In Memory Data Grid Patterns Demos from Devoxx France!
Devoxx France 2017 was a blast!! Emmanuel and I would like to thank all attendees to our in-memory data grids patterns talk. The room was full and we thoroughly enjoyed the experience!
During the talk we presented a couple of small demos that showcased some in-memory data grid use cases. The demos are located here, but I thought it’d be useful to provide some step-by-step here so that you can get them running as quickly as possible.
Before we start with any of the demos, it’s necessary to run some set up steps:
1. Check out git repository:
2. Download Infinispan Server 9.0.0.Final and at the same level as the git repository.
3. Go into the datagrid-patterns directory, start the servers and wait until they’ve started:
cd datagrid-patterns ./run-servers.sh
4. Install Anaconda for Python 3, this is required to run Jupyter notebook for plotting.
5. Install Maven 3.
Once the set up is complete, it’s time to start with the individual demos.
Both demos shown below work with the same application domain: rail transport systems. In this domain, we differentiate between physical stations, trains, station boards which are located in stations, and finally stops, which are individual entries in station boards.
Analytics Demo
The first demo is focused on how you can use Infinispan for doing offline analytics. In particular, this demo tries to answer the following question:
Q. What is the time of the day when there is the biggest ratio of delayed trains?
To answer this question, Infinispan data grid will be loaded with 3 weeks worth of data from station boards. Once the data is loaded, we will execute a remote server task which will use Infinispan Distributed Java Streams to calculate the two pieces of information required to answer the question: per hour, how many trains are going through the system, and out of those, how many are delayed.
An important aspect to bear in mind about this server tasks is that it will only be executed in one of the nodes in the cluster. It does not matter which one. In turn, this node will will ship the lambdas required to do the computation to each of the nodes so that they can executed against their local data. The other nodes will reply with the results and the node where the server task was invoked will aggregate the results.
Then, these results are sent back to the client, which in turn, stores the results as JSON in an intermediate cache. Once the results are in place, we will use a Jupyter notebook to read those results and plot the result.
Let’s see these steps in action:
1. First, we need to install the server tasks in the running servers above:
cd datagrid-patterns/analytics
mvn clean install package -am -pl analytics-server
mvn wildfly:deploy -pl analytics-server
2. Open the datagrid-pattern repo with your favourite IDE and run delays.java.stream.InjectApp class located in analytics/analytics-server project. This command will inject the data into the cache. On my environment, it takes between 1 and 2 minutes.
3. With the data loaded, we need to run the remote task that will calculate the total number of trains per hour and how many of those are delayed. To do that, execute delays.java.stream.AnalyticsApp class located in analytics/analytics-server project from your IDE.
4. You can verify that the results have been calculating by going to the following address:
5. With the results in place, it’s time to start the Jupyter notebook:
cd datagrid-patterns/analytics/analytics-jupyter
~/anaconda/bin/jupyter notebook
6. Once the notebook opens, click open live-demo.ipynb notebook and execute each of the cells in order. You should end up seeing a plot like this:
So, the answer to the question:
Q. What is the time of the day when there is the biggest ratio of delayed trains?
is 2am! That’s because last connecting trains of the day wait for each other to avoid leaving passengers stranded.
Real Time Demo
The second demo that we presented uses the same application domain as above, but this time we’re trying to use our data grid as a way of storing the station board state of each station at a given point in time. So, the idea is to use Infinispan as an in memory data grids for working with real time data.
So, what can we do with this type of data? In our demo, we will create a centralised dashboard of delayed trains around the country. To do that, we will take advantage of Infinispan’s Continuous Query functionality which allows us to find those station boards which contain stops that are delayed, and as new delayed trains appeared these will be pushed to our dashboard.
To run this demo, keep the same servers running as above and do the following:
-
Run delays.query.continuous.FxApp application located in real-time project inside the datagrid-patterns demo. This app will inject some live station board data and will launch a JavaFX dashboard that shows delayed trains as they appear. It should look something like this:
Conclusion
This has been a summary of the demos that we run in our talk at Devoxx France with the intention of getting you running these demos as quickly as possible. The repository contains more detailed information of these demos. If there’s anything unclear or any of the instructions above are not working, please let us know!
Thanks to Emmanuel Bernard for partnering with me for this Devoxx France talk and for the continuous feedback while developing the demos. Thanks as well to Tristan Tarrant for the input in the demos and many thanks to all Devoxx France attendees who attended our talk :)
A very special thanks to Alexandre Masselot whose "Swiss Transport in Real Time: Tribulations in the Big Data Stack" talk at Soft-Shake 2016 was the inspiration for these demos. @Alex, thanks a lot for sharing the demos and data with me and the rest of the community!!
In a just a few weeks I’ll be at Great Indian Developer Summit presenting these demos and much more! Stay tuned :)
Cheers,
Galder


The submit
defines a submit button which
submits all form values to a form-handler and is typically a server page with a script for
processing the input data and is specified in
the form's action attribute.
Example:
Define a submit button:
<form action="/action_page.php">
First name:
<input type="text" id="fname" name="fname">

Last name:
<input type="text" id="lname" name="lname">

<input type="submit" value="Submit">
</form>
If you click the "Submit" button, the form-data will be sent to a page called "/action_page.php".


Amistad is a masterpiece that will shake your foundations. Since he's Jewish, it was easy to understand how Steven Spielberg could have found the emotional power he displayed in Schindler's List. But he's definitely not black. Amistad shows that the depth of his talent and perception knows no bounds. It does everything for the black experience that Schindler's List did for the Jews, and with just as much passion. He understands the power of pain that crosses all boundaries. It's a film about integrity and the inescapable power and attraction of freedom. It's classic cinema that takes risks, full of the type of graphic realism Spielberg has always avoided. For the first time, he doesn't blink at violence, but forces you to confront your darkest fears about man's inhumanity.
It's an epic about the slave trade, and I admit I expected the film to start in Africa with the rounding up of the slaves, but it starts on the ship.  For ten minutes tears poured down my face while watching the graphic truth of the slave ships. It's so powerful it's almost unbearable. I can't believe people were ever treated like this. Schindler's List was just practice for Amistad. With this film, Spielberg becomes not only America's premiere filmmaker but our premiere educator. I can't believe I wasn't taught about this in school. This is the incident that made the civil war inevitable. It resonates with compassion, full of brilliant performances, an amazing score, and incredible writing. Spielberg is at the absolute peak of his powers. Don't miss it.
MD
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{% include header.html %} RevolutionUC Hackathon at the University of Cincinnati - March 2nd and 3rd, 2019 Interested? Register to participate! Register Now ⌄ {{ content }} {% include footer.html %}
Hackathon at the University of Cincinnati - March 2nd and 3rd, 2019
Interested? Register to participate!


Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 6
For the meatballs, beat the eggs and milk together in a bowl. Add the minced venison, breadcrumbs, parmesan and garlic and mix well.
Mould the meatball mixture into balls, each the size of a golf ball.
Place the meatballs onto a plate or tray and chill in the fridge for 20 minutes.
Heat the oil in a frying pan over a medium heat.
Remove the meatballs from the fridge and place into a hot frying pan. Fry for 8-10 minutes, or until golden-brown all over.
For the ragu, heat the oil in a separate frying pan. Fry the onion, carrot, celery and garlic for 5-6 minutes, or until softened.
Add the tomato purée and cook for a further 7-8 minutes, until the sauce is thick and deep red in colour.
Add the wine, chicken stock and rosemary and bring to boil. Reduce the heat and simmer for 45 minutes to an hour.
Add the meatballs to the sauce and simmer for 5-6 minutes, until the meatballs are completely cooked through.
To serve, place a generous portion of cooked spaghetti into each serving bowl. Spoon over equal amounts of the meatballs and sauce, and finish with grated parmesan.


Returns true if an object was created by the
Object
constructor.
Install with npm:
$ npm install --save is-plain-object
Use isobject if you only want to check if the value is an object and not an array or null.
var isPlainObject = require('is-plain-object');
true when created by the Object
constructor.
isPlainObject(Object.create({}));
//=> true
isPlainObject(Object.create(Object.prototype));
//=> true
isPlainObject({foo: 'bar'});
//=> true
isPlainObject({});
//=> true
false when not created by the Object
constructor.
isPlainObject(1);
//=> false
isPlainObject(['foo', 'bar']);
//=> false
isPlainObject([]);
//=> false
isPlainObject(new Foo);
//=> false
isPlainObject(null);
//=> false
isPlainObject(Object.create(null));
//=> false
Pull requests and stars are always welcome. For bugs and feature requests, please create an issue.
| Commits | Contributor |
|---|---|
| 17 | jonschlinkert |
| 6 | stevenvachon |
| 3 | onokumus |
| 1 | wtgtybhertgeghgtwtg |
(This project's readme.md is generated by verb, please don't edit the readme directly. Any changes to the readme must be made in the .verb.md readme template.)
To generate the readme, run the following command:
$ npm install -g verbose/verb#dev verb-generate-readme && verb
Running and reviewing unit tests is a great way to get familiarized with a library and its API. You can install dependencies and run tests with the following command:
$ npm install && npm test
Jon Schlinkert
Copyright © 2017, Jon Schlinkert. Released under the MIT License.
This file was generated by verb-generate-readme, v0.6.0, on July 11, 2017.


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Welcome to Your Library!
| Creation Center |
| My Library |
| My Creations |
| Explore Books |
Background Themes:
Books Galore
Space Cats
Inside The Matrix
Ancient History
Sailors Wake
Sci-Fi Fantasy
Reading is dreaming with Open eyes.
Library starts here.


Full stack web developer and recent Northwestern University School of Communications graduate with extensive experience with coding and computer science theory. While at Northwestern, I took many computer science classes concerning theory and back-end development skills, specifically in C. Recently graduated from Northwestern University Coding Boot Camp obtaining skills in Javascript, React, and Database work.
During the past year, has been tutoring full-stack software development as well as becoming a software developer at DonationXChange, learning new technologies like VueJS and ASP.NET . Extensive history in communication and team-leading: held multiple leadership positions throughout high school and at Northwestern, specifically in the realm of communication. My history of theater and communication enables purposeful explanation and clear writing of goals and ideas.
Over the past few months I have been personally building a fullstack application which allows users to log in, view , save and bid on items. Each event dynamically styles its items and its event information using a VueJS front-end server, while connecting to a mySQL database using ASP.NET routes. The styling is designed to replace an existing site.
Technologies Used: VueJS, HTML, CSS, Javascript, MySQL, JQuery
Front-End on Heroku (login, favoriting, and event switching not be permitted due to lack of database connection)
My main responsibilities were building the database and back-end routes, as well as connecting the back-end to the user experience by building the results page and connecting scores to users.
Technologies Used: HTML, CSS, Paralax, Express, Passport, MongoDB, NodeJS, React
As for my last project, my responsibilities were mainly to build the database routes for submitting recipes and comments, as well as working to bring the data on the user side.
Technologies Used: HTML, CSS, Express, MySQL, Sequelize. NodeJS
Besides being a developer, I am an aspiring comedian. I write sketches and stand up sets regularly. Right now I am taking long form improv classes at the improvOlympic in Chicago and performing stand up , both of which are passions of mine.
I love hiking, windsurfing, surfing, all of which are hard to do in Chicago. I am a dog lover, video game player, music fan, and Dungeons and Dragons junkie.
For any business inquiries, please email me at: kempatkinson@gmail.com


Birth Information
* or approximate, if known. Any service can be completed if unknown, but exact time and place provide the greatest detail and insight.


DOAG |
|
Package body pit_pkg |
| Program units |
| string_to_table | Helper to convert a string with a given separator into an instance of type ARGS |
| raise_event | Helper to raise an event |
| get_id | Helper to get a new message id |
| get_context_modules | Helper to read in a list of all available output modules for a given context |
| get_context | Helper to read the actual context settings for PIT |
| create_default_context | Helper to create a default context as defined in the parameters for PIT procedure to create the default context from parameter table |
| load_modules | Helper to load all output modules installed extending PIT_MODULE |
| get_available_module_list | Helper to create a |-separated list of available output modules |
| load_adapter | Helper to load and instantiate an adapter to read client information |
| report_module_status | Procedure to report all modules that have not succesfully loaded |
| log_me | Helper function to decide whether a message shall be logged. |
| trace_me | Helper function to decide whether an entry or leave shall be traced. |
| pop_stack | Helper to maintain a call stack |
| initialize | INTERFACE |
| get_active_modules | MODULE MAINTENANCE |
| get_available_modules | |
| set_context | CONTEXT MAINTENANCE |
| reset_active_context | In Default, extend visibility of parameter according to the type of CONTEXT by setting user_name and/or client_id to a predefined CONTEXT_PKG.C_NO_VALUE Available Types: |
| reset_context | |
| log | CORE |
| purge | intantiate message |
| enter | |
| leave | Module des aktuellen Kontextes lesen |
| finalize entry in call stack and pass to output modules | |
| raise_error | |
| get_message |
| Types |
| module_list_type | list of modules |
| context_rec | debug and trace settings, stored within a context |
| call_stack_tab | call stack |
| Variables |
| g_module_list | all modules that could be instantiated |
| g_available_modules | all modules which are available for logging (meaning: status = msg.MODULE_INSTANTIATED) |
| g_call_stack | |
| g_active_adapter | package vars |
| g_language | |
| g_user_name | |
| g_client_id | |
| g_owner | |
| g_name | |
| g_lineno | |
| g_caller_t | |
| g_ctx |
| Constants |
| c_param_group | parameter constants |
| c_base_module | |
| c_focus_active | log level constants |
| c_focus_default | |
| c_log_level | |
| c_trace_level | |
| c_trace_timing | |
| c_modules | |
| c_log_modules | |
| c_adapter_preference | |
| c_global_context | context constants |
| c_context_type | |
| c_context_group | |
| c_env | c_trace_session constant varchar2(20) := 'TRACE_SESSION'; |
| c_client_id | |
| c_true | generic constants |
| c_false | |
| c_event_focus_all | "events" |
| c_event_focus_active | |
| c_context_event | |
| c_log_event | |
| c_purge_event | |
| c_print_event | |
| c_enter_event | |
| c_leave_event |
module_list_type
type module_list_type is table of pit_module index by varchar2(30 char);
list of modules
context_rec
type context_rec is record ( log_level pls_integer, trace_level pls_integer, trace_timing char(1), active_log_modules module_list_type, context_type varchar2(30));
debug and trace settings, stored within a context
g_module_list
g_module_list module_list_type;
all modules that could be instantiated
g_available_modules
g_available_modules module_list_type;
all modules which are available for logging (meaning: status = msg.MODULE_INSTANTIATED)
call_stack_tab
type call_stack_tab is table of call_stack_type index by binary_integer;
call stack
g_call_stack
g_call_stack call_stack_tab;
c_param_group
c_param_group constant varchar2(20) := 'PIT';
parameter constants
c_base_module
c_base_module constant varchar2(20) := 'PIT_MODULE';
c_focus_active
c_focus_active constant varchar2(15) := 'ACTIVE';
log level constants
c_focus_default
c_focus_default constant varchar2(10) := 'DEFAULT';
c_log_level
c_log_level constant varchar2(15) := '_LOG_LEVEL';
c_trace_level
c_trace_level constant varchar2(15) := '_TRACE_LEVEL';
c_trace_timing
c_trace_timing constant varchar2(20) := '_TRACE_TIMING';
c_modules
c_modules constant varchar2(15) := '_MODULES';
c_log_modules
c_log_modules constant varchar2(20) := '_LOG_MODULES';
c_adapter_preference
c_adapter_preference constant varchar2(20) := 'ADAPTER_PREFERENCE';
c_global_context
c_global_context constant varchar2(20) := 'PIT_CTX';
context constants
c_context_type
c_context_type constant varchar2(30) := c_global_context || '_TYPE';
c_context_group
c_context_group constant varchar2(30) := 'CONTEXT';
c_env
c_env constant varchar2(10) := 'USERENV';
c_trace_session constant varchar2(20) := 'TRACE_SESSION';
c_client_id
c_client_id constant varchar2(20) := 'CLIENT_IDENTIFIER';
c_true
c_true constant char(1) := 'Y';
generic constants
c_false
c_false constant char(1) := 'N';
c_event_focus_all
c_event_focus_all constant varchar2(10) := 'ALL';
"events"
c_event_focus_active
c_event_focus_active constant varchar2(10) := 'ACTIVE';
c_context_event
c_context_event constant integer := 1;
c_log_event
c_log_event constant integer := 2;
c_purge_event
c_purge_event constant integer := 3;
c_print_event
c_print_event constant integer := 4;
c_enter_event
c_enter_event constant integer := 5;
c_leave_event
c_leave_event constant integer := 6;
g_active_adapter
g_active_adapter default_adapter;
package vars
g_language
g_language varchar2(30);
g_user_name
g_user_name varchar2(30);
g_client_id
g_client_id varchar2(64);
g_owner
g_owner varchar2(30);
g_name
g_name varchar2(30);
g_lineno
g_lineno number;
g_caller_t
g_caller_t varchar2(30);
g_ctx
g_ctx context_rec;
string_to_table
Function string_to_table(p_string_value in varchar2, p_delimiter in varchar2 := ':') return args
Helper to convert a string with a given separator into an instance of type ARGS
| Usage |
|
internal helper to split a string into an array of chars. |
| Parameters |
| p_string_value |
List of items to be split | |
| p_delimiter |
character that separates the items in p_string_value
|
| Returns |
Array of items (up to 50) of varchar2(50)
|
raise_event
Procedure raise_event(p_event in integer, p_event_focus in varchar2, p_call_stack in call_stack_type default null, p_date_before in date default null, p_message message_type default null)
Helper to raise an event
| Usage |
|
Called internally as a generic helper to throw messages to output modules |
| Parameters |
| p_event |
Integer indicating the type of "event" (i.e. LOG|PRINT|ENTER etc.) thrown by PIT | |
| p_event_focus |
Flag to indicate whether the message shall be broadcasted to all available modules or only to active ones. | |
| p_call_stack |
Instance of the actual call stack in the event of ENTER|LEAVE | |
| p_date_before |
Date indicating the point in time, up to when the log shall be purged | |
| p_message |
Instance of the message to raise |
get_id
Function get_id return number
Helper to get a new message id
| Usage |
|
Every message or call stack instance are provided with a unique id which is sorted even across RAC environments to allow for a solid ordering of the messages raised. |
| Returns |
|
New message ID |
get_context_modules
Function get_context_modules(p_requested_modules in varchar2) return module_list_type
Helper to read in a list of all available output modules for a given context
| Usage |
|
This function is used to fill a list of available modules as requested by the actual context. These modules will then be used to log to. |
| Parameters |
| p_requested_modules |
Colon-separated list of requested output modules |
| Returns |
|
Collection of available and requested module instances |
get_context
Procedure get_context
Helper to read the actual context settings for PIT
| Usage |
|
This function is called to read the actual context from the globally accessed context. |
| Returns |
|
Active context, if available, default context else. |
create_default_context
Procedure create_default_context
Helper to create a default context as defined in the parameters for PIT procedure to create the default context from parameter table
| Usage |
|
Is called during initialization of the package to set up the default logging context as requested by the parameters. |
load_modules
Procedure load_modules
Helper to load all output modules installed extending PIT_MODULE
| Usage |
|
procedure loads and instantiates all output modules which are create under PIT_MODULE and at the same time listed in parameter DEFAULT_LOG_MODULES. After instantiation, all modules are made available in PL/SQL table MODULES |
get_available_module_list
Function get_available_module_list return varchar2
Helper to create a |-separated list of available output modules
| Usage |
|
Is called to prepare a pipe separated list of available modules to store it in the context |
| Returns |
|
String of pipe-separated list of available output modules |
load_adapter
Procedure load_adapter
Helper to load and instantiate an adapter to read client information
| Usage |
procedure loads and instantiates adapters which are created under DEFAULT_ADAPTER plus DEFAULT_ADAPTER itself.Which modules are instantiated depends on parameter ADAPTER_PREFERENCE LOAD_ADAPTER tries to instantiate the adpaters in the order specified in the parameter and stops upon first successful instantiation.The instantiated module provides information about the actual environment. |
report_module_status
Procedure report_module_status
Procedure to report all modules that have not succesfully loaded
| Usage |
|
This procedure is called after initialization of the output modules. It will emit warnings for any module that has not loaded succesfully. |
log_me
Function log_me(p_level in integer) return boolean
Helper function to decide whether a message shall be logged.
| Usage |
|
This function is called during logging to decide whether the actual settings allow for logging. The decision is based on the requested log level as well as upon settings in the global context |
| Parameters |
| p_level |
log_level for which a decision is requested |
| Returns |
|
flag that indicates whether settings allow for logging. |
trace_me
Function trace_me(p_level in integer) return boolean
Helper function to decide whether an entry or leave shall be traced.
| Usage |
|
This function is called during tracing to decide whether the actual settings allow for tracing. The decision is based on the requested trace level as well as upon settings in the global context |
| Parameters |
| p_level |
trace_level for which a decision is requested |
| Returns |
|
flag that indicates whether settings allow for tracing. |
pop_stack
Function pop_stack(p_call_stack in call_stack_type) return call_stack_type
Helper to maintain a call stack
| Usage |
|
Called internally to maintain a call stack of the calle blocks. |
| Parameters |
| p_call_stack |
instance of call_stack_type to pop to the stack |
| Returns |
|
Last entry found on the call stack. Used to maintain timing etc. |
initialize
Procedure initialize
INTERFACE
get_active_modules
Function get_active_modules return args
MODULE MAINTENANCE
get_available_modules
Function get_available_modules return args
set_context
Procedure set_context(p_log_level in integer, p_trace_level in integer, p_trace_timing in boolean, p_module_list in varchar2, p_focus in varchar2 default 'ACTIVE')
CONTEXT MAINTENANCE
reset_active_context
Procedure reset_active_context
In Default, extend visibility of parameter according to the type of CONTEXT by setting user_name and/or client_id to a predefined CONTEXT_PKG.C_NO_VALUE Available Types:
GLOBAL | FORCE_USER | ORCE_CLIENT_ID | FORCE_USER_CLIENT_ID |
PREFER_CLIENT_ID | PREFER_USER_CLIENT_ID | SESSION
reset_context
Procedure reset_context
log
Procedure log(p_level in integer, p_message_name in varchar2, p_arg_list in msg_args)
CORE
purge
Procedure purge(p_date_before in date)
intantiate message
enter
Procedure enter(p_action in varchar2, p_module in varchar2, p_params in msg_params, p_trace_level in number)
leave
Procedure leave(p_trace_level in number)
Module des aktuellen Kontextes lesen
Procedure print(p_message_name in varchar2, p_arg_list msg_args default null)
finalize entry in call stack and pass to output modules
raise_error
Procedure raise_error(p_message_name varchar2, p_arg_list in msg_args)
get_message
Function get_message(p_message_name in varchar2, p_arg_list msg_args) return message_type


Error: Don't leave fields empty.
Confirm Data
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Git/Github(Git Repository) and Basic Web Concept
Dec 12, 2014
Hello World! This is my first week in learning web development. I am exciting as my dog, Brian, when he heard the word "Cookie"!
Learning new thing could be intimidating experience but I found it facinating. I haven't been a best learner but I can tell you I have always big passion about learning a new stuff. I can say I am a professional learner.
As I learn more and more about web development everyday I would like to share with all of you about my progress. I hope that this will help many new learners and dreamers in web developing world!
Today's topic is Git / Github
First, what is git/github? It is a part of a version control system. Git and github are different but many use as interchangeable term. 'Git' is a program lives in your system(local computer) and help track the changes been made by using a terminal (or whatever command). 'Github' is a sister program lives in the network(internet) and collects every changes being pushed by local computers through 'git'. So they are same sisters but lives in different places. Essentially, GitHub and Git have a same goal - "Version Control". If you are working on a project and you are the only person working on the project at the same place(your computer) than version control doesn't mean anything for you (I will describe what version control is in the next question). When you need some sort of version control, you need some sort of system to track the changes in the project.
It is also worth to note that Github is developed as a side project of Linus. I mean you can imagine how big and how many engineers working for Linus project. They must have tons of people involved. They need some sort of system to control their master copy and changes they proposed. Github or any version control system will make sure that changes are tracked and verified before recognized as a master copy. Github also provide full traceability on every changes being made so anyone (with right permission) can retrive previous versions if things go wild. That's how github helps version control on a project.
So it is pretty clear that why many developers are using a version control system. Well, without a version contorl, developers must rely on very strict rules about how to propose and implement changes and store data. It is also requires to have structured security system to ensure who can access, edit and publish verified project. Lots of work for sure.
How does git control changes?
Simply, they protect an original copy, which is a verified and most up-to-dated version, and then add all the change with description and user id(git log).
- original - this is an original and the most perfect version of the copy
- Forked copy - this is on your page on github repo(repository). Baiscally, this is your own copy on github
- Cloned copy - You can clone the copy on your local machine and work with your favorite text editor.
There are several terminology you can learn. (Type: git --help) The most commonly used git commands are:
- Add - Add file contents to the index
- Branch - List, create, or delete branches
- Checkout - Checkout a branch or paths to the working tree
- Clone - Clone a repository from github into a new directory on your local machine
- Commit - Record changes to the repository
- Fetch - Download objects and refs from another repository. Wildly used for review request to peers
- Init - Create an empty Git repository or reinitialize an existing one
- Log - Show commit logs
- Merge - Join two or more development histories together
- Pull - Fetch from and integrate with another repository or a local branch. Pull is great function to check all repos are up-do-dated
- Push - Update remote refs along with associated objects
- Reset - Reset current HEAD to the specified state
- Status - Show the working tree status
Why use GitHub to store your code?
Github can be used as repository of your project on different server. If something happens on your computer and server, github can be a backup source. Also it allows you retriving any previous versions if things go wild.
13 Dec, 2014
What is Internet?
There are plenty of good places explains about internet. I found this 5 mininutes vidio clip explains the best about How The Internet Works
Basically, internet is a 'wire' that connects other computers. Unless you have a super computer or sort, most of devices are connected through ISP(Internet Servie Providers).
What is Web Browser?
A program(desktop software like Chrome) that allows you browsing a web and enjoy other fantastic stuff
What is DNS?
Domain Name System that works like yellowpage. Apparently there is a issue with DNS delaying that it takes up to 24 hours for new web address becomes available. When a browser request a website, the ISP send a request to DNS to look up the record of the website. Because they want to reduce the web traffic and time to show the website, they generally copy DNS record. That's called "cashing". Mostly, cashing process works great (it makes web search faster!) but when there is a new website was not in that particular copy of DNS, you are scrwed. You can't access it. Generally they are updating the copy within several hours so don't worry, it will eventually let you go sarahkwak.com
What is Domain name and IP address?
Those are same thing but in different language. Domain name is English name for IP address, which looks like <IP_ADDRESS> - good luck with remembering them. So they give something more memorable and make sense name like google.com or youtube.com
Different between website and web app?
Web app: The key word is "Interaction". Web apps are the basement works of website (presentation). It includes all programs makes dynamic interaction between server and database. Website is the final product of web apps. In general, website can be composed by simple HTML, CSS and maybe Javascript.
What is www?
World wide web is a program allows to sharing information, red and interact with other computers through a web browser.


2.1.7
- Replaced the
has-own
module with has
.
2.1.6
- Fixes an issue where the module would discard at-rules that were defined in
@media
& @supports
rules as well as the root. As this is legal to do in
CSS, the module now checks to see if the candidate rule has the same parent
as the cached rule. If it does, the rules are merged.
2.1.5
- Now compiled with babel 6.
2.1.4
- Fixed a range error which happened when duplicated at rules were found
in the stylesheet.
2.1.3
- Fixed an infinite loop regression in the last patch.
2.1.2
- Fixes a bug where sometimes values would be substituted by JS code.
2.1.1
- Updates postcss-value-parser to version 3 (thanks to @TrySound).
2.1.0
- Replaced css-list with postcss-value-parser, reduced AST iterations from 4
to 1 for increased performance.
2.0.0
1.0.2
- Minor boost in performance with reduced stringify passes.
1.0.1
- Fixes an issue where duplicated keyframes with the same name would cause
an infinite loop.
1.0.0


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{% endif %}


Making a long story short
InOut prides itself as India’s leading Hackathon since it's inception in April 2015. Attracting the bunch of passionate developers & designers who are engaged with the latest technologies, InOut is actively contributing to the conversation of what the next decade of technology will look like!


Function vrt_write_if_context¶
Defined in File vrt_write.h
Function Documentation¶
-
VRT_WARN_UNUSED int32_t
vrt_write_if_context
(const struct vrt_if_context *if_context, void *buf, int32_t words_buf, bool validate)¶ Low-level function that writes VRT IF context section.
- Return
Number of written 32-bit words, or a negative number if error.
- Warning
Fields represented as double but with an underlying 64-bit fixed precision format, i.e. bandwidth, if_reference_frequency, rf_reference_frequency, rf_reference_frequency_offset, if_band_offset, and sample_rate, may in rare cases lose precision since double only has 53 bits of precision.
- Parameters
if_context
: IF context to write.buf
: Buffer to write to. This must point to the first context section word in the packet, i.e. directly following the fields section.words_buf
: Size of buf in 32-bit words.validate
: True if validation shall be done. If false, only buffer size is validated.
- Return Value
VRT_ERR_BUFFER_SIZE
: Buffer is too small.VRT_ERR_BOUNDS_BANDWIDTH
: Bandwidth is outside valid bounds (< 0 Hz or > 8.79 THz).VRT_ERR_BOUNDS_IF_REFERENCE_FREQUENCY
: IF reference frequency is outside valid bounds (< ~-8.79 or > ~8.79 THz).VRT_ERR_BOUNDS_RF_REFERENCE_FREQUENCY
: RF reference frequency is outside valid bounds (< ~-8.79 or > ~8.79 THz).VRT_ERR_BOUNDS_RF_REFERENCE_FREQUENCY_OFFSET
: RF reference frequency offset is outside valid bounds (< ~-8.79 or > ~8.79 THz).VRT_ERR_BOUNDS_IF_BAND_OFFSET
: IF band offset is outside valid bounds (< ~-8.79 or > ~8.79 THz).VRT_ERR_BOUNDS_REFERENCE_LEVEL
: Reference level is outside valid bounds (< -256 or > ~256 dBm).VRT_ERR_BOUNDS_GAIN
: Gain stage 1 or 2 outside valid bounds (< -256 or > ~256 dB).VRT_ERR_GAIN_STAGE2_SET
: Gain stage 1 must be used instead of stage 2 when only one of them is set.VRT_ERR_BOUNDS_SAMPLE_RATE
: Sample rate is outside valid bounds (< 0 Hz or > ~8.79 THz).VRT_ERR_BOUNDS_TEMPERATURE
: Temperature is outside valid bounds (< -273.15 or > ~512 degrees C).VRT_ERR_BOUNDS_OUI
: OUI is outside valid bounds (> 0x00FFFFFF).VRT_ERR_INVALID_PACKING_METHOD
: Packing method is an invalid value.VRT_ERR_INVALID_REAL_OR_COMPLEX
: Real/Complex is an invalid value.VRT_ERR_INVALID_DATA_ITEM_FORMAT
: Data item format is an invalid value.VRT_ERR_BOUNDS_EVENT_TAG_SIZE
: Event tag size is outside valid bounds (> 0x07).VRT_ERR_BOUNDS_CHANNEL_TAG_SIZE
: Channel tag size is outside valid bounds (> 0x0F).VRT_ERR_BOUNDS_ITEM_PACKING_FIELD_SIZE
: Item packing fields size is outside valid bounds (> 0x3F).VRT_ERR_BOUNDS_DATA_ITEM_SIZE
: Data item size is outside valid bounds (> 0x3F).VRT_ERR_INVALID_TSI
: TSI is an invalid value.VRT_ERR_INVALID_TSF
: TSF is an invalid value.VRT_ERR_SET_INTEGER_SECOND_TIMESTAMP
: Integer second timestamp is not 0xFFFFFFFF when TSI is VRT_TSI_UNDEFINED.VRT_ERR_SET_FRACTIONAL_SECOND_TIMESTAMP
: Integer second timestamp is not 0xFFFFFFFF when TSI is VRT_TSI_UNDEFINED.VRT_ERR_BOUNDS_REAL_TIME
: TSF is VRT_TSF_REAL_TIME but fractional timestamp is outside valid bounds (><PHONE_NUMBER>99 ps).VRT_ERR_BOUNDS_LATITUDE
: Latitude is outside valid bounds (< -90 or > 90 degrees).VRT_ERR_BOUNDS_LONGITUDE
: Longitude is outside valid bounds (< -180 or > 180 degrees).VRT_ERR_BOUNDS_ALTITUDE
: Altitude is outside valid bounds (< ~-67108 or > ~67108 km).VRT_ERR_BOUNDS_SPEED_OVER_GROUND
: Speed over ground is outside valid bounds (< 0 or > ~65536 m/s).VRT_ERR_BOUNDS_HEADING_ANGLE
: Heading angle outside valid bounds (< 0 or > ~360 degrees).VRT_ERR_BOUNDS_TRACK_ANGLE
: Track angle is outside valid bounds (< 0 or > ~360 degrees).VRT_ERR_BOUNDS_MAGNETIC_VARIATION
: Magnetic variation is outside valid bounds (< -180 or > 180 degrees).VRT_ERR_BOUNDS_POSITION
: Position x, y, or z is outside valid bounds (< ~-67108 or > ~67108 km).VRT_ERR_BOUNDS_ATTITUDE
: Attitude alpha, beta, or gamma is outside valid bounds (< -512 or > ~512 degrees).VRT_ERR_BOUNDS_VELOCITY
: Velocity dx, dy, or dz is outside valid bounds (< -32768 or > ~32768 m/s).VRT_ERR_BOUNDS_SOURCE_LIST_SIZE
: Source list size is outside valid bounds (> 0x01FF).VRT_ERR_BOUNDS_SYSTEM_LIST_SIZE
: System list size is outside valid bounds (> 0x01FF).VRT_ERR_BOUNDS_CHANNEL_LIST_SIZE
: Channel list size is outside valid bounds (> 0x7FFF).


The wallpaper isn't always immediately made available at the beginning of each month. It sometimes takes a few days for the Logos Hope people to upload the new wallpaper. (I'm sure they're busy people!) You can check the website to make sure.
This can happen if the wallpaper file didn't download properly. Since the file is already downloaded, clicking Set Wallpaper will simply set the wallpaper to the already-downloaded file, which won't solve the problem. In this case, you need to click the Clear Cache button below. That will remove the damaged file. Clicking Set Wallpaper will then download the wallpaper again.
Since the name of the file being set as the wallpaper hasn't changed, the system may not bother reading the file again. (It assumes it hasn't changed.) Try setting your wallpaper to something else, then clicking Set Wallpaper again.
Please don't hesitate to get in touch with me at<EMAIL_ADDRESS>I'd be happy to help — maybe you found a bug that I missed!


Neural Networks as Universal Approximators¶
Original Source:
Most materials are from A visual proof that neural nets can compute any function by Michael Nielsen. Few materials are of my own
About the Authors:
Michael Aaron Nielsen (born January 4, 1974) is a quantum physicist, science writer, and computer programming researcher living in San Francisco. In 2004 Nielsen was characterized as Australia’s “youngest academic” and secured a Federation Fellowship at the University of Queensland; the fellowship was for five years. He worked at the Los Alamos National Laboratory, as the Richard Chace Tolman Prize Fellow at Caltech, and a Senior Faculty Member at the Perimeter Institute for Theoretical Physics. Nielsen obtained his PhD in physics in 1998 at the University of New Mexico.
With Isaac Chuang he is the co-author of a popular textbook on quantum computing. As of December 2019, the book was cited more than 36,000 times.
In 2007, Nielsen announced a marked shift in his field of research: from quantum information and computation to “the development of new tools for scientific collaboration and publication”. This work, for which he gave up a tenured academic position, includes “massively collaborative mathematics” projects like the Polymath project with Timothy Gowers. Besides writing books and essays, he has also given talks about Open Science. He was a member of the Working Group on Open Data in Science at the Open Knowledge Foundation.
Introduction¶
One of the most striking facts about neural networks is that they can compute any function at all. That is, suppose someone hands you some complicated, wiggly function, \(f(x)\):
No matter what the function, there is guaranteed to be a neural network so that for every possible input, \(x\), the value \(f(x)\) (or some close approximation) is output from the network, e.g.:
This result holds even if the function has many inputs, \(f=f(x_1,…,x_m)\), and many outputs. For instance, here’s a network computing a function with \(m=3\) inputs and \(n=2\) outputs:
This result tells us that neural networks have a kind of universality. No matter what function we want to compute, we know that there is a neural network which can do the job.
What’s more, this universality theorem holds even if we restrict our networks to have just a single layer intermediate between the input and the output neurons - a so-called single hidden layer. So even very simple network architectures can be extremely powerful.
The universality theorem is well known by people who use neural networks. But why it’s true is not so widely understood. Most of the explanations available are quite technical. For instance, one of the original papers proving the result did so using the Hahn-Banach theorem, the Riesz Representation theorem, and some Fourier analysis. If you’re a mathematician the argument is not difficult to follow, but it’s not so easy for most people. That’s a pity, since the underlying reasons for universality are simple and beautiful.
In this chapter I give a simple and mostly visual explanation of the universality theorem. We’ll go step by step through the underlying ideas. You’ll understand why it’s true that neural networks can compute any function. You’ll understand some of the limitations of the result. And you’ll understand how the result relates to deep neural networks.
Universality theorems are a commonplace in computer science, so much so that we sometimes forget how astonishing they are. But it’s worth reminding ourselves: the ability to compute an arbitrary function is truly remarkable. Almost any process you can imagine can be thought of as function computation. Consider the problem of naming a piece of music based on a short sample of the piece. That can be thought of as computing a function. Or consider the problem of translating a Chinese text into English. Again, that can be thought of as computing a function. Or consider the problem of taking an mp4 movie file and generating a description of the plot of the movie, and a discussion of the quality of the acting. Again, that can be thought of as a kind of function computation. Universality means that, in principle, neural networks can do all these things and many more.
Of course, just because we know a neural network exists that can (say) translate Chinese text into English, that doesn’t mean we have good techniques for constructing or even recognizing such a network. This limitation applies also to traditional universality theorems for models such as Boolean circuits. Neural networks have powerful algorithms for learning functions. That combination of learning algorithms \(+\) universality is an attractive mix. Here, we focus on universality, and what it means.
Two caveats¶
Before explaining why the universality theorem is true, I want to mention two caveats to the informal statement “a neural network can compute any function”.
First, this doesn’t mean that a network can be used to exactly compute any function. Rather, we can get an approximation that is as good as we want. By increasing the number of hidden neurons we can improve the approximation. For instance, earlier I illustrated a network computing some function \(f(x)\) using three hidden neurons. For most functions only a low-quality approximation will be possible using three hidden neurons. By increasing the number of hidden neurons (say, to five) we can typically get a better approximation:
And we can do still better by further increasing the number of hidden neurons.
To make this statement more precise, suppose we’re given a function \(f(x)\) which we’d like to compute to within some desired accuracy \(\epsilon > 0\). The guarantee is that by using enough hidden neurons we can always find a neural network whose output \(g(x)\) satisfies \(|g(x)−f(x)|<\epsilon\), for all inputs \(x\). In other words, the approximation will be good to within the desired accuracy for every possible input.
The second caveat is that the class of functions which can be approximated in the way described are the continuous functions. If a function is discontinuous, i.e., makes sudden, sharp jumps, then it won’t in general be possible to approximate using a neural net. This is not surprising, since our neural networks compute continuous functions of their input. However, even if the function we’d really like to compute is discontinuous, it’s often the case that a continuous approximation is good enough. If that’s so, then we can use a neural network. In practice, this is not usually an important limitation.
Summing up, a more precise statement of the universality theorem is that neural networks with a single hidden layer can be used to approximate any continuous function to any desired precision. In this chapter we’ll actually prove a slightly weaker version of this result, using two hidden layers instead of one. In the problems I’ll briefly outline how the explanation can, with a few tweaks, be adapted to give a proof which uses only a single hidden layer.
Universality with one input and one output¶
To understand why the universality theorem is true, let’s start by understanding how to construct a neural network which approximates a function with just one input and one output:
It turns out that this is the core of the problem of universality. Once we’ve understood this special case it’s actually pretty easy to extend to functions with many inputs and many outputs.
To build insight into how to construct a network to compute \(f\), let’s start with a network containing just a single hidden layer, with two hidden neurons and an output layer containing a single output neuron:
To get a feel for how components in the network work, let’s focus on the top hidden neuron.
We can simplify our analysis quite a bit by increasing the weight so much that the output really is a step function, to a very good approximation. Below I’ve plotted the output from the top hidden neuron when the weight is \(w=999\).
It’s actually quite a bit easier to work with step functions than general sigmoid functions. The reason is that in the output layer we add up contributions from all the hidden neurons.  And so it makes things much easier to assume that our hidden neurons are outputting step functions. More concretely, we do this by fixing the weight w to be some very large value, and then setting the position of the step by modifying the bias. Of course, treating the output as a step function is an approximation, but it’s a very good approximation, and for now we’ll treat it as exact. I’ll come back later to discuss the impact of deviations from this approximation.
At what value of \(x\) does the step occur? Put another way, how does the position of the step depend upon the weight and bias? With a little work you should be able to convince yourself that the position of the step is proportional to \(b\), and inversely proportional to \(w\). In fact, the step is at position \(s=−b/w\), as you can see by modifying the weight and bias in the following diagram:
It will greatly simplify our lives to describe hidden neurons using just a single parameter, \(s\), which is the step position, \(s=−b/w\).
Up to now we’ve been focusing on the output from just the top hidden neuron. Let’s take a look at the behavior of the entire network. In particular, we’ll suppose the hidden neurons are computing step functions parameterized by step points \(s_1\) (top neuron) and \(s_2\) (bottom neuron). And they’ll have respective output weights \(w1\) and \(w2\). Here’s the network:
Note
By the way, that the output from the whole network is \(σ(w_1 a_1+w_2 a_2+b)\), where \(b\) is the bias on the output neuron. Obviously, this isn’t the same as the weighted output from the hidden layer, which is what we’re plotting here. We’re going to focus on the weighted output from the hidden layer right now, and only later will we think about how that relates to the output from the whole network.
Of course, we can rescale the bump to have any height at all. Let’s use a single parameter, \(h\), to denote the height. To reduce clutter we’ll also remove the \(s_1...\) and \(w_1...\) notations.
We can use our bump-making trick to get two bumps, by gluing two pairs of hidden neurons together into the same network. More generally, we can use this idea to get as many peaks as we want, of any height. In particular, we can divide the interval \([0,1]\) up into a large number, \(N\), of subintervals, and use \(N\) pairs of hidden neurons to set up peaks of any desired height. Let’s see how this works for \(N=5\).
Let’s think back to the function I plotted at the beginning of the chapter:
That’s obviously not a trivial function. We are going to figure out how to compute it using a neural network.
In our networks above we’ve been analyzing the weighted combination \(\sum w_ja_j\) output from the hidden neurons. We now know how to get a lot of control over this quantity. But, as I noted earlier, this quantity is not what’s output from the network. What’s output from the network is \(σ(∑w_j a_j+b)\) where \(b\) is the bias on the output neuron. Is there some way we can achieve control over the actual output from the network?
The solution is to design a neural network whose hidden layer has a weighted output given by \(σ−1 \bullet f(x)\), where \(σ−1\) is just the inverse of the \(σ\) function. That is, we want the weighted output from the hidden layer to be:
If we can do this, then the output from the network as a whole will be a good approximation to \(f(x)\).
Note
We have set the bias on the output neuron to 0.
We’ve now figured out all the elements necessary for the network to approximately compute the function \(f(x)\)! It’s only a coarse approximation, but we could easily do much better, merely by increasing the number of pairs of hidden neurons, allowing more bumps.
In particular, it’s easy to convert all the data we have found back into the standard parameterization used for neural networks. Let me just recap quickly how that works:
The first layer of weights all have some large, constant value, say \(w=1000\).
The biases on the hidden neurons are just \(b=−ws\). So, for instance, for the second hidden neuron s=0.2 becomes \(b=−1000×0.2=−200\).
The final layer of weights are determined by the \(h\) values. So, for instance, the value you’ve chosen above for the first \(h\), \(h= -0.9\), means that the output weights from the top two hidden neurons are \(-0.9\) and \(0.9\), respectively. And so on, for the entire layer of output weights.
Finally, the bias on the output neuron is \(0\).
That’s everything: we now have a complete description of a neural network which does a pretty good job computing our original goal function. And we understand how to improve the quality of the approximation by improving the number of hidden neurons.
What’s more, there was nothing special about our original goal function, \(f(x)=0.2+0.4x^2+0.3sin(15x)+0.05cos(50x)\). We could have used this procedure for any continuous function from \([0,1]\) to \([0,1]\). In essence, we’re using our single-layer neural networks to build a lookup table for the function. And we’ll be able to build on this idea to provide a general proof of universality.
Many input variables¶
The idea pitched above is appicable for 2 input variables as well, just that in that case there will be an increase in dimensionality.
By gluing together many such networks we can get as many towers as we want, and so approximate an arbitrary function of three variables. Exactly the same idea works in \(m\) dimensions. The only change needed is to make the output bias \((−m+1/2)h\), in order to get the right kind of sandwiching behavior to level the plateau.
Okay, so we now know how to use neural networks to approximate a real-valued function of many variables. What about vector-valued functions \(f(x_1,…,x_m)\epsilon R_n\)? Of course, such a function can be regarded as just \(n\) separate real-valued functions, \(f^1(x_1,…,x_m),f^2(x_1,…,x_m)\), and so on. So we create a network approximating \(f_1\), another network for \(f_2\), and so on. And then we simply glue all the networks together. So that’s also easy to cope with.
Extension beyond sigmoid neurons¶
We’ve proved that networks made up of sigmoid neurons can compute any function. Recall that in a sigmoid neuron the inputs \(x_1,x_2,…\) result in the output \(σ(∑w_jx_j+b)\), where \(w_j\) are the weights, \(b\) is the bias, and \(σ\) is the sigmoid function. What if we consider a different type of neuron, one using some other activation function, \(s(z)\)?
We can use this activation function to get a step function, just as we did with the sigmoid.
What properties does s(z) need to satisfy in order for this to work?
We do need to assume that s(z) is well-defined as \(z→−∞\) and \(z→∞\). These two limits are the two values taken on by our step function.
We also need to assume that these limits are different from one another. If they weren’t, there’d be no step, simply a flat graph! But provided the activation function \(s(z)\) satisfies these properties, neurons based on such an activation function are universal for computation.
Note
Rectified Linear Unit(ReLU) does not satisfy the conditions just given for universality.
Fixing up the step functions¶
Up to now, we’ve been assuming that our neurons can produce step functions exactly. That’s a pretty good approximation, but it is only an approximation. In fact, there will be a narrow window of failure, illustrated in the following graph, in which the function behaves very differently from a step function:
In these windows of failure the explanation given for universality will fail. Now, it’s not a terrible failure. By making the weights input to the neurons big enough we can make these windows of failure as small as we like. Certainly, we can make the window much narrower than I’ve shown above - narrower, indeed, than our eye could see. So perhaps we might not worry too much about this problem.
Nonetheless, it’d be nice to have some way of addressing the problem.
In fact, the problem turns out to be easy to fix. Let’s look at the fix for neural networks computing functions with just one input and one output. The same ideas work also to address the problem when there are more inputs and outputs.
In particular, suppose we want our network to compute some function, f. As before, we do this by trying to design our network so that the weighted output from our hidden layer of neurons is \(σ^{−1}\dot f(x)\). If we were to do this using the technique described earlier, we’d use the hidden neurons to produce a sequence of bump functions:
Again, I’ve exaggerated the size of the windows of failure, in order to make them easier to see. It should be pretty clear that if we add all these bump functions up we’ll end up with a reasonable approximation to \(σ−1\dot f(x)\), except within the windows of failure.
Suppose that instead of using the approximation just described, we use a set of hidden neurons to compute an approximation to half our original goal function, i.e., to \(σ^{−1}\dot f(x)/2\). Of course, this looks just like a scaled down version of the last graph:
And suppose we use another set of hidden neurons to compute an approximation to \(σ^{−1}\dot f(x)/2\), but with the bases of the bumps shifted by half the width of a bump:
Now we have two different approximations to \(σ^{−1}\dot f(x)/2\). If we add up the two approximations we’ll get an overall approximation to \(σ^{−1}\dot f(x)\)). That overall approximation will still have failures in small windows. But the problem will be much less than before. The reason is that points in a failure window for one approximation won’t be in a failure window for the other. And so the approximation will be a factor roughly \(2\) better in those windows.
We could do even better by adding up a large number, \(M\), of overlapping approximations to the function \(σ^{−1}\dot f(x)/M\). Provided the windows of failure are narrow enough, a point will only ever be in one window of failure. And provided we’re using a large enough number \(M\) of overlapping approximations, the result will be an excellent overall approximation.
Conclusion¶
The explanation for universality we’ve discussed is certainly not a practical prescription for how to compute using neural networks! In this, it’s much like proofs of universality for NAND gates and the like. For this reason, I’ve focused mostly on trying to make the construction clear and easy to follow, and not on optimizing the details of the construction. However, you may find it a fun and instructive exercise to see if you can improve the construction.
Although the result isn’t directly useful in constructing networks, it’s important because it takes off the table the question of whether any particular function is computable using a neural network. The answer to that question is always “yes”. So the right question to ask is not whether any particular function is computable, but rather what’s a good way to compute the function.
The universality construction we’ve developed uses just two hidden layers to compute an arbitrary function. Furthermore, as we’ve discussed, it’s possible to get the same result with just a single hidden layer. Given this, you might wonder why we would ever be interested in deep networks, i.e., networks with many hidden layers. Can’t we simply replace those networks with shallow, single hidden layer networks?
While in principle that’s possible, there are good practical reasons to use deep networks. Deep networks have a hierarchical structure which makes them particularly well adapted to learn the hierarchies of knowledge that seem to be useful in solving real-world problems. Put more concretely, when attacking problems such as image recognition, it helps to use a system that understands not just individual pixels, but also increasingly more complex concepts: from edges to simple geometric shapes, all the way up through complex, multi-object scenes. Deep networks do a better job than shallow networks at learning such hierarchies of knowledge. To sum up: universality tells us that neural networks can compute any function; and empirical evidence suggests that deep networks are the networks best adapted to learn the functions useful in solving many real-world problems.
Code Snippet¶
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from sklearn.preprocessing import MinMaxScaler
from IPython.display import clear_output # to clear output before printing
import matplotlib.pyplot as plt
np.random.seed(7)
X = np.linspace(0.0 , 2.0 * np.pi, 10000).reshape(-1, 1)
Y = np.sin(X)
x_scaler = MinMaxScaler()
y_scaler = MinMaxScaler()
X = x_scaler.fit_transform(X)
Y = y_scaler.fit_transform(Y)
def plotgraph(X, Y, res,i): # to plot the graph
plt.plot(X, res, label = "predicted")
plt.plot(X, Y, label = "true")
plt.title("Epoch Count: " + str(i))
plt.legend()
plt.show()
for i in range(0,10): # for loop is needed just to visualize the training progress, it can be removed
model = Sequential()
model.add(Dense(50, input_dim=X.shape[1], activation='relu'))
model.add(Dense(50, input_dim=X.shape[1], activation='relu'))
model.add(Dense(1, activation='linear'))
model.compile(loss='mse', optimizer='adam', metrics=['mae'])
model.fit(X, Y, epochs=i, batch_size=32, verbose=0)
res = model.predict(X, batch_size=32)
res_rscl = y_scaler.inverse_transform(res)
Y_rscl = y_scaler.inverse_transform(Y)
plotgraph(X, Y, res, i)
clear_output(wait=True)


If extreme mountain biking is your thing—or if you are interested in finding out how to get started—you are in the right place! Our products are chosen by bike enthusiasts especially for whatever your needs might be. We can help you prepare for your next adventure.
We had the pleasure of attending the Outerbike Trade show in Killington, Vermont in July 2019, and we will be looking forward to meeting many more of you this year, as restrictions for gatherings are lifted. Bicycling has been a great way to exercise and maintain social distancing for the last year, and as we move into the coming year, we will be seeing you in person at a show near you.
We have a wide selection of stock bikes for purchase and now orders for custom bikes can be placed on our website. Easily search our full line of accessories to make your cycling experience more perfect, or find replacement parts to make repairs at home. Our prices will leave you feeling like you got the best deal out there.
Can’t find what you are looking for? Our customer service team uses our products and we are the most knowledgable in the business. Contact us and we will help get you outfitted with everything you need.


Moreover, in order to have a wide range of skills, I learnt to evaluate the usability of a website, following the guidelines that in the early 90's Jakob Nielsen and Rolf Molich created evaluating usability with their heuristic evaluation, a method that identifies design problems.
In this case, the heuristic evaluation is applicable to the Blackboard system.
For this coursework, I wrote a report available on request.


Developing Strategies for Mental & Physical Well-Being During a Pandemic
Jaymie Rosen
<EMAIL_ADDRESS>COVID-19
What is COVID-19?
Coronaviruses are a large family of viruses which may cause illness (flu-like illness) in animals or humans. COVID-19 is the infectious disease caused by the most recently discovered coronavirus. This new virus and disease were unknown before the outbreak began in Wuhan, China, in December 2019.
Symptoms include: fever, tiredness, dry cough, shortness of breath, aches and pains, nasal congestion, runny nose, and a sore throat.
The virus can spread through droplets from the nose or mouth from another person.
Resource: Q&A on coronaviruses (COVID-19)
How to Protect Yourself
-
Wash your hands with soap (for at least 20 seconds) regularly, especially after coming back from being in public
-
Maintain a 1 metre (3 feet) distance away from others
-
14 day quarantine if you have been around someone who has had symptoms/infected with COVID-19, or you feel the symptoms
-
Even if you feel healthy, stay home as much possible, unless you need food, supplies, etc.
-
Do not touch your face
-
Cough into your elbow; if you sneeze/blow your nose into a tissue, immediately it out
Physical Distancing
"Social distancing makes it sound like people should stop communicating with one another,
while instead we should be preserving as much community as we can even while we keep our physical distance from one another"
Resource: Why 'physical distancing' is better than 'social distancing'
Isolation
-
Enforced isolation: feel like you're losing connection to your family, friends and co-workers
-
Feelings of hopelessness (especially if you are not working/lost your job), loneliness, feeling lazy, lack of motivation, depression, anxiety, agoraphobia/germaphobia
-
Worries about money (rent, food, supplies), how will you support yourself financially, financially taking care of family members/children/pets, etc.
-
Use technology to communicate during this time
-
How to get help: Shoppers Drug Mart, camh, online therapy, Stella's Place, online doctors
Working from home
-
Declutter and set up an "office space"; make it look like your desk at work
-
Set up work space near natural light
-
Keep a similar routine (ex. going for lunch at same time)
-
Communicate with co-workers (ex. have a virtual lunch)
-
Take breaks often (ex. go for a quick walk outside)
FACE COVID
are a set of practical steps for responding effectively to the COVID-19 crisis; uses the principles of acceptance and commitment therapy (ACT)
Resource: https://drive.google.com/file/d/1w5sySK40Wn9tvYd5KMYicufaUlX9cO-g/view?usp=sharing
Focus on what you can control
- When facing a crisis of any sort, fear and anxiety are inevitable; they are normal, natural responses to challenging situations infused with danger and uncertainty
- Instead of focusing on the uncertainty, focus on what you can control
- Take one day at a time
Acknowledge your thoughts and feelings
- Recognize when you are having anxious thoughts: "I’m noticing anxiety", "There’s my mind worrying", or "I’m having thoughts about getting sick"
- Write down your feelings
Come back into your body
- Ground yourself: breathing, sit down on a hard surface, meditate, clasping hands together, do yoga, etc.
- Eat well, keep your immune system strong, drink water, get good sleep, exercise, manage stress
- Gain as much control as possible over your physical actions, even though you can’t control your feelings
Engage in what you're doing
- Get a sense of where you are, and re-focus your attention
- Notice what you can see, hear, smell, and what you're doing
Drop anchor & do ACE
Curb negative or worrying thoughts, be more engaged/focused, grounded, steadying yourself in problematic situations, and curb obsession over worrying
Committed action:
action you take because it’s truly important to you; actions (besides handwashing, physical distancing) you take even if it brings up difficult thoughts & feelings
- Looking out for yourself: self-care, physical exercise, cooking/baking, cleaning, playing games with friends online
- Looking out for others: comfort, communicate, offer to help, volunteering opportunities
Opening up
- Acknowledge that fear, anxiety, anger, sadness, guilt, confusion and frustration are normal
- Self-kindness and self-compassion
- Self-care is important
- eBook on self-compassion: https://drive.google.com/file/d/1__Q3UcT9Q8VuSbiRm7x7-xjaxy5xkrba/view
Beyoncé said she only gives herself one day to feel sorry for herself when she's down, but in those 24 hours she allows herself to truly FEEL it, then she pushes past it because she can't sit and dwell for too long. Queen advice.
— lani 🌱 (@kailaniskye) April 20, 2019
Values
- What can you give back as a person during and after the crisis is over?
- Values may be respect, humour, patience, courage, caring -- how can you use these values to give back?
- Gain new values, like appreciation and gratitude
- Support local small businesses, helping the elderly, awareness about climate change, hygiene, health care systems, how connected the world is, being more resourceful
Identify resources
- Find reliable (aka not social media*) resources for help, assistance, support and advice (helpful resources here here)
- Finding trustworthy resources online can help you better understand the situation with facts
- *Limit social media activity -- conflict, misinformation, confusion
- Don't obessively look at the news
Disinfect & practice physical distance
- Disinfect your hands regularly and practice as much social distancing as realistically possible, for yourself and others
- Keep your place of residence clean, as well and disinfect objects you touch frequently (ex. phone)
- Physical distancing ≠ cutting off emotionally
Important reminders
Important reminders
This is temporary.
Helpful Resources
-
If you can't work or lost your job, visit: CERB Application


Welcome
Program Structure
Objects
Booleans
Numbers
Strings
Nothing
Tuples
Records
Functions
Quotations
Expressions
Messages
Calls
Operators
Variables
Blocks
Flow Control
Classes
Constructors
Fields
Methods
Getters and Setters
Mixins
Delegates
Types
Interfaces
Generics
Internals
The Heart of Magpie
Hacking on Magpie
Design Questions
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Please refer to the information provided here first. If it cannot help you to solve your issue, then contact us via email. Thank You very much, and we are very sorry that you are experiencing difficulties using virtual machines.
User Documentation
Up to date documentation can found at our development site:
Turku University, documentation
Primarily, you should be interested in the User's Manual:
Other documentation therein is more technical and may not be of interest to most who simply want to use virtual machines.
Other documentation that may be useful
Design Document (04.12.2019)
UTU appliance guide for students (26.6.2020)
Documents in Finnish - Suomenkieliset ohjeet
Opiskelijan opas - virtuaalikoneen asennus, käyttöönotto ja hallintaOpiskelijan opas - Java-koneen ohjelmistojen käyttö
Pieni Git-opas
Contact Us
Your problem was not found or could not be solved with what was found in this page? That's no good! It's time you send us an email and let us try to help you. When you do, please try to do the following:
- Try to be very descriptive. Attaching screen captures or other images will help a lot!
- If the issue is related to hypervisor, please also let us know your operating system, version, language plus of course which hypervisor and version are you having problems with.
- Let us know which VM image you are trying to run, unless that is irrelevant to your issue.
Support email address<EMAIL_ADDRESS>Please allow up to 2-3 days for replies. Personnel helping you are available only 2 days per week.


Hello {{name}},
Welcome to the Alumni Request Gateway! A staff member at the Alumni Center should get back to you very soon to
approve your account. You will be notified at that time.
You can also check out your console at AlumniRequestGW to check the situation.
- The Alumni Request Portal Team


Thats me on the left Thank you for checking out my website. There is a lot of things about me and I can't say everything about me in one page. So, Wanna know more about me? Check it out


The Group on Earth Observations - Biodiversity Observation Network (GEO BON) is working towards the implementation and progression of Essential Biodiversity Variables (EBVs) that could serve as the foundation for interoperable sub-national, national, regional and global monitoring initiatives. This explorer aims to provide a mechanism that helps communities developing EBVs discover data that is freely and openly available today, and has been connected through the work of the EU BON partner organisations.


The SOCI Firebird backend is currently supported for use with Firebird 1.5.
Other versions of Firebird may work as well, but they have not been tested by the SOCI team.
| Firebird version | Operating System | Compiler |
|---|---|---|
| <IP_ADDRESS>31 | SunOS 5.10 | g++ 3.4.3 |
| <IP_ADDRESS>31 | Windows XP | Visual C++ 8.0 |
| <IP_ADDRESS>70 | Windows XP | Visual C++ 8.0 Professional |
The Firebird backend requires Firebird's libfbclient
client library.
To establish a connection to a Firebird database, create a Session object using the firebird backend factory together with a connection string:
BackEndFactory const &backEnd = firebird; Session sql(backEnd, "service=/usr/local/firbird/db/test.fdb user=SYSDBA password=masterkey");
or simply:
Session sql(firebird, "service=/usr/local/firbird/db/test.fdb user=SYSDBA password=masterkey");
The set of parameters used in the connection string for Firebird is:
The following parameters have to be provided as part of the connection string : service, user, password. Role and charset parameters are optional.
Once you have created a Session
object as shown above, you can use it to access the database, for example:
int count; sql << "select count(*) from user_tables", into(count);
(See the SOCI basics and exchanging data documentation for general information on using the Session
class.)
The Firebird backend supports the use of the SOCI Row
class, which facilitates retrieval of data whose type is not known at compile time.
When calling Row::get<T>()
, the type you should pass as T depends upon the underlying database type. For the Firebird backend, this type mapping is:
| Firebird Data Type | SOCI Data Type | Row::get<T> specializations |
|---|---|---|
| numeric, decimal (where scale > 0) |
eDouble |
double |
| numeric, decimal [1] (where scale = 0) |
eInteger, eDouble |
int, double |
| double precision, float | eDouble |
double |
| smallint, integer | eInteger |
int |
| char, varchar | eString |
std::string |
| date, time, timestamp | eDate |
std::tm
|
[1] There is also 64bit integer type for larger values which is currently not supported.
(See the dynamic resultset binding documentation for general information on using the Row
class.)
In addition to binding by position, the Firebird backend supports binding by name, via an overload of the use()
function:
int id = 7; sql << "select name from person where id = :id", use(id, "id")
It should be noted that parameter binding by name is supported only by means of emulation, since the underlying API used by the backend doesn't provide this feature.
The Firebird backend has full support for SOCI's bulk operations interface. This feature is also supported by emulation.
Transactions are also fully
supported by the Firebird backend. In fact, there is always a transaction which is automatically commited in Session's
destructor.
See the Configuration options section for more details.
The Firebird backend supports working with data stored in columns of type Blob, via SOCI's BLOB
class.
It should by noted, that entire Blob data is fetched from database to allow random read and write access. This is because Firebird itself allows only writing to a new Blob or reading from existing one - modifications of existing Blob means creating a new one. Firebird backend hides those details from user.
This feature is not supported by Firebird backend.
This feature is not supported by Firebird backend.
Firebird stored procedures can be executed by using SOCI's Procedure class.
SOCI provides access to underlying datbabase APIs via several getBackEnd() functions, as described in the beyond SOCI documentation.
The Firebird backend provides the following concrete classes for navite API access:
| Accessor Function | Concrete Class |
|---|---|
SessionBackEnd* Session::getBackEnd() |
FirebirdSessionBackEnd |
StatementBackEnd* Statement::getBackEnd() |
FirebirdStatementBackEnd |
BLOBBackEnd* BLOB::getBackEnd() |
FirebirdBLOBBackEnd |
RowIDBackEnd* RowID::getBackEnd() |
FirebirdRowIDBackEnd |
The Firebird backend can throw instances of class FirebirdSOCIError
,
which is publicly derived from SOCIError
and has an
additional public status_
member containing the Firebird status vector.
The Firebird backend recognize the following configuration macros :
SOCI_FIREBIRD_NORESTARTTRANSACTION
-
Transactions will not be restarted automatically after commit() or rollback().
The default is to restart transactions.Copyright © 2004-2006 Maciej Sobczak, Stephen Hutton, Rafal Bobrowski


This website will give you a brief background of Docker, Containerization, Virtualization, and Linux. There are some tutorials to further your understanding of common Docker uses!
This website was built because it is an assignment for IS601, the Web Systems Development course at NJIT. Teammates Lauren Rega and Sally Khalil worked together to create this site. You can learn more about them on the Team page. They created this website using Bootstrap, a front-end development framework that allows developers to build websites quickly and easily. They used the development environment, PyCharm, and the version control system, Git.


Built-In Namespace _global_
| Field Attributes | Field Name and Description |
|---|---|
| <private> |
Array that contains the possible types of values when pressing mouse button.
|
| <private> |
Cache '' value.
|
| <private> |
Properties of event to be defined.
|
| <private> |
Cache false boolean value.
|
| <private> |
Cache null value.
|
| <private> |
Cache 0 value.
|
| <private> |
Cache 0 value.
|
| <private> |
Object where save the no system events.
|
| <private> |
This callback auto executed returns an object with all the events in systems as keys and values.
|
| <private> |
Prevents default process of events.
|
| <private> |
Stop bubbling / propagation of events.
|
| <private> |
Cache true boolean value.
|
| Method Attributes | Method Name and Description |
|---|---|
| <private> |
checkPropInDom(sProp)
Check property exist in DOM.
|
| <private> |
hasEvent(oElement, sType)
Check if oElement has events of the specified type.
|
| <private> |
hasEvents(oElement)
Check if oElement exist in oEvents
|
| <private> |
isArray(oObject)
Check if oObject is an array or not.
|
| <private> |
Checks for cancelBubble to avoid browser sniffing
|
| <private> |
Checks for returnValue to avoid browser sniffing
|
| <private> |
isSystemEvent(sType)
Check if the event is a system event.
|
| <private> |
unique(aArray)
Cleans the array if some elements is repeated on the array.
|
Field Detail
<private>
{Array}
BUTTON_MOUSE_DOWN
Array that contains the possible types of values when pressing mouse button.
Defined in: EH.js.
Defined in: EH.js.
<private>
{String}
EMPTY
Cache '' value. Increase readability reduce size.
Defined in: EH.js.
Defined in: EH.js.
<private>
{Object}
EVENT_PROPERTIES
Properties of event to be defined.
Keys are the properties to be defined in Eve, values are the default values if are not defined in the original Event.
Defined in: EH.js.
Defined in: EH.js.
<private>
{Boolean}
FALSE
Cache false boolean value. Helps to reduce final size.
Defined in: EH.js.
Defined in: EH.js.
<private>
{Object}
NULL
Cache null value. Reduce size.
Defined in: EH.js.
Defined in: EH.js.
<private>
{Number}
NUMBER_NEG
Cache 0 value. Increase readability reduce size.
Defined in: EH.js.
Defined in: EH.js.
<private>
{Number}
NUMBER_ZERO
Cache 0 value. Increase readability.
Defined in: EH.js.
Defined in: EH.js.
<private>
{Object}
oEvents
Object where save the no system events.
Defined in: EH.js.
Defined in: EH.js.
<private>
{Object}
oSystemEvents
This callback auto executed returns an object with all the events in systems as keys and values.
Defined in: EH.js.
Defined in: EH.js.
<private>
preventDefault
Prevents default process of events.
Use Lazy Pattern to be defined
Defined in: EH.js.
Defined in: EH.js.
<private>
stopPropagation
Stop bubbling / propagation of events.
Use Lazy Pattern to be defined
Defined in: EH.js.
Defined in: EH.js.
<private>
{Boolean}
TRUE
Cache true boolean value. Helps to reduce final size.
Defined in: EH.js.
Defined in: EH.js.
Method Detail
<private>
checkPropInDom(sProp)
Check property exist in DOM.
To do it correctly we need to create a new layer.
Defined in: EH.js.
Defined in: EH.js.
- Parameters:
- sProp
- is the key of the property to check in dom element
- Returns:
- Boolean
<private>
hasEvent(oElement, sType)
Check if oElement has events of the specified type.
Defined in: EH.js.
Defined in: EH.js.
- Parameters:
- oElement
- is the DOM element to check if exist.
- sType
- is the type of event to be checked
- Returns:
- Boolean
<private>
hasEvents(oElement)
Check if oElement exist in oEvents
Defined in: EH.js.
Defined in: EH.js.
- Parameters:
- oElement
- is the DOM element to check if exist.
- Returns:
- Boolean
<private>
isArray(oObject)
Check if oObject is an array or not.
Defined in: EH.js.
Defined in: EH.js.
- Parameters:
- oObject
- the object to be checked
- Returns:
- Boolean
<private>
isCancelBubble()
Checks for cancelBubble to avoid browser sniffing
Defined in: EH.js.
Defined in: EH.js.
- Returns:
- Boolean
<private>
isReturnValue()
Checks for returnValue to avoid browser sniffing
Defined in: EH.js.
Defined in: EH.js.
- Returns:
- Boolean
<private>
isSystemEvent(sType)
Check if the event is a system event.
Defined in: EH.js.
Defined in: EH.js.
- Parameters:
- sType
- is the type of event to be checked
- Returns:
- Boolean
<private>
unique(aArray)
Cleans the array if some elements is repeated on the array.
Defined in: EH.js.
Defined in: EH.js.
- Parameters:
- aArray
- the array to remove repeated elements.
- Returns:
- Array


WITNESS LIST Elections Committee (Subcommittee on Subcommittee on HB 643, HB 1001) February 25, 1997-2:45P HB 643 On: Steve McDonald (Tx Dem Pty) Tom Harrison (TX ETHICS COMMISSION) HB 1001 For: Margaret Walker (ACLU) Against: Mary Ann Collins (Repub Pty of Tx) On: Melinda Nickless (Secy of State) HJR 56 On: Steve McDonald (Tx Dem Pty)


TRUE CRIME A film review by Mark R. Leeper
Capsule: In a story that takes place almost entirely in one day a reporter covering a Death Row execution tries to prove the condemned man is innocent. Clint Eastwood stars, produces, and directs. The mainline story is cliched melodrama, but the writing and especially the well-developed minor characters give the plot a royal treatment. Rating: 7 (0 to 10), +2 (-4 to +4)
Steve "Ev" Everett (Clint Eastwood) is a bad-boy reporter who refuses to follow any rules. Right now he is holding back on his drinking, but he is smoking and, oh yes, sleeping with his editor's wife. This does not make for good relationships around the office, but Ev carries on (in several different senses). When a fellow reporter is killed in a (gratuitously spectacular) car accident, Ev picks up her responsibilities including the interviewing and writing a human interest sidebar about Frank Beachum (Isaiah Washington), a Death Row inmate scheduled to be executed the next night at midnight. But in reviewing the trial from six years earlier Ev starts questioning whether the story makes sense. There appear to be problems in the trial testimony. But Ev now mistrusts his own once powerful talent to "smell out" when there is something suspicious with a story. And as he traces the story he sabotages his own effectiveness by not following anybody's rules but his own.
Eastwood creates more believable characters for the minor roles than the character he creates for himself. But then going back to THE OUTLAW JOSEY WALES he has frequently done the same thing. Bernard Hill plays the role of Luther Plunkett, the prison warden. It would be cliche to play him as officious and unfeeling. Instead he turns out to be a genuinely caring person. On the other hand Isaiah Washington has been getting some favorable press as the condemned Frank Beachum. We see a lot of him, but he plays the simon-pure innocent to the hilt. Where an actor should have personality he has only virtue. Though his character was not always so, we see him he is the perfect husband and father. The film intentionally contrasts his ultra-perfect family values with those of Everett which have ripped apart Everett's family. We feel for Beachum, but other than in his moments of greatest pain it is more for his predicament than for his character. His family is just a little too wholesome. James Woods plays Eastwood's boss at the newspaper in a role only a little less slimy than his usual. Woods is one of the few actors who can steal attention away from Eastwood. Other familiar actors include Anthony Zerbe and an almost unrecognizable William Windom as a bartender.
Clint Eastwood is really a very good director in a very controlled film. However he has the same Achilles Heel that Woody Allen has. He has to paint himself as being the great lover. His character seems to be able to seduce any woman he wants. The problem is that he is getting on in years. His youthful good looks have given way to an older chiseled look. Eastwood seems to be doing his locker room bragging on the wide screen. His even raspier voice is now a sort that lost actors careers when sound came to films. Perhaps Eastwood, the gifted director, should consider if he needs a better star than Eastwood, the actor. On the other hand playing the character himself thematically gives the film one big advantage. Eastwood almost invariably plays the outlaw. He is Kurosawa's samurai Sanjuro, a law unto himself, transplanted to America. But in his younger spaghetti Western and Dirty Harry days he has played that character as hero. As he has aged Eastwood has begun to look at that character more deeply than Kurasawa ever did. In UNFORGIVEN he began re-examining the hard man who was this character he had created on the screen. He began questioning on film if the violence that was the former screen persona's daily bread did not exact a toll. Was the man with the big gun not dehumanized and desensitized by carrying and using that weapon. In TRUE CRIME Eastwood shows us how that character gets to middle age and can no longer make his personal relationships work. In this film he clearly envies the man who, though once a criminal, has reformed and built a strong family, even if that relationship turns out to be only temporarily.
Just as an aside, an interesting visual allusion is used. When Ev is interviewing Beachum, Eastwood has himself shot through the bars recreating the poster from his ESCAPE FROM ALCATRAZ. It is a reminder of the days when Eastwood was building his reputation and his films were just solid entertainment. TRUE CRIME is certainly a good film, but lacks the fun of his earlier work. Still I would give it a 7 on the 0 to 10 scale and a +2 on the -4 to +4 scale.
Mark R. Leeper<EMAIL_ADDRESS>Copyright 1999 Mark R. Leeper
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Depei Zhang
P1: LC167. Two Sum II - Input array is sorted
Leetcode runtime & memory (cpp): 4 ms (96.84%) & 10 MB
Solution:
P3: LC973. K Closest Points to Origin
Leetcode runtime & memory (cpp): 196 ms (98.60%) & 37.1 MB
Solution:
P6: LC315. Count of Smaller Numbers After Self
Leetcode runtime & memory (cpp): 28 ms (88.69%) & 26.1 MB
Solution:


This is the Javadoc for the Boot Accounts Gradle Project in the O'REILLY Learning Bath Learning Spring and Spring Boot.


| AnnotationFinder |
ClassFinder searches the classpath of the specified classloader for
packages, classes, constructors, methods, or fields with specific annotations.
|
| AnnotationUtil |
|
| AppFinder |
|
| AppFinder.AppContextTransformer |
|
| AppFinder.AppOrWebContextTransformer |
|
| AppFinder.WebBeansContextTransformer |
|
| ArrayEnumeration<T> |
|
| AsynchronousRunner |
Utility class used to invoke methods asynchronously, using a given Executor .
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| Base64 |
Provides Base64 encoding and decoding as defined by RFC 2045.
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| Classes |
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| CollectionsUtil |
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| ConfUtils |
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| Connect |
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| ContainerClassesFilter |
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| ContainerClassesFilter.OptimizedExclusionFilter |
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| Contexts |
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| CountingLatch |
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| DaemonThreadFactory |
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| Debug |
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| Debug.Trace |
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| Debug.Trace.Node |
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| DirectoryMonitor |
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| Duration |
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| DurationEditor |
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| Enumerator |
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| Exceptions |
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| ExecutorBuilder |
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| HeapDump |
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| HexConverter |
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| Index<K,V> |
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| IntrospectionSupport |
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| JarCreator |
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| JarExtractor |
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| JavaSecurityManagers |
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| JndiTreeBrowser |
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| Join |
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| Join.ClassCallback |
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| Join.FileCallback |
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| Join.MethodCallback |
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| JuliLogStream |
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| JuliLogStream.OpenEJBLogRecord |
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| JuliLogStreamFactory |
default conf = jre conf
user conf used transparently
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| JuliLogStreamFactory.OpenEJBLogManager |
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| JuliLogStreamFactory.OpenEJBSimpleLayoutHandler |
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| LengthInputStream |
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| LinkedListStack |
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| LinkResolver<E> |
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| ListConfigurator |
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| ListSetters |
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| Log4j2LogStreamFactory |
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| Log4jLogStream |
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| Log4jLogStreamFactory |
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| Log4jPrintWriter |
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| LogCategory |
Contains Logger categories used in OpenEJB.
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| Logger |
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| Logger.LoggerKey |
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| LoggingPrintWriter |
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| LogStreamAsync |
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| Memoizer<K,V> |
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| Messages |
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| NetworkUtil |
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| ObjectRecipeHelper |
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| OpenEJBErrorHandler |
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| OpenEJBScripter |
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| OpenEJBScripter.BeanManagerHelper |
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| OpenEjbVersion |
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| OptionsLog |
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| OutputScanner |
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| PassthroughFactory |
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| PassthroughFactory.Create |
xbean-reflect seems to sometimes get confused.
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| PaxLogStreamFactory |
A PAX logging friendly version of the log4j stream factory.
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| PerformanceTimer |
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| Pipe |
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| PojoExternalization |
Works with objects that have a public no-arg constructor
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| PojoSerialization |
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| Pool<T> |
Any successful pop() call requires a corresponding push() or discard() call.
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| Pool.Builder<T> |
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| Pool.HardReference<T> |
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| PropertiesHelper |
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| PropertiesService |
Manages properties so any property modifications are handled here.
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| PropertyPlaceHolderHelper |
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| References |
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| SafeToolkit |
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| Saxs |
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| ServiceManagerProxy |
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| SetAccessible |
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| SimpleJSonParser |
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| Slf4jLogStream |
Log the output to Slf4j
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| Slf4jLogStreamFactory |
Simple Slf4jLogStream, not much configuration needed, as slf4j is just a facade
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| Strings |
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| StringTemplate |
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| SuperProperties |
Properties is a Hashtable where the keys and values must be Strings.
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| Tee |
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| UniqueDefaultLinkResolver<E> |
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| UpdateChecker |
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| URISupport |
Swiped verbatim from ActiveMQ...
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| URISupport.CompositeData |
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| UrlCache |
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| UrlComparator |
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| URLs |
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{% extends "layout.html" %} {% import "bootstrap/wtf.html" as wtf %} {% block title %} | User Reg{% endblock %} {% block content %}
This form will make domain user creation easier. All fields is required to make an account.


As large scale applications required the content translation based upon the URL alike /en/user or fr/user, We prefer without putting our brain to resolve the content based upon URL language code, Let library manage this work elegantly 👏.
Show me codeThis gives us full control to change the language based upon our business logic. For changing the language we resolve the RxTranslateService class through component constructor and use the available method for changing the language.
Show me codeSome of the shared component we may need to show the text in anyone fixed language rather than the application current language. You can achieve this by simply passing the language value in the @translate decorator.
Show me code


This version uses AngularJS in a similar fasion to the knockout example.
It uses jquery.bsAlerts.js to display notices, which is bound via a div tag. They are set to fade out after 3 seconds.
This example demonstrates the use of JsonCallbackAnonFunc to pass an anonymous function to a JavaScript module that calls a JValue => JsCmd function on the server. The returned JsCmd is then executed on the client.
Submit the form and see in the logs the data sent via ajax to the server and sent back to the client via a notice.
This demonstrates the use of AjaxCallbackAnonFunc to pass an anonymous function to a JavaScript module that calls a () => JsCmd function on the server. The returned JsCmd is then executed on the client.
This just sends a success notice back to the client.
This demonstrates triggering the "add-alerts" event to send a warning directly to the client. No server calls are made.


type TRenderMode = (rnmFull, rnmConstrained);
Description
TRenderMode defines how the synthetic image will render or rasterize.
- rnmFull a color information from the foreground image completely replaces the background data;
- rnmConstrained – foreground and background colors are blended together using per-pixel
opacity of the foreground bitmap (stored in its alpha channel), multiplied
by its MasterAlpha;


Syrian state media say 2 civilians have been killed, others wounded in
DAMASCUS, Syria (AP) - Syrian state media say 2 civilians have been killed, others wounded in series of Israeli airstrikes near Damascus.
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adabag: Applies Multiclass AdaBoost.M1, SAMME and Bagging
It implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging
algorithm using classification trees as individual classifiers. Once these classifiers have been
trained, they can be used to predict on new data. Also, cross validation estimation of the error can
be done. Since version 2.0 the function margins() is available to calculate the margins for these
classifiers. Also a higher flexibility is achieved giving access to the rpart.control() argument
of 'rpart'. Four important new features were introduced on version 3.0, AdaBoost-SAMME (Zhu
et al., 2009) is implemented and a new function errorevol() shows the error of the ensembles as
a function of the number of iterations. In addition, the ensembles can be pruned using the option
'newmfinal' in the predict.bagging() and predict.boosting() functions and the posterior probability of
each class for observations can be obtained. Version 3.1 modifies the relative importance measure
to take into account the gain of the Gini index given by a variable in each tree and the weights of
these trees. Version 4.0 includes the margin-based ordered aggregation for Bagging pruning (Guo
and Boukir, 2013) and a function to auto prune the 'rpart' tree. Moreover, three new plots are also
available importanceplot(), plot.errorevol() and plot.margins(). Version 4.1 allows to predict on
unlabeled data. Version 4.2 includes the parallel computation option for some of the functions.
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=adabag
to link to this page.


A toolkit for analyzing stratified population genetic data. Functions are provided for summarizing and checking loci (haploid, diploid, and polyploid), single stranded DNA sequences, calculating most population subdivision metrics, and running external programs such as structure and fastsimcoal. The package is further described in Archer et al (2016) <doi:10.1111/1755-0998.12559>.
| Version: | 2.0.2 |
| Depends: | R (≥ 3.2.0), apex, ape, adegenet |
| Imports: | data.table, copula, DT, ggplot2, graphics, grid, gridExtra, Hmisc, methods, pegas, phangorn, RColorBrewer, Rcpp, shiny, shinyFiles, stats, survival (≥ 2.40.1), swfscMisc, utils |
| LinkingTo: | Rcpp |
| Suggests: | knitr, rmarkdown, testthat |
| Published: | 2017-04-11 |
| Author: | Eric Archer [aut, cre], Paula Adams [aut], Brita Schneiders [aut], Sarina Fernandez [aut], Warren Asfazadour [aut] |
| Maintainer: | Eric Archer <eric.archer at noaa.gov> |
| BugReports: | https://github.com/EricArcher/strataG/issues |
| License: | GPL-2 | GPL-3 [expanded from: GNU General Public License] |
| URL: | https://github.com/EricArcher/strataG |
| NeedsCompilation: | yes |
| Citation: | strataG citation info |
| Materials: | README |
| CRAN checks: | strataG results |
| Reference manual: | strataG.pdf |
| Vignettes: |
6. Installing External Programs 1. Creating and Manipulating gtypes Objects 4. Tests of Population Structure 5. Quality Assurance / Quality Control 3. Working With Sequences 2. By-Locus Summaries |
| Package source: | strataG_2.0.2.tar.gz |
| Windows binaries: | r-devel: strataG_2.0.2.zip, r-release: strataG_2.0.2.zip, r-oldrel: strataG_2.0.2.zip |
| OS X binaries: | r-release: strataG_2.0.2.tgz, r-oldrel: strataG_2.0.2.tgz |
| Old sources: | strataG archive |
| Reverse imports: | skeleSim |
Please use the canonical form https://CRAN.R-project.org/package=strataG to link to this page.


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