---
layout: post
title: AMA - Mario Zechner - Author of Beginning Android Games
categories: []
tags:
- development
- games
status: publish
type: post
published: true
meta:
_edit_last: '1'
---
The following is a reddit AMA article by the Mario Zechner, the maintainer (and creator?) of libgdx
He includes several links to free resources for people interested in mobile game development on Android.


Workflow.Rd
Class for creating and managing a workflow.
list_workflows()
Lists all the workflows in the account.
Workflow$list_workflows(max_items = 100, client = NULL, html = FALSE)
max_items
(int, optional): The maximum number of items to be returned. (default: 100)
client
(SFN.Client, optional): sfn
client to use for the query. If
not provided, a default sfn
client for Step Functions will be
automatically created and used. (default: None)
html
(bool, optional): Renders the list as an HTML table (If running in an IRkernel environment). If the parameter is not provided, or set to FALSE, a R list is returned. (default: False)
list: The list of workflows. Refer to :meth:`.SFN.Client.list_state_machines()` for the response structure.
new()
Initialize Workflow Class
Workflow$new( name, definition, role, tags = list(), execution_input = NULL, timeout_seconds = NULL, comment = NULL, version = NULL, state_machine_arn = NULL, format_json = TRUE, client = NULL )
name
(str): The name of the workflow. A name must not contain:
whitespace
brackets ``< > [ ]``
wildcard characters ``? *``
special characters ``\" # % \ ^ | ~ ` $ & , ; : /``
control characters (``U+0000-001F``, ``U+007F-009F``)
definition
(State or Chain): The Amazon States Language https://states-language.net/spec.html definition of the workflow.
role
(str): The Amazon Resource Name (ARN) of the IAM role to use for creating, managing, and running the workflow.
tags
(list): Tags to be added when creating a workflow. Tags are key-value pairs that can be associated with Step Functions workflows and activities. (default: [])
execution_input
(ExecutionInput, optional): Placeholder collection that defines the placeholder variables for the workflow execution. This is also used to validate inputs provided when executing the workflow. (default: None)
timeout_seconds
(int, optional): The maximum number of seconds an execution of the workflow can run. If it runs longer than the specified time, the workflow run fails with a `States.Timeout` Error Name. (default: None)
comment
(str, optional): A human-readable description of the workflow. (default: None)
version
(str, optional): The version of the Amazon States Language used in the workflow. (default: None)
state_machine_arn
(str, optional): The Amazon Resource Name (ARN) of the workflow. (default: None)
format_json
(bool, optional): Boolean flag set to `True` if workflow definition and execution inputs should be prettified for this workflow. `False`, otherwise. (default: True)
client
(SFN.Client, optional): paws client to use for creating, managing,
and running the workflow on Step Functions. If not provided, a
default sfn
client for Step Functions will be automatically created
and used. (default: None)
attach()
Factory method to create an instance attached to an exisiting workflow in Step Functions.
Workflow$attach(state_machine_arn, client = NULL)
state_machine_arn
(str): The Amazon Resource Name (ARN) of the existing workflow.
client
(SFN.Client, optional): sfn
client to use for attaching the existing
workflow in Step Functions to the Workflow object. If not provided,
a default sfn
client for Step Functions will be automatically
created and used. (default: None)
Workflow: Workflow object attached to the existing workflow in Step Functions.
create()
Creates the workflow on Step Functions.
Workflow$create()
str: The Amazon Resource Name (ARN) of the workflow created. If the workflow already existed, the ARN of the existing workflow is returned.
update()
Updates an existing state machine by modifying its definition and/or role. Executions started immediately after calling this method may use the previous definition and role.
Workflow$update(definition = NULL, role = NULL)
definition
(State or Chain, optional): The Amazon States Language https://states-language.net/spec.html definition to update the workflow with. (default: None)
role
(str, optional): The Amazon Resource Name (ARN) of the IAM role to use for creating, managing, and running the workflow. (default: None)
str: The state machine definition and/or role updated. If the update fails, None will be returned.
execute()
Starts a single execution of the workflow.
Workflow$execute(name = NULL, inputs = NULL)
name
(str, optional): The name of the workflow execution. If one is not provided, a workflow execution name will be auto-generated. (default: None)
inputs
(str, list, optional): Input data for the workflow execution. (default: None)
stepfunctions.workflow.Execution: An execution instance of the workflow.
list_executions()
Lists the executions for the workflow.
Workflow$list_executions(max_items = 100, status_filter = NULL, html = FALSE)
max_items
(int, optional): The maximum number of items to be returned. (default: 100)
status_filter
(ExecutionStatus, optional): If specified, only list the executions whose current status matches the given filter. (default: None)
html
(bool, optional): Renders the list as an HTML table (If running in an IRKernel environment). If the parameter is not provided, or set to FALSE, a R list is returned. (default: False)
list: List of workflow run instances.
delete()
Deletes the workflow, if it exists.
Workflow$delete()
render_graph()
Renders a visualization of the workflow graph.
Workflow$render_graph(portrait = FALSE)
portrait
(bool, optional): Boolean flag set to `True` if the workflow graph should be rendered in portrait orientation. Set to `False`, if the graph should be rendered in landscape orientation. (default: False)
get_cloudformation_template()
Returns a CloudFormation template that contains only the StateMachine resource. To reuse the CloudFormation template in a different region, please make sure to update the region specific AWS resources (e.g: Lambda ARN, Training Image) in the StateMachine definition.
Workflow$get_cloudformation_template()
format()
class formatting
Workflow$format()
print()
print class
Workflow$print()
clone()
The objects of this class are cloneable with this method.
Workflow$clone(deep = FALSE)
deep
Whether to make a deep clone.


| Top |
Functions
| void | (*NcmFitMCResample) () |
| NcmFitMC * | ncm_fit_mc_new () |
| void | ncm_fit_mc_free () |
| void | ncm_fit_mc_clear () |
| void | ncm_fit_mc_set_data_file () |
| void | ncm_fit_mc_set_mtype () |
| void | ncm_fit_mc_set_rtype () |
| void | ncm_fit_mc_set_nthreads () |
| void | ncm_fit_mc_keep_order () |
| void | ncm_fit_mc_set_fiducial () |
| void | ncm_fit_mc_set_rng () |
| void | ncm_fit_mc_start_run () |
| void | ncm_fit_mc_end_run () |
| void | ncm_fit_mc_reset () |
| void | ncm_fit_mc_set_first_sample_id () |
| void | ncm_fit_mc_run () |
| void | ncm_fit_mc_run_lre () |
| void | ncm_fit_mc_mean_covar () |
| NcmMSetCatalog * | ncm_fit_mc_get_catalog () |
Properties
| NcmMSet * | fiducial | Read / Write / Construct |
| NcmFit * | fit | Read / Write / Construct Only |
| gboolean | keep-order | Read / Write / Construct |
| NcmFitRunMsgs | mtype | Read / Write |
| guint | nthreads | Read / Write |
| NcmFitMCResampleType | rtype | Read / Write / Construct |
Functions
ncm_fit_mc_new ()
NcmFitMC * ncm_fit_mc_new (NcmFit *fit
,NcmFitMCResampleType rtype
,NcmFitRunMsgs mtype
);
FIXME
ncm_fit_mc_set_data_file ()
void ncm_fit_mc_set_data_file (NcmFitMC *mc
,const gchar *filename
);
FIXME
ncm_fit_mc_set_first_sample_id ()
void ncm_fit_mc_set_first_sample_id (NcmFitMC *mc
,gint first_sample_id
);
FIXME
ncm_fit_mc_run ()
void ncm_fit_mc_run (NcmFitMC *mc
,guint n
);
Runs the Monte Carlo until it reaches the n
-th realization. Note that
if the first_id is non-zero it will run n
- first_id realizations.
ncm_fit_mc_get_catalog ()
NcmMSetCatalog *
ncm_fit_mc_get_catalog (NcmFitMC *mc
);
Gets the generated catalog of mc
.
Property Details
The “fiducial”
property
“fiducial” NcmMSet *
Fiducial model to sample from.
Owner: NcmFitMC
Flags: Read / Write / Construct
The “keep-order”
property
“keep-order” gboolean
Whether keep the catalog in order of sampling.
Owner: NcmFitMC
Flags: Read / Write / Construct
Default value: TRUE
The “mtype”
property
“mtype” NcmFitRunMsgs
Run messages type.
Owner: NcmFitMC
Flags: Read / Write
Default value: NCM_FIT_RUN_MSGS_SIMPLE
The “nthreads”
property
“nthreads” guint
Number of threads to run.
Owner: NcmFitMC
Flags: Read / Write
Allowed values: <= 100
Default value: 0
The “rtype”
property
“rtype” NcmFitMCResampleType
Monte Carlo run type.
Owner: NcmFitMC
Flags: Read / Write / Construct
Default value: NCM_FIT_MC_RESAMPLE_FROM_MODEL


BA 186 - Activity Guide
Week 1 - Information Systems Development Environment
We will have a discussion activity for this topic.
Instructions
We will do a 1-3-2 for this. Each student shall post 1 reflection on the questions we raised. Each student will react/ respond to at least 3 other students’ reflections. Let’s try to make sure each student gets at least 2 replies. Each student will respond/ reply to at least 2 responses to their reflections.
Questions Raised
-
SDLC is sometimes defined as: Plan – Analyze – Design – Implement – Maintain and sometimes it is defined as: Analysis – Requirements – Design – Development – Implementation . With which do you agree more? Why?
-
From what you’ve studied, if you were to have your own personal Software Development Life Cycle, what would the steps be?
Netiquette
-
Participate: This is a shared learning environment. No lurking in the cyberspace background. It is not enough to log in and read the discussion thread of others. For the maximum benefit to all, everyone must contribute.
-
Avoid Repetition: Read everything in the discussion thread before replying. This will help you avoid repeating something someone else has already contributed. Acknowledge the points made with which you agree and suggest alternatives for those with which you don’t.
-
Use Proper Writing Style: The academic environment expects a higher-order language. Write as if you were writing a term paper. Correct spelling, grammatical construction, and sentence structure are expected in every other writing activity associated with scholarship and academic engagement. Online discussions are no different. Avoid profanity.
-
Cite Your Sources: Another big must! If your contribution to the conversation includes the intellectual property (authored material) of others, e.g., books, newspaper, magazine, or journal articles—online or in print—they must be given proper attribution.
-
Respect Diversity: It’s an ethnically rich and diverse, multi-cultural world in which we live. Use no language that is—or that could be construed to be—offensive toward others. Racists, sexist, and heterosexist comments and jokes are unacceptable, as are derogatory and/or sarcastic comments and jokes directed at religious beliefs, disabilities, and age. We all come with different perspectives, so please be respectful and resist the urge to tell anyone they are wrong. Understand they have had different life experiences and all of our world views are simply different.
-
No YELLING! Using bold upper-case letters is bad form, like stomping around and yelling at somebody (NOT TO MENTION BEING HARD ON THE EYE).
-
Remember, You Can't Un-Ring the Bell: Language is your only tool in an online environment. Be mindful. How others perceive you will be large—as always—up to you. Once you've hit the send button, you've rung the bell. Review your written posts and responses to ensure that you’ve conveyed exactly what you intended. This is an excellent opportunity to practice your proofreading, revision, and rewriting skills—valuable assets in the professional world for which you are now preparing. Read your post out loud before hitting the send button. This will tell you a lot about whether your grammar and sentence structure are correct, your tone is appropriate, and your contribution clear or not.
Adapted from Colorado State University
Week 2 - The Origins of Software
We will have a zoom webinar for this.
Instructions
- Login using the zoom link that will be provided.
- Rename using surname first.
- Discuss with the class about the sources of software.
- All sources of software originate from ideas of how someone's life can be made better with technology.
- The more clearly imagined the idea, the more possible it is to make real, like the ipod.
- The ideas that are tied to how feel, are more real to you.
- Think of 3 pain points, problems that you feel deeply about. List them and send them to me via chat.
- Now think of the opposites of these problems. What would that look like? Describe them in 1 sentence each. Send them to me via chat.
- Now think of a mobile app that, if existed, would ba part of the solution that would bring about the situation you desire. What would the app do? How will it help? Answer these in 2 sentences for each app. Send me your app descriptions via chat.
- Go into prof-assigned breakout rooms of 3-5 students each.
- Get to know each other. Turn on your video when you introduce yourself.
- Assign a group secretary
- Take 2-minute turns introducing your App Ideas while the secretary lists
- The secretary will then share screen and the group discusses and eliminates ideas that are vague or unpalatable for the group to pursue until 3 app ideas remain. Send me your group's top 3 via chat.
- Think about personas for each of the 3 app ideas. Tell me about the persona, the problem and the app when I visit your breakup room.
Netiquette
Remember: * Participate * Respect differing opinions * Each individual is important * NO YELLING! * Think before you speak
Week 3 - Identifying and Selecting Systems Development Projects
We will have an inter-group discussion activity for this topic.
Instructions
We will do a 1-4-3 for this. Each group shall post 1 article based on the questions for this week. Each group will react/ respond to at least 4 other groups' articles. Let’s try to make sure each group gets at least 3 substantial comments. Each group will respond/ reply to at least 3 comments to their articles.
Use the zoom breakout room to jointly create your article, while the secretary shares her screen and types in UVLE.
Questions
- What is the problem/ pain point / need that your app is trying to address?
- What's the big idea / value behind your app?
- Put into words the Measurable Organizational Value (MOV) of your app for the company/ group who will use it.
Netiquette
Remember: * Participate * Respect differing opinions * Each individual is important * NO YELLING! * Think before you speak/ type
Week 4 - Managing the Information Systems Project
We will have a reflection paper for this topic.
Instructions
- Make a 1-2 page reflection paper on the most unforgettable experience you had as a Project Manager. You may attach pictures, links, Gantt charts and the like for bonus points.
- In case, you haven't been a Project Manager before, reflect on an experience where you were a part of the team that took care of an event or created a project.
- Relate your experience to the things we discussed this week in class.
Weeks 5 - 11
We'll have Class Discussions with updates on group deliverables with respect to their project assignments
Week 12
We'll have a mini lecture
Weeks 13 - 14
Agile-style stand-up meetings for group presentations and class production


PostIt
Web based email marketing contact application
{{message}}
To:
Example: u1<EMAIL_ADDRESS>or Like<EMAIL_ADDRESS>Subject:
Send


please check that you are visiting the correct URL
Email
Mobile
Password
© 2017-2021. All rights reserved


{% extends "base_generic.html" %} {% block content %}
Welcome {{user_name}}, a website developed by Karl Dedow!
This User Has Some Information:


{% extends '../Layout/layout.html' %} {% block content %}
Oops! The page you are looking for does not exist. It might have been moved or deleted.


CSC 120 Lab 1 Part 3
Part 3: Declare, Instantiate, and Use objects from existing Java classes.
- The last two videos in the "Watch: Week 1 Getting Started" activity showed a Java program that Declares, Instantiates, and Uses objects from
the Balloon class. Now we will practice with a Java program that uses Flag objects. You may want to review those videos for help
if you need it when completing these activities.
- Create your Lab1 project:
- Create a new NetBeans project called Lab1.
- Visit the CSC 120 Resources web page,
Right-click on the link to MUPanel.java
in the Starter Templates section of the page,
Choose "Save Link As..." or "Save File As...",
and save the file in your CSC120\Java\Lab1\src folder:
- Again on the CSC 120 Resources web page,
Left-click on the link to MU Java Classes in the JavaDocs and Java Classes section at the bottom of the page,
Right-click on the link to Flag.java
on the MU Classes page,
Choose "Save Link As..." or "Save File As...",
and save the file in your CSC120\Java\Lab1\src folder
- Before moving to the next step in this lab, be sure that your
Lab1
project contains two files in the src
folder that are named Flag.java
and MUPanel.java
.
- Declare a Flag object in the appropriate place in the MUPanel.java file, like this:
private Flag flag1;
- In the appropriate place in the MUPanel.java file instantiate (construct) your Flag object.
To do this you'll need to know what parameters to pass to the constructor method of the Flag
class. To find that out you can click on the MU Java Docs page for Balloon, Flag, etc.
from the CSC 120 Resources page, and from there click on the docs for the Flag class.
Now scroll down and find the description of the constructor method to see what parameters
it expects. You'll need to add a line of code in the MUPanel.java file in the space
where you instantiate objects. It should look something like this:
flag1 = new Flag( ... parameters go here ... );
There are 4 values that you must specify to instantiate a new Flag object: you must first
specify 2 Integer values that represent the screen position of the upper-left corner of
the Flag, and then you specify two different Color values; one Color for the Flag's
background, and a second Color for the horizontal stripe across the middle of the Flag. The
parameters in parentheses are always separated by commas.
- Finally, in the paintComponent method of MUPanel, add a line of code that calls the
draw method of your Flag object. Don't forget to pass it the Graphics object it expects.
flag1.draw(g);
- Now declare 3 more Flag objects, instantiate them, and ask them to draw themselves.
Make sure your 4 Flag objects each use different flag & stripe color combinations,
are in different locations, and are visible (not outside of the MUPanel window).
Here is a list of color names you can use
in Java.
- Change the String used in the "
setName
" method of the MUPanel constructor
so that your name replaces Samantha's in the title of the output window when the program is run. Next, edit
the comment header at the top of the MUPanel class to show your name
and today's date instead of Samantha Student and the words "Today's Date". Finally, add a brief
description of what your program does.
- Run your program so that the output window appears on your screen. Take a screen capture of just the output window that includes
the title bar showing your name (in Windows, this can be done in one of two ways:
- Click on the output window so that it is selected, then use the [Alt]-[PrtSc] keystroke. Next, open Windows Paint (type
"Paint" in the Windows search area), paste in your screen capture, and save the image as a
.png
file
- Use the Windows Snip & Sketch tool (search for "Snip" in the Windows search area), then save your screen snip as a
.png
file
Ask your
lab instructor for help if needed with this process. You will save your screen capture as a .png
file to be submitted along with
your NetBeans project.
- Hand in your project by following these steps:
- Click on the icon at the bottom of the screen
to open File Explorer, and browse to the
location of your
CSC120\Java
folder.
- Right-click on the folder named
Lab1
and choose "Send To ▶ Compressed (zipped) folder".
This will create a new file named Lab1.zip
in the current folder, which is a compressed file that contains your
entire NetBeans project.
- Use a web browser to login to D2L,
and go to the CSC 120 course in your course list.
- Find the Lab01 dropbox folder (the Course Tour video showed you how to find the dropbox for an assignment). This will
display the "Lab01 Submit Files" page,
and you will want to find the "Add a File" button about halfway down the page — click this button now.
- Click on the "Upload" button in the dialog window that appears, browse to your
CSC120\Java
folder,
and select the file named "Lab01.zip". Click the blue "Add" button.
- Click the "Upload" button again, and this time browse to the folder in which you saved your screen capture .png file. Select
that file and click the blue "Add" button.
- Click the blue "Submit" button.
If you did this correctly, you should receive a "File submission successful" message in your browser. You will also receive
an e-mail message confirming that you submitted the files to the CSC 120 Lab01 dropbox.
If you have any questions about submitting work, ask your instructor for clarification or help.
- If time remains in this class period, create a new NetBeans project called Lab1B that draws 3
differently-colored Flags
at the same vertical position across the bottom part of the display window, with a Balloon
located above each Flag. Each Balloon should be the same color as the central stripe on each
Flag. Show your results to your instructor if you are able to get this to work before the
end of today's class session. You won't need to submit the Lab1B project to D2L.
Grading Rubric:
Lab01 Grading Rubric


Publications
Profile
Friends
Friends
Search Friends
Message
Create Message
Received Messages {{total}}
Chat


Eugenio Cabezas (born 1855 in Santa Cruz, Manila) was a goldsmith who was a Freemason and Katipunero.
He was married to Luisa Antonio of Cavite by whom he had seven children.
He owned a jewellery and watch repair shop on Calle Real (now called Trece Martires Avenue) in Cavite
which was used by the Katipunan as a meeting place.


This model is useful if multiple connections shall be made to a port of a volume model exposing a state, like a pipe with ModelStructure av_vb. The mixing is shifted into the volume connected to port_a and the result is propagated back to each ports_b.
If multiple connections were directly made to the volume, then ideal mixing would take place in the connection set, outside the volume. This is normally not intended.
| Name | Description |
|---|---|
| positiveMax | |
| Medium |


CGIVariable()
语法
Result$ = CGIVariable(Name$)概要
Gets the specified CGI environment variable content. When the CGI is loaded, many information are sent from the web server to the CGI application trough environment variables.
参数
Name$ The name of the variable to get. It can be a custom value or one of following predefined constants: #PB_CGI_AuthType: the authentication method used by the web browser if any authentication method was used. this is not set unless the script is protected. #PB_CGI_ContentLength: used for scripts that are receiving form data using the POST method. This variable tells the byte length of the CGI input data stream. This is required to read the data from the standard input with the POST method. #PB_CGI_HeaderContentType: tells the media type of data being received from the user. this is used for scripts called using the POST method. #PB_CGI_DocumentRoot: the root path to the home HTML page for the server. #PB_CGI_GatewayInterface: the version of the common gateway Interface (CGI) specification being used to exchange the data between the client and server. this is usually CGI/1.1 for the current revision level of 1.1. #PB_CGI_PathInfo: extra path information added to the end of the URL that accessed the server side script program. #PB_CGI_PathTranslated: a translated version of the PATH_INFO variable translated by the webserver from virtual to physical path information. #PB_CGI_QueryString: this string contains any information at the end of the server side script path that followed a question mark. Used to Return data if the GET method was used by a form. There are length restrictions to the QUERY_STRING. #PB_CGI_RemoteAddr: the IP address of the client computer. #PB_CGI_RemoteHost: the fully qualified domain name of the client machine making the HTTP request. It may not be possible to determine this name since many client computers names are not recorded in the DNS system. #PB_CGI_RemoteIdent: the ability to use this variable is limited to servers that support RFC 931. This variable may contain the client machine's username, but it is intended to be used for logging purposes only, when it is available. #PB_CGI_RemotePort: the clients requesting port. #PB_CGI_RemoteUser: if the CGI script was protected and the user had to be logged in to get access to the script, this value will contain the user's log in name. #PB_CGI_RequestURI: the path to the requested file by the client. #PB_CGI_REquestMethod: describes the request method used by the browser which is usually GET, POST, Or HEAD. #PB_CGI_ScriptName: the virtual path of the CGI script being executed. #PB_CGI_ScriptFilename: the local filename of the script being executed. #PB_CGI_ServerAdmin: the e-mail address of the server administrator. #PB_CGI_ServerName: the server hostname, IP address Or DNS alias name shown as a self referencing URL. This does not include the protocol identifier such As "HTTP:", the machine name, or port number. #PB_CGI_ServerPort: the port number the HTTP requests and responses are being sent on. #PB_CGI_ServerProtocol: this value is usually HTTP which describes the protocol being used between the client and server computers. #PB_CGI_ServerSignature: server information specifying the name and version of the web server and the port being serviced. #PB_CGI_ServerSoftware: the name and version of the web server. #PB_CGI_HttpAccept: the media types of data that the client browser can accept. these data types are separated by commas. #PB_CGI_HttpAcceptEncoding: the encoding type the client browser accepts. #PB_CGI_HttpAcceptLanguage: the language the client browser accepts. #PB_CGI_HttpCookie: used as an environment variable that contains cookies associated with the server domain from the browser. #PB_CGI_HttpForwarded: the forwarded page URL. #PB_CGI_HttpHost: hostname from where the HTPP requests comes from. #PB_CGI_HttpPragma: HTTP pragmas #PB_CGI_HttpReferer: the page address where the HTTP request originated. #PB_CGI_HttpUserAgent: the name of the client web browser being used to make the request.
返回值
Returns the value of the specified CGI environment variable.
示例
If Not InitCGI() Or Not ReadCGI() End EndIf WriteCGIHeader(#PB_CGI_HeaderContentType, "text/html", #PB_CGI_LastHeader) ; Write the headers to inform the browser of the content format WriteCGIString("<title>PureBasic - variables</title>") Procedure WriteCGIConstant(Constant$) WriteCGIString(Constant$ + ": " + CGIVariable(Constant$)+"
") EndProcedure WriteCGIConstant(#PB_CGI_AuthType) WriteCGIConstant(#PB_CGI_ContentLength) WriteCGIConstant(#PB_CGI_HeaderContentType) WriteCGIConstant(#PB_CGI_DocumentRoot) WriteCGIConstant(#PB_CGI_GatewayInterface) WriteCGIConstant(#PB_CGI_PathInfo) WriteCGIConstant(#PB_CGI_PathTranslated) WriteCGIConstant(#PB_CGI_QueryString) WriteCGIConstant(#PB_CGI_RemoteAddr) WriteCGIConstant(#PB_CGI_RemoteHost) WriteCGIConstant(#PB_CGI_RemoteIdent) WriteCGIConstant(#PB_CGI_RemotePort) WriteCGIConstant(#PB_CGI_RemoteUser) WriteCGIConstant(#PB_CGI_RequestURI) WriteCGIConstant(#PB_CGI_RequestMethod) WriteCGIConstant(#PB_CGI_ScriptName) WriteCGIConstant(#PB_CGI_ScriptFilename) WriteCGIConstant(#PB_CGI_ServerAdmin) WriteCGIConstant(#PB_CGI_ServerName) WriteCGIConstant(#PB_CGI_ServerPort) WriteCGIConstant(#PB_CGI_ServerProtocol) WriteCGIConstant(#PB_CGI_ServerSignature) WriteCGIConstant(#PB_CGI_ServerSoftware) WriteCGIConstant(#PB_CGI_HttpAccept) WriteCGIConstant(#PB_CGI_HttpAcceptEncoding) WriteCGIConstant(#PB_CGI_HttpAcceptLanguage) WriteCGIConstant(#PB_CGI_HttpCookie) WriteCGIConstant(#PB_CGI_HttpForwarded) WriteCGIConstant(#PB_CGI_HttpHost) WriteCGIConstant(#PB_CGI_HttpPragma) WriteCGIConstant(#PB_CGI_HttpReferer) WriteCGIConstant(#PB_CGI_HttpUserAgent) WriteCGIString("")
参阅
ReadCGI()
已支持操作系统
所有


|
|
|||||||||||||
|
Tested on Firefox 40.0.3, Chrome 45.0, Safari 7.0.6, Edge/Spartan, Android 5.0
Works on any web browser with the Web Audio API support and a decent JavaScript engine. :)
No plugin required. Very low CPU usage, similar to the native version (Thanks to the asm.js support).
The sources are available on GitHub.
Emscripten was used to generate the JavaScript code from the HxCMOD C sources code.
Drap and drop any MOD music file on this page to play it or click on one of the above MOD files.
Current Mode : Unknown
Last update: 2022-10-23 07:48:43
(C) HxC2001 / Jean-François DEL NERO


A basic driving agent, which processes the following inputs at each time step:
Run this agent within the simulation environment with enforce_deadline set to False (see run function in agent.py), and observe how it performs. In this mode, the agent is given unlimited time to reach the destination. The current state, action taken by your agent and reward/penalty earned are shown in the simulator.
The agent eventually makes it to the target location quite a few times when the actions are chosen at random. However, it also misses it's target location and hits a hard stop a few times. It is purely chance that the agent reaches it's destination and often does so way past the deadline. It's equally likely that it doesn't reach the deadline either.
Further, the following was noticed here with respect to the agent, environment and rewards:
The agent had no regard for traffic rules, lights, right-of-way, waypoint and sense of direction.
It seems to be penalized for the following actions as seen from the results:
a. -0.5
when, the action taken doesn't lead to the next waypoint.
Ex: {'tripId': 1, 'right': None, 'light': 'green', 'oncoming': None, 'deadline': 23, 'waypoint': 'forward', 'action': 'right', 'reward': -0.5, 'left': None}
when, the light is red and an action to turn right is executed provided there is no traffic at the waypoint.
Ex: {'right': None, 'tripId': 2, 'light': 'red', 'oncoming': None, 'deadline': 12, 'waypoint': 'forward', 'action': 'right', 'reward': -0.5, 'left': None}
b. -1
When, the action taken doesn't obey the traffic signal or when it takes an action against the U.S. right-of-way.
Ex: {'tripId': 100, 'right': None, 'light': 'red', 'oncoming': None, 'deadline': -64, 'waypoint': 'right', 'action': 'forward', 'reward': -1.0, 'left': None}
No reward is given when no action is taken by the agent while it could have instead.
Ex: {'action': nan, 'deadline': -50, 'left': None, 'light': 'green', 'oncoming': None, 'reward': 0.0, 'right': None, 'tripId': 100, 'waypoint': 'right'}
A positive reward is given when the agent executes actions that takes it to the next_waypoint
without disrupting traffic.
Ex: {'action': 'right', 'deadline': -83, 'left': None, 'light': 'green', 'oncoming': None, 'reward': 2.0, 'right': None, 'tripId': 7, 'waypoint': 'right'}
However, this is arguably not the desired behavior expected from a smartcab. It must be able to plan and move in an optimal path from the source to the destination. Choosing a random action suggests that there is no learning and hence nothing smart about the cab.
Identify a set of states that you think are appropriate for modeling the driving agent. The main source of state variables are current inputs, but not all of them may be worth representing. Also, you can choose to explicitly define states, or use some combination (vector) of inputs as an implicit state.
At each time step, process the inputs and update the current state. Run it again (and as often as you need) to observe how the reported state changes through the run.
A state is representative of the condition that the agent is in with respect to the environment at any particular given point in time. It seems appropriate to represent it as a combination of sensory inputs and the responses derived from the environment. Hence, we may choose to represent the state with the following inputs:
Light: ['Red','Green']
Waypoint: ['None','Left','Right','Forward']
Left: ['None','Left','Right','Forward']
Oncoming: ['None','Left','Right','Forward']
Right: ['None','Left','Right','Forward']
deadline: Number of points in the Grid
Light
, action
and oncoming
captures information about traffic rules in the environment and the waypoint
guides the agent to the next waypoint that will lead it to the destination.
Adding the deadline
increases the state space and we wish to keep the state space as small as possible. Further, it's felt that since the waypoint
is already guiding the agent toward the deadline already deadline
seems redundant and unnecessary.
The final state space is now composed of light
, waypoint
, left
, oncoming
and right
resulting in 2*4*4*4*4 = 512
possible states.
Implement the Q-Learning algorithm by initializing and updating a table/mapping of Q-values at each time step. Now, instead of randomly selecting an action, pick the best action available from the current state based on Q-values, and return that.
Each action generates a corresponding numeric reward or penalty (which may be zero). Your agent should take this into account when updating Q-values. Run it again, and observe the behavior.
The agent reaches the destination more often and is significantly faster after Q-Learning is implemented. It's however, important to note that the agent begins to learn only as the number of trials increase. In the beginning (first 10 trips) although the agent reaches the destination on time it takes longer and collects more negative rewards to reach the destination while towards the end (last 10 trips) it begins to accumulate more positive rewards and reaches the destination much faster.
This shows that the agent learns from its mistakes progressively and learns the behavior that the environment rewards as the trips progress. Further, it was noticed that the agent explores the environment in the beginning and begins to exploit as the trip count increases.
At this point the learning rate
(α) and the discount rate
(γ) have been arbitarily chosen to be 0.5
and 0.5
respectively. Further, no greedy-exploration of the environment is implemented yet.
Apply the reinforcement learning techniques you have learnt, and tweak the parameters (e.g. learning rate, discount factor, action selection method, etc.), to improve the performance of your agent. Your goal is to get it to a point so that within 100 trials, the agent is able to learn a feasible policy - i.e. reach the destination within the allotted time, with net reward remaining positive.
The action selection method is already quite optimal and the vannila Q-learning implementation proved to be quite effective. Toward the end of 100 trips it can be seen that the agent seldom collects negative feedback.
However, we could tinker with the learning rate
(α) and the discount rate
(γ) to optimize this further. We can also implement greedy limit infinite exploration
so that the agent explores the environment to ensure that it reaches the destination in the minimum possible time.
Further, the state space could be reduced. The car in the right
although present adds no value with the current reward system in place. Further, in the U.S. right-of-way rules we are concerned with the car oncoming
and the car on the left
. Hence, it makes sense to ignore right
while reducing the state space.
Since, we wish to change the hyperparameters of learning i.e. α , γ and ε we must have a metric or measure to judge how well the agent is doing and must be able to compare the results using this metric/measure. While there could be multiple measures/metrics we could use, it was decided that we shall go with MeanNetReward/Action
. Mathematically, this is: $$\frac{NetRewards}{Actionstaken}$$ Here,
NetReward = Sum of all rewards in 100 trials
Actionstaken = Total number of actions taken in 100 trials
Further, it also makes sense to track the number of times the agent reaches the destination during the 100 trials
These have been recorded in the tables below for all the three attempts:
| Trial | MeanReward/Action | Reached | Not Reached |
| 1 | 0.03326023392 | 23 | 77 |
| 2 | 0.01367488444 | 19 | 81 |
| 3 | 0.05433130699 | 24 | 76 |
| 4 | 0.01201550388 | 22 | 78 |
| 5 | 0.01397168405 | 23 | 77 |
| 6 | 0.08922226609 | 30 | 70 |
| 7 | 0.008302583026 | 19 | 81 |
| 8 | -0.004234297812 | 19 | 81 |
| 9 | 0.03193960511 | 22 | 78 |
| 10 | 0.03458049887 | 23 | 77 |
| Mean | 0.02870642686 | 22.4 | 77.6 |
| Trial | MeanReward/Action | Reached | Not Reached |
| 1 | 1.930419269 | 100 | 0 |
| 2 | 1.741129032 | 97 | 3 |
| 3 | 1.656502572 | 97 | 3 |
| 4 | 1.799159664 | 98 | 2 |
| 5 | 1.740930599 | 100 | 0 |
| 6 | 1.664661654 | 96 | 4 |
| 7 | 1.624905944 | 96 | 4 |
| 8 | 1.603606789 | 98 | 2 |
| 9 | 1.701638066 | 96 | 4 |
| 10 | 1.707354056 | 96 | 4 |
| Mean | 1.717030764 | 97.4 | 2.6 |
| Trial | MeanReward/Action | Reached | Not Reached |
| 1 | 1.789624183 | 99 | 1 |
| 2 | 2.005597015 | 99 | 1 |
| 3 | 1.837073982 | 98 | 2 |
| 4 | 1.900690846 | 99 | 1 |
| 5 | 1.926165803 | 100 | 0 |
| 6 | 1.802474062 | 99 | 1 |
| 7 | 1.848229342 | 100 | 0 |
| 8 | 1.893435635 | 100 | 0 |
| 9 | 1.753949447 | 99 | 1 |
| 10 | 1.938958707 | 100 | 0 |
| Mean | 1.869619902 | 99.2 | 0.8 |
The enhanced agent is quite close to finding an optimal policy. It reaches the destination in very little time when compared to the vanilla Q-learning implementation. This is visible from the fact that on average of 10 sets of 100 trials the agent with an α= 1
and γ= 0.1
has a better MeanRewards per Action performed and reaches the target on a average 99.2 times.
Furthermore, it was quite counter-intuitive to notice that implementing the Greedy limit - Infinite exploration actually reduced the performance of the Agent although ε was 0.1
and hence this was reverted back


Home
Products
PS2
PS3
ps Vita
Wii
Xbox one
Buy Now
The Gamer
Online Order Form
Please fill in the following details:
First name:
Last name:
Address:
City:
State:
Zip:
How would you like to pay for this console?
Mastercard
Visa
American Express
Maestro
Please enter your card number:
Please take care that everything is filled up correctly!


run_sceptre_gRNA_gene_pair.Rd
Runs SCEPTRE on a given gRNA-gene pair. The function requires as arguments the gene expression vector, the gRNA indicator vector, and the covariate matrix.
run_sceptre_gRNA_gene_pair( expressions, gRNA_indicators, covariate_matrix, side = "left", gene_precomp_size = NULL, B = 500, seed = NULL, reduced_output = TRUE, verbose = TRUE )
| expressions | a vector a gene expressions |
|---|---|
| gRNA_indicators | a vector of gRNA indicators |
| covariate_matrix | the data frame of cell-specific covariates |
| side | (optional; default "left") side of the test; one of "left," "right," and "both." |
| gene_precomp_size | (optional) the pre-computed size of the gene NB distribution; if not supplied, will be estimated |
| B | (optional; default 500) number of conditional randomization test resamples |
| seed | (optional; default 4) seed of the random number generator |
| reduced_output | (optional; default TRUE) return the reduced output? |
| verbose | (optional; default TRUE) print status updates? |
If reduced_output is TRUE, a data frame containing the following columns: p-value, skew-t fit success, skew-t fit MLEs (xi, omega, alpha, nu), the original signed test statistic, and the number of successful resamples; if reduced_output is FALSE, a list containing all of the above, plus the vector of resampled test statistics.
data(expressions) data(gRNA_indicators) data(covariate_matrix) result <- run_sceptre_gRNA_gene_pair(expressions, gRNA_indicators, covariate_matrix, verbose = FALSE) result#> p_value skew_t_fit_success xi omega alpha nu #> 1 0.1952004 TRUE 0.8196897 1.021334 -0.3667235 59745.98 #> z_value n_successful_resamples #> 1 -0.3037842 500


25 October 2007
The Ministry of Health of Indonesia has announced a new case of human infection of H5N1 avian influenza. A 5-year-old female from the Tangerang District, Banten Province developed symptoms on 14 October, was hospitalized on 20 October and died in an AI referral hospital on 22 October. The investigation found that there were poultry deaths in the case's neighborhood in the two weeks prior to her onset of symptoms.
Of the 110 cases confirmed to date in Indonesia, 89 have been fatal.


Sorry about the mix up! We depend on our users to keep our info updated. What seems to be the issue?
All fields are required.
We were tired of military websites that made it difficult to find important services around base, so we created something that (we think) is simpler and works better.
This is still a work in progress, and we know there are errors on a lot of the locations. If you find an incorrectly listed location, please click the "report error" button and let us know so that we can make it better!
Contact us at:
militaryfinder@gmail.com


Gatekeeper Teranu Mardui:
Traveler of the west wind, leave this place at once! This is the land of the Varka! No foreigner's feet may touch this sacred land!


Ends on {{ deal.end_date }}
{% if deal.start_date <= now and deal.end_date > now %}
Active
{% elif deal.start_date <= now and deal.end_date <= now %}
Ended
{% elif deal.start_date > now %}
Coming soon
{% endif %}
{{ deal.short_desc }}
{% endfor %}
{% if not deals%}
No deals found!
{% endif %}
{% include "pigination.html" %}
{% endblock %}


Symbol Index for "FFA/State"
Symbol Index for diagram
"FFA/State"
Discard Focus (Transition)
Establish Focus (Transition)
Established Focus (State)
Rejected Focus (State)
Relevant Focus (State)


ROOMS
Here you can access room categories and availability
Select your room below
Select a room category
Meeting room
Games room
Quiet time room
Learning room
Working room
Check Availability


Home
Into to HTML
Into to CSS3
Restaurant Site
JavaScript Basics
JavaScript Application
×
Home
Into to HTML
Into to CSS3
Restaurant Site
JavaScript Basics
JavaScript Application
HTML, CSS, and Javascript for Web Developers
Learning to use HTML, CSS, and Javascript


Jake's sudoku solver
clear
solve
"Error in solving the board. make sure the board you entered is valid and that you only entered numbers between 1 and 9"


Home
About
Contact
Log in
Sign up
×
Sign up into Brightly as
Contact Us
Experiencing any issues with Brightly? Let us know how we can help
Name*
Email*
Phone*
Report*
Send


Emotional Intelligence, or EQ, is the ability to identify and manage one’s own emotions as well as the emotions of people around you.
EQ is totally different to IQ, While EQ is mostly concerned with how we deal with our emotions, IQ (or intelligence Quotient) refers to your intellectual ability. IQ tests usually measures one’s ability to use logic and reasoning to solve and answer problems.
Recent studies show that EQ is a better determinant of success and happiness than IQ. This means that people with high EQ are generally more successful and happier in life. Now who wouldn’t want that?
The good news is: EQ is a skill. And like any skill, anyone can improve one’s EQ skills through training. The video below shows some tips on how we can improve our Emotional Intelligence.


{{ $baseURL := .Site.BaseURL }}
To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video


COVID-19
WORLD WIDE DATA
{% csrf_token%}
{% for country in mylist %}
{{country}}
> {% endfor %}
{{selectedcountry}}
{{total}}
total
{{recovered}}
recovered
{{active}}
active
{{new}}
new
{{critical}}
critical
{{deaths}}
death
DEVELOPED BY :
MOHIT PANDEY (top_dog_baker)
>


With innovations in LED technology, household LED bulbs are able to cast any color- from sunlight to traditional lamplight!
LEDs, unlike compact flourescents, turn on instantly, providing full light to a room without any delay!
LED bulbs are cool burning, meaning that even when on for extended periods of time they remain at room temperature. This is possible because of how little power they use!
Unlike CFLs, the older option for efficient lighting, LEDs contain no mercury, meaning they release no toxins into the air when broken.


Toggle navigation
Doubler
about us
affiliate
our partners
faq
support
createnow
(current)
sign in
Item
Item
Item


Password Generator
Generated Password
click to copy
copied
CLICK GENERATE
length:
setting
Include Uppercase
Include Lowercase
Include Numbers
Include Symbols
Generate Password
Created by
iEmmad
social media
info
All Rights Reserved © 2021


Provide enums for various publicly exposed result code.
More...
Namespaces |
| namespace | bmqt |
Detailed Description
- Outline
-
-
- Purpose:
- Provide enums for various publicly exposed result code.
-
- Classes:
-
-
- Description:
- This file contains a list of Enums (
bmqt::GenericResult
, bmqt::AckResult
, bmqt::CloseQueueResult
, bmqt::EventBuilderResult
, bmqt::OpenQueueResult
, bmqt::ConfigureQueueResult
, and bmqt::PostResult
) that are publicly exposed to Application (via bmqa), but whose value comes from the internal implementation (bmqimp). Having them defined in bmqt allows bmqa to return the enum returned by bmqimp with no transformation.
- All enums are using the convention that < 0 values are errors, while > 0 are warnings.
- The
GenericStatus
enum contains values that are common for most or all of the other status enums, such as success
, timeout
, ... The values of its members can range from -99 to 99.
- Each other enum should duplicate any values from the
GenericStatus
one that it intends to be able to represent (aliasing the values to the ones from the GenericStatus
enum) and extend with specialized values in the ]..., -99[
or ]99, ...[
ranges.


Our Work & The Role Of Incubators “Democratic governance needs the citizen to be legally empowered to ask questions, file complaints and be a part of the corrective process” The above quote is by Aruna Roy from an article that was published in The Hindu recently. Jun 2, 2018
22% to 78% | The Rise & Rise of Attendance In PTMs A few months back, the Parent Teacher Meeting (PTM) held at a Government School in Kundli , Sonipat saw only 22% parents turn up. While the PTM held in August saw the the attendance jump to 78%. Sep 13, 2018 ·
Google inks pact for new 35-storey office That dominion stars lights dominion divide years for fourth have don't stars is that he earth it first without heaven in place seed it second morning saying. 15 Jan 2019 ·


class: middle, center # Cutting-plane Method and Its Amazing Oracles Wai-Shing Luk 2019-04-04 --- class: middle, right > When you have eliminated the impossible, whatever remains, however > improbable, must be the truth. *Sir Arthur Conan Doyle, stated by Sherlock Holmes* --- class: middle, center # Introduction --- ## Some History of the Ellipsoid Method - Introduced by Shor and Yudin and Nemirovskii in 1976 - Used to show that linear programming (LP) is polynomial-time solvable (Kachiyan 1979), settled the long-standing problem of determining the theoretical complexity of LP. - In practice, however, the simplex method runs much faster than the method, although its worst-case complexity is exponential. --- ## Common Perspective of the Ellipsoid Method - It is commonly believed that it is inefficient in practice for large-scale problems. - The convergent rate is slow, even with the use of deep cuts. - Cannot exploit sparsity. - Since then, it was supplanted by interior-point methods. - Only treated as a theoretical tool for proving the polynomial-time solvability of combinatorial optimization problems. --- ## But... - The ellipsoid method works very differently compared with the interior point method. - Only require a separation oracle. Can play nicely with other techniques. - The oracle can exploit sparsity. --- ## Consider Ellipsoid Method When... - The number of optimization variables is moderate, e.g. ECO flow, analog circuit sizing, parametric problems - The number of constraints is large, or even infinite - Oracle can be implemented efficiently. --- class: middle, center # Cutting-plane Method Revisited --- ## Basic Idea - Let $\mathcal{K} \subseteq \mathbb{R}^n$ be a convex set. - Consider the feasibility problem: - Find a point $x^* \in \mathbb{R}^n$ in $\mathcal{K}$, - or determine that $\mathcal{K}$ is empty (i.e., no feasible solution) .pull-left70[ ![](ellipsoid.files/region.svg) ] .pull-right30[ ] --- ## Separation Oracle - When a separation oracle $\Omega$ is *queried* at $x_0$, it either - asserts that $x_0 \in \mathcal{K}$, or - returns a separating hyperplane between $x_0$ and $\mathcal{K}$: $$g^\mathsf{T} (x - x_0) + \beta \leq 0, \; \beta \geq 0, g \neq 0, \; \forall x \in \mathcal{K}. $$ .pull-left[ ![](ellipsoid.files/cut.svg) ] .pull-right[ ] --- ## Separation oracle (cont'd) - $(g, h)$ called a *cutting-plane*, or cut, since it eliminates the halfspace $\{x \mid g^\mathsf{T} (x - x_0) + h > 0\}$ from our search. - If $h=0$ ($x_0$ is on the boundary of halfspace that is cut), cutting-plane is called *neutral cut*. - If $h>0$ ($x_0$ lies in the interior of halfspace that is cut), cutting-plane is called *deep cut*. --- ## Subgradient - $\mathcal{K}$ is usually given by a set of inequalities $f_j(x) \le 0$ or $f_j(x) < 0$ for $j = 1 \cdots m$, where $f_j(x)$ is a convex function. - A vector $g \equiv \partial f(x_0)$ is called a subgradient of a convex function $f$ at $x_0$ if $f(z) \geq f(x_0) + g^\mathsf{T} (z - x_0)$. - Hence, the cut $(g, h)$ is given by $(\partial f(x_0), f(x_0))$ Remarks: - If $f(x)$ is differentiable, we can simply take $\partial f(x_0) = \nabla f(x_0)$ --- ## Key components of Cutting-plane method - Cutting plane oracle $\Omega$ - A search space $\mathcal{S}$ initially big enough to cover $\mathcal{K}$, e.g. - Polyhedron $\mathcal{P}$ = $\{z \mid C z \preceq d \}$ - Interval $\mathcal{I}$ = $[l, u]$ (for one-dimensional problem) - Ellipsoid $\mathcal{E}$ = $\{z \mid (z-x_c)P^{-1}(z-x_c) \leq 1 \}$ --- ## Generic Cutting-plane method - **Given** initial $\mathcal{S}$ known to contain $\mathcal{K}$. - **Repeat** 1. Choose a point $x_0$ in $\mathcal{S}$ 2. Query the separation oracle at $x_0$ 3. **If** $x_0 \in \mathcal{K}$, quit 4. **Else**, update $\mathcal{S}$ to a smaller set that covers: $$\mathcal{S}^+ = \mathcal{S} \cap \{z \mid g^\mathsf{T} (z - x_0) + \beta \leq 0\}. $$ 5. **If** $\mathcal{S}^+ = \emptyset$ or it is small enough, quit. --- ## Python code ```python def cutting_plane_feas(evaluate, S, options=Options()): feasible = False status = 0 for niter in range(options.max_it): cut, feasible = evaluate(S.xc) if feasible: # feasible sol'n obtained break status, tau = S.update(cut) if status != 0: break if tau < options.tol: status = 2 break return S.xc, niter+1, feasible, status ``` --- ## Convex Optimization Problem (I) $$\begin{array}{ll} \text{minimize} & f_0({\color{blue}x}), \\ \text{subject to} & x \in \mathcal{K}. \end{array}$$ - The optimization problem is treated as a feasibility problem with an additional constraint $f_0({\color{blue}x}) < {\color{purple}t}$ - $f_0({\color{blue}x})$ could be a convex function or a quasiconvex function. - ${\color{purple}t}$ is the best-so-far value of $f_0({\color{blue}x})$. --- ## Convex Optimization Problem (II) - Problem can be reformulated as: $$\begin{array}{ll} \text{minimize} & {\color{purple}t}, \\ \text{subject to} & \Phi({\color{blue}x}, {\color{purple}t}) < 0, \\ & {\color{blue}x} \in \mathcal{K}, \end{array}$$ where $\Phi({\color{blue}x}, {\color{purple}t}) < 0$ is the ${\color{purple}t}$-sublevel set of $f_0({\color{blue}x})$. - Note: $\mathcal{K}_t \subseteq \mathcal{K}_u$ if and only if $t \leq u$ (monotonicity). - One easy way to solve the optimization problem is to apply the binary search on ${\color{purple}t}$. --- ## Corresponding Python code ```python def bsearch(evaluate, I, options=Options()): # assume monotone feasible = False l, u = I t = l + (u - l)/2 for niter in range(options.max_it): if evaluate(t): # feasible sol'n obtained feasible = True u = t else: l = t tau = (u - l)/2 t = l + tau if tau < options.tol: break return u, niter+1, feasible ``` --- ```python class bsearch_adaptor: def __init__(self, P, E, options=Options()): self.P = P self.E = E self.options = options @property def x_best(self): return self.E.xc def __call__(self, t): E = self.E.copy() self.P.update(t) x, _, feasible, _ = cutting_plane_feas( self.P, E, self.options) if feasible: self.E._xc = x.copy() return True return False ``` --- - Another possible way is, to update the best-so-far ${\color{purple}t}$ whenever a feasible solution $x_0$ is found such that $\Phi({\color{blue}x_0}, {\color{purple}t}) = 0$. - We assume that the oracle takes the responsibility for that. --- ## Generic Cutting-plane method (Optim) - **Given** initial $\mathcal{S}$ known to contain $\mathcal{K}_{\color{purple}t}$. - **Repeat** 1. Choose a point $x_0$ in $\mathcal{S}$ 2. Query the separation oracle at $x_0$ 3. **If** $x_0 \in \mathcal{K}_{\color{purple}t}$, update ${\color{purple}t}$ such that $\Phi({\color{blue}x_0}, {\color{purple}t}) = 0$. 4. Update $\mathcal{S}$ to a smaller set that covers: $$\mathcal{S}^+ = \mathcal{S} \cap \{z \mid g^\mathsf{T} (z - x_0) + h \leq 0\}. $$ 5. **If** $\mathcal{S}^+ = \emptyset$ or it is small enough, quit. --- ## Corresponding Python code ```python def cutting_plane_dc(evaluate, S, t, options=Options()): feasible = False # no sol'n x_best = S.xc for niter in range(options.max_it): cut, t1 = evaluate(S.xc, t) if t != t1: # best t obtained feasible = True t = t1 x_best = S.xc status, tau = S.update(cut) if status == 1: break if tau < options.tol: status = 2 break return x_best, t, niter+1, feasible, status ``` --- ## Example: Profit Maximization Problem $$\begin{array}{ll} \text{maximize} & p(A x_1^\alpha x_2^\beta) - v_1 x_1 - v_2 x_2, \\ \text{subject to}& 0 \le x_1 \le k, x_2 \ge 0. \end{array}$$ - $p(A x_1^\alpha x_2^\beta)$ : Cobb-Douglas production function - $p$: the market price per unit - $A$: the scale of production - $\alpha, \beta$: the output elasticities - $x$: input quantity - $v$: output price - $k$: a given constant that restricts the quantity of $x_1$ --- ## Example: Profit maximization (cont'd) - The formulation is not in the convex form. - Rewrite the problem in the following form: $$\begin{array}{ll} \text{maximize} & {\color{purple}t}, \\ \text{subject to} & {\color{purple}t} + v_1 x_1 + v_2 x_2 < p A x_1^{\alpha} x_2^{\beta}, \\ & 0 \le x_1 \le k, x_2 \ge 0. \end{array}$$ --- ## Profit maximization in Convex Form - By taking the logarithm of each variable: - ${\color{blue}y}_1 = \log x_1$, ${\color{blue}y}_2 = \log x_2$. - We have the problem in a convex form: $$\begin{array}{ll} \text{max} & {\color{purple}t}, \\ \text{s.t.} & \log({\color{purple}t} + v_1 e^{{\color{blue}y}_1} + v_2 e^{{\color{blue}y}_2}) - (\alpha {\color{blue}y}_1 + \beta {\color{blue}y}_2) < \log(pA), \\ & {\color{blue}y}_1 \le \log k. \end{array}$$ --- ## Python code (Profit oracle) ```python class profit_oracle: def __init__(self, params, a, v): p, A, k = params self.log_pA = np.log(p * A) self.log_k = np.log(k) self.v = v; self.a = a def __call__(self, y, t): fj = y[0] - self.log_k # constraint if fj > 0.: g = np.array([1., 0.]) return (g, fj), t log_Cobb = self.log_pA + np.dot(self.a, y) x = np.exp(y) vx = np.dot(self.v, x) te = t + vx fj = np.log(te) - log_Cobb if fj < 0.: te = np.exp(log_Cobb) t = te - vx; fj = 0. g = (self.v * x) / te - self.a return (g, fj), t ``` --- ## Python code (Main program) ```python import numpy as np from profit_oracle import * from cutting_plane import * from ell import * p, A, k = 20.0, 40.0, 30.5 params = p, A, k alpha, beta = 0.1, 0.4 v1, v2 = 10.0, 35.0 y0 = np.array([0.0, 0.0]) # initial x0 E = ell(200, y0) P = profit_oracle(params, alpha, beta, v1, v2) yb1, fb, iter, feasible, status = \ cutting_plane_dc(P, E, 0.0) print(fb, iter, feasible, status) ``` --- ## Area of Applications - Robust convex optimization - oracle technique: affine arithmetic - Parametric network potential problem - oracle technique: negative cycle detection - Semidefinite programming - oracle technique: Cholesky factorization --- class: middle, center # Robust Convex Optimization --- ## Robust Optimization Formulation - Consider: $$\begin{array}{ll} \text{minimize} & \sup_{q \in \mathbb Q} f_0({\color{blue}x},q), \\ \text{subject to} & f_j({\color{blue}x},q) \leq 0, \; \forall q \in {\mathbb Q}, \; j = 1,2,\cdots,m, \end{array}$$ where $q$ represents a set of varying parameters. - The problem can be reformulated as: $$\begin{array}{ll} \text{minimize} & {\color{purple}t} \\ \text{subject to} & f_0({\color{blue}x},q) < {\color{purple}t} \\ & f_j({\color{blue}x},q) \leq 0, \; \forall q \in {\mathbb Q}, \; j = 1,2,\cdots,m. \end{array}$$ --- ## Oracle in Robust Optimization Formulation - The oracle only needs to determine: - If $f_j({\color{blue}x_0}, q) > 0$ for some $j$ and $q = q_0$, then - the cut $(g, h)$ = $(\partial f_j({\color{blue}x_0}, q_0), f_j({\color{blue}x_0}, q_0))$. - If $f_0({\color{blue}x_0}, q) \geq {\color{purple}t}$ for some $q = q_0$, then - the cut $(g, h)$ = $(\partial f_0({\color{blue}x_0}, q_0), f_0({\color{blue}x_0}, q_0) - {\color{purple}t})$. - Otherwise, $x_0$ is feasible, then - Let $q_{\max} = \text{argmax}_{q \in \mathbb Q} f_0({\color{blue}x_0}, q)$. - ${\color{purple}t} := f_0({\color{blue}x_0}, q_{\max})$. - The cut $(g, h)$ = $(\partial f_0({\color{blue}x_0}, q_{\max}), 0)$. --- ## Example: Profit Maximization Problem (convex) $$\begin{array}{ll} \text{max} & {\color{purple}t} \\ \text{s.t.} & \log({\color{purple}t} + \hat{v}_1 e^{{\color{blue}y}_1} + \hat{v}_2 e^{{\color{blue}y}_2}) - (\hat{\alpha} {\color{blue}y}_1 + \hat{\beta} {\color{blue}y}_2) \le \log(\hat{p}\,A) \\ & {\color{blue}y}_1 \le \log \hat{k}. \end{array}$$ - Now assume that: - $\hat{\alpha}$ and $\hat{\beta}$ vary $\bar{\alpha} \pm e_1$ and $\bar{\beta} \pm e_2$ respectively. - $\hat{p}$, $\hat{k}$, $\hat{v}_1$, and $\hat{v}_2$ all vary $\pm e_3$. --- ## Example: Profit Maximization Problem (oracle) By detail analysis, the worst case happens when: - $p = \bar{p} + e_3$, $k = \bar{k} + e_3$ - $v_1 = \bar{v}_1 - e_3$, $v_2 = \bar{v}_2 - e_3$, - If ${\color{blue}y}_1 > 0$, $\alpha = \bar{\alpha} - e_1$, else $\alpha = \bar{\alpha} + e_1$. - If ${\color{blue}y}_2 > 0$, $\beta = \bar{\beta} - e_2$, else $\beta = \bar{\beta} + e_2$. > **Remark**: for more complicated problems, affine arithmetic could be > used. --- ## `profit_rb_oracle` ```python class profit_rb_oracle: def __init__(self, params, a, v, vparams): ui, e1, e2, e3 = vparams self.uie = [ui * e1, ui * e2] self.a = a p, A, k = params p -= ui * e3 k -= ui * e3 v_rb = v.copy() v_rb += ui * e3 self.P = profit_oracle((p, A, k), a, v_rb) def __call__(self, y, t): a_rb = self.a.copy() for i in [0, 1]: a_rb[i] += self.uie[i] * (+1. if y[i] <= 0. else -1.) self.P.a = a_rb return self.P(y, t) ``` --- class: middle, center # Parametric Network Potential Problem --- ## Parametric Network Potential Problem Given a network represented by a directed graph $G = (V, E)$. Consider: $$\begin{array}{ll} \text{minimize} & {\color{purple}t}, \\ \text{subject to} & {\color{darkred}u_i} - {\color{darkred}u_j} \le h_{ij}({\color{blue}x}, {\color{blue}t}), \; \forall (i, j) \in E,\\ \text{variables} &{\color{blue}x}, {\color{darkred}u} , \end{array}$$ - $h_{ij}({\color{blue}x}, {\color{blue}t})$ is the weight function of edge $(i, j)$, - Assume: network is large but the number of parameters is small. --- ## Network Potential Problem (cont'd) Given $x$ and $t$, the problem has a feasible solution if and only if $G$ contains no negative cycle. Let $\mathcal{C}$ be a set of all cycles of $G$. $$\begin{array}{ll} \text{minimize} & {\color{purple}t}, \\ \text{subject to} & W_k({\color{blue}x}, {\color{purple}t}) \ge 0, \forall C_k \in C ,\\ \text{variables} & x \end{array}$$ - $C_k$ is a cycle of $G$ - $W_k({\color{blue}x}, {\color{purple}t}) = \sum_{ (i,j)\in C_k} h_{ij}({\color{blue}x}, {\color{purple}t})$. --- ## Oracle in Network Potential Problem - The oracle only needs to determine: - If there exists a negative cycle $C_k$ under $x_0$, then - the cut $(g, h)$ = $(-\partial W_k(x_0), -W_k(x_0))$ - If $f_0(x_0) \geq {\color{purple}t}$, then - the cut $(g, h)$ = $(\partial f_0(x_0), f_0(x_0) - {\color{purple}t})$. - Otherwise, $x_0$ is feasible, then - ${\color{purple}t} := f_0(x_0)$. - The cut $(g, h)$ = $(\partial f_0(x_0), 0)$. --- ```python class network_oracle: def __init__(self, G, f, p): self.G = G self.f = f self.p = p # partial derivative of f w.r.t x self.S = negCycleFinder(G) def __call__(self, x): def get_weight(G, e): return self.f(G, e, x) self.S.get_weight = get_weight C = self.S.find_neg_cycle() if C is None: return None, 1 f = -sum(self.f(self.G, e, x) for e in C) g = -sum(self.p(self.G, e, x) for e in C) return (g, f), 0 ``` --- ## Example: Optimal Matrix Scaling - Given a sparse matrix $A = [a_{ij}] \in \mathbb{R}^{N\times N}$. - Find another matrix $B = U A U^{-1}$ where $U$ is a nonnegative diagonal matrix, such that the ratio of any two elements of $B$ in absolute is as close to 1 as possible. - Let $U = \mathrm{diag}([u_1, u_2, \ldots, u_N])$. Under the min-max-ratio criterion, the problem can be formulated as: $$\begin{array}{ll} \text{minimize} & \pi/\psi, \\ \text{subject to} & \psi \leq u_i |a_{ij}| u_j^{-1} \leq \pi, \; \forall a_{ij} \neq 0 , \\ & \pi, \, \psi, u, \text{positive}, \\ \text{variables} & \pi, \psi, u \, . \end{array}$$ --- ## Optimal Matrix Scaling (cont'd) By taking the logarithms of variables, the above problem can be transformed into: $$\begin{array}{ll} \text{minimize} & {\color{purple}t}, \\ \text{subject to} & {\color{blue}\pi'} - {\color{blue}\psi'} \leq {\color{purple}t}, \\ & {\color{darkred}u_i'} - {\color{darkred}u_j'} \leq {\color{blue}\pi'} - a_{ij}', \; \forall a_{ij} \neq 0 \,, \\ & {\color{darkred}u_j'} - {\color{darkred}u_i'} \leq a_{ij}' - {\color{blue}\psi'}, \; \forall a_{ij} \neq 0 \,, \\ \text{variables} & {\color{blue}\pi'}, {\color{blue}\psi'}, {\color{darkred}u'} \,, \end{array}$$ where $k'$ denotes $\log( | k | )$ and $x = ({\color{blue}\pi'}, {\color{blue}\psi'} )^\mathsf{T}$. --- ```python def con(G, e, x): u, v = e if u < v: return x[0] - G[u][v]['cost'] else: return G[u][v]['cost'] - x[1] def pcon(G, e, x): u, v = e if u < v: return np.array([1., 0.]) else: return np.array([0., -1.]) class optscaling_oracle: def __init__(self, G): self.network = network_oracle(G, con, pcon) def __call__(self, x, t): cut, feasible = self.network(x) if not feasible: return cut, t s = x[0] - x[1] fj = s - t if fj < 0.: t = s fj = 0. return (np.array([1., -1.]), fj), t ``` --- ## Example: clock period & yield-driven co-optimization $$\begin{array}{cll} \text{minimize} &T_{CP} - w_{\color{blue}\beta} {\color{blue}\beta}, \\ \text{subject to} & u_i - u_j \le T_{CP} + F_{ij}^{-1}(1 - {\color{blue}\beta}), & \forall (i,j) \in E_s \,,\\ & u_j - u_i \le F_{ij}^{-1}(1 - {\color{blue}\beta}), & \forall (j,i) \in E_h \,, \\ & T_{CP} \ge 0, \, 0 \le {\color{blue}\beta} \le 1 \, , \\ \text{variables} &T_{CP}, {\color{blue}\beta}, u. \end{array}$$ - Note that $F_{ij}^{-1}(x)$ is not concave in general in $[0,1]$. - Fortunately, we are most likely interested in optimizing circuits for high yield rather than the low one in practice. - Therefore, by imposing an additional constraint to $\beta$, say $\beta \geq 0.8$, the problem becomes convex. --- ## Inverse CDF ![img](ellipsoid.files/Fig2-b-invcdf.svg) --- class: middle, center # Matrix Inequalities --- ## Problems With Matrix Inequalities Consider the following problem: $$\begin{array}{ll} \text{minimize} & {\color{purple}t}, \\ \text{subject to} & F({\color{blue}x}, {\color{purple}t}) \succeq 0, \end{array}$$ - $F({\color{blue}x}, {\color{purple}t})$: a matrix-valued function - $A \succeq 0$ denotes $A$ is positive semidefinite. --- ## Problems With Matrix Inequalities - Recall that a matrix $A$ is positive semidefinite if and only if $v^\mathsf{T} A v \ge 0$ for all $v \in \mathbb{R}^N$. - The problem can be transformed into: $$\begin{array}{ll} \text{minimize} & {\color{purple}t}, \\ \text{subject to} & v^\mathsf{T} F({\color{blue}x}, {\color{purple}t}) v \ge 0, \; \forall v \in \mathbb{R}^N . \end{array}$$ - Consider $v^\mathsf{T} F({\color{blue}x}, {\color{purple}t}) v$ is concave for all $v \in \mathbb{R}^N$ w. r. t. ${\color{blue}x}$, then the above problem is a convex programming. - Reduce to *semidefinite programming* if $F({\color{blue}x}, {\color{purple}t})$ is linear w.r.t. ${\color{blue}x}$, i.e., $F({\color{blue}x}) = F_0 + x_1 F_1 + \cdots + x_n F_n$ --- ## Oracle in Matrix Inequalities The oracle only needs to: - Perform a *row-based* Cholesky factorization such that $F(x_0, t) = R^\mathsf{T} R$. - Let $A_{:p,:p}$ denotes a submatrix $A(1:p, 1:p) \in \mathbb{R}^{p\times p}$. - If Cholesky factorization fails at row $p$, - there exists a vector $e_p = (0, 0, \cdots, 0, 1)^\mathsf{T} \in \mathbb{R}^p$, such that - $v = R_{:p,:p}^{-1} e_p$, and - $v^\mathsf{T} F_{:p,:p}(x_0) v < 0$. - The cut $(g, h)$ = $(-v^\mathsf{T} \partial F_{:p,:p}(x_0) v, -v^\mathsf{T} F_{:p,:p}(x_0) v)$. --- ```python class lmi_oracle: ''' Oracle for LMI constraint F*x <= B ''' def __init__(self, F, B): self.F = F self.F0 = B self.A = np.zeros(B.shape) def __call__(self, x): n = len(x) def getA(i, j): self.A[i, j] = self.F0[i, j] self.A[i, j] -= sum(self.F[k][i, j] * x[k] for k in range(n)) return self.A[i, j] Q = chol_ext(getA, len(self.A)) if Q.is_spd(): return None, 1 v = Q.witness() p = len(v) g = np.array([v.dot(self.F[i][:p, :p].dot(v)) for i in range(n)]) return (g, 1.), 0 ``` --- ## Example: Matrix Norm Minimization - Let $A({\color{blue}x}) = A_0 + x_1 A_1 + \cdots + x_n A_n$. - Problem $\min_x \| A({\color{blue}x}) \|$ can be reformulated as $$\begin{array}{ll} \text{minimize} & {\color{purple}t}, \\ \text{subject to} & \left( \begin{array}{cc} {\color{purple}t}\,I & A({\color{blue}x}) \\ A^\mathsf{T}({\color{blue}x}) & {\color{purple}t}\,I \end{array} \right) \succeq 0, \end{array}$$ - Binary search on ${\color{purple}t}$ can be used for this problem. --- ```python class qmi_oracle: def __init__(self, F, F0): self.F = F; self.F0 = F0 self.Fx = np.zeros(F0.shape) self.A = np.zeros(F0.shape) self.t = None; self.count = -1 def update(self, t): self.t = t def __call__(self, x): self.count = -1; nx = len(x) def getA(i, j): if self.count < i: self.count = i; self.Fx[i] = self.F0[i] self.Fx[i] -= sum(self.F[k][i] * x[k] for k in range(nx)) self.A[i, j] = -self.Fx[i].dot(self.Fx[j]) if i == j: self.A[i, j] += self.t return self.A[i, j] Q = chol_ext(getA, len(self.A)) if Q.is_spd(): return None, 1 v = Q.witness(); p = len(v) Av = v.dot(self.Fx[:p]) g = -2.*np.array([v.dot(self.F[k][:p]).dot(Av) for k in range(nx)]) return (g, 1.), 0 ``` --- ## Example: Estimation of Correlation Function $$\begin{array}{ll} \min_{{\color{blue}\kappa}, p} & \| \Omega({\color{blue}p}) + {\color{blue}\kappa} I - Y \|, \\ \text{s. t.} & \Omega({\color{blue}p}) \succcurlyeq 0, {\color{blue}\kappa} \geq 0 \; .\\ \end{array}$$ - Let $\rho(h) = \sum_i^n {\color{blue}p}_i \Psi_i(h)$, where - $p_i$'s are the unknown coefficients to be fitted - $\Psi_i$'s are a family of basis functions. - The covariance matrix $\Omega({\color{blue}p})$ can be recast as: $$\Omega({\color{blue}p}) = {\color{blue}p}_1 F_1 + \cdots + {\color{blue}p}_n F_n$$ where $\{F_k\}_{i,j} =\Psi_k( \| s_j - s_i \|_2)$ --- ## Experimental Result ![BSpline vs. Polynomail](ellipsoid.files/corr_fn.svg) --- class: center, middle Questions? ==========


{% extends "main/base.html" %} {% load static %} {% block content %}
Founder and Principal
Hi my name is Michael Janko. I am a senior at the University of North Carolina Charlotte seeking a degree in the Computer Science field with a concentration in data science. I am detail oriented, hard working, and organized. Hockey has always been a passion of mine so to be able to provide an in-depth statistical website for hockey analytics was important to me.
Project Programmer
Hi my name is Shiv Parekh, I am a computer science major/math minor at unc charlotte.  You can create games, websites, bots, and many other things. Some things I like to do for fun is play the piano, play basketball, and run.


--- layout: post title: IRB Tricks date: 2013-09-30 20:53:43.000000000 -04:00 type: post published: true status: publish categories: - Linux - Ruby tags: [] meta: _edit_last: '2' bwps_enable_ssl: '' _css_code: "div#social a img {\r\n padding: 0px;\r\n margin: 10px 3px 0 0;\r\n}" author: login<EMAIL_ADDRESS>email<EMAIL_ADDRESS>display_name: Aimee Knight first_name: Aimee last_name: Knight ---
Here's another cool trick I learned through pairing today during my apprenticeship. In IRB, you can use an underscore to retrieve your last return value. Seems like the perfect shortcut whenever you realize too late that you should have stored a value in a variable! Here's a quick example.
$ 1 + 2
=> 3
$ _ + 3
=> 6


[§502-4] Rules. The department of land and natural resources may adopt rules pursuant to chapter 91 necessary for the purposes of this chapter. [L 1993, c 141, §1]


Profile
Send Feedback
License
Sign Out
General
Vaccinations
Allergies
Infections
Medications
Exams
Veterinary
Notes


{% extends "base.html" %} {% block title %}
Welcome, {{ new_user.username }} Successfully Registered in our website! Your account has been successfully created!


在Linux上构建AirSim
目前验证并推荐的Linux版本为 Ubuntu 16.04 LTS。理论上您可以在其他版本或者OSX上构建，但是我们目前并未验证。
安装以及Build
- 确保您已 注册了Epic Games。这一步用以获取虚幻引擎的代码。
- 克隆虚幻引擎到您期望的目录，然后Build。注意：我们目前仅提供对UE4.18版本的支持。
bash git clone -b 4.18 https://github.com/EpicGames/UnrealEngine.git cd UnrealEngine ./Setup.sh ./GenerateProjectFiles.sh make
- 克隆AirSim然后build:
bash git clone https://github.com/Microsoft/AirSim.git cd AirSim ./setup.sh ./build.sh
Build Unreal Environment
最终，您需要一个内有环境 (environment) 的项目 (project) 来运行AirSim。AirSim内置"Blocks Environment"，或者您也可以参考设置虚幻环境来创建自己的项目。
配置遥控器控制功能（仅限无人机）
如果您想手动控制无人机，您需要一个遥控器。请查阅 遥控器控制 获取更多细节。
当然，您也可以通过 APIs 编写程序来进行操控，或者在 计算机视觉模式 下使用键盘来预览环境。
如何使用AirSim
一旦您完成了上述步骤，您就可以
- 进入
UnrealEngine
文件夹，然后运行UnrealEngine/Engine/Binaries/Linux/UE4Editor
。 - 当UE提示打开或创建项目时，选择浏览并选择
AirSim/Unreal/Environments/Blocks
（或者您自己 客制 的项目）。 - If you get prompts to convert project, look for 当提示您需要转换（convert）项目时，找到并配置More选项或者Convert-In-Place选项。如果提示您build，选择yes，如果提示您禁用AirSim插件，选择no。
- 加载Unreal Editor后，单击“开始”按钮，小提示：进入“Edit->Editor Preferences“并搜索”CPU“，然后确保 'Use Less CPU when in Background' 未被勾选。
参阅 使用 APIs 以及 settings.json 来开启或设置更多功能。
FAQ
提示错误" could not be compiled. Try rebuilding from source manually"。
This could either happen because of compile error or the fact that your gch files are outdated. Look in to your console window. Do you see something like below?
fatal errorfatal error: : file '/usr/include/linux/version.h''/usr/include/linux/version.h' has been modified since the precompiled header
If this is the case then look for *.gch file(s) that follows after that message, delete them and try again. Here's relevant thread on Unreal Engine forums.
If you see other compile errors in console then open up those source files and see if it is due to changes you made. If not, then report it as issue on GitHub.
Unreal crashed! How do I know what went wrong?
Go to the MyUnrealProject/Saved/Crashes
folder and search for the file MyProject.log
within its subdirectories. At the end of this file you will see the stack trace and messages. You can also take a look at the Diagnostics.txt
file.
How do I use an IDE on Linux?
You can use Qt Creator or CodeLite. Instructions for Qt Creator are available here.
Can I cross compile for Linux from a Windows machine?
Yes, you can, but we haven't tested it. You can find the instructions here.
What compiler and stdlib does AirSim use?
We use the same compiler that Unreal Engine uses, Clang 5.0, and stdlib, libc++. AirSim's setup.sh
will automatically download them both. The libc++ source code is cloned into the llvm-source-(version)
folder and is built into the llvm-build
folder, from where CMake uses libc++.
What version of CMake does the AirSim build use?
3.9.0 or higher. This is not the default in Ubuntu 16.04 so setup.sh installs it for you. You can check your CMake version using cmake --version
. If you have an older version, follow these instructions or see the CMake website.
Can I compile AirSim in BashOnWindows?
Yes, however, you can't run Unreal from BashOnWindows. So this is kind of useful to check a Linux compile, but not for an end-to-end run. See the BashOnWindows install guide. Make sure to have the latest version (Windows 10 Creators Edition) as previous versions had various issues. Also, don't invoke bash
from Visual Studio Command Prompt
, otherwise CMake might find VC++ and try and use that!


Company profile - Canadian Company Capabilities
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Hi-Point Industries (1991) Ltd.
Legal/Operating Name:
Hi-Point Industries (1991) Ltd.
Mailing Address:
P.O. Box 779BISHOPS FALLS, Newfoundland and Labrador
A0H 1C0
Location Address:
141 Sunset Dr.BISHOPS FALLS, Newfoundland and Labrador
A0H 1C0
Tel.:<PHONE_NUMBER>
Tel.:<PHONE_NUMBER>
Fax:<PHONE_NUMBER>
Company Profile
Hi-Point Industries was incorporated in 1983, with the business objective of producing environmentally safe, all natural oil absorbents for the containment of hydrocarbon spills. Millions of dollars were spent on research and the construction of a custom-built factory. In 1984, Oclansorb® was born and our process patented. Oclansorb® is an organic, nontoxic, non-abrasive, lightweight industrial oil absorbent. Oclansorb® is made from naturally occurring blonde sphagnum peat moss harvested from the bogs of Newfoundland - it is the original peat based absorbent.
Oclansorb® has proven to be effective in large spills worldwide. Oclansorb® has helped mitigate environmental damage at major spills such as the Exxon Valdez spill in Alaska, the Iron Baron spill in Australia, and at a catascropic spill in Uruguay. Equally important, Oclansorb® and associated products are used daily as an ecological and economical absorbent for small spills. Oclansorb® is used by manufacturing plants, pulp and paper companies, oil storage facilities, oil refineries, petrochemical and chemical plants, transportation depots, airports, railways hydro and utility companies, fire and police departments, military bases and numerous other facilities.
Oclansorb® is also produced and packaged under private label for several large United States and Canadian Corporations. Oclansorb® is packaged in Sorb-Sox® (boom) and Spillow® (pillow) formats to provide spill responders the capability to control any spill scenario. Both Sorb-Sox® and Spillow® are available in standard and custom sizes. Hi-Point also stocks its own line of private label polypropylene pads, rolls, and booms at it's facility in Bishop's Falls. Hi-Point Industries have formed a joint venture with one of the oldest and most respected manufacturers of environmental bio cultures in the world.
Their product forms the key microbial components of Oclansorb® Plus, a biological oil absorbent that accelerates the natural biodegradation of residual oil left after a spill. These cultures are selected blends of naturally occurring, environmentally safe, nonpathogenic bacteria which have been specifically bred for more than 30 years to aggressively and enzymatically metabolize many petroleum hydrocarbons and other wastes. These cultures were fully authorized by the U.S. EPA Oil Spill National Contingency Plan (NCP) as an approved biotreatment agent for marine oil spills.
Existing Distributor Network: Abu Dhabi, Alaska, Argentina, Australia, Canada, Chile, Columbia, Ecuador, Germany, Hong Kong, Hungary, Italy, Japan, Mexico, Norway, Peru, Saudi Arabia, Spain, South Africa, Switzerland, Taiwan, Trinidad, United Kingdom, Uruguay, United States, Venezuela.
Full profile
Full profile
Contact information
Company description
Product / Service / Licensing
Oclansorb® was used at the Exxon Valdez spill for this purpose. A large volume of Oclansorb® was used on Applegate Island in Prince William Sound, Alaska. The product was used to protect a sensitive area from oil contamination. The area is used by mother seals to give birth to their pups. Oclansorb acted as a barrier protecting mother and baby from the oil sticking to their fur. Oclansorb® was applied at the land/water interface. As the oil contacted the Oclansorb®, it was immediately encapsulated saving the seals from imminent death. This unique and proven capability has resulted in Oclansorb® being utilized at the Iron Baron spill in Australia and a catastrophic spill in Uruguay for this important purpose. Oclansorb®, a product of nature, helping nature.
Oclansorb® is an efficient, economical clean-up product that instantly absorbs hydrocarbons on land and water. This lightweight material is easy to apply and spread. Hand, mechanical, airborne applicators, and boats designed specifically for the application and retrieval of Oclansorb® can be used. It is quickly picked up with brooms, squeegees, screened forks, various mechanical devices, and the boats previously mentioned. Oclansorb® leaves an oil sheen free surface on water, and a clean oil-free surface on concrete and asphalt, and is very effective at removing free oil from the ground.
Oclansorb® is safe and ecological, it is completely natural and non-toxic, it can be used liberally without harming the environment. Disposal is quick and inexpensive in either incinerators or landfills, local regulations permitting. Oclansorb® has passed the toxicity tests required by various governments. Oclansorb has passed the North American Leachate Test -TCLP Procedure 1311, and the Paint Filtration 9095 Test, it will not release captured hydrocarbons into the environment.
Oclansorb® is economical, it requires less product for storage and disposal. 1 Lb. of Oclansorb® will absorb approximately 1 gallon of oil, or up to 12 times its weight, resulting in inexpensive clean-up costs.
Be prepared, use Oclansorb as your first response for immediate hydrocarbon containment and clean-up at the spill site.
When broadcast over a crude oil, fuel oil or other petroleum hydrocarbon spill in marshes or along shorelines, the specially processed peat moss absorbent in Oclansorb® Plus traps the oil (a rich carbon supply) with very high efficiency, while also providing good moisture retention and oxygen transfer. Within hours of use, the timed release of large numbers of high performance oil degrading microbes and microbial nutrients present in Oclansorb® Plus immediately begin to accelerate the natural biodegradation of the contaminant in situ. The cultures in Oclansorb® Plus are selected blends of naturally occurring, environmentally safe, non-pathogenic bacteria which have been specifically bred for over 30 years to aggressively and enzymatically metabolize many petroleum hydrocarbons. These cultures are fully authorized by the U.S. EPA Oil Spill National Contingency Plan (NCP) as an approved biotreatment agent for marine oil spills.
Oclansorb® Plus is designed for use in marshes and wetlands and any open water environment which cannot be efficiently cleaned by manual methods. Oclansorb® Plus with non-toxic food grade gelling agents is designed for use along shorelines, beaches, land and inter tidal zones which cannot be efficiently cleaned by manual techniques. Oclansorb® Plus is also recommended when economic conditions or the presence of sensitive ecosystems require residual oil to be removed from the environment quickly.
Market profile
Industry sector market interests:
- Environment
- Manufacturing
- Medical/Biotechnology/Chemical
- Mining/Petroleum/Gas
- Service Industry
- Transportation
Geographic markets:
Export experience:
- Algeria
- Argentina
- Australia
- Belgium
- Brazil
- Chile
- Colombia
- Ecuador
- France
- Germany
- Hong Kong
- Hungary
- Italy
- Japan
- Luxembourg
- Mexico
- Netherlands
- Norway
- Peru
- Saudi Arabia
- South Africa
- Spain
- Switzerland
- Taiwan
- Trinidad and Tobago
- United Arab Emirates
- United Kingdom
- United States
- Uruguay
- Venezuela, Bolivarian Republic of
- Alabama
- Alaska
- Arkansas
- California
- Colorado
- Delaware
- Florida
- Illinois
- Indiana
- Iowa
- Kansas
- Kentucky
- Louisiana
- Maine
- Massachusetts
- Michigan
- Minnesota
- Mississippi
- Missouri
- Nebraska
- New Jersey
- New York
- North Dakota
- Ohio
- Oklahoma
- Pennsylvania
- South Dakota
- Tennessee
- Texas
- Virginia
- Washington
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Contacts
Contact information
Description
Company description
Products, services and licensing
Product / Service / Licensing
Oclansorb® was used at the Exxon Valdez spill for this purpose. A large volume of Oclansorb® was used on Applegate Island in Prince William Sound, Alaska. The product was used to protect a sensitive area from oil contamination. The area is used by mother seals to give birth to their pups. Oclansorb acted as a barrier protecting mother and baby from the oil sticking to their fur. Oclansorb® was applied at the land/water interface. As the oil contacted the Oclansorb®, it was immediately encapsulated saving the seals from imminent death. This unique and proven capability has resulted in Oclansorb® being utilized at the Iron Baron spill in Australia and a catastrophic spill in Uruguay for this important purpose. Oclansorb®, a product of nature, helping nature.
Oclansorb® is an efficient, economical clean-up product that instantly absorbs hydrocarbons on land and water. This lightweight material is easy to apply and spread. Hand, mechanical, airborne applicators, and boats designed specifically for the application and retrieval of Oclansorb® can be used. It is quickly picked up with brooms, squeegees, screened forks, various mechanical devices, and the boats previously mentioned. Oclansorb® leaves an oil sheen free surface on water, and a clean oil-free surface on concrete and asphalt, and is very effective at removing free oil from the ground.
Oclansorb® is safe and ecological, it is completely natural and non-toxic, it can be used liberally without harming the environment. Disposal is quick and inexpensive in either incinerators or landfills, local regulations permitting. Oclansorb® has passed the toxicity tests required by various governments. Oclansorb has passed the North American Leachate Test -TCLP Procedure 1311, and the Paint Filtration 9095 Test, it will not release captured hydrocarbons into the environment.
Oclansorb® is economical, it requires less product for storage and disposal. 1 Lb. of Oclansorb® will absorb approximately 1 gallon of oil, or up to 12 times its weight, resulting in inexpensive clean-up costs.
Be prepared, use Oclansorb as your first response for immediate hydrocarbon containment and clean-up at the spill site.
When broadcast over a crude oil, fuel oil or other petroleum hydrocarbon spill in marshes or along shorelines, the specially processed peat moss absorbent in Oclansorb® Plus traps the oil (a rich carbon supply) with very high efficiency, while also providing good moisture retention and oxygen transfer. Within hours of use, the timed release of large numbers of high performance oil degrading microbes and microbial nutrients present in Oclansorb® Plus immediately begin to accelerate the natural biodegradation of the contaminant in situ. The cultures in Oclansorb® Plus are selected blends of naturally occurring, environmentally safe, non-pathogenic bacteria which have been specifically bred for over 30 years to aggressively and enzymatically metabolize many petroleum hydrocarbons. These cultures are fully authorized by the U.S. EPA Oil Spill National Contingency Plan (NCP) as an approved biotreatment agent for marine oil spills.
Oclansorb® Plus is designed for use in marshes and wetlands and any open water environment which cannot be efficiently cleaned by manual methods. Oclansorb® Plus with non-toxic food grade gelling agents is designed for use along shorelines, beaches, land and inter tidal zones which cannot be efficiently cleaned by manual techniques. Oclansorb® Plus is also recommended when economic conditions or the presence of sensitive ecosystems require residual oil to be removed from the environment quickly.
Market
Market profile
Industry sector market interests:
- Environment
- Manufacturing
- Medical/Biotechnology/Chemical
- Mining/Petroleum/Gas
- Service Industry
- Transportation
Geographic markets:
Export experience:
- Algeria
- Argentina
- Australia
- Belgium
- Brazil
- Chile
- Colombia
- Ecuador
- France
- Germany
- Hong Kong
- Hungary
- Italy
- Japan
- Luxembourg
- Mexico
- Netherlands
- Norway
- Peru
- Saudi Arabia
- South Africa
- Spain
- Switzerland
- Taiwan
- Trinidad and Tobago
- United Arab Emirates
- United Kingdom
- United States
- Uruguay
- Venezuela, Bolivarian Republic of
- Alabama
- Alaska
- Arkansas
- California
- Colorado
- Delaware
- Florida
- Illinois
- Indiana
- Iowa
- Kansas
- Kentucky
- Louisiana
- Maine
- Massachusetts
- Michigan
- Minnesota
- Mississippi
- Missouri
- Nebraska
- New Jersey
- New York
- North Dakota
- Ohio
- Oklahoma
- Pennsylvania
- South Dakota
- Tennessee
- Texas
- Virginia
- Washington


Tests for a typename
provider live at spec/unit/puppet/typename_spec.rb
and their corresponding fixture (starting file) under spec/fixture/unit/puppet/typename/
.
Use an rspec context section per fixture and have multiple examples within the section using it.
Tests use real resources which are applied via the AugeasSpec::Fixtures::apply
method to a temporary file created from the original fixture. Once applied, the temporary file is tested using one of two methods:
aug_open
helper) and perform match/get queries to test particular features of the tree.{ }
syntax and use augparse
or augparse_filter
helpers to compare the file against the expected tree.The latter will be much easier and more robust as it will compare all aspects of the tree, while the first might be needed for some edge cases (empty files etc).
See also this writeup on testing techniques for Puppet providers using Augeas which shows this process for augeasproviders.
Execute rake spec
in the root directory to run all tests.
After applying the resource, there are a few ways we could test the results of the file.
Install bundler and run bundle install
to get all gems required for development or see the contents of Gemfile.
Please send pull requests via GitHub, or patches via git send-email to the author.


The more details about your project you include in your message, the faster I will get back to you. You’ll hear back from me within 2–4 business days. I look forward to hearing from you.
© Copyright 2021 AMY. All right Reserved


react-date-context
A set of React components for managing realtime UI updates that depend on the current time.
Rendering and updating React components that depend on the real time is hard.
Testing React component trees that use new Date()
directly is even more
difficult.
This project can be installed using npm. Go check them out if you don't have them locally installed.
$ npm install react-date-context
See doc site.
Feel free to dive in! Open an issue or submit PRs.


Delivering AI Applications with Open Source Platforms at Scale
How upstarts can play in mission-critical industries
- Pantelis Monogioudis, Gurudutt Hosanghadi, Lorant Farkas
- Augmented Intelligence & Devices Research Lab, Bell-Labs
A nautical analogy on where we are today with AI
DARPA's AI development in waves
Symbolic GOFAI
> 1980
Connectionism
> 2010
AGI
2020s
Meaning and resoning in NLP, Inference and Representation of Causality, Uncertainty Representations, Long-term Goal Planning, World Models
World Models: generative augmented physical (CFD) modeling from CT Scans
What we need from AI Pipelines
-
Composability
pointing to microservices and atomic operations. -
Scalability & Resilience
pointing to cloud-native (Kubernetes and FaaS) software architectures. -
Portability
across workstation, training rigs, dedicated edge devices and public clouds.
Many moving parts other than model code.
-
Model compression multiple targets
-
Model & ML Frameworks
-
Specialized (5G-enabling) Target Devices
-
Multi-Cloud (Edge/Core) Deployment
-
OS
Application Orchestration Operating System & Drivers
Linux Foundation - Deep Learning Foundation
www.acumos.org - the need for AI marketplaces
Acumos.org Architecture
New augmented intelligence business models - Health
New augmented intelligence business models - Transportation & Energy
New augmented intelligence business models - Security
New augmented intelligence business models - Defence
Lessons Learned
- Be here but think global.
- There is no such thing as "AI startup" - you need to have a solid business model that is by itself sustained without AI.
- Rethink mission critical industries - partner with local providers to make their business models work better.
- Health, Transportation, Energy, Security are being disrupted not so by AI but by the drammatic cost reduction of the platforms (cloud infrastructure, robotic manufacturing) that allow entries from many, unseen before, players.


|
App Engine Python SDK
v1.6.9 rev.445
The Python runtime is available as an experimental Preview feature.
|
Public Member Functions | |
| def | __init__ |
| def | get_keys_to_reserve |
| def | alias_old_names |
| def | create_entity |
| def | generate_key |
| def | handle_entity |
| def | initialize |
| def | finalize |
| def | generate_records |
Static Public Member Functions | |
| def | RegisterLoader |
| def | RegisteredLoaders |
| def | RegisteredLoader |
Static Public Attributes | |
| kind = None | |
A base class for creating datastore entities from input data. To add a handler for bulk loading a new entity kind into your datastore, write a subclass of this class that calls Loader.__init__ from your class's __init__. If you need to run extra code to convert entities from the input data, create new properties, or otherwise modify the entities before they're inserted, override handle_entity. See the create_entity method for the creation of entities from the (parsed) input data.
| def google.appengine.tools.bulkloader.Loader.__init__ | ( | self, | |
| kind, | |||
| properties | |||
| ) |
Constructor. Populates this Loader's kind and properties map. Args: kind: a string containing the entity kind that this loader handles properties: list of (name, converter) tuples. This is used to automatically convert the input columns into properties. The converter should be a function that takes one argument, a string value from the input file, and returns a correctly typed property value that should be inserted. The tuples in this list should match the columns in your input file, in order. For example: [('name', str), ('id_number', int), ('email', datastore_types.Email), ('user', users.User), ('birthdate', lambda x: datetime.datetime.fromtimestamp(float(x))), ('description', datastore_types.Text), ]
| def google.appengine.tools.bulkloader.Loader.alias_old_names | ( | self | ) |
Aliases method names so that Loaders defined with old names work.
| def google.appengine.tools.bulkloader.Loader.create_entity | ( | self, | |
| values, | |||
key_name = None , |
|||
parent = None |
|||
| ) |
Creates a entity from a list of property values. Args: values: list/tuple of str key_name: if provided, the name for the (single) resulting entity parent: A datastore.Key instance for the parent, or None Returns: list of db.Model The returned entities are populated with the property values from the argument, converted to native types using the properties map given in the constructor, and passed through handle_entity. They're ready to be inserted. Raises: AssertionError: if the number of values doesn't match the number of properties in the properties map. ValueError: if any element of values is None or empty. TypeError: if values is not a list or tuple.
| def google.appengine.tools.bulkloader.Loader.finalize | ( | self | ) |
Performs finalization actions after the upload completes.
| def google.appengine.tools.bulkloader.Loader.generate_key | ( | self, | |
| i, | |||
| values | |||
| ) |
Generates a key_name to be used in creating the underlying object. The default implementation returns None. This method can be overridden to control the key generation for uploaded entities. The value returned should be None (to use a server generated numeric key), or a string which neither starts with a digit nor has the form __*__ (see https://developers.google.com/appengine/docs/python/datastore/entities), or a datastore.Key instance. If you generate your own string keys, keep in mind: 1. The key name for each entity must be unique. 2. If an entity of the same kind and key already exists in the datastore, it will be overwritten. Args: i: Number corresponding to this object (assume it's run in a loop, this is your current count. values: list/tuple of str. Returns: A string to be used as the key_name for an entity.
| def google.appengine.tools.bulkloader.Loader.generate_records | ( | self, | |
| filename | |||
| ) |
Subclasses can override this to add custom data input code. This method must yield fixed-length lists of strings. The default implementation uses csv.reader to read CSV rows from filename. Args: filename: The string input for the --filename option. Yields: Lists of strings.
| def google.appengine.tools.bulkloader.Loader.get_keys_to_reserve | ( | self | ) |
Returns keys with ids in their paths to be reserved. Returns: A list of keys used to advance the id sequences associated with each id to prevent collisions with future ids.
| def google.appengine.tools.bulkloader.Loader.handle_entity | ( | self, | |
| entity | |||
| ) |
Subclasses can override this to add custom entity conversion code. This is called for each entity, after its properties are populated from the input but before it is stored. Subclasses can override this to add custom entity handling code. The entity to be inserted should be returned. If multiple entities should be inserted, return a list of entities. If no entities should be inserted, return None or []. Args: entity: db.Model Returns: db.Model or list of db.Model
| def google.appengine.tools.bulkloader.Loader.initialize | ( | self, | |
| filename, | |||
| loader_opts | |||
| ) |
Performs initialization and validation of the input file. This implementation checks that the input file exists and can be opened for reading. Args: filename: The string given as the --filename flag argument. loader_opts: The string given as the --loader_opts flag argument.
|
static |
Returns the loader instance for the given kind if it exists.
|
static |
Returns a dict of the Loader instances that have been created.
|
static |
Register loader and the Loader instance for its kind. Args: loader: A Loader instance.


This is one of the experiment i m doing in public folder
here Web server first look into the public folder, if it will not able to find a file in public folder then it will go to rails framework (checking for the routes)


MENU
Home
Top 10 Music
Top 10 Artists
Rate US
Contact US
Top 10 Artists
Here you find the best artists based on Bilboard
The Bealtles
Members:
John Lennon
Paul McCartney
George Harrison
Ringo Starr
Visit The Bealtles Bilboard
Guns N' Roses
Members:
Axl Rose
Dizzy Reed
Richard Fortus
Frank Ferrer
Slash
Visit Guns N' Roses Bilboard
Green Day
Members:
Billie Joe Armstrong
Mike Dirn
Tré Cool
Visit Green Day Bilboard
AC/DC
Members:
Angus Young
Stevie Young
Chris Slade
Brian Johnson
Visit AC/DC Bilboard
Queen's
Members:
Freddie Mercury
Brian May
John Deacon
Roger Taylor
Visit Queen's Bilboard
R.E.M.
Members:
Peter Buck
Mike Mills
Michael Stipe
Bill Berry
Visit R.E.M. Bilboard
Linkin Park
Members:
Chester Bennington
Mike Shinoda
Rob Bourdon
Brad Delson
Visit Linkin Park Bilboard
Imagine Dragons
Members:
Dan Reynolds
Ben McKee
Wayne "Wing" Sermon
Dan Platzman
Visit Imagine Dragons Bilboard
Arctic Monkeys
Members:
Alex Turner
Matt Helders
Jamie Cook
Andy Nicholson
Visit Arctic Monkeys Bilboard
Panic! at the Disco
Members:
Brendon Urie
Nicole Row
Dan Pawlovich
Mike Naran
Visit Panic! at the Disco Bilboard


Magister Gauen:
Please put them in that box over there. Thanks to you, we can continue to conduct highly productive and effective research. Your contribution will be duly noted.
Would you like to help us some more?


TVBEATS
Movies
Series
Our Team
Ziyad saleh
Software Engineer
Contact
Emad Idris
Computer Science
Contact
Omar Ali
Software Engineer
Contact
Mohamad Alhasan
Software Engineer
Contact
Hala Almasharfeh
Software Engineer
Contact