous Formula 1 team.
Contact: http://blogs.solidq.com/dsarka/Home.aspx
Dejan Sarka, MCT and Data Platform MVP, is an independent trainer and consultant that focuses on development of database and business intelligence applications. Besides projects, he spends about half of his time on training and mentoring. He is the founder of the Slovenian SQL Server and .NET Users Group. Dejan Sarka is the main author or co-author of eighteen books about databases and SQL Server. Dejan Sarka has also developed many courses and seminars for Microsoft, Radacad, SolidQ, and Pluralsight.
LinkedIn: http://si.linkedin.com/in/matijalah/
Contact: http://milambda.blogspot.com
Matija Lah has more than a decade of experience working with Microsoft SQL Server, mostly architecting data-centric solutions in the legal domain. His contributions to the SQL Server community have led to the Microsoft Most Valuable Professional award in 2007 (Data Platform), which he held until 2017. In 2008 Matija joined SolidQ as a Mentor, located in Central and Eastern Europe. He spends most of his time on projects involving advanced information management, and natural language processing.
LinkedIn: https://it.linkedin.com/in/sqlbi
Contact: http://sqlblog.com/blogs/marco_russo
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://lnkd.in/VC3U97
Contact: http://ianmeadedotdotdot.blogspot.ie/
The following is a list of sponsors that helped fund the event.


What are the two complex roots of the following quadratic equation?
x^2 = x - 1
x = \quad
/
+ \sqrt{}
i/
x = \quad
/
- \sqrt{}
i/
two fractions like 1/3
or -2/3
What are the two complex roots of the following quadratic equation?
x^2 = -1
x = \quad
+ \;
i
x = \quad
- \;
i
two integers like 6
or -6
You may want to first convert the quadratic equation to its standard form
ax^2 + bx + c = 0
. This makes it easier to apply a formula to
find the roots.
Remember that the roots of a quadratic equation in its standard form is
x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a}
.
If you are unsure of how to use this formula, you may find it helpful to watch
this video.


Hello, I am a creative software developer with extensive experience in programming languages and development frameworks, including JavaScript, React, Angular, Node.js, Vue, Dart, and Flutter. I am passionate about trying new technologies and tools in my development work and finding innovative solutions to problems.
I am proficient in both front-end and back-end development and excel at creating unique user experiences and features. I am skilled in JavaScript and various libraries and frameworks, such as React and Angular, which allow me to build powerful and one-of-a-kind applications for clients.
In terms of Node.js, I can create efficient and scalable server-side code that integrates with various databases and external APIs. I also have rich experience in Vue, Dart, and Flutter, which enable me to create beautiful and efficient web and mobile applications for clients.
As a creative software developer, I enjoy challenging myself and existing solutions, exploring different programming techniques and approaches to provide innovative and unique solutions for clients. I believe that continuous learning and exploration of new technologies and ideas can help create better experiences and value for clients.
In summary, if you are looking for a creative software developer to create innovative solutions for you, I am eager to help. Please feel free to contact me at any time to create amazing software products together.
I am proficient in both front-end and back-end development and excel at creating unique user experiences and features. I am skilled in JavaScript and various libraries and frameworks, such as React and Angular, which allow me to build powerful and one-of-a-kind applications for clients.
In my downtime you’ll find me hiking, walking my dog, or stargazing with friends.


Tractorization request:
Farm Specification:
- {{ orderSummary.propertyType }}, {{data.numberOfRooms}} hectares in size
with {{orderSummary.propertyFeatures}}.
- {{orderSummary.location}}
- {{orderSummary}}
Grand Total:
NGN{{orderSummary.totalAmount}}
Notes:


NPP Nasara Coordinators' organiser dies
The organiser for the NPP's National Nasara Coordinators has reportedly died, GhanaWeb has learned.
According to GhanaWeb sources, Salihu Yahaya Bo, who drove himself to Kumasi from Accra Saturday night was found dead by family this morning.
Even though the cause of death is not yet known, the late Yahaya Bo will be buried at the Old Tafo Cemetery in Kumasi at 2pm today in accordance to the Islamic culture.
Alhaj Bo, as his friends called him, was the deputy Ashanti regional Secretary for the New Patriotic Party from 1997 to 2001. He also served as Ashanti regional Nasara Coordinator from 2001 to 2005.


Package algorithms.util
Class VeryLargeNumber
- java.lang.Object
-
- algorithms.util.VeryLargeNumber
-
- All Implemented Interfaces:
java.lang.Cloneable
,java.lang.Comparable<VeryLargeNumber>
public class VeryLargeNumber extends java.lang.Object implements java.lang.Comparable<VeryLargeNumber>, java.lang.Cloneable
NOTE: still testing this class. it needs more testing for operations that change the instance value from positive to negative. A class to hold numbers that can be larger than 64 bits after adds. The class was created w/ Pascal's triangle as a use case in order to be able to print out numbers larger than ((1<<63) -1) for level >= 64 and an operation pattern that does not create a new instance for each result. the core of the constructor and add methods were started with http://www.cs.utexas.edu/users/djimenez/utsa/cs3343/lecture20.html and then added to and adapted afterwards. the core of the Euclidean division started with: https://en.wikipedia.org/wiki/Division_algorithm- Author:
- nichole
-
-
Field Summary
Fields Modifier and Type Field Description static int
BASE
-
Constructor Summary
Constructors Constructor Description VeryLargeNumber(double value)
a temporary work around for holding the results of inverse operations.VeryLargeNumber(int number)
-
Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
add(VeryLargeNumber addThis)
add addThis to this instance.(package private) static VeryLargeNumber
add(VeryLargeNumber number1, VeryLargeNumber addThis)
VeryLargeNumber
clone()
make a copy of this instance with a different identity but same values in the member variables.int
compareTo(VeryLargeNumber other)
compare the number within this instance to the number within other and return -1 if this is smaller, 0 if this is equal to other, and +1 if this is larger than other.(package private) int
countNumberOfDigits(int number)
static VeryLargeNumber
createMaxLong()
convenience method to create an instance with the value of Long.MAX_VALUE, ((1<<63)-1)java.lang.String
divideByAndPrint(VeryLargeNumber divisor)
divide internal number by the divisor and return a string.boolean
equals(java.lang.Object other)
compare the number value of this instance to another and return true if they are the same.double
getDoubleValueIfExists()
protected int[]
getInternalArray()
get a copy of the internal array aprotected int
getInternalArraySize()
return the size of the internal array aint
hashCode()
void
increment()
increment this number valuevoid
incrementNegative()
an increment specifically for use when this number is a negative numbervoid
incrementPositive()
an increment specifically for use when this number is a positive number.(package private) double
inverse()
(package private) double
inverseByEuclidean(VeryLargeNumber number)
using Euclidean division, divide this by divisor and return the result.boolean
isOdd()
boolean
isPositive()
whether this is a positive numberboolean
isZero()
whether this number value is 0(package private) static VeryLargeNumber
karatsuba(VeryLargeNumber num1, VeryLargeNumber num2)
https://en.wikipedia.org/wiki/Karatsuba_algorithm runtime O(n_digits^(lg2(3))) Let x and y be represented as n-digit strings in some base B.(package private) static void
moveDown(int[] in, int shift)
(package private) static void
moveUp(int[] in, int length, int shift)
(package private) static int
moveUpIfStartsWithZeros(int[] in, int length)
if the given array in starts with 0's, moves the elements below it (at higher indexes) up to remove the zero's and returns the new total length of the usable portion of array in (that is highIndex + 1).void
multiply(VeryLargeNumber number)
protected void
multiplyOneDigit(int[] in, int[] out, int d)
http://www.cs.utexas.edu/users/djimenez/utsa/cs3343/lecture20.html NOTE: in and out must have same lengths (and have been created with the same BASE).protected VeryLargeNumber
multiplySmall(VeryLargeNumber num2)
the O(N^2) method for multiplication: http://www.cs.utexas.edu/users/djimenez/utsa/cs3343/lecture20.htmlVeryLargeNumber
pow(int x)
For exponentiation: https://en.wikipedia.org/wiki/Exponentiation_by_squaringvoid
resetTo(VeryLargeNumber copyThis)
reset the content of this instance to be the same as the copied content of copyThis.void
reversePolarity()
reverse the sign of this number.protected void
setInternalArray(int[] b, int newNLen, boolean sign)
method purely for testing.(package private) VeryLargeNumber[]
splitAt(int m)
a split of the array at BASE^m is performed and the returned array is new VeryLargeNumber[]{highDigits, lowDigits}.void
subtract(VeryLargeNumber subtractThis)
subtract subtractThis from this instance.java.lang.String
toString()
return the number value of this instance as a string.
-
-
-
Field Detail
-
BASE
public static final int BASE
- See Also:
- Constant Field Values
-
-
Constructor Detail
-
VeryLargeNumber
public VeryLargeNumber(int number)
-
VeryLargeNumber
VeryLargeNumber(double value)
a temporary work around for holding the results of inverse operations. retrieve the result with getDoubleValueIfExists(). NOTE that this instance is not compatible with any other operations at this time. It's merely a way to return a value from the pow operation.- Parameters:
value
-
-
-
Method Detail
-
getDoubleValueIfExists
public double getDoubleValueIfExists()
-
add
public void add(VeryLargeNumber addThis)
add addThis to this instance.- Parameters:
addThis
- number to add to this
-
add
static VeryLargeNumber add(VeryLargeNumber number1, VeryLargeNumber addThis)
-
countNumberOfDigits
int countNumberOfDigits(int number)
-
moveUpIfStartsWithZeros
static int moveUpIfStartsWithZeros(int[] in, int length)
if the given array in starts with 0's, moves the elements below it (at higher indexes) up to remove the zero's and returns the new total length of the usable portion of array in (that is highIndex + 1).- Parameters:
in
-length
-- Returns:
-
moveUp
static void moveUp(int[] in, int length, int shift)
-
moveDown
static void moveDown(int[] in, int shift)
-
subtract
public void subtract(VeryLargeNumber subtractThis)
subtract subtractThis from this instance.- Parameters:
subtractThis
-
-
multiply
public void multiply(VeryLargeNumber number)
-
multiplySmall
protected VeryLargeNumber multiplySmall(VeryLargeNumber num2)
the O(N^2) method for multiplication: http://www.cs.utexas.edu/users/djimenez/utsa/cs3343/lecture20.html- Parameters:
num2
-
-
multiplyOneDigit
protected void multiplyOneDigit(int[] in, int[] out, int d)
http://www.cs.utexas.edu/users/djimenez/utsa/cs3343/lecture20.html NOTE: in and out must have same lengths (and have been created with the same BASE). To account for overflow, out and in should be one larger than the original length of in. output = input * d- Parameters:
in
-out
-d
-
-
karatsuba
static VeryLargeNumber karatsuba(VeryLargeNumber num1, VeryLargeNumber num2)
https://en.wikipedia.org/wiki/Karatsuba_algorithm runtime O(n_digits^(lg2(3))) Let x and y be represented as n-digit strings in some base B. x = x_1*B^m + x_0 y = y_1*B^m + y_0, where x_0 and y_0 are less than B^m x*y = (x_1*B^m + x_0)(y_1*B^m + y_0) x*y = z_2*B^{2m} + z_1*B^m + z_0 z_2 = x_1*y_1 z_1 = x_1*y_0 + x_0*y_1 z_0 = x_0*y_0 z_1 = x_1*y_0 + x_0*y_1 z_1 = (x_1 + x_0)(y_1 + y_0) - x_1*y_1 - x_0*y_0 z_1 = (x_1 + x_0)(y_1 + y_0) - z_2 - z_0 z_2 = x_1*y_1 z_0 = x_0*y_0 x*y = (b^2 + b)*x_1*y_1 - b*(x_1 - x_0)(y_1 - y_0) + (b + 1)*x_0*y_0 where b is the power where the split occurs of x_1. Below, is an iterative version of karatsuba. Java doesn't use tail recursion at this time, so cannot use a recursive version of the algorithm for very large numbers.
-
splitAt
VeryLargeNumber[] splitAt(int m)
a split of the array at BASE^m is performed and the returned array is new VeryLargeNumber[]{highDigits, lowDigits}.- Parameters:
index
-- Returns:
-
pow
public VeryLargeNumber pow(int x)
For exponentiation: https://en.wikipedia.org/wiki/Exponentiation_by_squaring
-
divideByAndPrint
public java.lang.String divideByAndPrint(VeryLargeNumber divisor)
divide internal number by the divisor and return a string. note that the method currently uses the simplest implementation, Euclidean division. a faster internal implementation can be made upon need.- Parameters:
divisor
-- Returns:
-
isZero
public boolean isZero()
whether this number value is 0- Returns:
-
isOdd
public boolean isOdd()
-
isPositive
public boolean isPositive()
whether this is a positive number- Returns:
-
reversePolarity
public void reversePolarity()
reverse the sign of this number.
-
inverse
double inverse()
- Parameters:
number
-- Returns:
-
inverseByEuclidean
double inverseByEuclidean(VeryLargeNumber number)
using Euclidean division, divide this by divisor and return the result.- Parameters:
number
-- Returns:
-
createMaxLong
public static VeryLargeNumber createMaxLong()
convenience method to create an instance with the value of Long.MAX_VALUE, ((1<<63)-1)- Returns:
-
compareTo
public int compareTo(VeryLargeNumber other)
compare the number within this instance to the number within other and return -1 if this is smaller, 0 if this is equal to other, and +1 if this is larger than other.- Specified by:
compareTo
in interfacejava.lang.Comparable<VeryLargeNumber>
- Parameters:
other
-- Returns:
-
increment
public void increment()
increment this number value
-
incrementNegative
public void incrementNegative()
an increment specifically for use when this number is a negative number
-
incrementPositive
public void incrementPositive()
an increment specifically for use when this number is a positive number.
-
clone
public VeryLargeNumber clone() throws java.lang.CloneNotSupportedException
make a copy of this instance with a different identity but same values in the member variables. it's expected that compareTo is used for comparison, but equals will also return true for comparison of the clone with the original instance.- Overrides:
clone
in classjava.lang.Object
- Returns:
- Throws:
java.lang.CloneNotSupportedException
-
equals
public boolean equals(java.lang.Object other)
compare the number value of this instance to another and return true if they are the same.- Overrides:
equals
in classjava.lang.Object
- Parameters:
other
-- Returns:
-
hashCode
public int hashCode()
- Overrides:
hashCode
in classjava.lang.Object
-
resetTo
public void resetTo(VeryLargeNumber copyThis)
reset the content of this instance to be the same as the copied content of copyThis.- Parameters:
copyThis
-
-
setInternalArray
protected void setInternalArray(int[] b, int newNLen, boolean sign)
method purely for testing. TODO: should be added to an aspect woven for tests only.- Parameters:
b
- array of numbers composing the large number. note that no checks are done to assert that the numbers are positive as this is a method meant to be used in testing only.newNLen
-sign
- the number array is positive or negative
-
getInternalArraySize
protected int getInternalArraySize()
return the size of the internal array a- Returns:
-
getInternalArray
protected int[] getInternalArray()
get a copy of the internal array a- Returns:
-
toString
public java.lang.String toString()
return the number value of this instance as a string. note that this will be a problem if the value is ><PHONE_NUMBER>854775807- Overrides:
toString
in classjava.lang.Object
- Returns:
-
-


Web-Dev
CSS
HTML5
- Q: HTML5 best practices; section/header/aside/article tags
- A: 1st part (Detailed) Explanation
- A: 2nd part Concrete answers to OP’s questions
- A: 3rd part Improved detailed HTML
Git
Jekyll
RegEx
- Regex to Match all Symbols:
/[$-/:-?{-~!"^_`\[\]]/
Ruby
Terminal


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Richard Cypher holds the key to the fate of three nations. But until he learns the Wizard's First Rule his chances of succeeding in his task look slim. And his biggest problem is admitting that magic exists at all.
It was an odd-looking vine. Dusty variegated leaves hunkered against a stem that wound in a stranglehold around the smooth trunk of a balsam fir. Sap drooled down the wounded bark, and dry limbs slumped, making it look as if the tree were trying to voice a moan into the cool, damp morning air. Pods stuck out from the vine here and there along its length, almost seeming to look warily about for witnesses.


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--- layout: post title: Japanese-ink Minecraft date: December 30, 2018 primary-language: C++ framework: OpenGL ide: QT Creator ---
Underconstruction!!!


PEARL HARBOR A film review by Christopher Null Copyright 2001 filmcritic.com filmcritic.com
There's a point in Pearl Harbor when Cuba Gooding Jr. leaps into a battleship's gun turret and starts shooting down Japanese planes while hell rages around him. It's a dramatic moment... until you realize that it's that "Show me the money!" guy from Jerry Maguire, shooting CGI bullets at a CGI plane... and you are reminded once again just how phony everything you've seen in Pearl Harbor has been.
Ironically, this incident, where ship's cook Dorie Miller took charge and shot back during America's worst hour on December 7, 1941, is just about the only true event to be found in the entire, oppressive three-hour film. (And our producers are quick to remind us of just how ripped-from-history this little vignette is. Never mind that Gooding has a pitiful excuse for a role with maybe five minutes of screen time.)
After more than a year of hype, Pearl Harbor finally hits theater screens this Memorial Day weekend, anxious to repeat that Titanic experience, by throwing us a love story set in the most inhospitable of locations. In Titanic, of course, it was a sinking ship. Here, it's in the middle of a military base while the bombs are dropping around the smoochie lovebirds.
The trailers have been coy with the plot, and I'll do my best to keep the various twists secret (except for that whole Japanese invasion bit... sorry). Pretty boy Rafe (Ben Affleck) and his scruffy best friend Danny (Josh Hartnett) grow up on a farm, dreaming of taking to the skies in the Air Force. They get their wish when they enlist in 1940, and as Rafe prepares to ship out, he falls madly in love with Evelyn (Kate Beckinsale), half nurse, half pin-up girl.
Rafe, "a slow reader" but damn if he doesn't look good in uniform, volunteers to serve in the British air force, surprising Evelyn and Danny, who head for the sunny, peaceful shores of Hawaii's Pearl Harbor naval base to "sit out" the war in Europe. While Rafe fights the Nazis, our other pals soak up some rays.
After much gnashing of teeth, we're thigh-deep in a love triangle. But wouldn't you know it? Just as the Titanic began to sink halfway through the film, the first bomb is dropped on Pearl Harbor at 1 hour, 30 minutes into Pearl Harbor. 35 minutes later, the siege is over, and we are left to contend with an iffy dénouement (which takes another 55 minutes to get resolved, as the U.S. plans its lame, first counterstrike).
Plot excepted, Pearl Harbor is a good-looking film. When the planes whoosh by we can almost feel the wind. When the bombs drop, we feel the shudder. And when lovable supporting characters get blown to bits, it's hard to suppress the waterworks.
However, anyone expecting a think piece on the war in the Pacific is going to be sorely disappointed. Unlike, say, Tora! Tora! Tora!, history gets short shrift in Pearl Harbor, as director Michael Bay's WWII for Dummies gives us laughably simplistic details about why the war with Japan was entered into. Bizarrely, yet in a blatant attempt to garner an international audience that is not offended in any way by the movie, the film virtually absolves Japan of any wrongdoing, excusing the sneak attack as their regrettable only option.
Thank God, at least, that we are mercifully spared the Saving Private Ryan and Titanic flash-forwards to a melodramatic, length-padding present. With a lame and drippy score courtesy of Hans Zimmer, there's plenty of sap already, thank you very much. As well, the film's few attempt at lightening things up garner few laughs, if ever.
Critics (myself included) are going to endlessly compare Pearl Harbor to Titanic, and for good reason. However, aside from the length and the love story, these are radically different pictures. Ultimately, Pearl Harbor is not a film made by a man fascinated with history (a la James Cameron), but rather a studio-manufactured tearjerker, with nothing left to chance.
Titanic was so opulent and obsessed with period detail it almost made you forget that Billy Zane can't act. Pearl Harbor is clearly the work of hundreds of computer experts dueling it out to see who can make the biggest explosion. Been there, done that, folks.
Titanic was a big risk that took guts to make. Pearl Harbor is just another step toward the Disneyfication of our collective memories, trying unsuccessfully to prey upon a resurgence of patriotism.
Titanic was epic, and it made you forget about the passage of time. Pearl Harbor is soulless and desperate in its cheesy attempt to appeal to women, running at three hours long mainly because it's all done in slow motion.
Which is funny, because the actual invasion in 1941 only lasted for less than two.

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In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
inputs_real = tf.placeholder(tf.float32,(None,image_width, image_height, image_channels), name='input_real')
inputs_z = tf.placeholder(tf.float32, (None,z_dim),name='input_z')
learn_rate = tf.placeholder(tf.float32, name='learn_rate')
#learning_rate = 0.1
return inputs_real, inputs_z, learn_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
alpha = 0.1
leaky_relu = lambda x: tf.maximum(alpha*x, x)
def conv(inputs, filters, kernel_size,strides, batch_norm = True):
outputs = tf.layers.conv2d(inputs, filters, kernel_size,strides, 'same')
if batch_norm:
outputs = tf.layers.batch_normalization(outputs, training=True)
return leaky_relu(outputs)
with tf.variable_scope('discriminator',reuse=reuse):
conv1 = conv(images, 64, 5, 2, batch_norm = False)
conv2 = conv(conv1, 128, 5, 2)
conv3 = conv(conv2, 256, 5, 2)
flat = tf.contrib.layers.flatten(conv3)
#h1 = tf.layers.dense(flat, 128, activation=None)
#h1 = tf.maximum(alpha*h1, h1)
#dropout = tf.nn.droput(h1, keep_prob=0.5)
logits = tf.layers.dense(flat, 1, activation=None)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
alpha = 0.1
leaky_relu = lambda x: tf.maximum(alpha*x, x)
def deconv(inputs, filters, kernel_size,strides, batch_norm = True, is_train = True):
outputs = tf.layers.conv2d_transpose(inputs, filters, kernel_size,strides, 'same')
if batch_norm:
outputs = tf.layers.batch_normalization(outputs, training=is_train)
return outputs
with tf.variable_scope('generator',reuse= not is_train):
h1 = tf.layers.dense(z, 7*7*512,activation=None)
h1 = tf.reshape(h1,[-1,7,7,512])
h1 = tf.layers.batch_normalization(h1, training=is_train)
h1 = leaky_relu(h1)
conv1 = deconv(h1, 256, 5, 1, is_train = is_train)
conv1 = leaky_relu(conv1)
conv2 = deconv(conv1, 128, 5, 2, is_train = is_train)
conv2 = leaky_relu(conv2)
conv3 = deconv(conv2, out_channel_dim, 5, 2, batch_norm = False)
out = tf.nn.tanh(conv3)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
smooth = 0.1
d_gen_logits = generator(input_z,out_channel_dim)
_, d_real_logits = discriminator(input_real, reuse=False)
_, d_fake_logits = discriminator(d_gen_logits, reuse=True)
d_real_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_real_logits,labels=tf.ones_like(d_real_logits) * (1 - smooth)))
d_fake_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits,labels=tf.zeros_like(d_fake_logits)))
d_gen_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits,labels=tf.ones_like(d_fake_logits)))
d_dis_loss = d_real_loss + d_fake_loss
return d_dis_loss, d_gen_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
d_opt = tf.train.AdamOptimizer(learning_rate=learning_rate,beta1=beta1).minimize(d_loss, var_list = d_vars)
g_opt = tf.train.AdamOptimizer(learning_rate=learning_rate,beta1=beta1).minimize(g_loss, var_list = g_vars)
return d_opt, g_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
show_n_images = 25
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
input_real, input_z, learn_rate = model_inputs(data_shape[1],data_shape[2],data_shape[3],z_dim)
d_dis_loss, d_gen_loss = model_loss(input_real,input_z,data_shape[3])
d_opt, g_opt = model_opt(d_dis_loss, d_gen_loss, learning_rate, beta1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
steps = 0
for batch_images in get_batches(batch_size):
# TODO: Train Model
steps += 1
batch_images = batch_images *2.0
batch_z = np.random.uniform(-1.0,1.0, size=(batch_size,z_dim))
_ = sess.run(g_opt, feed_dict={input_z:batch_z,input_real:batch_images})
_ = sess.run(d_opt, feed_dict={input_z:batch_z,input_real:batch_images})
if steps % 100 == 0:
train_loss_d = sess.run(d_dis_loss, feed_dict={input_z:batch_z,input_real:batch_images})
train_loss_g = sess.run(d_gen_loss, feed_dict={input_z:batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
if steps % 100 == 0:
show_generator_output(sess, show_n_images, input_z, data_shape[3], data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 32
z_dim = 128
learning_rate = 0.0008
beta1 = .5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 64
z_dim = 128
learning_rate = .0001
beta1 = .5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


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| Service | Status | Name | Address | Capacity | |
|---|---|---|---|---|---|
| Online | {{ $server.Name }} | {{ $server.Address }} | {{ if $server.Locked }} {{ else }} {{end}} | {{ if $server.MaxPlayers }} {{ $server.MaxPlayers }} {{ else }} ∞ {{ end }} |
| Hostname / IP | Status | OS |
|---|---|---|
| {{ $infrastructure.Hostname }} ({{ $infrastructure.Address }}) | Online | {{ $infrastructure.Os }} |


SMBSTATUS(1) User Commands SMBSTATUS(1) NAME smbstatus - report on current Samba connections SYNOPSIS smbstatus [-P] [-b] [-d <debug level>] [-v] [-L] [-B] [-p] [-S] [-s <configuration file>] [-u <username>] DESCRIPTION This tool is part of the samba(7) suite. smbstatus is a very simple program to list the current Samba connections. OPTIONS -P|--profile If samba has been compiled with the profiling option, print only the contents of the profiling shared memory area. -b|--brief gives brief output. -d|--debuglevel=level level is an integer from 0 to 10. The default value if this parameter is not specified is 0. The higher this value, the more detail will be logged to the log files about the activities of the server. At level 0, only critical errors and serious warnings will be logged. Level 1 is a reasonable level for day-to-day running - it generates a small amount of information about operations carried out. Levels above 1 will generate considerable amounts of log data, and should only be used when investigating a problem. Levels above 3 are designed for use only by developers and generate HUGE amounts of log data, most of which is extremely cryptic. Note that specifying this parameter here will override the smb.conf.5.html# parameter in the smb.conf file. -V|--version Prints the program version number. -s|--configfile <configuration file> The file specified contains the configuration details required by the server. The information in this file includes server-specific information such as what printcap file to use, as well as descriptions of all the services that the server is to provide. See smb.conf for more information. The default configuration file name is determined at compile time. -l|--log-basename=logdirectory Base directory name for log/debug files. The extension ".progname" will be appended (e.g. log.smbclient, log.smbd, etc...). The log file is never removed by the client. -v|--verbose gives verbose output. -L|--locks causes smbstatus to only list locks. -B|--byterange causes smbstatus to include byte range locks. -p|--processes print a list of smbd(8) processes and exit. Useful for scripting. -S|--shares causes smbstatus to only list shares. -h|--help Print a summary of command line options. -u|--user=<username> selects information relevant to username only. VERSION This man page is correct for version 3 of the Samba suite. SEE ALSO smbd(8) and smb.conf(5). AUTHOR The original Samba software and related utilities were created by Andrew Tridgell. Samba is now developed by the Samba Team as an Open Source project similar to the way the Linux kernel is developed. The original Samba man pages were written by Karl Auer. The man page sources were converted to YODL format (another excellent piece of Open Source software, available at ftp://ftp.icce.rug.nl/pub/unix/) and updated for the Samba 2.0 release by Jeremy Allison. The conversion to DocBook for Samba 2.2 was done by Gerald Carter. The conversion to DocBook XML 4.2 for Samba 3.0 was done by Alexander Bokovoy. Samba 3.6 06/22/2012 SMBSTATUS(1)


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This is an antique-clock with numbers in Roman Numerals.
It is a pocket watch with orange background.
This is a leafy illustration with turquoise background.
This is a girl with mask and open hair.


dcl:096 shouldn't appear anywhere?
It is not anywhere in the ShotList nor in the master file. I thought it was just the typo in the MARC record?
Is dcl:096 assigned to any other fiche scans at the moment?
Huh hum. I'll have to check in with Bary about this. Maybe it is possible as you suggested but please let me know if dcl:096 appears on anything else than the Christianarum title.
Hey,
As you said it is just the the one wrong scan of the microfiche header in the very beginning. The rest of the scan looks good. So it would only be the header of that one fiche that needs to be refilmed.
I checked the other title that Tortebat title belongs to and they have very similar dcl numbers (dcl:rbv and dcl:rvb) so there might have been a switcheroo because of that (also I wouldn't know how that is possible?) but the Tortebat in itself looks fine and does not contain the missing header. Alas, that would have been to easy.
[unrelated to original issue: the Tortebat has a weird image at the end that might be a faulty scan: https://figgy.princeton.edu/catalog/83fd93b2-1cf6-4458-9fc4-e566f2260cb4]
@smallfind hey! for this I would suggest just adding the year of publication (from the title page)? Just something that helps a user navigate and identify what they are looking for and that is not just the same title for all. In other examples it would be a volume designator or for if the titles vary much like in the Trattato and accompanying Disegno example we had here yesterday. Does that make sense? I am not fixed on any particular solution here, just something that makes one more aware what might hide between the different structmap sections.
cheers!
mentioned
by David Platt at 2020-05-30 19:21:15.subscribed
by David Platt at 2020-05-30 19:21:15.convert_to_draft
by James R. Griffin III at 2020-05-30 00:04:12.ready_for_review
by James R. Griffin III at 2020-05-30 00:05:13.referenced
by James R. Griffin III at 2020-05-30 00:06:33.merged
by James R. Griffin III at 2020-05-30 00:06:33.closed
by James R. Griffin III at 2020-05-30 00:06:33.head_ref_deleted
by James R. Griffin III at 2020-05-30 00:06:37.This should be ready to be merged. Unfortunately, the runlevel issue is actually due to the fact that the base image for the Docker container does not have systemd
installed. SysVinit is instead used, and attempting to install systemd
atop of this requires a reboot (one cannot really support this for a Docker provisioning pipeline).
head_ref_force_pushed
by James R. Griffin III at 2020-05-29 22:37:17.head_ref_force_pushed
by James R. Griffin III at 2020-05-29 22:50:47.referenced
by James R. Griffin III at 2020-05-30 00:03:47.merged
by James R. Griffin III at 2020-05-30 00:03:47.closed
by James R. Griffin III at 2020-05-30 00:03:48.head_ref_deleted
by James R. Griffin III at 2020-05-30 00:03:51.In order to import the latest scsb dump we need to follow the steps @mzelesky listed to exclude private records.
mentioned
by None at 2020-05-29 22:05:34.subscribed
by None at 2020-05-29 22:05:34.Coverage increased (+0.006%) to 96.576% when pulling a1a017290c68cb3a3b444d9621ec2bf548c4cfcb on requests into 21881ca33ccdaa4ce852e6fa76b0807bebfde685 on master.
deployed
by Trey Pendragon at 2020-05-29 22:31:22.deployed
by Trey Pendragon at 2020-05-30 12:37:53.head_ref_force_pushed
by None at 2020-05-30 12:59:22.head_ref_force_pushed
by None at 2020-05-30 14:12:36.renamed
by None at 2020-05-30 14:13:25.referenced
by None at 2020-05-30 18:51:34.merged
by None at 2020-05-30 18:51:34.closed
by None at 2020-05-30 18:51:34.head_ref_deleted
by None at 2020-05-30 18:51:41.labeled
by Kim Leaman at 2020-05-30 16:09:43.labeled
by Kim Leaman at 2020-05-30 16:09:43.mentioned
by None at 2020-05-30 16:09:43.subscribed
by None at 2020-05-30 16:09:43.referenced
by Christina Chortaria at 2020-05-30 12:36:16.merged
by Christina Chortaria at 2020-05-30 12:36:16.closed
by Christina Chortaria at 2020-05-30 12:36:16.it looks good .
👍 That looks good to me.
assigned
by None at 2020-05-30 12:03:59.closed
by Christina Chortaria at 2020-05-30 12:36:16.


About
This simple web app will show you the precise solar time of your location, using HTML5 Geolocation.
To see the local solar time for your location, click allow when prompted to allow this page access to your location. This is to calculate what the true solar time of your location is to nearest second based on your longitude position on the globe.


Girlguiding: My Queens Guide Award JourneySian Reis
Sian's QGA JourneyThe Highest award in Guiding
Over the last 3 years I've completed the five sections: Service in guiding, Outdoor challenge, Personal skill development, Community action, Residential


--- layout: %%PATH_TO_BUILTIN_VIEWS%%/layouts/localdev-theme.html --- {{##body:
{{= page.data.stack }}


| Pedigree of : |
| Staghorn's Resusci Annie (a.k.a. Annie) |
| Born: January 13, 2008 |
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Int. Ch Autumn's Thunder Vision JH |
Ch Breamar Oakmead Magnum Force
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Ch Sandyland's Flash Harry CD |
| Ch Breamar Thistle CDXr | |||
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Vision's Lady Alexi |
Int World Ch Sandylands Rip Van Winkle | ||
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Ch Hunterleigh Kriscoland Summer
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Am Ch Dickendall Arnold
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Dickendall A-HA
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Tabatha's Brazos Truffle
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Wyndcall's Druid O'Tabatha | ||
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Parents
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Grand Parents
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Great Grand Parents
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Great Great Grand Parents
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Kriscoland's Plum Crazy |
Ch Kriscoland's Blast Off
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Tabatha's Hunterleigh Rocket
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Ch Kriscolands Purify Me Claire MBISS
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Ch Kriscoland's Perpetual Breeze
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Am Can Ch Kriscoland's Distinctive Mark WC
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| Kriscoland's Nell | |||
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Ch Kriscoland's West Coast Breeze
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Wingmstr's Just Another Fella | ||
| Ch Kriscoland Hunterleigh Pearl |
Copyright © 2008 Harold
deGroot - Last Update :
November 1, 2008
All Rights Reserved
Webmaster: webmaster@staghorn-labs.com


SETENV
Section: Linux Programmer's Manual (3)
Updated: 2013-10-11
Index
Return to Main Contents
NAME
setenv - change or add an environment variable
SYNOPSIS
#include <stdlib.h>
int setenv(const char *name, const char *value, int overwrite);
int unsetenv(const char *name);
Feature Test Macro Requirements for glibc (see
feature_test_macros(7)):
setenv(),
unsetenv():
-
_BSD_SOURCE || _POSIX_C_SOURCE >= 200112L || _XOPEN_SOURCE >= 600
DESCRIPTION
The
setenv()
function adds the variable
name
to the
environment with the value
value,
if
name
does not
already exist.
If
name
does exist in the environment, then
its value is changed to
value
if
overwrite
is nonzero;
if
overwrite
is zero, then the value of
name
is not changed (and
setenv()
returns a success status).
This function makes copies of the strings pointed to by
name
and
value
(by contrast with
putenv(3)).
The
unsetenv()
function deletes the variable
name
from
the environment.
If
name
does not exist in the environment,
then the function succeeds, and the environment is unchanged.
RETURN VALUE
The
setenv()
function returns zero on success,
or -1 on error, with
errno
set to indicate the cause of the error.
The
unsetenv()
function returns zero on success,
or -1 on error, with
errno
set to indicate the cause of the error.
ERRORS
- EINVAL
-
name
is NULL, points to a string of length 0,
or contains an '=' character.
- ENOMEM
-
Insufficient memory to add a new variable to the environment.
CONFORMING TO
4.3BSD, POSIX.1-2001.
NOTES
POSIX.1-2001 does not require
setenv()
or
unsetenv()
to be reentrant.
Prior to glibc 2.2.2,
unsetenv()
was prototyped
as returning
void;
more recent glibc versions follow the
POSIX.1-2001-compliant prototype shown in the SYNOPSIS.
BUGS
POSIX.1-2001 specifies that if
name
contains an '=' character, then
setenv()
should fail with the error
EINVAL;
however, versions of glibc before 2.3.4 allowed an '=' sign in
name.
SEE ALSO
clearenv(3),
getenv(3),
putenv(3),
environ(7)
Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- RETURN VALUE
-
- ERRORS
-
- CONFORMING TO
-
- NOTES
-
- BUGS
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 02:54:59 GMT, September 18, 2014


Lambda
A -> B
Code that accepts a Lamba
Stream<A>#map :: (A -> B) -> Stream<B>;
Given Stream of A and a function from A to B, we can make a Stream of B
Stream<A>#map :: (A -> B) -> Stream<B>;
Sadly this method is only on the new Stream class, not List
Given a Stream of A and a function from A to Stream of B, we can make a Stream of B
Stream<A>#flatMap :: (A -> Stream<B>) -> Stream<B>
What if we just used map?
Stream<A>#map :: (A -> Stream<B>) -> Stream<Stream<B>>
This should be old-hat, but this is a good time to stop and check in.
Optionals can be constructed in one of three ways
I recommend you avoid Optional.of
Optional<A>#map :: (A -> B) -> Optional<B>;
Chain together a series of transformations without checking for presence or absence until the end.
If the Optional is empty, map is a no-op.
Optional<A>#flatMap :: (A -> Optional<B>) -> Optional<B>;
Chain together a series of transformations that might each fail, without checking for presence or absence explicitly.
flatMap is a no-op if the Optional is empty.
By now you should see the similarities between Stream and Optional.
In both cases, you don't have to check to see if the stream/optional is empty, and you can chain together operations that independent of each other.
Optional<A>#isPresent :: boolean;
Optional<A>#get :: A; // Throws exceptions!
Optional<A>#orElse :: A -> A;
Old Way
String x = Helper.functionOne();
if (x != null) {
x = Helper.functionTwo(x);
x = Helper.functionThree(x);
if (x != null) {
...
}
}
New Way
Optional<String> x = Helper.functionOne()
.map(Helper::functionTwo)
.flatMap(Helper::functionThree);
Either is a concept lifted from ML.
No official version, but several third party versions are available.
Either is usually represented an Either<L, R>
It represents either the existence of a L (left), or a R (right)
L and R may be the same types, or they might be distinct
Either.left(L);
Either.right(R);
Eithers commonly represent success/failure scenarios
Traditionally, left is used for failure, right for success
Either<L, R>#map :: (R -> R2) -> Either<L, R2>;
Just like Optional#map, if we have a failure case (left), this is a no-op.
Either<L, R>#biMapL :: (L -> L2) -> Either<L2, R>;
Either<L, R>#biMapR :: (R -> R2) -> Either<L, R2>;
biMapR is a synonym for map
Either<L, R>#flatMap :: (R -> Either<L, R2>) -> Either<L, R2>;
Either<L, R>#match :: (L -> T), (R -> T) -> T
This is called unifying.
public HttpResponse handle(HttpRequest request) {
return JsonParser.parse(request.getBody())
.flatMap(Validator::validate)
.map(ServiceObject::businessLogic)
.flatMap(JsonGenerator::generate)
.match(l -> HttpResponse.internalServerError(l.getMessage()),
r -> HttpResponse.ok(r));
}
Obviously this is an interesting novelty, but I haven't sold you on why you should like these.
Especially Optional and Either, one of which is new, the other of which is Optional (pun intended).
The advantage of list is obvious
Obviously superior to a large number of similarly typed fields
Neatly represents the presence of 0 or more values at a type level
Represents the existence of 0 or 1 item at a type level.
Users forced to deal with the Optional, cannot forget the null check
Easily chain together a series of operations that may or may not fail with ease
Represent the exclusive choice of L or R at a type level.
Make it impossible to accidentally mis-configure an object (e.g. two nulls or not enough nulls when fields are mutually exclusive)
Chain together a sequence of actions, each of which might fail with a reason together in a type safe way.
HackerNews street cred
Either is a special CoProduct
Can be used to represent 2 or more mutually exclusive values
CoProduct3<A, B, C>
Use them!
Use it any place that you expect nulls.
More type-safe than null, forces users to do the null check.
Warning: Too many Optionals in argument lists is a sign that you've got latent polymorphism happening
Fits very nicely in two places
Cumbersome because of Java's syntax
Only recommended if you grok Optional and Either completely.
I recommend combining tiny types with these types.


TestNG groups add great flexibility to the Citrus test execution. We are able to divide all tests into several groups reaching a sophisticated seperation of concerns in our test setup. As an example I want to classify some of my functional Citrus tests as “long-running”. These tests may not apply to continuous execution every time I package my project. Instead of this I want to set up a scheduled integration build to execute those long-running tests in a time schedule.
We simply declare the TestNG groups in the Java part of the Citrus tests, like this:
In Maven I can configure the TestNG groups to be included and excluded during build lifecycle. For better usability I add properties and profiles to my Maven POM, so the configuration looks like follows:
With this setup I am not forced to wait for the long-running tests every time I build the project locally as these test group is excluded by default. However with Maven profiles I am able to run all TestNG groups together or explicitly execute a single group:
TestNG groups in combination with Maven are extremely useful to break down Citrus tests into logical units. For additional reading and other ways to execute TestNG groups without using Maven please see the official TestNG documentation.


Callback Me Maybe
After taking a class on Javascript during my Spring Quarter of Freshman year, I realized that I finally had all the tools needed to build my own personal website. However, I was intimidated by other CS majors and thought that I could never work on my own project because I'm not as good as a programmer as they are.
A New Career
Luckily, one of my friends brought me to a hackathon and forced me to build him a website. I did so and that's when I realized how easy it was now that I had learned Javascript. Right after the hackathon, I went straight to work on my own website and finished it within a week. After publishing it on the web, one of my friends thought I did a really good job and wondered if I could build a website for the mom's cafe. I immediately jumped on this opportunity and built the website for her within a couple of weeks. She was so happy about it and I loved seeing how excited my client was about a project I completed.
After creating my first two websites, I then decided to employ myself as a freelance web designer. Now I have two clients that I work for and have been designing their website for over 3 months. I'm glad that my friend pushed me to pursue web development and not give up on my goals because of an insecurity.


The #1 source for fantasy sports and pools
A sharpgroup is a tool to create a custom pool for you and your bros. We are NOT a gambling website. We also can not stop you from charging all your friends 10 bucks to enter the sharpgroup. We also can't stop you from paying out the leader of your sharpgroup. And we also can't stop you from shaming the guy who doesn't pay his dues on time by drawing boobs on his face when he passes out at the tailgate after-party.
Our most popular games are playoff fantasy football and fantasy sportsbook. Sign up, create your sharpgroup, invite your friends and enjoy the fun.
Fantasy SportsbookFantasy Sportsbook is a game that is loosely based on sports betting. You make wagers with fake money AKA sharps. Whoever has the most money at the end of the defined season wins the sharpgroup. This game can be played at any time of the year, for any combinition of available sports currently in season. Sports include NFL, NBA, MLB, NHL, PGA Tour, Triple Crown Races, NCAAB and NCAAF
Playoff Fantasy Football registration is currently closed. Check back closer to the 2016 NFL playoffs to sign up.
Masters Pick'em registration is currently closed. Check back closer to the 2015 Masters tournament to sign up.
We have plenty of other ideas rolling around in our heads and we plan to stay busy here at sharpgroups. Keep an eye out for more games coming in the future.


Methods are created using "def" followed by the method name. Method names should begin with a lowercase letter. Methods can take any number of arguments and the arguments can be given default values as well.
Classes
Classes are created using "class" followed by the class name. Class names should begin with a uppercase letter. Classes can contain a multitude of methods and have instance variables which can be used throughout all the methods within the class. There are also reader and writer attributes that can be applied to classes that allow interaction with the variables outside of the class.
Array
Arrays are a collection of objects ordered by an index. The first object is indexed at 0 and the last can be indexed at -1.
 You can access values by calling upon the keys.
Strings
Strings are an object which can be made up of most things held within single or double quotes.
Integers
Objects that are whole numbers.
Boolean expressions
a == b
Returns true when a is equal to b.
a != b
Returns true when a is not equal to b.
a > b
Returns true when a is greater than b.
a < b
Returns true when a is less than b.
a >= b
Returns true when a is greater than or equal to b.
a <= b
Returns true when a is less than or equal to b.
Ruby Operators
+
For adding the values on both side of the operator
-
For subtracting the values on both side of the operator
*
For multiplying the values on both side of the operator
/
For dividing the values on both side of the operator
%
For finding the remainder of the values on both sides of the operator
&&
If both sides of the operator return true, then the condition is true.
||
If either side of the operator return true, then the condition is true.
!
Reverses the logical state. If a condition is true, it will be false and vice versa.
Array methods
collect
Passes given elements to the block. Creates new array containing new values. Syntax and examples
Returns the number of elements unless an argument is given. If an argument is given, returns the number of elements equal to the given argument. Syntax and examples
delete_if
Deletes every element where the given block is true for. Syntax and examples