new Imager($imageElement, options)
Parameters:
| Name | Type | Description | ||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
$imageElement |
Element to attach to | |||||||||||||||||||||||||
options |
Object | Options
Properties
|
Methods
(static) getImagerFor($img) → {Imager|undefined}
Tries to find Imager instance associated with provided img element.
Parameters:
| Name | Type | Description |
|---|---|---|
$img |
HTMLImageElement | jQuery |
- Source:
Returns:
- Type
- Imager | undefined
adjustCanvasSize()
Takes image's real size (naturalWidth & naturalHeight)
and adjust canvas size to match that
but with respect to aspect ratio of preview viewport size.
- Source:
adjustEditContainer()
Positions $editContained with absolute coordinates
to be on top of $imageElement.
- Source:
commitChanges(operationMessage)
Stores current image to history, then renders current canvas into image.
Parameters:
| Name | Type | Description |
|---|---|---|
operationMessage |
- Source:
detectPlatform()
Crude detection of device and platform.
Sets this.platform and this.touchDevice.
- Source:
- To Do:
-
- this is BAD. Use more precise methods or some lib
fixImageSizeAndRotation($image) → {jQuery.Deferred.<string>}
Prepares image after first loading. It checks image EXIF data and fixes it's rotation,
scales image down if it's too large.
Parameters:
| Name | Type | Description |
|---|---|---|
$image |
HTMLImageElement |
- Source:
Returns:
Image data base64 string
- Type
- jQuery.Deferred.<string>
getDataSize() → {number}
Returns current image data in bytes.
- Source:
Returns:
Bytes number
- Type
- number
getImageFormat(imageSrc)
Detects image format for either base64 encoded string or http:// url.
Parameters:
| Name | Type | Description |
|---|---|---|
imageSrc |
string |
- Source:
getPluginInstance(pluginName) → {*}
Returns plugin instance by its name
Parameters:
| Name | Type | Description |
|---|---|---|
pluginName |
- Source:
Returns:
- Type
- *
handleImageElementSrcChanged()
This function should be called when image's `src` attribute is changed from outside of the imager.
It checks `src` attribute, detects image format, prepares image (rotates it according to EXIF for example)
and triggers `ready` event on imager.
- Source:
pluginSort()
Sorts plugins based in their `weight`
- Source:
render(ctxopt)
Renders image on temporary canvas and then invokes plugin methods
that shoul modify image.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
ctx |
<optional> |
Context on which to draw image. |
- Source:
setPreviewSize(width, height)
Sets preview area dimensions.
Note that this affects only the size of image that user sees.
Internal image size is not affected.
Parameters:
| Name | Type | Description |
|---|---|---|
width |
number | |
height |
number |
- Source:
setWaiting(waiting)
Parameters:
| Name | Type | Description |
|---|---|---|
waiting |
boolean | Waiting status. TRUE for adding 'waiting' text, false to remove. |
- Source:
setZindex(zIndexValue)
Change the container's z-index property.
Parameters:
| Name | Type | Description |
|---|---|---|
zIndexValue |
- Source:


BACKGROUND
For the last 6 years, Iíve been on and off (mostly on) several different SSRIís for depression/anxiety, which makes sense considering the history of mental illness in my family. Now, Iíve only been able to successfully treat my depression with two medicines: Effexor and Celexa, and since Effexor caused me constipation and actually increased my anxiety, I opted for the libido-bashing Celexa.

Miserable while I know too well how lucky I am in life. Thatís why I take 60mg of Celexa every morning. I would take less, but I canít even manage on 55mg.
TRIP #1
Recently I asked my girlfriend to roll a joint I had prepared with some junk weed and approximately 30mg of 5-MeO-DMT sprinkled evenly on top. I would roll it myself, but I canít. Physically canít.
Being the nice sitter she is, my girl joined me on the deck where I smoked half of the spliff while lying back in a lawn chair.  Weird.
Winter sky. I stared up at the skeleton-like trees. The more I smoked the heavier my body felt. Even in the dark, subtle lines and colors began to poke out with beautiful clarity. The closed-eye visuals were not there. Darkness like with the 2C-Ďs. I tried to force a CEV trip with no successÖsomething thatís easy for me on DXM.
I imagine I would have experienced more visual effects if I had been off of my SSRI for the appropriate 3-day minimum. Itís a bitch to do, but I find that 6 to 8 days is the ideal vacation time from my anti-depressants in order to get a proper trip off of the usual doses of LSD, MDMA, psilocybin, and other like drugs. Still, going into a trip stressed and depressed is the WORST IDEA.
Since the trip was weak and over in about 20 minutes, on the following Saturday afternoon, I decided on that I would extend the life of the remaining half joint with the aid of an MAOI. I wonder what the tolerance is like on this 5-MeO-DMT
THE POLY-DRUG PROBLEM
I have experienced a few days of the milder symptoms of Serotonin Syndrome, when I combined Hawaiian Baby Woodrose seeds with Syrian Rue.
Surely itís smarter to simply get off the SSRI or tricyclic antidepressants for a week before taking any MAOI, but making well thought-out decisions frequently isnít my strength.
Iíve found that I can safely take a tablespoon or 9g of fresh Syrian Rue (approximately 200-600 mg Harmala, assuming a 4% MAOI alkaloid content of dry weight seeds). Itís my safety threshold, and I have never exceeded it while on Celexa. Once again, if I were smart, I would have taken a week or two off of the SSRI.
TRIP #2
Okay, itís a gorgeous Saturday afternoon. I have an empty stomach and about 5 hours to burn with no responsibilities and an empty house. I measure out a tablespoon of Rue and chop it to a powder in a coffee grinder, carefully trying my best not to inhale that funky, nauseous, toffee smell. I empty out several 1000mg Vitamin C gel tabs and fill them with the brown powder. After two glasses of water, the tabs are gone. Now I would have done a proper ascorbic acid extract, but the mere thought of breathing in the bitter stank of boiling RueÖ Ack! Ack!
I sit down on the couch, play a video game, and in about an hour or so, I begin to feel the lethargic drawl of the Harmala. I save the game, grab the joint, and head to the backyard. I lie down in the grass. I light up and start some more tree staring.
Now this is definitely differentÖ Branches begin to shimmer and wiggle as I focus my eyes. Fallen leaves drift back and forth on the lawn as if the blades of grass were gently moving tides. My weight feels heavier than even before, like Iím anchored to the soil. I begin to notice various insects. Bugs crawling everywhere! Creatures everywhere!!! I had no idea how truly alive my backyard was.
Time passes quickly. Iím guessing 40 minutes had passed after the half-joint had been burned down to an empty roach. I spent the following hour and a half calmly listening to some gansta rap. Limited subject material but the metaphor and turn of phrase feels plenty fun to dissect.
Anyway, the trip tapered off after a total of about 3 hours, and physical and mental fatigue ensued for the rest of the evening.
CONCLUSIONS
These last couple of trips has taught me that patience and respect must be given in ample amounts to hallucinogens.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


The Java EE 7 Tutorial
47.1 Overview of Java EE Security
Every enterprise that has either sensitive resources that can be accessed by many users or resources that traverse unprotected, open networks, such as the Internet, needs to be protected.
Enterprise tier and web tier applications are made up of components that are deployed into various containers. These components are combined to build a multitier enterprise application. Security for components is provided by their containers. A container provides two kinds of security: declarative and programmatic.
-
Declarative security expresses an application component's security requirements by using either deployment descriptors or annotations.
A deployment descriptor is an XML file that is external to the application and that expresses an application's security structure, including security roles, access control, and authentication requirements. For more information about deployment descriptors, read Using Deployment Descriptors for Declarative Security.
Annotations, also called metadata, are used to specify information about security within a class file. When the application is deployed, this information can be either used by or overridden by the application deployment descriptor. Annotations save you from having to write declarative information inside XML descriptors. Instead, you simply put annotations on the code, and the required information gets generated. For this tutorial, annotations are used for securing applications wherever possible. For more information about annotations, see Using Annotations to Specify Security Information.
-
Programmatic security is embedded in an application and is used to make security decisions. Programmatic security is useful when declarative security alone is not sufficient to express the security model of an application. For more information about programmatic security, read Using Programmatic Security.
47.1.1 A Simple Application Security Walkthrough
The security behavior of a Java EE environment may be better understood by examining what happens in a simple application with a web client, a user interface, and enterprise bean business logic.
In the following example, which is taken from the Java EE 7 Specification, the web client relies on the web server to act as its authentication proxy by collecting user authentication data from the client and using it to establish an authenticated session.
<IP_ADDRESS> Step 1: Initial Request
In the first step of this example, the web client requests the main application URL. This action is shown in Figure 47-1.
Since the client has not yet authenticated itself to the application environment, the server responsible for delivering the web portion of the application, hereafter referred to as the web server, detects this and invokes the appropriate authentication mechanism for this resource. For more information on these mechanisms, see Security Mechanisms.
<IP_ADDRESS> Step 2: Initial Authentication
The web server returns a form that the web client uses to collect authentication data, such as user name and password, from the user. The web client forwards the authentication data to the web server, where it is validated by the web server, as shown in Figure 47-2. The validation mechanism may be local to a server or may leverage the underlying security services. On the basis of the validation, the web server sets a credential for the user.
<IP_ADDRESS> Step 3: URL Authorization
The credential is used for future determinations of whether the user is authorized to access restricted resources it may request. The web server consults the security policy associated with the web resource to determine the security roles that are permitted access to the resource. The security policy is derived from annotations or from the deployment descriptor. The web container then tests the user's credential against each role to determine whether it can map the user to the role. Figure 47-3 shows this process.
The web server's evaluation stops with an "is authorized" outcome when the web server is able to map the user to a role. A "not authorized" outcome is reached if the web server is unable to map the user to any of the permitted roles.
<IP_ADDRESS> Step 4: Fulfilling the Original Request
If the user is authorized, the web server returns the result of the original URL request, as shown in Figure 47-4.
Figure 47-4 Fulfilling the Original Request
Description of "Figure 47-4 Fulfilling the Original Request"
In our example, the response URL of a web page is returned, enabling the user to post form data that needs to be handled by the business-logic component of the application. See Chapter 48, "Getting Started Securing Web Applications" for more information on protecting web applications.
<IP_ADDRESS> Step 5: Invoking Enterprise Bean Business Methods
The web page performs the remote method call to the enterprise bean, using the user's credential to establish a secure association between the web page and the enterprise bean, as shown in Figure 47-5. The association is implemented as two related security contexts: one in the web server and one in the EJB container.
Figure 47-5 Invoking an Enterprise Bean Business Method
Description of "Figure 47-5 Invoking an Enterprise Bean Business Method"
The EJB container is responsible for enforcing access control on the enterprise bean method. The container consults the security policy associated with the enterprise bean to determine the security roles that are permitted access to the method. The security policy is derived from annotations or from the deployment descriptor. For each role, the EJB container determines whether it can map the caller to the role by using the security context associated with the call.
The container's evaluation stops with an "is authorized" outcome when the container is able to map the caller's credential to a role. A "not authorized" outcome is reached if the container is unable to map the caller to any of the permitted roles. A "not authorized" result causes an exception to be thrown by the container and propagated back to the calling web page.
If the call is authorized, the container dispatches control to the enterprise bean method. The result of the bean's execution of the call is returned to the web page and ultimately to the user by the web server and the web client.
47.1.2 Features of a Security Mechanism
A properly implemented security mechanism will provide the following functionality:
-
Prevent unauthorized access to application functions and business or personal data (authentication)
-
Hold system users accountable for operations they perform (non-repudiation)
-
Protect a system from service interruptions and other breaches that affect quality of service
Ideally, properly implemented security mechanisms will also be
-
Easy to administer
-
Transparent to system users
-
Interoperable across application and enterprise boundaries
47.1.3 Characteristics of Application Security
Java EE applications consist of components that can contain both protected and unprotected resources. Often, you need to protect resources to ensure that only authorized users have access. Authorization provides controlled access to protected resources. Authorization is based on identification and authentication. Identification is a process that enables recognition of an entity by a system, and authentication is a process that verifies the identity of a user, device, or other entity in a computer system, usually as a prerequisite to allowing access to resources in a system.
Authorization and authentication are not required for an entity to access unprotected resources. Accessing a resource without authentication is referred to as unauthenticated, or anonymous, access.
The characteristics of application security that, when properly addressed, help to minimize the security threats faced by an enterprise include the following.
-
Authentication: The means by which communicating entities, such as client and server, prove to each other that they are acting on behalf of specific identities that are authorized for access. This ensures that users are who they say they are.
-
Authorization, or access control: The means by which interactions with resources are limited to collections of users or programs for the purpose of enforcing integrity, confidentiality, or availability constraints. This ensures that users have permission to perform operations or access data.
-
Data integrity: The means used to prove that information has not been modified by a third party, an entity other than the source of the information. For example, a recipient of data sent over an open network must be able to detect and discard messages that were modified after they were sent. This ensures that only authorized users can modify data.
-
Confidentiality, or data privacy: The means used to ensure that information is made available only to users who are authorized to access it. This ensures that only authorized users can view sensitive data.
-
Non-repudiation: The means used to prove that a user who performed some action cannot reasonably deny having done so. This ensures that transactions can be proved to have happened.
-
Quality of Service: The means used to provide better service to selected network traffic over various technologies.
-
Auditing: The means used to capture a tamper-resistant record of security-related events for the purpose of being able to evaluate the effectiveness of security policies and mechanisms. To enable this, the system maintains a record of transactions and security information.


This file lives in public/about.html. Feel free to edit!
After you run the application, visit /about.html to see this page rendered in the browser.


Input your cards ({{$ctrl.mainPlayer.cards.length}}):
Requirements:
All card fields are filled
All card fields are unique
NOTE: player inputs must be in the order of play, starting from you.
| Name | Cards |
|---|---|
Requirements:
All player name fields are filled
All player names are unique
The total number of cards adds up to 18 (current: {{$ctrl.getCardCount()}})


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{{ bokeh_css_resources|indent(4)|safe }} {{ bokeh_js_resources|indent(4)|safe }}
Made first as a serious boardgame, then as a digital prototype for the Smart Charging Challenge 2018.
Team: Jana Endemann, Daphne van Leeuwen, Felix Claessen (Seita B.V.) and Nicolas Höning (Seita B.V.).
We explain the underlying game mechanics and data sources here.
What happens after you plug in your electric car? A big underlying problem of the energy transition is this: who should charge when?
This game challenges you to find the best schedule. Can you beat the score of your friends?
Remember: The smartest charge is doing it for him/herself, but also for a balanced grid and for clean energy!
In this game you are the operator of several EV charging stations. EV owners will come to one of your stations and want to charge their cars. When they leave, the car should be charged (you get 50 per energy token). But if you let the car leave empty, you will have to pay a penalty (100).
On the other hand, you must buy the energy that you charge with from the grid.
You pay a variable price, depending on the supply in the system at the moment.
Note: You'll always buy or sell the next cheapest token. Exactly like in a market.
When there is little energy (negative imbalance, i.e. more demand than supply), you pay a higher price than when there is a lot of energy available (imbalance is positive, i.e. more supply than demand).
You can also use this to your advantage, by using the stationed cars as batteries. Charge when energy is cheap and discharge when energy is expensive. But be careful not to let the cars leave empty!
By balancing your import and export to the grid while having all the cars leave fully charged will give you the greatest profit. All the while you are helping balance the grid and charging cars for users. Win-win-win!!!
Each turn you have several options available. For each car currently at one of your stations you can decide to:
When you're happy with the changes in all your chargers, you can commit them by clicking 'next turn'. Shortcut: [Enter] The transactions are now included in the game grid and the active turn-line now moves down one.
Your score will increase or decrease with each turn you play. At the start, you will have to make investments to buy energy for the charging EV owners. Soon though you may start seeing your money back in rewards.
Finished (or screwed up beyond repair)? Better reset the game. Shortcut: [Shift+r]
Balance and charge to the max to max your score.
Below we explain the game dynamics and show some of the underlying data. This data can easily be changed with data fitting a specific real-world case. One of our main design challenges for the hackathon was to use realistic data that still fits the game into an 8 by 8 matrix (where the dimensions represent time and market quantities) and is fun to play.
The supply data represents solar production in Castricum, Noord-Holland on March 9, 2018 (9am to 16pm). We got the data from the iCarus API, and downsampled and divided it until it could be represented by eight tokens.
The demand data represents two well-known peaks in energy markets in the morning and evening when people get up or come home. The peaks are shifted to improve game play.
The liquidity of our in-game market has a fixed range, meaning there is only a limited number of energy tokens. When demand and supply are equal, the in-game market has 4 tokens. This represents that there is ample opportunity to both buy and sell. When demand exceeds supply by 4, there are no tokens to buy. When supply exceeds demand by 4, there are 8 tokens on the market. The market is then full, meaning there is no possibility to sell.
The in-game market is a generic function representing pricing dynamics on real energy markets: supply and demand that change throughout the day, and prices that increase non-linearly with scarcity. The highlighted number of tokens shows the price for supply and demand being in balance. It's cheap to buy tokens to the right of it, but expensive to sell to the left of it. This market motivates players to 1) make most of their purchases when the market has the most abundant supply, and 2) to feed back energy to the grid when the market is coming up way short.
Probabilities bring an element of chance (and luck) into games. In this game, cars arrive at charging stations throughout the day with a certain probability of arrival and duration. For each new game the board is initialised using random draws from the shown probability distribution for car arrivals. The time that each car spends at a charging station is also randomised. They can remain at a charging station for up to 8 hours, but 4 hours of parking is the most common. The probabilities used here are based on data from office parking stations, but it's easy to set up the game for other situations, such as parking on public streets or at houses.
([ESC] to close)
{{ move_summary | safe }}


DATA SCIENTIST - Shreveport, LA 71105
ROMPH & POU is not a typical Advertising & Marketing Agency. We also play with numbers, too.
Our Culture: Our philosophy is that through data, all things are created. We love collaboration, and we let our team members shine. We want our clients to win!
What We Do: We dig into client data for better understanding, all the while creating our proprietary SCORPIO® system. We’re industry leaders.
Average Day: Each day is a different mix. Some projects are 2 weeks, some come-and-go in a day. Either way, the load is never stale. We work until we find an answer!
Typical Responsibilities
- Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies.
- Maintain, improve, and contribute to the company’s data science code base.
- Keep code base up to date and running smoothly.
- Develop custom algorithms to run in a production environment.
- Analyze marketing campaign results and present conclusions to management.
- Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
- Assess the effectiveness and accuracy of new data sources and data gathering techniques.
Desired Qualifications
- Solid understanding of a variety of statistical techniques/concepts, such as:
- probability laws/rules and distributions
- parametric and non-parametric hypothesis testing
- kernel density estimation
- Solid understanding of a variety of machine learning techniques, such as:
- linear regression
- logistic regression
- kNN regression and classification
- regression tress and classification trees
- Must have proficiency with the following tech stack:
- Python (NumPy, pandas, scikit-learn, SciPy, TensorFlow)
- T-SQL
- Git
- Basic understanding of a variety of computer science topics, such as:
- data structures
- algorithm design and analysis
- object oriented design
- Additional proficiencies that are nice to have:
- C/C , R (Shiny)
- Hadoop, Hive, and Pig
Benefits
- Medical Coverage
- Dental Coverage
- Vision Coverage
- 401K
- Life Insurance (both Automatic and Supplemental up to $100,000)
- Paid Vacation Days/Personal Days/Sick Days
- 11 Weeks of Short-Term Disability Coverage
- Rewards Program for Length of Service


[1]
at least until they have replaced all of us.
[2]
at least until they have replaced all of their mattresses.


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Interface Summary
Interface
Description
Property
Represents objects that are property types
A property knows its type and its images
Record Summary
Record
Description
Property.Boom
The BOOM property
An element with BOOM will instantly disappear, along with all elements directly next to it
Property.Defeat
The DEFEAT property
An element with DEFEAT will make a superimposed YOU element disappear
Property.Hot
The HOT property
An element with HOT will make a superimposed MELT element disappear
Property.Melt
The MELT property
An element with MELT disappears when superimposed with an element with HOT
Property.Push
The PUSH property
An element with PUSH can be pushed (therefore, cannot be walked over)
Property.Sink
The SINK property
An elements with SINK will make any superimposed element disappear along with itself
Property.Stop
The STOP property
An element with STOP cannot be walked over
Property.Win
The WIN property
An element with WIN will make the state of the level as won if superimposed with a YOU element
Property.You
The YOU property
An element with YOU can be controlled by the player,
will disappear if superimposed with a DEFEAT element,
and will make the state of the level as won if superimposed with a WIN element


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LogFactor5 Overview
What is LogFactor5? - LogFactor5 is a Swing based GUI
that leverages the power of log4j logging framework and
provides developers with a sophisticated, feature-rich,
logging interface for managing log messages.
How does it work? - LogFactor5 seamlessly plugs into
log4j and provides developers with an alternate destination
for outputting messages.
Benefits:
- Quickly isolate problems in your application.
- Enable only the categories that you are interested in without affecting
other messages.
- Filter out priority levels you don't need.
- Filter out records based on NDC.
- Reduce the time required to locate specific messages.
- Read in and view log4j log files from either a file or a URL.
- Start the LogFactor5 GUI up independent of your application.
Features:
- Real-time category and log level filtering.
- Read log files from a file.
- Read log files from a URL.
- Customizable Log Table view.
- Category level tree navigation.
- NDC record filtering.
- Full text searching on logged messages.
- Save configuration and filtering settings for later sessions.
- Configure the number of log records to be displayed.
- Customizable record colors.
- Configurable font face and size.
- Customizable Log Table column layout.
- Full support for all log4j levels.
- Full support for custom levels.
- Dynamic message counting.
LogFactor5 System Requirements:
LogFactor5 requires Java (TM) Virtual Machine 1.2.2 and log4j
1.2 or later.
Documentation and Help
- All documentation is in HTML format.
- For additional documentation on logging, please see the Suggested Reading
section.
Platform Support
- LogFactor5 is written in 100% Java and will work with any
system supporting the Java 2 platform.


Glassic is a simple dashboard interface built using Angular and Dotnet Core.
Currently, Glassic is under development. Expect these features in the near future:
New landing page
Database connected login page
Custom and configureble dashboard UI


John & Emily Get Married!
home
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friends
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travel & accomodations
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dress code
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party
Our friends
We have wonderful friends who are getting married, too:
Paige and Nick
Jenna and Daniel


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--- layout: post title: I’ve been a little down lately. date: '2013-09-05T23:04:00-05:00' tags: - Baseball - Life tumblr_url: http://theabellsix.tumblr.com/post/60423743483/ive-been-a-little-down-lately-not-a-lot-down ---
I’ve been a little down lately. Not a lot down. But a little.  Good times.—Rob Neyer — My favorite baseball writer, even though we are opposites politically. Today, I commiserate with him.


There are many configuration parameters that affect the behavior of Pgpool-II. In the first section of this chapter we describe how to interact with configuration parameters. The subsequent sections discuss each parameter in detail.


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pygaussmarkov package¶
Submodules¶
pygaussmarkov.CircleSolver module¶
Created on Sun May 14 11:11:17 2017
@author: john
-
class
pygaussmarkov.CircleSolver.
CircleSolver
(xPos, yPos, xPosUncert, yPosUncert, deltaChiSqToStop=0.01, dampingFactor=1, useDampedGaussNeutonLineSearch=False, recordHistory=False)[source]¶ Bases:
pygaussmarkov.CombinedSolver.CombinedSolver
Simple y=mx+c fitting
pygaussmarkov.CombinedSolver module¶
@author: john
-
class
pygaussmarkov.CombinedSolver.
CombinedSolver
(deltaChiSqToStop=0.01, dampingFactor=1, useDampedGaussNeutonLineSearch=False, recordHistory=False)[source]¶ Bases:
object
Class to solve problems of the type f(x,l) = 0. Must be implemented by a user class and the requred abstract functions implemented The code is broken down into many small functions which can allow a child class to overload any of the methods to change the performance of different methods.
-
computeATBPinvBTinv
(AT, BPinvBTinv)[source]¶ Computes AT*BPinvBTinv AT the transpose of the determinante matrix BPinvBTinv the weight matrix return AT*BPinvBTinv
-
computeXhat
(ATBPinvBTinvAinv, ATBPinvBTinv, w)[source]¶ Computes ATBPinvBTinvAinv * ATBPinvBTinv ATBPinvBTinvAinv = the inverse of the design matrix ATBPinvBTinv = the transpose of the determinant matrix multiplied by the weight matrix w = the residual vecotr (f(x,l)) retuns the negetive value of the update vector (ATBPinvBTinvAinv * ATBPinvBTinv).w
-
computerATBPinvBTinvA
(ATBPinvBTinv, A)[source]¶ Computes ATBPinvBTinv*A the design matrix ATBPinvBTinv the transpose of the determinante matrix multiplied by the weight matrix A the determinant matrix return ATBPinvBTinv*A the design matrix
-
getATBPinvBTinvAandATBPinvBTinv
(variables, BpinvBTinv)[source]¶ Computes ATBPinvBTinvA and ATBPinvBTinv by calling sub functions variables = the current variables p = the weight matrix returns ATBPinvBTinvA and ATBPinvBTinv
-
getATBPinvBTinvAinvandATBPinvBTinvandATBPinvBTinvA
(variables, BpinvBTinv)[source]¶ Computes ATBPinvBTinvAinv, ATBPinvBTinv and ATBPinvBTinvA by calling sub functions variables = the current variables p = the weight matrix returns ATBPinvBTinvAinv, ATBPinvBTinv and ATBPinvBTinvA
-
getAandAT
(variables)[source]¶ User implemented method. Computers the determinant matrix A and its transpose AT variables = the current variables return two numpy matricies A and AT
-
getBandBT
(variables)[source]¶ User implemented method. Computers the determinant matrix A and its transpose AT variables = the current variables return two numpy matricies A and AT
-
getChiSq
(w, BTpinvBinv)[source]¶ Computes w.p.w w = the residual vector, the difference between f(x) and the measurements returns value of w.p.w
-
getFreeVariables
(variables)[source]¶ Returns a list of np array with only the free variables variables = np array of all the variables
return = np array of the free variables
-
getFxl
(variables)[source]¶ User implemented method. Gets the values for the given variables. variables = the current variables return a numpy array
-
getListOfParameterNames
()[source]¶ Returns a list of strings. There should be one entry per parameter The order should be the same as the order in the the A matrix
-
getNewVariables
(variables, alpha, xHat)[source]¶ Combines the current variables with the update vector xHat and the scale parameter alpha The method takes account of the fixed and free variables variables = the current variables. This should inclule all the variables either free or fixed alpha the scale factor for xHat. this is a scalar value xHat the update vector. This should only include the updates for the free parameters returns the new variables vector
-
getPinv
()[source]¶ User implemented method. Gets the error matrix ((sig**2)) this is also the inverse of the weight matirx p return a single numpy matrix
-
getUncertaintyList
(ATBPinvBTinvAInv)[source]¶ takes the inverse of the design matrix and returns a list of the the uncertainities for the parameters ATBPinvBTinvAInv is the inverse of the design matix returns a list of length number parmetrs, for the uncertainities for each parmeter. If the parameter is fixed then the value will be 0.0
-
pygaussmarkov.GaussMarkovUtilities module¶
Contains untility functions which can be used in Gaus Markov implementatinos @author: john
-
pygaussmarkov.GaussMarkovUtilities.
generatePMatrix
(errors, noMeasurements)[source]¶ Takes an error vectror and returns a diagional weight matrix. the weight is 1/error**2 errors = np list of length equal to noMeasuremetns or 1. If length =1 it will assume that this error is the same for all measurements noMeasurements = the number of measurements.
returns a diagional matrix for weights of size noMeasurements X noMeasurements
-
pygaussmarkov.GaussMarkovUtilities.
generatePinvMatrix
(errors, noMeasurements)[source]¶ Takes an error vectror and returns a diagional weight matrix. the weight is 1/error**2 errors = np list of length equal to noMeasuremetns or 1. If length =1 it will assume that this error is the same for all measurements noMeasurements = the number of measurements.
returns a diagional matrix for weights of size noMeasurements X noMeasurements
pygaussmarkov.ParametricSolver module¶
module contains the ParametricSolver object @author: john
-
class
pygaussmarkov.ParametricSolver.
ParametricSolver
(deltaChiSqToStop=0.01, dampingFactor=1, useDampedGaussNeutonLineSearch=False, recordHistory=False)[source]¶ Bases:
object
Class to solve problems of the type f(x) = L. Must be implemented by a user class and the requred abstract functions implemented The code is broken down into many small functions which can allow a child class to overload any of the methods to change the performance of different methods.
-
computeATP
(AT, P)[source]¶ Computes AT*P AT the transpose of the determinante matrix P the weight matrix return AT*P
-
computeXhat
(ATPAinv, ATP, w)[source]¶ Computes ATPAinv * ATP ATPAinv = the inverse of the design matrix ATP = the transpose of the determinant matrix multiplied by the weight matrix w = the residual vecotr (f(x)-measurements) retuns the negetive value of the update vector (ATPAinv * ATP).w
-
computerATPA
(ATP, A)[source]¶ Computes ATP*P the design matrix ATP the transpose of the determinante matrix multiplied by the weight matrix A the determinant matrix return ATP*A the design matrix
-
getATPAandATP
(variables, p)[source]¶ Computes ATPA and ATP by calling sub functions variables = the current variables p = the weight matrix returns ATPA and ATP
-
getATPAinvATPandATPA
(variables, p)[source]¶ Computes ATPAinv, ATP and ATPA by calling sub functions variables = the current variables p = the weight matrix returns ATPAinv, ATP and ATPA
-
getAandAT
(variables)[source]¶ User implemented method. Computers the determinant matrix A and its transpose AT variables = the current variables return two numpy matricies A and AT
-
getChiSq
(w, p)[source]¶ Computes w.p.w w = the residual vector, the difference between f(x) and the measurements returns value of w.p.w
-
getFreeVariables
(variables)[source]¶ Returns a list of np array with only the free variables variables = np array of all the variables
return = np array of the free variables
-
getFx
(variables)[source]¶ User implemented method. Gets the values for the given variables. variables = the current variables return a numpy array
-
getListOfParameterNames
()[source]¶ Returns a list of strings. There should be one entry per parameter The order should be the same as the order in the the A matrix
-
getNewVariables
(variables, alpha, xHat)[source]¶ Combines the current variables with the update vector xHat and the scale parameter alpha The method takes account of the fixed and free variables variables = the current variables. This should inclule all the variables either free or fixed alpha the scale factor for xHat. this is a scalar value xHat the update vector. This should only include the updates for the free parameters returns the new variables vector
-
getP
()[source]¶ User implemented method. Gets the weight matrix (1/(sig**2)) return a single numpy matrix
-
getUncertaintyList
(ATPAInv)[source]¶ takes the inverse of the design matrix and returns a list of the the uncertainities for the parameters ATPAInv is the inverse of the design matix returns a list of length number parmetrs, for the uncertainities for each parmeter. If the parameter is fixed then the value will be 0.0
-
getW
(measurements, fx)[source]¶ Computes f(x) - measurements measurements = the measurements fx = the calcualted values returns fx-measurements
-
getXHatandATPA
(variables, p, w)[source]¶ Computes xHat by calling subfunctions. variables = the current variables p = the weight matrix w = the residual vecotr (f(x)-measurements) returns -xHat(subtract from current x to get new x), ATPAInv
-
pygaussmarkov.TriangulationSolver module¶
module contains the TriangulationSover object wihch can sovle triangulatin problems @author: john
-
class
pygaussmarkov.TriangulationSolver.
TriangulationSolver
(xPos, yPos, zPos, measurements, uncertainties, deltaChiSqToStop=0.01, dampingFactor=1, useDampedGaussNeutonLineSearch=False, recordHistory=False)[source]¶ Bases:
pygaussmarkov.ParametricSolver.ParametricSolver
triangulation with parametric solver
pygaussmarkov.nthOrderFitParametricSolver module¶
@author: john
-
class
pygaussmarkov.nthOrderFitParametricSolver.
NthOrderFit
(order, xData, yData, uncertainties, deltaChiSqToStop=0.01, dampingFactor=1, useDampedGaussNeutonLineSearch=False, recordHistory=False)[source]¶ Bases:
pygaussmarkov.ParametricSolver.ParametricSolver
Nth Order Fitter


Earth Spirit
Tips
- You can Boulder Smash allies out of Chronosphere (post-Agh Grip doesn't work, though).
- You can drop a Remnant while you're revving up in Rolling Boulder.
- You can double tap the Remnant hotkey to automatically drop one in front of yourself.
- You can use items while Rolling (except Blink). This is mainly useful for TPs, Shiva's,
Sheepstick/Orchid, and wards. If you use a Force Staff while it's revving up, you'll get the Force and
continue rolling. If you use Force while rolling, you'll get the Force but the Boulder will break apart.
- You can pick up runes while Rolling.
- You can use/target spells while you're in your Boulder. This means that you can Smash an ally while
you're rolling into them or do a drive-by Gripping.
- When you hit an enemy hero with Rolling Boulder you'll always appear behind them and at the same angle
from which you approached them. You can target your Smash on them while Rolling and you'll immediately
kick them back along the path you came.
- Rolling Boulder destroys trees along the path that you roll.
- Neither Smash nor Grip can interrupt ally or enemy channels when directly targeted on the hero.
- Reduce, reuse, recycle. Use the same Remnant you just Gripped to Roll into. Smash a Remnant into an
enemy then spam Grip to catch it while it's still moving.
- The disadvantage of recycling is that you end up stacking your total disables on top of each other (some
of the silence duration is wasted because the enemy is stunned anyway). If you have Remnants to burn,
stagger your spells.
- The trickiest part about using Magnetize effectively is getting it started. The initial Magnetize radius
is a tiny AoE around Earth Spirit himself. But once you get it cracking on one unit, then the good times
can start to roll. Use Force Staff or Boulder Smash to manipulate the Magnetized unit's position to get
closer to other heroes, then cast a Remnant near it.
- The trigger radius on a Remnant from a magnetized unit is larger than the activation/refresh radius to
Magnetize new enemies, so place fresh Remnants closer to un-Magnetized enemies.
- The Magnetize DoT lasts for 6 seconds on an enemy. Space out re-Magnetizations to squeeze out the most
damage.
- Rolling into an Enchant Remnant (Agh skill) will immediately take the hero out of Remnant form.
Counters
- Kaolin Showdown isn't invulnerable inside the Rolling Boulder. He's fully targeted and can be stunned
and damaged as per usual.
- When ganking an Earth Spirit, stand in front of his Rolling Boulder and make him hit you to prevent him
from rolling out of the situation.
- Earth Spirit needs a lot of time to recharge Remnants after a teamfight. Keep high aggression when
playing against an Earth Spirit.
- Magnetize deals some substantial amounts of hurt. Don't stand next to your team if you're Magnetized and
they're not.
- Magnetize can be dispelled.


Palak
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Tswalu Kalahari Reserve
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Half-Life: Alyx trailer shows the G-Man is back for March 2020
It may not be Half-Life 3 but the first gameplay trailer for VR title Half-Life: Alyx promises everything you’ve ever wanted from a new sequel.
Not for the first time this week, if you take into account Shenmue 3, the impossible seems to have happened and a video game sequel many expected never to see is about to become a reality… a virtual reality anyway.
Half-Life: Alyx isn’t Half-Life 3 but it is instead a VR spin-off set between Half-Life 1 and 2.
As you might guess from the title, you play as Alyx Vance, who together with her father Eli is attempting to ‘mount an early resistance to the Combine’s brutal occupation of Earth’.
The trailer looks great and Valve has hinted at a lot of interesting details, telling The Verge that the game will last as long as Half-Life 2, which ran to at least 13 hours on a first go.
Advertisement Advertisement
That’s extremely long for a VR title and since the whole game exists partly to drum up interest in the Valve Index headset you can guarantee Valve will be pulling out all the high-tech stops.
You won’t actually need Valve Index to play the game though, as it will work with any PC compatible headset, including Oculus Rift and HTC Vive.
Everyone will get Source 2 tools for modding the game but if you have a Valve Index you’ll also be able to explore environments from the game in your SteamVR Home space, use alternate gun skins, and access exclusive Half-Life: Alyx content in CS:GO and Dota 2.
The game is up for pre-order now on Steam for £46.49, although it’ll be 10% off (£41.84) up until launch in March 2020. Valve Index owners though, will get it for free.
The trailer looks much closer to a traditional Half-Life experience than many were expecting, although the floating hands are somewhat surprising and a little distracting. But we guess that means full virtual bodies still aren’t really viable.
What’s most interesting for long-term Half-Life fans is Valve’s curious statement that you should play Half-Life 2: Episode Two before playing the new game, which seems to make little sense since it’s set sometime afterwards.
Is that a hint that Half-Life: Alyx will attempt to resolve the cliffhanger ending from Episode Two? Or does it just mean there’ll be a few references that you might not otherwise notice?
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The G-Man is in the game and he’s always seemed to have control over dimensional travel, and has managed to stop time on at least one occasion, so perhaps time travel will figure as part of the story?
There’s no way to tell at the moment, but thankfully it won’t be long till we find out for sure.
Email<EMAIL_ADDRESS>leave a comment below, and follow us on Twitter


Hi guys,
I am trying to read a sensor temp from the gpio of raspberry pi...
I don't know how to send data to emoncms :S
I don't understand correctly the work flow from imput->feed->dashboard, maybe to easy, and i don't understand it, i don't know.
Is there any documentation about protocol formats or something...it seems easy from the cms, but I don't know what I need.
Firstly i will do it with bash, for testing, after with c language, I don't know maybe its easy and I don't get the point :S
I can Already see the temps from the ds18b20 sensor, using bash...but i don't know how to log them to emoncms...
I created a input called room_temp, and a feed for it...afther that I assigned the feed to the graphs in dashboard...but i think I need some programing to send data to emoncms right?
Can any one help me with the workflow of this?
Thanks in advance
Re: temperature readings with ds18b20 1 wire sensor
Hi,
I created a input for server temp...
Node: name Description Process list
1 1 server_temp log
after that a feed have benn created:
Id Name Tag Datatype Public Updated Value
5 server_temp REALTIM "private" inactive 0.00
using jason api helper... I am doing this...
#!/bin/bash
while :
do
info_temp0=$(/opt/vc/bin/vcgencmd measure_temp)
temp0=${info_temp0:5:4}
echo $temp0
curl "http://localhost/emoncms/input/post.json?json={server_temp:$temp0}"
sleep 5
done
but the feed appears inactive :S?!
Nothing happens... In dash I associated a gauge...but I don't get feedback from emoncms!
What i need to do?
thanks in advance
regards tux
Re: temperature readings with ds18b20 1 wire sensor
You need to add the api write key for the user you are logging data for to the url as detailed in /emoncms/input/api
Regards
Re: temperature readings with ds18b20 1 wire sensor
Ok...
I got it :S
its to much easy... kudos for you guys...the inputs are created automatically with APIKey.
I have a problem now with the returning Ok string...
I am redirecting the output "ok" to /dev/null...but curl will show some statistic data...
To solve that we can use -s(to suppress this statistic data when redirecting or when a error ocurrs)
I readed some where that apikey puts some more processing on the server side...maybe because of validation key?is that processing power very high?
EDIT: in the smoothie graph visualization...what the parameter Ufac means??
thanks in advance
regards
tux


9.6 ALL_USERS
The ALL_USERS view provides information on all user names.
Name
Type
Description
username
VARCHAR2
Name of the user.
user_id
Numeric user id assigned to the user.


If you want to work or just talk with me, your best bet is probably email. I try to respond within a day, so just shoot me a message anytime!
You can email me at<EMAIL_ADDRESS>or check out my profiles on the buttons below.


Test purpose
- Check that we can load the Delaunator JS library into a module from a remote CMS.
- Create a scene using a particle emitter.
- Use the emitter particles as the source points for the Delaunay triangulation calculations.
- Render Delauney triangulation lines using Delaunator data.
- Render Voronoi cell lines using Delaunator data.
- Animate the scene.
- Check that we can include mouse coordinates in the Delaunay calculations.
Touch test: should work as expected
Annotated code


A function is a reusable block of code. We can use a function to output a value, or do anything else we want. We can easily define our own function by using the keyword "def".
def product(x,y): return x*y print product(2,3) [out]: 6 print product(5,10) [out]: 50
The keyword "def" is followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, and must be indented. The product() function above has "x" and "y" as its parameters. A function doesn't necessarily have parameters:
def say_hi(): print "Welcome to QuantConnect' say_hi() [out]: Welcome to QuantConnect


Drag and drop image files here.
Image files supported are:
- .gif
- .jpg, .jpeg
- .pdf
- .ps
- .xbm
- .png
- And so many more! The last few are maybe's. They are a bit old, so dont count on them working.
After dropping the image will show in the browser.
When your done, simply click the back arrow to return to this page!


Mandala Generator
Range of random numbers
Amount of random numbers
Graph Random Mandala
Take Screenshot


Cow Kingdom: Easter Egg Rampage
A real-time-strategy game, with a twist. You can choose to see your enemy or not.
 Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore
Kevin Xiao: The Demon Hunting Code Master
Has lead hundreds of League of Legends and Starcraft teams to victory and can produce thousands of lines of code to commandeer your computers
Kim Usi: Professional Space Software Ninja
Has relevant experiences in game development and pair programming
Barbara Xu: Experienced Marketing Warrior
Has critical thinking and analyzing skills as well as marketing experience
Cynthia Huang: Project-Oriented Business Expert
Has relevant technical project experience during co-op and business analytical skills developed through new venture projects
Alice Ma: Innovative Computing Champion
Has critical analysis skills, programming knowledge, effective writing skills


You are the Magic Love Fairy, you have arrived to this world to fulfill your heart wish to spread love throughout the universe
You need to release at least 8 hearts in the world to complete your first mission.
You can move using the arrows on your keyboard and press "h" to release a heart :)


Julius Tens
FOSS developer and social democrat based in Berlin.
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A build is normally a fairly sequential process, and in a big project, a full execution can easily take hours. While one could create such a job on Jenkins, the long turn-around time to the result tends to reduce the value of continuous integration. This page discusses a technique to cope with this problem.
The idea to is to split a big build into multiple stages. Each stage is executed sequentially for a particular build run, but this works like a CPU pipeline and increase the throughput of CI, and also reduces the turn-around time by reducing the time a build sits in the build queue.
In this situation, your earlier stage needs to pass files to later stages. A general way to do this is as follows:
- An earlier stage archives all the files into a zip/tgz file at the end of the build.
- Tell Jenkins to archive this zip/tgz file as a post-build action, take a fingerprint of it, then trigger the next stage.
- The first thing the next stage does in its build is to obtain this bundle through the permalink for the last successful artifact, then unzip it. Do keep this archive file around because we'll take a fingerprint of it here, too.
- The build proceeds by using the files obtained from the earlier stage.
- Tell Jenkins to fingerprint the zip/tgz file. This allows you to correlate executions of these stages to track the flow.
If you have more than 2 stages, you can repeat this process. In some cases, this "zip/tgz" file would have to contain the entire workspace. If so, the next stage can use the URL SCM plugin to simplify the retrieval.
JENKINS-682 keeps track of an RFE to more explicitly and better support this use case. Please feel free to add yourself to CC, vote, and comment on the issue.
Note: there is a new plugin which helps with this problem: http://wiki.jenkins-ci.org/display/JENKINS/Clone+Workspace+SCM+Plugin


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You username is required.
Username is too short.
Username is too long.
You must enter a valid email address.
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Password too short, must be at least 8 characters long.
Your passwords MUST match!
You must enter your phone number.
Please select a plan.
Please enter your credit card number.


Last Updated on May 16, 2006 9:03 PM .
Adding new instructions to Agilla is easy. This tutorial describes the process.
For this example, we will create a new instruction, findmatch
, that takes
a variable as a parameter, and searches through the heap to find an equivalent
variable. If it finds a match, it stores the index on the opstack and sets
the condition code equal to 1, otherwise it sets the condition code equal to
1.
The built-in Agilla instructions are located in <agilla>/opcodes
. All
instructions must provide interface BytecodeI, which is located in <agilla>/interfaces/BytecodeI.nc
.
ByteCodeI defines a single command:
command result_t execute(uint8_t instr, AgillaAgentContext* context);
This command is called when the instruction is executed. It is passed the instruction's byte code to allow a single component to implement multiple instructions. It is also passed the context of the agent that executed the instruction.
Usually, an instruction is implemented using a configuration and a module. Here is the implementation of findmatch:
configuration OPfindMatch{
provides interface BytecodeI;
}
implementation {
components OPfindMatchM, OpStackC, TupleUtilC, ErrorMgrProxy;
BytecodeI = OPfindMatchM;
OPfindMatchM.OpStackI -> OpStackC;
OPfindMatchM.TupleUtilI -> TupleUtilC;
OPfindMatchM.Error -> ErrorMgrProxy;
}
includes Agilla; /** * Searches through the heap to find the index of a matching variable. */ module OPfindMatchM { provides interface BytecodeI; uses { interface OpStackI; interface TupleUtilI; // For comparing variables interface ErrorMgrI as Error; } } implementation { command result_t BytecodeI.execute(uint8_t instr, AgillaAgentContext* context) { AgillaVariable arg; // the variable to search for dbg(DBG_USR1, "VM (%i:%i): Executing findMatch\n", context->id.id, context->pc-1); if (call OpStackI.popOperand(context, &arg)) { int16_t i; for (i = 0; i < AGILLA_HEAP_SIZE; i++) { if (context->heap.pos[i].vtype != AGILLA_TYPE_INVALID) { if (call TupleUtilI.fEquals(&arg, &context->heap.pos[i])) { call OpStackI.pushValue(context, i); // save index on stack context->condition = 1; // indicate match found return SUCCESS; } } } context->condition = 0; // indicate no match found return SUCCESS; } else return FAIL; } }
Next choose a bytecode for your instruction. Open <Agilla>/AgillaOpcodes.h. This file contains enumerations of all instructions in Agilla. The main enumeration is BasicInstruction. These are the primary instructions in Agilla that are listed here. Since all of the bytecodes in the Basic instruction set are used, Agilla provides 13 extended instruction sets, each of which can contain an additional 256 instructions. In this case, we will assign findmatch bytecode 0x1c within enumeration ExtendedISA1.
typedef enum { ... IOPfindMatch = 0x1c, ... } ExtendedISA1;
Now open <Agilla>/Agilla.nc and wire the opcode to AgillaEngineC.
implementation { ... components OPfindMatch; ... AgillaEngineC.ExtendedISA1[IOPfindMatch] -> OPfindMatch; ... }
You now have to recompile and install the Agilla firmware on the motes.
The final step is to modify the AgentInjector to recognize the new instruction. Go into edu.wustl.mobilab.agilla.opcodes and open the opcode class that the new instruction is part of (in this case ExtendedOpcodes1.java). Add the following line to it:
public static final short OPfindMatch = 0x1c;
This specifies the instruction and its opcode to the Agilla assembler.
If the instruction contains embedded operands, you need to specify the number operands and the number of bits dedicated to it using:
public static final int ArgNumOP<opcode> = <num operands>; public static final int ArgNumOP<opcode> = <num bits>;
Sometimes instructions have predefined arguments. For example, you can pushc
photo
. To specify this, create a two-dimensional array where each row
contains the argument and its corresponding value. For example, the specification
for pushc is as follows:
public static final String[][] ArgValOPpushc = new String[][] { {"temperature", "1"}, {"temp", "1"}, {"photo", "2"}, {"mic", "3"}, {"microphone", "3"}, {"magx", "4"}, {"magnetometerx", "4"}, {"magy", "5"}, {"magnetometery", "5"}, {"accelx", "6"}, {"accelerometerx", "6"}, {"accely", "7"}, {"accelerometery", "7"}, // the following are for changing an agent's description {"unknown", "3"}, {"unspecified","0"}, {"cargo","1"}, {"fire","2"} };


How smart is today's artificial intelligence?
Artificial intelligence is creeping into our everyday lives through technology like check-scanning machines and GPS navigation. How far away are we from making intelligent machines that actually have minds of their own? Hari Sreenivasan reports on the ethical considerations of artificial intelligence as part of our Breakthroughs series.


FULLTEXT(coll, attribute, query, limit) → docArray
Return all documents from collection coll, for which the attribute attribute matches the fulltext search phrase query, optionally capped to limit results.
Note: the FULLTEXT() function requires the collection coll to have a fulltext index on attribute. If no fulltext index is available, this function will fail with an error at runtime. It doesn't fail when explaining the query however.
FULLTEXT() is not meant to be used as an argument to FILTER, but rather to be used as the expression of a FOR statement:
FOR oneMail IN FULLTEXT(emails, "body", "banana,-apple")
RETURN oneMail._id
query is a comma-separated list of sought words (or prefixes of sought words). To distinguish between prefix searches and complete-match searches, each word can optionally be prefixed with either the prefix: or complete: qualifier. Different qualifiers can be mixed in the same query. Not specifying a qualifier for a search word will implicitly execute a complete-match search for the given word:
FULLTEXT(emails, "body", "banana") Will look for the word banana in the attribute body of the collection collection.
FULLTEXT(emails, "body", "banana,orange") Will look for both words banana and orange in the mentioned attribute. Only those documents will be returned that contain both words.
FULLTEXT(emails, "body", "prefix:head") Will look for documents that contain any words starting with the prefix head.
FULLTEXT(emails, "body", "prefix:head,complete:aspirin") Will look for all documents that contain a word starting with the prefix head and that also contain the (complete) word aspirin. Note: specifying complete is optional here.
FULLTEXT(emails, "body", "prefix:cent,prefix:subst") Will look for all documents that contain a word starting with the prefix cent and that also contain a word starting with the prefix subst.
If multiple search words (or prefixes) are given, then by default the results will be AND-combined, meaning only the logical intersection of all searches will be returned. It is also possible to combine partial results with a logical OR, and with a logical NOT:
FULLTEXT(emails, "body", "+this,+text,+document") Will return all documents that contain all the mentioned words. Note: specifying the + symbols is optional here.
FULLTEXT(emails, "body", "banana,|apple") Will return all documents that contain either (or both) words banana or apple.
FULLTEXT(emails, "body", "banana,-apple") Will return all documents that contain the word banana, but do not contain the word apple.
FULLTEXT(emails, "body", "banana,pear,-cranberry") Will return all documents that contain both the words banana and pear, but do not contain the word cranberry.
No precedence of logical operators will be honored in a fulltext query. The query will simply be evaluated from left to right.


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Woman, 21, who married man, 74, cheats on him with another pensioner
Milijana Bozic has been seen on Serbian reality TV show 'Parovi' kissing 60-year-old Nemanja Stamatovic Zabac - weeks after she was snapped naked in bed with another older man
by Joseph Wilkes, Ana Marjanovic, https://www.facebook.com/dailymirror/A 21-year-old woman who married a 74-year-old cheated on him with a pensioner just weeks after sleeping with another elderly man, according to reports.
Milijana Bozic has been seen on Serbian reality TV show 'Parovi' - which translates as 'Couples' in English - kissing 60-year-old Nemanja Stamatovic Zabac.
This despite being married to Milojko Bozic since September this year.
And local media reports this latest incident comes just weeks after Milijana was snapped naked in bed with another older man.
In the photos taken from the show, Milijana can be seen kissing Mr Zabac and sitting on his lap.
Other images show them in bed together and local media reports she had sex with the other, older man while her husband slept next to them.
The unnamed older man, who is reportedly older than husband Milojko, even attended their highly-publicised wedding.
He is also said to be married with children, although Milijana denies having a fling with him.
She said: "The person who posted those photos will be sued. They are selfies I took and I know who I sent the pictures to, and I know who posted them.
"I have seen this man a couple of times, but I have not been in a relationship with him."
And in the previous season of the reality show 'Parovi', Milijana confessed that she had oral sex with 35-year-old married contestant Aleksandar Pozgaj after Milojko was eliminated.
At the time, she said: "We were intimate. We didn't have full sex, I satisfied him orally and he did the same for me. There was also touching and cuddling."
Despite the apparent indiscretion, Milojko forgave his partner.
But this time he does not believe that this is only a game and he stated: "If you set it like this, our conflict will continue after the reality show."
He later added: "She's playing me and is making a fool out of me. This is not a game this is real! Miliana is playing both of us."
Milijana tried to explain to her husband that it was just a game.
She reportedly said that it is difficult for her to find a free elderly man because her target group is much older gentlemen.
Zabac is divorced and has three adult children, reportedly telling Milijana he no longer needed his wife in his life.
Local media report Zabac has previously served a ten-year prison sentence.


s6
Software
skarnet.org
An overview of s6
s6 is a collection of utilities revolving around process supervision and
management, logging, and system initialization. This page is a high-level
description of the different parts of s6.
Process supervision
At its core, s6 is a process supervision suite, like its ancestor
daemontools and its
close cousin
runit.
Concept
The concept of process supervision comes from several observations:
- Unix systems, even minimalistic ones, need to run
long-lived processes, aka daemons. That is one of the
core design principles of Unix: one service → one daemon.
- Daemons can die unexpectedly. Maybe they are missing a vital
resource and cannot handle a certain failure; maybe they tripped on a bug;
maybe a misconfigured administration program killed them; maybe the
kernel killed them. Processes are fragile, but daemons are vital to a
Unix system: a fundamental discrepancy that needs to be solved.
- Automatically restarting daemons when they die is generally a good
thing. In any case, sysadmin intervention is necessary, but at least the
daemon is providing service, or trying to, until the sysadmin can log in
and investigate the underlying problem.
- Ad-hoc shell scripts that restart daemons suck, for
several reasons that would each justify their own page. The difficulty of
keeping track of the PID, explained below, is one of those reasons.
- It is sometimes necessary to send signals to a daemon. To kill it,
of course, but also to make it read its config file again, for instance;
signalling a daemon is a natural and very common way of sending it
simple commands.
- Generally, to send a signal to a daemon, you need to know its PID.
Without a supervision suite, knowing the proper PID is hard. Most
non-supervision systems use a hack known as .pid files, i.e.
the script that starts the daemon stores its PID into a file, and other
scripts read that file. This is a bad mechanism for several reasons, and
the case against .pid files would also justify its own page; the most
important drawback of .pid files is that they create race conditions
and management scripts may kill the wrong process.
- Non-supervision systems provide scripts to start and stop daemons,
but those scripts may fail at boot time even though they work when run
manually,
and vice versa. If a sysadmin logs in and runs the script to restart a
daemon that has died, the result might not be the same as if the whole
system had been rebooted, and the daemon may exhibit strange behaviours!
This is because the boot-time environment and the restart-time environment
are not the same when the script is run; and a non-supervision system
just cannot ensure reproducibility of the environment. This is a core
problem of non-supervision systems: countless bugs have been falsely
reported because of simple environment differences or configuration errors,
countless man-hours have been wasted to try and understand what was
going on.
A process supervision system organizes the process hierarchy in a
radically different way.
- A process supervision system starts an independent hierarchy of
processes at boot time, called a supervision tree. This
supervision tree never dies: when one of its components dies, it is
restarted automatically. To ensure availability of the supervision
tree at all times, it should be rooted in process 1, which cannot die.
- A daemon is never started, either manually or in a script, as a
scion of the script that starts it.
Instead, to start a daemon, you configure a
specific directory which contains all the information about your daemon;
then you send a command to the supervision tree. The supervision tree
will start the daemon as a leaf. In a process supervision
system, daemons are always spawned by the supervision tree, and
never by an admin's shell.
- The parent of your daemon is a supervisor. Since your
daemon is its direct child, the supervisor always knows the
correct PID of your daemon.
- The supervisor watches your daemon and can restart it when it
dies, automatically.
- The supervision tree always has the same environment, so starting
conditions are reproducible. Your daemon will always be started with the
same environment, whether it is at boot time via init scripts or for the
100th automatic - or manual - restart.
- To send signals to your daemon, you send a command to its
supervisor, which will then send a signal to the daemon on your behalf.
Your daemon is identified by the directory containing its information,
which is stable, instead of by its PID, which is not stable; the supervisor
maintains the correct association without a race condition or the other
problems of .pid files.
Implementation
s6 is a straightforward implementation of those concepts.
- The s6-svscan and
s6-supervise programs are the components
of the supervision tree. They are long-lived programs.
- s6-supervise is a daemon's
supervisor, its direct parent. For every long-lived process on a
system, there is a corresponding s6-supervise
process watching it. This is okay, because every instance of
s6-supervise uses very few resources.
- s6-svscan is, in a manner of speaking,
a supervisor for the supervisors. It watches and maintains a collection of
s6-supervise processes: it is the branch
of the supervision tree that all supervisors are stemming from. It can be
run and
supervised
by your regular init process, or it can
run as
process 1 itself. Running s6-svscan as process 1 requires
some effort from the user, because of the inherent non-portability of
init processes; the
s6-linux-init
package automates that effort and allows users to run s6 as an init
replacement.
- The configuration of a daemon to be supervised by
s6-supervise is done via a
service directory.
- The place to gather all service directories to be watched by a
s6-svscan instance is called a
scan directory.
- The command that controls a single supervisor, and allows you to
send signals to a daemon, is
s6-svc. It is a short-lived program.
- The command that controls a set of supervisors, and allows you to
start and stop supervision trees, is
s6-svscanctl. It is a short-lived
program.
These four programs,
s6-svscan,
s6-supervise,
s6-svscanctl and
s6-svc,
are the very core of s6. Technically, once you have them, you have a
functional s6 installation, and the other utilities are just a bonus.
Practical usage
To use s6's supervision features, you need to perform the following steps:
- For every daemon you potentially want supervised, write a
service directory. Make sure that
your daemon does not background itself when started in the
./run script! Auto-backgrounding is a historical hack
that was implemented when supervision suites did not exist; since
you're using a supervision suite, auto-backgrounding is unnecessary
and in this case detrimental.
- Write a single scan directory for
the set of daemons you want to actually run. This set can be modified
at run time.
- At some point in your initialization scripts, run
s6-svscan on the scan directory. This will
start the supervision tree, including your set of daemons. The exact
way of running s6-svscan depends on your system: it is not quite the same
when you want to run it as process 1 on a real machine, or under another
init on a real machine, or as process 1 in a
Docker container, or in another
context entirely.
- Alternatively, you can start s6-svscan
on an empty scan directory, then populate it step by step and send an
update command to s6-svscan via
s6-svscanctl whenever the supervision
tree should pick up the differences and start the services you added.
- That's it, your services are running. To control them manually,
you can use the s6-svc command.
- At the end of the system's lifetime, you can use
s6-svscanctl to bring down the supervision
tree.
Service-specific logging
s6-svscan can monitor a supervision tree,
but it can also do one more thing. It can ensure that a daemon's log,
i.e. what the daemon outputs to its stdout (or stderr if you redirect it),
gets processed by another, supervised, long-lived process, called a
logger; and it can make sure that the logs are never lost
between the daemon and the logger - even if the daemon dies, even if the
logger dies.
If your daemon is outputting messages, you have a decision to make
about where to send them.
- You can do as non-supervision systems do, and send the messages
to syslog. It's entirely possible with a supervision system too.
However, like auto-backgrounding, syslog is a historical mechanism that
predates supervision suites, and is technically inferior; it is
recommended that you do not use it whenever you can avoid it.
- You can send them to the daemon's stdout/stderr and do nothing special
about it. The logs will then be sent to s6-svscan's stdout/stderr;
what mechanism will read them depends on how you started s6-svscan.
- You can use s6-svscan's service-specific logging mechanism and
dedicate a logger process to your daemon's messages.
s6 provides you with a long-lived process to use as a logger:
s6-log. It will store your logs in one (or
more) specific directory of your choice, and rotate them automatically.
Helpers for run scripts
Creating a working
service directory, and especially a good
run script, is the most important part of the work when
adapting a daemon to a supervision framework.
If you can find your daemon's invocation script on a non-supervision system,
for instance a System V-style init script, you can see the exact
options that the daemon is being run with: environment variables,
uid and gid, open descriptors, etc. This is what you
need to replicate in your run script.
(Do not replicate the auto-backgrounding, or things like
start-stop-daemon
invocation: start-stop-daemon and its friends are hideous and kludgy
attempts to work around the lack of proper supervision mechanisms. Now
that you have s6, you should remove them from your system, throw them
into a bonfire, and dance and laugh while they burn. Generally speaking,
as a system administrator you want daemons that have been designed
following the principles described
here,
or at least you want to use the command-line options that make them
behave in such a way.)
The vast majority of the tools provided by s6 are meant to be used in
run scripts: they help you control the process state and
environment in your script before it executes into your daemon. Or,
sometimes, they are daemons themselves, designed to be supervised.
s6, like other skarnet.org
software, makes heavy use of
chain
loading, also known as "Bernstein chaining": a lot of s6 tools will
perform some action that changes the process state, then execute into the
rest of their command line. This allows the user to change the process state
in a very flexible way, by combining the right components in the right
order. Very often, a run script can be reduced to a single command line -
likely a long one, but still a single one. (That is the main reason why
using the
execline language
to write run scripts is recommended: execline makes it natural to handle
long command lines made of massive amounts of chain loading. This is by no
means mandatory, though: a run script can be any executable file you want,
provided that running it eventually results in a long-lived process with
the same PID.)
Some examples of s6 programs meant to be used in run scripts:
- The s6-log program is a long-lived
process. It is meant to be executed into by a ./log/run
script: it will be supervised, and will process what it reads on
its stdin (i.e. the output of the ./run daemon).
- The s6-envdir program is a
short-lived process that will update its current environment according
to what it reads in a given directory, then execute into the rest of its
command line. It is meant to be used in a run script to adjust the
environment with which the final daemon will be executed into.
- Similarly, the s6-softlimit program
adjusts its resource limits, then executes into the rest of its command
line: it is meant to set the resources the final daemon will have
access to.
- The s6-applyuidgid program,
part of the s6-*uidgid family, drops root privileges before
executing into the rest of its command line: it is meant to be used
in run scripts that need root privileges when starting but do not
need it for the execution of the long-lived process.
- s6-ipcserverd is a daemon that
listens to a Unix socket and spawns a program for every connection.
It is meant to be supervised, so it should be used in a run script,
and it's also meant to be a flexible super-server that you can use
for different applications: so it is a building block that may appear in
several of your run scripts defining
local services.
Readiness notification and dependency management
Now that you have a supervision tree, and long-lived processes running
supervised, you may want to introduce dependencies between them: do not
perform an action (e.g. start (with s6-svc -u)
the Web server connecting to a database)
before a given daemon is up and running (e.g. the database server).
s6 provides tools to do that:
- The s6-svwait,
s6-svlisten1 and
s6-svlisten programs will wait until a set of
daemons is up, ready, down (as soon as the ./run process dies) or
really down (when the ./finish process has also died).
- Unfortunately, a daemon being up does not mean that it is
ready:
this page goes into the details. s6
supports a simple mechanism: when a daemon wants to signal that it is
ready, it simply writes a newline to a file descriptor of its
choice, and s6-supervise will pick that
notification up and broadcast the information to processes waiting for
it.
- s6 also has a legacy mechanism for daemons that do not
notify their own readiness but provide a way for an external program
to check whether they're ready or not:
s6-notifyoncheck.
This is polling, which is bad, but unfortunately necessary for
many daemons as of 2019.
s6 does not provide a complete dependency management framework,
i.e. a program to automatically start (or stop) a set of services in a
specific order - that order being automatically computed from a graph of
dependencies between services.
That functionality belongs to a service manager, and is
implemented for instance in the
s6-rc package.
Fine-grained control over services
s6 provides you with a few more tools to control and monitor your
services. For instance:
- s6-svstat gives you access to
the detailed state of a service
- s6-svperms allows you to configure
what users can read that state, what users can send control
commands to your service, and what users can be notified of
service start/stop events
- s6-svdt
allows you to see what caused the latest deaths of a supervised
process
These tools make s6 the most powerful and flexible of the existing
process supervision suites.
Additional utilities
The other programs in the s6 package are various utilities that may be
useful in designing servers, and more generally multi-process software.
They can be used with or without a supervision environment, although
it is of course recommended to have one; but they are not part of the core s6
functionality, and you may safely ignore them for now if you are just getting
into the supervision world.
Generic inter-process notification
The s6-ftrig* family of programs allows notifications between
unrelated processes: a set of processes can subscribe to a certain
channel - identified by a directory in the filesystem - and ask to be
notified of certain events on that channel; another set of processes can
send events to the channel.
The underlying mechanism is the same as the one used by the supervision
tree for readiness notification, but the s6-ftrig* tools provide
a more generic access to that mechanism.
Helpers for designing local services
Local services, i.e. daemons listening to a Unix domain socket, are a
powerful and flexible mechanism, especially with modern Unix systems
that allow client authentication. s6 includes tools to take advantage
of that mechanism.
- The s6-ipc* family of programs is about designing clients
or servers that communicate over Unix domain sockets.
- The s6-*access* and s6-connlimit
family of programs is about client access control.
- The s6-sudo* family of programs is about using a local
service in order to give selected
clients the ability to run a command line with the privileges of the
server, without using suid programs.
Keeping file descriptors open
Sometimes you want to keep a file descriptor open, even if the program
normally using it dies - so the program can restart and use the same
file descriptor without losing any data. To do that, you need to
hold the descriptor in another process, i.e. that process
should have it open but do nothing with it.
s6-svscan, for instance, holds the pipe
existing between a supervised daemon and its logger, so even if the
daemon or the logger dies while there are logs in the pipe, the pipe
remains open and the logs are not lost.
s6 provides a mechanism to store and retrieve open file descriptors
in a totally generic way: the s6-fdholder* family of programs.
- The s6-fdholder-daemon program
is a daemon (or, rather, executes into the
s6-fdholderd daemon), meant to be
supervised, that will hold file descriptors on its clients' behalf.
- Other programs in the family, such as
s6-fdholder-store, are client
programs that interact with this daemon to store and retrieve file
descriptors.
Note that "socket activation", one of the main advertised benefits of the
systemd
init system, sounds similar to fd-holding.
The reality is that socket activation is a mixture of several different
mechanisms, one of which is fd-holding; s6 allows you to implement the
healthy parts of socket activation.
Other miscellaneous utilities
This page does not list or classify every s6 tool. Please
explore the "Reference" section of the
main s6 page for details on a specific program.


Please find here all related information about registered applications, roles, users and their current permissions.
Please navigate using the top menu from above or the options under Tools.


Multi-Agent Reinforcement Learning Paper Lists
Published:
Multi-Agent Reinforcement Learning (MARL) is a very interesting research area, which has strong connections with single-agent RL, multi-agent systems, game theory, evolutionary computation and optimization theory.
This is a collection of research and review papers of multi-agent reinforcement learning (MARL). The Papers are sorted by time. Any suggestions and pull requests are welcome.
The sharing principle of these references here is for research. If any authors do not want their paper to be listed here, please feel free to contact Lantao Yu (Email: lantaoyu [AT] hotmail.com).
Tutorial and Books
- Multiagent Reinforcement Learning by Daan Bloembergen, Daniel Hennes, Michael Kaisers, Peter Vrancx. ECML, 2013.
- Multiagent systems: Algorithmic, game-theoretic, and logical foundations by Shoham Y, Leyton-Brown K. Cambridge University Press, 2008.
Review Papers
- Deep Reinforcement Learning Variants of Multi-Agent Learning Algorithms by Castaneda A O. 2016.
- Evolutionary Dynamics of Multi-Agent Learning: A Survey by Bloembergen, Daan, et al. JAIR, 2015.
- Game theory and multi-agent reinforcement learning by Nowé A, Vrancx P, De Hauwere Y M. Reinforcement Learning. Springer Berlin Heidelberg, 2012.
- Multi-agent reinforcement learning: An overview by Buşoniu L, Babuška R, De Schutter B. Innovations in multi-agent systems and applications-1. Springer Berlin Heidelberg, 2010
- A comprehensive survey of multi-agent reinforcement learning by Busoniu L, Babuska R, De Schutter B. IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews, 2008
- If multi-agent learning is the answer, what is the question? by Shoham Y, Powers R, Grenager T. Artificial Intelligence, 2007.
- From single-agent to multi-agent reinforcement learning: Foundational concepts and methods by Neto G. Learning theory course, 2005.
- Evolutionary game theory and multi-agent reinforcement learning by Tuyls K, Nowé A. The Knowledge Engineering Review, 2005.
- An Overview of Cooperative and Competitive Multiagent Learning by Pieter Jan ’t HoenKarl TuylsLiviu PanaitSean LukeJ. A. La Poutré. AAMAS’s workshop LAMAS, 2005.
Research Papers
Framework
- Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments by Lowe R, Wu Y, Tamar A, et al. arXiv, 2017.
- Deep Decentralized Multi-task Multi-Agent RL under Partial Observability by Omidshafiei S, Pazis J, Amato C, et al. arXiv, 2017.
- Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games by Peng P, Yuan Q, Wen Y, et al. arXiv, 2017.
- Robust Adversarial Reinforcement Learning by Lerrel Pinto, James Davidson, Rahul Sukthankar, Abhinav Gupta. arXiv, 2017.
- Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning by Foerster J, Nardelli N, Farquhar G, et al. arXiv, 2017.
- Multiagent reinforcement learning with sparse interactions by negotiation and knowledge transfer by Zhou L, Yang P, Chen C, et al. IEEE transactions on cybernetics, 2016.
- Decentralised multi-agent reinforcement learning for dynamic and uncertain environments by Marinescu A, Dusparic I, Taylor A, et al. arXiv, 2014.
- CLEANing the reward: counterfactual actions to remove exploratory action noise in multiagent learning by HolmesParker C, Taylor M E, Agogino A, et al. AAMAS, 2014.
- Bayesian reinforcement learning for multiagent systems with state uncertainty by Amato C, Oliehoek F A. MSDM Workshop, 2013.
- Multiagent learning: Basics, challenges, and prospects by Tuyls, Karl, and Gerhard Weiss. AI Magazine, 2012.
- Classes of multiagent q-learning dynamics with epsilon-greedy exploration by Wunder M, Littman M L, Babes M. ICML, 2010.
- Conditional random fields for multi-agent reinforcement learning by Zhang X, Aberdeen D, Vishwanathan S V N. ICML, 2007.
- Multi-agent reinforcement learning using strategies and voting by Partalas, Ioannis, Ioannis Feneris, and Ioannis Vlahavas. ICTAI, 2007.
- A reinforcement learning scheme for a partially-observable multi-agent game by Ishii S, Fujita H, Mitsutake M, et al. Machine Learning, 2005.
- Asymmetric multiagent reinforcement learning by Könönen V. Web Intelligence and Agent Systems, 2004.
- Adaptive policy gradient in multiagent learning by Banerjee B, Peng J. AAMAS, 2003.
- Reinforcement learning to play an optimal Nash equilibrium in team Markov games by Wang X, Sandholm T. NIPS, 2002.
- Value-function reinforcement learning in Markov game by Littman M L. Cognitive Systems Research, 2001.
- Hierarchical multi-agent reinforcement learning by Makar, Rajbala, Sridhar Mahadevan, and Mohammad Ghavamzadeh. The fifth international conference on Autonomous agents, 2001.
Joint action learning
- AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents by Conitzer V, Sandholm T. Machine Learning, 2007.
- Extending Q-Learning to General Adaptive Multi-Agent Systems by Tesauro, Gerald. NIPS, 2003.
- Multiagent reinforcement learning: theoretical framework and an algorithm. by Hu, Junling, and Michael P. Wellman. ICML, 1998.
- The dynamics of reinforcement learning in cooperative multiagent systems by Claus C, Boutilier C. AAAI, 1998.
- Markov games as a framework for multi-agent reinforcement learning by Littman, Michael L. ICML, 1994.
Cooperation and competition
- Multi-agent Reinforcement Learning in Sequential Social Dilemmas by Leibo J Z, Zambaldi V, Lanctot M, et al. arXiv, 2017. [Post]
- Opponent Modeling in Deep Reinforcement Learning by He H, Boyd-Graber J, Kwok K, et al. ICML, 2016.
- Multiagent cooperation and competition with deep reinforcement learning by Tampuu A, Matiisen T, Kodelja D, et al. arXiv, 2015.
- Emotional multiagent reinforcement learning in social dilemmas by Yu C, Zhang M, Ren F. International Conference on Principles and Practice of Multi-Agent Systems, 2013.
- Multi-agent reinforcement learning in common interest and fixed sum stochastic games: An experimental study by Bab, Avraham, and Ronen I. Brafman. Journal of Machine Learning Research, 2008.
- Combining policy search with planning in multi-agent cooperation by Ma J, Cameron S. Robot Soccer World Cup, 2008.
- Collaborative multiagent reinforcement learning by payoff propagation by Kok J R, Vlassis N. JMLR, 2006.
- Learning to cooperate in multi-agent social dilemmas by de Cote E M, Lazaric A, Restelli M. AAMAS, 2006.
- Learning to compete, compromise, and cooperate in repeated general-sum games by Crandall J W, Goodrich M A. ICML, 2005.
- Sparse cooperative Q-learning by Kok J R, Vlassis N. ICML, 2004.
Coordination
- Coordinated Multi-Agent Imitation Learning by Le H M, Yue Y, Carr P. arXiv, 2017.
- Reinforcement social learning of coordination in networked cooperative multiagent systems by Hao J, Huang D, Cai Y, et al. AAAI Workshop, 2014.
- Coordinating multi-agent reinforcement learning with limited communication by Zhang, Chongjie, and Victor Lesser. AAMAS, 2013.
- Coordination guided reinforcement learning by Lau Q P, Lee M L, Hsu W. AAMAS, 2012.
- Coordination in multiagent reinforcement learning: a Bayesian approach by Chalkiadakis G, Boutilier C. AAMAS, 2003.
- Coordinated reinforcement learning by Guestrin C, Lagoudakis M, Parr R. ICML, 2002.
- Reinforcement learning of coordination in cooperative multi-agent systems by Kapetanakis S, Kudenko D. AAAI/IAAI, 2002.
Security
- Markov Security Games: Learning in Spatial Security Problems by Klima R, Tuyls K, Oliehoek F. The Learning, Inference and Control of Multi-Agent Systems at NIPS, 2016.
- Cooperative Capture by Multi-Agent using Reinforcement Learning, Application for Security Patrol Systems by Yasuyuki S, Hirofumi O, Tadashi M, et al. Control Conference (ASCC), 2015
- Improving learning and adaptation in security games by exploiting information asymmetry by He X, Dai H, Ning P. INFOCOM, 2015.
Self-Play
- Deep reinforcement learning from self-play in imperfect-information games by Heinrich, Johannes, and David Silver. arXiv, 2016.
- Fictitious Self-Play in Extensive-Form Games by Heinrich, Johannes, Marc Lanctot, and David Silver. ICML, 2015.
Learning To Communicate
- EMERGENCE OF LANGUAGE WITH MULTI-AGENT GAMES: LEARNING TO COMMUNICATE WITH SEQUENCES OF SYMBOLS by Serhii Havrylov, Ivan Titov. ICLR Workshop, 2017.
- Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning by Abhishek Das, Satwik Kottur, et al. arXiv, 2017.
- Emergence of Grounded Compositional Language in Multi-Agent Populations by Igor Mordatch, Pieter Abbeel. arXiv, 2017. [Post]
- Cooperation and communication in multiagent deep reinforcement learning by Hausknecht M J. 2017.
- Multi-agent cooperation and the emergence of (natural) language by Lazaridou A, Peysakhovich A, Baroni M. arXiv, 2016.
- Learning to communicate to solve riddles with deep distributed recurrent q-networks by Foerster J N, Assael Y M, de Freitas N, et al. arXiv, 2016.
- Learning to communicate with deep multi-agent reinforcement learning by Foerster J, Assael Y M, de Freitas N, et al. NIPS, 2016.
- Learning multiagent communication with backpropagation by Sukhbaatar S, Fergus R. NIPS, 2016.
- Efficient distributed reinforcement learning through agreement by Varshavskaya P, Kaelbling L P, Rus D. Distributed Autonomous Robotic Systems, 2009.
Transfer Learning
- Transfer Learning for Multiagent Reinforcement Learning Systems by da Silva, Felipe Leno, and Anna Helena Reali Costa. IJCAI, 2016.
- Accelerating multi-agent reinforcement learning with dynamic co-learning by Garant D, da Silva B C, Lesser V, et al. Technical report, 2015
- Transfer learning in multi-agent systems through parallel transfer by Taylor, Adam, et al. ICML, 2013.
- Transfer learning in multi-agent reinforcement learning domains by Boutsioukis, Georgios, Ioannis Partalas, and Ioannis Vlahavas. European Workshop on Reinforcement Learning, 2011.
- Transfer Learning for Multi-agent Coordination by Vrancx, Peter, Yann-Michaël De Hauwere, and Ann Nowé. ICAART, 2011.
Inverse Reinforcement Learning
- Cooperative inverse reinforcement learning by Hadfield-Menell D, Russell S J, Abbeel P, et al. NIPS, 2016.
- Comparison of Multi-agent and Single-agent Inverse Learning on a Simulated Soccer Example by Lin X, Beling P A, Cogill R. arXiv, 2014.
- Multi-agent inverse reinforcement learning for zero-sum games by Lin X, Beling P A, Cogill R. arXiv, 2014.
- Multi-robot inverse reinforcement learning under occlusion with interactions by Bogert K, Doshi P. AAMAS, 2014.
- Multi-agent inverse reinforcement learning by Natarajan S, Kunapuli G, Judah K, et al. ICMLA, 2010.
Application
- Collaborative Deep Reinforcement Learning for Joint Object Search by Kong X, Xin B, Wang Y, et al. arXiv, 2017.
- Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving by Shalev-Shwartz S, Shammah S, Shashua A. arXiv, 2016.
- Applying multi-agent reinforcement learning to watershed management by Mason, Karl, et al. Proceedings of the Adaptive and Learning Agents workshop at AAMAS, 2016.
- Crowd Simulation Via Multi-Agent Reinforcement Learning by Torrey L. AAAI, 2010.
- Traffic light control by multiagent reinforcement learning systems by Bakker, Bram, et al. Interactive Collaborative Information Systems, 2010.
- Multiagent reinforcement learning for urban traffic control using coordination graphs by Kuyer, Lior, et al. oint European Conference on Machine Learning and Knowledge Discovery in Databases, 2008.
- A multi-agent Q-learning framework for optimizing stock trading systems by Lee J W, Jangmin O. DEXA, 2002.
- Multi-agent reinforcement learning for traffic light control by Wiering, Marco. ICML. 2000.


Qantas's cracking planes are not a safety concern — here's why
by Paige CockburnAmid calls for Qantas to ground 75 planes and a global review of some cracking Boeing 737s, even confident fliers might be feeling uneasy about their next trip.
Australia's national carrier has revealed hairline cracks were found in three of its Boeing 737s during visual checks this week.
The airline has removed the affected planes for repair but the Australian Licensed Aircraft Engineers Association (ALAEA) wants Qantas to ground all its 737 planes.
However the cracks — which are on a part of the plane called the "pickle fork" — should not scare travellers according to Australia's Civil Aviation Safety Authority (CASA) and a leading aviation consultancy firm.
Here's why.
What is a 'pickle fork'?
The pickle forks strengthen the connection between the wing and the body of the plane.
They are not the only thing holding the wing on.
"Even if the crack speared right through, the wing wouldn't fall off," Neil Hansford from Strategic Aviation Solutions said.
Infographic: Pickle forks help to hold the wings to the body of a plane. (ABC News)
The pickle fork is supposed to last the life of an aircraft — which means roughly 90,000 take-off and landing cycles for a 737.
It's a known problem in the Boeing 737s and last month the US Federal Aviation Administration (FAA) issued an urgent directive for all airlines to check their 737s that had completed more than 30,000 cycles for pickle fork cracks.
However, none of Qantas's planes have done that many.
CASA spokesperson Peter Gibson described the cracking as "a serious problem" but said there was "no evidence" to suggest Qantas's whole fleet should be grounded.
"Boeing came up with a procedure to inspect these aircraft in a specific area and that's being done right around the world, including here in Australia," he said.
"Any aircraft that are cracked go out of service until they're repaired."
Qantas's high safety standards
The ALAEA is one of several unions that represent people in the aviation sector.
"The area where the crack is takes the load off the wing and all the fuel it carries," ALAEA boss Steven Purvinas said.
"As long as Qantas is unaware which aircraft do or don't have cracks, they should ground the entire fleet until they know which are safe to fly."
Boeing maintains the cracks do not compromise safety.
As of October 9, cracks had been found in the pickle forks of about 5 per cent of the 737s inspected worldwide.
Although Qantas does not have any 737s with more than 30,000 cycles, they have so far inspected 33 of their 737s which have done more than 22,600.
Mr Hansford said the public should trust Qantas, because it was "boringly conservative" about safety.
"If there was a legitimate problem they would ground voluntarily," he said.
Mr Gibson said there was no reason for Qantas to ground their fleet.
Virgin inspected 19 of their 737s with more than 22,600 cycles earlier this month and did not find any cracking.


Barack Obama
@BarackObama
It’s clear our communities are crying out for change—and what that change looks like is up to us.
That’s why it’s
important to take a hard look at all the perspectives and proposals out there, even the ones we
disagree with. It’s how
we’ll come up with solutions that last.
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