SELinux contexts - what to look for
SELINUX RESTORE, ADD, DELETE CONTEXTS
RESTORING CONTEXTS
- Could use autorelabel
- touch /.autorelabel
- /.autorelabel
- On next reboot it will relabel everything based on
proper context
or use restorecon command.
CONTEXTS CONTROLLED BY
semanage fcontext -l
- Reccomended to use context as defined in semanage fcontext ONLY
- NOT set them manually.
- You can use restorecon to restore context based on defaults
ADDING CONTEXT TO DIRECTORY
let us say you set apache DocumentRoot and DirectoryIndex to /content instead of /var/www
[root@j0nn91 content]# ls -Z
-rw-r--r--. root root unconfined_u:object_r:default_t:s0 index.html
Problem is index.html does not have httpd context, so httpd service will not have access to the file. Therefore page will be "permission denied" REGARDLESS OF PERMISSIONS.
so you need to set the semanage context for this directory to reflect /var/www:::
/var/www(/.*)? all files system_u:object_r:httpd_sys_content_t:s0
COMMAND TO ADD CONTEXT TO DIRECTORY
semanage fcontext -l | grep /var/www --> can take context and also (/.*) if you forget
semanage fcontext -a -t httpd_sys_content '/content(/.*)?'
[root@j0nn91 content]# semanage fcontext -a -t httpd_sys_content_t '/content(/.*)?'
[root@j0nn91 content]# ls -Z
-rw-r--r--. root root unconfined_u:object_r:default_t:s0 index.html
But file is still default_t. Need to do restorecon to restore to semanage defaults.
RESTORECON TO SET CONTEXT BACK TO DEFAULTS
restorecon -Rv /content
TO DELETE NEWLY SET CONTEXT
[root@j0nn91 content]# semanage fcontext -l | grep /content
/content(/.*)? all files system_u:object_r:httpd_sys_content_t:s0
[root@j0nn91 content]# semanage fcontext -d "/content(/.*)?"
The biggest thing to know with SELinux when you are troubleshooting is improper context and incorrect labelling of files can be the main culprits.


background information: i have experience with 2C-E, THC, opiates, benzos, alcohol, salvia divinorum, cocaine, DXM, and a couple others.
BDFLY is the 2nd psychedelic i've ever ingested, 2C-E being the first
here is my experience, the highlight of my experience was when i was outside. i HIGHLY recommend going outside and watching the sunrise on any psychedelic voyage.
1:45 am - ingested ~250ug bdfly sublingually
2:00 am - feeling some auditory changes, i'm sure its just placebo, though one can never know
2:15 am - same feeling as above
2:30 am - feeling of 'something' happening, although i can't exactly pinpoint whats happening
2:45 am - time dilation starting to become apparent same feelings as mentioned above still present
3:00 am - feeling positive mood :)
3:15 am - great mood uplift feeling of well being and happy all throughout
3:30 am - great warm feeling in the lower abdomen lots of gently rushing euphoria coming in waves feels amazing no complaints so far :)
3:45 am - feeling something i would like to call... comfortable anxiety
4:00 am - went outside found the backyard very interesting and amusing :)
4:15 am - still outside
4:30 am - just came inside starting to feeeeel
4:45 am - just got in shower feeling good
5:00 am - just got out of shower feeling fine
5:15 am - playing some online games feeling alright
5:30 am - had a phone convo my cognitive thinking seems to be improved not impaired
5:45 am - planning to watch the sunrise; will report back for details
6:00 am - heading off to watch the sunrise
6:00 am to 8:45 am - vivid awesome hallucinations like i have never seen before on any type of enthenogen, +++ on shulgin scale almost got to a ++++ gorgeous Open eye visuals and Closed eye visuals. I was absolutely engulfed in the visuals beautiful hexagonal snowflake like (mandalas?) patterns in the sky rotating slowly clock wise and counter clock wise they were huge and they seemed so real it was amazing... i could have had a ++++ but i chose to not let my mind drift in that direction, i was not prepared to experience what bromodragonfly had in store for me... so i 'drove' the trip back towards home
10:09 am - still tripping heavily
12:08 pm - effects slightly diminished still a ++ on shulgin scale
1:00 am - effects almost completely gone ready to go to sleep! took some melatonin and Valerian root to calm down whatever psychedelic fuzz remained.
4:00 am - finally go to sleep
next day i felt amazing and refreshed and happy to be alive, i feel the experience has definitely softened my ego and showed me the beauty of nature
i felt so much ability to think about ideas and communicate with people while on bdfly.
it was a mind opening life changing experience... the most powerful i've had to date.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


| 011:014 Khan | : |
If then they answer you not, know then that the Revelation (this Qur'an) is sent down with the Knowledge of Allah and that La ilaha illa Huwa: (none has the right to be worshipped but He)! Will you then be Muslims (those who submit to Islam)? |
| 011:014 Maulana | : |
But if they answer you not, then know that it is revealed by Allah's knowledge, and that there is no God but He. Will you then submit? |
| 011:014 Pickthal | : |
And if they answer not your prayer, then know that it is revealed only in the knowledge of Allah; and that there is no Allah save Him. Will ye then be (of) those who surrender? |
| 011:014 Rashad | : |
If they fail to meet your challenge, then know that this is revealed with GOD's knowledge, and that there is no god except He. Will you then submit? |
| 011:014 Sarwar | : |
If they will not respond to you, know that God has sent it with His knowledge and that He is the only God. Will you then become Muslims?" |
| 011:014 Shakir | : |
But if they do not answer you, then know that it is revealed by Allah's knowledge and that there is no god but He; will you then submit? |
| 011:014 Sherali | : |
And if they do not accept your challenge, then know that it has been revealed comprising that which is only within ALLAH's knowledge and there is no god but HE. Will you then submit. |
| 011:014 Yusufali | : |
"If then they (your false gods) answer not your (call), know ye that this revelation is sent down (replete) with the knowledge of Allah, and that there is no god but He! will ye even then submit (to Islam)?" |
Concepts/Themes Covered: أركان الإسلام > (محمد (صلى الله عليه وسلم > تأييد رسالته Pillars of Islam> The Blessed Muhammad(PBUH)> Supporting his Message القرآن الكريم > محاججة المنكرين والجاحدين The Holy Quran> Arguing the Denyers and the Unbelievers
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


{% include "content/includes/header.html" %}
{% include "content/includes/breadcrumbs.html" %}
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{% when 'level2-index' %}
{% include "content/includes/level2-index.html" %}
{% when 'detail-page' %}
{% include "content/includes/detail-page.html" %}
{% when 'topic-landing' %}
{% include "content/includes/topic-landing.html" %}
{% when 'L2-section-landing' %}
{% include "content/includes/L2-section-landing.html" %}
{% when '1-topic-landing' or 'alt-1-topic-landing' %}
{% include "content/includes/1-topic-landing.html" %}
{% when '2-detailed-landing' %}
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{% when '3-panel-filters' %}
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{% when '4-action-page' %}
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{% when '6-info-page' %}
{% include "content/includes/6-info-page.html" %}
{% when '7-get-page' or '7-get-page-alt' %}
{% include "content/includes/7-get-page.html" %}
{% else %}
{{ contents }}
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{% if path != 'employment' AND template != 'level2-index' AND template != 'topic-landing' AND template != 'detail-page' %}
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Welcome to my portfolio
Hello! It's very nice to meet you. Who am I? I am 28 years young, currently living in Austin, Texas. Orginally, I wanted a career in Criminal Psychology but after supporting a contract with the FBI and getting a taste of what my life would be like long-term, I decided that I would rather have a job that was more flexible, creativity enabled, and always changing. I love working out and enjoy the outdoors and all of its many facets, so my dream would be to eventually be a developer for an athleisure brand (i.e. Lululemon) or an hunting/fishing brand (i.e. OnX maps or First Lite). Make sure to watch my video above that talks a little more about me, click on the picture of me in the top right corner, and visit my projects page at the top navigation bar.


Top 10 Anime
- Full Metal Alchemist Brotherhood (Great Story):
This, is clearly the best anime out there.  With his supreme alchemy skills, Edward binds Alphonse's soul to a large suit of armor. -
Death Note (Master-Mind):
Death note is one of the best anime to come out for a long time. An intelligent high school student (Light Yagami) goes on a secret crusade to eliminate criminals from the world after discovering a notebook capable of killing anyone whose name is written into it. -
Attack on Titan (Awesome Just Awesome):
This anime blew my mind each episode resides something new and interesting. I was restless to watch the next episode. Even, wasn’t able to wait for the season 3 and read the manga completely to 101 chapters the next day. People thinking season 2 is best, wait for the book in basement. It will totally blow your Mind. -
Spirited Away (Amazing!!):
In this animated movie of Japanese director Hayao Miyazaki, a 10-year-old Chihiro and her parents stumble upon a seemingly abandoned amusement park. After her mother and father are turned into giant pigs, she needs to work in the resort for the supernatural beings who take bath after the time spent in earthly realms to free her parents. -
One Piece (Treasure):
This anime is beyond my expectation. It may feel slow and childish in the start but keep watching and you’ll know what a fantastic anime it is. Follow the adventures of Monkey D. Luffy and his friends in order to find the greatest treasure ever left by the legendary Pirate, Gol D Roger. The famous mystery treasure named "One Piece". -
Code Geass (Revolution):
It is perhaps the second-best big-robot show of the year, but given that Tengen Toppa Gurren Laggann is sitting on the number one slot. The Holy Empire of Britannia conquered the country known as Japan and now call it Area 11. Its residents lost their rights to self-govern and are now called Elevens. The Empire uses powerfully destructive robotic weapons called Knightmares to ensure control, but someone is about to stand up against it. Lelouch, the black prince, has endless ambition and uses the power of the Geass to build a world based on his ideals. -
Naruto (BATTLE):
The story is absolutely beautiful. You have epic fights combined with deep emotions and unpredictable story changes that will blow your mind. Naruto Uzumaki, is a loud, hyperactive, adolescent ninja who constantly searches for approval and recognition, as well as to become Hokage, who is acknowledged as the leader and strongest of all ninja in the village. -
Cowboy Bebop (BANG):
“One Ship, One Crew, One Destiny”. This is an anime Classic. This futuristic misadventures and tragedies of an easygoing bounty hunter and his partners is an interesting anime. At Most basic level it is a space western. -
Gintama (Hilarious):
Every anime has it's funny, even hilarious moments. But not many are built around these moments. Gintama, for the most part definitely is. In an era where aliens have invaded and taken over feudal Tokyo, an unemployed samurai finds work however he can. -
Boku No Hero Academia (Plus Ultra):
This anime is really filled with strong emotions and zeal to become a hero. The story sets in a world, where 80% of the population is having some superpowers. A superhero-loving boy without any powers is determined to enroll in a prestigious hero academy and learn what it really means to be a hero.
Follow Me on Social Media


Contents
- Add dependencies and download the data.
- Data overview.
- Call GLMestimatesingletrial with default parameters.
- Important outputs.
- Plot a slice of brain with GLMSingle outputs.
- Run standard GLM.
- Compare GLM results.
- Calculate reliability index.
- Plot reliability index as an overlay.
- Plot median reliability for each GLM.
Add dependencies and download the data.
% add path to GLMsingle % You also need fracridge repository to run this code % https://github.com/nrdg/fracridge.git addpath(genpath('./../')) addpath(genpath('/Users/jankurzawski/Documents/fracridge')) clear clc close all dataset = 'nsdcore'; % Download the data to data directory if ~exist('./data','dir') mkdir('data') end if ~exist('./data/nsdcoreexampledataset.mat','file') !curl -L --output ./data/nsdcoreexampledataset.mat https://osf.io/k89b2/download end load('./data/nsdcoreexampledataset.mat') % Data comes from subject1, session1 from NSD dataset % https://www.biorxiv.org/content/10.1101/2<IP_ADDRESS>2340v1.full.pdf
Data overview.
clc whos % data -> Consists of several runs of 4D volume files (x,y,z,t) where % (t)ime is the 4th dimention. % design -> Each run has a corresponding design matrix where each colum % describes single condition (conditions are repeated across runs). Each % design matrix is binary with 1 specfing the time (TR) when stimulus is % presented on the screen. % ROI -> Manually defined region in the occipital cortex. fprintf('There are %d runs in total.\n',length(design)); fprintf('The dimensions of the data for the first run are %s.\n',mat2str(size(data{1}))); fprintf('The stimulus duration is %.6f seconds.\n',stimdur); fprintf('The sampling rate (TR) is %.6f seconds.\n',tr); figure(1);clf % Show example design matrix. for d = 1 imagesc(design{d}); colormap gray; drawnow xlabel('Conditions') ylabel('TRs') title(sprintf('Design matrix for run%i',d)) set(gca,'YDir','normal') axis image end set(gcf,'Position',[ 1000 786 861 552])
Name Size Bytes Class Attributes ROI 145x186 215760 double data 1x12 388369344 cell dataset 1x7 14 char design 1x12 69408 cell stimdur 1x1 8 double tr 1x1 8 double There are 12 runs in total. The dimensions of the data for the first run are [145 186 1 300]. The stimulus duration is 3.000000 seconds. The sampling rate (TR) is 1.000000 seconds.
Show an example slice of the first fMRI volume
figure(2);clf imagesc(makeimagestack(data{1}(:,:,:,1))); colormap(gray); axis equal tight; c=colorbar; title('fMRI data (first volume)'); set(gcf,'Position',[ 1000 786 861 552]) axis off c.Label.String = 'T2*w intensity'; set(gca,'FontSize',15)
Call GLMestimatesingletrial with default parameters.
Outputs and figures will be stored in folder in the current directory or saved to the results variable which is the only output of GLMestimatesingletrial
% Optional parameters below can be assigned to a variable i.e % opt by creating fields (i.e opt.wantlibrary). Options are the 6th input % to GLMestimatesingletrial. % DEFAULT OPTIONS: % wantlibrary = 1 -> Fit hRF to each voxel % wantglmdenoise = 1 -> Use GLMdenoise % wantfracridge = 1 -> Use ridge regression to improve beta estimates % chunknum = 5000 -> is the number of voxels that we will process at the % same time. For setups with lower memory deacrease this number. % wantmemoryoutputs is a logical vector [A B C D] indicating which of the % four model types to return in the output <results>. The user must be careful with this, % as large datasets can require a lot of RAM. If you do not request the various model types, % they will be cleared from memory (but still potentially saved to disk). % Default: [0 0 0 1] which means return only the final type-D model. % wantfileoutputs is a logical vector [A B C D] indicating which of the % four model types to save to disk (assuming that they are computed). % A = 0/1 for saving the results of the ONOFF model % B = 0/1 for saving the results of the FITHRF model % C = 0/1 for saving the results of the FITHRF_GLMDENOISE model % D = 0/1 for saving the results of the FITHRF_GLMDENOISE_RR model % Default: [1 1 1 1] which means save all computed results to disk. % [results] = GLMestimatesingletrial(design,data,stimdur,tr,dataset); load('results')
Important outputs.
% R2 -> is model accuracy expressed in terms of R^2 (percentage). % modelmd -> is the full set of single-trial beta weights (X x Y x Z x % TRIALS). Beta weights are arranged in a chronological order) % HRFindex -> is the 1-index of the best fit HRF. HRFs can be recovered % with getcanonicalhrflibrary(stimdur,tr) % FRACvalue -> is the fractional ridge regression regularization level % chosen for each voxel.

Additionally, for comparison purposes we are going to run standard GLM without hrf fitting, GLMdenoise or Ridge regression regularization.
opt.wantlibrary= 0; % switch off hrf fitting opt.assume = 1; % assume one hrf opt.wantglmdenoise = 0; % switch off glmdenoise opt.wantfracridge = 0; % switch off Ridge regression opt.wantfileoutputs =[0 0 0 0]; opt.wantmemoryoutputs =[0 1 0 0]; [ASSUME_HRF] = GLMestimatesingletrial(design,data,stimdur,tr,NaN,opt); % Results are going to be stored in ASSUME_HRF variable.
*** FITTING TYPE-A MODEL (ONOFF) *** *** FITTING TYPE-B MODEL (FITHRF) *** working on chunk 1 of 1.
cmap = [0.2314 0.6039 0.6980 0.8615 0.7890 0.2457 0.8824 0.6863 0 0.9490 0.1020 0]; models = {'';'TYPEB_FITHRF.mat';'TYPEC_FITHRF_GLMDENOISE.mat';'TYPED_FITHRF_GLMDENOISE_RR.mat'}; %No need to load ASSUME_HRF as it is already in matlab's memory model_names = {'ASSUME_HRF','FIT_HRF','FIT_HRF_GLMDENOISE','FIT_HRF_GLMDENOISE_RR'};
Compare GLM results.
To compare the results of different GLMs we are going to calculate the reliablity voxel-wise index for each model. Reliablity index represents a correlation between beta weights for repeated presentations of the same stimuli. In short, we are going to check how reliable/reproducible are single trial responses to repeated images estimated with each GLM type.
% First, we are going to locate the indices in the beta weight GLMsingle % outputs modelmd(x,y,z,trials) that correspond to repated images. Here we % only consider stimuli that have been repeated once. For the purpose of % the example we ignore the 3rd repetition of the stimulus. condition = zeros(size(design{1},1),length(design)); newdesign = design; for r = 1 : length(design) dm = full(design{r}); newdm = zeros(size(dm)); for cond = 1 : size(dm,2) tmp = dm(:,cond) == 1; newdm(tmp,cond) = cond; end condition(:,r) = sum(newdm,2); end condition(condition==0) = []; unique_betas = unique(condition); duplicates = cell(1,length(unique_betas))'; for u = 1:length(unique_betas) duplicates{u} = find(condition==unique_betas(u)); end % Find images that have been repeated at least twice. Idx = cellfun(@(x) (length(x) > 1), duplicates); duplicates=(duplicates(Idx)); maxRep = max(cellfun(@(x) (length(x)), duplicates)); duplicates_mat = NaN(length(duplicates),maxRep); for d = 1 : length(duplicates_mat) duplicates_mat(d,1:length(duplicates{d})) = duplicates{d}; end fprintf('There are %i repeated images in the experiment \n',length(duplicates)) % For each voxel we are going to correlate beta weights describing the % response to images presented for the first time for each condition % with beta weights describing the response from the repetition of the same % image. With 136 repeated images R value for each voxel will correspond % to correlation between vectors with 136 beta weights.
There are 136 repeated images in the experiment
Calculate reliability index.
vox_reliabilities = cell(1,length(models)); for m = 1 : 4 %load GLM model if m == 1 betas=ASSUME_HRF{2}.modelmd; %No need to laod the data as it is stored in matlab's memory in %ASSUME_HRF variable else load(sprintf('./nsdcore/%s',models{m})) betas = modelmd; end dims = size(betas); Xdim = dims(1); Ydim = dims(2); Zdim = dims(3); vox_reliability = NaN(Xdim, Ydim, Zdim); %For each voxel we find beta weights corresponding to the first %presentation of the image and second presentaion of the same image. for i = 1:Xdim for j = 1:Ydim for k = 1:Zdim vox_data = squeeze(betas(i,j,k,:)); repetition_1 = vox_data(duplicates_mat(:,1)); repetition_2 = vox_data(duplicates_mat(:,2)); r = corr(repetition_1, repetition_2); vox_reliability(i,j,k) = r; end end end %We store results for each model vox_reliabilities{m} = vox_reliability; end
Plot reliability index as an overlay.
figure(4);clf for m = 1 : 4 vox_reliability = vox_reliabilities{m}; subplot(2,2,m); data = meanvol(:,:); ROI(ROI~=1) = NaN; overlay = vox_reliability.*ROI; imagesc(data); colormap gray freezeColors hold on; imAlpha = ones(size(overlay)); imAlpha(isnan(overlay)) = 0; imagesc(overlay, 'AlphaData', imAlpha,[-0.5 0.5]); colormap hot axis image off title(model_names{m},'Interpreter','None') drawnow c = colorbar; c.Label.String = 'Reliabiliy Index'; set(gca,'Fontsize',15) end set(gcf,'Position',[ 1000 786 861 552])
Plot median reliability for each GLM.
figure(5);clf % For each GLM type we calculate median reliability for voxels within the % visual ROI. for m = 1 : 4 bar(m,nanmedian(vox_reliabilities{m}(ROI==1)),'FaceColor','None','Linewidth',3,'EdgeColor',cmap(m,:)); hold on end % xticks([1:4]) ylabel('Median reliability') legend(model_names,'Interpreter','None','Location','NorthWest') set(gca,'Fontsize',16) set(gca,'TickLabelInterpreter','none') xtickangle(0) xticks([]) ylim([0.1 0.2]) set(gcf,'Position',[ 1000 585 650 753]) title('mean voxel split-half reliability of GLM models')


OPC UA
, Information Model
, Address Space
, Digital Twin
, Process Observer
, State Observer
An important issue for process control is to restore its state and behavior so as to guarantee appropriate quality indicators. The main goal of the project is to prove that a stand-alone implementation of the OPC UA Address Space concept is possible so that it can be used for selected purposes requiring real-time creation of an information replica of an external process.
Computer science is a research discipline of using computers to process information. One of the domains of application is real-time process automation. In this approach, an important issue is to restore the state and behavior of the process for the purposes of the process control so as to guarantee appropriate quality indicators. There are many concepts known from the literature, including OPC UA Address Space, Digital Twin, Process Observer, State Observer, etc. Each of these concepts has its advantages and disadvantages that will be investigated in this project. OPC UA Address Space seems to be the most promising, however, this concept is currently used only as a component embedded in the communication server.
The main goal of the project is to prove that a stand-alone implementation of the OPC UA Address Space concept is possible so that it can be used for selected use cases requiring the real-time creation of an external process information replica. The selected use cases include but it is not limited to
As part of the project, references containing a description of the OPC UA Address Space concept, including the OPC UA specification, articles describing this concept, etc., will be selected and analyzed. An important contribution of the project is also the selection and analysis of available implementations of this concept, which will serve as reference solutions. Since the purpose of the project is to implement a certain concept, it will be necessary to select the design environment that will be used to achieve this goal. Then, these tools will be used to implement the OPC UA Address Space concept as an independent library so that it can be reused in various applications. As confirmation of the correctness of the proposed approach, two selected library use cases will be implemented.
Finally, as a result of the project, an independent library will be developed in the selected technology. Its public interface (API) will at least comply with the needs of research resulting from the analysis of selected applications. The API may contain additional elements resulting from the needs indicated in the analytical part. The correctness of the implementation of the concept will be confirmed by the implementation of independent solutions (proof of concept). These implementations will serve as a kind of integration test. The correctness of individual parts of the library will be verified using unit tests.
The project is to be harmonized with the Master's Thesis of my student. The implementation of the Address Space is to be derived from the following existing libraries. The main goal of harmonization is to make sure that the library will be reused by all the mentioned applications.


COP LAND
It is nice to see Stallone in something other than his macho, daredevil, machine gun-firing roles. And he is much more likable playing Freddy Heflin, a wannabe policeman who is rejected because one ear has been damaged when, as a teenager, he dove into the water to rescue the drowning Liz (Annabella Sciorra). He is an acknowledged hero and is given the job of sheriff in the town of Garrison, NJ, just across the river from NYC. There is not much happening in Garrison except possibly for a jackknifed big rig.
Garrison is called "Copland" because so many of New York's finest are fed up with NYC's crime rate of one every eight seconds. In Garrison, no one commits a crime because there are too many cops living there. Sheriff or no, the cops really control the town. For instance, when Cindy (Janeane Garofalo), a new deputy, wants to cite Ray Donlan, (Harvey Keitel) a top ranking cop for speeding, Freddy has to tell her that is a "no-no." We do not give tickets to cops. Nice and peaceful town.
Nice and peaceful until Officer Murray Babitch (Michael Rappaport) who is on his way home after a fun evening in NYC gets sideswiped by a couple of black men who are having their own joy ride. Infuriated, he gives chase. When one of the men leans out the window with a gun pointed at Babitch, he fires his own revolver and causes the car he is chasing to crash and explode, killing both occupants.
That is not good . . . but worse is that when the detectives investigate, they find that there was no gun involved, just a steering wheel lock. That is bad. He can easily lose his status as a hero for having rescued people in a fire and suddenly find himself the subject of a police brutality investigation. But what are friends for? A fellow officer plants a gun in the crashed vehicle. To insure there will be no further inquiries they pretend that Babitch must have jumped to his death from the George Washington Bridge into the Hudson River. End of story? Hardly.
Two things then happen. As Donlan drives away after the ticket incident, Freddy sees the "drowned" Babitch looking out the back window. Further, there is the NYC police Internal Affairs investigator, Moe Tilden (played with great humor and flair by Robert De Niro) who smells there may be a cover up. Tilden tries to talk Freddie into helping him by reporting anything suspicious but Freddie is uncertain about what he should do; go against his cop friends . . . or uphold the law? The combined efforts of Tilden and Freddy could forever change how Garrison is perceived and run.
For the most part, the film is exciting. Much of that is due to a superb cast. Aside from those already mentioned, there are Ray Liotta, Peter Berg, Robert Patrick in supporting roles.
Written and based on his own experiences in a similar town and Directed by James Mangold
2.5 Bytes
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Copyright 1997 Ben Hoffman
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Problem Description
Why do Google Suite products matter?
When I was a high school student, I commonly helped out my friends when they struggled in the mathematics courses, especially AP Statistics and AP Calculus (both AB and BC), and not to mention the mathematics sections on the SAT (as you can tell, I was pretty much the go-to math geek when it came to asking for help). Working in-person with my friends to go over their troubles in their math class, I often worked out practice problems on a scratch sheet of paper, and for additional resources, I shared with them a Google Slides presentation that they could walk through to recall certain tips and tricks. Using Google Suite products really helped me out in these cases as they allowed me edit these presentations whenever I wanted to and wherever I was. The benefit of working on the cloud is far greater than depending on perfect health, especially when it comes to learning remotely during the era of COVID-19.
Apart from these purposes, I commonly used Google Suite tools such as Google Slides and Google Docs to communicate with my group members during a group project. We would list all of our ideas (scripts when it came to filming, tables when it came to documenting what each person does, etc.) and meet up to complete our project based on the guide we listed in the documents.
However, there was a detriment to using Google Suite tools:
Google Suite is Blocked on the School Network.
Expected output when navigating to Google Docs
Unwanted output when navigating to Google Docs
As a result of these blocked websites, we commonly had to use Google Suite tools on our phones when we needed to use it for sharing documents and other information we otherwise did not have access to under the school's network.
Substitute Goods
What are substitute Goods?
School administrators cited their number one reason for blocking Google Suite products was because they wanted us to use Microsoft's products instead. With a school-wide license, everyone has access to Microsoft Office 365, . . . but working with them is not as user-friendly as Google Suite's tools. Being completely free and 100% (at least for individual use) and on the web for general tools like document editors, knowing how to use Google products was practically required knowledge when it came to working on group projects.
This ties in with the economics of substitute goods. According to their websites, Microsoft Office 365 for Education is $3.25 per person (max) per month school wide, whereas getting the Google Suite for Education plan with the same number of features costs about $4 per per person per month. Looking at the data, and knowing that the high school has to provide approximately 16000 accounts. With Google's plan, my high school would have had to pay just under $800,000, however, if my high school continues to stick with Microsoft Office 365, the administration would have to pay approximately $600,000. The supply curves are shown below in the interactable graph.
If the graph loaded properly, you should see two lines: one red and one blue in the right pan and some text and equations in the left pan. You can interact with the graph by clicking and dragging the graph. Usse your page scroller (two fingers on the trackpad or the scroller on your mouse) or the buttons in the top right to zoom the graph in or out. To see the equations that developed the graph, you may scroll through the pan on the left.
Economical Analysis
It is clear from the supply curves for the Google Suite for Education and Microsoft's Office 365 for Education that the optimal product to use is Microsoft's Office 365 for Education. The functions for these were adapted from the websites for each of the products, which are included below.
Analyzing this in terms of economics, we know from experience using both Google Suite tools and Microsoft Office tools that they are substitute goods to each other. As a result, we can state that as the price of one object increases, the other would be preferred, and so its demand increases. So if the price of the Microsoft's Office 365 for Education plan increases, then my high school would prefer to change their Office Suite tool to Google's product. Considering that the demand is currently higher for Microsoft's Office 365 tool, for Microsoft to maintain that high demand is to maintain the price of its product such that it will be less than Google's product. Microsoft may raise the service's price per student per month according to the substitution effect of a price rise such that the price remains lower than that of its competitor good.
Revisiting the Original Question
So, why did the school administration block Google Suite from being used under their network, despite its utility? Clearly, the school administration could have simply left it alone, letting the students use whatever product they want to use. However, taking into consideration that the administration spends about $600,000 (maybe considerably more because of hidden costs), it makes sense that if people do not use the product, this price paid for this product becomes a sunk cost (service not being used). This sunk cost implies that the school administration "loses" money for whenever a student chooses to not make use of their Microsoft Office license. As such, to answer the posed question, to reduce the sunk costs from little use of an expensive product (Microsoft Office 365 for Education), Google Suite was blocked to direct more students to using the Microsoft Office 365 tools.
Glossary
- Microsoft Office 365: A set of professional tools for document editing and presentation
- Google Suite: A set of professional tools for document editing and presentation as a susbtitute for Microsoft Office
- Substitute Goods: A pair of goods considered alternatives to each other
- Sunk Costs: The costs that cannot be recovered after its use
Sources
- Microsoft Office 365: https://www.microsoft.com/
- Google Suite: https://edu.google.com/


Teaching
ESPM-157: Data Science & Global Change Ecology
- Next offered: Fall Semester
- Course website: https://espm-157.carlboettiger.info
This is an upper-division undergraduate course designed to introduce CNR majors to the fundamentals of data science, while also introducing students majoring in Data Science or related fields to key issues in global change ecology.
ESPM-288: Reproducible and Collaborative Data Science
- Next offered: Spring Semester
- Course website: https://espm-288.carlboettiger.info
This is a graduate-level course aimed at students enrolled in UC Berkeley NSF Research Trainee Program Environment and Society: Data Sciences for the 21st Century and other interested students in ecological, environmental or social science programs.
ESPM-88B: Data Science in Ecology and the Environment
- Last offered: Spring 2016
- Next offered: TBD
- Course website: https://berkeley.carlboettiger.info/espm-88
This course is a "connector course" that will be open to freshmen enrolled in UC Berkeley's Foundations of Data Science course (DS-8/Stat-94/CS-94).
UGIS-192: Independent Undergraduate Reseach, via URAP
- Offered: Every Semester
- Current available projects: http://urapprojects.berkeley.edu/projects/detail.php?id_list=Env0730


Your Flux Capacitor stopped working and you need to get back to 1985
You have a large complex application state
Presentation Intent
Warning:
Pseudo Code Ahead
Example Simple Application:
Baseline
Seperation of concerns is the main point of Redux
The value of separation of concerns is simplifying development and maintenance of computer programs.
When concerns are well-separated, individual sections can be reused, as well as developed
and updated independently.
The pieces of Redux
Actions
Reducer
Store
Actions
WHAT happened not HOW it happened
Use indirection to decouple.
Indirection is the ability to reference something using a name, reference, or container instead of the value itself.
Example Simple Application:
Actions
Reducer
(current state, action metadata) => new state
Do not mutate, Create a new state obj
why, because predictable changes are deterministic.
Troubleshooting is really easy, and this makes timetravel possible
A deterministic model will thus always produce the same output from a given starting condition or initial state.
Deterministic system is a system in which no randomness is involved in the development of future states of the system.
Example Simple Application
Reducer
Store
State retention
Pub/Sub system
Example Simple Application: Actions
State
Intial State
+
List of Previous Actions
=
Current State


autoGAM.Rmd
In this vignette, two different examples on two different well-known datasets are proposed. The goal is to cover all different aspects of workflow based procedures included this package through these examples.
Basically, autoGAM has 4 functions that communicate with each other to create a workflow with the aim of evaluating different user-specified forms of included continuous predictors and find the best Generalized Additive Model (GAM). These functions are:
1- autoGAM_frame: The base of all other procedures! Evaluates user’s specified form(s) on continuous predictor(s) and obtains the best form (among pre-specified form(s)) for each continuous predictor based on the chosen metric (AIC, AICc or BIC) values of GLM fits. This function creates the base platform for creating the final GAM adn the remaining functions are modifiers of this base platform.
Note: User can specify whether outliers should be ignored in fitting each form on predictor(s) or not. outliers in case of response are detected by car::outlierTest()
which is based on studentized residuals of records and outliers in case of the predictor are determined as records that have hat-values > 2p (where p is the number of parameters inside the model). If user decides to ignore outliers in evaluation process, ignore.outliers
argument can be set to TRUE
but when one is not sure about the presence of outliers, it’s highly recommended to leave the default value which is FALSE
to obtain more accurate results.
2- backward_select: An independent function for performing p-value based backward elimination process that also modified to simply be used on returns of class autoGAM_frame
.
3- autoGAM_fit: A wrapper function that obtains and adds the final and best GAM to the platform.
4- plot: Plot method for returns of autoGAM_frame
to automatically create plots of the evaluated forms of continuous predictors and plots related to the best forms of predictors.
All the mentioned procedures can be used almost back and forth but user must remember that autoGAM_frame()
is the base that creates the base platform and other functions can be used on it in different orders.
library(autoGAM)
#> Loading required package: tidyverse
#> -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
#> v ggplot2 3.3.3 v purrr 0.3.4
#> v tibble 3.1.2 v dplyr 1.0.6
#> v tidyr 1.1.3 v stringr 1.4.0
#> v readr 1.4.0 v forcats 0.5.1
#> -- Conflicts ------------------------------------------ tidyverse_conflicts() --
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> Loading required package: magrittr
#>
#> Attaching package: 'magrittr'
#> The following object is masked from 'package:purrr':
#>
#> set_names
#> The following object is masked from 'package:tidyr':
#>
#> extract
#> Loading required package: gam
#> Loading required package: splines
#> Loading required package: foreach
#>
#> Attaching package: 'foreach'
#> The following objects are masked from 'package:purrr':
#>
#> accumulate, when
#> Loaded gam 1.20
#> Loading required package: fastDummies
#> Loading required package: car
#> Loading required package: carData
#>
#> Attaching package: 'car'
#> The following object is masked from 'package:dplyr':
#>
#> recode
#> The following object is masked from 'package:purrr':
#>
#> some
For the first example, we use mtcars dataset. We want to evaluate x, log(x) with 1 & 3 as its base, logb(x), power(x) with degrees 3 & 4, poly(x) (polynomial form) with degrees 3 & 4, ns(x) (natural cubic spline form) with 3 degrees of freedom and cut(x) with 3 & 4 partitions as our desired forms on disp, hp, drat & wt as our chosen continuous variables and then we want to obtain the best GAM made out of the obtained best forms of mentioned continuous variables and vs as our only desired categorical variable:
my_mtcars <- mtcars %>% mutate_at('vs',as.factor)
(cars_frame <- autoGAM_frame(formula = mpg~disp+hp+drat+wt+vs,
forms = list('identity'=c(),'log'=c(1,3),'logb',
'power'=3:4,'poly'=3:4,'ns'=3,'cut'=3:4),
data = my_mtcars))
#> Warning: autoGAM removed log(disp,1), log(hp,1), log(drat,1), log(wt,1) due to
#> the inclusion of NaN, Inf or -Inf.
#>
#> Forms evaluation was done in: 1.499888 secs.
#> $`best forms`
#> [1] "poly(disp,3,raw=F)" "cut(drat,4)" "cut(hp,4)"
#> [4] "poly(wt,4,raw=F)"
#>
#> $`final predictors`
#> [1] "poly(disp,3,raw=F)" "cut(drat,4)" "cut(hp,4)"
#> [4] "poly(wt,4,raw=F)" "vs"
Best forms of continuous predictors and final predictors to be used in the final GAM are the default print outputs but they are part of a bigger list. Try exploring this list by calling different parts of it by cars_frame$
.
Note: As we can see, autoGAM automatically removes any function (form) that creates NaN
, Inf
or -Inf
on specific predictors and gives out a Warning message on which forms of predictor(s) were removed.
All the included functions in the forms list has the form: f(x) or f(x,degree) and they all output a vector or a matrix (As they should!). All of these functions are well known functions from base R or familiar packages (that are included as dependencies of autoGAM) including: identity, log2, logb, log, exp, poly, bs & ns (from splines package), s (from gam package) & cut. Only power function is an exception which is a pre-defined function inside autoGAM. power(x,degree) is simply x^degree.
Note: You can always include your own function in the mentioned way if it has the f(x) or f(x,degree) form and it outputs a vector or a matrix. We will get back to this shortly in iris example.
‘identity’ (f(x)=x) and logb has the form: f(x) so we can only pass their name (like what we did for ‘logb’) or we can determine that they don’t have degree argument by passing c() in front of their names (like ‘identity’=c()), we call this singular determination. All other functions have the form: f(x,degree) and the desired degrees, degrees of freedom were passed in front of their names.
Note: If a hypothetical function g has the form g(x,degree) and you pass it singularly in the forms list (‘g’ or ‘g’=c()), default value for degree in g(x,degree) will be used, otherwise if there is no default value for degree, you will get an error until you determine degree value(s) by passing them in front of ‘g’ name in the forms list.
Now that we have our base platform and the best forms of our included continuous predictors, it’s time to fit the best GAM. it can’t be any simpler! just call autoGAM_fit()
on the created frame:
(cars_fit <- autoGAM_fit(cars_frame))
#>
#> Call:
#> glm(formula = final_formula, family = family, data = data)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -2.68924 -1.65509 0.02335 1.04505 2.90671
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 22.4362 1.6734 13.407 1.81e-10 ***
#> poly(disp, 3, raw = F)1 -14.6151 11.5412 -1.266 0.2225
#> poly(disp, 3, raw = F)2 10.9831 6.9698 1.576 0.1335
#> poly(disp, 3, raw = F)3 -10.5256 4.0493 -2.599 0.0187 *
#> cut(drat, 4)(3.3,3.84] 1.1281 2.2370 0.504 0.6205
#> cut(drat, 4)(3.84,4.39] 1.1326 2.1907 0.517 0.6118
#> cut(drat, 4)(4.39,4.93] 1.3703 2.8535 0.480 0.6372
#> cut(hp, 4)(123,194] -3.5973 1.6377 -2.197 0.0422 *
#> cut(hp, 4)(194,264] -7.8049 2.9773 -2.621 0.0179 *
#> cut(hp, 4)(264,335] -6.7004 3.6635 -1.829 0.0850 .
#> poly(wt, 4, raw = F)1 1.7170 8.7863 0.195 0.8474
#> poly(wt, 4, raw = F)2 -0.4116 6.7857 -0.061 0.9523
#> poly(wt, 4, raw = F)3 -3.0134 3.4524 -0.873 0.3949
#> poly(wt, 4, raw = F)4 2.1585 2.7949 0.772 0.4505
#> vs1 -0.5373 1.6504 -0.326 0.7487
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for gaussian family taken to be 4.617075)
#>
#> Null deviance: 1126.05 on 31 degrees of freedom
#> Residual deviance: 78.49 on 17 degrees of freedom
#> AIC: 151.52
#>
#> Number of Fisher Scoring iterations: 2
Simple as that. Again this is the print output of autoGAM_fit()
. Actually the fitted model object was added to the base list created by autoGAM_frame()
. You can still explore everything by using cars_fit$
. For example let’s extract the fitted model object that was added and we saw its summary:
cars_fit$`final fit`
#>
#> Call: glm(formula = final_formula, family = family, data = data)
#>
#> Coefficients:
#> (Intercept) poly(disp, 3, raw = F)1 poly(disp, 3, raw = F)2
#> 22.4362 -14.6151 10.9831
#> poly(disp, 3, raw = F)3 cut(drat, 4)(3.3,3.84] cut(drat, 4)(3.84,4.39]
#> -10.5256 1.1281 1.1326
#> cut(drat, 4)(4.39,4.93] cut(hp, 4)(123,194] cut(hp, 4)(194,264]
#> 1.3703 -3.5973 -7.8049
#> cut(hp, 4)(264,335] poly(wt, 4, raw = F)1 poly(wt, 4, raw = F)2
#> -6.7004 1.7170 -0.4116
#> poly(wt, 4, raw = F)3 poly(wt, 4, raw = F)4 vs1
#> -3.0134 2.1585 -0.5373
#>
#> Degrees of Freedom: 31 Total (i.e. Null); 17 Residual
#> Null Deviance: 1126
#> Residual Deviance: 78.49 AIC: 151.5
As you saw, we first created the base platform and evaluated our desired forms of continuous predictors in it and then fitted the GAM by using the best forms of continuous predictors and our one categorical predictor. What if we wanted all the insignificant predictors in the last outpur gone? Again it can’t be any easier. Just call backward_select()
function on the base platform then fit the final GAM by autoGAM_fit()
.
(cars_backward_fit <- cars_frame %>%
backward_select(backward.alpha=.1,backward.test='Rao') %>%
autoGAM_fit)
#>
#> Call:
#> glm(formula = final_formula, family = family, data = data)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -3.11983 -1.16499 0.05868 1.43673 2.66941
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 22.3680 0.7316 30.576 < 2e-16 ***
#> poly(disp, 3, raw = F)1 -16.5277 3.7439 -4.415 0.00017 ***
#> poly(disp, 3, raw = F)2 9.9757 2.2816 4.372 0.00019 ***
#> poly(disp, 3, raw = F)3 -10.7006 1.9112 -5.599 7.98e-06 ***
#> cut(hp, 4)(123,194] -2.9287 1.1174 -2.621 0.01471 *
#> cut(hp, 4)(194,264] -6.4395 1.7815 -3.615 0.00132 **
#> cut(hp, 4)(264,335] -4.9531 2.2178 -2.233 0.03471 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for gaussian family taken to be 3.552615)
#>
#> Null deviance: 1126.047 on 31 degrees of freedom
#> Residual deviance: 88.815 on 25 degrees of freedom
#> AIC: 139.48
#>
#> Number of Fisher Scoring iterations: 2
From the output we can see that backward elimination was done. The sequential details of this backward process were also added to the list from autoGAM_frame. let’s check it out:
cars_backward_fit$`backward info`
#> $step0
#> Df Deviance AIC scaled Rao sc. Pr(>Chi)
#> poly(disp, 3, raw = F) 3 127.11898 160.9525 10.5323626 0.01454296
#> cut(drat, 4) 3 79.87272 146.0824 0.2994191 0.96013770
#> cut(hp, 4) 3 112.58882 157.0683 7.3853130 0.06057964
#> poly(wt, 4, raw = F) 4 85.85985 146.3954 1.5961573 0.80948262
#> vs 1 78.97962 149.7226 0.1059860 0.74476113
#>
#> $`step1: removed cut(drat, 4) [Pr(>Chi) = 0.9601]`
#> Df Deviance AIC scaled Rao sc. Pr(>Chi)
#> poly(disp, 3, raw = F) 3 130.58149 155.8124 12.6973953 0.005338874
#> cut(hp, 4) 3 125.94498 154.6556 11.5364218 0.009152265
#> poly(wt, 4, raw = F) 4 88.81200 141.4772 2.2383824 0.692008743
#> vs 1 80.98379 144.5245 0.2782113 0.597876021
#>
#> $`step2: removed poly(wt, 4, raw = F) [Pr(>Chi) = 0.692]`
#> Df Deviance AIC scaled Rao sc. Pr(>Chi)
#> poly(disp, 3, raw = F) 3 359.41473 180.2118 7.312599e+01 9.134616e-16
#> cut(hp, 4) 3 136.49324 149.2293 1.288508e+01 4.891831e-03
#> vs 1 88.81537 139.4784 9.115434e-04 9.759141e-01
#>
#> $`step3: removed vs [Pr(>Chi) = 0.9759]`
#> Df Deviance AIC scaled Rao sc. Pr(>Chi)
#> poly(disp, 3, raw = F) 3 380.2083 180.0115 82.02211 1.130401e-17
#> cut(hp, 4) 3 138.4360 147.6816 13.96734 2.949949e-03
Now nothing gathers up information in one place better than a good plot. plot method for autoGAM_frame
was designed to create plots related to evaluation of different forms of continuous predictors and to create final forms of included continuous predictors.
Note that plot can be called on any object with the autoGAM_frame
base.
plot(cars_frame,type='forms')
The plot above was a bit crowded, wasn’t it? It’s always good to see a formal plot for final best form of each continuous predictor that was included in the final GAM.
Based on full model:
plot(cars_fit,type='final')
Based on reduced model after backward elimination process:
plot(cars_backward_fit,type='final')
Now that we have the general idea about how autoGAM works, it’s time to wrap everything up by mentioning final details in the form of an example.
This time we are interested in evaluation of two completely random functions that one of them has a vector output and the other one has a matrix output.
We first perform some modifications on iris data and then we introduce our desired functions:
(my_iris <- iris %>% filter(Species %in% unique(Species)[1:2]) %>%
as_tibble %>% mutate_at('Species',as.character))
#> # A tibble: 100 x 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ... with 90 more rows
We filtered iris to only have the information of setosa & versicolor Species. So our version of iris (dat) has all numerical predictors and a categorical response with 2 levels (Species).
Now we introduce our forms (functions) to be evaluated on our predictors
temp_vec <- function(x) 3*sqrt(x)+5
temp_mat <- function(x,d) as.list((1:d)*-1) %>% map(~x^.) %>% reduce(cbind)
Both of these specified functions have f(x) or f(x,degree) form and their outputs are either a vector or a matrix so they can be used in the list of forms argument.
iris_fit <- autoGAM_frame(formula=Species~.,
data=my_iris,
forms=list('temp_vec','temp_mat'=1:3),
family=binomial(link='logit'),
metric='AICc',
parallel=T) %>% autoGAM_fit
#>
#> Forms evaluation was done in: 14.45891 secs.
plot(iris_fit,type='forms')
One last thing! You saw that base category of the Species variable was chosen to be setosa. This is because by default the first category of the response will become the base. There’s no difference in how the model is going to predict but one might want to change this base for the sake of interpretation. For example what if we wanted versicolor as the base category of the response? We can easily set base category of response with resp.base argument.
iris_frame <- autoGAM_frame(formula=Species~.,
data=my_iris,
forms=list('temp_vec','temp_mat'=1:3),
family=binomial(link='logit'),
metric='AICc',
parallel=T,
resp.base='versicolor')
#>
#> Forms evaluation was done in: 11.48609 secs.
plot(iris_frame,type='forms')
plot(iris_frame)
From update (1.0.0) that came with total rebuild of the package’s structure, backward_select()
became an independent function so it can act as a function on autoGAM_frame
objects to match the new workflow based structure. A valuable side effect of this independence is that this function can also act as an independent function for only backward selection procedures based on formula inputs. In technical form, object
argument can be both a formula or an object of class autoGAM_frame
to participate in autoGAM’s workflow. The usage is pretty easy and straightforward but let’s check out an example:
backward_select(object=mpg~hp+poly(disp,2)+cut(drat,3)+vs,data=my_mtcars)
#> $step0
#> Df Deviance AIC scaled dev. Pr(>Chi)
#> hp 1 177.3535 159.6092 0.05513119 0.814363717
#> poly(disp, 2) 2 274.0842 171.5385 13.98442936 0.000919009
#> cut(drat, 3) 2 222.5434 164.8724 7.31839905 0.025753119
#> vs 1 178.3336 159.7855 0.23148621 0.630424008
#>
#> $`step1: removed hp [Pr(>Chi) = 0.8144]`
#> Df Deviance AIC scaled dev. Pr(>Chi)
#> poly(disp, 2) 2 426.7211 183.7047 28.0955479 7.927373e-07
#> cut(drat, 3) 2 232.9316 164.3324 8.7232019 1.275795e-02
#> vs 1 179.1663 157.9346 0.3254306 5.683622e-01
#>
#> $`step2: removed vs [Pr(>Chi) = 0.5684]`
#> Df Deviance AIC scaled dev. Pr(>Chi)
#> poly(disp, 2) 2 678.2967 196.5352 42.600650 5.615474e-10
#> cut(drat, 3) 2 233.3932 162.3957 8.461117 1.454426e-02
The detailed information of the sequential backward elimination process will be returned directly. When object
is an object of class autoGAM_frame
instead of a formula, the above list will be added to the final frame list as we saw earlier.


10-30 minutes, or more if your data need a lot of cleaning!
Today’s GIS exercises are all about add your fieldwork data to the map. These data are the information you have collected at each location of your field surveys. On this page we cover how to ensure that your data are formatted correctly for use in a GIS project, and on the next page we import them into QGIS
Before importing your field observations into QGIS, you need to ensure they are clean and consistent. A good way to check for problems is to open your data in Excel and use the Filter or Pivot Table tools to examine them
If you’re working with the water beetle dataset, you can download it from the Resources section of the course above
With all new and unfamiliar datasets, we always recommend that you look at the data to familiarise yourself with it before doing anything further, such as adding it to your GIS project or analysing it
Open the .csv file in your usual spreadsheet software (e.g. Excel) and view the column headers. Do they make sense to you? Do you understand what data each column contains?


Evolution takes a step forward. K. Mumford 2017
Current Word
Wrong Guesses Remaining: 10
Wins: 0 Losses: 0


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Welcome to RackStep
Check the github page for more informations:
https://github.com/mfdavid/RackStep


This is a test of Goatee templates. It's not a comprehensive test quite by
any means. I trust the original tests from Mustache for that. This is just
a smoke test to confirm that my change to the template delimiters isn't
obviously broken. If you see the word "Elephant" in big letters below, then
goatee.js is working.


Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
Contrary to popular belief, Lorem Ipsum is not simply random text.  Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source.
Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32.
Know More...
SVCE ACM Chapters 15th May, 2017
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
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A short workshop to help boost placements. 15th June, 2017
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
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This one will be better than the last. 15th December, 2030
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
Know More...
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s.
Know More...
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s.
Know More...
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s.
Know More...
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s.
Know More...
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s.
©SVCE ACM CHAPTERS. All Rights Reserved.
Lorem Ipsum is simply dummy text of the printing and typesetting industry.


Note that NO TITLE WILL BE RENDERED if there isn't a title defined in the YFM of the "current" page.
This project shows the amazing things you can accomplish with Assemble and the helpers from the handlebars-helpers library.
npm install
to install Assemble, Grunt and any other dependencies.grunt assemble
to build the example project.If you haven't used Grunt before, be sure to check out the Getting Started guide, as it explains how to create a Gruntfile, as well as install and use Grunt plugins.
handlebars-helpers is the maintained by the core team of Assemble, a component, site and documentation generator based on Grunt.js. The helpers in this project can be used with our without Assemble.
If you want to see the types of helpers that are included, either visit the handlebars-helpers repo or just pull down this project, install the dependencies (npm install
), and run grunt build
.
Then go to the ./src
and ./dest
directories to look around.
Visit http://assemble.io to learn about Assemble.
Jon Schlinkert
Brian Woodward
In lieu of a formal styleguide, take care to maintain the existing coding style. Add unit tests for any new or changed functionality. Use Assemble to build and maintain your gh-pages, blog or documentation. Lint and test your code using Grunt.


A content delivery network (CDN) refers to a geographically distributed group of servers that provide fast delivery of assets.
A CDN enables the quick transfer of Internet assets including HTML pages, Javascript files, Stylesheets, etc. Also, a properly configured CDN can help protect websites against some common malicious attacks, e.g. Distributed Denial of Service (DDOS) attacks.
A CDN does not host the actual web content, it caches content at the network edge to improve performance. Also, caching reduces hosting bandwidth to improve security.
Benefits of using a CDN:
Shorter loading times
Global distributed nature of a CDN means reduce distance between users and website sources.
A CDN is optimized for load balancing and better read performance
Smaller file size using minification and file compression methods
TLS/SSL certificates connection reuse and TLS false start enabled
Less bandwidth costs
Result is cached
Reduce routing distance from/to server
Better content availability and redundancy
Load balancing distributes network traffic to multiple servers


The Federation of Saint Kitts and Nevis is a democratic and sovereign country. The impacts of Climate Change are global and St. Kitts and Nevis, along with the International community are committed to addressing the adverse effects. In addition, climate change is one of the most critical issues facing the Caribbean region and it is already being experienced through rising sea levels, increasing mean temperatures and changes in rainfall and weather patterns. All these impacts, in many ways, affect the socio-economic development of the country in sectors such as St. Kitts and Nevis Tourism, Agriculture and Water.
St. Kitts and Nevis sustainability, as a nation, is dependent upon the collective and global response to aggressively reduce St. Kitts and Nevis Greenhouse gas emissions (GHG). In this regard the Federation of St. Kitts and Nevis proposes an emissions reduction target of 22% and 35% of St. Kitts and Nevis GHG emissions projected in the business as usual (BAU) scenario for 2025 and 2030 respectively.
The National Conservation and Environmental Protection Act (NCEPA) articulates strategic approaches to environmental protection, and serves as a framework for the declaration of sensitive ecological and historic sites that presents clearly vulnerability to climate change and vulnerability.
The Federation of Saint Kitts and Nevis, hereby communicates its Intended Nationally Determined Contribution (iNDC) towards achieving the UNFCCC objective as set out in Article 2 of the Convention, and in accordance with decisions 1/CP.19 and 1/CP.20.
| The reference point | 22% of the absolute GHG from the Business as Usual (BAU) in 2025. 35% of the absolute GHG from the BAU in 2030. | ||||||||||||||||
| Time frame and/or Implementation period | The time frame to implement the iNDC is from 2020-2030, mid-term review in 2025. | ||||||||||||||||
| Type of commitment or contribution | St. Kitts and Nevis type of commitment is in terms of absolutes numbers GHG reduction from the BAU. | ||||||||||||||||
| Scope and coverage | All the economic sectors are covered and targeted into St. Kitts and Nevis’ national contributions, but with special attention to the Energy and Transport sectors, since they are the highest contributors to the GHG national matrix. The high percentage of consumption is based on fossil fuels. | GHG Coverage: CO2 | |||||||||||||||
| Geographic Coverage: National | |||||||||||||||||
| Percentage of National coverage: 100% | |||||||||||||||||
| Planning processes | The iNDC preparation has been built upon from previous climate and non-climate activities and includes a consultative process, involving key actors to obtain useful information at the sectoral level.
The implementation process is still under discussion to ensure that there is highest participation and ownership from the key actors. A very comprehensive plan for the implementation phase must take place to track and monitor the policies and measures within the iNDC. |
||||||||||||||||
| Assumptions and methodological approaches | The methodology used to measure and monitor St. Kitts and Nevis’ national contributions is “Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories”.
St. Kitts and Nevis’ iNDC is conditional and based on the availability of financing and technological support. St. Kitts and Nevis supports the inclusion of the International Carbon Markets and any other Market Mechanisms in a post-2020 agreement on climate change and any future emission reductions pre-2020, should be accounted as part of our contributions. |
||||||||||||||||
| Level of ambition in iNDC | St. Kitts and Nevis is committed to tackling the adverse impacts of climate change. It considers its iNDC to be ambitious and continues to exhibit evidence of environmentally sound development. Furthermore, St. Kitts and Nevis’ inventories of GHGs demonstrate that the country is a net sink for global carbon emissions, although like other Small Island Developing States, it is expected to bear the brunt of the impacts.
St. Kitts and Nevis proposes to reduce its GHG emissions by focusing on electricity generation and the transport sector. Under its proposed mitigation actions it is intended that the policies and measures would increase the use of renewable energy sources by 50%, taking into consideration that this ambitious target could be considered risky within the short time frame. To reduce the risk St. Kitts and Nevis must ensure that the relevant policies and measures are created within its natural, financial, technological and human resources to implement the measures necessary to achieve the intended emissions reduction |
||||||||||||||||
| Policies and Measures | * Details in Annex1 |
||||||||||||||||
| Estimation of GHG Impacts |
|
The Federation of Saint Kitts and Nevis is committed to building resilience and mitigating the negative impacts of external shocks including natural disasters, according to NCEPA, and many others institutional and regulation requirement that have been developed in the country.
For St. Kitts and Nevis the most vulnerable sectors and areas include:
Climate change is one of the major threats for key vulnerable sectors that are linked to vital pillars for sustainable development. Forestry and water supply are intimately linked to potable water, tourism, and agriculture. Moreover, St. Kitts and Nevis, a twin island state, is abundant in nearshore and marine resources which provide the basis for a range of economic and social activity relevant to the tourism and fishing industries. Some of these marine resources include coral reefs, beaches, mangroves, freshwater lagoons and sea-grass beds. Tourism, water supply and coastal infrastructure, are also being affected by the sea-level rise, saline intrusion and flooding.
St. Kitts and Nevis wishes to highlight three areas in its iNDC that are considered important to its social and economic sustainability and their interlinkages with others sectors.
St. Kitts and Nevis plans develop a comprehensive plan to build resilience in the following sectors:
St. Kitts and Nevis is aware of the importance of developing a comprehensive plan which must serve as the preparation stage for the implementation process.
In order to ensure the effective implementation of the plan proposed above, the local actors must be involved and therefore empowered and prepared to execute the necessary tasks. The preparation process may require, inter alia, technical training, capacity building workshops, expert guidance, and feasibility and technical studies. It is important to highlight the relevance of the institutional capacity, as well as the necessary establishment of institutional coordination and political support to meet the desired national goals. Furthermore, technical and economically feasibility studies for all levels of implementation (actions, projects, programs, policies), as well as a comprehensive analysis for policy implication would be required. It is also relevant to prepare a sectorial financing plan with specific funding sources and disbursement planning to implement the necessary policies and measures.


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SaveInstruction Class
Definition at line
378
of
Instruction.hpp
Constructors
SaveInstruction
()
Member Functions
void
Write
(CodeFormatter& formatter,
Function
& function,
Context
& context)
Constructor Details
SaveInstruction Constructor
cmsxi::SaveInstruction::SaveInstruction()
Definition at line
692
of
Instruction.cpp
:
692
:
Instruction
(
)
693
{
694
}
Declaration at line
381
of
Instruction.hpp
Member Function Details
Write Member Function
void
cmsxi::SaveInstruction::Write(CodeFormatter& formatter,
Function
& function,
Context
& context)
Definition at line
696
of
Instruction.cpp
:
697
{
698
formatter
.
Write
(
"save"
)
;
699
}
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Examples are checked into the GitHub repository. You can take a look with:
> git clone git://github.com/firebase/firepad.git > open firepad/examples/index.html
Or you can view them below and click "View Source on GitHub" to jump to the source.
Use Firepad's rich text features for collaborative blogging, note taking, proofreading and much more!
Combined with CodeMirror's extensive code editing features, Firepad is great for pair programming!
You can code collaboratively with Ace too!
You can easily add user presence information and other features.


Part 5: Running our Scraper
Putting all the pieces together to scrape our data.
scrapy.cfg file
Scrapy needs a configuration file to direct it to where our project lies. The config file needs to be in the project root directory. An example of our directory structure for our application:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | .
├── my_scraper
│ ├── scraper_app
│ │ ├── __init__.py
│ │ ├── items.py
│ │ ├── models.py
│ │ ├── pipelines.py
│ │ ├── settings.py
│ │ └── spiders
│ │ ├── __init__.py
│ │ └── livingsocial_spider.py
│ └── scrapy.cfg # we must create this file now
├── README.md
└── requirements.txt
|
Create a file called scrapy.cfg
within /my_scraper/
directory. This file should contain the name of the python module that defines the project settings:
[settings]
default = scraper_app.settings
Manually run the scraper
Within your terminal, with your ScrapeProj virtualenv activated:
1 2 | (ScrapeProj) $ cd my_scraper/scraper_app
(ScrapeProj) $ scrapy crawl livingsocialscraper
|
Where livingsocialscraper
is the BOT_NAME
defined in settings.py
.
Next, to see the data that's saved into the database, start up Postgres and enter these commands:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | $ psql -h localhost
psql (9.1.4, server 9.1.3)
Type "help" for help.
lynnroot=# \connect scrape
psql (9.1.4, server 9.1.3)
You are now connected to database "scrape" as user "lynnroot".
scrape=# select * from deals limit 5;
id | title | description | link | location | category | original_price | price
----+---------------------------------------+------------------------------------+----------------------------------------------------------------------------+--------------+------------+----------------+-------
1 | Mini Box | Deck of Photo Playing Cards | /cities/1719-newyork-citywide/deals/614972-deck-of-photo-playing-cards | national | | 29 | 9
2 | Paintball: LivingSocial Original: | Paintball + BBQ Day Trip | /cities/1719-newyork-citywide/deals/575448-paintball-bbq-day-trip | NYC Citywide | activities | | 69
3 | Medieval Times | Medieval Times: Meal + Show Ticket | /cities/1719-newyork-citywide/deals/627242-medieval-times-meal-show-ticket | NYC Citywide | activities | 41 | 27
4 | '80s Boat Cruise: LivingSocial Ori... | NYC Boat Cruise + '80s Concert | /cities/1719-newyork-citywide/deals/610320-nyc-boat-cruise-80s-concert | NYC Citywide | activities | | 29
5 | New York Magazine | 50 Issues of New York Magazine | /cities/1719-newyork-citywide/deals/594056-50-issues-of-new-york-magazine | NYC Citywide | | 30 | 15
(5 rows)
|
Try a few of these select queries:
1 2 3 4 5 | scrape=# select * from deals where title like ('%Yoga');
scrape=# select * from deals where description like ('%Yoga%');
scrape=# select * from deals where description like ('%Dinner');
scrape=# select link from deals where description like ('%Photography%');
scrape=# select title from deals limit 30;
|
Notice how the strings we’re searching for contains %
– this is a wildcard character. This is essentially saying “find deals where the title contains “Yoga”. Learn more about querying Postgres.
Hook up to a Cron job
It’d be pretty annoying if we had to manually run this script regularly. This is where cron jobs come in.
We’ll first create a bash script that simulates what we would do if we were to run scrapy manually. There is a sample one in the new-coder/scrape/living_social/
directory called scrape.sh
. Edit the bash script to where your (ScrapeProj)
virtualenv is as well as where your scraper root directory is (where the scrapy.cfg
file lies).
Next, within your terminal, type:
1 | $ crontab -e
|
to edit your crontab file. This opens up the editor for your cron tab. Add a line:
0 13 * * * sh ~/Projects/new-coder/scrape/living_social/scrapy.sh
This says that ever day at hour 13 (1pm, relative to your local machine time), run the scrapy.sh
script. To schedule your cron job at a different time, check out Wiki's overview.
NOTE: The cron job will run automatically for whenever you schedule it to run (in this example, daily at 1pm). But! It will only run when your computer is on (not hibernate/sleep or powered off), and in particular with this script, connected to the internet. To run the cron job regardless of the state your computer is in, you would host the scraper code + the bash script and cron job on a separate server (either your own, or a company's) that will always be 'on'. You can host your own cron jobs on OpenShift.


Fastest Growing Countries: Healthcare and Education Expenditure
Annual growth is an important measurement for a country's economy, and this section focuses on the growth of healthcare and education expenditure for each of the eleven countries over a period of nine years for healthcare expenditure and five years for education expenditure. The tables show each country's annual growth in terms of fixed value and percentage, and the interactive maps show percentage growth, with lighter shades of blue representing lower growth and darker shades of blue representing higher growth.
Fastest Growing Countries in Healthcare Expenditure (2010-2018)
| Country | Annual Growth: Fixed Value (USD Millions) | Annual Growth: Percentage |
|---|---|---|
| Australia | $46,000 | 58% |
| Canada | $42,062 | 30% |
| France | $69,867 | 27% |
| Germany | $141,423 | 40% |
| Italy | $23,380 | 13% |
| Japan | $195,488 | 48% |
| Korea | $71,145 | 76% |
| Mexico | $34,495 | 33% |
| Turkey | $37,442 | 61% |
| United Kingdom | $89,530 | 50% |
| United States | $1,015,146 | 41% |
Fastest Growing Countries in Education Expenditure (2010-2014)
| Country | Annual Growth: Fixed Value (USD Millions) | Annual Growth: Percentage |
|---|---|---|
| Australia | $12,181 | 19% |
| France | $6,782 | 5% |
| Germany | $23,946 | 14% |
| Italy | $(4,802) | -5% |
| Mexico | $14,580 | 27% |
| United Kingdom | $18,489 | 13% |
| United States | $51,323 | 7% |


module PublicSuffix
PublicSuffix is a Ruby domain name parser based on the Public Suffix List.
The [Public Suffix List](publicsuffix.org) is a cross-vendor initiative to provide an accurate list of domain name suffixes.
The Public Suffix List is an initiative of the Mozilla Project, but is maintained as a community resource. It is available for use in any software, but was originally created to meet the needs of browser manufacturers.
Public Suffix¶ ↑
Domain name parser based on the Public Suffix List.
Copyright © 2009-2017 Simone Carletti<EMAIL_ADDRESS>Public Suffix¶ ↑
Domain name parser based on the Public Suffix List.
Copyright © 2009-2017 Simone Carletti<EMAIL_ADDRESS>Public Suffix¶ ↑
Domain name parser based on the Public Suffix List.
Copyright © 2009-2017 Simone Carletti<EMAIL_ADDRESS>Public Suffix¶ ↑
Domain name parser based on the Public Suffix List.
Copyright © 2009-2017 Simone Carletti<EMAIL_ADDRESS>Public Suffix¶ ↑
Domain name parser based on the Public Suffix List.
Copyright © 2009-2017 Simone Carletti<EMAIL_ADDRESS>Constants
- BANG
- DOT
- STAR
- VERSION
The current library version.
Public Class Methods
private
# File lib/public_suffix.rb, line 148 def self.decompose(name, rule) left, right = rule.decompose(name) parts = left.split(DOT) # If we have 0 parts left, there is just a tld and no domain or subdomain # If we have 1 part left, there is just a tld, domain and not subdomain # If we have 2 parts left, the last part is the domain, the other parts (combined) are the subdomain tld = right sld = parts.empty? ? nil : parts.pop trd = parts.empty? ? nil : parts.join(DOT) Domain.new(tld, sld, trd) end
Attempt to parse the name and returns the domain, if valid.
This method doesn't raise. Instead, it returns nil if the domain is not valid for whatever reason.
@param [String, to_s] name The domain name or fully qualified domain name to parse. @param [PublicSuffix::List] list The rule list to search, defaults to the default {PublicSuffix::List} @param [Boolean] ignore_private @return [String]
# File lib/public_suffix.rb, line 139 def self.domain(name, **options) parse(name, **options).domain rescue PublicSuffix::Error nil end
Pretend we know how to deal with user input.
# File lib/public_suffix.rb, line 163 def self.normalize(name) name = name.to_s.dup name.strip! name.chomp!(DOT) name.downcase! return DomainInvalid.new("Name is blank") if name.empty? return DomainInvalid.new("Name starts with a dot") if name.start_with?(DOT) return DomainInvalid.new("%s is not expected to contain a scheme" % name) if name.include?("://") name end
Parses name
and returns the {PublicSuffix::Domain} instance.
@example Parse a valid domain
PublicSuffix.parse("google.com") # => #<PublicSuffix::Domain:0x007fec2e51e588 @sld="google", @tld="com", @trd=nil>
@example Parse a valid subdomain
PublicSuffix.parse("www.google.com") # => #<PublicSuffix::Domain:0x007fec276d4cf8 @sld="google", @tld="com", @trd="www">
@example Parse a fully qualified domain
PublicSuffix.parse("google.com.") # => #<PublicSuffix::Domain:0x007fec257caf38 @sld="google", @tld="com", @trd=nil>
@example Parse a fully qualified domain (subdomain)
PublicSuffix.parse("www.google.com.") # => #<PublicSuffix::Domain:0x007fec27b6bca8 @sld="google", @tld="com", @trd="www">
@example Parse an invalid (unlisted) domain
PublicSuffix.parse("x.yz") # => #<PublicSuffix::Domain:0x007fec2f49bec0 @sld="x", @tld="yz", @trd=nil>
@example Parse an invalid (unlisted) domain with strict checking (without applying the default * rule)
PublicSuffix.parse("x.yz", default_rule: nil) # => PublicSuffix::DomainInvalid: `x.yz` is not a valid domain
@example Parse an URL (not supported, only domains)
PublicSuffix.parse("http://www.google.com") # => PublicSuffix::DomainInvalid: http://www.google.com is not expected to contain a scheme
@param [String, to_s] name The domain name or fully qualified domain name to parse. @param [PublicSuffix::List] list The rule list to search, defaults to the default {PublicSuffix::List} @param [Boolean] ignore_private @return [PublicSuffix::Domain]
@raise [PublicSuffix::DomainInvalid]
If domain is not a valid domain.
@raise [PublicSuffix::DomainNotAllowed]
If a rule for +domain+ is found, but the rule doesn't allow +domain+.
# File lib/public_suffix.rb, line 67 def self.parse(name, list: List.default, default_rule: list.default_rule, ignore_private: false) what = normalize(name) raise what if what.is_a?(DomainInvalid) rule = list.find(what, default: default_rule, ignore_private: ignore_private) # rubocop:disable Style/IfUnlessModifier if rule.nil? raise DomainInvalid, "`#{what}` is not a valid domain" end if rule.decompose(what).last.nil? raise DomainNotAllowed, "`#{what}` is not allowed according to Registry policy" end # rubocop:enable Style/IfUnlessModifier decompose(what, rule) end
Checks whether domain
is assigned and allowed, without
actually parsing it.
This method doesn't care whether domain is a domain or subdomain. The validation is performed using the default {PublicSuffix::List}.
@example Validate a valid domain
PublicSuffix.valid?("example.com") # => true
@example Validate a valid subdomain
PublicSuffix.valid?("www.example.com") # => true
@example Validate a not-listed domain
PublicSuffix.valid?("example.tldnotlisted") # => true
@example Validate a not-listed domain with strict checking (without applying the default * rule)
PublicSuffix.valid?("example.tldnotlisted") # => true PublicSuffix.valid?("example.tldnotlisted", default_rule: nil) # => false
@example Validate a fully qualified domain
PublicSuffix.valid?("google.com.") # => true PublicSuffix.valid?("www.google.com.") # => true
@example Check an URL (which is not a valid domain)
PublicSuffix.valid?("http://www.example.com") # => false
@param [String, to_s] name The domain name or fully qualified domain name to validate. @param [Boolean] ignore_private @return [Boolean]
# File lib/public_suffix.rb, line 122 def self.valid?(name, list: List.default, default_rule: list.default_rule, ignore_private: false) what = normalize(name) return false if what.is_a?(DomainInvalid) rule = list.find(what, default: default_rule, ignore_private: ignore_private) !rule.nil? && !rule.decompose(what).last.nil? end


E~farm
Home
(current)
About
Vegetables
Fruits
Contact
Signup
Welcome To e-Farm
purchase your favorite fruits and vegetables and get them delivered to your doorstep


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|
Mulberry uses a tab-delimited text format for storage of local address books. On network address books, the format is stored slightly differently depending on which protocol (IMSP or ACAP) is used. For the practical purposes of importing external address books, however, the field order for importing to a network address book is the same as a local Mulberry address book. Mulberry currently can import the following address book formats: Mulberry can also export to any of these formats. Hint: more than one Mulberry user has observed that Mulberry can be a very easy to use tool if you need to translate from one of the above formats to another, as at this writing these other programs don't understand one another's formats. Cute! The field order for a Mulberry address book is as follows. <tab> represents a 'tab' character, and <LINEEND> represents a line end character sequence for the OS on which the address book is used, and marks the end of a single entry. Nick-name <tab> (aka the alias) Fields listed in boldface above can contain more than one line of data, and can contain tab characters. To indicate this in a source document for translation, use "C" programming language style 'escape' characters: To show a tab character, type \t
Full Name <tab> (Note: full name is stored lastname, firstname)
Email <tab>
Company <tab>
Address <tab>
Phone Work <tab>
Phone Home <tab>
Fax <tab>
URL <tab>
Notes <LINEEND>
To show a carriage return, type \r
To show a linefeed return, type \n


Happy Birthday Abbas!
27 Mar 2015Today is Mahmoud Abbas’s birthday and Fatah congratulated him on Facebook with this post:
The picture says “Another year and our President is doing great” (literally 1000x fine), which is the Arabic equivalent of “Happy Birthday.”
Of course it’s another year and “President” Abbas is doing great because this beacon of democracy hasn’t had to stand for election since his first one in 2005. Sure he was only elected for a 4 year term, but since Abbas is the moderate leader of Israel’s peace partner, the Palestinian Authority, the world can forgive this minor infraction.
While Israel’s democracy is questioned by the AP, and accusations of Apartheid are chanted after Israeli Arab Supreme Court Justice Salim Joubran oversaw the elections, it is nice to know that Abbas is having another good year of dictatorship next door.
So from all of us here at the Judean People’s Front, to our benevolent, peaceful, and moderate partner - who completely accepts the two-state solution despite personally turning down offer after offer, and who continues to reject the idea of a Jewish State and insist on the “Right of Return” for millions of Palestinians who never lived here to Israel - on the occasion of your 80th birthday, we would like to wish you a very heartfelt:
##كل عام وأنت بألف بخير


This is exciting content! It comes in paragraphs. Apparently lines will exist as well, surrounding said box.
This is another paragraph! Bring on the paragraphs! Look for empty lines before and after.


The Qnique Quilting Machine
We set out to make the perfect mid-arm quilting machine for the quilting enthusiast. We carefully crafted all of the high-end long-arm features you love into a truly affordable quilting machine.
For over 25 years the Grace Company has been the leading manufacturer of high quality quilting frames and quilting accessories. Now we are pleased to announce a revolutionary new quilting machine, designed to take your quilting experience to a whole new level!
Expand Your Reach!
The extended length and height of the work area on the Qnique gives you the freedom to quilt larger patterns or blocks. The Qnique features a 15 inch throat space from the needle to the back of the work area.
The Perfect Stitch
Built-in stitch regulation keeps your stitch length consistent, even as you speed up and slow down. The Qnique quilting machine features four different sewing modes, all conveniently displayed on the easy to navigate menu in the OLED screen.
Professional Quiltng Features
LED Sewing Lights
Bring your projects to lightThe Qnique features high powered, daylight color-balanced LED work lights to illuminate your projects while quilting. You can even control the light brightness.
Full Color OLED Screen
Simplified view of all sewing settingsAll of the most important quilt functions and settings can be changed and are seen right on the display! So you'll always have quick access to stitching mode, stitch speed or stitch length, and needle position.
Built-in Bobbin Winder
Quick and easy bobbin refillsThe included bobbin winder on the Qnique Quilter runs its own seperate built-in motor, so you can refill bobbins without having to run the sewing machine.
Easy-Access Bobbin
Bobbin changing doesn't get any easier than thisThe bobbin of the Qnique quilting machine has been designed for easy and quick access. Changing or checking on your bobbin is now more user-freindly than ever.
Large M-Class Bobbin
So you can keep on quilting, and quilting, and quilting...The Qnique Quilter comes standard with a large class M Bobbin. This lets you quilter for longer periods of time before needing to change or refill your bobbin/p>
Quick-Acess Controls
All the sewing power, right at your fingertipsWe designed the control of the quilting machine to be as quilter-friendly as possible. All major functions and settings can be controlled right from the handles and seen on the full-color display.
 Each port provides power to the accessory you plug in.
Perfectly Paired With a Quilting Frame
Save More With a Bundled Frame Package
They were made for eachotherThe Qnique can be purchased bundled with your choice of quilting frame, and will come with everything you need to get quilting! Compare some of the available quilting frame packages below.
Already Own a Quilting Frame?
The Qnique is a perfect fitThe Qnique is designed to work seamlessly with many existing quilting frames, so you can upgrade to your dream quilting system. When you buy a Grace Qnique quilting machine, it will come with everything you need, including instructions, for partnering it perfectly with your frame.
Frame Compatibility ListComplete Quilting System Packages
Qnique Frame Package
- Sets up in sizes:
- Maximum Length:
- Special Feature
- Made specifically for the Qnique Machine, optional 4th rail available
- King, Crib
- 122 Inches
Gracie King Package
- Sets up in sizes:
- Maximum Length:
- Special Feature
- Flip-up rail to quickly inspect underside stitching
- King, Queen, Crib
- 136 Inches
Gracie Queen Package
- Sets up in sizes:
- Maximum Length:
- Special Feature
- Flip-up rail to quickly inspect underside stitching
- Queen, Crib
- 136 Inches
SR-2 Frame Package
- Sets up in sizes:
- Maximum Length:
- Special Feature
- 2-rail system and "Quilt-Clips" for easiest fabric loading ever
- Queen, Crib
- 136 Inches


Creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned. The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/). Keywords: networks, food webs, linnet, systems biology, bioinformatics.
| Version: | 0.3.42 |
| Depends: | R (≥ 3.0) |
| Imports: | grid, plyr, jpeg, png, RColorBrewer, utils, stats, rgl, tkrgl, tcltk |
| Suggests: | FuncMap, knitr, sna, xtable, bipartite |
| Published: | 2017-07-28 |
| Author: | Bryan A. Hanson [aut, cre], Vesna Memisevic [ctb], Jonathan Chung [ctb] |
| Maintainer: | Bryan A. Hanson <hanson at depauw.edu> |
| BugReports: | https://github.com/bryanhanson/HiveR/issues |
| License: | GPL-3 |
| URL: | https://github.com/bryanhanson/HiveR |
| NeedsCompilation: | no |
| Citation: | HiveR citation info |
| Materials: | NEWS |
| CRAN checks: | HiveR results |
| Reference manual: | HiveR.pdf |
| Vignettes: |
HiveR: 2D and 3D Hive Plots for R |
| Package source: | HiveR_0.3.42.tar.gz |
| Windows binaries: | r-devel: HiveR_0.3.42.zip, r-release: HiveR_0.3.42.zip, r-oldrel: HiveR_0.3.42.zip |
| OS X binaries: | r-release: HiveR_0.3.42.tgz, r-oldrel: HiveR_0.3.42.tgz |
| Old sources: | HiveR archive |
Please use the canonical form https://CRAN.R-project.org/package=HiveR to link to this page.


Our Marijuana comes from only local farms that are certified organic and sustainable.
Our testing is done through THC Pharmaceuticals, based our of Corvallis, OR.
Based out of Corvallis, Highdrate's manufacturing is done in our Corvallis factory and shipped all across the US.


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Sleeve Patches, otherwise known as Shoulder Sleeve Insignias (SSI) are worn by United States Army members and other militia units across the globe. In the US Army, SSI are worn on the upper left sleeve of the uniform. An additional SSI may be worn on the upper right sleeve to denote former wartime service. Generally speaking, SSI come in two versions - camouflaged or full color. Camouflaged or subdued SSI are worn on combat uniforms while full color SSI are reserved for Class A and Mess Dress uniforms. A full description of SSI can be found on Wikipedia
Here on SleevePatches, we have broken our catalog of SSI into multiple groups based on usage. Each picture of SSI comes with a link to the Wikipedia page of the military unit.


{{Proposed deletion/dated
|concern = Made up, not a recognized poker concept; in fact, triple crown in a poker context generally means a different concept "to win three different tournaments on three different online sites over the course of one week"
|timestamp =<PHONE_NUMBER>4438
}}
The '''Triple Crown of Poker''' is made up of the [[European Poker Tour]], the [[World Poker Tour]] and a [[World Series of Poker]]. In order to complete the Triple Crown of Poker you must win an EPT main event, a WPT main event and a WSOP main or side event.
To date only five individuals have accomplished this feat. They are: [[Davidi Kitai]], [[Gavin Griffin]], [[Roland De Wolfe]], [[Jake Cody]] and [[Bertrand Grospellier]].
{| class="wikitable sortable"
!Player
!EPT Title
!WPT Title
!WSOP Bracelet
|-
|Gavin Griffin[{{cite web|url=http://www.worldpokertour.com/Shared/Players/Bios/G/Gavin_Griffin.aspx|title=Gavin Griffin|publisher=theHendonMob.com|accessdate=2010-06-14}}]
|2007
|2008
|2004
|-
|Roland De Wolfe[{{cite web|url=http://pokerdb.thehendonmob.com/player.php?a=r&n=29083|title=Roland DeWolfe|publisher=theHendonMob.com|accessdate=2010-06-14}}]
|2006
|2005
|2009
|-
|Jake Cody[{{cite web|url=http://pokerdb.thehendonmob.com/player.php?a=r&n=126180|title=Jake Cody|publisher=theHendonMob.com|accessdate=2010-06-14}}]
|2010
|2010
|2011
|-
|Bertrand Grospellier[{{cite web|url=http://pokerdb.thehendonmob.com/player.php?a=r&n=39879|title=Bertrand Grospellier|publisher=theHendonMob.com|accessdate=2010-06-14}}]
|2008
|2008
|2011
|-
|Davidi Kitai[{{cite web|url=http://pokerdb.thehendonmob.com/player.php?a=r&n=73283|title=Davidi Kitai|publisher=theHendonMob.com|accessdate=2010-06-14}}]
|2012
|2011
|2008
|}
== Notes ==
{{Reflist}}
{{wikipedia-deleted|2005}}


--- layout: post title: Rumi date: 2006-06-12 00:00:00 category: personal ---
"Last night you left me and slept
your own deep sleep. Tonight you turn
and turn. I say,
"You and I will be together
till the universe dissolves."

- Rumi


public class Player
extends java.lang.Object
| Constructor and Description |
|---|
Player(FloorGrid grid,
RoundManager roundManager)
constructor Creates a player object with the floor grid (map) and the round
manager to update
|
| Modifier and Type | Method and Description |
|---|---|
static void |
changeLifeAmount(int amount)
Static method changes the amount of lives the player has
|
static boolean |
changeMoneyAmount(int amount)
Static method changes the amount of money the player has if the money can be
infact altered with out causeing it to go int to the negatives then it is
aletered and true is returned other wise false
|
FloorGrid |
getGrid() |
java.util.ArrayList<MonkeyTower> |
getListOfMonkeyTowers() |
static int |
getLivesLeft() |
static int |
getMoney()
Generated Getters And Setters
|
RoundManager |
getRoundManager() |
MonkeyTower |
getTemporaryTower() |
MonkeyTower |
getTowerUnderMouse()
Returns the tower under the mouse by looping through the list of towers and
find the tower matching the x and y position of the floor under the mouse
|
void |
initialize()
initializes the player money and life count
|
boolean |
isHoldingTower() |
void |
pickTower(MonkeyTower t)
Picks the tower for the player to place and to draw it where the mouse isand
sets the holding tower to true and the temporary tower to the tower selected
|
void |
setGrid(FloorGrid grid) |
void |
setHoldingTower(boolean holdingTower) |
void |
setListOfMonkeyTowers(java.util.ArrayList<MonkeyTower> listOfMonkeyTowers) |
static void |
setLivesLeft(int livesLeft) |
static void |
setMoney(int money) |
void |
setRoundManager(RoundManager roundManager) |
void |
setTemporaryTower(MonkeyTower temporaryTower) |
void |
tick()
Updates the player object and at the same time checking if the player has
chosen a tower to place or has started the round or not
|
public Player(FloorGrid grid, RoundManager roundManager)
public void tick()
public void initialize()
public MonkeyTower getTowerUnderMouse()
public void pickTower(MonkeyTower t)
public static boolean changeMoneyAmount(int amount)
public static void changeLifeAmount(int amount)
public static int getMoney()
public static void setMoney(int money)
public static int getLivesLeft()
public static void setLivesLeft(int livesLeft)
public FloorGrid getGrid()
public void setGrid(FloorGrid grid)
public RoundManager getRoundManager()
public void setRoundManager(RoundManager roundManager)
public java.util.ArrayList<MonkeyTower> getListOfMonkeyTowers()
public void setListOfMonkeyTowers(java.util.ArrayList<MonkeyTower> listOfMonkeyTowers)
public boolean isHoldingTower()
public void setHoldingTower(boolean holdingTower)
public MonkeyTower getTemporaryTower()
public void setTemporaryTower(MonkeyTower temporaryTower)