This is a simple demo of Matlab's memmapfile class
Here we memory map the MNIST digit data.
Contents
function memoryMapDemo()
if isOctave() fprintf('Sorry this demo will only work in Matlab\n'); return end
load the mnist data
[Xtrain, ytrain, Xtest, ytest] = setupMnist('keepSparse', false);
whos
Name Size Bytes Class Attributes Xtest 10000x784 62720000 double Xtrain 60000x784 376320000 double ytest 10000x1 80000 double ytrain 60000x1 480000 double
Save the data to a binary file using fwrite
Here we save the data as int16 and int8, but double works as well if the data is not integer typed. Note, however, that double access can be considerably slower and take up much more memory.
fname = fullfile(tempdir(), 'mnist.dat'); fid = fopen(fname, 'w'); fwrite(fid, Xtrain, 'int16'); fwrite(fid, ytrain, 'int8'); fwrite(fid, Xtest, 'int16'); % max int16 value is 32767 fwrite(fid, ytest, 'int8'); % max int8 value is 127 fclose(fid);
Create the memory map
For each section of the data, we specify the data type, size, and a name.
mmap = memmapfile(fname, 'Writable', true, 'Format', ... {'int16', size(Xtrain), 'Xtrain'; 'int8', size(ytrain), 'ytrain'; 'int16', size(Xtest), 'Xtest'; 'int8', size(ytest), 'ytest'; });
random access to data
Access works just like a regular matlab struct. Our data is stored under the 'Data' field. The first time a region is requested, access can be slow,
tic
X4000 = mmap.Data.Xtrain(4000, :); % 1x784
y4000 = mmap.Data.ytrain(4000);
toc
Elapsed time is 1.092368 seconds.
but once the region is cached, access is usually faster.
tic X4000 = mmap.Data.Xtrain(4000, :); y4000 = mmap.Data.ytrain(4000); toc
Elapsed time is 0.002007 seconds.
Cast data
If we want the data returned to be of type double, as required by many functions, we can simply cast it.
class(X4000) X4000 = double(X4000); class(X4000)
ans = int16 ans = double
Set data
Since we specified that the map was writable, we can set values too.
mmap.Data.Xtrain(1, 30:35) = 255; mmap.Data.Xtrain(1, 30:35)
ans = 255 255 255 255 255 255
Permform usual matlab operations on data
However, if Xtest is too big to fit all at once, you will have to caluculate the mean 'online' and load it in chunks.
xbar = mean(mmap.Data.Xtest, 2);
Clean up
clear mmap
delete(fname);
end


--- layout: post title: Use BFG to clear secret Git history date: 2015-05-19 16:01:25.000000000 +01:00 type: post parent_id: '0' published: true password: '' status: publish categories: - git tags: [] meta: _edit_last: '1' _wpas_done_all: '1' author: Noel permalink: "/use-bfg-to-clear-secret-git-history/" ---
Pasted those nuclear launch codes in to a file and committed the damn thing to Git again? Well you can lose a bunch of time doing it with plain git commands or download BFG, then use these simple commands once you've pushed a clean version of the file:
cd /tmp git clone --mirror https://github.com/.../something.git java -jar ~/Downloads/bfg-1.12.3.jar --delete-files file_to_eradicate.txt something.git cd something.git git reflog expire --expire=now --all && git gc --prune=now --aggressive git push
Now delete your main repository and get a fresh clone from GitHub or wherever.


Changelog¶
Here you can see the full list of changes between each Nightfall release.
Version 0.4.0¶
Released on June 20, 2021
Add debug logs to Nightfall module
Change primary class name from
Api
toNightfall
to make things a bit more clear when this library is used in other programs and allowfrom nightfall import Nightfall
Warning
This is a breaking change, since the previous version of this SDK
imported Nightfall using from nightfall.api import Api
Version 0.3.0¶
Released on June 13, 2021
Implement basic chunking algorithm to split payloads per the API limts.
Version 0.2.0¶
Note
This is an initial Beta release.
Released on June 13, 2021
- Basic project tooling put into place.
Continuous Integration with GitHub Actions
Packaging and uploading to PyPI
Code Coverage
Testing with unittest
Basic Documentation in place
Add support for using the API with a token
Add support for scan API endpoint


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Vestassapede et donec ut est liberos sus et eget sed eget. Quisqueta habitur augue magnisl magna phas ellus sagit titor ant curabi turpis.
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Vestassapede et donec ut est liberos sus et eget sed eget. Quisqueta habitur augue magnisl magna phas ellus sagit titor ant curabi turpis.
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Vestassapede et donec ut est liberos sus et eget sed eget. Quisqueta habitur augue magnisl magna phas ellus sagit titor ant curabi turpis.


Add some information about Double Cube, the author, or any other background context. Make it a few sentences long so folks can pick up some informative tidbits. Then, link them off to some social networking sites or contact information.


Retto is an online platform designed to help and simplify the conventional method of renting with maximum support for each side
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Welcome to the homepage for binary puzzle. Here you will find links to every version of the puzzle
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- Accessing Hive Metastore
- Autoscaling HDInsight Spark Cluster using Unravel API
- Backing-up, Disaster Recovery and Reverting to Prior Version
- Cluster Wide Report
- Configurations
- Custom
Configurations
- Configure Permission for Unravel daemons on CDH Sentry Secured Cluster
- Configuring Multiple Hosts for Unravel Server
- Creating Multiple Workers for High Volume Data
- Defining a Custom Web UI Port
- Email Alerts
- HBASE Configuration
- Run Unravel Daemons with Custom User
- Setting Retention Time in Unravel Server
- Spark Properties for Spark Worker daemon @ Unravel
- Security
Configurations
- Adding More Admins to Unravel Web UI
- Adding SSL and TLS to Unravel Web UI
- Alternate Kerberos Principal for Cluster Access on CDH
- Configure HDFS permission for Unravel on CDH Sentry Secured Cluster
- Configure Hive Metastore Permissions
- Disabling Browser Telemetry
- Disabling Support/Comments Panel
- Enabling TLS to Unravel Web UI Directly
- Encrypting Passwords in Unravel Properties and Settings
- Integrating LDAP Authentication for Unravel Web UI
- Kafka Security
- Restrict direct access to Web UI
- Using a Private Certificate Authority with Unravel
- Unravel Properties
- Custom
Configurations
- Connecting to/Configuration of a Kafka Stream
- Creating Active Directory Kerberos Principals and Keytabs for Unravel
- Creating an AWS RDS CloudWatch Alarm for FreeStorageSpace
- Creating Application Tags
- EMR Support Matrix
- Installing Ondemand
- Roles and Role Based Access Control
- Running Verification Scripts and Benchmarks
- Unravel APIs
- Unravel Monitoring Service
- Unravel Servers and Sensors
- Workflows
Advanced Topics¶
Advanced Topics¶
Unravel 4.4 : Advanced Topics


RCMP
Royal Canadian Mounted Police
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CSIS
Canadian Security Intelligence Service
est. 1984
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CSE
Communications Security Establishment
est. 1946
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Budget: 538M (2015-2016)


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3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed) |
|
Finkel et al. (2002) Study 1
|
MD(N=89) = -.65 ± .45 |
MD(N=120) = -.25 ± .46 |
Yong & Li (2016)
|
no signal - inconsistent (imprecise) |
commitment priming boosts forgiveness effect (exit forgiveness DV) |
RRR5 #3 |
high vs. low commitment priming (dependence vs. independence from partner) |
forgiveness to hypothetical betrayals |
very close |
DV assessed via computer rather than paper-and-pencil |
α = .91 on subjective commitment measure |
3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed) |
|
Finkel et al. (2002) Study 1
|
MD(N=89) = -.65 ± .45 |
MD(N=111) = -.23 ± .38 |
Goldberg, Sinclair, Ritchey et al. (2016)
|
no signal - inconsistent |
commitment priming boosts forgiveness effect (exit forgiveness DV) |
RRR5 #4 |
high vs. low commitment priming (dependence vs. independence from partner) |
forgiveness to hypothetical betrayals |
very close |
DV assessed via computer rather than paper-and-pencil |
α = .91 on subjective commitment measure |
3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed) |
|
Finkel et al. (2002) Study 1
|
MD(N=89) = -.65 ± .45 |
MD(N=160) = -.22 ± .41 |
Cheung, Campbell & LeBel (2016)
|
no signal - inconsistent |
commitment priming boosts forgiveness effect (exit forgiveness DV) |
RRR5 #5 |
high vs. low commitment priming (dependence vs. independence from partner) |
forgiveness to hypothetical betrayals |
very close |
DV assessed via computer rather than paper-and-pencil |
α = .91 on subjective commitment measure |
3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed) |
|
Finkel et al. (2002) Study 1
|
MD(N=89) = -.65 ± .45 |
MD(N=124) = -.21 ± .58 |
Fuglestad, Leone & Kim (2016)
|
no signal - inconsistent (imprecise) |
commitment priming boosts forgiveness effect (exit forgiveness DV) |
RRR5 #6 |
high vs. low commitment priming (dependence vs. independence from partner) |
forgiveness to hypothetical betrayals |
very close |
DV assessed via computer rather than paper-and-pencil |
α = .92 on subjective commitment measure |
3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed) |
|
Finkel et al. (2002) Study 1
|
MD(N=89) = -.65 ± .45 |
MD(N=162) = -.12 ± .43 |
Vranka, Bahnik, & Houdek (2016)
|
no signal - inconsistent |
commitment priming boosts forgiveness effect (exit forgiveness DV) |
RRR5 #7 |
high vs. low commitment priming (dependence vs. independence from partner) |
forgiveness to hypothetical betrayals |
very close |
DV assessed via computer rather than paper-and-pencil |
α = .93 on subjective commitment measure |
3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed) |
|
Finkel et al. (2002) Study 1
|
MD(N=89) = -.65 ± .45 |
MD(N=102) = -.11 ± .42 |
Collins, Bowen, Winczewski et al. (2016)
|
no signal - inconsistent |
commitment priming boosts forgiveness effect (exit forgiveness DV) |
RRR5 #8 |
high vs. low commitment priming (dependence vs. independence from partner) |
forgiveness to hypothetical betrayals |
very close |
DV assessed via computer rather than paper-and-pencil |
α = .94 on subjective commitment measure |
3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed) |
|
Finkel et al. (2002) Study 1
|
MD(N=89) = -.65 ± .45 |
MD(N=105) = -.04 ± .49 |
DiDonato & Golom (2016)
|
no signal - inconsistent (imprecise) |
commitment priming boosts forgiveness effect (exit forgiveness DV) |
RRR5 #9 |
high vs. low commitment priming (dependence vs. independence from partner) |
forgiveness to hypothetical betrayals |
very close |
DV assessed via computer rather than paper-and-pencil |
α = .91 on subjective commitment measure |
3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed) |
|
Finkel et al. (2002) Study 1
|
MD(N=89) = -.65 ± .45 |
MD(N=187) = -.03 ± .45 |
Sucharyna & Morry (2016)
|
no signal - inconsistent |
commitment priming boosts forgiveness effect (exit forgiveness DV) |
RRR5 #10 |
high vs. low commitment priming (dependence vs. independence from partner) |
forgiveness to hypothetical betrayals |
very close |
DV assessed via computer rather than paper-and-pencil |
α = .90 on subjective commitment measure |
3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed) |
|
Finkel et al. (2002) Study 1
|
MD(N=89) = -.65 ± .45 |
MD(N=100) = .03 ± .39 |
Carcedo & Fernandez-Rouco (2016)
|
no signal - inconsistent |
commitment priming boosts forgiveness effect (exit forgiveness DV) |
RRR5 #11 |
high vs. low commitment priming (dependence vs. independence from partner) |
forgiveness to hypothetical betrayals |
very close |
DV assessed via computer rather than paper-and-pencil |
α = .86 on subjective commitment measure |
3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed) |
|
Finkel et al. (2002) Study 1
|
MD(N=89) = -.65 ± .45 |
MD(N=142) = .12 ± .43 |
Aykutoglu, Gündogdu-Aktürk et al. (2016)
|
no signal - inconsistent |
commitment priming boosts forgiveness effect (exit forgiveness DV) |
RRR5 #12 |
high vs. low commitment priming (dependence vs. independence from partner) |
forgiveness to hypothetical betrayals |
very close |
DV assessed via computer rather than paper-and-pencil |
α = .93 on subjective commitment measure |
3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed) |
|
Finkel et al. (2002) Study 1
|
MD(N=89) = -.65 ± .45 |
MD(N=209) = .16 ± .35 |
Hoplock & Stinson (2016)
|
no signal - inconsistent |
commitment priming boosts forgiveness effect (exit forgiveness DV) |
RRR5 #13 |
high vs. low commitment priming (dependence vs. independence from partner) |
forgiveness to hypothetical betrayals |
very close |
DV assessed via computer rather than paper-and-pencil |
α = .88 on subjective commitment measure |
3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed) |
|
Finkel et al. (2002) Study 1
|
MD(N=89) = -.65 ± .45 |
MD(N=173) = .16 ± .38 |
Cobb, Pink, Millman & Logan (2016)
|
no signal - inconsistent |
commitment priming boosts forgiveness effect (exit forgiveness DV) |
RRR5 #14 |
high vs. low commitment priming (dependence vs. independence from partner) |
forgiveness to hypothetical betrayals |
very close |
DV assessed via computer rather than paper-and-pencil |
α = .90 on subjective commitment measure |
3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed) |
|
Finkel et al. (2002) Study 1
|
MD(N=89) = -.65 ± .45 |
MD(N=127) = .23 ± .43 |
Caprariello (2016)
|
no signal - inconsistent |
commitment priming boosts forgiveness effect (exit forgiveness DV) |
RRR5 #15 |
high vs. low commitment priming (dependence vs. independence from partner) |
forgiveness to hypothetical betrayals |
very close |
DV assessed via computer rather than paper-and-pencil |
α = .90 on subjective commitment measure |
3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed) |
|
Finkel et al. (2002) Study 1
|
MD(N=89) = -.65 ± .45 |
MD(N=101) = .29 ± .46 |
Tidwell & Kraus (2016)
|
no signal - inconsistent (imprecise) |
commitment priming boosts forgiveness effect (exit forgiveness DV) |
RRR5 #16 |
high vs. low commitment priming (dependence vs. independence from partner) |
forgiveness to hypothetical betrayals |
very close |
DV assessed via computer rather than paper-and-pencil |
α = .90 on subjective commitment measure |
3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed) |
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=87) = -.58 ± .83 |
Özdogru (2016)
|
no signal - inconsistent (imprecise) |
facial feedback hypothesis |
RRR6 #1 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=110) = -.24 ± .52 |
Oosterwijk et al. (2016)
|
no signal - inconsistent |
facial feedback hypothesis |
RRR6 #2 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=110) = -.20 ± .54 |
Wayand (2016)
|
no signal - inconsistent |
facial feedback hypothesis |
RRR6 #3 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=100) = -.19 ± .54 |
Koch (2016)
|
no signal - inconsistent |
facial feedback hypothesis |
RRR6 #4 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=120) = -.13 ± .63 |
Pacheco-Unguetti et al. (2016)
|
no signal - inconsistent |
facial feedback hypothesis |
RRR6 #5 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=117) = -.11 ± .58 |
Capaldi et al. (2016)
|
no signal - inconsistent |
facial feedback hypothesis |
RRR6 #6 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=94) = -.05 ± .60 |
Chasten et al. (2016)
|
no signal - inconsistent |
facial feedback hypothesis |
RRR6 #7 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=115) = -.02 ± .50 |
Benning et al. (2016)
|
no signal - inconsistent |
facial feedback hypothesis |
RRR6 #8 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=112) = .02 ± .54 |
Talarico & DeCicco (2016)
|
no signal - inconsistent |
facial feedback hypothesis |
RRR6 #9 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=101) = .02 ± .67 |
Korb et al. (2016)
|
no signal - inconsistent |
facial feedback hypothesis |
RRR6 #10 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=101) = .12 ± .55 |
Bulnes et al. (2016)
|
no signal - inconsistent |
facial feedback hypothesis |
RRR6 #11 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=139) = .14 ± .53 |
Albohn et al. (2016)
|
no signal - inconsistent |
facial feedback hypothesis |
RRR6 #12 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=130) = .15 ± .42 |
Wagenmakers et al. (2016)
|
no signal - inconsistent |
facial feedback hypothesis |
RRR6 #13 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=125) = .16 ± .59 |
Allard & Zetzer (2016)
|
no signal - inconsistent |
facial feedback hypothesis |
RRR6 #14 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=99) = .20 ± .55 |
Holmes et al. (2016)
|
no signal - inconsistent |
facial feedback hypothesis |
RRR6 #15 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=108) = .35 ± .53 |
Zeelenberg et al. (2016)
|
no signal - inconsistent |
facial feedback hypothesis |
RRR6 #16 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Strack et al. (1988) Study 1
|
MD(N=60) = .82 ± .77 |
MD(N=126) = .37 ± .55 |
Lynott et al. (2016)
|
no signal - inconsistent |
facial feedback hypothesis |
RRR6 #17 |
hold pen with teeth vs. lips |
amusement of cartoons (4 cartoons) |
close |
New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale |
|
|
|
Kenrick et al. (1989) Study 2
|
Δd(N=65) = -.53 ± .64 |
Δd(N=223) = .29 ± .46 |
Balzarini et al. (2015) Study 1
|
no signal - inconsistent |
playboy effect |
individual effort |
Playboy centerfolds vs. control; Participant sex |
Love for partner (Rubin Love-scale) |
close |
Updated pictures of abstract art & male/female nudes; Two attention check questions |
Nudes rated as more pleasant than abstract art |
|
|
Kenrick et al. (1989) Study 2
|
Δd(N=65) = -.53 ± .64 |
Δd(N=263) = .30 ± .42 |
Balzarini et al. (2015) Study 2
|
no signal - inconsistent |
playboy effect |
individual effort |
Playboy centerfolds vs. control; Participant sex |
Love for partner (Rubin Love-scale) |
close |
Updated pictures of abstract art & male/female nudes; Two attention check questions |
Nudes rated as more pleasant than abstract art |
|
|
Kenrick et al. (1989) Study 2
|
Δd(N=65) = -.53 ± .64 |
Δd(N=225) = -.38 ± .46 |
Balzarini et al. (2015) Study 3
|
no signal - inconsistent |
playboy effect |
individual effort |
Playboy centerfolds vs. control; Participant sex |
Love for partner (Rubin Love-scale) |
close |
Updated pictures of abstract art & male/female nudes; Two attention check questions |
Nudes rated as more pleasant than abstract art |
|
|
Isen & Levin (1972) Study 2
|
PD(N=41) = 84% ± 18% |
PD(N=50) = 3% ± 30% |
Blevins & Murphy (1974)
|
no signal - inconsistent |
positive mood boosts helping effect |
individual effort |
positive mood (finding dime) vs. control (not finding dime) |
helping (picking up dropped papers) |
close |
|
|
|
|
Levin & Isen (1975) Study 1
|
PD(N=24) = 60% ± 30% |
PD(N=106) = 25% ± 29% |
Weyant & Clark (1977) Study 1
|
no signal - inconsistent |
positive mood boosts helping effect |
individual effort |
positive mood (finding dime) vs. control (not finding dime) |
helping (mailing 'forgotten' letter) |
close |
|
|
|
|
Levin & Isen (1975) Study 1
|
PD(N=24) = 60% ± 30% |
PD(N=32) = -7% ± 16% |
Weyant & Clark (1977) Study 2
|
no signal - inconsistent |
positive mood boosts helping effect |
individual effort |
positive mood (finding dime) vs. control (not finding dime) |
helping (mailing 'forgotten' letter) |
close |
|
|
|
|
Kidd & Castano (2013) Study 1
|
d(N=86) = .56 ± .43 |
d(N=300) = -.08 ± .23 |
Panero et al. (2016) Study 1
|
no signal - inconsistent |
reading fiction boosts empathy effect |
individual effort |
literary fiction vs. nonfiction |
empathy (Reading the mind in the eyes test [RMET]) |
very close |
|
|
|
|
Kidd & Castano (2013) Study 3
|
d(N=69) = .36 ± .21 |
d(N=300) = .14 ± .23 |
Dijkstra et al. (2015)
|
no signal - inconsistent |
reading fiction boosts empathy effect |
individual effort |
literary fiction vs. nonfiction |
empathy (Reading the mind in the eyes test [RMET]) |
very close |
|
|
|
|
Kidd & Castano (2013) Study 3
|
d(N=69) = .36 ± .21 |
d(N=303) = .04 ± .22 |
Panero et al. (2016) Study 2
|
no signal - inconsistent |
reading fiction boosts empathy effect |
individual effort |
literary fiction vs. popular fiction |
empathy (Reading the mind in the eyes test [RMET]) |
very close |
|
|
|
|
Kidd & Castano (2013) Study 5
|
d(N=237) = .25 ± .25 |
d(N=369) = .10 ± .20 |
Panero et al. (2016) Study 3
|
no signal - inconsistent |
reading fiction boosts empathy effect |
individual effort |
literary fiction vs. popular fiction |
empathy (Reading the mind in the eyes test [RMET]) |
very close |
|
|
|
|
Bem (2011) Study 1
|
d(N=100) = .25 |
d(N=200) = -.05 |
Wagenmakers et al. (2012)
|
no signal - inconsistent |
pre-cognition (erotic premonition) |
individual effort |
n/a |
future location of erotic photo |
close |
different erotic photos |
|
|
|
Bem (2011) Study 8
|
DR%(N=100) = 2.3% ± 2.3% |
DR%(N=112) = -1.2% ± 1.6% |
Galak et al. (2012) Study 1
|
no signal - inconsistent |
pre-cognition (retroactive memory recall) |
individual effort |
rehearsed vs. non-rehearsed words (after memory test) |
free recall memory test |
close |
|
|
|
|
Bem (2011) Study 8
|
DR%(N=100) = 2.3% ± 2.3% |
DR%(N=158) = 0% ± 1.3% |
Galak et al. (2012) Study 2
|
no signal - inconsistent |
pre-cognition (retroactive memory recall) |
individual effort |
rehearsed vs. non-rehearsed words (after memory test) |
free recall memory test |
close |
|
|
|
|
Bem (2011) Study 8
|
DR%(N=100) = 2.3% ± 2.3% |
DR%(N=124) = 1.2% ± 1.4% |
Galak et al. (2012) Study 3
|
no signal - inconsistent |
pre-cognition (retroactive memory recall) |
individual effort |
rehearsed vs. non-rehearsed words (after memory test) |
free recall memory test |
exact |
|
|
|
|
Bem (2011) Study 8
|
DR%(N=100) = 2.3% ± 2.3% |
DR%(N=2469) = -.1% ± .1% |
Galak et al. (2012) Study 7
|
signal - inconsistent (opposite) |
pre-cognition (retroactive memory recall) |
individual effort |
rehearsed vs. non-rehearsed words (after memory test) |
free recall memory test |
close |
|
|
|
|
Bem (2011) Study 9
|
DR%(N=50) = 4.2% ± 3.0% |
DR%(N=109) = 1.6% ± 1.1% |
Galak et al. (2012) Study 4
|
signal - inconsistent (smaller) |
pre-cognition (retroactive memory recall) |
individual effort |
rehearsed vs. non-rehearsed words (after memory test) |
free recall memory test |
exact |
|
|
|
|
Bem (2011) Study 9
|
DR%(N=50) = 4.2% ± 3.0% |
DR%(N=211) = -.5% ± 1.5% |
Galak et al. (2012) Study 5
|
no signal - inconsistent |
pre-cognition (retroactive memory recall) |
individual effort |
rehearsed vs. non-rehearsed words (after memory test) |
free recall memory test |
exact |
|
|
|
|
Bem (2011) Study 9
|
DR%(N=50) = 4.2% ± 3.0% |
DR%(N=106) = -.3% ± 1.5% |
Galak et al. (2012) Study 6
|
no signal - inconsistent |
pre-cognition (retroactive memory recall) |
individual effort |
rehearsed vs. non-rehearsed words (after memory test) |
free recall memory test |
exact |
|
|
|
|
Bem (2011) Study 9
|
DR%(N=50) = 4.2% ± 3.0% |
DR%(N=50) = .2% ± 3.5% |
Ritchie et al. (2012) Study 1
|
no signal - inconsistent (imprecise) |
pre-cognition (retroactive memory recall) |
individual effort |
rehearsed vs. non-rehearsed words (after memory test) |
free recall memory test |
exact |
|
|
|
|
Bem (2011) Study 9
|
DR%(N=50) = 4.2% ± 3.0% |
DR%(N=50) = -2.7% ± 3.5% |
Ritchie et al. (2012) Study 2
|
no signal - inconsistent (imprecise) |
pre-cognition (retroactive memory recall) |
individual effort |
rehearsed vs. non-rehearsed words (after memory test) |
free recall memory test |
exact |
|
|
|
|
Bem (2011) Study 9
|
DR%(N=50) = 4.2% ± 3.0% |
DR%(N=50) = -.6% ± 3.5% |
Ritchie et al. (2012) Study 3
|
no signal - inconsistent (imprecise) |
pre-cognition (retroactive memory recall) |
individual effort |
rehearsed vs. non-rehearsed words (after memory test) |
free recall memory test |
exact |
|
|
|
|
Bem (2011) Study 9
|
DR%(N=50) = 4.2% ± 3.0% |
DR%(N=50) = -1.6% ± 3.4% |
Robinson (2011)
|
no signal - inconsistent (imprecise) |
pre-cognition (retroactive memory recall) |
individual effort |
rehearsed vs. non-rehearsed words (after memory test) |
free recall memory test |
exact |
|
|
|
|
Carney, Cuddy & Yap (2010)
|
OR(N=42) = 3.13 [.94,10.4] |
OR(N=200) = .72 [.43,1.21] |
Ranehill et al. (2015)
|
no signal - inconsistent |
power posing effect (risk-taking DV) |
individual effort |
pose (expansive vs contractive) |
risky gambling decision (yes vs. no) |
close |
3-minute poses instead of 1-minute; 5 additional gain domain gambling decisions |
|
2 other DVS (testosterone level, cortisol level; same pattern of results observed) |
|
Carney, Cuddy & Yap (2010)
|
OR(N=42) = 3.13 [.94,10.4] |
OR(N=305) = .67 [.38,1.18] |
Garrison et al. (2016)
|
no signal - inconsistent |
power posing effect (risk-taking DV) |
individual effort |
pose (expansive vs contractive) |
risky gambling decision (yes vs. no) |
close |
added ultimatum game dominance DV; also manipulated eye gaze (orthogonal to pose IV) |
|
|
|
Carney, Cuddy & Yap (2010)
|
OR(N=42) = 3.13 [.94,10.4] |
OR(N=94) = 1.38 [.65,2.92] |
Bailey, LaFrance et al. (2017)
|
no signal - inconsistent |
power posing effect (risk-taking DV) |
CRSP special issue |
pose (expansive vs contractive) |
risky gambling decision (yes vs. no) |
close |
2 tokens for snacks instead of $2 |
|
|
|
Carney, Cuddy & Yap (2010)
|
OR(N=42) = 3.13 [.94,10.4] |
OR(N=108) = .53 [.17,1.59] |
Ronay, Tybur et al. (2017)
|
no signal - inconsistent |
power posing effect (risk-taking DV) |
CRSP special issue |
pose (expansive vs contractive) |
risky gambling decision (yes vs. no) |
very close |
Dutch undergraduates |
|
2 other DVS (testosterone level, cortisol level; same pattern of results observed) |
|
Carney, Cuddy & Yap (2010)
|
OR(N=42) = 3.13 [.94,10.4] |
OR(N=200) = 1.14 [.68,1.89] |
Bombari, Schmid et al. (2017)
|
no signal - inconsistent |
power posing effect (risk-taking DV) |
CRSP special issue |
pose (expansive vs contractive) |
risky gambling decision (yes vs. no) |
close |
Swiss undergrad or Master's students |
|
|
|
Carney, Cuddy & Yap (2010)
|
OR(N=42) = 3.13 [.94,10.4] |
OR(N=276) = 1.17 [.91,1.51] |
Keller, Johnson et al. (2017)
|
no signal - inconsistent |
power posing effect (risk-taking DV) |
CRSP special issue |
pose (expansive vs contractive) |
risky gambling decision (yes vs. no) |
close |
2 tickets to win $50 gift card instead of $2 |
|
impromptu job interview performance (same pattern of results observed) |
|
Bargh et al. (1996) Study 2a
|
d(N=30) = 1.02 ± .76 |
d(N=31) = .56 ± .72 |
Hull et al. (2002) Study 1a
|
no signal - inconsistent |
elderly priming |
individual effort |
exposure to elderly cues vs. control (scrambled sentence) |
walking speed |
close |
|
|
|
|
Bargh et al. (1996) Study 2a
|
d(N=30) = 1.02 ± .76 |
d(N=40) = .53 ± .63 |
Hull et al. (2002) Study 1b
|
no signal - inconsistent |
elderly priming |
individual effort |
exposure to elderly cues vs. control (scrambled sentence) |
walking speed |
close |
|
|
|
|
Bargh et al. (1996) Study 2a
|
d(N=30) = 1.02 ± .76 |
d(N=45) = .22 ± .58 |
Cesario et al. (2007) Study 2
|
no signal - inconsistent |
elderly priming |
individual effort |
exposure to elderly cues vs. control (scrambled sentence) |
walking speed |
very close |
|
|
|
|
Bargh et al. (1996) Study 2a
|
d(N=30) = 1.02 ± .76 |
d(N=66) = -.22 ± .48 |
Pashler et al. (2008)
|
no signal - inconsistent |
elderly priming |
individual effort |
exposure to elderly cues vs. control (scrambled sentence) |
walking speed |
very close |
|
|
|
|
Bargh et al. (1996) Study 2a
|
d(N=30) = 1.02 ± .76 |
d(N=120) = -.07 ± .36 |
Doyen et al. (2012) Study 1
|
no signal - inconsistent |
elderly priming |
individual effort |
exposure to elderly cues vs. control (scrambled sentence) |
walking speed |
close |
|
|
|
|
Slepian et al. (2012) Study 1
|
d(N=40) = .78 ± .62 |
d(N=314) = .19 ± .22 |
Perfecto et al. (2012)
|
no signal - inconsistent |
embodiment of secrets effect |
individual effort |
big vs. small secret (write about) |
hill steepness estimate (open-ended) |
very close |
|
|
|
|
Slepian et al. (2012) Study 1
|
d(N=40) = .78 ± .62 |
d(N=240) = .18 ± .25 |
LeBel & Wilbur (2014) Study 1
|
no signal - inconsistent |
embodiment of secrets effect |
individual effort |
big vs. small secret (write about) |
hill steepness estimate (open-ended) |
very close |
|
|
|
|
Slepian et al. (2012) Study 1
|
d(N=40) = .78 ± .62 |
d(N=90) = -.32 ± .41 |
LeBel & Wilbur (2014) Study 2
|
no signal - inconsistent |
embodiment of secrets effect |
individual effort |
big vs. small secret (write about) |
hill steepness estimate (open-ended) |
very close |
|
|
|
|
Slepian et al. (2012) Study 1
|
d(N=40) = .78 ± .62 |
d(N=100) = .08 ± .39 |
Pecher et al. (2015) Study 1
|
no signal - inconsistent |
embodiment of secrets effect |
individual effort |
big vs. small secret (write about) |
hill steepness estimate (open-ended) |
very close |
|
|
|
|
Slepian et al. (2012) Study 1
|
d(N=40) = .78 ± .62 |
d(N=100) = .21 ± .39 |
Pecher et al. (2015) Study 2
|
no signal - inconsistent |
embodiment of secrets effect |
individual effort |
big vs. small secret (write about) |
hill steepness estimate (open-ended) |
very close |
|
|
|
|
Slepian et al. (2012) Study 2
|
d(N=36) = .81 ± .73 |
d(N=60) = .31 ± .52 |
Cobb et al. (2014)
|
no signal - inconsistent |
embodiment of secrets effect |
individual effort |
meaningful vs. trivial secret (recall task) |
beanbag toss distance |
close |
|
|
|
|
Slepian et al. (2012) Study 2
|
d(N=36) = .81 ± .73 |
d(N=118) = .21 ± .36 |
Pecher et al. (2015) Study 3
|
no signal - inconsistent |
embodiment of secrets effect |
individual effort |
meaningful vs. trivial secret (recall task) |
beanbag toss distance |
very close |
|
|
|
|
Zhong & Liljenquist (2006) Study 2
|
r(N=27) = .45 ± .31 |
r(N=36) = .04 ± .33 |
Gamez et al. (2011) Study 2
|
no signal - inconsistent (imprecise) |
Macbeth effect (cleansing need) |
individual effort |
moral purity threat (transcribe unethical vs. ethical act) |
need to cleanse oneself (cleaning products desirability) |
very close |
|
|
|
|
Zhong & Liljenquist (2006) Study 2
|
r(N=27) = .45 ± .31 |
r(N=335) = -.04 ± .11 |
Siev (2012) Study 1
|
no signal - inconsistent |
Macbeth effect (cleansing need) |
individual effort |
moral purity threat (transcribe unethical vs. ethical act) |
need to cleanse oneself (cleaning products desirability) |
very close |
|
|
|
|
Zhong & Liljenquist (2006) Study 2
|
r(N=27) = .45 ± .31 |
r(N=148) = -.09 ± .16 |
Siev (2012) Study 2
|
no signal - inconsistent |
Macbeth effect (cleansing need) |
individual effort |
moral purity threat (transcribe unethical vs. ethical act) |
need to cleanse oneself (cleaning products desirability) |
very close |
|
|
|
|
Zhong & Liljenquist (2006) Study 2
|
r(N=27) = .45 ± .31 |
r(N=153) = .00 ± .16 |
Earp et al. (2014) Study 1
|
no signal - inconsistent |
Macbeth effect (cleansing need) |
individual effort |
moral purity threat (transcribe unethical vs. ethical act) |
need to cleanse oneself (cleaning products desirability) |
very close |
|
|
|
|
Zhong & Liljenquist (2006) Study 2
|
r(N=27) = .45 ± .31 |
r(N=156) = -.07 ± .16 |
Earp et al. (2014) Study 2
|
no signal - inconsistent |
Macbeth effect (cleansing need) |
individual effort |
moral purity threat (transcribe unethical vs. ethical act) |
need to cleanse oneself (cleaning products desirability) |
very close |
|
|
|
|
Zhong & Liljenquist (2006) Study 2
|
r(N=27) = .45 ± .31 |
r(N=286) = -.11 ± .11 |
Earp et al. (2014) Study 3
|
signal - inconsistent (opposite) |
Macbeth effect (cleansing need) |
individual effort |
moral purity threat (transcribe unethical vs. ethical act) |
need to cleanse oneself (cleaning products desirability) |
very close |
|
|
|
|
Zhong & Liljenquist (2006) Study 3
|
r(N=32) = .38 ± .30 |
r(N=210) = .03 ± .14 |
Fayard et al. (2009) Study 1
|
no signal - inconsistent |
Macbeth effect (product choice) |
individual effort |
moral purity threat (recall unethical vs. ethical deed) |
product choice (cleaning-related vs. unrelated) |
very close |
|
|
|
|
Zhong & Liljenquist (2006) Study 3
|
r(N=32) = .38 ± .30 |
r(N=45) = .15 ± .29 |
Gamez et al. (2011) Study 3
|
no signal - inconsistent |
Macbeth effect (product choice) |
individual effort |
moral purity threat (recall unethical vs. ethical deed) |
product choice (cleaning-related vs. unrelated) |
very close |
|
|
|
|
Zhong & Liljenquist (2006) Study 4
|
r(N=45) = .33 ± .26 |
r(N=115) = .01 ± .18 |
Fayard et al. (2009) Study 2
|
no signal - inconsistent |
Macbeth effect (volunteerism) |
individual effort |
moral purity threat (recall unethical vs. ethical deed) |
volunteerism (help student RA) |
close |
minutes volunteering instead of binary choice to help |
|
|
|
Zhong & Liljenquist (2006) Study 4
|
r(N=45) = .33 ± .26 |
r(N=28) = .19 ± .36 |
Gamez et al. (2011) Study 4
|
no signal - consistent (imprecise) |
Macbeth effect (volunteerism) |
individual effort |
moral purity threat (recall unethical vs. ethical deed) |
volunteerism (help student RA) |
very close |
|
|
|
|
Zhong & Liljenquist (2006) Study 4
|
r(N=45) = .33 ± .26 |
r(N=29) = .39 ± .31 |
Reuven et al. (2013)
|
signal - consistent |
Macbeth effect (volunteerism) |
individual effort |
moral purity threat (recall unethical vs. ethical deed) |
volunteerism (help student RA) |
close |
3-choice instead of binary choice DV |
|
|
|
Bargh & Shalev (2012) Study 1a
|
r(N=51) = .57 ± .19 |
r(N=197) = -.13 ± .14 |
Donnellan et al. (2015a) Study 9
|
no signal - inconsistent |
warmth embodiment (hot shower loneliness effect) |
individual effort |
UCLA Loneliness scale (10-item v1) |
showering/bathing behaviors (3-items) |
very close |
|
|
|
|
Bargh & Shalev (2012) Study 1a
|
r(N=51) = .57 ± .19 |
r(N=228) = -.10 ± .13 |
Donnellan et al. (2015a) Study 4
|
no signal - inconsistent |
warmth embodiment (hot shower loneliness effect) |
individual effort |
UCLA Loneliness scale (20-item v3) |
showering/bathing behaviors (3-items) |
close |
online (SurveyMonkey); slightly different scale response options; 20-item (instead of 10-item) UCLA Loneliness Scale |
|
|
|
Bargh & Shalev (2012) Study 1a
|
r(N=51) = .57 ± .19 |
r(N=235) = -.06 ± .13 |
Donnellan et al. (2015a) Study 1
|
no signal - inconsistent |
warmth embodiment (hot shower loneliness effect) |
individual effort |
UCLA Loneliness scale (20-item v3) |
showering/bathing behaviors (3-items) |
close |
slightly different scale response options; 20-item (instead of 10-item) UCLA Loneliness Scale |
|
|
|
Bargh & Shalev (2012) Study 1a
|
r(N=51) = .57 ± .19 |
r(N=291) = -.04 ± .11 |
Donnellan et al.(2015b)
|
no signal - inconsistent |
warmth embodiment (hot shower loneliness effect) |
individual effort |
UCLA Loneliness scale (10-item v1) |
showering/bathing behaviors (3-items) |
very close |
|
|
|
|
Bargh & Shalev (2012) Study 1a
|
r(N=51) = .57 ± .19 |
r(N=365) = -.03 ± .10 |
Ferrell et al. (2013)
|
no signal - inconsistent |
warmth embodiment (hot shower loneliness effect) |
individual effort |
Revised UCLA Loneliness scale (3-item; Hughes et al., 2004) |
showering/bathing behaviors (3-items) |
close |
online (UT Austin) |
|
|
|
Bargh & Shalev (2012) Study 1a
|
r(N=51) = .57 ± .19 |
r(N=356) = -.02 ± .10 |
McDonald & Donnellan (2015)
|
no signal - inconsistent |
warmth embodiment (hot shower loneliness effect) |
individual effort |
Revised UCLA Loneliness scale (3-item; Hughes et al., 2004) |
showering/bathing behaviors (3-items) |
close |
online Israeli sample |
|
|
|
Bargh & Shalev (2012) Study 1a
|
r(N=51) = .57 ± .19 |
r(N=480) = -.01 ± .09 |
Donnellan et al. (2015a) Study 2
|
no signal - inconsistent |
warmth embodiment (hot shower loneliness effect) |
individual effort |
UCLA Loneliness scale (20-item v3) |
showering/bathing behaviors (3-items) |
close |
online (mTurk); slightly different scale response options; 20-item (instead of 10-item) UCLA Loneliness Scale |
|
|
|
Bargh & Shalev (2012) Study 1a
|
r(N=51) = .57 ± .19 |
r(N=365) = .02 ± .10 |
Donnellan et al. (2015a) Study 8
|
no signal - inconsistent |
warmth embodiment (hot shower loneliness effect) |
individual effort |
UCLA Loneliness scale (10-item v1) |
showering/bathing behaviors (3-items) |
very close |
|
|
|
|
Bargh & Shalev (2012) Study 1a
|
r(N=51) = .57 ± .19 |
r(N=311) = .02 ± .11 |
Donnellan et al. (2015a) Study 7
|
no signal - inconsistent |
warmth embodiment (hot shower loneliness effect) |
individual effort |
UCLA Loneliness scale (10-item v1) |
showering/bathing behaviors (3-items) |
very close |
|
|
|
|
Bargh & Shalev (2012) Study 1a
|
r(N=51) = .57 ± .19 |
r(N=531) = .04 ± .08 |
Donnellan & Lucas (2014)
|
no signal - inconsistent |
warmth embodiment (hot shower loneliness effect) |
individual effort |
UCLA Loneliness scale (10-item v1) |
showering/bathing behaviors (3-items) |
very close |
|
|
|
|
Bargh & Shalev (2012) Study 1a
|
r(N=51) = .57 ± .19 |
r(N=553) = .06 ± .08 |
Donnellan et al. (2015a) Study 6
|
no signal - inconsistent |
warmth embodiment (hot shower loneliness effect) |
individual effort |
UCLA Loneliness scale (10-item v1) |
showering/bathing behaviors (3-items) |
very close |
online (mTurk) |
|
|
|
Bargh & Shalev (2012) Study 1a
|
r(N=51) = .57 ± .19 |
r(N=494) = .10 ± .09 |
Donnellan et al. (2015a) Study 5
|
signal - inconsistent (smaller) |
warmth embodiment (hot shower loneliness effect) |
individual effort |
UCLA Loneliness scale (10-item v1) |
showering/bathing behaviors (3-items) |
very close |
online (mTurk) |
|
|
|
Bargh & Shalev (2012) Study 1a
|
r(N=51) = .57 ± .19 |
r(N=210) = .13 ± .13 |
Donnellan et al. (2015a) Study 3
|
signal - inconsistent (smaller) |
warmth embodiment (hot shower loneliness effect) |
individual effort |
UCLA Loneliness scale (20-item v3) |
showering/bathing behaviors (3-items) |
close |
online (SurveyMonkey); slightly different scale response options; 20-item (instead of 10-item) UCLA Loneliness Scale |
|
|
|
Bargh & Shalev (2012) Study 2
|
r(N=75) = .29 ± .21 |
r(N=260) = .01 ± .11 |
Wortman et al. (2014)
|
no signal - inconsistent |
warmth embodiment (cold packs boost loneliness effect) |
individual effort |
holding cold vs. warm pack |
UCLA Loneliness scale (10-item v1) |
very close |
|
Cold packs rated as colder than warm packs (d = +3.07 ± .92) |
|
|
Vess (2012) Study 1
|
d(N=56) = .60 ± .55 |
d(N=219) = .03 ± .27 |
LeBel & Campbell (2013) Study 1
|
no signal - inconsistent |
anxious attachment warm food effect |
individual effort |
anxious attachment; recall breakup vs. control |
warm food desirability ratings (3 items) |
very close |
slightly different instructions & scale anchors for attachment measure |
Known sex differences in food preferences (women liked vegetables, fruits, candy, and wine more than men whereas men liked meats, chili pepper, and beer more than women; Logan & Smith, 1986) |
|
|
Vess (2012) Study 1
|
d(N=56) = .60 ± .55 |
d(N=233) = .05 ± .26 |
LeBel & Campbell (2013) Study 2
|
no signal - inconsistent |
anxious attachment warm food effect |
individual effort |
anxious attachment; recall breakup vs. control |
warm food desirability ratings (3 items) |
exact |
|
Known sex differences in food preferences (women liked vegetables, fruits, candy, and wine more than men whereas men liked meats, chili pepper, and beer more than women; Logan & Smith, 1986) |
|
|
Correll (2008) Study 2
|
d(N=71) = .59 ± .51 |
d(N=148) = .16 ± .34 |
Madurski & LeBel (2015) Study 1
|
no signal - inconsistent |
modulation of 1/f noise racial bias emission effect |
individual effort |
avoid/use race information vs. control instructions |
1/f noise (weapon identification task) |
very close |
Canadian rather than US undergraduates; keyboard used instead of response box; |
Racial bias (in terms of RT) higher in use and avoid race (compared to control) conditions (d = .34 ± .35); 1/f noise observed within each of the 3 conditions (p<.0001) |
|
|
Correll (2008) Study 2
|
d(N=71) = .59 ± .51 |
d(N=148) = -.09 ± .34 |
Madurski & LeBel (2015) Study 2
|
no signal - inconsistent |
modulation of 1/f noise racial bias emission effect |
individual effort |
avoid/use race information vs. control instructions |
1/f noise (weapon identification task) |
very close |
Canadian rather than US undergraduates; keyboard used instead of response box; |
Racial bias (in terms of RT) higher in use and avoid race (compared to control) conditions (d = .44 ± .35); 1/f noise observed within each of the 3 conditions (p<.0001) |
|
|
Gable & Harmon-Jones (2008) Study 2
|
η²(N=32) = .53 [.26,.67] |
η²(N=79) = .24 [.09,.39] |
Domachowska et al. (2016) Study 1
|
signal - inconsistent (smaller) |
high-approach positive affect constricts attention effect |
individual effort |
Affect (High-approach positive affect vs. neutral) |
Attentional breadth (Navon, 1977 letter task RTs) |
very close |
German language; Mood (PANAS) assessed at study outset |
Neutral mood global bias effect observed, p < .01; Successful valence manipulation check, dz = 1.14 |
|
|
Gable & Harmon-Jones (2008) Study 2
|
η²(N=32) = .53 [.26,.67] |
η²(N=80) = .08 [.00,.21] |
Domachowska et al. (2016) Study 2
|
signal - inconsistent (smaller) |
high-approach positive affect constricts attention effect |
individual effort |
Affect (High-approach positive affect vs. neutral) |
Attentional breadth (Navon, 1977 letter task RTs) |
close |
German-normed stimuli; Low-approach positive affect within-subject condition also included |
Neutral mood global bias effect observed, p < .05; Successful valence manipulation check, dz = 2.51 |
|
|
Rasinski et al. (2005)
|
d(N=62) = 1.21 ± .55 |
d(N=149) = .18 ± .33 |
Pashler, Rohrer, & Harris (2012) Study 1
|
no signal - inconsistent |
honesty priming |
individual effort |
honesty vs. neutral words exposure (synonym judgment task) |
candor (alcohol drinking questions) |
very close |
|
|
|
|
Rasinski et al. (2005)
|
d(N=62) = 1.21 ± .55 |
d(N=152) = -.13 ± .33 |
Pashler, Rohrer, & Harris (2012) Study 2
|
no signal - inconsistent |
honesty priming |
individual effort |
honesty vs. neutral words exposure (synonym judgment task) |
candor (alcohol drinking questions) |
very close |
UCSD online sample |
|
|
|
Rasinski et al. (2005)
|
d(N=62) = 1.21 ± .55 |
d(N=151) = -.14 ± .32 |
Pashler, Rohrer, & Harris (2012,online) Study 3
|
no signal - inconsistent |
honesty priming |
individual effort |
honesty vs. neutral words exposure (synonym judgment task) |
candor (alcohol drinking questions) |
close |
US mTurk sample; BIDR also measured |
|
|
|
Rasinski et al. (2005)
|
d(N=62) = 1.21 ± .55 |
d(N=153) = .04 ± .32 |
Pashler, Rohrer, & Harris (2012,online) Study 4
|
no signal - inconsistent |
honesty priming |
individual effort |
honesty vs. neutral words exposure (synonym judgment task) |
candor (alcohol drinking questions) |
close |
Indian mTurk sample; BIDR also measured |
|
|
|
Bargh et al. (2001) Study 1
|
d(N=78) = .70 ± .46 |
d(N=106) = -.24 ± .40 |
Harris, Rohrer, & Pashler (2013) Study 1
|
no signal - inconsistent |
achievement priming |
individual effort |
high-achievement vs. neutral words exposure (word search puzzle) |
word search performance (3 IV-unrelated puzzles) |
very close |
|
|
|
|
Bargh et al. (2001) Study 3
|
d(N=72) = NR |
d(N=66) = -.03 ± .49 |
Harris, Rohrer, & Pashler (2013) Study 2
|
no signal |
achievement priming (5-minute delay before DV) |
individual effort |
high-achievement vs. neutral words exposure (word search puzzle) |
word search performance (3 IV-unrelated puzzles) |
close |
for each DV puzzle, 5 of 10 words were different (not described in original paper) |
|
|
|
Devoe & House (2012) Study 3
|
d(N=145) = .75 ± .52 |
d(N=266) = -.25 ± .27 |
Connors, Campbell et al. (2016) Study 1
|
no signal - inconsistent |
time is money reminder reduces pleasure (enjoyment DV) |
individual effort |
time/money mindset vs. control; compensation vs. no compensation |
enjoyment (3-items) |
exact |
|
|
1 other DV (impatience; same pattern of results observed) |
|
Devoe & House (2012) Study 3
|
d(N=145) = .75 ± .52 |
d(N=254) = .21 ± .29 |
Connors, Campbell et al. (2016) Study 2
|
no signal - inconsistent |
time is money reminder reduces pleasure (enjoyment DV) |
individual effort |
time/money mindset vs. control; compensation vs. no compensation |
enjoyment (3-items) |
exact |
|
|
1 other DV (impatience; same pattern of results observed) |


| Quantity | Price | Infused Price |
|---|---|---|
| 6 | $10 | $12 |
| 12 | $12 | $20 |
| 18 | $20 | $25 |
| 24 | $25 | $30 |
| 32 | $30 | $40 |
| 40 | $45 | $50 |
Please contact us for prices of unlisted quantities.
| Quantity | Price |
|---|---|
| 6 | $5 |
| 12 | $11 |
| 18 | $15 |
| 24 | $20 |
| 32 | $25 |
Please contact us for prices of unlisted quantities.
| Quantity | Price |
|---|---|
| 6 | $6 |
| 12 | $10 |
| 18 | $15 |
| 24 | $18 |
| 32 | $25 |
Please contact us for prices of unlisted quantities.
| Quantity | Price |
|---|---|
| 6 | $15 |
| 12 | $25 |
| 18 | $35 |
| 24 | $45 |
| 32 | $55 |
| 40 | $65 |
| 50 | $80 |
| 100 | $95 |
Please contact us for prices of unlisted quantities.
A strawberry cake with a fluffy strawberry buttercream topped with a strawberry shortcake crumble and a fresh strawberry.
A red velvet cake (contains cocoa), with a cream cheese icing topped with walnuts (your choice).
A vanilla cake with oreo add ins with a fluffy oreo buttercream topped with a whole or oreo crumble.
A chocolate cake with chocolate buttercream topped with a mini chocolate candy bar.
A chocolate cake with marshmallow filling, a marshmallow flavored buttercream topped with crushed graham cracker and a mini hershey.


AnnoFlex provides for beginners a simple and short tutorial consisting of three parts each in the form of a scanner. Part one starts with a minimal scanner definition which shows how a scanner definition is structured in principle. Part two is based on part one and additionally contains the generated code of the scanner. Part three shows how this code can be customized.
You can use the following links to open the tutorial files:
AnnoFlex uses its own parsers for Java source code files and regular expressions. The scanners of both parsers are written with AnnoFlex and thus well-suited for the use as examples. As both scanners have dependencies to other classes they are not available as a single source code file in this examples directory. Instead they are referenced via a link into the source code directory of the AnnoFlex installation package.
If you want to investigate these scanners, it is recommended to use the entire source code of
AnnoFlex instead of the single scanner files. To do this create a new project in your favorite IDE and
copy the entire content of the source
folder into this project and add this folder as a
source folder. As the code itself does not have any dependencies to other libraries this can be
performed within a few minutes.
The JavaScanner
is used by AnnoFlex to scan Java source code files. It is a very
simple scanner which mainly consists of lexical rules for keywords and special symbols. Only comments
and literals step out of line and are more difficult to understand.
You can use the following link to open the file:
../source/org/annoflex/jdt/parser/JavaScanner.java
The RegExScanner
is used by AnnoFlex to scan regular expressions. This scanner makes
use of lexical states and state stacks. It is a good reference if you want to investigate how this
technique works.
You can use the following link to open the file:
../source/org/annoflex/regex/parser/RegExScanner.java


--- layout: post title: Bookmarks for September 24th from 09:26 to 10:15 tags: - bogart - capistrano - cd - ci - code - continuous - couchdb - cube - data - design - development - gem - google+ - gui - javascript - kids - learning - mongodb - node.js - nosql - OO - principles - ruby - rvm - SOLID - storm - tumble - twitter - unicorn - visualization status: publish type: post published: true meta: aktt_notify_twitter: 'no' ---
These are my links for September 24th from 09:26 to 10:15:


variant QML Basic Type
a generic property type.
The variant
type is a generic property type. It is obsolete and exists only to support old applications; new applications should use var type properties instead.
A variant type property can hold any of the basic type values:
Item { property variant aNumber: 100 property variant aString: "Hello world!" property variant aBool: false }
When integrating with C++, note that any QVariant value passed into QML from C++ is automatically converted into a variant
value, and vice-versa.
Using Scarce Resources with the variant Type
A variant
type property can also hold an image or pixmap. A variant
which contains a QPixmap or QImage is known as a "scarce resource" and the declarative engine will attempt to automatically release such resources after evaluation of any JavaScript expression which requires one to be copied has completed.
Clients may explicitly release such a scarce resource by calling the "destroy" method on the variant
property from within JavaScript. They may also explicitly preserve the scarce resource by calling the "preserve" method on the variant
property from within JavaScript.
Storing Arrays and Objects
The variant
type can also hold:
- An array of basic type values
- A map of key-value pairs with basic-type values
For example, below is an items
array and an attributes
map. Their contents can be examined using JavaScript for
loops. Individual array values are accessible by index, and individual map values are accessible by key:
Item { property variant items: [1, 2, 3, "four", "five"] property variant attributes: { 'color': 'red', 'width': 100 } Component.onCompleted: { for (var i = 0; i < items.length; i++) console.log(items[i]) for (var prop in attributes) console.log(prop, "=", attributes[prop]) } }
While this is a convenient way to store array and map-type values, you must be aware that the items
and attributes
properties above are not QML objects (and certainly not JavaScript object either) and the key-value pairs in attributes
are not QML properties. Rather, the items
property holds an array of values, and attributes
holds a set of key-value pairs. Since they are stored as a set of values, instead of as an object, their contents cannot be modified individually:
Item { property variant items: [1, 2, 3, "four", "five"] property variant attributes: { 'color': 'red', 'width': 100 } Component.onCompleted: { items[0] = 10 console.log(items[0]) // This will still be '1'! attributes.color = 'blue' console.log(attributes.color) // This will still be 'red'! } }
Since it is not possible to individually add or remove items from a list or object stored in a variant
, the only way to modify its contents is to reassign a new value. However, this is not efficient, as it causes the value to be serialized and deserialized.
Additionally, since items
and attributes
are not QML objects, changing their individual values do not trigger property change notifications. If the above example had onNumberChanged
or onAnimalChanged
signal handlers, they would not have been called. If, however, the items
or attributes
properties themselves were reassigned to different values, then such handlers would be called.
JavaScript programmers should also note that when a JavaScript object is copied to an array or map property, the contents of the object (that is, its key-value properties) are copied, rather than the object itself. The property does not hold a reference to the original JavaScript object, and extra data such as the object's JavaScript prototype chain is also lost in the process.
This basic type is provided by the QML language.
See also QML Basic Types.


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each case should have a message next to each form on the page with a list of warnings if any
This form shouldn't have any warnings attached to it
This form should have warnings for each of the missing hidden fields
The div that wraps this form should be missing the class "moonray-form-p2cXXXfXXXXc"
Should get warnings surrounding stating that the page doesn't a script or link with the uid in the href / source
even though is a non op form it should still get a message stating that it doesnt appear to be one


Subnetting is the deepening of the network/host hierarcy into a network/subnet(s)/host one.
The terms network and subnet both identify computer networks. Subnet is just used to imply that we are talking about a part of some larger address space.
Subnetting means taking a single network address and partitioning it into multiple logical networks.
Subnets are networks, so as with any multiple networks you need routing to exchange traffic between subnets.
As in every network, for every subnet you need to configure:
What we'll learn about subnetting will apply to both public and private IPv4 address space. Subnetting public addresses is an ISP concern, we'll focus on subnetting private blocks.
So we start with private IP address ranges:
and then we ask ourselves these questions:
How many subnets? What belongs to each subnet? How many hosts for each? What will I need in the future?
Network address = Network part + all-0s host part, distinguished thanks to the subnet mask (or prefix).
Subnets are created by taking one or more bits of the host part and making them "network bits".
The subnet mask is expanded to account for the bits borrowed from the host portion (previously 0s in the mask) that are now part of the network portion (becoming 1s).
The more bits are borrowed the more subnets are created, but with each additional subnet the available addresses decrease. Each borrowed bit doubles the subnets and halves the addresses.
1 bit borrowed
▼
NNN.NNN.NNN. HHHHHHHH
Original Network Address 192.168. 1. 00000000 = <IP_ADDRESS>/24
Original Subnet Mask 255.255.255. 00000000 = <IP_ADDRESS>
By borrowing 1 bit we can create 2 different network addresses, and thus 2 different subnets.
NNN.NNN.NNN. NHHHHHHH
Subnet 0 Network Address 192.168. 1. 00000000 = <IP_ADDRESS>/25
Subnet 1 Network Address 192.168. 1. 10000000 = <IP_ADDRESS>/25
New Subnet Mask 255.255.255. 10000000 = <IP_ADDRESS>
Before we assign addresses to devices in the subnet we must understand the available range:
NNN.NNN.NNN. N HHHHHHH
Subnet0 Network Address 192.168. 1. 0 0000000 = <IP_ADDRESS>/25
Subnet0 1st Host Address 192.168. 1. 0 0000001 = <IP_ADDRESS>
Subnet0 Last Host Address 192.168. 1. 0 1111110 = <IP_ADDRESS>
Subnet0 Broadcast Address 192.168. 1. 0 1111111 = <IP_ADDRESS>
NNN.NNN.NNN. N HHHHHHH
Subnet1 Network Address 192.168. 1. 1 0000000 = <IP_ADDRESS>/25
Subnet1 1st Host Address 192.168. 1. 1 0000001 = <IP_ADDRESS>
Subnet1 Last Host Address 192.168. 1. 1 1111110 = <IP_ADDRESS>
Subnet1 Broadcast Address 192.168. 1. 1 1111111 = <IP_ADDRESS>
Where
For every network, the network address address and the broadcast address are not valid addresses to assign an host, so 2 addresses must be removed from the total.
How many bits I need to borrow? 22 = 4. So: 2 bits.
2 bit borrowed
▾▾
NNN.NNN.NNN. HH HHHHHH
Original Network Address 192.168. 2. 00 000000 = <IP_ADDRESS>/24
Original Subnet Mask 255.255.255. 00 000000 = <IP_ADDRESS>
Each subnet will have 26 - 2 = 64 - 2 = 62 hosts.
NNN.NNN.NNN. NN HHHHHH
Subnet0 Network Address 192.168. 2. 00 000000 = <IP_ADDRESS>/26
Subnet1 Network Address 192.168. 2. 01 000000 = <IP_ADDRESS>/26
Subnet2 Network Address 192.168. 2. 10 000000 = <IP_ADDRESS>/26
Subnet3 Network Address 192.168. 2. 11 000000 = <IP_ADDRESS>/26
New Subnet Mask 255.255.255. 11 000000 = <IP_ADDRESS>
NNN.NNN.NNN. NN HHHHHH
Subnet0 Network Addr. 192.168. 2. 00 000000 = <IP_ADDRESS>/26
Subnet0 1st Host Addr. 192.168. 2. 00 000001 = <IP_ADDRESS>
Subnet0 Last Host Addr. 192.168. 2. 00 111110 = <IP_ADDRESS>
Subnet0 Broadcast Addr. 192.168. 2. 00 111111 = <IP_ADDRESS>
Subnet1 Network Addr. 192.168. 2. 01 000000 = <IP_ADDRESS>/26
Subnet1 1st Host Addr. 192.168. 2. 01 000001 = <IP_ADDRESS>
Subnet1 Last Host Addr. 192.168. 2. 01 111110 = <IP_ADDRESS>
Subnet1 Broadcast Addr. 192.168. 2. 01 111111 = <IP_ADDRESS>
Subnet2 Network Addr. 192.168. 2. 10 000000 = <IP_ADDRESS>/26
Subnet2 1st Host Addr. 192.168. 2. 10 000001 = <IP_ADDRESS>
Subnet2 Last Host Addr. 192.168. 2. 10 111110 = <IP_ADDRESS>
Subnet2 Broadcast Addr. 192.168. 2. 00 111111 = <IP_ADDRESS>
Subnet3 Network Addr. 192.168. 2. 11 000000 = <IP_ADDRESS>/26
Subnet3 1st Host Addr. 192.168. 2. 11 000001 = <IP_ADDRESS>
Subnet3 Last Host Addr. 192.168. 2. 11 111110 = <IP_ADDRESS>
Subnet3 Broadcast Addr. 192.168. 2. 11 111111 = <IP_ADDRESS>
If we need 5 subnets, we have to borrow 3 bits (24 = 8 > 5).
The remaing 5 bits in the host part allow for 25 - 2 = 30 hosts.
▾▾▾
NNN.NNN.NNN. HHH HHHHH
Original Network Addr. 192.168. 3. 000 00000 = <IP_ADDRESS>/24
Original Subnet Mask 255.255.255. 000 00000 = <IP_ADDRESS>
NNN.NNN.NNN. NNN HHHHH
Subnet0 Network Addr. 192.168. 3. 000 00000 = <IP_ADDRESS>/27
Subnet1 Network Addr. 192.168. 3. 001 00000 = <IP_ADDRESS>/27
Subnet2 Network Addr. 192.168. 3. 010 00000 = <IP_ADDRESS>/27
Subnet3 Network Addr. 192.168. 3. 011 00000 = <IP_ADDRESS>/27
Subnet4 Network Addr. 192.168. 3. 100 00000 = <IP_ADDRESS>/27
NNN.NNN.NNN. NNN HHHHH
Subnet0 Network Addr. 192.168. 3. 000 00000 = <IP_ADDRESS>/27
Subnet0 1st Host Addr. 192.168. 3. 000 00001 = <IP_ADDRESS>
Subnet0 Last Host Addr. 192.168. 3. 000 11110 = <IP_ADDRESS>
Subnet0 Broadcast Addr. 192.168. 3. 000 11111 = <IP_ADDRESS>
Subnet1 Network Addr. 192.168. 3. 001 00000 = <IP_ADDRESS>/27
Subnet1 1st Host Addr. 192.168. 3. 001 00001 = <IP_ADDRESS>
Subnet1 Last Host Addr. 192.168. 3. 001 11110 = <IP_ADDRESS>
Subnet1 Broadcast Addr. 192.168. 3. 001 11111 = <IP_ADDRESS>
Subnet2 Network Addr. 192.168. 3. 010 00000 = <IP_ADDRESS>/27
Subnet2 1st Host Addr. 192.168. 3. 010 00001 = <IP_ADDRESS>
Subnet2 Last Host Addr. 192.168. 3. 010 11110 = <IP_ADDRESS>
Subnet2 Broadcast Addr. 192.168. 3. 010 11111 = <IP_ADDRESS>
Subnet3 Network Addr. 192.168. 3. 011 00000 = <IP_ADDRESS>/27
Subnet3 1st Host Addr. 192.168. 3. 011 00001 = <IP_ADDRESS>
Subnet3 Last Host Addr. 192.168. 3. 011 11110 = <IP_ADDRESS>
Subnet3 Broadcast Addr. 192.168. 3. 011 11111 = <IP_ADDRESS>
Subnet4 Network Addr. 192.168. 3. 100 00000 = <IP_ADDRESS>/27
Subnet4 1st Host Addr. 192.168. 3. 100 00001 = <IP_ADDRESS>
Subnet4 Last Host Addr. 192.168. 3. 100 11110 = <IP_ADDRESS>
Subnet4 Broadcast Addr. 192.168. 3. 100 11111 = <IP_ADDRESS>
If we need 100 subnets, we need to borrow at least 7 bits from the host part of our address block: 27 = 128 > 100.
If our address block is a /24 network, that would generate a /31 prefix and leave 1 bit in the host part, so 21-2 = 0 hosts!
This means we need more host bits to borrow from, and therefore a larger starting address block. For instance...
▾▾▾▾▾▾▾
NNN.NNN.HHHHHHH H.HHHHHHHH
Original Network Add. 172. 20.0000000 0.00000000 = <IP_ADDRESS>/16
Original Subnet Mask<PHONE_NUMBER>000 0.00000000 = <IP_ADDRESS>
NNN.NNN.NNNNNNN H.HHHHHHHH
New Subnet Mask<PHONE_NUMBER>111 0.00000000 = <IP_ADDRESS>
Subnet0 Network Addr. 172. 20.0000000 0.00000000 = <IP_ADDRESS>/23
Subnet1 Network Addr. 172. 20.0000001 0.00000000 = <IP_ADDRESS>/23
Subnet2 Network Addr. 172. 20.0000010 0.00000000 = <IP_ADDRESS>/23
Subnet3 Network Addr. 172. 20.0000011 0.00000000 = <IP_ADDRESS>/23
[..............................CUT...............................]
Subnet126 Netw. Addr. 172. 20.1111110 0.00000000 = <IP_ADDRESS>/23
Subnet127 Netw. Addr. 172. 20.1111111 0.00000000 = <IP_ADDRESS>/23
Subnetting is done in the 3rd octect, host bits share part of the 3rd, plus the whole 4th octect.
Total hosts per subnet: 29 - 2 = 512 - 2 = 510.
Let's say we need subnets capable of > 5000 hosts. We:
This could very well not be enough for most needs. The solution is to start from a larger block, like <IP_ADDRESS>/8.
In a "10/8", after reserving the last 13 bits for our 5000+ hosts, we are left with 11 bits to borrow for subnetting. That's 211 = 2048 subnets with a /19 prefix (<IP_ADDRESS>).
▾▾▾▾▾▾▾▾ ▾▾▾
NNN.HHHHHHHH.HHH HHHHH.HHHHHHHH
Original Net. Addr. 10.00000000.000 00000.00000000 = <IP_ADDRESS>/8
Original Mask<PHONE_NUMBER>0.000 00000.00000000 = <IP_ADDRESS>
New Subnet Mask<PHONE_NUMBER>1.111 00000.00000000 = <IP_ADDRESS>
NNN.NNNNNNNN.NNN HHHHH.HHHHHHHH
Subnet0 Net. Addr. 10.00000000.000 00000.00000000 = <IP_ADDRESS>/19
Subnet1 Net. Addr. 10.00000000.001 00000.00000000 = <IP_ADDRESS>/19
Subnet2 Net. Addr. 10.00000000.010 00000.00000000 = <IP_ADDRESS>/19
[..............................CUT...............................]
Subnet8 Net. Addr. 10.00000001.000 00000.00000000 = <IP_ADDRESS>/19
[..............................CUT...............................]
Subnet2045 Net.Addr. 10.11111111.101 00000.00000000 = <IP_ADDRESS>/19
Subnet2046 Net.Addr. 10.11111111.110 00000.00000000 = <IP_ADDRESS>/19
Subnet2047 Net.Addr. 10.11111111.111 00000.00000000 = <IP_ADDRESS>/19
The key to basic subnetting is to balance number of subnets, and hosts for each, based on requirements.
With basic subnetting, every subnet has the same size. When the number of hosts needed in each is very different, that would lead to significant waste and unused addresses.
Subnets too large for the actual need will also limit growth by reducing the number of available subnets.
The solution to these scenarios is a subnetting method called Variable Length Subnet Mask (VLSM).
It does what the name says: the number of bits borrowed (hence, the subnet mask) varies with each subnet.
The difference is it's not a single-pass process anymore:
Starting from a /24: we need 9 subnets, so 4 bits to borrow. It means 16 subnets with a /28 mask, each capable of 14 hosts.
This means 12 wasted address on each PtP link, 48 total!
And 14 hosts per subnet are a bit low, aren't they?
Operating with traditional, basic subnetting:
Original Network Address <IP_ADDRESS>/24
Original Subnet Mask <IP_ADDRESS>
New Subnet Mask <IP_ADDRESS> = /28
Subnet 0 <IP_ADDRESS>/28
Subnet 1 <IP_ADDRESS>/28
Subnet 2 <IP_ADDRESS>/28
Subnet 3 <IP_ADDRESS>/28
[...]
Subnet 14 <IP_ADDRESS>/28
Subnet 15 <IP_ADDRESS>/28
Wasteful. Let's take the first /28 subnet and subnet it again into 4 /30 subnets of 2 hosts each: 0 wasted addresses!:
Subnet0 - PtP #1 <IP_ADDRESS>/30 # subnetted from <IP_ADDRESS>/28
Subnet1 - PtP #2 <IP_ADDRESS>/30 # subnetted from <IP_ADDRESS>/28
Subnet2 - PtP #3 <IP_ADDRESS>/30 # subnetted from <IP_ADDRESS>/28
Subnet3 - PtP #4 <IP_ADDRESS>/30 # subnetted from <IP_ADDRESS>/28
Subnet4 <IP_ADDRESS>/28
Subnet5 <IP_ADDRESS>/28
Subnet6 <IP_ADDRESS>/28
[...]
Subnet12 <IP_ADDRESS>/28
Subnet13 <IP_ADDRESS>/27 # 30 hosts capable
Subnet14 <IP_ADDRESS>/27 # 30 hosts capable
Subnet15 <IP_ADDRESS>/27 # 30 hosts capable
In addition to the perfect fit for the PtP links, VLSM also allows us to create more flexible subnetting.
In this example we ditched the last 6 /28 subnets for 3 /27 subnets in which we can host up to 30 devices.
When using VLSM:
We plan the addressing scheme for our network for the following purposes:
It is best to assign addresses to hosts based on a recognizable pattern, which will help the administrator and the documentation process.
IPv4 Subnetting is done to manage address scarcity.
Subnetting IPv6 is done for an entirely different reason: to support hierarchical and logical network design.
An IPv6 (public) address block with a /48 (Global Routing) prefix has 16 bits for subnet ID (65536 /64 subnets).
A dedicated Subnet ID field means there's no need to borrow bits from Interface ID (host portion).
Subnets created by the Subnet ID are easy to identify and represent, it just works like an hexadecimal counter.
 ]
Subnet 65533 2001:0DB8:ACAD:FFFD::/64
Subnet 65534 2001:0DB8:ACAD:FFFE::/64
Subnet 65535 2001:0DB8:ACAD:FFFF::/64
Subnetting IPv6 is not that much different from IPv4.
The leading practice is to receive at least a /48 prefix from an ISP. This leaves 280 bits to manipulate for subnetting.
RFC 4291 says the current smallest recommended prefix is /64. You can assign a /64 to PtP links and not feel guilty.
Using a prefix smaller than /64 is actually strongly discouraged because it breaks IPv6 features that rely on a 64-bit Interface ID, like SLAAC.
Some ISPs insist to give customers a single /64 IPv6 subnet, so they can't use the Subnet ID field to do subnetting.
You can of course borrow bits from the Interface ID to have a deeper hierarchy. The best practice is to borrow 4 bits at a time and used aligned netmasks (/68, /72, /76, etc.). Why?
A nibble is a 4 bit group, or one hex digit. By subnetting on nibble boundaries we create subnets just by adding 0x01.


Created By: Sivalingam
Date: Nov 2015
Venue: Electronic city
Place: Bangalore
The Hindu tradition relates diamonds to the planet Venus. Diamond, king of all crystals represent faithfulness, love, purity, innocence, and relationships filled with love. It removes certain types of deviations The Hindu tradition relates diamonds to the planet Venus. Diamond, king of all crystals represent faithfulness, love, purity, innocence, and relationships filled with love.
The Hindu tradition relates diamonds to the planet Venus. Diamond, king of all crystals represent faithfulness, love, purity, innocence, and relationships filled with love. It removes certain types of deviations
-
The Hindu tradition relates diamonds
-
The Hindu tradition relates diamonds
Sathish Kumar
11 Nov 2015 03:10:12 pm


Profile informations
{{ user }} tasks
Home
News
Contact
About
Profile of {{ user }}
You have {{ nb_tasks }} tasks :
{% for id, name, start, end, status in tasks %}
{{ name }}
{% endfor %}
name
starting date
ending date
not started
in progress
done
Written by
Lucas TABIS
and
Paul BRUNNER
and
Lucas HEMMERLÉ
Students from
EPITECH


Meet the team
Connect with us
Explore Lincoln
Housing
Food
Transportation
Entertainment
{% block content %}{% endblock %}
Connect with us


Pick a task, set a timer for 25 minutes (or more), work on that task only until the timer ends, then take a short break.
You can create and send invoices for the time you tracked. Fast and simple, from any device.
Get access to a detailed time log and learn where you've spent the time. Filter by projects, sources and dates. Import and export the logs in .CSV format.
Get a bird's-eye view of the sources, the projects you're working on and your daily summary.
$8
per month billed annually
Single use license, best suited for freelancers.
$14
per month billed annually
Maximum 10 licenses, best suitable for agencies.
$59
per month billed annually
Unlimited licenses, best suitable for large companies.
Everything is smooth and super easy to use. I'm really happy with my choice.
My experience with Ascension has been great from the very start. Super easy to use software, I love it.
Bootstrap takes care of its customers and I love that.
It's awesome, I highly recommend it.


CWPIL No. 168 of 2017 14.8.2018 Present: Mr. Dheeraj K.Vashisht, Advocate, as Amicus Curiae.
.
Mr. T.S. Chauhan, Advocate for respondent No.1.
Mr. Ashok Sharma, Advocate General with M/s Ranjan Sharma, Ritta Goswami & Nand Lal Thakur, Additional Advocates General for respondent-State Mr. C.N. Singh,Advocate for respondents No.4, 6 & 7.
Mr. Goverdhan Lal Sharma, Advocate for respondent No.5.
Affidavit dated 13.8.2018 filed by the Chairman, HP State Pollution Control Board Shimla, is neither in consonance nor in the spirit of our order dated 10.8.2018.
At this stage, Mr. T.S. Chauhan, learned counsel for respondent No.1 seeks time to file a better affidavit. Let needful be done within a period of two weeks.
We clarify that we would not hesitate of passing appropriate orders issuing directions to the State for taking appropriate action in accordance with Section 62 of the Water (Prevention and Control of Pollution) Act, 1974 and other environmental laws.
Mr. T.S. Chauhan, learned counsel for respondent No.1 has placed on record communication dated 19 th July, 2018. Conscious of the fact that H.P. Pubic Service Commission is not a party to the present petition, however, finding Mr. D.K. Khanna, learned Counsel, who normally appears for Himachal Pradesh Public Service Commission, we direct him to ascertain the factual position in this regard.
List on 17.8.2018.
(Sanjay Karol) Acting Chief Justice (Ajay Mohan Goel) Judge 14th August, 2018 (Guleria) ::: Downloaded on - 16/08/2018 23:00:25 :::HCHP


Cascading JS Variables let CSS authors include cascading variables in the stylesheets they write.
What does it mean for a variable to 'cascade'? In this case it means variable assignments are inherited globally, but can be reassigned for individual elements and children of those elements differently. Another feature of cascading variables is their ability to change in value (responsively via CSS, or via JS) in different situations making them a very flexible tool for the expression of dynamic values with a straightforward notation inside CSS.
--variable: value;
var(--variable);
The syntax supported by this plugin for assigning and using a variable is the same as the syntax for native CSS variables. Each variable name is represented by a property name beginning with two hyphens: --
. To reference the value of a variable inside CSS values, use the function var()
with the name of the variable between the brackets.
data-variable="value"
This plugin also allows HTML authors to assign cascading variables by defining a custom data attribute beginning with data-
and followed by the variable name, with the desired variable value set as the value of the attribute in HMTL.
JavaScript authors are also able to use this custom attribute interface to more easily assign and reassign different values to elements in the DOM.
cascadingVariables(selector, rule)
selector
is a CSS selector
rule
is a semicolon-separated list of CSS property declarations
This plugin is a JavaScript function that accepts a CSS selector and properties, including variable assignment and references. The plugin will find any elements in the DOM matching the given CSS selector, mark them with a unique attribute, and return CSS text with styles written targeting elements by these unique attributes.
To set the variable --example
to green
we can accomplish that a few ways:
cascadingVariables(':root', '--example: green;')
This would have the effect of setting the following attribute on the HTML tag:
data-example="green"
Which could also be achieved in HTML by writing:
<html data-example="green">
Or added programmtically via JS at any point with something like:
document.documentElement.setAttribute('data-example', 'green')
In any of these cases, whether assigned from CSS, HTML, or JavaScript, the plugin would be able to read a rule like this and use the value `green` for the variable:
cascadingVariables(':root', 'background: var(--example);')


LoadedTranslationType
Specifies available translation types used by translation processor to have
knowledge about specifics of loaded translations. It helps later to decide
what is needed to do with LoadedTranslation
’s content
property.
-
Pair where value is dictionary that contains dictionary with expression and length variations.
"car-sentence": { "one": { "@100": "one car", "@200": "just one car", "@300": "you've got just one car" }, "more": { "@100": "%d cars", "@300": "you've got %d cars" }, "two": "two cars" }
Declaration
Swift
case WithExpressionsAndLengthVariations
-
Pair where value is a dictionary with length variations.
"forgot-password": { "@100": "Forgot Password? Help.", "@200": "Forgot Password? Get password Help.", "@300": "Forgotten Your Password? Get password Help." }
Declaration
Swift
case WithLengthVariations
-
Pair where value is a dictionary with expressions.
"cars": { "one": "1 car", "ie:x=2": "2 cars", "more": "%d cars" }
Declaration
Swift
case WithExpressions
-
Simple key-value pair.
"welcome": "welcome"
Declaration
Swift
case Simple


UCFR(1) Debian GNU/Linux manual UCFR(1) NAME ucfr - Update Configuration File Registry: associate packages with con‐ figuration files SYNOPSIS ucfr [options] <Package> <Path to configuration file> DESCRIPTION Where Package is the package associated with the configuration file (and, in some sense, its owner), and Path to configuration file is the full path to the location (usually under /etc) where the configuration file lives, and is potentially modified by the end user. This script maintains an association between configuration files and packages, and is meant to help provide facilities that dpkg provides conffiles for configuration files and not shipped in a Debian package, but handled by the postinst by ucf instead. This script is idempotent, associating a package to a file multiple times is not an error. It is normally an error to try to associate a file which is already associated with another package, but this can be over ridden by using the --force option. OPTIONS -h, --help Print a short usage message -n, --no-action Dry run. Print the actions that would be taken if the script is invoked, but take no action. -d [n], --debug [n] Set the debug level to the (optional) level n (n defaults to 1). This turns on copious debugging information. -p, --purge Removes all vestiges of the association between the named package and the configuration file from the registry. The association must already exist; if the configuration file is associated with some other package, an error happens, unless the option --force is also given. In that case, the any associations for the configuration file are removed from the registry, whether or not the package name matches. This action is idempotent, asking for an association to be purged multiple times does not result in an error, since attempting to remove an non-existent association is silently ignored unless the --verbose option is used (in which case it just issues a diagnostic). -v, --verbose Make the script be very verbose about setting internal variables. -f, --force This option forces operations requested even if the configuration file in consideration is owned by another package. This allows a package to hijack a configuration file from another package, or to purge the association between the file and some other package in the registry. --state-dir /path/to/dir Set the state directory to /path/to/dir instead of the default /var/lib/ucf. Used mostly for testing. USAGE The most common case usage is pretty simple: a single line invocation in the postinst on configure, and another single line in the postrm to tell ucfr to forget about the association with the configuration file on purge (using the --purge option) is all that is needed (assuming ucfr is still on the system). FILES /var/lib/ucf/registry, and /var/lib/ucf/registry.X, where X is a small integer, where previous versions of the registry are stored. /etc/ucf.conf EXAMPLES If the package foo wants to use ucfr to associate itself with a configu‐ ration file foo.conf, a simple invocation of ucfr in the postinst file is all that is needed: ucfr foo /etc/foo.conf On purge, one should tell ucf to forget about the file (see detailed examples in /usr/share/doc/examples): ucfr --purge foo /etc/foo.conf SEE ALSO ucf(1), ucf.conf(5). AUTHOR This manual page was written Manoj Srivastava<EMAIL_ADDRESS>for the Debian GNU/Linux system. Debian Apr 11 2006 UCFR(1)


Phone number to time zone mapping functionality
>>> import phonenumbers
>>> from phonenumbers.timezone import time_zones_for_number
>>> ro_number = phonenumbers.parse("+40721234567", "RO")
>>> tzlist = time_zones_for_number(ro_number)
>>> len(tzlist)
1
>>> str(tzlist[0])
'Europe/Bucharest'
>>> dual_number = phonenumbers.parse("+976136234567", "US")
>>> tzlist = time_zones_for_number(dual_number)
>>> len(tzlist)
2
>>> str(tzlist[0])
'Asia/Choibalsan'
>>> str(tzlist[1])
'Asia/Ulaanbaatar'
Functions |
| | |
- time_zones_for_geographical_number(numobj)
- Returns a list of time zones to which a phone number belongs.
This method assumes the validity of the number passed in has already been
checked, and that the number is geo-localizable. We consider fixed-line
and mobile numbers possible candidates for geo-localization.
Arguments:
numobj -- a valid phone number for which we want to get the time zones
to which it belongs
Returns a list of the corresponding time zones or a single element list
with the default unknown time zone if no other time zone was found or if
the number was invalid
- time_zones_for_number(numobj)
- As time_zones_for_geographical_number() but explicitly checks the
validity of the number passed in.
Arguments:
numobj -- a valid phone number for which we want to get the time zones to which it belongs
Returns a list of the corresponding time zones or a single element list with the default
unknown time zone if no other time zone was found or if the number was invalid
|
Data |
| | |
UNKNOWN_TIMEZONE = 'Etc/Unknown'
__all__ = ['UNKNOWN_TIMEZONE', 'time_zones_for_geographical_number', 'time_zones_for_number'] |


My Team
Bob
Manager
ID: 1
Email:
<EMAIL_ADDRESS>Office number: 13
Sally
Engineer
ID: 2
Email:
<EMAIL_ADDRESS>GitHub:
sallysaysso
Sam
Intern
ID: 3
Email:
<EMAIL_ADDRESS>School: UNC


Displays the difference between two branches.
git diff <branch>..<other-branch>
to view the difference between <branch>
and <other-branch>
.git diff <branch>..<other-branch>
git diff patch-1..patch-2
# Displays the difference between branches `patch-1` and `patch-2`


An abstract base class extending [page:Curve]. A CurvePath is simply an array of connected curves, but retains the api of a curve.
The constructor take no parameters.
See the base [page:Curve] class for common properties.
The array of [page:Curve Curves].
Whether or not to automatically close the path.
See the base [page:Curve] class for common methods.
Add a curve to the [page:.curves] array.
Adds a [page:LineCurve lineCurve] to close the path.
Get list of cumulative curve lengths of the curves in the [page:.curves] array.
divisions -- number of pieces to divide the curve into. Default is
`12`.
Returns an array of points representing a sequence of curves. The
`division` parameter defines the number of pieces each curve is divided
into. However, for optimization and quality purposes, the actual sampling
resolution for each curve depends on its type. For example, for a
[page:LineCurve], the returned number of points is always just 2.
divisions -- number of pieces to divide the curve into. Default is
`40`.
Returns a set of divisions + 1 equi-spaced points using getPointAt( u ).
[link:https://github.com/mrdoob/three.js/blob/master/src/[path].js src/[path].js]


A note on frustration
Argh, I'm trying to build some iPhone apps, and it looks like building on Leopard is a difficult nut to crack - I think right now the problem is the headers I have aren't correct. Bugger!  There's some tomfoolery in getting the tools to work, and that may be the real problem. Argh!
About this entry
You"re currently reading "A note on frustration," an entry on a crick in the net
- Published:
- 12.01.07 / 6pm
- Category:
- Code Geekery, Gadget-o-rama


{% extends "base.html" %} {% block page_header %}
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Michael Mose Biskjaer, Peter Dalsgaard, Kim Halskov
Interesting focus on interaction design, specifically the ideation and concept development phase, so very relevant for firmware design.
"The field of interaction design deals with the development and use of interactive products, systems and services. It is an interventionist discipline in that it aims at transforming the current state of events through the introduction of something novel. Some interaction design projects radically rethink and transform the situation, whereas others incrementally develop existing systems and practices; however, innovation and creativity play a crucial role in most interaction design projects due to this transformative agenda."
Interaction Design


This is the JavaScript version of MicroPython, plus the
LittlevGL bindings for MicroPython, plus secp256k1, extended hashlib and bitcoin library.
You can type your own Python code into the prompt in the usual way, or by using the editor. When you want to run your customized script, click Restart.


|
iDreamSky Game Engine
v1.0.0
|
公開屬性 | |
| Vector3 | contactNormalWS |
| contact Normal | |
| Vector3 | contactPointWS |
| raycast Hit Point | |
| float | suspensionLength |
| suspension length | |
| Vector3 | hardPointWS |
| raycast starting point | |
| Vector3 | wheelDirectionWS |
| direction in worldspace | |
| Vector3 | wheelAxleWS |
| axle in worldspace | |
| bool | isInContact |
| Matrix4 | worldTransform |
| world Transform | |