August 2019
Version 1.4
This document will act as a user-guide for the KTAB: Spatial Model of Politics GUI. The guide will cover the whole process of using KTAB for the SMP model, taking the user through downloading and Installing the application, running the model, and visualizing the results. This document assumes a detailed understanding of the SMP as instantiated in KTAB. For more details, see two Discussion Papers published by King Abdullah Petroleum studies and Research Center (KAPSARC) An Introduction to the KAPSARC Toolkit for Behavioral Analysis (KTAB) and Multidimensional Bargaining using KTAB
KAPSARC Toolkit for behavioral analysis (KTAB) is an open source platform for building models to allow the systematic and rigorous analysis of Collective Decision-Making Processes (CDMPs). KTAB is under continual development by KAPSARC to meet the need for widely available, state- of-the-art, supported, and open source software that facilitates the modeling and analysis of collective decision making. The software libraries and source code for KTAB are released on github1 as open source under the MIT license.
CDMPs are those in which a group of individual actors interacts to arrive at a single decision. In both general and technical parlance, CDMPs may be termed bargaining or negotiating. To avoid confusion with legacy interpretations of these words we adopt the term CDMP, but the general meaning remains the same. The deliberations of a corporate board, the internal debates of ruling parties, the voting of electorates, and the haggling between seller and buyer at a market stall can all be viewed as CDMPs, albeit with different actors operating according to different rules. KTAB is intended to support reasonable analyses of the potential outcomes of CDMPs involving a range of modeled groups. In any group, the actors hold different values, positions, and views, which they bring to the CDMP.
KTAB is intended to be a platform that contains a number of models that can simulate CDMPs. The initial model that has been instantiated in KTAB is called the Spatial Model of Politics (SMP). The class of models represented by the SMP has a long history in the academic discipline of political science. More detail on the SMP, and references to the appropriate literature, can again be found in articles available for download on the main KTAB page. The majority of the development team’s current efforts are focused on producing a GUI for the SMP. The official version with a working GUI is now available for download.
The initial model that has been instantiated in KTAB is called the Spatial Model of Politics (SMP). The class of models represented by the SMP has a long history in the academic discipline of political science. The key distinguishing feature of the SMP is that the model assumes that positions can be arrayed along a linear, spatial dimension (usually a single line).
Fundamentally, this implies that differences in position, or advocacy, can be ordered along a position spectrum. Differences in actors’ advocacy (their position) reflect the ordering and magnitude of the political difficulty in moving from one position to another. The “space” between two points on the line measures the level of difference between two positions.
A non-spatial approach has also been implemented in KTAB. We call this the Enumerated Model of Politics (EMP).
This is for Windows (64-bits)
1- Download and install the Visual C++ Runtime libraries for Visual Studio 2015 or more recent, from the Microsoft support official website.
https://support.microsoft.com/en-us/help/2977003/the-latest-supported-visual-c-downloads
2- Download the latest SMP version from the KTAB official GitHub page
http://kapsarc.github.io/KTAB/.
3- Extract the contents of the .zip archive to your PC, maintaining the directory structure in the archive.
1- Download the latest SMP version for the Mac OS X from the KTAB official GitHub page http://kapsarc.github.io/KTAB/.
2- Extract the contents of the .zip archive to your PC, maintaining the directory structure in the archive.
Important note: to use the smpc and smpDyn from the downloaded bundle you need to access the contents of the KTAB_SMP bundle. You will find both the smpc and smpcDyn executables in the bundle folder KTAB_SMP.app/Contents/MacOS/
Once we have configured and installed all the dependencies. the easiest way to build KTAB on Linux is to run the scripts located in the home directory of KTAB. These are clean.sh
, reconfig.sh
, and build.sh
– they should be run in this order to rebuild all the libraries and models for KTAB.
The home directory will store your default model outputs. To change your home directory, go to
File > Change Home Directory
You can save the model log output in different ways by indicating which logging option you prefer.
You can set the database configuration to either record your model output in SQLite or PosgreSQL databases from
File > Configure Database
or from the database icon:
Minimum logging allows the user to limit database logging. Checking this disables logging except for: model / data information. You can find the Minimum Logging checkbox within the Model Parameters panel
Note: If you need complete database output for more advanced data analysis, you should keep this option unchecked.
The KTAB version of the Spatial Model of Politics is highly parameterized, with many options that can be set to model collective decision-making processes based on your understanding of how actors behave. We briefly describe the parameters that users can modify without modifying the source code. These parameters can be set either in the GUI application, or stored in an xml-format input file. As previously mentioned, the following descriptions of the model parameters assume a detailed understanding of the SMP model as instantiated in KTAB.
The default value for each model parameter is highlighted in blue in the descriptions below.
| VictoryProbModel | Linear | A 2:1 ratio gives a probability of 2/3 to the stronger coalition |
| Square | A 2:1 ratio gives a probability of 4/5 to the stronger coalition | |
| Quartic | A 2:1 ratio gives a probability of 16/17 to the stronger coalition | |
| Octic | A 2:1 ratio gives a probability of 256/257 to the stronger coalition | |
| Binary | Any significant percentage difference gives a probability of 1 to the stronger coalition |
| ProbCondorcetElection | Conditional | PCE uses single-step conditional probabilities |
| Markov Incentive | PCE uses a Markov process in which challenge probabilities are proportional to the influence of the initiating actor(s) | |
| Markov Uniform | PCE uses a Markov process in which challenge probabilities are uniform |
| StateTransition | Deterministic | The bargain which has the strongest coalition wins, even with a very small margin |
| Stochastic | The probability of winning for each bargain is proportional to its relative coalition strength |
| VotingRule | Prop | The vote is linearly proportional to the difference in utilities |
| PropBin | The vote is proportional to the weighted average of Prop (80%) and Binary (20%) | |
| Binary | The actor exerts all influence, regardless of the difference in utilities | |
| PropCbc | The vote is proportional to the average of Prop and Cubic | |
| ASymProsp | Influence is exerted asymmetrically: It is proportional to the difference of utilities if negative (a loss in utility). It is proportional to 2/3 of the difference, if positive (a gain in utility). | |
| Cubic | The vote is proportional to the cubed difference in utilities |
| BigRAdjust | OneThird | Actor i estimates actor j's risk attitude by interpolating between them, such that its estimate is closer (2/3 anchored, 1/3 adjusted) to its risk attitude |
| None | Actor i judge’s actor j's risk attitude as being identical to its risk attitude | |
| Half | Actor i estimates actor j's risk attitude by interpolating midway between its risk attitude and actor j's actual risk attitude | |
| TwoThirds | Actor i estimates actor j's risk attitude by interpolating between them, such that its estimate is closer (1/3 anchored, 2/3 adjusted) to actor j's risk attitude | |
| Full | Actor i judge’s actor j's risk attitude correctly |
| BigRRange | |
| Min | Sets risk tolerances in the range [0,1] such that actors with the most probable position are perfectly risk averse (1), while actors holding the least probable position are perfectly risk tolerant (0) |
| Mid | Sets risk tolerances in the range [-1⁄2,1] such that actors with the most probable position are perfectly risk averse (1), while actors holding the least probable position are somewhat risk seeking, with an aversion of -1⁄2 |
| Max | Sets risk tolerances in the range [-1,1] such that actors with the most probable position are perfectly risk averse (1), while actors holding the least probable position are perfectly risk seeking (-1) |
| ThirdPartyCommit | |
| NoCommit | controls how committed a third-party actor k is in a challenge between actors i and j. |
| SemiCommit | If the coalition actor joined by k loses, k must take the position of the winning coalition; otherwise it does not need to change position |
| FullCommit | Actor k is fully committed to the coalition it joins, and must adopt the position of the winning coalition |
| InterVecBrgn | |
| S1P1 | Proposed positions for each actor are computed as a weighted average of their current positions, where the weights are the products of salience and probability of success |
| S2P2 | Proposed positions for each actor are computed as a weighted average of their current positions, where the weights are the squared products of salience*probability of success |
| S2PMax | Proposed positions for each actor are computed as asymmetric shifts from their current positions, which is a function of squared salience and truncated difference in probability of success |
| BargnModel | InitOnlyInterp | Bargains are only computed from the initiating actor's perspective |
| InitRcvrInterp | Bargains are computed from the perspective of both the initiating actor and receiving actor | |
| PWCompInterp | Bargains are computed as an effective power- weighted average of both actor's perspectives | |
| TwoThirds | Actor i estimates actor j's risk attitude by interpolating between them, such that its estimate is closer (1/3 anchored, 2/3 adjusted) to actor j's risk attitude | |
| Full | Actor i judge’s actor j's risk attitude correctly |
The affinity matrix is currently unused in the model; it is a placeholder for intended future development of the algorithm.
KTAB SMP uses two main data input formats: Comma Separated Value (CSV) files and Extensible Markup Language (XML) files. We assume in this tutorial that the user understands how to obtain appropriate data.
You can find CSV and XML templates in the KTAB_SMP\sample_data
folder for the Windows release folder or you can find it in the official KTAB folder of the main GitHub repository under KTAB\examples\smp\doc\
These templates show how a proper CSV or XML file input is structured for a KTAB SMP input.
The Database file .db is another type of input, holding data from a previously-run model.
Note: Influence, Position, and Salience are typically numeric values ranging from 0 – 100
To start creating new actor data you can either;
• Go to File > Create New Actor Data
• Or Click on the icon from the tool bar
• Or use the Keyboard shortcut “Ctrl+N”
Insert the number of dimensions for your new data-set
To start typing in your data, you will need to set the Scenario Name and Description
You can increase or decrease the number of actors using the buttons +/- buttons
You can also click on the “Add Dimension” button to add more dimensions
Type in your data in the table
You can also Add/Delete dimensions, insert new actors and rename column headers by right clicking on a column from the table.
Once you are done creating your new data-set, click on “Done” to save the data-set on your computer.
You can either save it in a CSV or an XML file format.
File > View/Modify Existing CSV File
You can then choose the CSV file that contains the data that you wish to use.
File > View/Modify Existing XML File
When you are done inserting your input data, the Run button will be highlighted in green for you to run the model.
The Actor’s Data table shows the current turn, actor’s name and description, actor’s influence, actor’s salience and the position for the current turn. You can navigate through turns with the slider.
The next section introduces the visualizations within the KTAB-SMP application.
You can Hide and View charts from the “View” option on the toolbar.
The line chart displays the change in the actors’ positions over turns. Each line corresponds to an actor, hovering over a line will trigger a tooltip showing the actor’s name, description, and influence.
In the actor’s controls on the right, you can show/hide the lines through the checkboxes for the actor. You can also switch between dimensions from the “Dimensions” section.
On the chart, you can click on the line to toggle the name of the actor and you can also zoom in/out to change the chart scale.
When you double click on an actor’s line, changing position for a specific turn, a small dialogue box will appear explaining why the actor’s position changed for that turn.
The bar chart displays the effective power landscape, showing the effective power and positions of actors for each turn.
Similar to the line chart, in the actor’s controls on the right, you can focus or unfocus bars through the checkboxes for the actors. You can also switch between dimensions from the “Dimensions” section. For the stacked bar chart, you can change the number of bars displayed by modifying “No. of Bars”.
You can change the scale options to automatic, max, or user defined.
You can enable the Quad Map view from View > QuadMap
The Quad Map plots the change in utility to both the initiator (y-axis) and receiver (x-axis) expected as a result of a challenge from a specified initiator to at least one other actor.
There are four ways to compute the perspective from which the utility changes are computed:
Initiator: All computations performed from the perspective of the initiator.
Receiver(s): All computations performed from the perspective of each receiver selected for plotting.
Objective: The utilities for the initiator are computed from their perspective, and the utilities to each receiver are computed from their own perspective.
Other: All utilities are computed from a specified third party.
The quad map is named as such because the plane is divided into four quadrants, between which the behavior of receivers can be expected to consistently vary. In the first (green) quadrant, both the initiator and receiver can expect to gain utility from a challenge. Hence, actors in this quadrant may be expected to cooperate. The second (red) quadrant contains the cases where the initiator is expected to gain utility, while the receiver is expected to lose. There is likely to be conflict between actors in this quadrant. In the grey third quadrant, both actors would expect to lose utility from a challenge, so it’s unlikely any would be made. The last (yellow) quadrant contains cases where the initiator would be expected to lose utility, but the receiver would gain. Again, it’s highly unlikely any challenge would be made. You can check the auto-scale option to change the scaling to focus on where the actors are scattered on the Quad Map.
To Change actors’ colors displayed in the charts you can;
Color Options > Change Actors Color
Color Options
> Import Actor Colors`Color Options > Export Actor Colors
.Color Options > Reset Actors Colors
As of version 1.4, the SMP application includes a new visualization dashboard that opens in your favorite browser window (Chrome, Firefox, Safari). This dashboard currently includes Line and Bar plots included in the SMP application. However, the dashboard includes an additional feature for the actor movements and the Network Diagram that displays the bargains happening between the actors as a directed network diagram. We also have plans to improve the current visualizations to make them more interactive and give you more control over the look and feel. Please note that, though the visualization dashboard opens in a browser, there is no online content, and no data is sent off your computer. The plots are all drawn using the most popular and powerful open-source set of JavaScript visualization libraries, called “D3” (Data-Driven Documents).

- The database file has to be less than 1.5GB as of KTAB-SMP V1.4.
- This dashboard is separate from the visualizations within the SMP application.
To open the visualization dashboard, you can either click on the Open Visualization menu item, or the visualization button from the tool bar
After the browser is launched click on the Browse button to load your database file
A dashboard will appear a few seconds after the data is loaded, showing you the currently available visualizations.
The bar chart is also like the chart embedded in the KTAB_SMP.
With the controls on the bar chart you can:
The Line chart is very similar to the chart embedded in the KTAB_SMP application.
There are several controls that you can use in this chart:
The Network Diagram is a scenario and turn specific bargains visualization. The diagram allows you to visualize the bargain proposals and acceptances for each actor in a single turn.
You can switch between charts from the tabs located above the chart.
You can also customize and download the charts, change the colors of actors and add actors to groups.
Note: if you do not have a CSV file with current format click on “Export CSV” to download the csv file and customize.
Note: You can find sample CSV files with desired format in /sample_data folder


Time Focus
Description
Time Out - Lorem Ipsum
Time Out - News Article
Time Out - Don't read too fast Mode - Lorem Ipsum
Time Out - Don't read too fast Mode - News Article


{% extends "home-base.html"%} {% block title %}Home Page{% endblock %} {% block head%} {{ super() }} {% endblock%} {% block page_content%}
I made a book depository so that an average user can add, delete, and edit books to this database for various purposes such as to keep track of various author's books or to keep track of the book he or she has read. The Author table is a one to many relationship in which one author can have many books. .


Pending vulnerability verification found by the current scan and not yet verified by WhiteHat. This only applies to Standard Edition. Caution: May contain false positives. WhiteHat recommends you provide higher threshold values for these findings.


Physical-organic Studies of Organotin Compounds
By Victor Seth Krimsley A.B. (Bucknell University) 1966
DISSERTATION
Submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in
Chemistry
in the GRADUATE DIVISION
of the
Approved:
se ee eee + DEGREE CONFERRED DEC. 19, 1970s...
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


Elton Chen is a Computer Science B.S. student at Unversity of California at San Diego. He holds a penchant for all things culinary as well as video games. He is currently trying his best to reach Gold Nova 1 in "Counter Strike: Global Offensive," a game in which he enjoys playing with his friends.
Here he is as "Sintle Kraus" as the top player of his team in Counter Strike


|
John E. du PontGreat-great grandson of E.I. du Pont, John E. du Pont possesses a net worth probably around $250M. A trained ornithologist, du Pont discovered and named two dozen new species of birds and wrote numerous articles of scholarship on the subject.He is, however, insane. After the death of his mother (with whom he lived most of his first 50 years), Du Pont converted his 800 acre Foxcatcher into a wrestling "training compound", complete with 14,400 square foot training facility costing over half a million dollars. He became the primary benefactor to the sport of amateur wrestling in the entire United States. A gun enthusiast, du Pont opened a firing range at Foxcatcher, which he named the J. Edgar Hoover Pistol Training Center. Du Pont really, umm, liked young male athletes. Evidence of gayness is circumstantial but compelling. Wrestling coach Richard Deschatelets: "Nobody would say out in the open that Du Pont's a homosexual, even though that's [what] just about everybody figured out just [by] the way he handled things." Ron Good, editor, Amateur Wrestling News: "I think that Du Pont was most likely a gay person, and he certainly would make attempts to do whatever he wanted to do with the wrestlers that he had in there." Former employee Andy Metzger sued du Pont for $555k, claiming the millionaire had made homosexual advances at him, and then was subsequently fired. The suit was settled out of court, though it is not known if Metzger took up du Pont's offer of a wrestling match to determine the outcome. Du Pont perfected an (illegal) wrestling move, the Foxcatcher Five, in which the opponent's testicles are cupped not-so-gently. He particularly enjoyed using the innovative move on the Foxcatcher newbies. We don't know why, but Du Pont shot Olympic wrestler David Schultz as the wrestler was fixing his car radio. The exchange went something like this: Schultz: "Hey Coach."A two day police standoff followed, SWAT teams biding their time under the assumption that madman du Pont possessed an arsenal. During trial, the defense claimed du Pont "fell into the abyss of insanity." A jury found him guilty of third degree murder, and the state of Delaware forced du Pont to pay $742,107 reimbursement for the trial.
Timeline
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Version control allows users to track changes made to a file within a project. Not only can you track your own files, but multiple people can keep track of the same files. It allows people such as developers to track changes to code as well as fall back to a working code set if they run into problems. People can go back to older "checkpoints" per say to add new files, fix bugs, etc. Version control is also beneficial in the way that it helps organize workflow, especially when numerous people are working on the same program or project. Without something such as version work may become jumbled and mistakes could be made for example someone may delete another's previous work. So essentially version control is a great organizational tool that allows users to have work to fall back on and helps promote a more dynamic work environment which is quickly the norm in today's world.
Let's start at the beginning--Version Control benefits. Version control is "controlling" the "version" of the code you are working on. Basically, it allows you to save previous versions of your code, just in case you make a mistake and crash the program. This is one of the benefits of version control. Your previous works are saved and changes are tracked, so that you may always refer back to them and see what was changed to either fix the program or cause it to crash. Another benefit of version control is that it allows you to view what other people change in the programming and when they did it. Let's say "Jim" was working on the project and changed something in the code that caused half of the inputs to not work, but he's out on vacation and you don't know what he changed. Version control allows you to view the recent changes and (pretty much) undo whatever Jim did.
This is where git comes into play. Git uses version control constantly to help maintain the productiveness of your program. Git registers and stores the time and date of when data was changed, who changed it, and what they had changed. Git also allows you to work on a project with others simultaneously, without stepping on each other's toes. Git creates "branches" (like a tree) which allows different members to test out certain code and not step on each other's toes and overwrite each other's work.
This site includes multiple links that will complement your learning experience with Git. Some are purely referential and informational while others are actual online quizzes that will really gauge your new found abilities. Mainly, our site is just a little cheat sheet for anyone interested in what Git is all about. Thanks for visiting!


@* * Copyright 2020 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import uk.gov.hmrc.timeoutdialogdemo.config.AppConfig @this(layout: Layout, hmrcTimeoutDialog: HmrcTimeoutDialog) @()(implicit request: Request[_], messages: Messages, appConfig: AppConfig) @layout(pageTitle = Some("timeout-dialog-demo")) {
@{messages("service.text")}
@* This is invalid but I'm trying to keep the example simple - this should really be part of the element*@ @hmrcTimeoutDialog(TimeoutDialog( name="abc", timeout= Some(70), countdown= Some(68), keepAliveUrl= Some("/"), signOutUrl= Some("/sign-out"), language= Some("en") )) }


object Version
Defines the contract from extracting a specification version from a Config object.
interface Extractor : Parser<Int>


Introducing The New VIP Travel Card
Features and Benefits of VIP Travel Card:
Free Comprehensive Travel Insurance
Exciting offers and discounts at your favourite destination
Superior security through 3D Secure and CHIP and PIN technology


Voice Messages in local language
Features are currently being tested.
Voice message feature will be available soon
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---
title: "Running Git with Github"
layout: "post"
permalink: "/2012/12/running-git.html"
uuid: "5718635464321317608"
guid: "tag:blogger.com,1999:blog-7751875784341660635.post-5718635464321317608"
date: "2012-12-21 09:44:00"
updated: "2012-12-21 09:46:06"
description:
blogger:
siteid: "7751875784341660635"
postid: "5718635464321317608"
comments: "0"
categories: [RSA, Git, SSH]
author:
name: "Yi Yang"
url: "https://www.blogger.com/profile/13529726289633825935?rel=author"
image: "https://img2.blogblog.com/img/b16-rounded.gif"
---
Running Git with Github on Mac seems very easy, with the help from XCode. However running such configuration on Windows lacks of proper instruction.
The steps are list as following:
- Download Git for Windows: http://git-scm.com/download/win
- Generate SSH Key, PuTTYgen is a great Windows tool: link
For mac user we can just use: ssh-keygen -t rsa -C<EMAIL_ADDRESS>- Paste your public key here: https://github.com/settings/ssh
- Test connection using:
ssh -T git@github.com


|
C++ Actor Framework 0.18
|
Simple wrapper for runnables. More...
#include <multiplexer.hpp>
Public Member Functions | |
| subtype_t | subtype () const override |
| Returns a subtype hint for this object. | |
| void | intrusive_ptr_add_ref_impl () override |
| Add a strong reference count to this object. | |
| void | intrusive_ptr_release_impl () override |
| Remove a strong reference count from this object. | |
| Public Member Functions inherited from caf::resumable | |
| virtual subtype_t | subtype () const |
| Returns a subtype hint for this object. | |
| virtual resume_result | resume (execution_unit *, size_t max_throughput)=0 |
| Resume any pending computation until it is either finished or needs to be re-scheduled later. | |
| virtual void | intrusive_ptr_add_ref_impl ()=0 |
| Add a strong reference count to this object. | |
| virtual void | intrusive_ptr_release_impl ()=0 |
| Remove a strong reference count from this object. | |
| Public Member Functions inherited from caf::ref_counted | |
| ref_counted (const ref_counted &) | |
| ref_counted & | operator= (const ref_counted &) |
| void | ref () const noexcept |
| Increases reference count by one. | |
| void | deref () const noexcept |
Decreases reference count by one and calls request_deletion when it drops to zero. | |
| bool | unique () const noexcept |
| Queries whether there is exactly one reference. | |
| size_t | get_reference_count () const noexcept |
Additional Inherited Members | |
| Public Types inherited from caf::resumable | |
| enum | resume_result { resume_later , awaiting_message , done , shutdown_execution_unit } |
Denotes the state in which a resumable returned from its last call to resume . | |
| enum | subtype_t { unspecified , scheduled_actor , io_actor , function_object } |
Denotes common subtypes of resumable . More... | |
| Protected Attributes inherited from caf::ref_counted | |
| std::atomic< size_t > | rc_ |
| Related Functions inherited from caf::ref_counted | |
| template<class T , class... Ts> | |
| intrusive_cow_ptr< T > | make_copy_on_write (Ts &&... xs) |
Constructs an object of type T in an intrusive_cow_ptr . | |
| template<class T , class... Ts> | |
| intrusive_ptr< T > | make_counted (Ts &&... xs) |
Constructs an object of type T in an intrusive_ptr . | |
| void | intrusive_ptr_add_ref (const ref_counted *p) |
| void | intrusive_ptr_release (const ref_counted *p) |
Simple wrapper for runnables.
|
overridevirtual |
Add a strong reference count to this object.
Implements caf::resumable.
|
overridevirtual |
Remove a strong reference count from this object.
Implements caf::resumable.
|
overridevirtual |
Returns a subtype hint for this object.
This allows an execution unit to limit processing to a specific set of resumables and delegate other subtypes to dedicated workers.
Reimplemented from caf::resumable.


When Should I Use Rust?
DISCLAIMER: I don’t claim to be an expert on this language, so I may have made mistakes. If there is anything you would say differently or anything that is just plain wrong, please open an issue on Github
Rust is an amazing language.
True, I say every language is amazing (although begrudgingly with JavaScript), so that may not mean much, but Rust is something special.
It’s a language I could imagine replacing C.
And I don’t say that lightly, I’m Mr. CFanBoy. C has been in my Top 5 Languages a Developer Needs to Know list longer than any other, and the unofficial number 1 in my heart.
But Rust just does everything that C does, except safer. It helps so much to finally have a good low level programming language without worrying every single moment of my life about the dreaded Segmentation Fault
.
But enough of that, let’s get into it…
What is Rust?
Rust is a language that will confuse you if you’ve never touched ML-based languages, because it seems so similar to C but things tend to not compile if you do them 100% “the C way”.
This is because Rust has taken influence from a weird set of languages, such as C, Haskell and OCaml.
But aside from semantics and syntax, Rust focuses on a safe, concurrent, practical language.
Once again, just like in Go, Rust prevents us from really screwing up memory unless we really want to. But then we have to mark the code as unsafe
.
Since Rust is kind enough to write down their greatest features in their website, we will talk about them one by one:
- zero-cost abstractions
- move semantics
- guaranteed memory safety
- threads without data races
- trait-based generics
- pattern matching
- type inference
- minimal runtime
- efficient C bindings
Zero-Cost Abstractions
Rust attempts to obey the C++ mantra:
C++ implementations obey the zero-overhead principle: What you don’t use, you don’t pay for [Stroustrup, 1994]. And further: What you do use, you couldn’t hand code any better.
– Stroustrup
And it succeeds beautifully thanks to Traits.
Traits are essentially Interfaces, ways to define a certain number of expected behaviours that a Type complies with. And since they are compiled away, this means that, very much like C++, any calls to a Trait method are zero-cost.
However they are also types and can be used as an element in a struct through indirection if you need to dynamically call a method for an type unknown at compile time.
So somehow, Rust has managed to actually get the best of both worlds here…
Move Semantics
This is a weird one for people not used to the idea: in Rust you usually move rather than copy values.
This means that this would result in a compiler error:
let v = Box::new(5);
println!("{}", v);
let v2 = v;
println!("{}", v);
Why? Because v has been moved to v2 and you can no longer use v.
Now that seems annoying, right? But don’t worry! If you need to just lend a value instead of move it completely, you can just do a “borrow” by instead using &v
.
And if you need to be able to modify the value, use &mut v
.
What this entails is that we can always know where we can access a variable from, which is the key that brings us to our next section:
Guaranteed Memory Safety
Read that again. Guaranteed. Memory. Safety. It’s ok if you cry a little.
If that didn’t blow your mind, this will: Rust uses no Garbage Collection. And this, like many of the beautiful things of rust, is due to the move semantics.
It’s simple, values can be moved or borrowed, but once they live out their scope, they are destroyed. Period.
At first it can sound scary, but really, it just means that whenever you want a value to be in a certain scope, you just move it there.
It also means that for 99% of things you won’t need to ever create a Close or Destroy function :)
Threads Without Data Races
Again Move Semantics to the rescue! Since a value can only be owned by a single scope (and thus a single thread), there are no ownership problems, but that’s not all!
Remember &v
and &mut v
? Well, at compile time Rust makes sure that there can never exist a &mut v
(read/write) reference when there is ANY other kind of reference to the object. However, any number of &v
(read) references can coexist.
This mean no race conditions. Whoa.
Trait-based generics
We already touched on this, but the key here is very simple: you can use Traits as Types that satisfy an Interface. Which means that you can declare a function for all of those Types at once.
For example:
fn print_hash<T: Hash>(t: &T) {
println!("The hash is {}", t.hash())
}
t
can be a borrow (note the &
) of any Type which implements the Hash Trait.
Pattern matching
Everything is an expression in Rust. This mean this is valid code:
let (x, y, z) = (5, "Potatoes", false);
And that you can declare functions such as:
fn compare_hash<T: Hash>(t: &T, t2: &T) -> bool
Where, you guessed right, not only do both t and t2 need to implement Hash, but they must be the same Type.
Minimal runtime
Rust is built on LLVM and it’s optimized like crazy. All those weird rules about moving and borrowing, and the way Traits work allow the runtime to be a very efficient thing that pretty much needs to do nothing but some initialization setting up a heap, some guards and backtraces.
This means Rust is stupidly fast. C-levels of fast. Like whoa.
Efficient C bindings
Rust is not here to displace C, although it very well might.
The truth is that, especially at low level, most things are C. So interfacing correctly with C is a must, really, if you want to be compatible with the existent way of doing things.
This also means that it’s simple to make a Rust extension to a C application, and so I feel like transitioning things from C to Rust is quite a nice process.
All languages are amazing, but I’ve yet to find one that is perfect, and Rust is no exception.
Although its problem are not as glaring as other programming languages which may or may not be called JavaScript, they still exist and must be considered:
- Rust is hard to learn and master
- Rust is young and doesn’t have as many libraries as you’d want
- Not really for the web
Hard to Learn and Master
Unless you’re lucky (or crazy) enough to already be profficient in an ML language, a C language and a functional language, chances are Rust is gonna look downright weird to you.
You will probably take a couple of months until you finally stop being surprised at random compiler errors for things that you’re sure should be working.
This also means you have to be reasonably sure you actually want to use Rust for something before getting on to learn it…
However, you really should learn Rust anyway, so get on it!
Not enought Libraries!
The language is young, still, so this will probably be a nonissue in a few years, and a large number of libraries are appearing every day (in part thanks to Redox, a complete functional OS written in Rust. Yes, I said an OS).
The fact remains that Rust is still in its infancy, and some things just aren’t available. You probably can’t reasonably create a TripleA game in Rust (yet).
So if you’re building something complex, that might turn you away, although you should consider contributing libraries yourself! :)
Not for the web
Yes, Iron exists. You technically CAN develop a Web App in Rust. But really, should you? It’s not designed for it. At all.
And this is probably its greatest problem nowadays, since 90% of everything being done is in the Web…
So… When should I use Rust?
If you need to create a fast, robust, complex app, Rust has your back.
Need a new accounting program? Rust. Something to coordinate signals between different programs? Rust. A driver for that new piece of hardware your company has been building? Rust, please, the world will thank you for it.
Rust: the safe, fast, concurrent language.


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As we continued to explore the automated process for the football data we then started to look into 2d designs. We learned how to convert a raster image into a vector image. After making it into a vecctor we were able to laser cut our designs out of cardboard. We then were able to make vinyl stickers out of the vector images we used for the laser cut out to stick to our chrome books. The process of converting it into a vector image wasn't that hard, it mainly was making sure everything was dimenioned correctly, because the laser engraver was very nit picky on that.


JavaScriptMVC's controllers are many things. They are a jQuery plugin factory. They can be used as a traditional view, making pagination widgets and grid controls. Or, they can be used as a traditional controller, initializing and controllers and hooking them up to models. Mostly, controller's are a really great way of organizing your application's code.
Controllers provide a number of handy features such as:
But controller's most important feature is not obvious to any but the most hard-core JS ninjas. The following code creates a tooltip like widget that displays itself until the document is clicked.
$.fn.tooltip = function(){
var el = this[0];
$(document).click(function(ev){
if(ev.target !== el){
$(el).remove()
}
})
$(el).show();
return this;
})
To use it, you'd add the element to be displayed to the page, and then call tooltip on it like:
$("
Some Info
")
.appendTo(document.body)
.tooltip()
But, this code has a problem. Can you spot it? Here's a hint. What if your application is long lived and lots of these tooltip elements are created?
The problem is this code leaks memory! Every tooltip element, and any tooltip child elements, are kept in memory forever. This is because the click handler is not removed from the document and has a closure reference to the element.
This is a frighteningly easy mistake to make. jQuery removes all event handlers from elements that are removed from the page so developers often don't have to worry about unbinding event handlers. But in this case, we bound to something outside the widget's element, the document, and did not unbind the event handler.
But within a Model-View-Controller architecture, Controllers listen to the View and Views listen to the Model. You are constantly listening to events outside the widget's element. For example, the nextPrev
widget from the $.Model
section listens to updates in the paginate model:
paginate.bind('updated.attr', function(){
self.find('.prev')[this.canPrev() ? 'addClass' : 'removeClass']('enabled')
self.find('.next')[this.canNext() ? 'addClass' : 'removeClass']('enabled');
})
But, it doesn't unbind from paginate! Forgetting to remove event handlers is potentially a source of errors. However, both the tooltip and nextPrev would not error. Instead both will silently kill an application's performance. Fortunately, $.Controller makes this easy and organized. We can write tooltip like:
$.Controller('Tooltip',{
init: function(){
this.element.show()
},
"{document} click": function(el, ev){
if(ev.target !== this.element[0]){
this.element.remove()
}
}
})
When the document is clicked and the element is removed from the DOM, $.Controller will automatically unbind the document click handler.
$.Controller can do the same thing for the nextPrev widget binding to the the paginate model:
$.Controller('Nextprev',{
".next click" : function(){
var paginate = this.options.paginate;
paginate.attr('offset', paginate.offset+paginate.limit);
},
".prev click" : function(){
var paginate = this.options.paginate;
paginate.attr('offset', paginate.offset-paginate.limit );
},
"{paginate} updated.attr" : function(ev, paginate){
this.find('.prev')[paginate.canPrev() ? 'addClass' : 'removeClass']('enabled')
this.find('.next')[paginate.canNext() ? 'addClass' : 'removeClass']('enabled');
}
})
// create a nextprev control
$('#pagebuttons').nextprev({ paginate: new Paginate() })
If the element #pagebuttons
is removed from the page, the Nextprev controller instance will automatically unbind from the paginate model.
Now that your appetite for error free code is properly whetted, the following details how $.Controller works.
$.Controller inherits from $.Class. To create a Controller class, call $.Controller( NAME, classProperties, instanceProperties )
with the name of your controller, static methods, and instance methods. The following is the start of a reusable list widget:
$.Controller("List", {
defaults : {}
},{
init : function(){ },
"li click" : function(){ }
})
When a controller class is created, it creates a jQuery helper method of a similar name. The helper method is primarily use to create new instances of controller on elements in the page. The helper method name is the controller's name underscored, with any periods replaced with underscores. For example, the helper for $.Controller('App.FooBar')
is $(el).appfoobar()
.
To create a controller instance, you can call new Controller(element, options)
with a HTMLElment or jQuery-wrapped element and an optional options object to configure the controller. For example:
new List($('ul#tasks'), {model : Task});
You can also use the jQuery helper method to create a List controller instance on the #tasks
element like:
$('ul#tasks').list({model : Task})
When a controller is created, it calls the controller's prototype init method with:
this.element
set to the jQuery-wrapped HTML element this.options
set to the options passed to the controller merged with the class's defaults
object. The following updates the List controller to request tasks from the model and render them with an optional template passed to the list:
$.Controller("List", {
defaults : {
template: "items.ejs"
}
},{
init : function(){
this.element.html( this.options.template, this.options.model.findAll() );
},
"li click" : function(){ }
})
We can now configure Lists to render tasks with a template we provide. How flexible!
$('#tasks').list({model: Task, template: "tasks.ejs"});
$('#users').list({model: User, template: "users.ejs"})
If we don't provide a template, List will default to using items.ejs.
As mentioned in $.Controller's introduction, it's most powerful feature is it's ability to bind and unbind event handlers.
When a controller is created, it looks for action methods. Action methods are methods that look like event handlers. For example, "li click"
. These actions are bound using jQuery.bind
or jQuery.delegate
. When the controller is destroyed, by removing the controller's element from the page or calling destroy on the controller, these events are unbound, preventing memory leaks.
The following are examples of actions with descriptions of what the listen for:
"li click"
- clicks on or within li
elements within the controller element."mousemove"
- mousemoves within the controller element."{window} click"
- clicks on or within the window.Action functions get called back with the jQuery-wrapped element or object that the event happened on and the event. For example:
"li click": function( el, ev ) {
assertEqual(el[0].nodeName, "li" )
assertEqual(ev.type, "click")
}
$.Controller supports templated actions. Templated actions can be used to bind to other objects, customize the event type, or customize the selector.
Controller replaces the parts of your actions that look like {OPTION}
with a value in the controller's options or the window.
The following is a skeleton of a menu that lets you customize the menu to show sub-menus on different events:
$.Controller("Menu",{
"li {openEvent}" : function(){
// show subchildren
}
});
//create a menu that shows children on click
$("#clickMenu").menu({openEvent: 'click'});
//create a menu that shows children on mouseenter
$("#hoverMenu").menu({openEvent: 'mouseenter'});
We could enhance the menu further to allow customization of the menu element tag:
$.Controller("Menu",{
defaults : {menuTag : "li"}
},{
"{menuTag} {openEvent}" : function(){
// show subchildren
}
});
$("#divMenu").menu({menuTag : "div"})
Templated actions let you bind to elements or objects outside the controller's element. For example, the Task model from the $.Model section produces a "created" event when a new Task is created. We can make our list widget listen to tasks being created and automatically add these tasks to the list like:
$.Controller("List", {
defaults : {
template: "items.ejs"
}
},{
init : function(){
this.element.html( this.options.template, this.options.model.findAll() );
},
"{Task} created" : function(Task, ev, newTask){
this.element.append(this.options.template, [newTask])
}
})
The "{Task} create"
gets called with the Task model, the created event, and the newly created Task. The function uses the template to render a list of tasks (in this case there is only one) and add the resulting html to the element.
But, it's much better to make List work with any model. Instead of hard coding tasks, we'll make controller take a model as an option:
$.Controller("List", {
defaults : {
template: "items.ejs",
model: null
}
},{
init : function(){
this.element.html( this.options.template, this.options.model.findAll() );
},
"{model} created" : function(Model, ev, newItem){
this.element.append(this.options.template, [newItem])
}
});
// create a list of tasks
$('#tasks').list({model: Task, template: "tasks.ejs"});
Now we will enhance the list to not only add items when they are created, but update them and remove them when they are destroyed. To do this, we start by listening to updated and destroyed:
"{model} updated" : function(Model, ev, updatedItem){
// find and update the LI for updatedItem
},
"{model} destroyed" : function(Model, ev, destroyedItem){
// find and remove the LI for destroyedItem
}
You'll notice here we have a problem. Somehow, we need to find the element that represents particular model instance. To do this, we need to label the element as belonging to the model instance. Fortunately, $.Model and $.View make labeling an element with an instance and finding that element very easy.
To label the element with a model instance within an EJS view, you simply write the model instance to the element. The following might be tasks.ejs
<% for(var i =0 ; i < this.length; i++){ %>
<% var task = this[i]; %>
<li <%= task %> > <%= task.name %>

<% } %>
tasks.ejs
iterates through a list of tasks. For each task, it creates an li
element with the task's name. But, it also adds the task to the element's jQuery data with: <li <%= task %> >
.
To later get that element given a model instance, you can call modelInstance.elements([CONTEXT])
. This returns the jQuery-wrapped elements the represent the model instance.
Putting it together, list becomes:
$.Controller("List", {
defaults : {
template: "items.ejs",
model: null
}
},{
init : function(){
this.element.html( this.options.template, this.options.model.findAll() );
},
"{model} created" : function(Model, ev, newItem){
this.element.append(this.options.template, [newItem])
},
"{model} updated" : function(Model, ev, updatedItem){
updatedItem.elements(this.element)
.replaceWith(this.options.template, [updatedItem])
},
"{model} destroyed" : function(Model, ev, destroyedItem){
destroyedItem.elements(this.element)
.remove()
}
});
// create a list of tasks
$('#tasks').list({model: Task, template: "tasks.ejs"});
It's almost frighteningly easy to create abstract, reusable, memory safe widgets with JavaScriptMVC.
Boo!


Ranking Source Code Comment Delimiters
I present a definitive ranking of source code comments. I included some of my favorites and least favorites. I rougly went off of the Rosetta Code list, but didn't put in many of the exotic languages. The ones I did put in were arbitrary, so if your favorite language isn't in these rankings please let me know so I can judge it.
Multiline Comments
#| |#
- (two way tie) looks weird but easier to type{- -}
- (two way tie) good but a little hard to type/* */
- only if it's nestable--[[ ]]--
- consistent with--
but too many characters%{ }%
- easy to change between line and nested, still ugly<!-- -->
- Clocking in at a whopping 7 characters, the HTML gets points docked for its ridiculous overindulgence. Not to mention its asymmetry.(* *)
- Parentheses and asterisks are already the most overused of characters. You have the whole keyboard to work with! Consider the ambiguity of parsing something like(* try (simpl in *) *)
?""" """
- is it a string? a comment? who knows???
Single Line Comments
#
- "The comment that will probably work"--
- elegant look but 2nd place because it has two characters;
- feels like you're hacking, good use of homerow//
not bad, but just is a little ostentatious, you know?%
is typeset so many different ways, looks crunched in monospace!
- drawing your attention needlessly"
or'
- leaves things... shudders unmatchedC
- what are we using, punch cards?dnl
- discard to next line, more like discard this comment scheme.REM
- seriously?⍝
- oh, APL.
Honorable mention goes to: BibTeX where everything is a comment unless you say otherwise.


| Projectname | Desription | Progress | Deadline |
|---|---|---|---|
| CIT Project | soo much fun |
|
01.01.2018 |
| CIT Project | soo much fun |
|
01.01.2018 |
| CIT Project | soo much fun |
|
01.01.2018 |
| CIT Project | soo much fun |
|
01.01.2018 |
| CIT Project | soo much fun |
|
01.01.2018 |
| CIT Project | soo much fun |
|
01.01.2018 |


This is heading cfed0089-89f7-457a-8734-59448113f9e4
This is text 573f8fe2-df4d-4239-84a7-1a8b2f98bde9This is heading 50e7a3aa-3859-4484-8fa9-93462cc08880
This is paragraph 4a6fd107-6719-4032-9470-6f6df2dee5bd
This is paragraph 4a6fd107-6719-4032-9470-6f6df2dee5bd


We've got the strings your guitar has been looking for.
With ReString Club you select the strings you want and the frequency that you need them.
Our strings are packaged in simple biodegradable envelopes to lower cost and keep the planet happy. Pause or cancel anytime.
Join ReString Club and get fresh strings when your guitar is over stressed.
ReString Club is the premier, subscription guitar string delivery service for the player who demands professional grade strings at a price that makes sense. Our strings are made with machine precision in the same factories as the strings on your guitar right now. Pay less, play more and choose how often you want strings. Twice a month? One every other month? No problem. Going on the road? Put your membership on hold at anytime with one click! Our mission is music and that starts with great tone. The rest is up to you.. Enjoy!


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|---|---|---|
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{{! Orders Hidden - End }}Generated on {{system.date.longdate}} @ {{system.date.shorttime}}.


KidsRuby
"Hackety Hack" by _why the lucky stiff
The inspiration for us to create KidsRuby was Hackety Hack. This is an edited version of the lessons from Hackety Hack, changed to match KidsRuby.
"Ruby4Kids" by Alberto Morales
Writing games is really fun, and is the reason why a lot of people want to learn to program. Alberto Morales has been working on a series of tutorials on his website at Ruby4kids.com to show kids how to create games using Ruby and a game package called Gosu.


Before Change
backgrounds = find_background_regions(access_chroms, target_chroms,
2 * INSERT_SIZE)
bg_arr = GA.from_rows(backgrounds)
bg_arr = bg_arr.subdivide(avg_bin_size, min_bin_size)
bg_arr["gene"] = "Background"
return bg_arr
After Change
logging.info("Skipping untargeted chromosomes %s",
" ".join(sorted(chroms_to_skip)))
skip_idx = accessible.chromosome.isin(chroms_to_skip)
accessible = accessible[~skip_idx]
else:
// Chromosome accessible sequence regions not known -- use heuristics
// (chromosome length is endpoint of last probe; skip initial
// <magic number> of bases that are probably telomeric)


 As the twenty-first century gets under way, a new excuse for contempt comes along: a disdain for people born out of the loving embrace of men and women, that is, people like you and me. What's so bad about being what we in the year 1997 consider insipidly ordinary? Just this: the vast majority of those living in the near future have been designed in a petri dish: a master race genetically programmed to perform specialized tasks just as purebred dogs are bred for specific traits. These offspring have been engineered like a genetically produced tomato to be free of the natural defects. Radial keratotomy is obsolete, as these new human beings will never become nearsighted. Forget Minoxidil: baldness has been programmed out of existence. No need for phen/fen or Prozac or nitroglycerine tablets as obesity, heart disease and depression simply cannot happen in this brave new world.
We can only guess why, but one couple decide to have their kid born the natural way. When Vincent emerges from the womb he is immediately scanned by the latest medical equipment, and the news is not good: he has an 89% possibility of heart disease, is burdened with Attention Deficit Disorder (though we never see symptoms), and is expected to live only 30.2 years. No wonder the folks decided the next kid, Anton, would go the genetic route.
"Gattaca," which is also the name of a corporation which sends specially designed human beings into space exploration, ranks with "Contact" as one of the two intelligent movies of its genre to hit the screens this year. While "Gattaca" is authentic sci-fi, it features no green people with antennas intent on destroying the earth, no Godzilla-like dinosaurs intent on trampling San Diego to a desert, no Buck Rogers laser guns or giant roaches or brilliant doctors who turn into ghouls without warning. This is adult sci-fi, the sort of picture which is about believable characters living in the very near future as an outgrowth of experiments actually being made today in space travel and genetic engineering. Like other films of its ilk such as "The Stepford Wives" (gender discrimination), and "Logan's Run" (age bigotry), it makes incisive, edgy commentary on our own contemporary world, by showing us the ugly face of prejudice--as experienced by the natural-born Vincent.
Each time Vincent (Ethan Hawke) reads books about space travel, he is warned by his parents to stop dreaming. Because of his genetic flaws--weak heart and attenuated life span--he is unfit for the Gattaca project and will have to content himself with being a sweeper under the supervision of Caesar (Ernest Borgnine) inside the ultra-modern, impeccably clean building which houses the astronauts. When the bespectacled Vincent is introduced by a DNA broker, German (Tony Shalhoub), to Jerome Eugene Morrow (Jude Law), a superior specimen who has been recently paralyzed, the three work out a deal by which Vincent would assume Jerome's identity and pass himself off as one of the elite--a Valid rather than an Invalid human being. Convinced that one need not play the hand which has been dealt, Vincent uses Jerome's blood and urine samples to pass himself off in his new identity, determined through rigorous training to defy the diagnosis given him at infancy by the doctors.
When the director of the space mission is murdered, detective Hugo (Alan Arkin) discovers an eyelash which is traced to Vincent, who is believed to have disappeared. As investigators close in on the disguised space traveler, Vincent plans his escape with the help of his new love, the genetically flawed Irene (Uma Thurman).
By keeping the action down-to-earth and only cautiously going beyond what has already been accomplished by science, New Zealand born Andrew Niccol's directorial debut is this side of awesome. Using his own screenplay, Mr. Niccol offers a film which obliquely comments on the willingness of so many people nowadays to avoid reaching, much less going beyond, their potential. A film which could be used to inspire schoolchildren, "Gattaca" is never didactic: it does not wear its message of hope and inspiration on its sleeve, but unfolds a story which is compelling science fiction as well as thematically heartening. Ethan Hawke has gone beyond the teen heartthrob role, having matured into a handsome and confident twenty-something actor, whose performance is given solid backing by the tongue-in-cheek Gore Vidal as Gattaca Corporation's wily director, Uma Thurman as the somewhat repressed love object, Jude Law as the despondent, wheelchair-bound hero, and Loren Dean as the genetically perfect brother who turns out to be weaker than the all-too-human Vincent. Alan Arkin is not as amusing as he was as the psychiatrist in "Grosse Pointe Blank" but acquits himself professionally as the insistent detective investigating the director's murder.
Writer-director Niccol is no Luddite. You do not emerge from his movie wanting to tear down the labs of geneticists. After all, if some piano sonatas are written so that only people with twelve fingers can play them--as is the case in this film-- let's design people with twelve fingers. In fact, he is not really against genetic engineering at all. He does, however, most effectively exploit current experiments in the field to fashion an inspirational movie. Even more important, he knows how to tell a crackling good tale. Rated R. Running Time: 105 minutes. (C) 1997 Harvey Karten
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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belongs to the author.
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Broken URLs inthe reviews are the responsibility of the author.
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We are looking for a smart, inquisitive data scientist to join our rapidly growing data science capability within Wunderman Thompson Data. You’ll be working in a team focussed on both scalable predictive modelling and unstructured approaches on text/image/video. In particular, we are looking for a strong python/spark coder with extensive experience building and applying various machine learning techniques, both supervised and unsupervised, to large scale datasets.
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All content is customized to your growth plan using our state of the art recommendation system.
Learn MoreQuickly build an effective pricing table for your potential customers with this Bootstrap example. It's built with default Bootstrap components and utilities with little customization.
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The FFT (Fast Fourier Transform) is an algorithm used to convert a time-domain signal into its frequency-domain representation. In music informatics, the FFT is important because it allows us to analyze and understand the different frequency components present in music. By analyzing the frequency spectrum, we can extract valuable information about the distribution and intensity of frequencies, aiding tasks such as audio classification, chord recognition, and audio effects processing.
Understanding the FFT (Fast Fourier Transform) is essential for research in AI and music. It enables the analysis of frequency components in audio, aiding tasks like music classification, genre recognition, and content-based recommendation. The FFT facilitates feature extraction, allowing AI models to understand the spectral and timbral characteristics of music. It is also valuable for music generation, composition, and audio effects processing. By leveraging the FFT in AI research, we can develop innovative applications, improve music understanding and creation, and push the boundaries of computational musicology.
The sketch above uses an FFT to split the audio in bass/middle/and treble, visualising the output as some bars. Follow the comments numbered 1. to 5. to make sense of the code.
Try replacing the sound with bee.mp3. Note where the frequency bands are most sensitive to changes (e.g. moving microphone, not bee noises).


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This virus is airborne so, properly wearing a mask can help stop the virus from getting out into the air.
More InformationBasically, masks block COVID particles that you have from going to other people. If it is medical-grade it can also block particles from other people getting to you.
Both are necessary because they both decrease the change of you or those around you getting COVID-19. So double the protection means double the chance you will not get sick.
Get a cotton mask so you can wash and reuse it. But if one-use masks are the only masks, available get a few of those. Do not get medical-grade masks because those should be saved for doctors, nurses, and other essential workers.


The opinions expressed herein are my own personal opinions and do not necessarily represent the opinion of any other organization or person, including (but not limited to) my fellow employees, my employer, its clients or their agents.
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CLOCKWATCHERS A film review by Steve Rhodes Copyright 1998 Steve Rhodes
RATING (0 TO ****): *** 1/2
Temps of the world unite!
The four brave temps in first-time director Jill Sprecher's irreverent CLOCKWATERS are ready to rebel against their oppression. Treated with worse than disdain, they are alternately ignored and ordered about like slave labor. Figuring that their latest employer, Global Credit, cannot function without them, they take a vow to skip work en masse one day. Follow-through, however, is not their strong suit.
Marvelously cast, the film stars Toni Collette (MURIEL'S WEDDING) as the timid and ambitionless worker, Iris, Parker Posey (THE HOUSE OF YES) as the rebellious leader, Margaret, Lisa Kudrow (ROMY AND MICHELE'S HIGH SCHOOL REUNION) as the no-talent, wannabe actress, Paula, and Alanna Ubach (DENISE CALLS UP) as the would-be married, Jane. Together they spend their hours looking busy while waiting for the clock to strike 5. "I can sit there and do nothing as good as anyone," Margaret explains in her rationale for why she should be considered just as competent as the workers she fills in for.
Pamela Marcotte's sets create a scarily sterile office environment that is creepily realistic. As devoid of personality as the landscape of the moon, the office has small partitions to separate the clerical workers and glass walls to ensconce the professional staff as the business equivalent of the aristocracy.
The wry script, by the director and her sister, Karen Sprecher, is full of subtle humor and poignancy. Even when the Dilbert-like situations are exaggerated, they always possess gems of frightening truth. One manager obsesses over office supply usage and another issues stern memos warning about the personal use of corporate resources, the phone system in this case. Whereas workers may not have encountered these exact forms of corporate repression, most will be able to identify with slight variations in the harassment.
Although the story is more a comedy of manners coupled with social satire, it takes the time to develop the four women into real characters with interesting backgrounds. The last half of the story concerns a mystery about an office kleptomaniac, but the movie doesn't leave much doubt about the identity of the miscreant.
The film creates four individuals whose plights are easy to empathize with and who are naturally funny without trying to be so. With its Kafkaesque plot, the characters are more pitiable than hilarious, although they are both and usually at the same time.
CLOCKWATHCERS runs 1:36. It is rated PG-13 for a little profanity and would be fine for kids around 10 and up although its office-based humor may even be over the heads of most teenagers.
The film is playing now in New York. It will hopefully come to the San Jose area later this summer, but it currently has no local booking.
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A drug that I've used for years can still surprise me utterly. I thought I knew what to expect, but with little warning I was thrust into alien territory. Or so it goes in this fable I just wrote, at least. It is based on a fever hallucination I had some months ago.
Sometimes fate has a sense of irony. Last year, I had been facing something I had put off for years: the obligatory military service period of my fair fatherland. I no longer had a legitimate reason to gain any further deferments, nor much motivation to fake one. Since I had no intention to become Jehovah's Witness or play a lunatic, neither of which have to go, the service was like the removal of rotting tooth: something that had to be faced before it got even worse.
  I chose civil service and made arrangements with a distant monastery for a 12-month service as a sort of jack-of-all-trades. Unfortunately this deal blew up, and I ended serving in the training center of civil servicemen.  Luckily, there was a constant flow of hippies, New Agers and potheads through the center, so there was an abundance of cannabis to soothe any frayed nerves and remove boredom.
Hiding somewhere in that constant flow of young men, a fellow traveller known as Influenza A entered the center. People started falling ill in droves, and one evening I too noticed the warning signs of rapidly rising fever. The next morning I got a sick leave, but decided to stay for one day and get a good night's sleep before starting the long trek home. Since I had woken in 6:15AM, was dizzy with fever and had muscle aches, this was a good decision, or at least the best I made that day.
That evening, my fever had reached a plateau. The feeling was not unpleasant. In fact, should there be a drug that replicated that sensation, I would be sorely tempted. My flesh glowed with warmth, but the flames under my skin weren't uncomfortably hot. I had a feeling of lightness, and sometimes when sitting or lying down, it was like I was floating. I felt a touch detached, and there was an oddly pleasing sluggishness to my thoughts, like running through knee-deep water on a hot summer day.
I could claim that it had impaired my judgment, as well, but looking back at the decisions I made, I fail to find any critical lack of reason. I was curious of how my fever and mild sleep deprivation would react with stoned state. I saw the possibility of unexpectedly intense reaction, but was quietly confident that I could handle it.
When an associate of my roommate came by and offered us a smoke, I accepted. Somehow, it escaped me that he had talked about a mix of high quality weed and Dutch hash. I inhaled again and again, for once matching the pace of my roommate, whose intake could take fairly heroic proportions whenever he got the chance. And I'm sensitive to psychoactives even in normal times.
We waltzed back to our room, with me feeling comfortably stoned. Of course that was only the fever at that point, with perhaps a tiny foretaste of what was to come. The main assault was still speeding through my veins, and when it broke through the blood-brain barrier, I went far beyond 'comfortably stoned' or even 'stoned'. But for a little while yet, I sat on my bed unaware of what I was due for, listening to psytrance and stoned babble.
Then, the first wave struck. Violently and suddenly, I was jerked further into the altered state. I noticed that I was very stoned now, and those feelings of lightness had disappeared. This was still the kind of stuff I was expecting, and smiling tranquilly, I emptied my mind and let it surf on the soundwaves bouncing around the room.
Suddenly, BOOM! The second wave hit me. At this point experience started to get derailed from its usual track. I literally felt gravity grip my suddenly heavy body. Surrendering to its pull, I laid down on my bed. My sensory perceptions felt distant and unpleasant, so I closed my eyes. I found colourful geometrical patterns pulsing and dancing on my eyelids.
It wasn't fun and games anymore. I was uncomfortably aware of the ever-present gravity, pulling me down and struggling against my chest every time it rose in an intake of breath. The body buzz was harsh, far more intense than anything you normally get from pot. And those visuals, for all their delicate beauty, were more intense also, with little of that paleness of colours and detachment which characterize my occasional cannabis visuals. All in all, the experience now lurked at boundaries of a proper trip. I felt mildly anxious, but rather than giving in, I looked at what was happening and adapted to it.
Something that felt like a minute passed. I was just beginning to get settled in, when the third wave rushed in. Gravity gained additional vigor, body buzz turned into electrical currents flowing through my body and visuals got much more vibrancy and depth, being now at par with those you could expect from shrooms or acid. My sense of balance started to malfunction, and lying there with my eyes closed, it was as if my body was slowly spinning and turning around. I began feeling dizzy, nauseous and weak.
It was still fairly easy to remain in control of my now considerable anxiety, but the unescapable fact was that my mind was far more altered than I had anticipated, and that I felt pretty awful. I tried to change the direction of trip by changing my sensory input. I rose to a sitting position and opened my eyes. For a little while, it cleared away some of that dizziness and heaviness. Yet soon the crushing weight returned, and those pulsing geometrical shapes started creeping inwards from the edges of my visual field, overlapping and covering what I was supposed to see with my eyes. I sank back on my bed.
The fourth wave came soon afterwards. My memory started getting fuzzy at this point, but generally, this wave turned the experience even harsher and brought with it intense tinnitus, infernal high-pitched scream in my ears. Sinking into my hallucinations and losing all grasp of my thoughts and emotions, I scrambled up once more. It wasn't a thought-out decision, since I no longer possessed a capacity for making those, but rather a purely reflexive attempt to escape, an act of misguided instinct.
Of course, it was a failure. Reportedly, I struggled up and tried to stand, but fell right on the floor, smashing my face into a chair in the process. Of this I had no clue whatsoever. There was no time and space, no awareness of self, nothing but pure chaos. The only thing that existed was mind static, white noise encompassing several senses. It looked and sounded much like a TV tuned on an empty channel, and felt like electricity. I regret I cannot really describe it further, but not only was it so alien to make it hard to capture with words, it was basically a very simple thing for all its oddness. I see nothing one could learn from it, nor anything that could be called 'spiritual'. It was propably nothing but static noise from rebooting brain.
I suddenly woke up, completely refreshed and happy as a clam. All the pressures had vanished, leaving me clear-headed and euphoric. Interestingly, I learned that my roommate had performed energetic healing on me before I woke up. I don't know if this had an effect on the way I woke up or not. Let those be certain who have found the truth. This could be compared to another mind static experience of mine years back, resulting from a combination of cannabis and antidepressant. Back then, I woke up on my own accord, also refreshed but not quite to the same extent. But, of course the different circumstances mean that any comparisions will be ultimately unconvincing.
Was this some transformative experience? Certainly not. Was it interesting, mostly fun and worthwhile? Definitely.
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.


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Chapter 6 Nonlinear and Other Regressions
6.1 Logit Regression
6.1.1 Binary Logit
Go back to fan’s REconTools Package, R Code Examples Repository (bookdown site), or Intro Stats with R Repository (bookdown site).
Data Preparation
<- mtcars
df_mtcars
# X-variables to use on RHS
<- c('mpg', 'qsec')
ls_st_xs <- c('mpg')
ls_st_xs <- c('qsec')
ls_st_xs <- c('wt')
ls_st_xs <- c('mpg', 'wt', 'vs')
ls_st_xs
<- 'hpLowHigh'
svr_binary <- 'LowHP'
svr_binary_lb0 <- 'HighHP'
svr_binary_lb1 <- 'am'
svr_outcome <- 'mtcars'
sdt_name
# Discretize hp
<- df_mtcars %>%
df_mtcars mutate(!!sym(svr_binary) := cut(hp,
breaks=c(-Inf, 210, Inf),
labels=c(svr_binary_lb0, svr_binary_lb1)))
<IP_ADDRESS> Logit Regresion and Prediction
logit regression with glm, and predict using estimation data. Prediction and estimation with one variable.
# Regress
<- glm(as.formula(paste(svr_outcome, "~", paste(ls_st_xs, collapse="+")))
rs_logit data = df_mtcars, family = "binomial")
,summary(rs_logit)
##
## Call:
## glm(formula = as.formula(paste(svr_outcome, "~", paste(ls_st_xs,
## collapse = "+"))), family = "binomial", data = df_mtcars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.73603 -0.25477 -0.04891 0.13402 1.90321
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 22.69008 13.95112 1.626 0.1039
## mpg -0.01786 0.33957 -0.053 0.9581
## wt -6.73804 3.01400 -2.236 0.0254 *
## vs -4.44046 2.84247 -1.562 0.1182
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 43.230 on 31 degrees of freedom
## Residual deviance: 13.092 on 28 degrees of freedom
## AIC: 21.092
##
## Number of Fisher Scoring iterations: 7
# Predcit Using Regression Data
$p_mpg <- predict(rs_logit, newdata = df_mtcars, type = "response") df_mtcars
<IP_ADDRESS>.1 Prediction with Observed Binary Input
Logit regression with a continuous variable and a binary variable. Predict outcome with observed continuous variable as well as observed binary input variable.
# Regress
<- glm(as.formula(paste(svr_outcome,
rs_logit_bi "~ factor(", svr_binary,") + ",
paste(ls_st_xs, collapse="+")))
data = df_mtcars, family = "binomial")
, summary(rs_logit_bi)
##
## Call:
## glm(formula = as.formula(paste(svr_outcome, "~ factor(", svr_binary,
## ") + ", paste(ls_st_xs, collapse = "+"))), family = "binomial",
## data = df_mtcars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.45771 -0.09563 -0.00875 0.00555 1.87612
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.8285 18.0390 0.212 0.8319
## factor(hpLowHigh)HighHP 6.9907 5.5176 1.267 0.2052
## mpg 0.8985 0.8906 1.009 0.3131
## wt -6.7291 3.3166 -2.029 0.0425 *
## vs -5.9206 4.1908 -1.413 0.1577
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 43.2297 on 31 degrees of freedom
## Residual deviance: 8.9777 on 27 degrees of freedom
## AIC: 18.978
##
## Number of Fisher Scoring iterations: 9
# Predcit Using Regresion Data
$p_mpg_hp <- predict(rs_logit_bi, newdata = df_mtcars, type = "response")
df_mtcars
# Predicted Probabilities am on mgp with or without hp binary
<- ggplot(df_mtcars, aes(x=p_mpg_hp, y=p_mpg)) +
scatter geom_point(size=1) +
# geom_smooth(method=lm) + # Trend line
geom_abline(intercept = 0, slope = 1) + # 45 degree line
labs(title = paste0('Predicted Probabilities ', svr_outcome, ' on ', ls_st_xs, ' with or without hp binary'),
x = paste0('prediction with ', ls_st_xs, ' and binary ', svr_binary, ' indicator, 1 is high'),
y = paste0('prediction with only ', ls_st_xs),
caption = 'mtcars; prediction based on observed data') +
theme_bw()
print(scatter)
<IP_ADDRESS>.2 Prediction with Binary set to 0 and 1
Now generate two predictions. One set where binary input is equal to 0, and another where the binary inputs are equal to 1. Ignore whether in data binary input is equal to 0 or 1. Use the same regression results as what was just derived.
Note that given the example here, the probability changes a lot when we
# Previous regression results
summary(rs_logit_bi)
##
## Call:
## glm(formula = as.formula(paste(svr_outcome, "~ factor(", svr_binary,
## ") + ", paste(ls_st_xs, collapse = "+"))), family = "binomial",
## data = df_mtcars)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.45771 -0.09563 -0.00875 0.00555 1.87612
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.8285 18.0390 0.212 0.8319
## factor(hpLowHigh)HighHP 6.9907 5.5176 1.267 0.2052
## mpg 0.8985 0.8906 1.009 0.3131
## wt -6.7291 3.3166 -2.029 0.0425 *
## vs -5.9206 4.1908 -1.413 0.1577
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 43.2297 on 31 degrees of freedom
## Residual deviance: 8.9777 on 27 degrees of freedom
## AIC: 18.978
##
## Number of Fisher Scoring iterations: 9
# Two different dataframes, mutate the binary regressor
<- df_mtcars %>% mutate(!!sym(svr_binary) := svr_binary_lb0)
df_mtcars_bi0 <- df_mtcars %>% mutate(!!sym(svr_binary) := svr_binary_lb1)
df_mtcars_bi1
# Predcit Using Regresion Data
$p_mpg_hp_bi0 <- predict(rs_logit_bi, newdata = df_mtcars_bi0, type = "response")
df_mtcars$p_mpg_hp_bi1 <- predict(rs_logit_bi, newdata = df_mtcars_bi1, type = "response")
df_mtcars
# Predicted Probabilities and Binary Input
<- ggplot(df_mtcars, aes(x=p_mpg_hp_bi0)) +
scatter geom_point(aes(y=p_mpg_hp), size=4, shape=4, color="red") +
geom_point(aes(y=p_mpg_hp_bi1), size=2, shape=8) +
# geom_smooth(method=lm) + # Trend line
geom_abline(intercept = 0, slope = 1) + # 45 degree line
labs(title = paste0('Predicted Probabilities and Binary Input',
'\ncross(shape=4)/red is predict actual binary data',
'\nstar(shape=8)/black is predict set binary = 1 for all'),
x = paste0('prediction with ', ls_st_xs, ' and binary ', svr_binary, ' = 0 for all'),
y = paste0('prediction with ', ls_st_xs, ' and binary ', svr_binary, ' = 1'),
caption = paste0(sdt_name)) +
theme_bw()
print(scatter)
<IP_ADDRESS>.3 Prediction with Binary set to 0 and 1 Difference
What is the difference in probability between binary = 0 vs binary = 1. How does that relate to the probability of outcome of interest when binary = 0 for all.
In the binary logit case, the relationship will be hump–shaped by construction between \(A_i\) and \(\alpha_i\). In the exponential wage cases, the relationship is convex upwards.
# Generate Gap Variable
<- df_mtcars %>% mutate(alpha_i = p_mpg_hp_bi1 - p_mpg_hp_bi0) %>%
df_mtcars mutate(A_i = p_mpg_hp_bi0)
# Binary Marginal Effects and Prediction without Binary
<- ggplot(df_mtcars, aes(x=A_i)) +
scatter geom_point(aes(y=alpha_i), size=4, shape=4, color="red") +
geom_abline(intercept = 0, slope = 1) + # 45 degree line
labs(title = paste0('Binary Marginal Effects and Prediction without Binary'),
x = 'P(binary=0) for all',
y = 'P(binary=1) - P(binary=0) gap',
caption = paste0(sdt_name)) +
theme_bw()
print(scatter)
<IP_ADDRESS>.4 X variables and A and alpha
Given the x-variables included in the logit regression, how do they relate to A_i and alpha_i
# Generate Gap Variable
<- df_mtcars %>% mutate(alpha_i = p_mpg_hp_bi1 - p_mpg_hp_bi0) %>%
df_mtcars mutate(A_i = p_mpg_hp_bi0)
# Binary Marginal Effects and Prediction without Binary
<- function(svr_x, df,
ggplot.A.alpha.x svr_alpha = 'alpha_i', svr_A = "A_i"){
<- ggplot(df, aes(x=!!sym(svr_x))) +
scatter geom_point(aes(y=alpha_i), size=4, shape=4, color="red") +
geom_point(aes(y=A_i), size=2, shape=8, color="blue") +
geom_abline(intercept = 0, slope = 1) + # 45 degree line
labs(title = paste0('A (blue) and alpha (red) vs x variables=', svr_x),
x = svr_x,
y = 'Probabilities',
caption = paste0(sdt_name)) +
theme_bw()
return(scatter)
}
# Plot over multiple
lapply(ls_st_xs,
ggplot.A.alpha.x,df = df_mtcars)
## [[1]]
##
## [[2]]
##
## [[3]]
6.2 Quantile Regression
6.2.1 Quantile Regression Basics
Go back to fan’s REconTools Package, R Code Examples Repository (bookdown site), or Intro Stats with R Repository (bookdown site).
<IP_ADDRESS> Estimate Mean and Conditional Quantile Coefficients using mtcars dataset
Here, we conduct tests for using the quantreg package, using the built-in mtcars dataset.
First, estimate the mean (OLS) regression:
<- lm(mpg ~ disp + hp + factor(am) + factor(vs), data = mtcars)
fit_mean summary(fit_mean)
##
## Call:
## lm(formula = mpg ~ disp + hp + factor(am) + factor(vs), data = mtcars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7981 -1.9532 0.0111 1.5665 5.6321
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.832119 2.890418 8.591 3.32e-09 ***
## disp -0.008304 0.010087 -0.823 0.41757
## hp -0.037623 0.013846 -2.717 0.01135 *
## factor(am)1 4.419257 1.493243 2.960 0.00634 **
## factor(vs)1 2.052472 1.627096 1.261 0.21794
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.812 on 27 degrees of freedom
## Multiple R-squared: 0.8104, Adjusted R-squared: 0.7823
## F-statistic: 28.85 on 4 and 27 DF, p-value: 2.13e-09
Now estimate conditional quantile regressions (not that this remains linear) at various quantiles, standard error obtained via bootstrap. Note that there is a gradient in the quantile hp coefficients as well as disp. disp sign reverses, also the coefficient on factor am is different by quantiles:
<- c(0.25, 0.50, 0.75)
ls_fl_quantiles <- rq(mpg ~ disp + hp + factor(am),
fit_quantiles tau = ls_fl_quantiles,
data = mtcars)
summary(fit_quantiles, se = "boot")
##
## Call: rq(formula = mpg ~ disp + hp + factor(am), tau = ls_fl_quantiles,
## data = mtcars)
##
## tau: [1] 0.25
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) 25.34665 1.50911 16.79575 0.00000
## disp -0.02441 0.00952 -2.56333 0.01603
## hp -0.01672 0.01641 -1.01845 0.31718
## factor(am)1 1.39719 1.43085 0.97648 0.33719
##
## Call: rq(formula = mpg ~ disp + hp + factor(am), tau = ls_fl_quantiles,
## data = mtcars)
##
## tau: [1] 0.5
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) 27.49722 1.78258 15.42554 0.00000
## disp -0.02253 0.01571 -1.43388 0.16268
## hp -0.02713 0.02329 -1.16494 0.25387
## factor(am)1 3.37328 2.09860 1.60739 0.11919
##
## Call: rq(formula = mpg ~ disp + hp + factor(am), tau = ls_fl_quantiles,
## data = mtcars)
##
## tau: [1] 0.75
##
## Coefficients:
## Value Std. Error t value Pr(>|t|)
## (Intercept) 28.06384 1.75630 15.97892 0.00000
## disp 0.00445 0.01427 0.31201 0.75734
## hp -0.06662 0.01781 -3.74146 0.00084
## factor(am)1 7.91402 2.48452 3.18533 0.00353
<IP_ADDRESS> Test Conditional Quantile Coefficients if Different
Use the rq.anova function frm the quantile regression packge to conduct WALD test. Remember WALD test says given unrestricted model’s estimates, test where null is that the coefficients satisfy some linear restrictions.
To test, use the returned object from running rq with different numbers of quantiles, and set the option joint to true or false. When joint is true: “equality of slopes should be done as joint tests on all slope parameters,” when joint is false: “separate tests on each of the slope parameters should be reported.” A slope parameter refers to one of the RHS variables.
Note that quantile tests are “parallel line” tests. Meaning that we should except to have different x-intercepts for each quantile, because they represents the levels of the conditional shocks distributions. However, if quantile coefficients for the slopes are all the same, then there are no quantile specific effects, mean effects would be sufficient.
see:
<IP_ADDRESS>.1 Test Statistical Difference between 25th and 50th Conditional Quantiles
Given the quantile estimates above, the difference between 0.25 and 0.50 quantiles exists, but are they sufficiently large to be statistically different? What is the p-value? Reviewing the results below, they are not statistically different.
First, joint = TRUE. This is not testing if the coefficien on disp is the same as the coefficient on hp. This is testing jointly if the coefficients for different quantiles of disp, and different quantiles of hp are the same for each RHS variable.
<- c(0.25, 0.50)
ls_fl_quantiles <- rq(mpg ~ disp + hp + factor(am),
fit_quantiles tau = ls_fl_quantiles,
data = mtcars)
anova(fit_quantiles, test = "Wald", joint=TRUE)
## Quantile Regression Analysis of Deviance Table
##
## Model: mpg ~ disp + hp + factor(am)
## Joint Test of Equality of Slopes: tau in { 0.25 0.5 }
##
## Df Resid Df F value Pr(>F)
## 1 3 61 0.8421 0.4761
Second, joint = False:
anova(fit_quantiles, test = "Wald", joint=FALSE)
## Quantile Regression Analysis of Deviance Table
##
## Model: mpg ~ disp + hp + factor(am)
## Tests of Equality of Distinct Slopes: tau in { 0.25 0.5 }
##
## Df Resid Df F value Pr(>F)
## disp 1 63 0.0305 0.8619
## hp 1 63 0.5461 0.4627
## factor(am)1 1 63 1.3500 0.2497
<IP_ADDRESS>.2 Test Statistical Difference between 25th, 50th, and 75th Conditional Quantiles
The 1st quartile and median do not seem to be statistically different, now include the 3rd quartile. As seen earlier, the quartiles jointly show a gradient. Now, we can see that idisp, hp and am are separately have statistically different
First, joint = TRUE:
<- c(0.25, 0.50, 0.75)
ls_fl_quantiles <- rq(mpg ~ disp + hp + factor(am),
fit_quantiles tau = ls_fl_quantiles,
data = mtcars)
anova(fit_quantiles, test = "Wald", joint=TRUE)
## Quantile Regression Analysis of Deviance Table
##
## Model: mpg ~ disp + hp + factor(am)
## Joint Test of Equality of Slopes: tau in { 0.25 0.5 0.75 }
##
## Df Resid Df F value Pr(>F)
## 1 6 90 3.3173 0.005367 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Second, joint = False:
anova(fit_quantiles, test = "Wald", joint=FALSE)
## Quantile Regression Analysis of Deviance Table
##
## Model: mpg ~ disp + hp + factor(am)
## Tests of Equality of Distinct Slopes: tau in { 0.25 0.5 0.75 }
##
## Df Resid Df F value Pr(>F)
## disp 2 94 5.4903 0.005558 **
## hp 2 94 6.7221 0.001868 **
## factor(am)1 2 94 7.2758 0.001154 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1