:: com :: sun :: star :: beans ::
|
| interface XMultiPropertyStates |
|
- Description
- makes it possible to query information about the state of
one or more properties.
The state of a property contains information about the source of
the value, e.g. the object itself, a default or a stylesheet.
For more information see PropertyState.
|
| Methods' Details |
getPropertyStates
- Returns
- a sequence of the states of the properties which are specified
by their names.
The order of the states is correlating to the order of the
given property names.
- Parameter aPropertyName
- specifies the names of the properties. All names must be unique.
This sequence must be alphabetically sorted.
|
|
setAllPropertiesToDefault
| void |
| setAllPropertiesToDefault(); |
- Description
- sets all properties to their default values.
Each value depends on the implementation of this interface.
If it is a bound property, you must change the value before
the change events are fired. If it is a constrained property, you
must fire the vetoable event before you change the property value.
|
|
setPropertiesToDefault
- Description
- sets the specified properties to their default values.
Each value depends on the implementation of this interface.
If it is a bound property, you must change the value before
the change events are fired. If it is a constrained property, you
must fire the vetoable event before you change the property value.
- Parameter aPropertyName
- specifies the names of the properties. All names must be unique.
This sequence must be alphabetically sorted.
- Throws
- UnknownPropertyException
if one of the properties does not exist.
|
|
getPropertyDefaults
- Returns
- the default values of the propertes with the specified names.
If no default exists, is not known, or is void,
then the return type at the corresponding position in the
sequence returned is void .
- Parameter aPropertyName
- specifies the names of the properties. All names must be unique.
This sequence must be alphabetically sorted.
- Throws
- UnknownPropertyException
if one of the propertes does not exist.
- Throws
- com::sun::star::lang::WrappedTargetException
if the implementation has an internal reason for the exception.
In this case the original exception is wrapped into that
::com::sun::star::lang::WrappedTargetException.
|
|
Top of Page
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


A Little About Luke
What's he up to now?
He is an aspiring software development engineer learning to code at Code Fellows!
Education:
- AA degree in Korean language
- BS in Biochemistry, Cellular and Molecular Biology
- MA in International Relations
- Global MBA
Work Experience:
- Global Marketing Strategist: Blockchain Platform Company (Seoul, South Korea
- Cryptologic Linguist: US Army (Various Locations)
Goals:
- Gain a good grasp on HTML, CSS, and JavaScript
- Maintain a Good Work-Life Harmony
- Go Explore the Mountain Surrounding Seattle
- Suceed at AWS
Hobbies:
- Hiking!
- Golf!
- Movies!
- Anything to Enjoy the Great Outdoors and Fresh Air!
Oh The Places You'll Go:
The Top 10 Places I Want to Go!
- Aruba
- Jamaica
- Bermuda
- Bahamas
- Key Largo
- Montego
- Kokomo
- Grand Canyon
- Yellowstone
- Tahiti


WITNESS LIST Public Safety Committee April 2, 1997-2:00P HB 580 For: A. Kent Waldrep (Natl. Paralysis Fdn.) Bruce Elfant (Travis Co. Constable) Dallas Patterson (Coal of Txns w/ Disab.) Joel Albrecht (Texas Parking Assn.) Rep. Coleman (Author HB 580) On: Jerry Dike (DPS) Maj. E.C. Sherman (DPS) HB 972 For: Rep. Jones (Author HB 972) On: Maggie Gillean (DPS) HB 1058 For: Mark Clark (CLEAT) On: James McLaughlin, Jr. (TX Police Chief's Assn.) HB 1153 For: Rep. Wise (Author HB 1153) On: Deanna Tidwell (DPS - Missing Persons) HB 1200 Committee Substitute (Oakley) For: Rep. Cuellar (Author HB 1200) On: Michael Greenberg (Comptroller's Office) Peary Perry (Himself) Robert H. Burroughs (DPS) HB 1366 Committee Substitute (Madden) For: B.J. Browning (Tx. Conf. Pol. & Sher.) Jim Lyde (Tx. Muni. Police Assoc.) Mark Clark (CLEAT) Rep. Talton (Author HB 1366) Against: James McLaughlin, Jr. (Tx. Pol. Chief's Assoc.) HB 1598 For: Bonita Blayney (SOCAN) John F. Arnaud, Jr. (Himself) On: Paul C. Jordan (DPS - Special Crimes) HB 1736 For: Bonita Blayney (SOCAN) John F. Arnaud, Jr. (Himself) On: Paul C. Jordan (DPS - Special Crimes) HB 1912 For: Rep. Wise (Author HB 1912) HB 2406 For: Rep. Hochberg (Author HB 2406) HB 3113 For: Bonita Blayney (SOCAN) John F. Arnaud, Jr. (Himself) On: Paul C. Jordan (DPS - Special Crimes)


Hello World!
A few facts about the world we live in
All about the world but i can not find the actual text so this will have to do.


The on-disk cache consists in a filesystem subtree rooted at
a location defined by the variable diskCacheRoot
, by default
"/var/cache/polipo/"
. This directory should normally be
writeable, readable and seekable by the user running Polipo. While it
is best to use a local filesystem for the on-disk cache, a NFSv3- or
AFS-mounted filesystem should be safe in most implementations. Do not
use NFSv2, as it will cause cache corruption 1.
If diskCacheRoot
is an empty string, no disk cache is used.
The value maxDiskEntries
(32 by default) is the absolute
maximum of file descriptors held open for on-disk objects. When this
limit is reached, Polipo will close descriptors on
a least-recently-used basis. This value should be set to be slightly
larger than the number of resources that you expect to be live at
a single time; defining the right notion of liveness is left as an
exercise for the interested reader.
The value diskCacheWriteoutOnClose
(64kB by default) is
the amount of data that Polipo will write out when closing a disk
file. Writing out data when closing a file can avoid subsequently
reopening it, but causes unnecessary work if the instance is later
superseded.
The integers diskCacheDirectoryPermissions
and
diskCacheFilePermissions
are the Unix filesystem permissions
with which files and directories are created in the on-disk cache;
they default to ‘0700’ and ‘0600’ respectively.
The variable maxDiskCacheEntrySize
specifies the maximum size,
in bytes, of an instance that is stored in the on-disk cache. If set
to -1 (the default), all objects are stored in the on-disk cache,


|
|
| SystemWindow (Application *application, std::shared_ptr< Window > window) |
| |
|
std::shared_ptr< Window > | window () const |
| |
|
virtual bool | render ()=0 |
| |
|
int | getX () const |
| |
|
int | getY () const |
| |
|
int | getWidth () const |
| |
|
int | getHeight () const |
| |
|
int | getSamples () const |
| |
|
int | getStencilBits () const |
| |
|
virtual void | setTitle (const std::string &title)=0 |
| |
|
virtual void | setFullscreen (bool fullscreen)=0 |
| |
|
virtual bool | getMinimized () const =0 |
| |
|
virtual void | setMinimized (bool minimized)=0 |
| |
|
virtual bool | getMaximized () const =0 |
| |
|
virtual void | setMaximized (bool maximized)=0 |
| |
|
virtual void | setVisible (bool visible)=0 |
| |
|
virtual void | setCursor (int cursor)=0 |
| |
|
virtual void | startDrag ()=0 |
| |
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virtual void | stopDrag ()=0 |
| |
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virtual void | setSize (int width, int height)=0 |
| |
|
virtual void | setPosition (int x, int y)=0 |
| |
|
|
Application * | _application |
| |
|
std::shared_ptr< Window > | _window |
| |
|
bool | _dragging {false} |
| |
|
int | _windowDragMouseX {0} |
| |
|
int | _windowDragMouseY {0} |
| |
|
int | _windowDragOffsetX {0} |
| |
|
int | _windowDragOffsetY {0} |
| |
|
double | _lastInteraction {0} |
| |
|
int | _stencilBits {8} |
| |
|
int | _samples {4} |
| |
|
int | _mouseX {0} |
| |
|
int | _mouseY {0} |
| |
|
int | _mouseState {0} |
| |
|
int | _modifiers {0} |
| |
|
int | _x {0} |
| |
|
int | _y {0} |
| |
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int | _width {0} |
| |
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int | _height {0} |
| |
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float | _pixelRatio = 0.f |
| |
|
bool | _focused {false} |
| |
|
bool | _minimized {false} |
| |
|
bool | _maximized {false} |
| |
The documentation for this class was generated from the following files:


Changes to the "Default" template and turns on template editing. This allows you to create a new template by simply editing the default template.
The default template is a special template that you cannot save to. If you save while editing the default template HexEdit prompts you for a file name.
The default template just has a single data element of type "none" and unspecified length. This accounts for all bytes to the end of file. It is useful to leave this data element at the end of the template, until it is finished,


class: center, middle, inverse, title-slide # Lecture 16 ## Using DAGs for Causal Inference ### Tyler Ransom ### ECON 6343, University of Oklahoma --- # Attribution Today's material is based on Cunningham (2021) and [lecture notes](https://github.com/andrewheiss/evalf20.classes.andrewheiss.com) by [Andrew Heiss](https://www.andrewheiss.com/) I have adjusted the materials slightly to fit the needs and goals of this course --- # Plan for the Day 1. What is a DAG? 2. How are DAGs useful? 3. What do familiar reduced-form causal models look like as DAGs? 4. How do we use a DAG to estimate causal effects with observational data? --- # What is a Directed Acyclic Graph (DAG)? .pull-left[ - .hi[Directed:] Each node has an arrow that points to another node - .hi[Acyclic:] You can't cycle back to a node; arrows are uni-directional - Rules out simultaneity - .hi[Graph:] It's a graph, in the sense of discrete mathematical graph theory ] .pull-right[  ] This DAG represents a model where `\(Z\)` determines `\(X\)` and `\(Y\)`, while `\(X\)` also determines `\(Y\)` --- # What is a Directed Acyclic Graph (DAG)? .pull-left[ - Graphical model of the DGP - Use mathematical operations called `\(do\)`-calculus - These tell you what to adjust for to isolate and identify causality - `\(do\)`-calculus is based on Bayesian Networks ] .pull-right[ ![](16slides_files/figure-html/simple-dag-1.png) ] --- # Judea Pearl: Father of using DAGs for causal inference .pull-left-narrow[

 ] .pull-right-wide.center[

 ] --- # How to draw a DAG e.g. .hi[What is the causal effect of an additional year of education on earnings?] 1. List variables 2. Simplify 3. Connect arrows 4. Use logic and math to determine which nodes and arrows to measure --- # 1. List variables - Education (treatment) → Earnings (outcome) - (Many) other factors to consider - Location - Cognitive & non-cognitive abilities (incl. risk tolerance, delaying gratification, ...) - Demographics - Family socioeconomic status - Genetics - Birth cohort - Compulsory schooling laws - Employment network --- # 2. Simplify - Education (treatment) → Earnings (outcome) - Background (Demographics, Abilities, Genetics, Family SES) - Location - Birth cohort - Compulsory schooling laws - Employment network --- # 3. Draw arrows .pull-left-narrow[ Education causes earnings ] .pull-right-wide[  ] --- # 3. Draw arrows .pull-left-narrow[ These factors all cause education: - Background - Birth cohort - Location - Job network - Schooling laws ] .pull-right-wide[  ] --- # 3. Draw arrows .pull-left-narrow[ These factors also cause earnings: - Background - Birth cohort - Location - Employment network ] .pull-right-wide[  ] --- # 3. Draw arrows .pull-left-narrow[ Education also causes employment network ] .pull-right-wide[  ] --- # 3. Draw arrows .pull-left-narrow[ - Location and background are probably related, but neither causes the other - Instead, something unobservable (U1) generates their relationship ] .pull-right-wide[  ] --- # Having the computer draw DAGs for you - [dagitty.net](http://www.dagitty.net/) - `ggdag` package in R --- # Causal identification .pull-left-narrow[ A causal effect is .hi[identified] if the association between treatment and outcome is .hi[properly stripped and isolated] ] .pull-right-wide[ ![](16slides_files/figure-html/edu-earn-full-1.png) ] --- # Paths and associations - Arrows in a DAG transmit associations - You can redirect and control those paths by "adjusting" or "conditioning" - There are different types of associations: 1. .hi[Confounding] 2. .hi[Causation] 3. .hi[Collision] --- # DAG depiction of the 3 types of associations .pull-left-3[ .center[.hi[Confounding]]  .center[.hi-lite[Common cause]] ] .pull-middle-3.center[ .hi[Causation]  .hi-lite[Mediation] ] .pull-right-3[ .center[.hi[Collision]]  .center[.hi-lite[Sample Selection]] ] --- # Confounding .pull-left-wide[  ] .pull-right-narrow[ - This situation is classic omitted variable bias - Resolve it by including `\(Z\)` as a regressor - If `\(Z\)` is unobserved, then the task becomes more difficult ] --- # Paths .pull-left-wide[ ![](16slides_files/figure-html/confounding-dag-big-1.png) ] .pull-right-narrow[ - There are two paths between `\(X\)` and `\(Y\)`: 1. `\(X\)` → `\(Y\)` 2. `\(X\)` ← `\(Z\)` → `\(Y\)` - `\(Z\)` is called a .hi[backdoor] ] --- # `\(d\)`-connection .pull-left-wide[ ![](16slides_files/figure-html/confounding-dag-big-1.png) ] .pull-right-narrow[ - `\(X\)` and `\(Y\)` are
.hi[_d_-connected] because associations can pass through `\(Z\)` - The relationship between `\(X\)` and `\(Y\)` is not identified / isolated - We .hi[close the door] by adjusting for `\(Z\)` ] --- # `\(d\)`-separation .pull-left[  ] .pull-right[ - If we adjust for `\(Z\)`,
 `\(X\)` and `\(Y\)` are now
.hi[_d_-separated] and the association is isolated ] --- # Closing backdoors .pull-left-narrow[ .box-inv-5[Block all backdoor paths to identify the main pathway you care about] ] .pull-right-wide[ ![](16slides_files/figure-html/edu-earn-full-1.png) ] --- # All paths .pull-left.left[ - Education → Earnings - Education → Job connections → Earnings - Education ← Background → Earnings - Education ← Background ← U1 → Location → Earnings - Education ← Location → Earnings - Education ← Location ← U1 → Background → Earnings - Education ← Year → Earnings ] .pull-right[ ![](16slides_files/figure-html/edu-earn-full-1.png) ] --- # All paths .pull-left.left[ - Education → Earnings - Education → Job connections → Earnings - Education ← Background → Earnings - Education ← Background ← U1 → Location → Earnings - Education ← Location → Earnings - Education ← Location ← U1 → Background → Earnings - Education ← Cohort → Earnings ] .pull-right[ ![](16slides_files/figure-html/edu-earn-full-1.png) ] --- # All paths .pull-left-narrow[ - Adjust for Location, Background and Cohort to isolate the Education → Earnings causal effect ] .pull-right-wide[  ] --- # Causation .pull-left-wide[  ] .pull-right-narrow[ - `\(X\)` causes `\(Y\)` - `\(X\)` also causes
 `\(Z\)` which causes `\(Y\)` - Should you adjust for `\(Z\)`? - Not if you want the causal effect of `\(X\)` on `\(Y\)`! - Sometimes called "overcontrolling" - Example: Personal rating in Harvard admissions ] --- # Colliders .pull-left-wide[  ] .pull-right-narrow[ - `\(X\)` causes `\(Z\)` - `\(Y\)` causes `\(Z\)` - Should you adjust for `\(Z\)`? - Not if you want the causal effect of `\(X\)` on `\(Y\)`! - Colliders always close open backdoor paths - If you condition on a collider, this opens a backdoor path ] --- # DAG for Instrumental Variables .pull-left-wide[  ] .pull-right-narrow[ - Relevance means `\(Z\)` affects `\(X\)` - Excludability means `\(Z\)` does not affect `\(Y\)` except through `\(X\)` ] --- # DAG for an RCT .pull-left-wide[  ] .pull-right-narrow[ - Randomization means `\(U\)` does not affect `\(X\)` - Hence `\(Corr(Y,X)\)` is causal ] --- # Other Resources - Another great resource for DAGs is Nick Huntington-Klein's intro [slides](https://nickch-k.github.io/introcausality/Lectures/Lecture_16_Back_Doors.html) - These are geared towards a class that hasn't learned regression yet - There are helpful practice questions and examples - All about closing backdoors and going through frontdoors - Huenermund and Bareinboim (2019) has excellent details on `\(do\)`-calculus - Also discusses how to resolve the Structural vs. Reduced Form debate --- # `\(do\)`-Calculus - `\(do\)`-Calculus is the Bayesian Network math describing how to obtain causality - We aim to obtain causality from .hi[observational data] using our DAG as the model - And making as few parametric assumptions as possible - Our DAG generates a bunch of testable implications which we can use for validity - e.g. `\(Educ \perp Earnings \vert Background, Network\)`, ... - The `\(do\)`-operator means setting the value of treatment to a specific value - Written `\(\mathbb{P}[Y\vert do(X=x)]\)` or `\(\mathbb{E}[Y\vert do(X=x)]\)` - e.g. `\(\mathbb{E}[\log wage\vert do(educ=13)]\)` (i.e. 1 year of college) - `\(do(X=x)\)` is graphically depicted by deleting all arrows leading into `\(X\)` --- # Interventions .pull-left[ .center[.hi[Observational DAG]]  ] .pull-right[ .center[.hi[Experimental DAG]]  ] --- # Returns to schooling example $$ \mathbb{E}[\log wage\ |\ do(\text{educ=16})] $$ .pull-left[ .center[.hi[Observational DAG]]  ] .pull-right[ .center[.hi[Experimental DAG]]  ] --- # Intuition of `\(do\)`-Calculus - `\(do()\)` is a causal operator, but we only have observational data - `\(\mathbb{P}[Y\ |\ do(X)] \neq \mathbb{P}(Y\ |\ X)\)` - So in order to estimate a causal effect, we need to get rid of the `\(do()\)`'s - There are three formal rules, but there are also two special cases 1. Backdoor adjustment 2. Frontdoor adjustment --- # Formal rules of `\(do\)`-Calculus Pearl (2012) 1. (Insertion/deletion of observations): `\begin{align*} \mathbb{P}(y\vert do(x), z, w) &= \mathbb{P}(y\vert do(x), w) \text{ if } (Y \perp Z\vert X, W)G_{\overline{X}} \end{align*}` 2. (Action/observation exchange): `\begin{align*} \mathbb{P}(y\vert do(x), do(z), w) &= \mathbb{P}(y\vert do(x), z, w) \text{ if } (Y \perp Z\vert X, W)G_{\overline{X}\underline{Z}} \end{align*}` 3. (Insertion/deletion of actions): `\begin{align*} \mathbb{P}(y\vert do(x), do(z), w) &= \mathbb{P}(y\vert do(x), w) \text{ if } (Y \perp Z\vert X, W)_{\overline{X}\overline{Z(W)}} \end{align*}` - `\(G_{\overline{X}}\)` is the DAG with X "done" (all incoming arrows deleted) - `\(G_{\underline{Z}}\)` is the DAG with all outgoing arrows from `\(Z\)` deleted - `\(G_{\overline{X}\underline{Z}}\)` is the DAG with `\(G_{\overline{X}}\)` and `\(G_{\underline{Z}}\)` applied --- # Backdoor adjustment $$ \mathbb{P}[Y\ |\ do(X)] = \sum_Z \mathbb{P}(Y\ |\ X, Z) \mathbb{P}(Z) $$ .pull-left[  ] .pull-right[ - This is the definition of adjusting for `\(Z\)` - This works because `\(Z\)` is observed - Our DAG presumes that there are no other confounders besides `\(Z\)` ] --- # Frontdoor adjustment  - `\(S \rightarrow T\)` is `\(d\)`-separated - `\(T \rightarrow C\)` is `\(d\)`-separated - combine the effects to find `\(S \rightarrow C\)` --- # Actually coding this up - We don't have time to go through `\(do\)`-Calculus more thoroughly - But you can consult Huenermund and Bareinboim (2019) for more details - You can also see an example from Andrew Heiss [here](https://www.andrewheiss.com/blog/2020/02/25/closing-backdoors-dags/#comparison-of-all-methods) - The R package `causaleffect` has many algorithms built-in - Near as I can tell, no one has programmed these up in Julia yet --- # Remaining issues - DAGs seem really useful for understanding intuition - I don't know how they deal with measurement issues - They can't deal with simultaneity - These issues might not actually be problematic, but I am unsure --- # References Cunningham, S. (2021). _Causal Inference: The Mixtape_. Yale University Press. URL: [https://www.scunning.com/causalinference_norap.pdf](https://www.scunning.com/causalinference_norap.pdf). Huenermund, P. and E. Bareinboim (2019). _Causal Inference and Data-Fusion in Econometrics_. Working Paper. arXiv. URL: [https://arxiv.org/abs/1912.09104](https://arxiv.org/abs/1912.09104). Pearl, J. (2012). "The Do-Calculus Revisited". In: _Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence_. Ed. by N. de Freitas and K. Murphy. Corvallis, OR: AUAI Press, pp. 4-11.


The genesis of this page is described on my blog. I was trying to create a way to visualize how the infection rates were changing, and how I could tell if there was a change in on direction or another to either be worried about, or happy about.
Cindy Bush, a former colleague now working at what she called her "dream job" at CDC recalled Statistical Process Control charts to me more than a year ago as we were trying to figure out what we should be doing with telemetry data.
I spent some time figuring out how to take data from various sources that I had access to, which resulted in me spending a lot of time in Excel. A video by Henry Reich over at minutephysics on You Tube made me rethink my math just a little bit, and after a few modifications, I came to a slightly modified way of computing my formulas. Kudos also to Aatish Bhatia who created the excellent visiualization in that video.
I hate manual data entry processes, especially when they get in the way of front line health workers, so when I found an online source I decided to put it together with what I previously described as a gem of a gist on github by a fellow named Chris Rooney, a PhD specializing in visualization out of Leeds.
That's how this all started. The source code is here. It's up to someone else to take it further, as there is another project keeping me sane that I'm working on.


Player Management System (C++)
This project helps the authority to manage their newcomer players. Talking about the project here, the user can manage their player profiles. He can search and update them. Also, this project helps in mitigating the risk of data loss. Whenever you save a player record it will be automatically added to the database in text format.
You can perform the CRUD operation in this project and the system also contains other features such as count number of players in all game, count the total number of player’s record and clear complete database of player’s record. Here, a player’s record can be searched with different searching methods which make the user work easier for the users.
The Functions used are as follows
- input(): Takes the Input of Player Information from user
- output(): Shows the last input information of Player
- writeInFile(): Writes the Input information into the File "PlayersRecord.txt"
- searchInRecord(): Searches the information such as Game, PlayerName, PlayerId, DormNo., bedNo. etc. in the File "PlayersRecord.txt"
- deleteInRecord(): Deletes the Player's Record using either PlayerId or PlayerName
- countPlayersInGame(): Counts the Players Enrolled in different Games
- countPlayers(): Counts the No. of Players Records available in File(Database)
- showAllRecords(): Shows complete Player's Records
- clearCompleteDatabase(): Clears the complete Player's Record Database
Player Management system is developed using C++ Programming Language and different variables, strings have been used for the development of it.This project provides the simplest system for managing player records.


NOTE: This is a companion package to both the {@link uk.ac.manchester.cs.hobo.modeller} and {@link uk.ac.manchester.cs.hobo.model.motor} packages, with each of which which it shares circular dependencies at the implementation level, though not at the API level. See {@link uk.ac.manchester.cs.hobo} for further details.


Welcome to Joe´s Coffee Store!
Welcome to the best Coffee store of the country Welcome to the best Coffee store of the country
Welcome to the best Coffee store of the country Welcome to the best Coffee store of the country
Welcome to the best Coffee store of the country Welcome to the best Coffee store of the country
Welcome to the best Coffee store of the country Welcome to the best Coffee store of the country
Bestsellers @ Joe's Coffee Store
Coffees
Coffee 1


See available instruments. This instrument was built with Koheron Software Development Kit. See instrument source code.


|
Hephaestus
0.0.4
|
A struct containing filename, funciton name, and line number. More...
#include <logging.hpp>
Public Member Functions | |
| code_location (const char *file, const char *func, unsigned line) | |
| Constructor for code locations. More... | |
Data Fields | |
| const char * | file |
| The name of the file this code location is in. More... | |
| const char * | func |
| The name of the function this code location is in. More... | |
| unsigned | line |
| The line number this code location is on. More... | |
A struct containing filename, funciton name, and line number.
Useful for passing code locations around for logging purposes. The HP_GET_CODE_LOC
macro automatically constructs a code_location
struct containing the appropriate info.
Definition at line 42 of file logging.hpp.
|
inline |
Constructor for code locations.
Can automatically built by the HP_GET_CODE_LOC
macro.
| file | The filename, see const char *file member. Expects __FILE__ |
| func | The function name, see const char *func member. Expects BOOST_CURRENT_FUNCTION |
| line | The line number, see unsigned line member. Expects __LINE__ |
Definition at line 75 of file logging.hpp.
| const char * hp::code_location::file |
The name of the file this code location is in.
Usually contains contents of __FILE__
macro.
Definition at line 48 of file logging.hpp.
| const char * hp::code_location::func |
The name of the function this code location is in.
Usually contains contents of BOOST_CURRENT_FUNCTION
macro, which aliases to the compiler/platform-specific function name macro.
Definition at line 55 of file logging.hpp.
| unsigned hp::code_location::line |
The line number this code location is on.
Usually contains contents of __LINE__
macro.
Definition at line 62 of file logging.hpp.


--- title: Decisions and Loss Functions - A more clinical focus author: Dan W Joyce date: '2020-04-02' slug: decisions-and-loss-functions output: blogdown::html_page: number_sections: true header-includes: \usepackage{amsmath} categories: [R code] tags: - posterior distribution - loss functions - risk - decisions image: caption: '' focal_point: '' bibliography: [./risk-dec.bib] ---
In the previous post, loss functions where considered in the context of estimating measures of central tendency for distributions. In this post, I want to look at the computation of loss functions in situations that might arise in a clinical predictive model. This is all textbook stuff – see Further Reading – but I wanted to summarise it in a way I understood when in a year’s time, I wonder what the code does.
I realised that the notation in the last post was sloppy, so for this post, I’ll adopt the conventions in (Berger 1985).
The basic setup is:
Then, our task will be to choose the best action from \(\mathscr{A}\) given information about states \(\Theta\).
Information about the states \(\Theta\) will come from a putative predictive model: as in the previous post, measurements (the “input” to the model) for a given patient \(x\) are given to the predictive model \(Y = F(x)\) that delivers scores (the “output”) as realisations of \(Y \in [0,1]\). Importantly, for any \(x\), we can access samples from the posterior distribution \(\pi_{F}(Y|x)\) (rather than relying on a single point prediction, such as the mean of the posterior).
To begin with, assume the simplest case of there being two states \(\Theta = \{ \theta_1, \theta_2 \}\) which correspond to a patient being “negative” or “positive” (respectively) for some event or outcome. Our repetoire of actions is \(\mathscr{A} = \{ a_1, a_2 \}\) representing “do nothing” and “intervene/treat” respectively. This only serves a pedagogical need when developing the ideas, not because it represents a principled or sound modelling decision.
For a single example patient \(x\), the output of the model suggests the they are most likely negative (i.e. the probability mass is concentrated near zero):
library(ggplot2)
library(gridExtra)
library(kableExtra)
library(latex2exp)
library(reshape2)
# globals for presentation
basictheme <- theme_minimal() +
theme(axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
plot.title = element_text(size = rel(1.25), face = "bold", hjust = 0.5 ))
set.seed(3141)
range01 <- function(x){(x-min(x))/(max(x)-min(x))}
samples <- range01( rgamma( 2000, shape = 2, scale = 2) )
df <- data.frame( y = samples )
ggplot( df, aes( y ) ) +
geom_density( fill = "#fa9fb5") +
ylab("Density\n") +
xlab("\nScore (Y)") + basictheme
We now require a mapping from the samples \(y \sim \pi_{F}(Y|x)\) to \(\Theta\) because the domain of \(\pi_{F}\) will be the interval \([0,1]\) and to get started, we need to “quantise” to two states.
Define the distribution over states \(\pi_{\Theta}\) as follows:
\[\begin{aligned} \pi_{\Theta}(\theta_{1}) &= \Pr_{\pi_{F}}( Y \leq 0.5 ) \\ \pi_{\Theta}(\theta_{2}) &= \Pr_{\pi_{F}}( Y > 0.5 ) \end{aligned}\]So, we basically histogram the samples into two bins either side of 0.5, representing the probability of a patient being negative (\(\theta_1\)) or positive (\(\theta_2\)). A terrible idea, which we will reverse later.
pdfFromSamples <- function(a, b, delta, samples) {
H <- hist( samples, plot = FALSE, breaks = seq(a, b, by = delta) )
ret <- data.frame(
mids = H$mids,
freq = H$counts
)
ret$P <- ret$freq / sum(ret$freq)
return(ret)
}
pdf.Y <- pdfFromSamples(0,1,delta = 1/2, samples)
pdf.Y$theta <- factor( c(1,2) )
pdf.plot <- ggplot( pdf.Y, aes( x = theta, y = P ) ) +
geom_col( fill = "#fa9fb5" ) +
scale_x_discrete(labels = pdf.Y$theta ) +
xlab(TeX("$\\theta")) +
ylab(TeX('$\\pi(\\theta)')) +
basictheme
print( pdf.plot )
According to our blunt assignment of states to output from the predictive model, the probability the patient is negative is \(\pi_{\Theta}(\theta_{1})\) = 0.921 and positive \(\pi_{\Theta}(\theta_{2})\) = 0.079.
With this setup, (two actions, two states) we can “tabulate” the combinations of actions and states (the Cartesian product: \(\Theta \times \mathscr{A}\)):
In each “cell” or combination \((\theta,a)\) we then assign a loss \(L(\theta,a) \leq 0\) which describes the cost incurred for taking action \(a\) when the state \(\theta\) obtains. Generally, we will adopt the convention that losses represent costs or penalties for actions with respect to states.
Equipped with this toy example we assign losses:
A <- c("a1:
do nothing","a2:
intervene")
Theta <- c("theta1:
negative", "theta2:
positive")
loss.matrix <- matrix( c( 0.0, -0.5,
-1.0, 0 ),
nrow = 2, ncol = 2, byrow = TRUE)
rownames(loss.matrix) <- Theta
colnames(loss.matrix) <- A
knitr::kable( loss.matrix,
format = "html",
align = 'c',
full_width = FALSE,
position = 'c',
escape = FALSE ) %>%
kable_styling(position = "center")
|
a1: do nothing |
a2: intervene |
|
|---|---|---|
|
theta1: negative |
0 | -0.5 |
|
theta2: positive |
-1 | 0.0 |
As a use example, assume we decide \(a_2\) (intervene) and the state of the patient turns out to be \(\theta_1\) (negative) we incur a loss of \(L(\theta_1,a_2) = -0.5\) to reflect unnecessary costs of e.g. further investigations, inconvenience to the patient etc. If we select \(a_1\) and the state is \(\theta_1\) (equating to doing nothing and the patient is negative) we incur zero loss because this was an appropriate action given the circumstances.
Following (Berger 1985), we define the Bayesian expected loss (BEL) for action \(a_j\) with respect to the discrete distribution \(\pi_{\Theta}\) as \[ \rho(\pi_{\Theta},a_j) = \mathbb{E}_{\pi_{\Theta}} \left[ L(\theta,a_j\right] = \sum_{i}L(\theta_i,a_j)\pi_{\Theta}(\theta_i) \]
# Bayesian expected loss (BEL)
BEL <- function( a, p.pi, loss.matrix ) {
sum( loss.matrix[ , a ] * p.pi )
}
# compute BEL for each action a:
rho.A <- data.frame(
A = factor(c("a1","a2")),
rho = rep(NA,2)
)
# for each action
for( j in 1:2 ) {
rho.A$rho[j] <- BEL( j, pdf.Y$P, loss.matrix )
}
bel.plot <- ggplot( rho.A, aes(x = A, y = rho) ) +
geom_col( fill = "#d6604d" ) +
basictheme
grid.arrange( pdf.plot, bel.plot, nrow = 1, ncol = 2 )
Note that the upper bound of the BEL is zero.
Having established the BEL for each action, the conditional bayes decision principle (CBD) for deciding on an action (Berger 1985) is:
In code: the resulting vector for \(\rho( \pi_{\Theta}, a )\)
knitr::kable( rho.A,
format = "html",
align = 'c',
full_width = FALSE,
position = 'c',
escape = FALSE ) %>%
kable_styling(position = "center")
| A | rho |
|---|---|
| a1 | -0.0790 |
| a2 | -0.4605 |
And the action that minimises the BEL:
print( min.bel.CBD <- which.max( rho.A$rho ) )
## [1] 1
In the example above, we find the action 1 (i.e. \(a_1\) = “do nothing”) minimises the BEL. This fits with our intuition given the patient is most likely negative: \(\pi_{\Theta}(\theta_1) > \pi_{\Theta}(\theta_1)\).
Consider a different patient where the posterior distribution \(\pi_{F}(Y|x)\), the output of the predictive model, looks like:
samples <- range01( c(rnorm( 1000, mean = 0, sd = 2 ), rnorm( 1000, mean = 10, sd = 3) ) )
df <- data.frame( y = samples )
ggplot( df, aes( y ) ) +
geom_density( fill = "#fa9fb5") +
ylab("Density\n") +
xlab("\nScore (Y)") + basictheme
In this example, there’s uncertainty about the patient being negative or positive.
This time, we’ll quantise into three equal-sized intervals over the range \([0,1]\) (again, an unprincipled decision made only for demonstration) and map to three states:
pdf.Y <- pdfFromSamples(0,1,delta=1/3,samples)
pdf.Y$theta <- factor( seq(1,3,by=1) )
pdf.plot <- ggplot( pdf.Y, aes( x = theta, y = P ) ) +
geom_col( fill = "#fa9fb5" ) +
scale_x_discrete(labels = pdf.Y$theta ) +
xlab(TeX("$\\theta")) +
ylab(TeX('$\\pi(\\theta)')) +
basictheme
print( pdf.plot )
The loss matrix will now be a \(3 \times 2\) matrix:
A <- c("a1:
do nothing","a2:
intervene")
Theta <- c("theta1:
negative",
"theta2:
equivocal",
"theta3:
positive")
loss.matrix <- matrix( c( 0.0, -0.5,
-0.5, -0.2,
-1.0, 0 ),
nrow = 3, ncol = 2, byrow = TRUE)
rownames(loss.matrix) <- Theta
colnames(loss.matrix) <- A
knitr::kable( loss.matrix,
format = "html",
align = 'c',
full_width = FALSE,
position = 'c',
escape = FALSE ) %>%
kable_styling(position = "center")
|
a1: do nothing |
a2: intervene |
|
|---|---|---|
|
theta1: negative |
0.0 | -0.5 |
|
theta2: equivocal |
-0.5 | -0.2 |
|
theta3: positive |
-1.0 | 0.0 |
Bearing in mind that states are uncertain, the logic behind this loss matrix is as follows:
For \(a_1\) (do nothing) : no cost is incurred if the patient is likely negative (\(\theta_1\)). If the patient is most likely positive (\(\theta_3\)) and we do nothing, this is evidently the wrong decision and we incur the maximum penalty of -1.0. If there is some equivocation \((\theta_2\)) – we penalise by half the maximum cost to discourage doing nothing (equating to loss aversion)
For \(a_2\) (intervene) : for \(\theta_1\) we incur a cost (-0.5) for intervening when unnecessary. Naturally, for \(\theta_3\), the correct thing to do is intervene so this has no penalty associated. For the equivocal case, \(\theta_2\), we should certainly not ignore these cases but simply intervening (i.e. with zero penalty) is inappropriate. So we incur a small penalty (-0.2) to nudge us away from intervening as the default.
Notice that in designing the loss matrix, we are trying to capture domain knowledge about the deployment of the model – for example, the loss attached to doing nothing (when there is equivocation about the negative/positive state of the patient) pushes us to be cautious and intervene.
Let’s look at the resulting BEL:
# compute BEL for each action a:
rho.A <- data.frame(
A = factor(c("a1","a2")),
rho = rep(NA,2)
)
# for each action
for( j in 1:2 ) {
rho.A$rho[j] <- BEL( j, pdf.Y$P, loss.matrix )
}
bel.plot <- ggplot( rho.A, aes(x = A, y = rho) ) +
geom_col( fill = "#d6604d" ) +
basictheme
grid.arrange( pdf.plot, bel.plot, nrow = 1, ncol = 2 )
knitr::kable( rho.A,
format = "html",
align = 'c',
full_width = FALSE,
position = 'c',
escape = FALSE ) %>%
kable_styling(position = "center")
| A | rho |
|---|---|
| a1 | -0.35125 |
| a2 | -0.30530 |
print( min.bel.CBD <- which.max( rho.A$rho ) )
## [1] 2
The action that minimises \(\rho(\pi_{\Theta},a)\) is \(a_2\) – as can be seen, the probability mass for \(\theta_2\) and \(\theta_3\) (and the associated losses) is driving the decision to intervene i.e. be cautious.
We can continue introducing more and more granularity in quantising the posterior predictions \(\pi_{F}(Y|x)\) to arrive at mappings to states \(\Theta\) and then specifying individual losses in the corresponding rows of the loss matrix. Instead, we’ll specify a loss function (although for coding convenience, we’ll continue with a matrix representation).
Sigmoid <- function( x, A, B, m, s ) {
# x = vector of values
# A = height of sigmoid
# B = translation on y axis
# m = value of x for which Sigmoid() = half max value
# s = steepness of linear component
exp.x <- exp( -(x-m)/s )
return(
( A + B * (1+exp.x) ) / (1+exp.x)
)
}
# plots to compare the quantised states to a more fine-grained version
pdf.Y1 <- pdfFromSamples(0, 1, delta= 1/50, samples)
pdf.plot.Y1 <- ggplot( pdf.Y1, aes( x = mids, y = P ) ) +
geom_col( fill = "#fa9fb5" ) +
xlab(TeX("$\\theta")) +
ylab(TeX('$\\pi(\\theta)')) +
basictheme
pdf.Y2 <- pdfFromSamples(0, 1, delta= 1/3, samples)
pdf.Y2$Theta <- factor(c("theta1","theta2","theta3"))
pdf.plot.Y2 <- ggplot( pdf.Y2, aes( x = Theta, y = P ) ) +
geom_col( fill = "#fa9fb5" ) +
scale_x_discrete(labels = pdf.Y2$theta ) +
xlab(TeX("$\\theta")) +
ylab(TeX('$\\pi(\\theta)')) +
basictheme
# loss functions for a1 and a2
loss.fun.a1 <- Sigmoid(pdf.Y1$mids, A = -1.0, B = 0, m = 0.5, s = 0.15 )
loss.fun.a2 <- Sigmoid(pdf.Y1$mids, A = 0.5, B = -0.5, m = 0.3, s = 0.08 )
# build a tabular version of loss function
loss.fun <- data.frame( Theta = pdf.Y1$mids,
L.a1 = loss.fun.a1,
L.a2 = loss.fun.a2
)
# show the loss function and 3 state quantised loss matrix
loss.fun.plot <- ggplot( loss.fun, aes( x = Theta ) ) +
geom_line( aes( y = L.a1 ), colour = "#fc8d59", size = 1) +
annotate( "label", x = 0.9, y = -0.75, label = "a1" ) +
geom_line( aes( y = L.a2 ), colour = "#91bfdb", size = 1 ) +
annotate( "label", x = 0.9, y = -0.15, label = "a2" ) +
ylab("Loss") +
xlab("\nTheta") +
basictheme
df.loss.matrix <- data.frame( Theta = factor( c("theta1","theta2","theta3") ),
L.a1 = loss.matrix[,1],
L.a2 = loss.matrix[,2]
)
loss.matrix.plot <- ggplot( df.loss.matrix ) +
geom_line( aes( x = Theta, y = L.a1, group = 1),
colour = "#fc8d59", size = 1) +
geom_point( aes( x = Theta, y = L.a1, group = 1),
colour = "#fc8d59", size = 4) +
annotate( "label",
x = 2.8, y = -0.7, label = "a1" ) +
geom_line( aes( x = Theta, y = L.a2, group= 1),
colour = "#91bfdb", size = 1.5 ) +
geom_point( aes( x = Theta, y = L.a2, group= 1),
colour = "#91bfdb", size = 4 ) +
annotate( "label",
x = 2.8, y = -0.2, label = "a2" ) +
ylab("Loss") +
xlab("\nTheta") +
basictheme
grid.arrange( pdf.plot.Y2, pdf.plot.Y1,
loss.matrix.plot, loss.fun.plot,
nrow = 2, ncol = 2 )
In the figure above, we show the three-state loss matrix underneath the distribution \(\pi_{\Theta}\) (the lines are to emphasise the trend in losses as we proceed from likely negative through positive). On the right, a finer-grained representation of the distribution \(\pi_{\Theta} \approx \pi_{F}(Y|x)\) with a sigmoid loss function over \(\Theta\) interpolating between the points in the loss matrix at the extremes (negative, positive) and midpoint (equivocal). Now, we can effectively use the whole of the posterior \(\pi_{F}(Y|x)\) more directly:
# the loss function x probability
pdf.Y1$L.a1 <- pdf.Y1$P * loss.fun$L.a1
pdf.Y1$L.a2 <- pdf.Y1$P * loss.fun$L.a2
# product of the posterior and loss function
loss.fun.plot2 <- ggplot( pdf.Y1, aes( x = mids ) ) +
geom_line( aes( y = L.a1 ), colour = "#fc8d59", size = 1.5) +
#annotate( "label", x = 0.9, y = -0.75, label = "a1" ) +
geom_line( aes( y = L.a2 ), colour = "#91bfdb", size = 1.5 ) +
#annotate( "label", x = 0.9, y = -0.15, label = "a2" ) +
ylab("Loss") +
xlab("\nTheta") +
basictheme
## The actual BEL
# we need a matrix representation of the loss function
loss.fun.matrix <- as.matrix( loss.fun[,2:3] )
colnames( loss.fun.matrix ) <- c("a1","a2")
# compute BEL for each action a:
rho.A <- data.frame(
A = factor(c("a1","a2")),
rho = rep(NA,2)
)
# for each action
for( j in 1:2 ) {
rho.A$rho[j] <- BEL( j, pdf.Y1$P, loss.fun.matrix )
}
bel.plot <- ggplot( rho.A, aes(x = A, y = rho) ) +
geom_col( fill = "#d6604d" ) +
basictheme
grid.arrange( pdf.plot.Y1, loss.fun.plot2, bel.plot, ncol = 2, nrow = 2)
Above, the top-left panel shows a finer-grained distribution function \(\pi_{\Theta}\) and the top-right panel shows the loss function for \(a_1\) and \(a_2\) weighted by \(\pi_{\Theta} \approx \pi_{F}(Y|x)\) – rather than the sum for each action as in \(\rho(\theta, a)\). This exposes that the Bayesian expected loss of an action is the integral over states (equivalently, the sum for discrete distributions) of the product of the loss function for an action in a certain state and the probability of that state. The bottom-left panel shows the resulting BEL where, as expected, \(a2\) minimises \(\rho(\theta,a)\).
If I were to try this again (rather than trying to piece together an understanding from wikipedia), I would proceed in this order:
Berger, James O. 1985. Statistical Decision Theory and Bayesian Analysis. 2nd ed. Springer.
Bernardo, José M, and Adrian FM Smith. 2009. Bayesian Theory. Vol. 405. John Wiley & Sons.
Bishop, Christopher M. 2006. Pattern Recognition and Machine Learning. Springer.
Duda, Richard O, Peter E Hart, and David G Stork. 2012. Pattern Classification. John Wiley & Sons.
Gneiting, Tilmann. 2011. “Making and Evaluating Point Forecasts.” Journal of the American Statistical Association 106 (494). Taylor & Francis: 746–62.
Savage, Leonard J. 1951. “The Theory of Statistical Decision.” Journal of the American Statistical Association 46 (253). Taylor & Francis Group: 55–67.


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Configure NOR-Flash or (P)SRAM. More...
#include <driver/fsmc/stm32/fsmc.hpp>
Classes | |
| struct | AsynchronousTiming |
| Timing for asynchronous access. More... | |
| struct | SynchronousTiming |
| Timing for synchronous access. More... | |
Public Types | |
| enum | Region { CHIP_SELECT_1, CHIP_SELECT_2, CHIP_SELECT_3, CHIP_SELECT_4 } |
| Corresponds to NE1..4. | |
| enum | WaitPolarity { WAIT_HIGH_ACTIVE, WAIT_LOW_ACTIVE } |
| enum | BusType { NO_MULTIPLEX_8BIT, NO_MULTIPLEX_16BIT, ADDRESS_DATA_MULIPLEX_8BIT, ADDRESS_DATA_MULIPLEX_16BIT } |
| enum | MemoryType { SRAM_ROM, PSRAM, NOR } |
| enum | AccessMode { MODE_A, MODE_B, MODE_C, MODE_D } |
| enum | ExtendedMode : uint32_t { Disable, Enable } |
Static Public Member Functions | |
| static void | resetRegion (Region region) |
| static void | enableRegion (Region region, bool enable=true) |
| static void | configureSynchronousRegion (Region region, BusType multiplex, MemoryType memoryType, SynchronousTiming timing) |
| Configure a region of Bank 1 for synchronous access. More... | |
| static void | configureAsynchronousRegion (Region region, BusType multiplex, MemoryType memoryType, AccessMode accessMode, AsynchronousTiming timing, ExtendedMode extended=ExtendedMode::Enable) |
| static void | enableAsynchronousWait (Region region, WaitPolarity polarity=WAIT_LOW_ACTIVE) |
| template<typename T > | |
| static constexpr T * | getRegionPointer (Region region) |
| Get mapped memory address of a region. More... | |
NOR non-multiplexed
NOR multiplexed
(P)SRAM
Options:
Mode 1 (SRAM/CRAM)
Mode A (CRAM)
Mode 2/B (NOR Flash)
Mode C (NOR Flash)
Mode D
Mode muxed
Async wait: only data phase stage is prolonged, address setup and address hold are not affected.
Memory Type (MTYP):
Multiplexed mode only valid for NOR and PSRAM
Synchronous => burst mode? NOR Flash and burst write?
|
static |
The region is disabled afterwards and has to be enabled via enableRegion().
|
inlinestatic |
Each region is 64 MB in size.


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/*****************************************************************************
* Source code information
* -----------------------
* Original author Ian Dickinson, HP Labs Bristol
* Author email<EMAIL_ADDRESS>* Package Jena 2
* Web http://sourceforge.net/projects/jena/
* Created 22-Aug-2003
* Filename $RCSfile: Main.java,v $
* Revision $Revision: 1.1 $
* Release status $State: Exp $
*
* Last modified on $Date: 2005/10/06 17:49:06 $
* by $Author: andy_seaborne $
*
* (c) Copyright 2002, 2003, 2004, 2005 Hewlett-Packard Development Company, LP
* (see footer for full conditions)
*****************************************************************************/
// Package
///////////////
package jena.examples.ontology.describeClass;
// Imports
///////////////
import java.util.Iterator;
import com.hp.hpl.jena.ontology.*;
import com.hp.hpl.jena.rdf.model.ModelFactory;
/**
*

* Execution wrapper for describe-class example
*

*
* @author Ian Dickinson, HP Labs
* (<a<EMAIL_ADDRESS>>email)
* @version CVS $Id: Main.java,v 1.1 2005/10/06 17:49:06 andy_seaborne Exp $
*/
public class Main {
// Constants
//////////////////////////////////
// Static variables
//////////////////////////////////
// Instance variables
//////////////////////////////////
// Constructors
//////////////////////////////////
// External signature methods
//////////////////////////////////
public static void main( String[] args ) {
// read the argument file, or the default
String source = (args.length == 0) ? "http://www.w3.org/TR/2003/CR-owl-guide-20030818/wine" : args[0];
// guess if we're using a daml source
boolean isDAML = source.endsWith( ".daml" );
OntModel m = ModelFactory.createOntologyModel(
isDAML ? OntModelSpec.DAML_MEM : OntModelSpec.OWL_MEM, null
);
// we have a local copy of the wine ontology
m.getDocumentManager().addAltEntry( "http://www.w3.org/TR/2003/CR-owl-guide-20030818/wine",
"file:./testing/reasoners/bugs/wine.owl" );
m.getDocumentManager().addAltEntry( "http://www.w3.org/TR/2003/CR-owl-guide-20030818/food",
"file:./testing/reasoners/bugs/food.owl" );
// read the source document
m.read( source );
DescribeClass dc = new DescribeClass();
if (args.length >= 2) {
// we have a named class to describe
OntClass c = m.getOntClass( args[1] );
dc.describeClass( System.out, c );
}
else {
for (Iterator i = m.listClasses(); i.hasNext(); ) {
// now list the classes
dc.describeClass( System.out, (OntClass) i.next() );
}
}
}
// Internal implementation methods
//////////////////////////////////
//==============================================================================
// Inner class definitions
//==============================================================================
}
/*
(c) Copyright 2002, 2003, 2004, 2005 Hewlett-Packard Development Company, LP
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
3. The name of the author may not be used to endorse or promote products
derived from this software without specific prior written permission.

*/


Select a csv file:
or put values in this textbox:
Filter out outliers ( < q5,> q95)
Analyze value columns
FFT window size
FFT window overlap
Spectogram frequency bins
Number of samples/sec
Test over time
Test spectogram multi window hann sampling
Select a column:
Input
Output (magnitude)
Spectogram
PSE:
0
Histogram over time
Entropy:
Mean
Median
Variance
Skewness
Kurtosis
Gini coefficient


[1]
Many features included; Many features included some mediocre
[2]
Simple, intuitive interface; Poor mounting options;
[3]
mapping software; bluetooth, poi


Do not wait for Tomorrow!


Results of a randomized, placebo-controlled, prospective 2-arm trial of rectal indomethacin (100 mg) vs. placebo prevent post-ERCP pancreatitis in 602 participants, as reported by Elmunzer, Higgins, et al. in 2012 in the New England Journal of Medicine (more details available below the variable definitions).
indo_rct
A data frame with 602 observations and 33 variables
subject id, first integer indicates center, integer, range:1001-4003
study site (center), factor, 1 = University of Michigan, 2= Indiana University, 3 = University of Kentucky, 4 = Case Western
age in years, numeric, range: 19-90
risk score, numeric, range: 1-5.5
male or female, factor, levels: 1_female, 2_male
sphincter of oddi dysfunction was present, a risk factor favoring post-ERCP pancreatitis, factor, levels: 0_no, 1_yes
previous post-ERCP pancreatitis (PEP), a risk factor for future PEP, factor, levels: 0_no, 1_yes
Recurrent Pancreatitis, a risk factor for future PEP, factor, levels: 0_no, 1_yes
a Pancreatic Sphincterotomy was performed, a risk factor for PEP, factor, levels: 0_no, 1_yes
a sphincter pre-cut was needed to enter the papilla, a risk factor for PEP, factor, levels: 0_no, 1_yes
Cannulation of the papilla was difficult, a risk factor for PEP, factor, levels: 0_no, 1_yes
Pneumatic dilation of the papilla was performed, a risk factor for PEP, factor, levels: 0_no, 1_yes
An Ampullectomy was performed for dysplasia or cancer, which could be a risk factor for PEP, factor, levels: 0_no, 1_yes
Contrast was injected into the pancreas during the procedure, a risk factor for PEP, factor, levels: 0_no, 1_yes
The pancreas appeared to have acinarization on imaging, which could be a risk factor for PEP, factor, levels: 0_no, 1_yes
Brushings were taken from the pancreatic duct, a possible risk factor favoring post-ERCP pancreatitis. factor, levels: 0_no, 1_yes
Aspirin was used at a dose of 81 mg per day, which may increase the risk of bleeding. factor, levels: 0_no, 1_yes
Aspirin was used at a dose of 325 mg per day, which may increase the risk of bleeding. factor, levels: 0_no, 1_yes
Aspirin was used (at a dose of 325 mg per day(at any dose), which may increase the risk of bleeding. factor, levels: 0_no, 1_yes
A pancreatic duct stent was placed at the end of the procedure per the judgement of the endoscopist (more often in high-risk cases), a potential protective effect against PEP, factor, levels: 0_no, 1_yes
A pancreatic duct stent was placed in order to treat a clinically significant narrowing of the pancreatic duct, a potential protective effect against PEP, factor, levels: 0_no, 1_yes
A pancreatic duct stent was placed at the end of the procedure for any reason, a potential protective effect against PEP, factor, levels: 0_no, 1_yes
Sphincter of oddi manometry was performed during the procedure for SOD, a risk factor for PEP, factor, levels: 0_no, 1_yes
A biliary sphincterotomy was performed, which could be a risk factor for PEP, factor, levels: 0_no, 1_yes
A biliary stent was placed to relieve significant biliary obstruction, factor, levels: 0_no, 1_yes
Choledocholithiasis (gallstones blocking the biliary duct) was present, factor, levels: 0_no, 1_yes
Malignancy of the biliary duct or pancreas was found, factor, levels: 0_no, 1_yes
A trainee participated in the ERCP, which could be a risk factor for PEP, factor, levels: 0_no, 1_yes
outcome of post-ercp pancreatitis, factor, levels: 0_no, 1_yes
outpatient status, factor, levels: 0_inpatient, 1_outpatient
Sphincter of Oddi dysfunction type/level - higher numbers are more severe with greater association with PEP, factor, levels: 0_no SOC, 1_type 1, 2_type 2, 3_type 3
treatment arm, factor, levels: 0_placebo, 1_indomethacin
A gastrointestinal bleed occurred (which could be a complication of indomethacin therapy), factor, levels: 1. no, 2. yes
This data set is sourced from the authors of the 2012 manuscript in the New England Journal of Medicine, entitled, A Randomized Trial of Rectal Indomethacin to Prevent Post-ERCP Pancreatitis, pages 1414-1422 volume 366, in the April 12, 2012 edition, authored by the Elmunzer, BJ, Higgins PDR, et al. You can find the manuscript at Indomethacin to Prevent Post-ERCP Pancreatitis.
ERCP, or endoscopic retrograde cholangio-pancreatogram, is a procedure performed by threading an endoscope through the mouth to the opening in the duodenum where bile and pancreatic digestive juices are released into the intestine. ERCP is helpful for treating blockages of flow of bile (gallstones, cancer), or diagnosing cancers of the pancreas, but has a high rate of complications (15-25%).
The occurrence of post-ERCP pancreatitis is a common and feared complication, as pancreatitis can result in multisystem organ failure and death, and can occur in ~ 16% of ERCP procedures.
The inflammatory cytokine storm that can result from this procedural complication can be quite severe. Several small randomized trials suggested that anti-inflammatory NSAID therapies at the time of ERCP could reduce the rate of this complication, but all were rather small single-center studies, and were not sufficiently convincing to change practice.
Elmunzer, Higgins, and colleagues performed a meta-analysis of these small trials, which suggested that this was a significant effect, and that indomethacin could result in a 64% reduction in post-ERCP pancreatitis.
The investigators took this as a possible over-estimate of the effect (due to publication bias), and designed a multicenter RCT of a planned 948 patients to see a reduction of 50% from a placebo rate of 10% to an indomethacin rate of 5%. Two interim analyses were performed, after 400 and 600 patients were enrolled, using an alpha spending function. The Data and Safety Monitoring Board stopped the study after 602 participants were enrolled because of the significantly positive effect of indomethacin, which reduced post-ERCP pancreatitis from 16% in the placebo group to 9% in the indomethacin group.
You can find the manuscript at Indomethacin to Prevent Post-ERCP Pancreatitis.


{% extends 'base.html' %} {% block title_block %}Login{% endblock %} {% block main_content %}
You are currently logged in as {{ user.username }}.
You are NOT logged in.
Add 1
session key: {{ session_key }}
current count: {{ add_cnt }}


Job Opportunities
Potential job descriptions
Web Analyst
IT Business Analyst
Monetization Analyst
Data Mining Specialist
Business Analyst
Data Scientist
Healthcare Analyst
Data
Data Visualisation
Business Intelligence
Dashboarding
Soft-skills
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Teamwork
Project-based work


25
1 While the Israelites were camped at a place called Acacia Grove, some of the men caused themselves to become unacceptable to God by sleeping with some of the women of the Moab people group who lived in that area.
2 Then those women invited the men to come when the sacrifices were being offered to their gods. The Israelite men accepted. They went to the feasts with the women and worshiped the gods of the Moab people group.
3 By doing that, the Israelite people joined the women in worshiping the god Baal who the Moab people group thought lived on Mount Peor. That caused Yahweh to become very angry with his people, and he sent a severe plague on many of the Israelite people.
4 Yahweh said this to Moses: "Seize all the leaders of those who are doing this and execute them while I am watching. Do that in the daytime. After you do that, I will no longer be angry with the Israelite people."
5 So Moses said to the other Israelite leaders, "Each of you must execute your men who have joined others in worshiping Baal."
6 But later, while many people were crying at the entrance of the sacred tent, one of the Israelite men brought a woman from the Midian people group into his tent and started to sleep with her. Moses and all the people heard of it.
7 When Phinehas, who was a grandson of Aaron, heard this, he grabbed a spear.
8 Then he rushed into the man's tent. He thrust the spear completely through the man's body and into the woman's belly and killed both of them. When he did that, the plague that had started to strike the Israelites stopped.
9 But 24,000 people had already died from that plague.
10 Then Yahweh said to Moses,
11 "Phinehas has caused me to stop being angry with the Israelite people, by being as eager as I am to stop this sinful behavior. I was ready to get rid of all the Israelite people because I was extremely angry, but Phinehas has prevented me from doing that.
12 Now tell him that I am making a special peace agreement with him.
13 In this agreement, I am promising to give to him and to his descendants the right to be priests. I am doing this because he showed that he was very eager to honor me, his God, by stopping this sinful behavior. He has satisfied my holy righteousness against the sinfulness of Israel by causing me to forgive them for their sin."
14 The Israelite man who was killed with the woman of the Moab people group was named Zimri son of Salu, who was the leader of a family from the tribe of Simeon.
15 The woman's name was Cozbi. She was the daughter of Zur, who was the leader of one of the clans of the Midian people group.
16 Then Yahweh said to Moses,
17 "Take your men and attack the Midian people group and kill them.
18 They have become your enemies, because they tricked you Israelite people and induced many of you to worship Baal, and because one of your men slept with Cozbi, who was the daughter of a leader of the Midian people group. She was killed at the time the plague started because of the people who sinned at Mount Peor."


All the functions that take place within a cell are performed through proteins. These proteins are coded within the DNA (Deoxyribonucleic acid) of the cell. A gene is a sequence of DNA that encodes for a particular protein. In order to make the necessary proteins, the transcriptional machinary of a cell makes special copies of the respective genes that can be translated to protein sequences. These special copies are called messenger RNAs (Ribonucleic acids).
The amount of mRNA produced by a specific gene is used as a surrogate marker for quantification of the gene activity. With Current highthroughput sequencing technologies (such as in this case RNAseq), all (fragmented) RNA molecules from a biological sample are sequenced. These sequences are then matched to the annotated genome sequence to identify to which gene each sequenced fragment belongs. More sequenced fragments mapping to a gene in one samples as compared to another, means that the specific gene had a higher activity.
This workflow uses ‘Rsubread’ package to perform mapping of sequencing reads to the genome and to quantify the number of reads mapped to each gene. This preprocessing step is mostly done outside R using tools such as STAR aligner, TopHat, HISAT2, kallisto, or Salmon.
There are 1 packages used in this workflow, which depend on ... additional packages (dependencies).
Used packages:
Package dependencies:
RNAseq fastq-files from Solaimani Kartalaei P, (2014) samples from SRA id: SRR1653121 to SRR1653136


Use the arrow keys to move the qubit around and cross the finish line with its head on the right way round!


This is a test version of the document. Please
purchase a plan
to upgrade to the full versions!
This is a test version of the document; a full version is not covered by the Basic plan.
Edit this document in
Bookalope
, and then refresh the changes in a new local document.


Didn't find what you were looking for? I check my email periodically and always have my phone on me. In case you missed it, my profiles for GitHub, LinkedIn, Facebook, and Twitter are at the bottom of the page.


skyux generate creates template items for your SKY UX project. This command requires two arguments. First, it requires a type argument to specify the type of item to create. Currently, component is the only available option for the type argument. And second, the command requires a name argument to provide a name for the template items.
Arguments
-
Specify the type of template item to create. Currently, component is the only available option for this argument, and it generates the TypeScript, HTML, CSS, and spec files for a new component.
-
Provide a name for the template items to create. You do not need to include file extension information. You can precede the name with an optional subfolder to create a subfolder in the src/app folder. If you do not specify a subfolder, the generate command places items directly in the src/app folder.
Options


Elixir Circuits provides libraries for working with hardware in Elixir
Elixir/ALE was the predecessor to the Elixir Circuits libraries.


Example with monospace
Monospace
can be used in section headers
This is a paragraph with
some words in monospace and in
bold monospace
.


Selects matched samples of the original treated and
control groups with similar covariate distributions – can be
used to match exactly on covariates, to match on propensity
scores, or perform a variety of other matching procedures. The
package also implements a series of recommendations offered in
Ho, Imai, King, and Stuart (2007) <doi:10.1093/pan/mpl013>.
| Version: |
3.0.2 |
| Depends: |
R (≥ 2.6) |
| Imports: |
MASS, Matching |
| Suggests: |
cem, nnet, optmatch, rpart, mgcv, WhatIf, R.rsp, testthat, rgenoud |
| Published: |
2018-01-09 |
| Author: |
Daniel Ho [aut],
Kosuke Imai [aut, cre],
Gary King [aut],
Elizabeth Stuart [aut],
Alex Whitworth [ctb] |
| Maintainer: |
Kosuke Imai <kimai at Princeton.Edu> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
http://gking.harvard.edu/matchit |
| NeedsCompilation: |
no |
| Citation: |
MatchIt citation info |
| In views: |
OfficialStatistics, SocialSciences |
| CRAN checks: |
MatchIt results |


{% extends 'base.html' %} {% load poll_extras %} {% block content %}
There is no employee available.
There is no poll available.


- Russia’s main criminal investigation body has launched a probe after a report that a nickel-processing plant was pumping water contaminated with heavy metals into the Arctic tundra.
- 40
- World - Russia
- Investigators Probe Reported Russian Tundra Pollution
- Associated Press
- news.usnews.com
- 6/28/2020 12:20:01 PM
- investigators-probe-reported-russian-tundra-pollution
- Investigators Probe Reported Russian Tundra Pollution
- Russia’s main criminal investigation body has launched a probe after a report that a nickel-processing plant was pumping water contaminated with heavy metals into the Arctic tundra.
- 345
- https://www.usnews.com/news/business/articles/2020-06-28/investigators-probe-reported-russian-tundra-pollution
- A young black bear is back in the wild in western Wyoming after being rescued as a cub.
- 40
- Wyoming
- Black Bear Rescued as Cub Released Back Into Wyoming Woods
- Associated Press
- news.usnews.com
- 6/28/2020 12:10:44 PM
- black-bear-rescued-as-cub-released-back-into-wyoming-woods
- Black Bear Rescued as Cub Released Back Into Wyoming Woods
- A young black bear is back in the wild in western Wyoming after being rescued as a cub.
- 344
- https://www.usnews.com/news/best-states/wyoming/articles/2020-06-28/black-bear-rescued-as-cub-released-back-into-wyoming-woods
- Lazarus Chakwera has been sworn in as Malawi’s new president after the announcement that he won the southern African country’s rerun elections.
- 40
- Lazarus Chakwera Inaugurated as Malawi's New President
- Associated Press
- news.usnews.com
- 6/28/2020 12:10:32 PM
- lazarus-chakwera-inaugurated-as-malawi-s-new-president
- Lazarus Chakwera Inaugurated as Malawi's New President
- Lazarus Chakwera has been sworn in as Malawi’s new president after the announcement that he won the southern African country’s rerun elections.
- 343
- https://www.usnews.com/news/world/articles/2020-06-28/lazarus-chakwera-inaugurated-as-malawis-new-president
- Worldwide confirmed coronavirus infections have hit the 10 million mark as voters in Poland and France went to the polls for virus-delayed elections.
- 40
- World - France, World - Poland
- Confirmed Cases Hit 10 Million; Pence Skips Campaign Rallies
- Associated Press
- news.usnews.com
- 6/28/2020 12:04:22 PM
- confirmed-cases-hit-10-million-pence-skips-campaign-rallies
- Confirmed Cases Hit 10 Million; Pence Skips Campaign Rallies
- Worldwide confirmed coronavirus infections have hit the 10 million mark as voters in Poland and France went to the polls for virus-delayed elections.
- 342
- https://www.usnews.com/news/world/articles/2020-06-28/china-virus-cases-stabilize-as-italy-sees-drop-in-deaths
- The reenergized movement against racial inequality and police brutality following the killing of George Floyd has amplified the voices of Black candidates across the country.
- 40
- Black Candidates Push Race Debate Into GOP-Held Districts
- Associated Press
- news.usnews.com
- 6/28/2020 11:59:12 AM
- black-candidates-push-race-debate-into-gop-held-districts
- Black Candidates Push Race Debate Into GOP-Held Districts
- The reenergized movement against racial inequality and police brutality following the killing of George Floyd has amplified the voices of Black candidates across the country.
- 341
- https://www.usnews.com/news/politics/articles/2020-06-28/black-candidates-push-race-debate-into-gop-held-districts
- Muriel Bowser’s national profile had never been higher, thanks to a Twitter beef with President Donald Trump and a renewed push to turn the nation’s capital into the 51st state.
- 40
- DC Mayor Caught Between Activists, Police in Funding Battle
- Associated Press
- news.usnews.com
- 6/28/2020 11:56:08 AM
- dc-mayor-caught-between-activists-police-in-funding-battle
- DC Mayor Caught Between Activists, Police in Funding Battle
- Muriel Bowser’s national profile had never been higher, thanks to a Twitter beef with President Donald Trump and a renewed push to turn the nation’s capital into the 51st state.
- 340
- https://www.usnews.com/news/politics/articles/2020-06-28/dc-mayor-caught-between-activists-police-in-funding-battle
- China's legislature is reviewing a controversial national security bill for Hong Kong that critics worldwide say will severely compromise human rights in the semi-autonomous Chinese region.
- 40
- World - China
- China Reviews Heavily Criticized Hong Kong Security Bill
- Associated Press
- news.usnews.com
- 6/28/2020 11:37:54 AM
- china-reviews-heavily-criticized-hong-kong-security-bill
- China Reviews Heavily Criticized Hong Kong Security Bill
- China's legislature is reviewing a controversial national security bill for Hong Kong that critics worldwide say will severely compromise human rights in the semi-autonomous Chinese region.
- 339
- https://www.usnews.com/news/world/articles/2020-06-28/china-reviews-heavily-criticized-hong-kong-security-bill
- Utah officials say fireworks caused a wildfire that forced out residents of several houses and from an apartment building early Sunday as flames threatened the homes.
- 40
- Utah
- Officials: Fireworks Spark Utah Wildfire, Evacuations
- Associated Press
- news.usnews.com
- 6/28/2020 11:23:28 AM
- officials-fireworks-spark-utah-wildfire-evacuations
- Officials: Fireworks Spark Utah Wildfire, Evacuations
- Utah officials say fireworks caused a wildfire that forced out residents of several houses and from an apartment building early Sunday as flames threatened the homes.
- 338
- https://www.usnews.com/news/best-states/utah/articles/2020-06-28/officials-fireworks-spark-utah-wildfire-evacuations
- Confirmed coronavirus infections have surpassed the 10 million mark worldwide.
- 40
- The Latest: Confirmed Coronavirus Cases Pass 10 Million
- Associated Press
- news.usnews.com
- 6/28/2020 11:06:10 AM
- the-latest-confirmed-coronavirus-cases-pass-10-million
- The Latest: Confirmed Coronavirus Cases Pass 10 Million
- Confirmed coronavirus infections have surpassed the 10 million mark worldwide.
- 337
- https://www.usnews.com/news/world/articles/2020-06-28/the-latest-pakistan-producing-own-ventilators-as-cases-rise
- The Taiwanese capital has held its annual LGBT pride parade, making it one of the few places in the world to proceed with such an event in the face of the coronavirus pandemic.
- 40
- Taiwan Holds LGBT Pride Parade Despite Coronavirus
- Associated Press
- news.usnews.com
- 6/28/2020 10:53:28 AM
- taiwan-holds-lgbt-pride-parade-despite-coronavirus
- Taiwan Holds LGBT Pride Parade Despite Coronavirus
- The Taiwanese capital has held its annual LGBT pride parade, making it one of the few places in the world to proceed with such an event in the face of the coronavirus pandemic.
- 336
- https://www.usnews.com/news/world/articles/2020-06-28/taiwan-holds-lgbt-pride-parade-despite-coronavirus
- Only the fans were missing as Bayern Munich celebrated its title Saturday and the Bundesliga breathed a sigh of relief.
- 40
- World - Germany
- How Germany Did It: Completing the Soccer Season in Europe
- Associated Press
- news.usnews.com
- 6/28/2020 10:31:35 AM
- how-germany-did-it-completing-the-soccer-season-in-europe
- How Germany Did It: Completing the Soccer Season in Europe
- Only the fans were missing as Bayern Munich celebrated its title Saturday and the Bundesliga breathed a sigh of relief.
- 335
- https://www.usnews.com/news/sports/articles/2020-06-28/how-germany-did-it-completing-the-soccer-season-in-europe
- France is holding the second round of municipal elections that got postponed due to the country's coronavirus outbreak.
- 40
- World - France
- France Holds Municipal Elections Postponed by Virus Crisis
- Associated Press
- news.usnews.com
- 6/28/2020 10:29:03 AM
- france-holds-municipal-elections-postponed-by-virus-crisis
- France Holds Municipal Elections Postponed by Virus Crisis
- France is holding the second round of municipal elections that got postponed due to the country's coronavirus outbreak.
- 334
- https://www.usnews.com/news/world/articles/2020-06-28/france-holds-municipal-elections-postponed-by-virus-crisis
- The U.S. special representative to Iran says a United Nations weapons embargo on Tehran must remain in place.
- 40
- World - Iran
- AP Interview: US Envoy Calls for Iran Arms Embargo Renewal
- Associated Press
- news.usnews.com
- 6/28/2020 10:20:49 AM
- ap-interview-us-envoy-calls-for-iran-arms-embargo-renewal
- AP Interview: US Envoy Calls for Iran Arms Embargo Renewal
- The U.S. special representative to Iran says a United Nations weapons embargo on Tehran must remain in place.
- 333
- https://www.usnews.com/news/world/articles/2020-06-28/ap-interview-us-envoy-calls-for-iran-arms-embargo-renewal
- The Rolling Stones are threatening President Donald Trump with legal action for using their songs at his rallies despite cease-and-desist directives.
- 40
- Rolling Stones Threaten to Sue Trump Over Using Their Songs
- Associated Press
- news.usnews.com
- 6/28/2020 10:13:55 AM
- rolling-stones-threaten-to-sue-trump-over-using-their-songs
- Rolling Stones Threaten to Sue Trump Over Using Their Songs
- The Rolling Stones are threatening President Donald Trump with legal action for using their songs at his rallies despite cease-and-desist directives.
- 332
- https://www.usnews.com/news/entertainment/articles/2020-06-28/rolling-stones-threaten-to-sue-trump-over-using-their-songs
- By Joanna Plucinska and Anna KoperWARSAW (Reuters) - Poles stood in long lines to vote on Sunday in a closely-fought presidential election that...
- 40
- Poles Face Long Lines, Coronavirus Limits in Presidential Vote
- Reuters
- news.usnews.com
- 6/28/2020 10:12:27 AM
- poles-face-long-lines-coronavirus-limits-in-presidential-vote
- Poles Face Long Lines, Coronavirus Limits in Presidential Vote
- By Joanna Plucinska and Anna KoperWARSAW (Reuters) - Poles stood in long lines to vote on Sunday in a closely-fought presidential election that...
- 331
- https://www.usnews.com/news/world/articles/2020-06-28/poles-face-long-lines-coronavirus-limits-in-presidential-vote
- TAIPEI (Reuters) - Hundreds of people thronged a central square in Taiwan's capital Taipei for a Pride event on Sunday, unfurling a giant rainbow...
- 40
- Giant Rainbow Flag Unfurled in Front of Taiwan Autocrat's Memorial Hall
- Reuters
- news.usnews.com
- 6/28/2020 9:58:13 AM
- giant-rainbow-flag-unfurled-in-front-of-taiwan-autocrat-s-memorial-hall
- Giant Rainbow Flag Unfurled in Front of Taiwan Autocrat's Memorial Hall
- TAIPEI (Reuters) - Hundreds of people thronged a central square in Taiwan's capital Taipei for a Pride event on Sunday, unfurling a giant rainbow...
- 330
- https://www.usnews.com/news/world/articles/2020-06-28/giant-rainbow-flag-unfurled-in-front-of-taiwan-autocrats-memorial-hall
- DUBAI (Reuters) - Iranian President Hassan Rouhani said on Sunday that his country is experiencing its toughest year because of U.S. sanctions...
- 40
- World - Iran
- U.S. Sanctions, Coronavirus Make for Iran's Toughest Year, Rouhani Says
- Reuters
- news.usnews.com
- 6/28/2020 9:51:21 AM
- u-s-sanctions-coronavirus-make-for-iran-s-toughest-year-rouhani-says
- U.S. Sanctions, Coronavirus Make for Iran's Toughest Year, Rouhani Says
- DUBAI (Reuters) - Iranian President Hassan Rouhani said on Sunday that his country is experiencing its toughest year because of U.S. sanctions...
- 329
- https://www.usnews.com/news/world/articles/2020-06-28/us-sanctions-coronavirus-make-for-irans-toughest-year-rouhani-says
- KINSHASA (Reuters) - Democratic Republic of Congo's prime minister protested on Sunday over the brief arrest of the justice minister, saying the...
- 40
- Congo PM Threatens Government Resignation Over Minister's Arrest
- Reuters
- news.usnews.com
- 6/28/2020 9:48:36 AM
- congo-pm-threatens-government-resignation-over-minister-s-arrest
- Congo PM Threatens Government Resignation Over Minister's Arrest
- KINSHASA (Reuters) - Democratic Republic of Congo's prime minister protested on Sunday over the brief arrest of the justice minister, saying the...
- 328
- https://www.usnews.com/news/world/articles/2020-06-28/congo-pm-threatens-government-resignation-over-ministers-arrest
- Poles have begun voting in a presidential election that had been scheduled for May but was delayed by the coronavirus pandemic.
- 40
- World - Poland
- A Divided Poland Holds Presidential Vote Delayed by Pandemic
- Associated Press
- news.usnews.com
- 6/28/2020 9:45:31 AM
- a-divided-poland-holds-presidential-vote-delayed-by-pandemic
- A Divided Poland Holds Presidential Vote Delayed by Pandemic
- Poles have begun voting in a presidential election that had been scheduled for May but was delayed by the coronavirus pandemic.
- 327
- https://www.usnews.com/news/world/articles/2020-06-28/poland-holds-presidential-election-delayed-by-pandemic
- A Jerusalem court has released a former Air Force general and leading activist opposed to Israeli prime minister Benjamin Netanyahu whose arrest prompted hundreds to protest outside the Israeli leader’s residence.
- 40
- Israeli Court Releases Anti-Netanyahu Activist After Arrest
- Associated Press
- news.usnews.com
- 6/28/2020 9:08:54 AM
- israeli-court-releases-anti-netanyahu-activist-after-arrest
- Israeli Court Releases Anti-Netanyahu Activist After Arrest
- A Jerusalem court has released a former Air Force general and leading activist opposed to Israeli prime minister Benjamin Netanyahu whose arrest prompted hundreds to protest outside the Israeli leader’s residence.
- 326
- https://www.usnews.com/news/world/articles/2020-06-28/israeli-court-releases-anti-netanyahu-activist-after-arrest


APIEndpoint
public struct APIEndpoint
Helper object that centralize all required parameters for an endpoint
-
root URL. I.e. github.com, developer.apple.com and so forth. Ending slash (
/
) is not required; in case a path is provided it’ll be automatically added for those cases.Declaration
Swift
public let host: String
-
HTTP method for this endpoint
Declaration
Swift
public let httpMethod: HTTPMethod
-
endpoint http scheme that defines what kind of connection is
Declaration
Swift
public let scheme: APIScheme
-
timeout before the request expires
Declaration
Swift
public let timeout: TimeInterval
-
Creates an
APIEndpoint
with the set of given valuesDeclaration
Swift
public init(host: String, httpMethod: HTTPMethod = .get, scheme: APIScheme = .safe, timeout: TimeInterval = 10)
Parameters
host
root URL. I.e. github.com, developer.apple.com and so forth. Ending slash (
/
) is not required; in case a path is provided it’ll be automatically added for those cases.httpMethod
type of HTTP method this endpoint will handle. Defaults to
.get
typescheme
whether or not this is a safe connection. Defaults to
.safe
type (https)timeout
timeout before the request expires. Defaults to
10
seconds -
Builds a complete URL from the
host
value, given a pathExample of usage:
let githubEndpoint = APIEndpoint(host: "github.com") githubEndpoint.buildRequest(for: "events") // returns -> https://github.com/events
Declaration
Swift
public func buildRequest(for path: String, with queryDictionary: [String : String] = [:]) -> URLBuilderResult
Parameters
path
path (with or without query parameters) to append to the existing host
queryDictionary
param query in the form of a dictionary to be assembled into the resulting URLRequest. Defaults to an empty one
Return Value
The assembled URL.


Friday, November 20th, 2015
How the Internet Works
Welcome, welcooooome! I've decided to take a break this week from boring any non-technical people about tech related stuff and instead talk about how the internet works. Well, I guess that's still technical, but this will be more digestible than most of my other posts. Also, wasn't really my decision to write it- was an asignment, but I'll just pretend it occured to me on my own.
Let's jump right in. When people visualize the internet, they usually picture something like this:
That is incorrect. While there is such a thing as "The Cloud," it is not the same as the internet. The internet is actually a line or a wire, which traverses the land either underground or above ground, but it is a tangible thing. It looks like the subway. In fact, here is a map of the internet with interactive capabilities on this link.
On this line, we have special computers called servers. What makes servers special is that they can connect directly to the internet, as opposed to our home computers, which need to access a server first in order to get to the internet. On these servers we have web pages that all have their own unique Internet Protocol Address, or IP address.
An IP address is much like a postal address and helps computers find each other. Even if you have your own web page you've created yourself, it still needs to be hosted by a server in order to be read by the rest of the world. IP address format is a 10 digit number with decimals, and each block of numbers ranges from 0 to 255. Why? Too complicated to explain here (A.K.A. I don't know the answer and am on a deadline, but I can tell you it has something to do with binary code. I'll let you google it.) Anywhooo...
It's true every server has a unique IP adddress, but can you imagine what a pain it would be to try to rememember all of your favorite web sites' IP addresses? That's why we have site names like "Google," "Facebook," "Yahoo" (no one uses Yahoo anymore but you get the idea.)
So what about our home or work computers? They don't connect directly to the internet, but instead connect to a server, or ISP, mentioned above, which in turn, connects to the internet. Personal computers are called "Clients" in internet terminology, because technically, they are clients of the ISP.
Let's say I want to connect to Google from my home laptop. I would hop on my computer, my computer connects to Optimum Online, my provider, which then takes me to Google. Simple enough. Now let's say I wanted to email my mom a picture of my dogs (TIME OUT! PUPPY BREAK!):
I mean, can you even? You can't, right? Sorry, I'm super A.D.D. Anyway, I'm sending this image to my mom over gmail from my home computer. My home computer then connects to Optimum, which connects to the internet, which connects to my mom's ISP, Comcast, which then delivers the image to her computer. It doesn't send it as one whole file though. In order to keep the (internet) wire from getting clogged,the image is sent in pieces. You can think of this idea in terms of the Holland tunnel. There's plenty of room for every car to get through during the entire day, but when they go all at once during rush hour, the tunnel gets congested and people get mad! SO mad! Continuing...
So my mom gets the picture of my pups, which she refers to as her "grand dogs", and she wants to write back, " SQQUUEEEE OMG so cute lol FOMO!" Just kidding, my mom doesn't write like that because she is an adult human over the age of 25. She wants to write, "Dear Jessie, my grand dogs are insurmountably precious! I enjoyed the photos. Love, Mom xoxo." She sends this email, which takes the same toute back to me, but this time, I'm at work, instead of at home. The pathway is essentially the same at first, but in reverse: Mom's computer to Comcast, to the internet, to Optimum, and back to my laptop. Again, the reply is broken down into smaller pieces for expedition purposes. The internet knows which computer to deliver this message to based on IP addresses and routers.
At every point where two different parts of the internet intersect, there is a router with a new IP address. To visualize this process, think of the file you're sending as being a package wrapped in another layer with each router it has to pass. When the reply file or message is on it's way back to you, each wrapper is removed, based on the IP address of the router it came through, to make it's way back to you. Almost like a very low stakes version of the fairy tale, Hansel and Gretel.
Ok, that's it for now. Hopefully you get the idea. PEACE!


Svante August Arrhenius (February 19 1859 – October 2 1927) a Swedish scientist, originally a physicist, but often referred to as a chemist. Arrhenius was one of the founders of the science of physical chemistry.
He received the Nobel Prize for Chemistry in 1903, becoming the first Swedish Nobel laureate. In 1905, he became director of the Nobel Institute, where he remained until his death.
Arrhenius was the first to use basic principles of physical chemistry to estimate the extent to which increases in atmospheric carbon dioxide are responsible for the Earth's increasing surface temperature.
Arrhenius's contributions to science are memorialized by the Arrhenius equation, Arrhenius acid, lunar crater Arrhenius, Martian crater Arrhenius, the mountain of Arrheniusfjellet, and the Arrhenius Labs at Stockholm University.


PART II. IRRIGATION CORPORATIONS
§101-41 Special power of eminent domain. Corporations organized to develop, store, convey, distribute, and transmit water for irrigation, and which shall have at least $50,000 of fully paid in capital represented by cash or by tangible property of a market value equal to the amount at which the property is entered as assets on the books of the company, shall have the right to exercise the power of eminent domain as hereinafter provided. [L 1911, c 124, §1; RL 1925, §828; RL 1935, §71; RL 1945, §323; RL 1955, §8-38; HRS §101-41]
Case Notes
Cited: 74 F.2d 596, 601; 36 H. 692, 694, 698.


Hello, myself Yash. I am a Frontend web Developer.
Meticulous Web developer with over 3 year of full stack web developement experience and passion for responsive design and mobile first approach. Professional experience with pharmaceutical clients and huge userbase.


class: center, middle, inverse, title-slide # Basics ## Day 2 ### Stat 331 --- class: center, middle # Functions --- # Important R Facts *Fact 1:* **R** is an **open-source** language. -- *Fact 2:* The power of **R** comes from **packages**. -- *Fact 3:* **R** is a **functional programming** language. -- (This means it is designed for using and writing *functions*.) --- # Anatomy of a function ```r rnorm(10, mean = 5, sd =3) ``` ``` ## [1] 1.693410 3.589854 5.349179 3.476249 9.176607 4.979510 4.793665 ## [8] 7.664087 7.134826 5.383950 ``` --- # Anatomy of a function ```r rnorm(10, mean = 5, sd =3) ``` -- Function name: ``` ## Warning in readLines(con, warn = FALSE): cannot open file ## 'Objects_Functions_Plots.Rmd': No such file or directory ```
rnorm(10, mean = 5, sd =3)
 -- Required arguments: ``` ## Warning in readLines(con, warn = FALSE): cannot open file ## 'Objects_Functions_Plots.Rmd': No such file or directory ```
rnorm(10, mean = 5, sd =3)
 (we often don't use the names for required arguments, but `n = 10` would be okay too) -- Optional arguments: ``` ## Warning in readLines(con, warn = FALSE): cannot open file ## 'Objects_Functions_Plots.Rmd': No such file or directory ```
rnorm(10, mean = 5, sd =3)
 (*always* use the argument names for optional arguments) --- class: center, middle # Troubleshooting Errors --- # Syntax Errors * Did you leave off a parenthesis? ``` ## Warning in readLines(con, warn = FALSE): cannot open file ## 'Objects_Functions_Plots.Rmd': No such file or directory ```
rnorm(10, mean = 5
 ``` ## Error: <text>:4:0: unexpected end of input ## 2: rnorm(10, mean = 5 ## 3: ## ^ ``` --- # Syntax Errors * Did you leave off a comma? ``` ## Warning in readLines(con, warn = FALSE): cannot open file ## 'Objects_Functions_Plots.Rmd': No such file or directory ``` ``` ## Warning in stri_locate_all_regex(string, pattern, omit_no_match = TRUE, : ## argument is not an atomic vector; coercing ```
rnorm(10 mean = 5)
 ``` ## Error: <text>:2:10: unexpected symbol ## 1: ## 2: rnorm(10 mean ## ^ ``` --- # Syntax Errors * Did you make a typo? ``` ## Warning in readLines(con, warn = FALSE): cannot open file ## 'Objects_Functions_Plots.Rmd': No such file or directory ```
rnrom(10, mean = 5)
 ``` ## Error in rnrom(10, mean = 5): could not find function "rnrom" ``` --- # Object type errors * Are you using the right **input** that the function expects? ``` ## Warning in readLines(con, warn = FALSE): cannot open file ## 'Objects_Functions_Plots.Rmd': No such file or directory ```
rnorm('hi', mean = 5)
 ``` ## Warning in rnorm("hi", mean = 5): NAs introduced by coercion ``` ``` ## Error in rnorm("hi", mean = 5): invalid arguments ``` --- # Object type errors * Are you expecting the right **output** of the function? ``` ## Warning in readLines(con, warn = FALSE): cannot open file ## 'Objects_Functions_Plots.Rmd': No such file or directory ```
my_obj <- rnorm(10, mean = 5)

 my_obj(5)
 ``` ## Error in my_obj(5): could not find function "my_obj" ``` --- class: center, middle # Object and Data Types --- # Object Types Is your object a... * function? * vector? * list? * data frame? * matrix? --- # Data Types Does your data consist of... -- * numbers? + integers? + doubles? -- * words? + characters/string? + factor? -- * something else??? --- class: center, middle # (Fill in the grid activity) --- class: center, middle # R Markdown --- # R Markdown **R Markdown** is an *editor*. (Like MS Word!) It is built for combining **code chunks** with text. -- Like any text document, you should **save** as you work! -- When you are ready to see the finished product, you should **knit** your file. --- class: center, middle # Let's try it!


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Level {% assign counter = 0 %}{% for item in site.posts %}{% unless item.published == false %}{% assign counter=counter | plus:1 %}{% endunless %}{% endfor %}{{ counter }} Adventurer


Become a venture and build your business
The first stage in our process focuses on understanding the scope of the problem, validating it with customers, designing a solution, and conducting initial competitive analysis. Some resources that are available to you in this stage include coaching office hours, IDEA workshops and events, templates and guides, and the Prototype Fund.
In the Set stage, ventures work to develop their business model. This includes establishing their initial product or service, considering revenue models, and creating a go-to-market strategy. Some resources that are available to you in this stage include a business model guide, access to service providers and to the McCarthy Venture Mentor Network, and being connected with an IDEA coach for a one-on-one relationship.
In the last stage, Go, ventures begin executing on business milestones to scale their companies in preparation of raising external capital, joining another accelerator, or becoming self-sustained on revenue. Some available resources in this stage include the gap funding, pitch preparation and coaching, and access to the IDEA investor network.
The IDEA Gap Fund is a $10,000 non-equity educational grant available to ventures in the Go stage of IDEA's process. Grants are available for applicants on a bi-monthly basis. Applications are reviewed by IDEA’s student Investment Committee before being selected to pitch in front of members of IDEA's Advisory Board.
With the help of our Entrepreneur-in-Residence, IDEA has a network of investors to advise and finance ventures. Whether ventures are looking for an angel investor for a first round of outside capital or a venture capital firm to raise a Series A round, IDEA connects ventures who have made it all the way through the stage-gate process to investors that are a good fit.
To support our ventures in their business model and go-to-market IDEA relies on Bob Lentz our Entrepreneur in Residence. Bob has extensive executive management and entrepreneurial experience in the technology industry having been involved in the growth and eventual sale of a number of successful technology ventures.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo.
New venture orientation will give you a overview of the idea process and how you can begin developing your concept.


IPFS is a peer-to-peer hypermedia protocol that allows users to seed and download files without the use of servers or trackers.
All you need to know to download a file is its hash (for example: QmfJZH5kmnsjGP5nzfbP1dJXLw3ut8JuuXoawqP1WeYGMF).
You can download IPFS from
here.
Alternatively, you can download files from the IPFS web gateway (linked to from the files page) however, the gateway reserves the right to censor files which contain
illicit content or violate copyright claims.
This website provides a searchable index for files stored on IPFS. The hash of each file, along with some metadata, is permanently recorded in the Bitcoin Cash blockchain
in such a way that the entire search index may be downloaded and rebuilt by anyone running compatible software.
Protocol
Add File:
OP_RETURN <flag> <cid> <description> <category>
Each data element is in its own
pushdata
| Element |
Tag |
Data |
| flag |
0x9F |
0x01 |
| cid |
0x00 |
IPFS Content ID |
| description |
0x01 |
UTF-8 string |
| category |
0x05 |
UTF-8 string |
Vote:
OP_RETURN <flag> <txid> <vote> <comment>
Each data element is in its own
pushdata
| Element |
Tag |
Data |
| flag |
0x9F |
0x02 |
| txid |
0x02 |
32 byte BCH txid |
| vote |
0x03 |
0x00 for false, 0x01 for true |
| comment |
0x04 |
UTF-8 string |


Hi, I'm Jelle. I've always had a passion for computers and the technology behind them. Thats why I started programming at the age of 9, in the mean time I have learned multiple languages by myself. Right now I'm studying Computer Science at the Leiden University of Applied Sciences, so I can make programming my job.
In my free time I like to cycle on my mountainbike as well as play games on my battlestation. I have build up quite the game library in my time gaming, with a lot of games in my library you could say I like collecting games.
Feel free to check out my game library or some of the coding projects I work on!
| Age | |
|---|---|
| Gender | Male |
| Country | Holland |
| Food | Sushi |
| Color | Green |
| Personality | INTP-A |
An easy to use program to download videos and playlists from YouTube and many more services.
DownloadView 360 degree photospheres and cubemaps with this fast and really straightforward program.
DownloadThe site you are on right now, with some information about me and the projects I work on.
VisitA simple but good looking website where paintings that are up for sale get showcased.
VisitA minecraft plugin derived from the popular GM4 datapacks which adds tons of new features to the game.
DownloadMy very own setup where I spend most of my days. The name obviously comes from Spongebob (don't ask why).
More infoDo you have a project you need a developer for? I have tons of experience with all kinds of programming languages and projects. Contact me below if you are interested in hiring me for a project, or if you simply want some more information about the services I can provide.