Police's Victim List
-
Ian Zhao
-
Zhanhe Wang - Lee's college rival in love
-
Jessica Saho - A rogue who took Lee's wallet
-
Guangyun Chen - Lee's first employer who fired him for no reason
-
Weigang Deng - Landlord before Lee's Secret Occupation
-
Lin Saho - Jessica Saho's brother
-
Guan Cheng - City guard who use violence to treat Lee
-
Xuyuan Cheng - A coder who claims that he invented Bit Coin
-
Zhang Hang - A banker who curses the Bit Coin Inventor
-
Zhang Ke - An old woman who cursed him on the subway
-
Nv You - Lee's ex who took all his saving when breaking up
-
Shehui Hei - Leader of a Gang who threated Lee to help him hack
-
Jana Li
-
Ren Nv - Landlord's brother, who has been a dick to Lee


 Build your resume. Enhance your knowledge and potential. For Click herermation send me your "Git" and "Stack Exchange" ID.
Hire Me. Maybe? :) With immense knowledge of Python, Django, Mezzanine-CMS, Object Oriented Programming and working knowledge of both SQLlite and PostgreSQL, I hope I won't dissapoint.
If you have any queries when working with Python and Django make sure you don't hesistate to ask me by just sending me an e-mail at<EMAIL_ADDRESS>I am an Electrical And Electronics Engieering student passionate about the idea about how you can simply provide common people with all the comforts by writing few lines of code. I am mainly into Python and Python related frameworks. Still I am trying to improve.
There was a day I was frightened after seeing lines of codes but now I seem to be in love with code. And with Python the possibilities are endless. I dont have any hatred or dislike for any language but I know one thing. Life is short. Learn Python.
WEB DEVELOPER/ PYTHON ENTHUSIAST


{% extends 'CodeShareIo/baseLogin.html' %} {% load static %} {% block customCss %}{% endblock %} {% block body %}
Find your CodeShare account. Enter your email, phone number, or username. If the user exist, an email will be sent to the account corresponding email address.


Setup Laravel
Introduction
Laravel Mix provides a clean, fluent API for defining basic webpack build steps for your Laravel application. Mix supports several common CSS and JavaScript pre-processors.
Note: If you are using single mode ( Dark, Light, RTL ) and dont need right side drawer to change layout then please remove css link from resources/views/layouts/heade.blade.php
and also remove js code initSettings();
method is declared inside into init() method in the file resources/js/app.js
You need to add these css for specific mode which you want to use in your project so there will be no any extra css files will be loaded.
Light
<link href="{{ URL::asset('assets/css/bootstrap.min.css')}}" id="bootstrap-light" rel="stylesheet" type="text/css" /> <link href="{{ URL::asset('assets/css/icons.min.css')}}" rel="stylesheet" type="text/css" /> <link href="{{ URL::asset('assets/css/app.min.css')}}" id="app-light" rel="stylesheet" type="text/css" />
Dark
<link href="{{ URL::asset('assets/css/bootstrap-dark.min.css')}}" id="bootstrap-dark" rel="stylesheet" type="text/css" /> <link href="{{ URL::asset('assets/css/icons.min.css')}}" rel="stylesheet" type="text/css" /> <link href="{{ URL::asset('assets/css/app-dark.min.css')}}" id="app-dark" rel="stylesheet" type="text/css" />
RTL
<link href="{{ URL::asset('assets/css/bootstrap.min.css')}}" id="bootstrap-light" rel="stylesheet" type="text/css" /> <link href="{{ URL::asset('assets/css/icons.min.css')}}" rel="stylesheet" type="text/css" /> <link href="{{ URL::asset('assets/css/app-rtl.min.css')}}" id="app-rtl" rel="stylesheet" type="text/css" />
Prerequisites
Please follow below steps to install and setup all prerequisites:
-
Composer
Make sure to have the Composer installed & running in your computer.
-
Nodejs
Make sure to have the Node.js installed & running in your computer. If you already have installed Node on your computer, you can skip this step if your existing node version is greater than 10.
-
Git
Make sure to have the Git installed globally & running on your computer. If you already have installed git on your computer, you can skip this step.
Installation
To setup the admin theme, follow below-mentioned steps:
-
Install Prerequisites
Make sure to have all above prerequisites installed & running on your computer.
After you finished with the above steps, you can run the following commands to run the project locally or build for production use:
| Command | Description |
|---|---|
npm install
|
This would install all the required dependencies in
the node_modules folder.
|
composer install
|
command to install all of the framework's dependencies. |
|
Please run the below command to generate the new key.
php artisan key:generate
Please fill your DB credentials in the .env file.
DB_CONNECTION=mysql
DB_HOST=<IP_ADDRESS> DB_PORT=3306 DB_DATABASE=Arconsult DB_USERNAME=root DB_PASSWORD= |
|
npm run prod
|
To compile all assets files like CSS, JS and will copy all images into the public directory. Run this command to get minify output for the production server. |
npm run watch
|
If you are working on the project and would like to compile all assets files then tun this command so it will auto compile all assets so you dont need to run "npm run dev" command multiple times. |
|
Please run the following commands to clear all cache from the project.
php artisan optimize
|
|
php artisan migrate
|
This will migrate the database tables. For more details visit https://laravel.com/docs/8.x/migrations |
php artisan serve
|
The development server is accessible at http://localhost:8000. To run on other port just run command : php artisan serve --port=8001 |
Multi Language Settings
Lets add french language.
- Create new file translation.php in the fr folder in the
resources/lang
folder and add the below code.<?php // translation.php return [ 'welcome' => 'Welcome Friend' ];
- You need to add the new language option in the topbar
resounrces/views/layouts/topbar.blade.php
. Make sure to add it flag image and option in the dropdown.
Tips
We suggest you to do not change any scss files from the resrouces/scss/custom
folders because to get new updates will might be
break your SCSS changes if any you have made. We strongly suggest you to create new custom.scss file and use that instead of overwrite any theme's custom scss files.


a
legislation
drug guidelines
clinical practices guidelines
appropriate care guidelines
circulars
Private Hospitals & Medical Clinics (PHMC) Guidelines
eServices
Newsroom
Forms
Research & Funding
About MOH
Careers
Enabling Singaporeans to receive appropriate care in the community and closer to home
services & forms
my health records
disease updates
resources
find circulars
access moh alerts
Login to access your shortcuts
View prices for procedures across public and private hospitals.
your scheme & subsidies
locate healthcare provider
3rd Oct 2017
Events
1st Oct 2017
News, Press Release
@sghealthministry
It’s time to talk about palliative care as we face an ageing population in this light .. fb.me/2dnuSHcC1
29 Oct 2017
@sporeMOH
Didnt you know that since 1975, worldwide obesity levels have nearly tripled?... fb.me/8DQNTNnew


After some months of hard work we finally published the first application built entirely using Boken Engine.
You can find it on the App Store:
https://apps.apple.com/app/id1580924283#?platform=ipad
Our goals at the beginning of the project were:
- Try to build a complete demo using all Boken Engine features.
- Make it fun.
Regarding our first goal we think Iakkai could be a good starting point to build your own app using Boken Engine. It could be used as a boilerplate for your own projects. We are planning to add more documentation about how to integrate Boken Engine on your own app but this sample should be useful to understand and play with all the Boken Engine features.
If you want to use Boken Engine we think you should try first to setup and run Iakkai. You can also follow the instructions on the Boken Engine documentation.
Regarding making it fun, we chose a game topic that some mantainers know very well. In fact, all the application contents were done by Yeray and Constantin including script and graphics.
We hope you enjoy it!


Taking a photo
Open
Camera
.
Frame the scene you want to capture. Let your camera focus automatically or touch the screen to focus the camera.
Touch
to take a single photo, or touch and hold
to take several photos in rapid succession.


5 Building and Applying Logistic Regression Models
5.1 Strategies in Model Selection
5.1.2 Example: Horseshoe Crab Satellites Revisited
\[\mathrm{logit}[P(Y=1)] = \alpha + \beta_1 weight + \beta_2 width + \beta_3 c_2 + \beta_4 c_3 + \beta_5 c_4 + \beta_6 s_2 + \beta_7 s_3\]
<- read.table("http://users.stat.ufl.edu/~aa/cat/data/Crabs.dat",
Crabs header = TRUE, stringsAsFactors = TRUE
)
<- glm(y ~ weight + width + factor(color) + factor(spine),
fit family = binomial, data = Crabs
)
summary(fit)
Call:
glm(formula = y ~ weight + width + factor(color) + factor(spine),
family = binomial, data = Crabs)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1977 -0.9424 0.4849 0.8491 2.1198
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -8.06501 3.92855 -2.053 0.0401 *
weight 0.82578 0.70383 1.173 0.2407
width 0.26313 0.19530 1.347 0.1779
factor(color)2 -0.10290 0.78259 -0.131 0.8954
factor(color)3 -0.48886 0.85312 -0.573 0.5666
factor(color)4 -1.60867 0.93553 -1.720 0.0855 .
factor(spine)2 -0.09598 0.70337 -0.136 0.8915
factor(spine)3 0.40029 0.50270 0.796 0.4259
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 225.76 on 172 degrees of freedom
Residual deviance: 185.20 on 165 degrees of freedom
AIC: 201.2
Number of Fisher Scoring iterations: 4
<- 1 - pchisq(225.76 - 185.20, 172 - 165)
pValue format(round(pValue, 8), scientific = FALSE)
[1] "0.00000098"
library(car)
Anova(fit)
Analysis of Deviance Table (Type II tests)
Response: y
LR Chisq Df Pr(>Chisq)
weight 1.4099 1 0.23507
width 1.7968 1 0.18010
factor(color) 7.5958 3 0.05515 .
factor(spine) 1.0091 2 0.60377
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
5.1.6 AIC and the Bias/Variance Tradeoff
\[\mathrm{AIC = -2(log\ likelihood) + 2(number\ of\ parameters\ in\ model).}\]
<- read.table("http://users.stat.ufl.edu/~aa/cat/data/Crabs.dat",
Crabs header = TRUE, stringsAsFactors = TRUE
)
<- glm(y ~ width + factor(color), family = binomial, data = Crabs)
fit
-2*logLik(fit)
'log Lik.' 187.457 (df=5)
AIC(fit) # adds 2(number of parameters) = 2(5) = 10 to -2*logLik(fit)
[1] 197.457
<- glm(y ~ width + factor(color) + factor(spine),
fit family = binomial, data = Crabs)
library(MASS)
stepAIC(fit)
Start: AIC=200.61
y ~ width + factor(color) + factor(spine)
Df Deviance AIC
- factor(spine) 2 187.46 197.46
<none> 186.61 200.61
- factor(color) 3 194.43 202.43
- width 1 208.83 220.83
Step: AIC=197.46
y ~ width + factor(color)
Df Deviance AIC
<none> 187.46 197.46
- factor(color) 3 194.45 198.45
- width 1 212.06 220.06
Call: glm(formula = y ~ width + factor(color), family = binomial, data = Crabs)
Coefficients:
(Intercept) width factor(color)2 factor(color)3 factor(color)4
-11.38519 0.46796 0.07242 -0.22380 -1.32992
Degrees of Freedom: 172 Total (i.e. Null); 168 Residual
Null Deviance: 225.8
Residual Deviance: 187.5 AIC: 197.5
library(tidyverse)
# need response variable in last column of data file
<- Crabs %>%
Crabs2 select(weight, width, color, spine, y)
library(leaps)
library(bestglm)
bestglm(Crabs2, family = binomial, IC = "AIC") # can also use IC="BIC"
Morgan-Tatar search since family is non-gaussian.
AIC
BICq equivalent for q in (0.477740316103793, 0.876695783647898)
Best Model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -10.0708390 2.8068339 -3.587971 3.332611e-04
width 0.4583097 0.1040181 4.406056 1.052696e-05
color -0.5090467 0.2236817 -2.275763 2.286018e-02
5.2 Model Checking
5.2.1 Goodness of Fit: Model Comparison Using the Deviance
\[G^2 = 2\sum\mathrm{observed[log(observed/fitted)]}\]
5.2.2 Example: Goodness of Fit for Marijuana Use Survey
library(tidyverse)
<- read.table("http://users.stat.ufl.edu/~aa/cat/data/Marijuana.dat",
Marijuana header = TRUE, stringsAsFactors = TRUE)
Marijuana
race gender yes no
1 white female 420 620
2 white male 483 579
3 other female 25 55
4 other male 32 62
<- glm(yes/(yes+no) ~ gender + race, weights = yes + no, family = binomial,
fit data = Marijuana)
summary(fit) # deviance info is extracted on the next two lines
Call:
glm(formula = yes/(yes + no) ~ gender + race, family = binomial,
data = Marijuana, weights = yes + no)
Deviance Residuals:
1 2 3 4
-0.04513 0.04402 0.17321 -0.15493
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.83035 0.16854 -4.927 8.37e-07 ***
gendermale 0.20261 0.08519 2.378 0.01739 *
racewhite 0.44374 0.16766 2.647 0.00813 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 12.752784 on 3 degrees of freedom
Residual deviance: 0.057982 on 1 degrees of freedom
AIC: 30.414
Number of Fisher Scoring iterations: 3
$deviance # deviance goodness-of-fit statistic fit
[1] 0.05798151
$df.residual # residual df fit
[1] 1
1 - pchisq(fit$deviance, fit$df.residual )
[1] 0.8097152
fitted(fit)
1 2 3 4
0.4045330 0.4541297 0.3035713 0.3480244
library(dplyr)
%>%
Marijuana mutate(fit.yes = (yes+no)*fitted(fit)) %>%
mutate(fit.no = (yes+no)*(1-fitted(fit))) %>%
select(race, gender, yes, fit.yes, no, fit.no)
race gender yes fit.yes no fit.no
1 white female<PHONE_NUMBER>9<PHONE_NUMBER>1
2 white male<PHONE_NUMBER>1<PHONE_NUMBER>9
3 other female 25 24.28571 55 55.71429
4 other male 32 32.71429 62 61.28571
5.2.4 Residuals for Logistic Models with Categorical Predictors
\[\mathrm{Standardized\ residual} = \frac{y_i-n_i\hat\pi_i}{SE}.\] ### 5.2.5 Example: Graduate Admissoins at University of Florida
library(tidyverse)
<- read.table("http://users.stat.ufl.edu/~aa/cat/data/Admissions.dat",
Admissions header = TRUE, stringsAsFactors = TRUE)
<- glm(yes/(yes+no) ~ department, family = binomial,
theModel data = Admissions,
weights = yes+no)
<- tibble(`Std. Res.` = rstandard(theModel, type = "pearson")) %>%
theResiduals mutate(`Std. Res.` = round(`Std. Res.`, 2)) %>%
::filter(row_number() %% 2 == 0) # every other record is female
dplyr
<- Admissions %>%
theTable mutate(gender = case_when(gender == 1 ~ "Female",
== 0 ~ "Male")) %>%
gender pivot_wider(id_cols = department, names_from = gender,
values_from = c(yes, no), names_sep = "") %>%
select(department, yesFemale, noFemale, yesMale, noMale) %>%
rename("Dept" = department, "Females (Yes)" = yesFemale, "Females (No)" =
"Males (Yes)" = yesMale, "Males (No)" = noMale) %>%
noFemale, bind_cols(theResiduals)
::kable(theTable) knitr
| Dept | Females (Yes) | Females (No) | Males (Yes) | Males (No) | Std. Res. |
|---|---|---|---|---|---|
| anthropol | 32 | 81 | 21 | 41 | -0.76 |
| astronomy | 6 | 0 | 3 | 8 | 2.87 |
| chemistry | 12 | 43 | 34 | 110 | -0.27 |
| classics | 3 | 1 | 4 | 0 | -1.07 |
| communicat | 52 | 149 | 5 | 10 | -0.63 |
| computersci | 8 | 7 | 6 | 12 | 1.16 |
| english | 35 | 100 | 30 | 112 | 0.94 |
| geography | 9 | 1 | 11 | 11 | 2.17 |
| geology | 6 | 3 | 15 | 6 | -0.26 |
| germanic | 17 | 0 | 4 | 1 | 1.89 |
| history | 9 | 9 | 21 | 19 | -0.18 |
| latinamer | 26 | 7 | 25 | 16 | 1.65 |
| linguistics | 21 | 10 | 7 | 8 | 1.37 |
| mathematics | 25 | 18 | 31 | 37 | 1.29 |
| philosophy | 3 | 0 | 9 | 6 | 1.34 |
| physics | 10 | 11 | 25 | 53 | 1.32 |
| polisci | 25 | 34 | 39 | 49 | -0.23 |
| psychology | 2 | 123 | 4 | 41 | -2.27 |
| religion | 3 | 3 | 0 | 2 | 1.26 |
| romancelang | 29 | 13 | 6 | 3 | 0.14 |
| sociology | 16 | 33 | 7 | 17 | 0.30 |
| statistics | 23 | 9 | 36 | 14 | -0.01 |
| zoology | 4 | 62 | 10 | 54 | -1.76 |
\[logit(\pi_{ik}) = \alpha + \beta_k.\]
5.2.6 Standardized versus Pearson and Deviance Residuals
<- glm(yes/(yes+no) ~ gender + race, weights = yes + no, family = binomial,
fit data = Marijuana)
summary(fit)
Call:
glm(formula = yes/(yes + no) ~ gender + race, family = binomial,
data = Marijuana, weights = yes + no)
Deviance Residuals:
1 2 3 4
-0.04513 0.04402 0.17321 -0.15493
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.83035 0.16854 -4.927 8.37e-07 ***
gendermale 0.20261 0.08519 2.378 0.01739 *
racewhite 0.44374 0.16766 2.647 0.00813 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 12.752784 on 3 degrees of freedom
Residual deviance: 0.057982 on 1 degrees of freedom
AIC: 30.414
Number of Fisher Scoring iterations: 3
::kable(
knitrbind_cols(Standardized = rstandard(fit, type = "pearson"),
Pearson = residuals(fit, type = "pearson"),
deviance = residuals(fit, type = "deviance"),
`std dev.` = rstandard(fit, type = "deviance"),
Race = Marijuana$race, Gender = Marijuana$gender) %>%
mutate_if(is.numeric, round, digits = 3)
)
| Standardized | Pearson | deviance | std dev. | Race | Gender |
|---|---|---|---|---|---|
| -0.241 | -0.045 | -0.045 | -0.241 | white | female |
| 0.241 | 0.044 | 0.044 | 0.241 | white | male |
| 0.241 | 0.174 | 0.173 | 0.240 | other | female |
| -0.241 | -0.155 | -0.155 | -0.241 | other | male |
5.2.8 Example: Heart Disease and Blood Pressure
<- read.table("http://users.stat.ufl.edu/~aa/cat/data/HeartBP.dat",
HeartBP header = TRUE, stringsAsFactors = TRUE)
<- glm(y/n ~ bp, family = binomial, data = HeartBP, weights = n)
theModel summary(theModel)
Call:
glm(formula = y/n ~ bp, family = binomial, data = HeartBP, weights = n)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.0617 -0.5977 -0.2245 0.2140 1.8501
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -6.082033 0.724320 -8.397 < 2e-16 ***
bp 0.024338 0.004843 5.025 5.03e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 30.0226 on 7 degrees of freedom
Residual deviance: 5.9092 on 6 degrees of freedom
AIC: 42.61
Number of Fisher Scoring iterations: 4
<- round(fitted(theModel)* HeartBP$n, 1)
predicted <- round(rstandard(theModel, type = "pearson"),2)
std_res
#influence.measures(theModel)
<- data.frame(dfbetas(theModel)) %>%
rDfbeta select(bp) %>%
transmute(DFbeta = round(bp, 2))
%>%
HeartBP rename("Blood Presure" = bp,
"Sample size" = n,
"Observed Disease" = y) %>%
bind_cols("Fitted Disease" = predicted,
"Standardized Residual" = std_res,
"Dfbeta (not SAS)" = rDfbeta)
Blood Presure Sample size Observed Disease Fitted Disease
1 111.5 156 3 5.2
2 121.5 252 17 10.6
3 131.5 284 12 15.1
4 141.5 271 16 18.1
5 151.5 139 12 11.6
6 161.5 85 8 8.9
7 176.5 99 16 14.2
8 191.5 43 8 8.4
Standardized Residual DFbeta
1 -1.11 0.56
2 2.37 -2.24
3 -0.95 0.34
4 -0.57 0.08
5 0.13 0.01
6 -0.33 -0.06
7 0.65 0.38
8 -0.18 -0.11
library(ggthemes)
<- predict(theModel, type = "response")
predicted %>%
HeartBP mutate(proportion=y/n) %>%
ggplot(aes(x= bp, y = proportion)) +
geom_point(shape = 4) +
geom_smooth(aes(y = predicted)) +
theme_few() +
scale_x_continuous(breaks = seq(110, 200, by = 10), limits = c(110, 200)) +
ggtitle("Figure 5.1")
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
5.3 Infinite Estimates in Logistic Regression
5.3.1 Complete and Quasi-Complete Separation: Perfeect Discrination
library(tidyverse)
library(ggthemes)
data.frame (x = c(10, 20, 30, 40, 60, 70, 80, 90),
y = c(0, 0, 0, 0, 1, 1, 1, 1)) %>%
ggplot(aes(x = x, y = y)) +
geom_point() +
theme_few() +
ggtitle("Figure 5.2") +
scale_y_continuous(breaks = c(0,1)) +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(angle = 0, vjust = 0.5),
plot.title = element_text(hjust = 0.5))
5.3.2 Example: Infinite Estimate for Toy Example
<- c(10, 20, 30, 40, 60, 70, 80, 90)
x <- c(0, 0, 0, 0, 1, 1, 1, 1)
y
<- glm(y ~ x, family = binomial) fit
Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
# P-value for Wald test of H0: beta = 0
# res deviance = 0 means perfect fit
# Fisher iterations: 25 shows very slow convergence
summary(fit)
Call:
glm(formula = y ~ x, family = binomial)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.045e-05 -2.110e-08 0.000e+00 2.110e-08 1.045e-05
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -118.158 296046.187 0 1
x 2.363 5805.939 0 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1.1090e+01 on 7 degrees of freedom
Residual deviance: 2.1827e-10 on 6 degrees of freedom
AIC: 4
Number of Fisher Scoring iterations: 25
# maximized log-likelihood = 0, so maximized likelihood = 1
logLik(fit)
'log Lik.' -1.09134e-10 (df=2)
#library(car)
# P-value for likelihood-ratio test of beta = 0 is more sensitive than p-value of 1 for Wald test
::Anova(fit) car
Analysis of Deviance Table (Type II tests)
Response: y
LR Chisq Df Pr(>Chisq)
x 11.09 1 0.0008678 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
library(profileModel) # ordinary confint function fails for infinite estimates
confintModel(fit, objective = "ordinaryDeviance", method = "zoom")
Preliminary iteration .. Done
Profiling for parameter (Intercept) ... Done
Profiling for parameter x ... Done
Zooming for parameter (Intercept) ...
Zooming for parameter x ...
Lower Upper
(Intercept) -Inf -2.66963
x 0.05876605 Inf
attr(,"fitted object")
fit
5.3.4 Example: Risk Factors for Endometrial Cancer Grade
library(tidyverse)
<- read.table("http://users.stat.ufl.edu/~aa/cat/data/Endometrial.dat",
Endo header = TRUE, stringsAsFactors = TRUE)
%>%
Endo filter(row_number() %in% c(1, 2, n()))
NV PI EH HG
1 0 13 1.64 0
2 0 16 2.26 0
3 0 33 0.85 1
xtabs(~NV + HG, data = Endo) # quasi-complete separation when NV = 1, no HG=0 cases occur
HG
NV 0 1
0 49 17
1 0 13
<- glm(HG ~ NV + PI + EH, family = binomial, data = Endo)
fit
# NV's true estimate is infinity
summary(fit)
Call:
glm(formula = HG ~ NV + PI + EH, family = binomial, data = Endo)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.50137 -0.64108 -0.29432 0.00016 2.72777
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 4.30452 1.63730 2.629 0.008563 **
NV 18.18556 1715.75089 0.011 0.991543
PI -0.04218 0.04433 -0.952 0.341333
EH -2.90261 0.84555 -3.433 0.000597 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 104.903 on 78 degrees of freedom
Residual deviance: 55.393 on 75 degrees of freedom
AIC: 63.393
Number of Fisher Scoring iterations: 17
logLik(fit)
'log Lik.' -27.69663 (df=4)
library(car)
Anova(fit) # NV P= .0022 vs Wald 0.99
Analysis of Deviance Table (Type II tests)
Response: HG
LR Chisq Df Pr(>Chisq)
NV 9.3576 1 0.002221 **
PI 0.9851 1 0.320934
EH 19.7606 1 8.777e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
library(profileModel) # ordinary confint function fails for infinite est
confintModel(fit, objective = "ordinaryDeviance", method = "zoom") # 95% profile likelihood CI for beta 1
Preliminary iteration .... Done
Profiling for parameter (Intercept) ... Done
Profiling for parameter NV ... Done
Profiling for parameter PI ... Done
Profiling for parameter EH ... Done
Zooming for parameter (Intercept) ...
Zooming for parameter NV ...
Zooming for parameter PI ...
Zooming for parameter EH ...
Lower Upper
(Intercept) 1.4290183 7.94717425
NV 1.2856920 Inf
PI -0.1370717 0.03810556
EH -4.7866976 -1.43682244
attr(,"fitted object")
fit
library(detectseparation) # new package
# 0 denotes finite est, Inf denotes infinite est.
glm(HG ~ NV + PI + EH, family = binomial, data = Endo, method = "detectSeparation")
Implementation: ROI | Solver: lpsolve
Separation: TRUE
Existence of maximum likelihood estimates
(Intercept) NV PI EH
0 Inf 0 0
0: finite value, Inf: infinity, -Inf: -infinity
5.4 Bayesian Inference, Penalized Likelihood, and Conditional Likelihood for Logistic Regression *
5.4.2 Example: Risk Factors for Endometrial Caner Revisited
<-
Endo2 %>%
Endo mutate(PI2 = scale(PI),
EH2 = scale(EH),
NV2 = NV - 0.5)
<- glm(HG ~ NV2 + PI2 + EH2, family = binomial, data = Endo2)
fit.ML summary(fit.ML)
Call:
glm(formula = HG ~ NV2 + PI2 + EH2, family = binomial, data = Endo2)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.50137 -0.64108 -0.29432 0.00016 2.72777
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 7.8411 857.8755 0.009 0.992707
NV2 18.1856 1715.7509 0.011 0.991543
PI2 -0.4217 0.4432 -0.952 0.341333
EH2 -1.9219 0.5599 -3.433 0.000597 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 104.903 on 78 degrees of freedom
Residual deviance: 55.393 on 75 degrees of freedom
AIC: 63.393
Number of Fisher Scoring iterations: 17
library(MCMCpack) # b0 = prior mean, B0 = prior precision = 1/variance
Loading required package: coda
##
## Markov Chain Monte Carlo Package (MCMCpack)
## Copyright (C) 2003-2023 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park
##
## Support provided by the U.S. National Science Foundation
## (Grants SES-0350646 and SES-0350613)
##
<- MCMClogit(HG ~ NV2 + PI2 + EH2, mcmc = 100000, b0 = 0, B0 = 0.01,
fitBayes data = Endo2) # prior var = 1/0.01 = 100, sd = 10
summary(fitBayes) # posterior deviation
Iterations = 1001:101000
Thinning interval = 1
Number of chains = 1
Sample size per chain = 1e+05
1. Empirical mean and standard deviation for each variable,
plus standard error of the mean:
Mean SD Naive SE Time-series SE
(Intercept) 3.2356 2.5948 0.008206 0.026682
NV2 9.1626 5.1639 0.016330 0.053109
PI2 -0.4711 0.4546 0.001437 0.006376
EH2 -2.1332 0.5879 0.001859 0.008450
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
(Intercept) -0.3391 1.2619 2.7161 4.6972 9.4865
NV2 2.1105 5.2437 8.1064 12.1133 21.6077
PI2 -1.4163 -0.7676 -0.4445 -0.1538 0.3538
EH2 -3.3781 -2.5087 -2.0977 -1.7227 -1.0786
mean(fitBayes[,2] < 0) # probability below 0 for 2n model parameter (NV2)
[1] 0.00016
5.4.3 Penalized Likelihood Reduces Bias in Logistic Regression
\[L^*(\beta) = L(\beta) - s(\beta),\]
5.4.4 Example: Risk Factors for Endometrial Cancer Revisited
library(logistf)
<- logistf(HG ~ NV2 + PI2 + EH2, family=binomial, data=Endo2)
fit.penalized options(digits = 4)
summary(fit.penalized)
logistf(formula = HG ~ NV2 + PI2 + EH2, data = Endo2, family = binomial)
Model fitted by Penalized ML
Coefficients:
coef se(coef) lower 0.95 upper 0.95 Chisq p method
(Intercept) 0.3080 0.7585 -0.9755 2.7888 0.1690 6.810e-01 2
NV2 2.9293 1.4650 0.6097 7.8546 6.7985 9.124e-03 2
PI2 -0.3474 0.3788 -1.2443 0.4045 0.7468 3.875e-01 2
EH2 -1.7243 0.4990 -2.8903 -0.8162 17.7593 2.507e-05 2
Method: 1-Wald, 2-Profile penalized log-likelihood, 3-None
Likelihood ratio test=43.66 on 3 df, p=1.786e-09, n=79
Wald test = 21.67 on 3 df, p = 7.641e-05
options(digits = 7)
5.5 Alternative Link Functions: Linear Probability and Probit Models *
5.5.2 Example: Political Ideology and Belief in Evolution
library(tidyverse)
<- read.table("http://users.stat.ufl.edu/~aa/cat/data/Evolution2.dat",
Evo header = TRUE, stringsAsFactors = TRUE)
%>%
Evo filter(row_number() %in% c(1, 2, n()))
ideology evolved
1 1 1
2 1 1
3 7 0
<- glm(evolved ~ ideology,
fit family=quasi(link=identity, variance = "mu(1-mu)"),
data = Evo)
summary(fit, dispersion = 1)
Call:
glm(formula = evolved ~ ideology, family = quasi(link = identity,
variance = "mu(1-mu)"), data = Evo)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0558 -1.2617 0.7247 1.0954 1.7440
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.108437 0.038932 2.785 0.00535 **
ideology 0.110102 0.008971 12.273 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasi family taken to be 1)
Null deviance: 1469.3 on 1063 degrees of freedom
Residual deviance: 1359.5 on 1062 degrees of freedom
AIC: NA
Number of Fisher Scoring iterations: 3
<- glm(evolved ~ ideology, family=gaussian(link=identity), data = Evo)
fit2
summary(fit2)
Call:
glm(formula = evolved ~ ideology, family = gaussian(link = identity),
data = Evo)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.8819 -0.5492 0.2290 0.4508 0.7836
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.10554 0.04272 2.47 0.0137 *
ideology 0.11091 0.01033 10.73 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 0.2247506)
Null deviance: 264.57 on 1063 degrees of freedom
Residual deviance: 238.69 on 1062 degrees of freedom
AIC: 1435.2
Number of Fisher Scoring iterations: 2
5.5.3 Probit Model and Normal Latent Variable Model
\[\mathrm{probit}[F(Y = 1)] = \alpha + \beta_1 x_1 + \cdots + \beta_px_p\]
\[y^* = \alpha + \beta_1 x_1+ \cdots + \beta_px_p + \epsilon.\]
5.5.4 Example: Snoring and Heart Disease Revisited
\[\mathrm{probit}[\hat P(Y = 1)] = -2.061 + 0.188x.\]
library(tidyverse)
<- read.table("http://users.stat.ufl.edu/~aa/cat/data/Heart.dat",
Heart header = TRUE, stringsAsFactors = TRUE)
Heart
snoring yes no
1 never 24 1355
2 occasional 35 603
3 nearly_every_night 21 192
4 every_night 30 224
# recode() is in dplyr and car...
<- Heart %>%
Heart mutate(snoringNights = dplyr::recode(snoring, never = 0, occasional = 2,
nearly_every_night = 4, every_night = 5))
<- glm(yes/(yes+no) ~ snoringNights, family = binomial(link = probit),
fit weights = yes+no, data = Heart)
summary(fit)
Call:
glm(formula = yes/(yes + no) ~ snoringNights, family = binomial(link = probit),
data = Heart, weights = yes + no)
Deviance Residuals:
1 2 3 4
-0.6188 1.0388 0.1684 -0.6175
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.06055 0.07017 -29.367 < 2e-16 ***
snoringNights 0.18777 0.02348 7.997 1.28e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 65.9045 on 3 degrees of freedom
Residual deviance: 1.8716 on 2 degrees of freedom
AIC: 26.124
Number of Fisher Scoring iterations: 4
options(digits = 4)
fitted(fit)
1 2 3 4
0.01967 0.04599 0.09519 0.13100
options(digits = 7)


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10 Minute Tutorial on Apache Shiro
Introduction
Welcome to Apache Shiro's 10 Minute Tutoral!
通过快速和简单的教程，您能够充分理解一个开发者是如何将shiro应用到他们的程序中。并且您有可能在十分钟内完成。
Overview
What is Apache Shiro?
Apache Shiro是一个功能强大且易于使用的java安全框架，为开发人员提供一个直观而全面的认证、授权、加密解决方案，和会话管理。
In practical terms, it achieves to manage all facets of your application's security, while keeping out of the way as much as possible. It is built on sound interface-driven design and OO principles, enabling custom behavior wherever you can imagine it. But with sensible defaults for everything, it is as "hands off" as application security can be. At least that's what we strive for.
实际上，它实现了对应用程序安全性的所有方面的管理，同时尽可能地避开了。 它建立在良好的接口驱动设计和OO原则的基础上，在您可以想象的地方启用自定义行为。但对所有的东西都有合理的默认值，这就像应用程序的安全性一样“不受欢迎”。至少这是我们奋斗的目标。
What can Apache Shiro do?
A lot . But we don't want to bloat the QuickStart. Please check out our Features page if you'd like to see what it can do for you. Also, if you're curious on how we got started and why we exist, please see the Shiro History and Mission page.
Ok. Now let's actually do something!
| Shiro can be run in any environment, from the simplest command line application
to the biggest enterprise web and clustered applications, but we'll use the simplest possible example in
a simple main method for this QuickStart so you can get a feel for the API.
Shiro可以在任何环境下运行，从最简单的命令行应用程序到最大的企业网络和集群应用程序， 但我们会尽可能使用最简单的例子，在一个简单的可以快速启动的main方法中以至于让你感觉在调用一个API一样。 |
Download
- Ensure you have JDK 1.5+ and Maven 2.2+ installed.
- Download the lastest "Source Code Distribution" from the Download page. In this example, we're using the 1.1.0 release distribution.
- Unzip the source package:
> unzip shiro-root-1.2.0-source-release.zip
- Enter the quickstart directory:
> cd shiro-root-1.2.0/samples/quickstart
- Run the QuickStart:
> mvn compile exec:java
This target will just print out some log messages to let you know what is going on and then exit. While reading this quickstart, feel free to look at the code found under samples/quickstart/src/main/java/Quickstart.java. Change that file and run the above mvn compile exec:java command as often as you like.
Quickstart.java
The Quickstart.java file referenced above contains all the code that will get you familiar with the API. Now lets break it down in chunks here so you can easily understand what is going on.
上面引用的Quickstart.java文件包含了所有使您熟悉API的代码。现在我们把它分解成块，这样你就可以很容易地理解发生了什么。
In almost all environments, you can obtain the currently executing user via the following call:
在几乎所有的环境中，您都可以通过以下调用获得当前执行的用户
Subject currentUser = SecurityUtils.getSubject();
Using SecurityUtils.getSubject(), we can obtain the currently executing Subject. A Subject is just a security-specific "view" of an application User. We actually wanted to call it 'User' since that "just makes sense", but we decided against it: too many applications have existing APIs that already have their own User classes/frameworks, and we didn't want to conflict with those. Also, in the security world, the term Subject is actually the recognized nomenclature. Ok, moving on...
使用 SecurityUtils.getSubject(), 我们可以获得当前执行的 Subject. A Subject 只是应用程序用户的特定于安全的“视图”。 实际上我们想把它称为“用户”，因为它“有意义”，但是我们决定不这样称谓它：: 太多的应用程序已经有已经有自己的用户类/框架的现有API，我们不想与它们发生冲突。此外，在安全领域中，术语 Subject 实际上是公认的术语. Ok, moving on...
The getSubject() call in a standalone application might return a Subject based on user data in an application-specific location, and in a server environment (e.g. web app), it acquires the Subject based on user data associated with current thread or incoming request.
Now that you have a Subject, what can you do with it?
If you want to make things available to the user during their current session with the application, you can get their session:
Session session = currentUser.getSession(); session.setAttribute( "someKey", "aValue" );
The Session is a Shiro-specific instance that provides most of what you're used to with regular HttpSessions but with some extra goodies and one big difference: it does not require an HTTP environment!
If deploying inside a web application, by default the Session will be HttpSession based. But, in a non-web environment, like this simple Quickstart, Shiro will automatically use its Enterprise Session Management by default. This means you get to use the same API in your applications, in any tier, regardless of deployment environment. This opens a whole new world of applications since any application requiring sessions does not need to be forced to use the HttpSession or EJB Stateful Session Beans. And, any client technology can now share session data.
So now you can acquire a Subject and their Session. What about the really useful stuff like checking if they are allowed to do things, like checking against roles and permissions?
Well, we can only do those checks for a known user. Our Subject instance above represents the current user, but who is the current user? Well, they're anonymous - that is, until they log in at least once. So, let's do that:
if ( !currentUser.isAuthenticated() ) { //collect user principals and credentials in a gui specific manner //such as username/password html form, X509 certificate, OpenID, etc. //We'll use the username/password example here since it is the most common. //(do you know what movie this is from? ;) UsernamePasswordToken token = new UsernamePasswordToken("lonestarr", "vespa"); //this is all you have to do to support 'remember me' (no config - built in!): token.setRememberMe(true); currentUser.login(token); }
That's it! It couldn't be easier.
But what if their login attempt fails? You can catch all sorts of specific exceptions that tell you exactly what happened and allows you to handle and react accordingly:
try { currentUser.login( token ); //if no exception, that's it, we're done! } catch ( UnknownAccountException uae ) { //username wasn't in the system, show them an error message? } catch ( IncorrectCredentialsException ice ) { //password didn't match, try again? } catch ( LockedAccountException lae ) { //account for that username is locked - can't login. Show them a message? } ... more types exceptions to check if you want ... } catch ( AuthenticationException ae ) { //unexpected condition - error? }
There are many different types of exceptions you can check, or throw your own for custom conditions Shiro might not account for. See the AuthenticationException JavaDoc for more.
| Handy Hint Security best practice is to give generic login failure messages to users because you do not want to aid an attacker trying to break into your system. |
Ok, so by now, we have a logged in user. What else can we do?
Let's say who they are:
//print their identifying principal (in this case, a username): log.info( "User [" + currentUser.getPrincipal() + "] logged in successfully." );
We can also test to see if they have specific role or not:
if ( currentUser.hasRole( "schwartz" ) ) { log.info("May the Schwartz be with you!" ); } else { log.info( "Hello, mere mortal." ); }
We can also see if they have a permission to act on a certain type of entity:
if ( currentUser.isPermitted( "lightsaber:weild" ) ) { log.info("You may use a lightsaber ring. Use it wisely."); } else { log.info("Sorry, lightsaber rings are for schwartz masters only."); }
Also, we can perform an extremely powerful instance-level permission check - the ability to see if the user has the ability to access a specific instance of a type:
if ( currentUser.isPermitted( "winnebago:drive:eagle5" ) ) { log.info("You are permitted to 'drive' the 'winnebago' with license plate (id) 'eagle5'. " + "Here are the keys - have fun!"); } else { log.info("Sorry, you aren't allowed to drive the 'eagle5' winnebago!"); }
Piece of cake, right?
Finally, when the user is done using the application, they can log out:
currentUser.logout(); //removes all identifying information and invalidates their session too.
Well, that's the core to using Apache Shiro at the application-developer level. And although there is some pretty sophisticated stuff going on under the hood to make this work so elegantly, that's really all there is to it.
But you might ask yourself, "But who is responsible for getting the user data during a login (usernames and passwords, role and permissions, etc), and who actually performs those security checks during runtime?" Well, you do, by implementing what Shiro calls a Realm and plugging that Realm into Shiro's configuration.
However, how you configure a Realm is largely dependent upon your runtime environment. For example, if you run a standalone application, or if you have a web based application, or a Spring or JEE container-based application, or combination thereof. That type of configuration is outside the scope of this QuickStart, since its aim is to get you comfortable with the API and Shiro's concepts.
When you're ready to jump in with a little more detail, you'll definitely want to read the Authentication Guide and Authorization Guide. Then can move onto other Documentation, in particularly the Reference Manual, to answer any other questions. You'll also probably want to join the user mailing list - you'll find that we have a great community with people willing to help whenever possible.
Thanks for following along. We hope you enjoy using Apache Shiro!


cocoliztli {border-style px;} #ffffff;} Mexico epidemics epidemic drought native precipitation million px; {font-family ().


Shinmai 30 km
Nagano JPN
Race Winners
(11 runnings)
29 Mar
1987
1:33:41
1:48:20
23 Mar
1986
1:35:21
1:56:13
24 Mar
1985
1:34:56
2:04:05
25 Mar
1984
1:33:09
2:16:23
27 Mar
1983
1:34:53
2:03:04
28 Mar
1982
1:31:48
---
29 Mar
1981
1:36:20
---
23 Mar
1980
1:34:52
---
25 Mar
1979
1:35:20
---
26 Mar
1978
1:32:37.2
---
03 Nov
1977
1:36:32.0
---
Note: 1977 was the inaugural year. This race was held in conjunction with the Shinmai Marathon.
Ryoichi Enaidani (JPN)
Eriko Asai (JPN)
Toru Kozasu (JPN)
Kumiko Fukuzawa-2
Koshiro Kawafuchi (JPN)
Kumiko Fukuzawa (JPN)
Takeyuki Nakayama (JPN)
Katsuko Tanaka (JPN)
Kazuhiro Motodaka (JPN)
Keiko Akutsu (JPN)
Kunimitsu Ito (JPN)
n/a
Masaaki Chiba-2
none
Masaaki Chiba (JPN)
none
Masato Chiba (JPN)
none
Takeshi So (JPN)
none
Osamu Kitahara (JPN)
none
Most Wins (1977-1987)
Men 2
Masaaki Chiba (JPN)
1980-81
Women
2
Kumiko Fukuzawa (JPN)
1985-86
Prize Money (ADR qualified runners)
No prize money has been awarded at this race according to available information.
Race Time Bias (sec/km)
Insufficienct data to calculate the race time bias.
Competitive Level
(1990-date)
Insufficienct data to calculate the competitive level.
Quality Performances
Men <1:32 1 <1:33 2 <1:34 6 <1:35 12 <1:36 29 Women <1:50 1 <1:52 1
Last Updated on 21 Dec 2004
Contributor: Shigenobu Ota (43)


Before Change
req2 = create_requirement_lib("req2")
req3 = create_requirement_lib("req3")
pants_lib = self.make_target(
spec=":pants_lib",
target_type=PythonLibrary,
dependencies=[
req1,
req2,
]
)
self.make_target(
spec=":pants_bin",
target_type=PythonBinary,
entry_point="pants.bin.pants_exe:main",
dependencies=[
pants_lib // Should be stripped in minify since pants_packaged provides these sources.
]
)
pants_packaged = self.make_target(
spec=":pants_packaged",
target_type=PythonLibrary,
provides=PythonArtifact(
name="pants_packaged",
version="0.0.0"
).with_binaries(
pants_bin=":pants_bin" // Should be stripped in minify since pants_packaged provides this.
)
)
contrib_lib = self.make_target(
spec=":contrib_lib",
target_type=PythonLibrary,
dependencies=[
pants_lib, // Should be stripped in minify since pants_packaged provides these sources.
req3,
]
)
contrib_plugin = self.make_target(
spec=":contrib_plugin",
target_type=PythonLibrary,
provides=PythonArtifact(
name="contrib",
version="0.0.0"
),
dependencies=[
contrib_lib,
pants_packaged,
req1
]
)
minified_dependencies = SetupPy.minified_dependencies(contrib_plugin)
assert minified_dependencies == OrderedSet([contrib_lib, req3, pants_packaged, req1])
After Change
contrib_plugin = self.create_python_library(
relpath="contrib/lib/src/python/pants/contrib",
name="plugin",
provides=dedent(
setup_py(
name="contrib",
version="0.0.0"
)
),
dependencies=[
"contrib/lib/src/python/pants/contrib/lib",
"src/python/pants:pants_packaged",


Technical Program Manager, Wussfinder.com
After revolutionizing the world of bullying in 2015 with Wussfinder.com, James Feore went on to pioneer new and exciting ways to "bring about disruptive change, in order to make the world a worse place for everyone." He quickly discovered the bullying potential of even simple games, and began to explore the medium. As part of the team at Four in a Row, James constantly strives to bring a voice to the often under-represented "arrogant jerk" programming community.
COO, Farcebook
Growing up on the rough streets of detriot, Max got into the world of underground Four In A Row. He climbed his way to the top of the scene and was evently discovered by Bret Landenburg who brought him to the public. Winning championship after championship the sponsorships and endorsements started rolling in. Nike picked him up with the official shoe of the Four in a row world, UnderArmor for specialy designed clothing to improve the game. Max also has several Olympic Gold medals for Four in a Row with his most recent being narrowly won against team Russia in a gripping final match that lasted well into the next day. He spends his time now as head of testing for FOURINAROW making sure it lives up to the league standards, when he's not training for the next event of course.
Senior Program Manager, Yourtube.com
Bret Ladenburg has been finding patterns of four ever since the fateful day when of course, he turned four years old. Born into a family of avid Four In A Row players, he has been playing and honing his skills for over 22 years until recently, when he was approached by 3 young game enthusiasts to work on a project with them. This of course became the game you see before you today. Now, Bret uses this new game as both entertainment, and tool, to find the best Four In A Row players out there and recruit them into his new clan where all Four In A Row enthusiasts can come rejoice.
Game Developer
Timothy Tran is a team member as game developer for Four In A Row since 2017. He plays important roll in create the themes in the project. He is currently working on several projects as a software develper.


{% extends 'common/base_main.html' %} {% block scripts %} {{ super() }} {% endblock %} {% block contenttitle %}
| Dimensions: | {{metadata.dimensions|join(", ")}} |
|---|---|
| Produced By: | {{metadata.producedBy|join(", ")}} |
| Is About: | {{metadata.isAbout|join(", ")}} |
| Acknowledges: | {{metadata.acknowledges|join(", ")}} |
| Spatial Coverage: | {{metadata.spatialCoverage|join(", ")}} |
| Other Dates: | {% for key, value in metadata.dates.items() %} {{ key }}: {{ value }} {{" -- " if not loop.last else "" }} {% endfor %} |
The following instructions require a basic understanding of UNIX/LINUX command lines. Future portal functionality may include downloads directly from the web browser. Dataset download is currently enabled through DataLad.
Note: The conp-dataset
requires version >=0.12.5 of DataLad
and version >=8.20200309 of git-annex.
To install DataLad on your system, please refer to the install section of the DataLad Handbook (installation via miniconda is recommended in order to obtain the latest version of DataLad).
To initiate the CONP dataset (conp-dataset
), run the following
command in the directory where you want CONP datasets to be installed:
datalad install https://github.com/CONP-PCNO/conp-dataset.git
To install the dataset, go into the created conp-dataset directory and run
datalad install
on the dataset {{data.name}}
:
cd conp-dataset
datalad install projects/{{data.name}}
Now that the DataLad dataset has been installed, go into the dataset
directory under projects/{{data.name}}
.
cd projects/{{data.name}}
Note that files visible in the dataset are symlinks and will need to be
downloaded manually using the
datalad get
command in the dataset directory:
datalad get <filepath>
Note, if you run datalad get *
command, all the files present
in the dataset directory will be downloaded.


#include <Godrays.hpp>
◆ Godrays()
◆ ~Godrays()
◆ bindGodrayBuffer()
| void Godrays::bindGodrayBuffer |
( |
int |
width, |
|
|
int |
height |
|
) |
| const |
|
inline |
◆ getColorTexture()
| uint32_t Godrays::getColorTexture |
( |
void |
| ) |
const |
|
inline |
◆ initGodrays()
| bool Godrays::initGodrays |
( |
int |
width, |
|
|
int |
height |
|
) |
| |
◆ unbindGodrayBuffer()
| void Godrays::unbindGodrayBuffer |
( |
int |
width, |
|
|
int |
height |
|
) |
| const |
|
inline |
◆ screenBuffer
The documentation for this class was generated from the following files:
- C:/Users/User/Desktop/Github/Procedural-Terrain-Estimator/Renderer/TerrainRenderer/Godrays.hpp
- C:/Users/User/Desktop/Github/Procedural-Terrain-Estimator/Renderer/TerrainRenderer/Godrays.cpp


Contact
If you'd like to know more about how this project was done, the data that was used, or our general thoughts about the future of the universe, please contact us or connect via a social channel or by email.
If you'd like to know more about how this project was done, the data that was used, or our general thoughts about the future of the universe, please contact us or connect via a social channel or by email.


Decoding after losing n-k symbols
Success rate:
0%
Test rate:
0/s
modulo
n
k
raw
generator
encoded
n-k erased
parity
decoded


Annah Gerletti
A multi-faceted web developer
About Me
Professional knowledge
See my past and present work experiance
LinkedIn Profile
Blog Posts
A mindfulness, technology, and experiance blog authored during my time at EDA.
Blog
My Work
Work in progress projects
Portfolio


Customer Manager
This app has been developed on MEANjs Framework.It helps the user to create,edit,remove and store customres.
Backend Technologies used
- NODE.JS runs on the server side
Frontend Technologies used
- Anular.js   manages the App on the client side
- Anular-routes.js   manages the client routes
- Anular-ui.js,Bootstrap   For user interface display
Features
- Add articles   Any user (even without an account) can post articles
which are then verified by the Admin
- Global Chat  The chat feature uses socket.io to enable realtime chat exprience
- Customers  Can be only accessed by registered Users It allowes the users to create,remove and edit a customer
- Todo  Can be only accessed by registered Users It allowes the users to create a todo list
-
For testing:
username: user
password: user


Segments a matrix in blocks with constant values. BRAULT V, CHIQUET J. and LEVY-LEDUC C. (2017) <doi:10.1214/17-EJS1270>.
| Version: | 0.5.0 |
| Imports: | Rcpp, Matrix, ggplot2, reshape2, methods, stats, shiny |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2018-07-03 |
| Author: | Authors@R: c( person("Julien", "Chiquet", role = c("aut", "cre"), email =<EMAIL_ADDRESS>comment = c(ORCID = "0000-0002-3629-3429")), person("Vincent", "Brault", email =<EMAIL_ADDRESS>role = "aut", comment = c(ORCID = "0000-0003-4228-6687"))) |
| Maintainer: | Vincent Brault <vincent.brault at univ-grenoble-alpes.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)] |
| URL: | https://github.com/jchiquet/blockseg (dev version) |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | blockseg results |
| Reference manual: | blockseg.pdf |
| Package source: | blockseg_0.5.0.tar.gz |
| Windows binaries: | r-devel: blockseg_0.5.0.zip, r-release: blockseg_0.5.0.zip, r-oldrel: blockseg_0.5.0.zip |
| OS X binaries: | r-release: blockseg_0.5.0.tgz, r-oldrel: blockseg_0.5.0.tgz |
| Old sources: | blockseg archive |
| Reverse imports: | CNVScope |
Please use the canonical form https://CRAN.R-project.org/package=blockseg to link to this page.


ONCE UPON A TIME IN CHINA AND AMERICA (1997) Chinese, with English subtitles.
 It's actually the sixth ONCE UPON A TIME IN CHINA movie. Movies one through five of the series follow the adventures of hero Wong Fei-hong in China, but ONCE UPON A TIME IN CHINA AND AMERICA takes places in the 1800s American West. The film portrays the Western adventures of ONCE UPON A TIME IN CHINA series hero Wong Fei-hong (Jet Li), who is simultaneously a Chinese sage, a kung fu teacher, and a doctor.
The story begins with Fei-hong and his fiancee Yee (Rosamund Kwan), and their faithful attendant Seven riding a stagecoach, intending to visit Sol, a student of Fei-hung's who - - after immigrating to America - - has invited Fei-hong to celebrate the one-year anniversary of his medicinal clinic in a small town in the wild and woolly West. When the stagecoach goes out of control, Fei-hong suffers a nasty bump on the head, and soon ends up lost down the river with amnesia, where he is adopted by a friendly Indian. Fei-hong is finally found by Yee and recovers, but now there is a racist sheriff and some nasty gunslinging bandits to contend with in town. Can Fei-hung, Seven, and their cowboy friend Billy use kung fu to clean up all the injustice?
Opinion: When you take a handful of good dramatic actors and actresses and surround them with college kids from the local frat house, you end up with an embarrassing Wild West, populated by straight-faced surfer dudes dressed as both cowboys and Indians. But at least the fight scenes are good.
ONCE UPON A TIME IN CHINA AND AMERICA is hilarious, but the unintentional parts are funnier than the intentional. Everyone plays their part with a straight face. For example, cowboy Billy has a perm with platinum bleached blond surfer hair and brown roots, but he takes his part seriously. As do all the Indian villagers who are poorly disguised white actors wearing yellow and blue paint. Towards the end of the film, the cowboy bandits start doing kung fu kicks and fifteen-foot twirling leaps. Meanwhile, here's an example of an intentional joke: "You're name is Seven, and I was the eleventh child in my family, so why don't we team up and open up a store called 7-Eleven?"
The movie turns serious when Fei-hong makes two speeches exhorting the Chinese Americans to be proud to be Chinese, and soon the Sheriff threatens to hang every Chinese American in the town. Then the final battle becomes humorous again when the cowboys start doing Shaolin moves.
Copyright © 1998 by David Sunga This review and others like it can be found at THE CRITIC ZOO: http://www.criticzoo.com email<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


| Module | ActionController::Caching::Fragments |
| In: |
/usr/lib/ruby/gems/1.8/gems/actionpack-3.0.0.beta4/lib/action_controller/caching/fragments.rb
|
Fragment caching is used for caching various blocks within templates without caching the entire action as a whole. This is useful when certain elements of an action change frequently or depend on complicated state while other parts rarely change or can be shared amongst multiple parties. The caching is done using the cache helper available in the Action View. A template with caching might look something like:
<b>Hello <%= @name %></b> <% cache do %> All the topics in the system: <%= render :partial => "topic", :collection => Topic.find(:all) %> <% end %>
This cache will bind to the name of the action that called it, so if this code was part of the view for the topics/list action, you would be able to invalidate it using expire_fragment(:controller => "topics", :action => "list").
This default behavior is of limited use if you need to cache multiple fragments per action or if the action itself is cached using caches_action, so we also have the option to qualify the name of the cached fragment with something like:
<% cache(:action => "list", :action_suffix => "all_topics") do %>
That would result in a name such as "/topics/list/all_topics", avoiding conflicts with the action cache and with any fragments that use a different suffix. Note that the URL doesn‘t have to really exist or be callable - the url_for system is just used to generate unique cache names that we can refer to when we need to expire the cache.
The expiration call for this example is:
expire_fragment(:controller => "topics", :action => "list", :action_suffix => "all_topics")
Removes fragments from the cache.
key can take one of three forms:
options is passed through to the cache store‘s delete method (or delete_matched, for Regexp keys.)
# File /usr/lib/ruby/gems/1.8/gems/actionpack-3.0.0.beta4/lib/action_controller/caching/fragments.rb, line 87 87: def expire_fragment(key, options = nil) 88: return unless cache_configured? 89: key = fragment_cache_key(key) unless key.is_a?(Regexp) 90: message = nil 91: 92: instrument_fragment_cache :expire_fragment, key do 93: if key.is_a?(Regexp) 94: cache_store.delete_matched(key, options) 95: else 96: cache_store.delete(key, options) 97: end 98: end 99: end
Given a key (as described in expire_fragment), returns a key suitable for use in reading, writing, or expiring a cached fragment. If the key is a hash, the generated key is the return value of url_for on that hash (without the protocol). All keys are prefixed with "views/" and uses ActiveSupport::Cache.expand_cache_key for the expansion.
# File /usr/lib/ruby/gems/1.8/gems/actionpack-3.0.0.beta4/lib/action_controller/caching/fragments.rb, line 33 33: def fragment_cache_key(key) 34: ActiveSupport::Cache.expand_cache_key(key.is_a?(Hash) ? url_for(key).split("://").last : key, :views) 35: end
Check if a cached fragment from the location signified by key exists (see expire_fragment for acceptable formats)
# File /usr/lib/ruby/gems/1.8/gems/actionpack-3.0.0.beta4/lib/action_controller/caching/fragments.rb, line 61 61: def fragment_exist?(key, options = nil) 62: return unless cache_configured? 63: key = fragment_cache_key(key) 64: 65: instrument_fragment_cache :exist_fragment?, key do 66: cache_store.exist?(key, options) 67: end 68: end
# File /usr/lib/ruby/gems/1.8/gems/actionpack-3.0.0.beta4/lib/action_controller/caching/fragments.rb, line 101 101: def instrument_fragment_cache(name, key) 102: ActiveSupport::Notifications.instrument("#{name}.action_controller", :key => key){ yield } 103: end
Reads a cached fragment from the location signified by key (see expire_fragment for acceptable formats)
# File /usr/lib/ruby/gems/1.8/gems/actionpack-3.0.0.beta4/lib/action_controller/caching/fragments.rb, line 50 50: def read_fragment(key, options = nil) 51: return unless cache_configured? 52: 53: key = fragment_cache_key(key) 54: instrument_fragment_cache :read_fragment, key do 55: result = cache_store.read(key, options) 56: result.respond_to?(:html_safe) ? result.html_safe : result 57: end 58: end
Writes content to the location signified by key (see expire_fragment for acceptable formats)
# File /usr/lib/ruby/gems/1.8/gems/actionpack-3.0.0.beta4/lib/action_controller/caching/fragments.rb, line 38 38: def write_fragment(key, content, options = nil) 39: return content unless cache_configured? 40: 41: key = fragment_cache_key(key) 42: instrument_fragment_cache :write_fragment, key do 43: content = content.html_safe.to_str if content.respond_to?(:html_safe) 44: cache_store.write(key, content, options) 45: end 46: content 47: end


--- layout: index title: ITC tagline: tags : [about, info] img : img-mobile : author : Marcin Koculak css: js: keywords: canonical: --- {% include JB/setup %}
ITC Conference Grant is fixed financial contribution which targets researchers from groups affiliated with an institution in an ITC countries participating in the COST Action.
ITC countries are: Albania, Bosnia-Herzegovina, Bulgaria, Cyprus, Czech Republic, Estonia, Croatia, Hungary, Lithuania, Latvia, Luxembourg, Malta, Montenegro, Moldova, Poland, Portugal, Romania, Slovenia, Slovakia, the former Yugoslav Republic of Macedonia, Republic of Serbia and Turkey.
Applicants must make an oral/poster presentation at the conference in question and must be listed in the official event/conference program. The main subject of presentation at the approved conference must be in relation to the objective of the Action and clearly express excellence of the applicant’s research. The COST support must be acknowledged.
The proposed conference to be attended must take place before November 30th, 2020. Attendance at European conferences is preferred. However, conferences held elsewhere can also be considered. Conferences, meetings and other activities organized by COST are not eligible.
Any e-COST user who is a PhD student or an Early Career Investigator (ECI) and is affiliated with an institution located in an ITC1 country participating in the Action can submit an application request for an ITC Conference Grant. The application should be submitted at least 45 days before the conference start date. The applicant must be engaged in an official research programme as a PhD Student or postdoctoral fellow.
For full eligibility criteria please check the Vademecum available HERE.
A Conference Grant is a fixed financial contribution and do not necessarily cover all of the expenses related to participating in a given conference. A Conference Grant is a contribution to the overall travel, accommodation and meal expenses of the selected Grantee. The calculation of the financial contribution for each Conference Grant must respect the following criteria:
Ideally, the application should be submitted at least 45 days before the conference start date.
The applicant submit an ITC CG application by logging into e-COST and clicking on the ITC Conference Grants tab.
The application form contains the following sections:
The submission of the report is mandatory or the Grant is cancelled. Late submission, beyond the deadline, can also lead to the cancellation of the Grant.
For further details see the ITC guidebook.


{% extends "frappe/www/message.html" %} {% block message_body %}
{{ _("Please confirm your action to {0} this document.").format(action) }}
{% if alert_doc_change %}{{ _("Note:")}} {{ _("This document has been modified after the email was sent.")}}
{% endif %} {{ action }} Cancel {% endblock %}


We hope to stay faithful to the Word of God, build genuine lasting friendships, and provide a safe space where students are welcome to come and discover who Jesus is. We want to be an encouragement to students as they deepen their faith in Jesus Christ and empower them to share the Gospel in their communities.
Interested? Send us a message or visit our social media.


{% extends "master.html" %} {% block content %} {% with messages = get_flashed_messages() %} {% if messages %} {% for message in messages %}
{{ message }}
{% endfor %} {% endif %} {% endwith %}DunderBands is a Guided Search Engine that seeks underrated underground albums that are
considered masterpieces by immediate fans.
The search string will begin to change if no result is found quickly. It will drop prefix first then it will restore the original search string and only drop country
Then it will drop both prefix and country leaving only subgenre and genre. The quicker the find, the better the album.
If a result still can't be found
it will ask that you double your efforts.
If you enjoy using DunderBands please check out my Kick Starter Campaign.
I'm raising money to put a team together so I can make DunderBands something really special that will help
anyone and everyone find Music they will cherish for the rest of their lives.
Thanks so much for trying DunderBands


Dummy implementation of logger that doesn't do anything. More...
Public Member Functions | |
| void | Debug (string correlationId, string message, params object[] args) |
| Logs a high-level debug information for troubleshooting. More... | |
| void | Debug (string correlationId, Exception error, string message=null, params object[] args) |
| Logs a high-level debug information for troubleshooting. More... | |
| void | Error (string correlationId, string message, params object[] args) |
| Logs recoverable application error. More... | |
| void | Error (string correlationId, Exception error, string message=null, params object[] args) |
| Logs recoverable application error. More... | |
| void | Fatal (string correlationId, string message, params object[] args) |
| Logs fatal (unrecoverable) message that caused the process to crash. More... | |
| void | Fatal (string correlationId, Exception error, string message=null, params object[] args) |
| Logs fatal (unrecoverable) message that caused the process to crash. More... | |
| void | Info (string correlationId, string message, params object[] args) |
| Logs an important information message More... | |
| void | Info (string correlationId, Exception error, string message=null, params object[] args) |
| Logs an important information message More... | |
| void | Log (LogLevel level, string correlationId, Exception error, string message, params object[] args) |
| Logs a message at specified log level. More... | |
| void | Trace (string correlationId, string message, params object[] args) |
| Logs a low-level debug information for troubleshooting. More... | |
| void | Trace (string correlationId, Exception error, string message=null, params object[] args) |
| Logs a low-level debug information for troubleshooting. More... | |
| void | Warn (string correlationId, string message, params object[] args) |
| Logs a warning that may or may not have a negative impact. More... | |
| void | Warn (string correlationId, Exception error, string message=null, params object[] args) |
| Logs a warning that may or may not have a negative impact. More... | |
Properties | |
| LogLevel | Level [get, set] |
| Gets the maximum log level. More... | |
| Properties inherited from PipServices3.Components.Log.ILogger | |
| LogLevel | Level [get, set] |
| Gets or sets the maximum log level. Messages with higher log level are filtered out. More... | |
Dummy implementation of logger that doesn't do anything.
It can be used in testing or in situations when logger is required but shall be disabled.
See ILogger
| void PipServices3.Components.Log.NullLogger.Debug | ( | string | correlationId, |
| string | message, | ||
| params object [] | args | ||
| ) |
Logs a high-level debug information for troubleshooting.
| correlationId | (optional) transaction id to trace execution through call chain. |
| message | a human-readable message to log. |
| args | arguments to parameterize the message. |
Implements PipServices3.Components.Log.ILogger.
| void PipServices3.Components.Log.NullLogger.Debug | ( | string | correlationId, |
| Exception | error, | ||
| string | message = null , |
||
| params object [] | args | ||
| ) |
Logs a high-level debug information for troubleshooting.
| correlationId | (optional) transaction id to trace execution through call chain. |
| error | an error object associated with this message. |
| message | a human-readable message to log. |
| args | arguments to parameterize the message. |
Implements PipServices3.Components.Log.ILogger.
| void PipServices3.Components.Log.NullLogger.Error | ( | string | correlationId, |
| string | message, | ||
| params object [] | args | ||
| ) |
Logs recoverable application error.
| correlationId | (optional) transaction id to trace execution through call chain. |
| message | a human-readable message to log. |
| args | arguments to parameterize the message. |
Implements PipServices3.Components.Log.ILogger.
| void PipServices3.Components.Log.NullLogger.Error | ( | string | correlationId, |
| Exception | error, | ||
| string | message = null , |
||
| params object [] | args | ||
| ) |
Logs recoverable application error.
| correlationId | (optional) transaction id to trace execution through call chain. |
| error | an error object associated with this message. |
| message | a human-readable message to log. |
| args | arguments to parameterize the message. |
Implements PipServices3.Components.Log.ILogger.
| void PipServices3.Components.Log.NullLogger.Fatal | ( | string | correlationId, |
| string | message, | ||
| params object [] | args | ||
| ) |
Logs fatal (unrecoverable) message that caused the process to crash.
| correlationId | (optional) transaction id to trace execution through call chain. |
| message | a human-readable message to log. |
| args | arguments to parameterize the message. |
Implements PipServices3.Components.Log.ILogger.
| void PipServices3.Components.Log.NullLogger.Fatal | ( | string | correlationId, |
| Exception | error, | ||
| string | message = null , |
||
| params object [] | args | ||
| ) |
Logs fatal (unrecoverable) message that caused the process to crash.
| correlationId | (optional) transaction id to trace execution through call chain. |
| error | an error object associated with this message. |
| message | a human-readable message to log. |
| args | arguments to parameterize the message. |
Implements PipServices3.Components.Log.ILogger.
| void PipServices3.Components.Log.NullLogger.Info | ( | string | correlationId, |
| string | message, | ||
| params object [] | args | ||
| ) |
Logs an important information message
| correlationId | (optional) transaction id to trace execution through call chain. |
| message | a human-readable message to log. |
| args | arguments to parameterize the message. |
Implements PipServices3.Components.Log.ILogger.
| void PipServices3.Components.Log.NullLogger.Info | ( | string | correlationId, |
| Exception | error, | ||
| string | message = null , |
||
| params object [] | args | ||
| ) |
Logs an important information message
| correlationId | (optional) transaction id to trace execution through call chain. |
| error | an error object associated with this message. |
| message | a human-readable message to log. |
| args | arguments to parameterize the message. |
Implements PipServices3.Components.Log.ILogger.
| void PipServices3.Components.Log.NullLogger.Log | ( | LogLevel | level, |
| string | correlationId, | ||
| Exception | error, | ||
| string | message, | ||
| params object [] | args | ||
| ) |
Logs a message at specified log level.
| level | a log level. |
| correlationId | (optional) transaction id to trace execution through call chain. |
| error | an error object associated with this message. |
| message | a human-readable message to log. |
| args | arguments to parameterize the message. |
Implements PipServices3.Components.Log.ILogger.
| void PipServices3.Components.Log.NullLogger.Trace | ( | string | correlationId, |
| string | message, | ||
| params object [] | args | ||
| ) |
Logs a low-level debug information for troubleshooting.
| correlationId | (optional) transaction id to trace execution through call chain. |
| message | a human-readable message to log. |
| args | arguments to parameterize the message. |
Implements PipServices3.Components.Log.ILogger.
| void PipServices3.Components.Log.NullLogger.Trace | ( | string | correlationId, |
| Exception | error, | ||
| string | message = null , |
||
| params object [] | args | ||
| ) |
Logs a low-level debug information for troubleshooting.
| correlationId | (optional) transaction id to trace execution through call chain. |
| error | an error object associated with this message. |
| message | a human-readable message to log. |
| args | arguments to parameterize the message. |
Implements PipServices3.Components.Log.ILogger.
| void PipServices3.Components.Log.NullLogger.Warn | ( | string | correlationId, |
| string | message, | ||
| params object [] | args | ||
| ) |
Logs a warning that may or may not have a negative impact.
| correlationId | (optional) transaction id to trace execution through call chain. |
| message | a human-readable message to log. |
| args | arguments to parameterize the message. |
Implements PipServices3.Components.Log.ILogger.
| void PipServices3.Components.Log.NullLogger.Warn | ( | string | correlationId, |
| Exception | error, | ||
| string | message = null , |
||
| params object [] | args | ||
| ) |
Logs a warning that may or may not have a negative impact.
| correlationId | (optional) transaction id to trace execution through call chain. |
| error | an error object associated with this message. |
| message | a human-readable message to log. |
| args | arguments to parameterize the message. |
Implements PipServices3.Components.Log.ILogger.
|
getset |
Gets the maximum log level.


RSS Bandit also supports some command line options to control how the application starts and what User Interface Language should be used. The command line syntax to be used:
RssBandit.exe [options] <feedUrl> [<feedUrl> ]*
The application accepts fully qualified feed URL's as <feedUrl> parameters you want to like to subscribe to.
Here is the complete list of supported options and their descriptions:
| Option | Description |
| -h[elp] | Opens a message box that lists all available command line options |
| -t[askbar] | Application starts minimized; it will be only visible as a small icon in the windows task notification area (where the clock runs) |
|
-c[ulture]:<text> |
Application User Interface will use the specified culture. The <text> parameter have to be spcified following the RFC 1766 standard in the format "<languagecode2>-<country/regioncode2>", where <languagecode2> is a lowercase two-letter code derived from ISO 639-1 and <country/regioncode2> is an uppercase two-letter code derived from ISO 3166. For example, U.S. English is "en-US". In cases where a two-letter language code is not available, the three-letter code derived from ISO 639-2 is used; for example, the three-letter code "div" is used for cultures that use the Dhivehi language. Some culture names have suffixes that specify the script; for example, "-Cyrl" specifies the Cyrillic script, "-Latn" specifies the Latin script. The supported language matrix provides a list of the current supported User Interface Languages valid for the available versions of RSS Bandit (please note: some of them need to have the appropriate operating system language extension package installed to get correctly displayed, e.g. Chinese-Simplified). |
|
-r[esetUI] |
Will reset the user interface to the default (by not loading the .settings.xml at startup) |