OpenLLM-France/wikimedia huggingface dataset:
# Ruby Programming/Strings

← Hello world | Alternate quotes →
Like Python, Java, and the .NET Framework, Ruby has a built-in String class.

## String literals

One way to create a String is to use single or double quotes inside a Ruby program to create what is called a string literal. We've already done this with our "hello world" program. A quick update to our code shows the use of both single and double quotes.
Being able to use either single or double quotes is similar to Perl, but different from languages such as C and Java, which use double quotes for string literals and single quotes for single characters.
So what difference is there between single quotes and double quotes in Ruby? In the above code, there's no difference. However, consider the following code:
Because "Betty's" contains an apostrophe, which is the same character as the single quote, in the second line we need to use a backslash to escape the apostrophe so that Ruby understands that the apostrophe is in the string literal instead of marking the end of the string literal. The backslash followed by the single quote is called an escape sequence.

## Single quotes

Single quotes only support two escape sequences.
* \' – single quote
* \\ – single backslash
Except for these two escape sequences, everything else between single quotes is treated literally.

## Double quotes

Double quotes allow for many more escape sequences than single quotes. They also allow you to embed variables or Ruby code inside of a string literal – this is commonly referred to as interpolation.

### Escape sequences

Below are some of the more common escape sequences that can appear inside of double quotes.
* \" – double quote
* \\ – single backslash
* \a – bell/alert
* \b – backspace
* \r – carriage return
* \n – newline
* \s – space
* \t – tab
Try out this example code to better understand escape sequences.
The result:
Notice that the newline escape sequence (in the last line of code) simply starts a new line.
The bell character, produced by escape code \a, is considered a control character. It does not represent a letter of the alphabet, a punctuation mark, or any other written symbol. Instead, it instructs the terminal emulator (called a console on Microsoft Windows) to "alert" the user. It is up to the terminal emulator to determine the specifics of how to respond, although a beep is fairly standard. Some terminal emulators will flash briefly.
Run the following Ruby code to check out how your terminal emulator handles the bell character.

## puts

We've been using the puts function quite a bit to print out text. Whenever puts prints out text, it automatically prints out a newline after the text. For example, try the following code.
The result:

## print

In contrast, Ruby's print function only prints out a newline if you specify one. For example, try out the following code. We include a newline at the end of print's argument list so that the shell prompt appears on a new line, after the text.
The result:
The following code produces the same output, with all the words run together.

## See also

String literals
# Ruby Programming/Syntax/Hooks

Ruby provides callbacks, to, for example, know when a new method is defined (later) in a class.
Here is a list of known callbacks.

## const_missing

or more
# Ruby Programming/Syntax/Lexicology
## Identifiers

An identifier is a name used to identify a variable, method, or class.
As with most languages, valid identifiers consist of alphanumeric characters (A-Za-z0-9) and underscores (_), but may not begin with a digit (0-9). Additionally, identifiers that are method names may end with a question mark (?), exclamation point (!), or equal sign (=).
There are no arbitrary restrictions to the length of an identifier (i.e. it may be as long as you like, limited only by your computer's memory). Finally, there are reserved words which may not be used as identifiers.
Examples:

## Comments

Line comments run from a bare '#' character to the end of the line. Code commenting and documentation is best implemented with Ruby Embedded Documentation. http://www.ruby-doc.org/docs/ProgrammingRuby/html/rdtool.html
Examples:

## Embedded Documentation

Example:

## Reserved Words

The following words are reserved in Ruby:
You can find some examples of using them here.

## Expressions

Example:
All variables, literals, control structures, etcetera are expressions. Using these together is called a program. You can divide expressions with newlines or semicolons (;) — however, a newline with a preceding backslash (\) is continued to the following line.
Since in Ruby control structures are expressions as well, one can do the following:
The above equivalent in a language such as C would generate a syntax error since control structures are not expressions in the C language.
# Ruby Programming/Syntax

← RubyGems | Reference →
# SwisTrack/Components/DynamicNearestNeighborTracking
## Description

Greedy nearest neighbor tracking, that automatically creates and destroys tracks.

## Parameters
### InitialWindowSize

The length in pixels of the side of a square to make an initial window size. It will always use the same size. Really it should set this automatically from the particle...

### FrameKillThreshold

The number of frames to keep the track around before killing it, if it hasn't been updated.

### MinNewTrackDistance

The minimum distance a new track must be from existing tracks.

### MaxDistance

Inherited from NearestNeighborTracking. If the distance between a particle and a track is larger than this distance, the particle will not be associated with the track. If in addition it is greater than MinNewTrackDistance, a new track will be created.

### TrackDistanceKillThreshold

If two tracks get less than this distance to each other, the shorter one will be destroyed.

### MaxNumber

The number of tracks. I think.
# SwisTrack/Components/ConvertBayerToColor
## Description

This component transforms an Bayer pattern image into a BGR image. More information on the Bayer pattern can be found on here.

## Input

A gray image containing a Bayer pattern.

## Output

A color image

## Parameters
### Bayer pattern type

Type of Bayer pattern: BG, GB, RG or GR. For more information, refer to the OpenCV documentation.
# SwisTrack/Components/ConvertToGray
## Description

This component converts the input frame into a grayscale image. If the input frame is a grayscale image already, the image is left untouched. In all other cases, an appropriate conversion is applied.

## Input

A grayscale or a color image.

## Output

A grayscale image.

## Parameters
### Channel

All converts the image to grayscale using the OpenCV standard BGR-to-grayscale conversion.
If a channel is selected, only the selected channel is retained.
# SwisTrack/Components/FilterParticles
## Description

Filters particles based on area. You could add other features too.

## Input

Particles.

## Output

Particles.

## Parameters
### MinParticleArea

The minimum area for a particle to be accepted.

### MaxParticleArea

The maximum area for a particle to be accepted.
# SwisTrack/Components/IDReaderRing
## Description

This component detects the ID and rotation of a marker by reading a circular barcode around previously detected particles. Both ID and rotation of a barcode are determined by correlating the barcode with all potential codes, which are defined in an XML file that looks as follows:
The algorithm also determines a confidence level for each detection, which might be helpful to reject wrongly detected particles (false positives).
The algorithm proceeds in two steps:
* All pixels lying on a ring around the particle are indexed with their angle. Angle and radius of a pixel are calculated using the center of the pixel and the (potentially sub-resolution) particle position.
* The so indexed pixels are correlated with all IDs under all possible angles in 1 degree intervals.
The best match (highest correlation) is assigned to the particle.
Please refer to the IDReaderRing example for scripts to generate suitable markers.
Note that the algorithm assumes a perfectly circular barcode, and therefore doesn't perform very well under large deformations. In addition, even though the algorithm is near-optimal in a well-configured setup, a fairly high resolution (and a well-focussed lens) is required to read such codes. E.g., 14-chip codes should appear as 20-by-20 pixels at least on the image.
Is there a specific name for this kind of marker code? Is it maybe a Wikipedia: ShotCode or some other Wikipedia: 2D barcode?

## Input

File:SwisTrack IDReaderRingMarkers.png
A set of particles detected with e.g. the BlobDetectionCircularHough component, plus a grayscale image with the circular barcodes. Note that the particle center must lie in the center of the circular barcode.

## Output

The same set of particles, augmented with the ID, orientation and confidence value.

## Parameters
### Outer radius

The outer radius of the ring.

### Inner radius

The inner radius of the ring. Note that the inner radius should be chosen such as the blob in the center of the marker (if any) is not part of the ring.

### Objects and codes

XML file with the object list (see description above).
# SwisTrack/Components/DoubleThresholdColorIndependent
## Description

This step intends to separate the pixels that belongs to tracked objects. The pixels kept are in white and the black pixels will be considered as the background. Most of the time, this step follows a preprocessing method like background subtraction. In this specific case, the output pixel is white if the three channel values of the input pixel is inside of the respective color boundaries. output= (Blue Low <= inputBlue <= Blue High) && (Green Low <= inputGreen <= Green High) && (Red Low <= inputRed <= Red High)

## Input

A color image.

## Output

A binary (black and white) image.

## Parameters
### Blue Low

Lower boundary of the blue channel.

### Blue High

Higher boundary of the blue channel.

### Green Low

Lower boundary of the green channel.

### Green High

Higher boundary of the green channel.

### Red Low

Lower boundary of the red channel.

### Red High

Higher boundary of the red channel.
# SwisTrack/Components/GrayMask
## Description

This component will mask certain areas of your image, and therefore allow you to remove particles that are not in your area of interest (e.g. the arena).

## Input

File:SwisTrack GrayMaskInput.png
A grayscale image.

## Output

File:SwisTrack GrayMaskOutput.png
A grayscale image.

## Parameters
### Mask image

A binary (black-and-white) image with the mask.

### Mode

One of the following combination modes:
* Black -> black: Black pixels in the mask image will be black in the output image. All other pixels remain unchanged.
* White -> white: White pixels in the mask image will be white in the output image. All other pixels remain unchanged.
* Black -> white: Black pixels in the mask image will be white in the output image. All other pixels remain unchanged.
* White -> black: White pixels in the mask image will be black in the output image. All other pixels remain unchanged.
# SwisTrack/Components/HSVBackgroundSubtractionColor
## Description

This component subtracts the background image from the input image. However, this subtraction is done in the HSV space, using only the first channel (Hue).
If the camera is fixed, only the objects added to the scene remain after this subtraction. However, lighting condition variations or the camera noise can also produce a difference between the two images. As we do the subtraction in the Hue channel, light intensity has nearly no influence on the result as we are comparing the color. However, if there is color like white, gray or black, the tracking is really bad as those color are not well defined in the hue band. A small change can make them go from the pure red to the pure blue. Moreover, destructive data compression like DivX modify the color on the border of the object a lot, as we can see in the example image used here.
Most of the time, a threshold is applied after this method, to separate the background from the tracked objects.

## Input

A color image.

## Output

A grayscale image.

## Parameters
### Background Image

Path to the background image file (color). The background image must be a picture of the arena without the object to track.
# SwisTrack/Components/ColorSwapper
## Description

This component swaps two colors in an image.

## Input

A color image.

## Output

A color image with one color swapped for another color.

## Parameters

=== SourceColor ===.
# SwisTrack/Components/ConvertToColor
## Description

This component converts the input frame into a 3-channel color image. In case the input is
This tools guaranty that the resulting image will be color (3 channels). In case of a color input, nothing is done. In case of another input, the conversion is computed.

## Input

A grayscale or a color image.

## Output

A color image.

## Parameters

None.
# R Programming/Using C or Fortran

R Programming
* R Basics
** Introduction
** Sample Session
** Manage your workspace
** Settings and Installation
** Documentation
** Control Structures
** Working with functions
** Debugging
** Using C or Fortran
** Utilities
** Estimation utilities
** Packages
* Data Management
** Data types
** Working with data frames
** Importing and exporting data
** Text Processing
** Times and Dates
* Graphics
** Grammar of graphics
* Publication quality output
* Descriptive Statistics
* Mathematics
** Optimization
** Probability Distributions
** Random Number Generation
* Statistical Core Methods
** Maximum Likelihood
** Method of Moments
** Bayesian Methods
** Bootstrap
** Multiple Imputation
** Nonparametric Methods
* Regression Models
** Linear Models
** Quantile Regression
** Binomial Models
** Multinomial Models
** Tobit And Selection Models
** Count Data Models
** Duration Analysis
* Time Series
* Factor Analysis
* Classification
** Ordination
** Clustering
* Network Analysis
* High Performance Computing
** Profiling R code
** Parallel computing with R
* Appendix
** Sources
** Index
For some tasks, R can be slow. In that case, it is possible to write a program in C or Fortran and to use it from R. This page is for advanced programmers only.
* See wikiversity Connecting Fortran and R
* Link C with R
# RadOnc Resident Wiki

Volunteer Editors:
Full Table of Contents
| Introduction Instructions for authors Author List | Epidemiology Cancer staging | Recent Changes |
| Central Nervous System Peripheral Nervous System Autonomic Nervous System Sellar and Parasellar Pineal Optic Pathway Brain Stem Choroid Plexus Meninges Scalp Head and Neck Skull Base Ocular Orbital Eye Lid Ocular Surface Lacrimal Gland Nasal and Paranasal Ear and Temporal Bone Lip and Oral Cavity Odontogenic Salivary Gland Thyroid and Parathyroid Thorax Breast Axillary Chest Wall Lung Pleural Mediastinal Tracheobronchial Esophageal Pericardial Heart and Great Vessels Blood Vessels Lymphatic Vessels Diaphragm Abdomen Abdominal Wall Peritoneal Retroperitoneal | Gastrointestinal Hepatobiliary Genitourinary Gynecological Splenic Adrenal Pelvis Inguinal Extremity Bone and Joint Soft Tissue Muscle Skin Vertebral and Paravertebral Spinal and Paraspinal Sacral and Presacral Hematologic Endocrine Neuroendocrine Germ Cell Pediatric Infancy Childhood Adolescents Young Adults Pregnancy Associated Geriatric Unknown Primary Benign Miscellaneous Palliation | Randomized Evidence Official Practice Guidelines Clinical Trial Information Online Tools Review |
| Medical Care Palliative Care Supportive Care | Cancer Rehabilitation Psycho-Oncology Alternative Treatments | Treatment Toxicity / NTCP Contraindications |
| Radiologic Anatomy Treatment Planning Treatment Techniques | Radiation Physics Brachytherapy Radiosurgery | Proton Therapy Heavy Ion Therapy Neutron Capture Therapy |
| Radiobiology Effect of Time Radiochemistry Radiation Chemistry | Physical Oncology Cancer Biology Cancer Syndromes Cancer Genetics | Tumor Markers Drugs Biological Therapy Radiopharmaceuticals |
* Fellowships
* Please see Table of Contents for full detail
# R Programming/Time Series

R Programming
* R Basics
** Introduction
** Sample Session
** Manage your workspace
** Settings and Installation
** Documentation
** Control Structures
** Working with functions
** Debugging
** Using C or Fortran
** Utilities
** Estimation utilities
** Packages
* Data Management
** Data types
** Working with data frames
** Importing and exporting data
** Text Processing
** Times and Dates
* Graphics
** Grammar of graphics
* Publication quality output
* Descriptive Statistics
* Mathematics
** Optimization
** Probability Distributions
** Random Number Generation
* Statistical Core Methods
** Maximum Likelihood
** Method of Moments
** Bayesian Methods
** Bootstrap
** Multiple Imputation
** Nonparametric Methods
* Regression Models
** Linear Models
** Quantile Regression
** Binomial Models
** Multinomial Models
** Tobit And Selection Models
** Count Data Models
** Duration Analysis
* Time Series
* Factor Analysis
* Classification
** Ordination
** Clustering
* Network Analysis
* High Performance Computing
** Profiling R code
** Parallel computing with R
* Appendix
** Sources
** Index

## Introduction

In the following examples we will use the data set Mpyr which is included in the R-package Ecdat, which can be loaded into R and viewed in R by the following code.

## Creating time-series objects

* The function ts is used to create time-series objects.
* The function as.ts coerces an object to a time-series.
* The function is.ts tests whether an object is a time-series.
Example:

## Creating lagged and differenced variables

* The function lag creates a lagged variable.
* The function diff creates a differenced variable.
Example:

## Plotting time-series objects

* The function plot.ts is used for plotting time-series objects.

## Fit Autoregressive Models to Time-series by OLS

In order to fit an autoregressive time series model to the data by ordinary least squares it is possible to use the function ar.ols which is part of the "stats" package.

## Autocorrelation function

The function acf computes (and by default plots) estimates of the autocovariance or autocorrelation function. Function pacf is the function used for the partial autocorrelations. Function ccf computes the cross-correlation or cross-covariance of two univariate series.

## Useful R-packages

* fBasics, tis, zoo, tseries, xts, urca, forecast

## References

* ↑ http://www.inside-r.org/r-doc/stats/acf
http://cran.r-project.org/web/views/TimeSeries.html
http://cran.r-project.org/doc/contrib/Ricci-refcard-ts.pdf
# R Programming/Times and Dates

R Programming
* R Basics
** Introduction
** Sample Session
** Manage your workspace
** Settings and Installation
** Documentation
** Control Structures
** Working with functions
** Debugging
** Using C or Fortran
** Utilities
** Estimation utilities
** Packages
* Data Management
** Data types
** Working with data frames
** Importing and exporting data
** Text Processing
** Times and Dates
* Graphics
** Grammar of graphics
* Publication quality output
* Descriptive Statistics
* Mathematics
** Optimization
** Probability Distributions
** Random Number Generation
* Statistical Core Methods
** Maximum Likelihood
** Method of Moments
** Bayesian Methods
** Bootstrap
** Multiple Imputation
** Nonparametric Methods
* Regression Models
** Linear Models
** Quantile Regression
** Binomial Models
** Multinomial Models
** Tobit And Selection Models
** Count Data Models
** Duration Analysis
* Time Series
* Factor Analysis
* Classification
** Ordination
** Clustering
* Network Analysis
* High Performance Computing
** Profiling R code
** Parallel computing with R
* Appendix
** Sources
** Index
R contains a set of object types for holding date and time information. The system time and date can also be requested.

## Format

Many time and date units are recognised. These include:
| Unit | Symbol | Example |
| 4 digit year | %Y | 1932 |
| 2 digit year | %y | 84 |
| Numerical Month | %m | 03 |
| Full Month | %B | January |
| Abbreviated Month | %b | Jan |
| Day of the month | %d | 31 |
| Full weekday | %A | Wednesday |
| Abbreviated weekday | %a | Wed |
| Hours (24hr clock) | %H | 16 |
| Minutes | %M | 35 |
| Seconds | %S | 52 |
The default format is yyyy-mm-dd hh:mm:ss or %Y-%m-%d %H:%M:%S
For example 2010-02-13 23:12:24

## System Date and Time

To get the system date and time:

## Convert strings to date/time objects

Convert a string representing the date or time into a Date/Time object:

## Extracting information from dates

Get weekday, month and an integer representing the number of days since the beginning of epoch:
Note that weekdays and months returns results in the local language. For instance, if you turn R into French, you can get weekdays and months in French :

## References

* ↑ Issue on Stackoverflow

## External links

* Do more with dates and times in R with lubridate 1.1.0 (a sample chapter from the “R in Action” book)
# RadOnc Resident Wiki/Abdomen

Tumors of the abdomen can involve various structures within the abdominal cavity and can be benign or malignant. The abdomen contains many organs, tissues, and structures, including the gastrointestinal tract, liver, pancreas, kidneys, adrenal glands, blood vessels, and connective tissues. Tumors that occur in the abdomen can arise from any of these structures. Here are some common types of abdominal tumors:
Symptoms and treatment options for abdominal tumors can vary widely depending on the type, location, size, and whether they are benign or malignant. Common symptoms of abdominal tumors may include abdominal pain, bloating, changes in bowel habits, weight loss, and abdominal masses. Diagnosis typically involves imaging studies such as CT scans, MRIs, and biopsies to determine the nature of the tumor.
Treatment options may include surgery, chemotherapy, radiation therapy, targeted therapies, immunotherapy, and sometimes a combination of these approaches. The prognosis for abdominal tumors depends on multiple factors, including the stage of cancer at diagnosis, the type of tumor, and the effectiveness of treatment. Early detection and timely intervention are crucial for improving outcomes in individuals with abdominal tumors. A multidisciplinary team of healthcare professionals, including surgeons, oncologists, radiologists, and pathologists, often collaborates in the diagnosis and treatment of these tumors.
# Technology Planning

A technology plan is an important document for every educational institution. It is so important that some states, such as Pennsylvania, have incorporated the technology plan into the state-required strategic plan. This book is based on the knowledge, experience, and learnings of a group of graduate students and their professor.
This goal of this book is to offer insight and methodology into the best practices that should be followed to for a valid technology plan that is created, supported, and accepted by all stakeholders. This book is a work in progress and does not yet offer a complete step-by-step guide to Technology Planning. This book does aim to offer a starting point for others to follow and build upon.

## Table of Contents

* Introduction
* Before We Plan
* Intro to Plan
* Dissemination and Public Relations
* Vision
* Current State
* Goals
* Implementation Plan
* Timeline
* Budget
* The Approval Process
* Monitoring and Evaluation
* Appendix