January 10, 2015
Entering into anything new can be overwhelming and intimidating. I should know, I have chosen to enter the field of web development and I'm attending Dev Bootcamp. Dev Bootcamp (DBC) is a development bootcamp geared at taking you from newbie to a "world class beginner" in 18 weeks. It is going to be one of the most challenging experiences of my life. This blog series is designed to be an introduction to the world of development for the novice developer by a novice developer. By the end of this series I hope that you have had an opportunity to grow as much as I have.
What are Regular Expressions
A journey of a thousand miles starts under one's feet.– Laozi, Tao Te Ching
And so does our journey into regular expressions. I will be completely honest, this is not a how to tutorial into the use of regular expressions. There is just too much to cover and, let's face it, I'm just learning. This is more a guide into what regular expressions are and what they can do for your code.
Stephen Kleene, a mathematician, originated regular expressions in 1956. However, it was not until 1968, when Ken Thompson built regular expressions into the QED text editor as a way to match patterns in text files, that we started to see regular expressions in use. Basically, regular expressions, or more commonly referred to as regex, are a sequence of characters that define a search pattern. Regex processors are built into many search engines, word processors, and programming languages, including JavaScript and Ruby.
So How Do Regular Expressions Work?
The challenge of regular expressions is both matching what you want– Kevin Skoglund, Lynda.com
and only what you want.
All regex statements begin with a /
and end with a /
. /apples/
is a regex statement that uses character literals to find a match, and are case sensitive. Our example /apples/
would find a match for apples
and applesauce
, but not find a match for Apples
.
Regex also uses metacharacters, which include, . [] ^ $ () \ * + ? {} |
.
.
Is often referred to as a wild card and will return any single character.[ ]
Matches any character or range of characters within the brackets.[^ ]
The^
has two meanings. First, when used at the beginning of a bracket statement, it matches any character or range of characters not found within the brackets.^
Second, only returns a match if the regex is at the beginning of a string or line.$
Matches any regex at the end of a string or line.( )
Matches the group of characters within the( )
.\
Is used to cancel out a metacharacter and returns the literal character.*
Matches the preceding element zero or more times.+
Matches the preceding element one or more times.?
Matches the preceding element zero or one time.{x}
Matches the preceding element x-times.{x,y}
Matches the preceding element at least x-times but no more than y-times.|
Matches the expression before or the expression after the operator.
/^([\w-]+(?:\.[\w-]+)*)@((?:[\w-]+\.)*\w[\w-]{0,66})\.([a-z]{2,6}(?:\.[a-z]{2})?)$/
could be used within JavaScript to validate an email address.
Regular expressions can add a whole new dimension to your code and is a complex subject, for additional information visit Wikipedia. An additional resource is an excellent tutorial by Kevin Skoglund on Lynda.com.


This tests that lines work in the same way in different browsers when you are using sub pixel coordinates.


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Across: 1. Medics adopt bad practices (6) 4. Free love rampant after first two classes at start of school (7) 9. Accused fed-up social worker about death (9) 10. Healthy boy without uniform comes to bad end! (5) 11. Girl’s reefer? (5) 12. Young egotist caught Russians wandering outside (9) 13. One top car raced around and competed in training (7) 15. Hold back partner with less resistance (6) 17. Body bag may be produced when cars crash on main road (3 3) 19. Attempt at humour by uncle about terrible scar (7) 22. Friendly, attractive artist starts drawing inside (9) 24. Cut off half of rose before father returns (5) 26. Come into part of broken terrace (5) 27. Copper gets back in shape without electronic communication system (9) 28. Convert unstable nutcase, Percy (7) 29. Universal panic again about lizard! (6) Down: 1. Removes from the French channels (7) 2. Understand no part of Dante’s work is missing (5) 3. 100 soldiers start to denigrate southern publicans (9) 4. One treats strange goddess (7) 5. Huss I cook in a fish dish (5) 6. Refined oil in a USA state (9) 7. Most senior Tory leader is behind Leeds regeneration (6) 8. Prevented group touring Tyneside (6) 14. Am I a toxic criminal? Is this true? (9) 16. Pushing article about problem with plant (9) 18. New truce encompasses John’s crop (4 3) 19. Tell tales about heartless party’s espionage (6) 20. Scholars grip edge of instrument (7) 21. Agree to one taking possession of Cedric’s semi (6) 23. Set out up Australian river (5) 25. Having lost Elizabeth I love a new girl (5)


class: center, middle, inverse, title-slide # A First Look at Keras ## Stat 696 ### Andrew Bates ### 2019-03-03 --- ![](keras-logo-2018-large-1200.png)<!-- --> ### What Is Keras From [the documentation](https://keras.io): Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano... --- ![](landing_icon-tf_logo_halo_480.jpg)<!-- --> ### What is TensorFlow From [the website](https://www.tensorflow.org): TensorFlow is an open source software library for high performance numerical computation... --- class: middle ### What Does That Mean TensorFlow does math good. Keras uses TensorFlow to do math for neural networks. ??? Both can be use from Python and R. --- ### What Keras Looks Like in Python ```python from keras.models import Sequential from keras.layers import Dense # initialize model model = Sequential() # add a hidden layer model.add(Dense(units = 10, activation = 'sigmoid', input_dim = p)) # add an output layer model.add(Dense(units = 1)) # set things up for training model.compile(loss = 'mse', optimizer = 'sgd', metrics = ['mse']) # train the model model.fit(x_train, y_train) ``` --- ### What Keras Looks Like in R ```r library(keras) library(dplyr) # for the %>% model <- keras_model_sequential() %>% # initialize a model layer_dense(units = 10, activation = "sigmoid", input_shape = p) %>% # add a hidden layer layer_dense(units = 1) # add an output layer # set things up for training model %>% compile( loss = "mse", optimizer = optimizer_sgd(), metrics = "mse" ) # train the model model %>% fit( x_train, y_train ) ``` --- class: middle ### The Common Workflow 1. Create a model. 2. Add layers to the model. 3. Specify loss function, optimization algorithm, etc. 4. Train the model. --- class: middle ### Resources [https://keras.io](https://keras.io) - Keras Python documentation [https://keras.rstudio.com](https://keras.rstudio.com) - Keras R documentation [https://blogs.rstudio.com/tensorflow/](https://blogs.rstudio.com/tensorflow/https://tensorflow.rstudio.com) - TensorFlow/Keras blog by RStudio [https://www.tensorflow.org](https://www.tensorflow.org) --- class: middle ### Demo Time! The files can be found at [https://github.com/asbates/neural-nets/tree/master/keras-tutorial](https://github.com/asbates/neural-nets/tree/master/keras-tutorial)


CogatPheno is a database of cognitive and behavioral assessments with questions that have been mapped to concepts and contrasts defined in the
Cognitive Atlas.
CogatPheno will (eventually) allow for a data-driven derivation of an individual's cognitive phenotype using one or more assessments. Given that the Cognitive Atlas is an ontology, this phenotype can be expressed at multiple levels of detail. We hope to be able to describe currently categorically defined mental illness using these dimensional concepts.
This work is being done by the
Poldracklab at Stanford University.


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---
layout: default
title: Friends
---
Our Friends
Here is a list of active bicycle and non-bicycle co-ops and noteworthy
organizations nearby. Please help us keep this list up to date by notifying us with any updates.
Bicycle organizations
Community bicycle repair co-ops:
Worker-owned bicycle co-ops:
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--- layout: post title: 'Studying a Masters in London: Glasgow Caledonian University' date: 2015-05-08 11:23:22.000000000 +01:00 categories: - General Advice tags: [] status: publish type: post published: true meta: _edit_last: '1' _thumbnail_id: '229' hefo_before: '0' hefo_after: '0' _yoast_wpseo_focuskw: Masters _yoast_wpseo_metadesc: I just finished studying my first semester abroad in London at Glasgow Caledonian University and here is my story. _wpt_view_count: '96' _wpas_done_all: '1' _yoast_wpseo_linkdex: '74' author: login<EMAIL_ADDRESS>email<EMAIL_ADDRESS>display_name: Evan first_name: '' last_name: '' excerpt: !ruby/object:Hpricot::Doc options: {} ---
I wanted to study in London as soon as possible. Here's how I went about choosing Glasgow Caledonian University:
The Search
Like many, I started my Masters search online. I ran through the list of universities on the League tables. I first stumbled upon Glasgow Caledonian University after noticing it offers January courses. Moreover, it allowed me to use my American 529 plan (make sure you check if your school qualifies). Most known universities, such as London School of Economics, Kings College, and Imperial College London, only offer Fall courses. I applied to a couple of universities that started in January and got accepted to all of them.
The Decision
I knew I could not spend a year and half without actually visiting the universities, so I took a week off from work and flew to London. I decided to visit 3 universities as I was looking for a Masters course in digital marketing. Two of the universities, London South Bank University and Glasgow Caledonian University, have January and September start dates, and they were the most affordable options (around 10,000 pounds for the entire course).
I visited London South Bank University in the neighborhood of Elephant and Castle. Upon exiting the tube station, I was met with an awful smell and crowd of people in a market. The neighborhood is in one of the poorer boroughs in London and does not have many food options besides chicken shops and markets. I went into the large university and the administration/admission staff could not help me and I could not meet anyone in the marketing department until next week. Moreover, the university seemed overwhelming with many buildings and facilities.
Next, I went to Glasgow Caledonian University in Shoreditch. Sitting right between Brick Lane and Spitalfields, the university had more affordable healthy and cheap food options than LSBU. The campus is very modern and the Internet is fast. The staff were very friendly, spoke with me one on one, and the students were very welcoming as the campus was a lot smaller than LBSU. Although the students were busy, they welcomed me and discussed the pros and cons of the school. They told me that the some of instructors were not qualified, but they learned a lot from the group projects. I sat on the decision for a couple weeks and decided to go to Glasgow Caledonian university
Preparation
The Tier 4 visa process is perhaps the most difficult and exhausting part in moving to London. The entire process took me about 2 months and cost me some $550. When you are applying, make sure to include your actual passport with your application, not a copy. Also, you have to prove you can afford living in London with your or your parent's bank statements. I forgot my passport and had to express ship it, so that they could include it in my application. I received my passport back with my visa on it, gave my 2 weeks notice, packed my bags, and flew to England!
Experience
Induction week was not special, although there were some fun social events that allowed me to meet with the students. I was the only native English speaking student in my program. Most of the students were either from the EU, Asia, or India. As expected, the lectures lacked deep analysis and seemed only touch the surface. However, the projects and curriculum were quite intensive and had direct applications to the marketing industry. I love the brand project, in which I created a brand and developed its positioning and marketing. There is not much class time, and the faculty expect you to study a lot on your own time, which is great for a taking a trip or two.
In the end, I ended up making a iPhone prototype application and developing a brand. The students are all very friendly and I made some great friends. Plus, I found the university perfectly situated, a 5-10 minute walk to the Silicon roundabout, where you can attend Google events etc.
Thoughts
In retrospect, this first semester has been a roller-coaster. From finding a place, looking for work and learning branding, marketing and strategy, it was all exhausting and exhilarating. I did have some time to travel to Dublin and explore London. However, I always found myself running to the tube and jumping through closing doors.
IMAGE USE: © Copyright Derek Harper and licensed for reuse under this Creative Commons Licence


My name is Joe Thor. I was born in Dubuque, Iowa. I graduated Luther College. I received my undergraduate degree in history and classics. For my classics degree I studied ancient Greek and Latin along with ancient mediterranean cultures. In my senior year, I worked closely with Dr. Dan Davis to developed a field guide on ancient amphorae. Amphorae are large ceramic jugs which were used to carry dry and wet goods such as grain, wine, olive oil, and fish sauce. They were ubiqitous in the ancient world and were unique enough in their dimensions and the make up of the ceramic to determine from where they originated. My work with Dr. Davis earned me an invitation to work aboard the Exploration Vessel Nautilus where we used sonar and remotely operated vehicles to explore ancient shipwrecks in the Black Sea.
Luther College is also where I met my wife, Emily. She studied nursing. She went on to earn a master’s degree in public health from the University of Iowa. Recently, she and I moved up to Minneapolis. Currently, works as a nurse with Hennepin County's Healthcare for the Homeless. I work at the University of Minnesota's Financial Helpline. Most of my day consists of walking our customers through the University's financial system. The financial system hosues all the transactions that are for external vendors, repaying employees, and moving money around. A lot of what I do is stepping callers through clearing their cache. I also am able to maintain our website which is all static html based on bootstrap.
This weekend we are getting ready to plant our garden. I have a spreadsheet laying everything out. In November, I planted seventeen heads of garlic. This spring, fourteen of them have sprouted. Soon we will have garlic scapes that will need to be trimmed. We are currenlty preparing our home for our first child. Our baby is due in October and we are very excited and a bit terrified. We have been aggresively budgeting and looking into daycares trying to figure out how people make this parenthood thing work.


The theme defines a set of CSS custom properties you can use to apply a consistent color palette across your application.
Importing the style module
If you are not importing any component themes, and want to use these colors, you need to import the style module explicitly.
Light and dark themes
The theme has a dark color variation available out of the box.
Each color has two variations: the default variation which is used for the light theme, and another variation used for the dark theme.
You need to import and include the “valo-color” style module before you can use the dark theme. It needs to be imported in the same style scope where you want to apply the dark colors.
1. Import and include the style module
2.a. Applying the dark theme for global scope
Apply the theme="dark" attribute on the HTML element:
<html theme="dark">
<!-- The whole page/app will use the dark theme colors -->
</html>
2.b. Applying the dark theme for a single element scope
You can apply the theme="dark" attribute on any element inside the style scope where you include the style module:
Browser compatibility
This example doesn’t work in Internet Explorer 11 or Edge 14. Read the compatibility instructions how to make the dark colors, scoped inside an element, work in all supported browsers.
Primary
The most prominent color in the theme. It is used to bring attention to certain elements in the interface. An accompanying contrast color is also defined.
10%50%100%
Grayscale
These colors form the foundation for your application colors. The grayscale colors consist of 10 different shades of “black” and “white”, each with a degree of transparency. They are not necessarily pure grayscale, but tinted slightly with the theme primary color
The grayscale colors are useful for dividing pages into sections with different backgrounds and borders, or used as text colors, for example.
Contrast
The theme defines properties for contrast colors that work automatically for both light and dark variations. For the light theme the contrast colors use “shades” and for the dark theme they use “tints”.
5%10%20%30%40%50%60%70%80%90%100%
Tint
5%10%20%30%40%50%60%70%80%90%100%
Shade
5%10%20%30%40%50%60%70%80%90%100%
Error/danger
Usually a red color, used for error indicators, error messages and buttons that can cause permanent data loss. An accompanying contrast color is also defined.
10%50%100%
Success
Usually a green color, used for success messages and buttons that complete a certain task. An accompanying contrast color is also defined.
10%50%100%
Text
The following text colors are designed to have the desired contrast with the default background color, --valo-base-color.
Header text
Normal text
Secondary text
Tertiary text
Disabled text
Primary text
Error text
Success text
Note, that --valo-primary-text-color is not necessarily the same as --valo-primary-color. The same applies for the “error” and “success” colors as well.


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The core package of Tkrzw-RPC provides a server program which manages databases of Tkrzw. This package provides a Python client library to access the service via gRPC protocol. Tkrzw is a library to mange key-value storages in various algorithms. With Tkrzw, the application can handle database files efficiently in process without any network overhead. However, it means that multiple processes cannot open the same database file simultaneously. Tkrzw-RPC solves the issue by using a server program which manages database files and allowing other processes access the contents via RPC.
The class "RemoteDBM" has a similar API to the local DBM API, which represents an associative array aka a Map in Java. Read the homepage for details.
DBM stores key-value pairs of strings. Each string is represented as a byte array in Java. Although you can also use methods with string arguments and return values, their internal representations are byte arrays.
All classes are defined under the package "tkrzw_rpc", which can be imported in source files of application programs.
import tkrzw_rpc.Status; import tkrzw_rpc.StatusException; import tkrzw_rpc.RemoteDBM; import tkrzw_rpc.Iterator;
An instance of the class "RemoteDBM" is used in order to handle a database. You can store, delete, and retrieve records with the instance. The result status of each operation is represented by an object of the class "Status". Iterator to access each record is implemented by the class "Iterator".
This package is independent of the core library of Tkrzw. You don't have to install the core library. Meanwhile, you have to install the library of gRPC for Java as described in the official document. JDK 9.0 or later is required to use this package.
Enter the directory of the extracted package then perform installation. The environment variable JAVA_HOME must be set properly.
./configure make sudo make install
When a series of work finishes, the JAR file "tkrzw_rpc.jar" is installed under "/usr/local/share/java". Let the class search path include "/usr/local/share/java/tkrzw.jar".
CLASSPATH="$CLASSPATH:/usr/local/share/java/tkrzw.jar" export CLASSPATH
The above settings can be specified by options of the compiler and runtime command.
javac -cp .:/usr/local/share/java/tkrzw.jar FooBarBaz.java ... java -cp .:/usr/local/share/java/tkrzw.jar FooBarBaz ...
To perform the integration tests, run these command on two respective terminals.
tkrzw_server make check
Before running these examples, you have to run a database server by the following command. It runs the server at the port 1978 on the local machine.
tkrzw_server
The following code is a simple example to use a database, without checking errors. Many methods accept both byte arrays and strings. If strings are given, they are converted implicitly into byte arrays.
import tkrzw_rpc.*; public class Example1 { public static void main(String[] args) { // Prepares the database. RemoteDBM dbm = new RemoteDBM(); dbm.connect("localhost:1978", -1); // Sets records. // Keys and values are implicitly converted into byte arrays. dbm.set("first", "hop"); dbm.set("second", "step"); dbm.set("third", "jump"); // Retrieves record values. // If the operation fails, null is returned. // If the class of the key is String, the value is converted into String. System.out.println(dbm.get("first")); System.out.println(dbm.get("second")); System.out.println(dbm.get("third")); System.out.println(dbm.get("fourth")); // Checks and deletes a record. if (dbm.contains("first")) { dbm.remove("first"); } // Traverses records. // After using the iterator, it should be destructed explicitly. Iterator iter = dbm.makeIterator(); iter.first(); while (true) { String[] record = iter.getString(); if (record == null) { break; } System.out.println(record[0] + ": " + record[1]); iter.next(); } iter.destruct(); // Closes the connection. // After using the database, it should be destructed explicitly. dbm.disconnect(); dbm.destruct(); } }
The following code is a typical example to use a database, checking errors. Usually, objects of RemoteDBM and Iterator should be destructed in "finally" blocks to avoid memory leak. Even if the connection is not closed, the destructor closes it implicitly. The method "orDie" throws an exception on failure so it is useful for checking errors.
import tkrzw_rpc.*; public class Example2 { public static void main(String[] args) { RemoteDBM dbm = new RemoteDBM(); try { // Prepares the database. The timeout is in seconds. Status status = dbm.connect("localhost:1978", 10); // Checks the status explicitly. if (!status.isOK()) { throw new StatusException(status); } // Sets the index of the database to operate. // The default value 0 means the first database on the server. // 1 means the second one and 2 means the third one, if any. dbm.setDBMIndex(0).orDie(); // Sets records. // Throws an exception on failure. dbm.set("first", "hop").orDie(); dbm.set("second", "step").orDie(); dbm.set("third", "jump").orDie(); // Retrieves record values. String[] keys = {"first", "second", "third", "fourth"}; for (String key : keys) { // Gives a status object to check. String value = dbm.get(key, status); if (status.isOK()) { System.out.println(value); } else { System.err.println(status); if (!status.equals(Status.NOT_FOUND_ERROR)) { throw new StatusException(status); } } } // Traverses records. Iterator iter = dbm.makeIterator(); try { iter.first(); while (true) { String[] record = iter.getString(status); if (!status.isOK()) { if (!status.equals(Status.NOT_FOUND_ERROR)) { throw new StatusException(status); } break; } System.out.println(record[0] + ": " + record[1]); iter.next(); } } finally { // Release the resources. iter.destruct(); } // Closes the connection. dbm.disconnect().orDie(); } finally { // Release the resources. dbm.destruct(); } } }


Colors, Icons, Labels and Badges
CdTabsComponent
is designed to manage some things of Ionic
Framework (colors, ion-icon, ion-label, ion-badge).
Colors
You can use the Ionic Colors
for CdTabBarComponent .
Rendering
Code HTML
Ionic Tab 1
Ionic Tab 2
Use the color:
{{value|titlecase}}
Icons and Labels
You can use the Ionic Icons and Ionic Labels to customize the
CdTabButtonComponent .
Rendering
Code HTML
Beer
Pizza
Coffee
Badges
You can use the Ionic Badges to
customize the CdTabButtonComponent .
Rendering
Code HTML
Beer
2
Pizza
1
Coffee
0


{% extends "layout.html" %} {% block page_title %} GOV.UK prototype kit {% endblock %} {% block content %} Pay to Request a Historic Service Record You can pay online for your application. Once you have successfully paid, you will receive a reference number. Include the reference number with your application as proof of payment. It can take several months for your application to be processed. Total amount: £30.00 {% endblock %}
You can pay online for your application. Once you have successfully paid, you will receive a reference number. Include the reference number with your application as proof of payment.
It can take several months for your application to be processed.
£30.00


THE APOSTLE (October) Starring: Robert Duvall, Farrah Fawcett, Miranda Richardson, John Beasley, Walter Goggins, June Carter Cash, Todd Allen, Billy Bob Thornton. Screenplay: Robert Duvall. Producer: Rob Carliner. Director: Robert Duvall. MPAA Rating: PG (adult themes, mild profanity, brief violence) Running Time: 130 minutes. Reviewed by Scott Renshaw.
A moment of mild understatement: people of Christian faith haven't fared well in film. When Hollywood was still afraid to offend, Christian characters were flawless, salt-of-the-earth folks; later, as cinematic taboos vanished through the 60s and 70s, religion became a sure sign of either mental illness or hypocrisy. Improbably extremes ruled, with few film-makers bothering to delve into the complex middle ground occupied by millions of Americans: flawed human beings who are nevertheless completely sincere and devoted to their faith.
Pentecostal minister Euliss F. "Sonny" Dewey, the title character in THE APOSTLE, may be the most wonderfully complex Christian character ever depicted on film, and every ounce of credit for that creation goes to Robert Duvall. Duvall wrote, directed and stars as Sonny, a man with an unfailing passion for his calling as a preacher. Unfortunately he also has an unfailing passion for women, leading his wife Jessie (Farrah Fawcett) to throw him out of the house and engineer his ouster from his church. The short-tempered Sonny responds by taking a baseball bat to the head of Jessie's new lover, then quickly taking it on the lam from his home in Texas to Bayou Boutte, Louisiana. There he re-christens himself "The Apostle E. F.," dedicating himself to beginning a new ministry in his new home.
Duvall works wonders with Sonny, depicting every possible facet of this complicated man's personality. An early scene, in which Sonny stops at the scene of an automobile accident to guide a critically injured young man to salvation, depicts the single-minded, necessarily arrogant conviction of the evangelist that everyone needs "saving" (as well as the accompanying prideful glee when he succeeds). Sonny's belief that his work as a preacher supercedes all other obligations unifies every action, from his rationalization of his infidelities to his decision to run from the law. Duvall even touches humorously on the reaction of others to Sonny's fervor -- one man who offers Sonny hospitality also sleeps warily with a shotgun by his side.
If there's one major stumbling block in THE APOSTLE, it is that the other characters in the film exist entirely to react to Sonny. While the performances are uniformly strong and naturalistic, the supporting players rarely seem to have lives of their own. From Jessie, to the young mechanic (Walter Goggins) who becomes Sonny's close friend, to a racist troublemaker (Billy Bob Thornton), all serve as spectators or functional props in the story of Sonny's redemption. Duvall commits so much energy to making this one character completely real that he has little left to spend on anyone else.
You may not end up minding all that much, since that one character becomes so fascinating. Duvall has long been one of America's finest actors, but this is the kind of role into which he disappears most completely -- slightly roguish but unmistakably human souls like his roles in TENDER MERCIES and LONESOME DOVE. When Sonny gives his final sermon in THE APOSTLE, in a sequence which lingers and builds in intensity, you realize exactly what Duvall is aiming at. It is possible for a man who is quick-tempered, stubborn and unfaithful to his wife to change lives for the better; it doesn't take a saint to be a good Christian. Ultimately, THE APOSTLE is a strange sort of character study, because Sonny doesn't really change all that much. Like most well-intentioned by imperfect people, he just keeps trying to be better.
On the Renshaw scale of 0 to 10 preacher features: 8.
Visit Scott Renshaw's MoviePage http://www.inconnect.com/~renshaw/ *** Subscribe to receive new reviews directly by email! See the MoviePage for details, or reply to this message with subject line "Subscribe".
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Related links: index of all rec.arts.movies.reviews reviews


Configuration
While the intention of core.io is to adhere to the idea of convention over configuration, it still grants you, the developer, full control over most aspects of your application by letting you override default values.
core.io configuration process is purportedly simple, a core.io application takes an options object with configuration parameters and override values. core.io does not really care how you come up with that object.
However, the Application
class provides a helper static method to collect, merge, and resolve dependencies of configuration files that are located in the ./config
folder of a project.
The resulting configuration object will be made available at runtime on the application context as context.config
.
When you create a new Application
instance you can pass an options
object to it's constructor.
This options
object has two purposes. If you define a configuration
key, it's value will be added to context.config
.
All other keys in this object will extend the application instance, like a [mixin][mixin]. The application instance extends itself with this object in it's init
method which is called directly from the constructor.
You can use it to override methods before the instance makes use of any of them or to add new methods to your instance.
const App = require('core.io').Application;
/*
* Autoload and merge files inside
* `config/` directory.
*/
const config = App.loadConfig({
//...default values
}, true);
const app = new App({
myCustomMethod: function(e) {
this.emit('custom.event', e);
},
config: config
});
app.myCustomMethod({});
Configuration instance
For convenience core.io wraps the config
object with a get
and set
methods.
This is so that you can access a deep object without fear of some object in the path not being defined. It also enables you to provide a default value for such cases.
/*
* Get the value of "environment" defined in config/app.js
* return "production" if undefined.
*/
let environment = context.config.get('app.environment', 'production');
It's more useful when you need to access a deeply nested object:
//Get the value of "repl.options.prompt", return "poke-repl >" if undefined.
let prompt = context.config.get('repl.options.prompt', 'poke-repl >');
Module configuration
When core.io registers a module, first it will require
the module and then will look for a key in context.config
that matches the module's moduleid. It will then call module.init
with a reference to the value of this key.
Pseudo code to illustrate:
let moduleId = 'persistence';
let config = this.config.get(moduleId, {});
module.init(this, config);
Configuration Files
Application.loadConfig
will load all configuration files found inside the ./config
directory of your application.
It will then load the files, and merge them in a single object using the file name as a key.
If you have a configuration file that has the same name as a given module's moduleId
then the contents of that file will be passed to the module during the initialization phase.
In a configuration file you can reference values from the same configuration object or from other configuration objects. Using two different syntaxes you can reference strings or objects:
- Strings:
${app.name}
- Objects:
@{app.locals}
The configuration solve routine will solve all cross references between configuration files. It runs after merging all files into a single object.
As an example, ${app.name}
will be resolved to config.app.name
:
config/app.js
:
module.exports = {
name: 'MyApplication'
};
config/repl.js
:
module.exports = {
prompt: '${app.name}'
};
There is also the possibility of processing the contents of a configuration file after it has been merged and loaded.
If you export a function named afterSolver
it will be called after all dependencies have been resolved. The function will be called with the whole configuration object.
module.exports.afterSolver = function(config) {
config.set('amqp.amqp', require('amqp'));
};
Configuration files are regular JavaScript files, which means you can build different logic into them.
Under the hood core.io uses the [simple config loader][scl] package. You can read more in the packages repository.
core.io provides a convenience method to collect these configuration files.
var App = require('core.io').Application;
/*
* Autoload and merge files inside
* `config/`
*/
var config = App.loadConfig({
//...default values
}, true);
var app = new App({
//Top level attributes will extend the application
//instance.
myCustomMethod: function(){},
config: config
});
You can specify the path from where to look for the configuration files.
Supporting Different Environments
Another aspect in which core.io tries to simplify the configuration process is by how it supports different development environments, like staging, development, production, etc.
In short: it does not.
To be more precise, core.io takes a very pragmatic stance and does not provide any way to (directly) manage different environments but has some recommendations that make having configuration files per environment unnecessary.
A lot of the things that need to change on each environment are secrets like service tokens or user keys. We chose to manage those by using process.env
and environment variables.
If you use the provided Application.loadConfig
then your configuration files are javascript files which, obviously, can have logic in it. Meaning that you can check the value of process.env.NODE_ENV
and export different objects based on that value.
Another benefit of Application.loadConfig
is that you can reference other parts of your configuration files and solve them at runtime, making your configuration files modular.
You can also make use of the afterSolver
facility which gets access to the merged configuration object. In it you can access the environment
key which holds the value of the current environment and modify your configuration file at runtime.
Case in point, you have options.
Ideally your configuration files should be logic lightweight in order to reduce possible errors and keep things simple, but you are free to do as you please.
You can also use an environment manager like [envset][envset] to dynamically populate your process.env
variables. All you need is an .envset
file where you define your environments, environment variables and their values:
[production]
NODE_AWS_SECRET_ACCESS_KEY=FS40N0QY22p2bpciAh7wuAeHjJURgXIBQ2cGodpJD3FRjw2EyYGjyXpi73Ld8zWO
NODE_AWS_ACCESS_KEY_ID=LSLhv74Q1vH8auQKUt5pFwnix0FUl0Ml
NODE_HONEYBADGER_KEY=LCgZgqsxKfhO
NODE_POSTGRES_ENDPOINT=<IP_ADDRESS>
NODE_POSTGRES_DATABASE=myproject
NODE_POSTGRES_PSWD=Pa$sW03d
NODE_POSTGRES_USER=myproject
[development]
NODE_AWS_SECRET_ACCESS_KEY=HN5ITok3lDaA1ASHxtEpy1U9XCjZwThmfgoIYZk8bkOqc5yk6sT7AWd3ooNeRFV9
NODE_AWS_ACCESS_KEY_ID=m35W4PGGVZfj1gOxYvztFxBD5F2605B3
NODE_HONEYBADGER_KEY=f3MNPUhZoki6
NODE_POSTGRES_ENDPOINT=localhost
NODE_POSTGRES_DATABASE=postgres
NODE_POSTGRES_PSWD=postgres
NODE_POSTGRES_USER=postgres
NOTE: If you use .envset
remember to add it to your .gitignore
file.
Lastly but more importantly, you can BYOS- bring your own solution- and use whatever configuration system you prefer.
Configuration files located in the config/
folder of projects will be merged together in a single object, which will be available at runtime as a property of your application instance, i.e. context.config
.
The top-level keys on the context.config
(i.e. context.config.repl
) object correspond to a particular configuration file name under your config/
directory (i.e. config/repl.js
). Most individual configuration files are specific to a module, with the exception of config/app.js
which should hold options for the current application, like the application's name, it's base directory, environment in run under, etc.
The intention of these files is to provide modules with configuration options. When a module is loaded, it will be called with the application's instance and a config
top-level key that matches the module's name.
Configuration dependencies
Your configuration files can contain references to a value found in other configuration files using a simple syntax that will get resolved after merging all files into a single object.
As an example, ${moduleA.name}
will be resolved with config.moduleA.name
:
config/moduleA.js
:
module.exports = {
name: 'ModuleA'
};
config/moduleB.js
:
module.exports = {
prompt: '${moduleA.name}'
};
core.io provides a convenience method to collect these configuration files.
const Application = require('application-core').Application;
/*
* Autoload and merge files inside
* `config/`
*/
const config = Application.loadConfig({
//...default values
}, true);
const app = new Application({config});
Configuration Extra Properties
The configuration process attaches two properties to the config object:
- package: contents of
package.json
file minus the readme - environment: value of
process.env.NODE_ENV
Application configuration file
- banner: String|Function
The ./config/app.js
is different than other configuration files in that the application will extend itself with the object like if it was a mixin.
Module configuration
When core.io registers a module, first it will require
the module and then will look for a key in context.config
that matches the module's moduleid. It will then call module.init
with a reference to the value of this key.
Pseudo code to illustrate:
let moduleId = 'persistence';
let config = this.config.get(moduleId, {});
module.init(this, config);
Solving Configuration Dependencies
Configuration files can have interpolated values where you reference the value of any attribute of the configuration object using the attribute's keypath.
You reference objects or properties by their keypath. A keypath is a string representing the location of a piece of data.
var data = {
user: {
name: 'Peperone',
address: {
city: 'New York'
}
}
};
You can reference strings or objects using two different syntaxes:
- Object interpolation:
@{user}
<EMAIL_ADDRESS>- String interpolation:
${user.name}
or${user.address.city}


Nyzo version 554 (commit on GitHub) improves cycle transaction handling.
This version affects the verifier. It also contains an insignificant change to HTML rendering that affects the documentation server.
In the CycleTransactionManager.registerTransaction() method, cycle transactions are now separated from their signatures before registration. This ensures that only valid, in-cycle signatures are added to the CycleTransactionManager.
This was not a security issue, because all cycle transaction signatures are also checked by the Verifier class before block assembly and by the BalanceManager in its enforceCycleTransactionRules() method. But it could have allowed an attacker to cause the CycleTransactionManager to store large numbers of invalid signatures, causing unnecessary memory usage.
In Transaction, the cycleTransaction() overload that reassembles a cycle transaction was changed from private to public. Previously, this method was used only by this class for reassembling cycle transactions from byte buffers. It was exposed to allow the CycleTransactionManager, in the change described above, to create a copy of the transaction without cycle signatures.
In CycleTransactionListCommand, the transaction signatures are now registered separately from the transaction. This is necessary because, as the comment explains, the registerTransaction() method does not register signatures attached to a transaction that is already known to the system. While the behavior of the registerTransaction() method is reasonable for operation of a verifier, it is not the desired behavior for the client, which will likely want to see signatures that have been received by a verifier since the last time the transaction was queried.
The Hr class has been modified to omit the closing tag. This class is only used, currently, by the documentation server.


Hello! All the fiels are neccesary.
Be sure to make the title short and descriptive.
In the second field you have to describe the problem briefly.
To add a code to your data please use {;} and add your code.
Then add the related tags of your question so that the questions are sorted and answered by experts.
Finally! Press the submit button and wait for the answers.


This dataset was created and used in the student research project Vision-based Page Rank Estimation by Timo Denk and Samed Güner. It contains screenshots and meta information of some of the top 100k most popular domains on the web. It was used to train a combination of CNN and graph network Battaglia et al. (2018) to determine the correlation between page rank and appearance of a web page.
The dataset contain 83,165 samples. Each sample is a screenshot of a given web page as a visitor would see it with a common web browser on a desktop machine. The dataset has the following folder structure (the rank is the image name):
\<domain-rank>.jpg
⋮
The dataset contains 83,165 samples. Each sample has a rank and represents an enriched directed graph of the link structure of a given domain. While nodes are web pages, edges represent hyperlinks between them. Each web page comes with mobile and desktop screenshot.
A sample is a directed graph with up to eight nodes. It is encoded in JSON format, where nodes contain the following information:
id
: identifier of the node, corresponds to the image numberbaseUrl
: base URL of the websiteclient_status
: client error status (e.g unverified SSL connection)server_status
: server status code (e.g 404 Not Found)startNode
: given node is the entry point to the domainloading_time
loading time in millisecondssize
: size of the web page in kBtitle
: web page titleurls
: list of hyperlinks to other web pages on the same domainThe dataset has the following folder structure:
\<domain-rank>\<domain-rank>.json
\img\<image-number>.jpeg
\img\ …
⋮
⋮
The websites and their ranks are taken from the top 100,000 websites of Open PageRank, which relies on the number of backlinks to rank websites.
Please cite our work on Vision-based Page Rank Estimation if you intend to use this dataset.


| Hackers (1995) | |
| 2000 AD (2000) | |
| aka "Hackers" | |
| Hackers (2001) (TV Episode) | |
| - Frontline (1983) (TV Series) | |
| Hacker's Game (2015) | |
| aka "The Hackers" |
| Amy Acker (Actress, Angel (1999)) | |
| Drew Van Acker (Actor, Pretty Little Liars (2010)) | |
| Buddy Hackett (Actor, The Little Mermaid (1989)) | |
| Emily Wickersham (Actress, I Am Number Four (2011)) |
hacker (180 titles)
| Douglas, Gorman, Rothacker & Wilhelm (DGRW) |
| Crackerjack Management |
| Bleeker Street Media |
| Bleeker Street Entertainment |


Owner:
{{ owner }}
Birthdate:
{{ owner.birthdate }}
Passport:
{{ owner.passport_num }}
Nationality:
{{ owner.nationality }}
Address:
{{ owner.address }}


For example, imagine you borrow $100 at 5% interest for 10 years. With simple interest, you would add 5% of $100 - $5 - each year for 10 years, for a total of $50 worth of interest. You would end up owing $150 after 10 years. If you were paying 5% interest compounded annually, though, you would take 5% of the amount each year - including any interest that has already accumulated.
in-context


Gene Set Enrichment Analysis
Author: Aravind Subramanian, Pablo Tamayo, David Eby, Broad Institute
Contact:
Contact the GenePattern team for GenePattern issues.
See the GSEA forum for GSEA questions.
GSEA Version: 3.0
Evaluates a genomewide expression profile and determines whether a priori defined sets of genes show statistically significant, cumulative changes in gene expression that are correlated with a phenotype. The phenotype may be categorical (e.g., tumor vs. normal) or continuous (e.g., a numerical profile across all samples in the expression dataset).
Gene Set Enrichment Analysis (GSEA) is a powerful analytical method for interpreting gene expression data. It evaluates cumulative changes in the expression of groups of multiple genes defined based on prior biological knowledge. It first ranks all genes in a data set, then calculates an enrichment score for each gene set, which reflects how often members of that gene set occur at the top or bottom of the ranked data set (for example, in expression data, in either the most highly expressed genes or the most underexpressed genes).
Microarray experiments profile the expression of tens of thousands of genes over a number of samples that can vary from as few as two to several hundreds. One common approach to analyzing these data is to identify a limited number of the most interesting genes for closer analysis. This usually means identifying genes with the largest changes in their expression values based on a t-test or similar statistic, and then picking a significance cutoff that will trim the list of interesting genes down to a handful of genes for further research.
Gene Set Enrichment Analysis (GSEA) takes an alternative approach to analyzing genomic data: it focuses on cumulative changes in the expression of multiple genes as a group, which shifts the focus from individual genes to groups of genes. By looking at several genes at once, GSEA can identify pathways whose several genes each change a small amount, but in a coordinated way. This approach helps reflect many of the complexities of co-regulation and modular expression.
GSEA therefore takes as input two distinct types of data for its analysis:
The GSEA GenePattern module uses either categorical or continuous phenotype data for its analysis. In the case of a categorical phenotype, a dataset would contain two different classes of samples, such as "tumor" and "normal." In the case of a continuous phenotype, a dataset would contain a numerical value for each sample. Examples of numerical profiles include the expression level of a specific gene or a measure of cell viability over the course of a time series experiment. The GSEA desktop application, available on the GSEA website, has additional functionalities. For instance, the GSEA desktop application can conduct an enrichment analysis against a ranked list of genes, or analyze the leading-edge subsets within each gene set. Many of these capabilities are also available in separate GP modules (see GSEAPreranked and GSEALeadingEdgeViewer).
GSEA first ranks the genes based on a measure of each gene's differential expression with respect to the two phenotypes (for example, tumor versus normal) or correlation with a continuous phenotype. Then the entire ranked list is used to assess how the genes of each gene set are distributed across the ranked list. To do this, GSEA walks down the ranked list of genes, increasing a running-sum statistic when a gene belongs to the set and decreasing it when the gene does not. A simplified example is shown in the following figure.
The enrichment score (ES) is the maximum deviation from zero encountered during that walk. The ES reflects the degree to which the genes in a gene set are overrepresented at the top or bottom of the entire ranked list of genes. A set that is not enriched will have its genes spread more or less uniformly through the ranked list. An enriched set, on the other hand, will have a larger portion of its genes at one or the other end of the ranked list. The extent of enrichment is captured mathematically as the ES statistic.
Next, GSEA estimates the statistical significance of the ES by a permutation test. To do this, GSEA creates a version of the data set with phenotype labels randomly scrambled, produces the corresponding ranked list, and recomputes the ES of the gene set for this permuted data set. GSEA repeats this many times (1000 is the default) and produces an empirical null distribution of ES scores. Alternatively, permutations may be generated by creating “random” gene sets (genes randomly selected from those in the expression dataset) of equal size to the gene set under analysis.
The nominal p-value estimates the statistical significance of a single gene set's enrichment score, based on the permutation-generated null distribution. The nominal p-value is the probability under the null distribution of obtaining an ES value that is as strong or stronger than that observed for your experiment under the permutation-generated null distribution.
Typically, GSEA is run with a large number of gene sets. For example, the MSigDB collection and subcollections each contain hundreds to thousands of gene sets. This has implications when comparing enrichment results for the many sets:
The ES must be adjusted to account for differences in the gene set sizes and in correlations between gene sets and the expression data set. The resulting normalized enrichment scores (NES) allow you to compare the analysis results across gene sets.
The nominal p-values need to be corrected to adjust for multiple hypothesis testing. For a large number of sets (rule of thumb: more than 30), we recommend paying attention to the False Discovery Rate (FDR) q-values: consider a set significantly enriched if its NES has an FDR q-value below 0.25.
For more information, see http://www.gsea-msigdb.org/gsea.
Input expression datasets with the character '-' or spaces in their file names causes GSEA to error.
The GSEA GenePattern module interprets the sample labels in categorical CLS files by their order of appearance, rather than via their numerical value, unlike some other GenePattern modules. For example, in the CLS file below:
13 2 1
# resistant sensitive
1 1 1 1 1 1 1 1 0 0 0 0 0
Most other GenePattern modules would interpret the first 8 samples to be sensitive and the remaining 5 to be resistant. However, GSEA assigns resistant to the first 8 samples and sensitive to the rest. This is because GSEA assigns the first name in the second line to the first symbol found on the third line.
If the sample labels are in numerical order, as below, no difference in behavior will be noted.
13 2 1
# resistant sensitive
0 0 0 0 0 1 1 1 1 1 1 1 1
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. PNAS. 2005;102(43);15545-15550. (Link)
Mootha VK, Lindgren CM, Eriksson K-F, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E, Houstis N, Daly MJ, Patterson N, Mesivor JP, Golub TR, Tamayo P, Spiegelman B, Lander ES, Hirschhorn JN, Altshuler D, Groop LC. PGC-1-α responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34:267-273. (link)
GSEA User Guide: http://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html
GSEA website: http://www.gsea-msigdb.org/
This version of the module is based on the new open-source GSEA v3.0 code base. See the Release Notes for new features and other notable changes, and this link for some notes on new feature usage. The algorithm is identical to the older v2.x code.
| Name | Description |
|---|---|
| expression dataset * | This is a file in either GCT or RES format that contains the expression dataset. |
| gene sets database * |
This parameter's drop-down allows you to select gene sets from the Molecular Signatures Database (MSigDB) on the GSEA website. This drop-down provides access to only the most current version of MSigDB. You can also upload your own gene set file(s) in GMT, GMX, or GRP format. If you want to use files from an earlier version of MSigDB you will need to download them from the archived releases on the website. |
| number of permutations * | Specifies the number of permutations to perform in assessing the statistical significance of the enrichment score. It is best to start with a small number, such as 10, in order to check that your analysis will complete successfully (e.g., ensuring you have gene sets that satisfy the minimum and maximum size requirements and that the collapsing genes to symbols works correctly). After the analysis completes successfully, run it again with a full set of permutations. The recommended number of permutations is 1000. Default: 1000 |
| phenotype labels * |
A phenotype label file defines categorical or continuous-valued phenotypes and for each sample in your expression dataset assigns a label or numerical value for the phenotype. This is a tab-delimited text file in CLS format. A categorical phenotype CLS file should contain only two labels, such as tumor and normal. A continuous phenotype CLS file may define one or more continuous-valued phenotypes. Each phenotype definition includes a profile, assigning a numerical value to each sample in the expression dataset. GSEA interprets CLS files differently than many GenePattern modules. See the Known Issue for more details. |
| target profile | Name of the target phenotype for a continuous phenotype CLS. This parameter must be left blank in the case of a categorical CLS file. |
| collapse dataset * |
Select true to have GSEA collapse each probe set in the expression dataset into a single line of data for the gene, which is identified by its HUGO gene symbol. Be sure that your gene sets and array annotations also use gene symbols as the gene identifier format. Select false to use your expression dataset as is, with its native feature identifiers. When you select this option, the chip annotation file (chip platform parameter) is optional and you must specify a gene set file (gene sets database file parameter) that identify genes using the same feature (gene or probe) identifiers as is used in your expression dataset. Default: true |
| permutation type * |
Type of permutations to perform in assessing the statistical significance of the enrichment score. Options are:
|
| chip platform |
This drop-down allows you to specify the chip annotation file, which lists each probe on a chip and its matching HUGO gene symbol, used for the expression array. This parameter is required if collapse dataset is set to true. The chip files listed here are from the GSEA website: http://www.gsea-msigdb.org/gsea/downloads.jsp. If you used a file not listed here, you will need to provide it (in CHIP format) using 'Upload your own file'. Please do not use the GENE_SYMBOL.chip or SEQ_ACCESSION.chip files. These are meant only for internal use by the GSEA program. |
| scoring scheme * |
The enrichment statistic. This parameter affects the running-sum statistic used for the enrichment analysis, controlling the value of p used in the enrichment score calculation. Options are:
|
| metric for ranking genes * |
GSEA ranks the genes in the expression dataset and then analyzes that ranked list of genes. Use this parameter to select the metric used to score and rank the genes. The default metric for ranking genes is the signal-to-noise ratio. To use this metric, your expression dataset must contain at least three (3) samples for each phenotype. For descriptions of the ranking metrics, see Metrics for Ranking Genes in the GSEA User Guide. |
| gene list sorting mode * | Specifies whether to sort the genes using the real (default) or absolute value of the gene-ranking metric score. |
| gene list ordering mode * | Specifies the direction in which the gene list should be ordered (ascending or descending). |
| max gene set size * | After filtering from the gene sets any gene not in the expression dataset, gene sets larger than this are excluded from the analysis. Default: 500 |
| min gene set size * | After filtering from the gene sets any gene not in the expression dataset, gene sets smaller than this are excluded from the analysis. Default: 15 |
| collapsing mode for probe sets with more than one match * |
Collapsing mode for sets of multiple probes for a single gene. Used only when the collapse dataset parameter is set to true. Select the expression values to use for the single probe that will represent all probe sets for the gene. Options are:
|
| normalization mode * |
Method used to normalize the enrichment scores across analyzed gene sets. Options are:
|
| randomization mode * |
Method used to randomly assign phenotype labels to samples for phenotype permutations. ONLY used for phenotype permutations. Options are:
|
| omit features with no symbol match * | Used only when collapse dataset is set to true. By default (true), the new dataset excludes probes/genes that have no gene symbols. Set to false to have the new dataset contain all probes/genes that were in the original dataset. |
| make detailed gene set report * | Create detailed gene set report (heat map, mountain plot, etc.) for each enriched gene set. Default: true |
| median for class metrics * | Specifies whether to use the median of each class, instead of the mean, in the metric for ranking genes. Default: false |
| number of markers * | Number of features (gene or probes) to include in the butterfly plot in the Gene Markers section of the gene set enrichment report. Default: 100 |
| plot graphs for the top sets of each phenotype * | Generates summary plots and detailed analysis results for the top x genes in each phenotype, where x is 20 by default. The top genes are those with the largest normalized enrichment scores. Default: 20 |
| random seed * | Seed used to generate a random number for phenotype and gene_set permutations. Timestamp is the default. Using a specific integer valued seed generates consistent results, which is useful when testing software. |
| save random ranked lists * | Specifies whether to save the random ranked lists of genes created by phenotype permutations. When you save random ranked lists, for each permutation, GSEA saves the rank metric score for each gene (the score used to position the gene in the ranked list). Saving random ranked lists is very memory intensive; therefore, this parameter is set to false by default. |
| output file name * | Name of the output file. The name cannot include spaces. Default: <expression.dataset_basename>.zip |
| create svgs * | Whether to create SVG images (compressed) along with PNGs. Saving PNGs requires a lot of storage; therefore, this parameter is set to false by default. |
| selected gene sets | Semicolon-separated list of gene sets from the provided gene sets database files (GMT/GMX/GRP). If you are using multiple files then you must prefix each selected gene set with its file name followed by '#' (like "my_file1.gmt#selected_gene_set1,my_file2.gmt#selected_gene_set2"). With a single file only the names are necessary. Leave this blank to select all gene sets. |
| alt delim | Optional alternate delimiter character for gene set names instead of comma for use with selected.gene.sets. If used, a semicolon is recommended. |
| create gcts * | Whether to save the dataset subsets backing the GSEA report heatmaps as GCT files; these will be subsets of your original dataset corresponding only to the genes of the heatmap. |
| create zip * | Create a ZIP bundle of the output files. This is true by default, matching the former behavior where a ZIP bundle was always created. |
* - required
1. expression dataset: GCT or RES file
This file contains the expression dataset.
2. gene sets database: GMT, GMX, or GRP file.
Gene set files, either your own or from the listed MSigDB files.
3. phenotype labels: CLS file
The GSEA module supports two kinds of class (CLS) files: categorical phenotype and continuous phenotype.
A categorical phenotype CLS file must define a single phenotype having two categorical labels, such as tumor and normal.
A continuous phenotype CLS may define multiple phenotypes. Each phenotype definition assigns a numerical value for each sample. This series of values defines the phenotype profile. For example,
4. chip platform: an optional CHIP file may be provided if you do not select a chip platform from the drop-down
1. Optional Enrichment Report archive: ZIP
ZIP file containing the result files. For more information on interpreting these results, see Interpreting GSEA Results in the GSEA User Guide. Note that in prior versions the ZIP bundle was created as the only output file. This behavior has been changed to give direct access to the results without the need for a download. The default is to create the ZIP bundle, matching the former behavior, but the report files will always be created directly.
2. Enrichment Report: HTML and PNG images
The GSEA Enrichment Report. As above, see the GSEA User Guide for more info.
3. Optional SVG images (compressed)
Identical to the PNGs in the Enrichment Report, but in SVG format for higher resolution. These are GZ compressed to reduce space usage; they can be decompressed using 'gunzip' on Mac or Linux and 7-Zip on Windows
3. Optional GCTs
The datasets backing all the heatmap images from the Enrichment Report for use in external visualizers or analysis tools. These will have the same name as the corresponding image but instead with a GCT extension.
Task Type:
Gene List Selection
CPU Type:
any
Operating System:
any
Language:
Java
| Version | Release Date | Description |
|---|---|---|
| 19.0.26 | 2019-10-10 | Updated to use the GSEA v3.0 open-source code base. Updated to give access to MSigDB v6.2. Unified the Gene Set DB selector parameters and better downloading of MSigDB files. Added selected.gene.sets, alt.delim, creat.gcts and create.svgs parameters. Better temp file clean-up and other internal code improvements. |
| 18 | 2017-05-18 | Updated to give access to MSigDB v6.0 |
| 17 | 2016-02-04 | Updated to give access to MSigDB v5.1 |
| 16 | 2015-12-03 | Updating the GSEA jar to deal with an issue with FTP access. Fixes an issue for GP@IU. |
| 15 | 2015-06-16 | Add built-in support for MSigDB v5.0, which includes new hallmark gene sets. |
| 14 | 2013-06-14 | Update the gene sets database list and the GSEA Java library, added support for continuous phenotypes.. |
| 13 | 2012-09-20 | Updated and sorted the chip platforms list, changed default value of num permutations to 1000, and updated the GSEA java library |
| 12 | 2011-04-08 | Fixed parsing of gene sets database file names which contain @ and # symbols and added gene sets containing entrez ids |
| 11 | 2010-11-05 | Fixed parsing of chip platform file names which contain @ and # symbols |
| 10 | 2010-10-01 | Updated selections for the gene sets database parameter to reflect those available in MSigDB version 3 |


We are currently pursuing our Electrical Engineering sophomore at IIT
Madras. We started working on this project to enance our skills set in
the domain of programming and web development.
We are also working on various other projects in the domain of
Artificial Intelligence, Embedded systems and Web development, if you
wish to collabrate with us on any of the project feel free to drop us a
mail or ping us on linkedin.
Instructions
1. Please make the number of nodes on your own and, enter the
number of nodes excluding the reference node. (External Tip: Make
the node that has more number of elements connected to it as the
reference node)
2. Also count all the elements in the given circuit and enter it.
3. Now choose the elements that are present in the circuit from
the dropdown menu.
4. Once this is done, start entering the requested values for each
element abiding to the passive sign convention, a picture as a
helping hand is shown for each of the element. Don't make any
mistake in this, then the resuls might not be define or it can be
wrong.
5. After ensuring all the entered values are correct, hit the
submit button and you are done the answer now pops up.


{% extends "base.html" %} {% block title %} {% if spoiled %}{{ puzzle.spoilery_title }}{% else %}{{puzzle.spoiler_free_title}}{%endif%} / Postprodding {% endblock %} {% block main %}
You should make a .zip
file that contains the following files:
index.html
, the main file in which your puzzle content resides. Don't include a <head>
section or anything, just the puzzle content itself.crossword_grid.png
solution/index.html
, the main file in which your solution content resides. Don't include a <head>
section or anything, just the puzzle content itself. Feel free to include author's notes at the bottom of this.solution
directory, e.g. solution/filled_in_grid.png
posthunt/index.html
and any supporting files, if your puzzle will need to look different after the hunt is over (e.g. it has an interactive or server-based component).More detailled information is available in the postprodding guide.
You are not spoiled on this puzzle. I'm not sure you want to be postprodding it.
If you really want to, you can click back to it and spoil yourself.
{% endif %} {% endblock %}


About Spinal Tap
Looking for: Drummer. The band Spinal Tap first appeared in a video aired as part of a 1979 sketch comedy special called "The TV Show", a project spearheaded by Rob Reiner and Micheal McKean.
Meet the Band
In 2006, Nigel Tufnel appeared in a Volkswagen TV commercial highlighting their offer of a free, exclusive First Act guitar with the purchase of qualifying automobiles. The guitar features knobs and inlays with the Volkswagen logo and pre-amps that allow it to be played through the car's stereo system. Also in 2006, the song "Gimme Some Money" was used in a TV commercial[citation needed] for Open from American Express, "Tonight I'm Gonna Rock You Tonight" appeared in Harmonix's video game Guitar Hero 2 and "Christmas with the Devil" appeared in BBC One promo spots for the network's Christmas program.