Hardware-Interfaces Commands¶
This section contains those interface commands that are related to hardware-interfaces:
Note
For a complete list of CLI Debug commands refer to the Debug CLI section of the Source Code Documents .
Show Bridge-Domain¶
Summary/Usage¶
show bridge-domain [bridge-domain-id [detail|int|arp| bd-tag ]]
Description¶
Show a summary of all the bridge-domain instances or detailed view of a single bridge-domain. Bridge-domains are created by adding an interface to a bridge using the set interface l2 bridge command.
Example Usage¶
Example of displaying all bridge-domains:
vpp# show bridge-domain
ID Index Learning U-Forwrd UU-Flood Flooding ARP-Term BVI-Intf
0 0 off off off off off local0
200 1 on on on on off N/A
Example of displaying details of a single bridge-domains:
vpp# show bridge-domain 200 detail
ID Index Learning U-Forwrd UU-Flood Flooding ARP-Term BVI-Intf
200 1 on on on on off N/A
Interface Index SHG BVI VLAN-Tag-Rewrite
GigabitEthernet0/8/0.200 3 0 - none
GigabitEthernet0/9/0.200 4 0 - none
Declaration and Implementation¶
Declaration: bd_show_cli (src/vnet/l2/l2_bd.c line 1151)
Implementation: bd_show
Show Hardware-Interfaces¶
Display more detailed information about all or a list of given interfaces. The verboseness of the output can be controlled by the following optional parameters:
brief: Only show name, index and state (default for bonded interfaces).
verbose: Also display additional attributes (default for all other interfaces).
detail: Also display all remaining attributes and extended statistics.
Note
To limit the output of the command to bonded interfaces and their slave interfaces, use the ‘bond’ optional parameter.
Summary/Usage¶
show hardware-interfaces [brief|verbose|detail] [bond] [<interface> [<interface> [..]]] [<sw_idx> [<sw_idx> [..]]].
Examples¶
Example of how to display default data for all interfaces:
vpp# show hardware-interfaces
Name Idx Link Hardware
GigabitEthernet7/0/0 1 up GigabitEthernet7/0/0
Ethernet address ec:f4:bb:c0:bc:fc
Intel e1000
carrier up full duplex speed 1000 mtu 9216
rx queues 1, rx desc 1024, tx queues 3, tx desc 1024
cpu socket 0
GigabitEthernet7/0/1 2 up GigabitEthernet7/0/1
Ethernet address ec:f4:bb:c0:bc:fd
Intel e1000
carrier up full duplex speed 1000 mtu 9216
rx queues 1, rx desc 1024, tx queues 3, tx desc 1024
cpu socket 0
VirtualEthernet0/0/0 3 up VirtualEthernet0/0/0
Ethernet address 02:fe:a5:a9:8b:8e
VirtualEthernet0/0/1 4 up VirtualEthernet0/0/1
Ethernet address 02:fe:c0:4e:3b:b0
VirtualEthernet0/0/2 5 up VirtualEthernet0/0/2
Ethernet address 02:fe:1f:73:92:81
VirtualEthernet0/0/3 6 up VirtualEthernet0/0/3
Ethernet address 02:fe:f2:25:c4:68
local0 0 down local0
local
Example of how to display verbose data for an interface by name and software index (where 2 is the software index):
vpp# show hardware-interfaces GigabitEthernet7/0/0 2 verbose
Name Idx Link Hardware
GigabitEthernet7/0/0 1 up GigabitEthernet7/0/0
Ethernet address ec:f4:bb:c0:bc:fc
Intel e1000
carrier up full duplex speed 1000 mtu 9216
rx queues 1, rx desc 1024, tx queues 3, tx desc 1024
cpu socket 0
GigabitEthernet7/0/1 2 down GigabitEthernet7/0/1
Ethernet address ec:f4:bb:c0:bc:fd
Intel e1000
carrier up full duplex speed 1000 mtu 9216
rx queues 1, rx desc 1024, tx queues 3, tx desc 1024
cpu socket 0
Clear Hardware-Interfaces¶
Clear the extended statistics for all or a list of given interfaces (statistics associated with the show hardware-interfaces command).
Summary/Usage¶
clear hardware-interfaces [<interface> [<interface> [..]]] [<sw_idx> [<sw_idx> [..]]].
Examples¶
Example of how to clear the extended statistics for all interfaces:
vpp# clear hardware-interfaces
Example of how to clear the extended statistics for an interface by name and software index (where 2 is the software index):
vpp# clear hardware-interfaces GigabitEthernet7/0/0 2


This, from Ptak Science Books, is simply magnificent:
M Maid Fainting
For the millions of Americans who were newly affluent in 1925 but not affluent enough to afford a fainting maid that they could attend to came the Maid Fainting Society Inc of America. Bonded in 15 states, MFSA sponsored overseas maids to faint in good American homes. They were hired out at $4/hour (a handsome hourly sum in 1925), the fainting maids being allowed to keep half. In 1926 a sub-cult of Extra-Tall Fainting Maids was born (an example of which is pictured above), and provided an income to thousands for several years. (One of the last silent films ever made, Fainting Maid Maiden, with Clara Bow, 1944, used this phenomenon as a basis for its story; by that time however the craze had passed, the war was on, and no one cared any more for silent films about fainting maids.)


Change a Game's Name
The name displayed for a game can be changed by selecting the game and then clicking on its name. Changing the name only changes the name used to display the game in Count It. This does not change the name of the app itelf.


Block Count: {{blockCount}}
Unchecked Blocks: {{uncheckedBlocks}}
Connected Peers: {{peerCount}}
xrb_31rgf5n8om6pja6y6xfcrq8f6j3rsq546ym9fmd3izoesjqw5ytimtbpdt9y


CrimPapers
Articles published on 8 February 2019
On 8 February 2019, 6 new articles were published in 4 journals.
Size and Scope of the Tobacco Trade on the Darkweb by Viviana Barrera, Aili Malm, David Décary-Hétu, Rasmus Munksgaard
Interethnic Conflict and Genocide in Myanmar by Afroza Anwary
Prescribed Correctional Treatment Dosage: Cautions, Commentary, and Future Directions by David J. Simourd, Mark Olver
The Effectiveness of A Problem-Solving Court For Individuals Charged With Misdemeanor Prostitution in Harris County Texas by Lisa R. Muftic, Alexander H. Updegrove
Back to the Future: A Retrospective Cost Benefit Analysis of An Education Dropout Program As An Intervention to Prevent Reincarceration by Robert J. Brent, Tina Maschi
Reducing Rape-Related Attitudes Utilizing A Cognitive Dissonance Paradigm by Sarah E. Steinmetz, Matt J. Gray, Elizabeth M. Raymond


Ooni, Nwodo, others root for national unity - The Nation Newspaper
Emma Elekwa, Onitsha
Ooni of Ife, Oba Adeyeye Enitan Ogunwusi has called for unity among Nigerians regardless of tribes, ethnicity and religious backgrounds.
He said such unity was capable of further enthrenching peaceful co-existence among the people of the country.
Speaking during the World Eri Festival at Enugwu Aguleri, Anambra State, the monarch who was represented by the Asoya of Ile Isoya Ife Kingdom,
Oba Murainu Adebanjo said he was happy to be part of the event, describing Ndigbo as great people.
He said, “I’m interested in national unity because it will guarantee peace as well as bring the best of the people into a more prosperous country where the safely, security and people’s happiness are guaranteed.
“A peaceful and prosperous country is all that we are praying for and we can only get it when we are strongly united as a people.”
The monarch congratulated the Eze Eri and his subject for organizing the event, just as he praised the African Americans that came to identify with their progenitor.
“My prayer is that the festival will continue to serve as a rallying point for integration of people across-board,” he added.
Also speaking, President General of Ohaneze Ndigbo Chief Nnia Nwodo said the Eri Kingdom has made Ndigbo proud through the festival.
Nwodo, represented by the President of Ohaneze in the State, Chief Damian Afam Ogene described Ooni’s visit as another version of handshake across the Niger from the cultural point of view.
“The festival not only showcases the culture of Ndigbo to the rest of the world but also helps to connect them with others in a manner that enhances friendship between them and their counterparts,” he added.
Nwodo who was honored with a Chieftaincy title of Okwulora (the voice) of Igboland by Eze Eri said the honor would inspire him to continue to serve his people.
READ ALSO: No compromise on national unity – Buhari
“Ohaneze is committed to promoting national unity and will always identify with activities aimed at entrancing national unity from all angles as cultural, religious or political,” he noted.
Earlier, the host and celebrant of the festival, Eze Chukwuemeka Eri said the occasion was a time the Igbo race come together to remember their progenitor Eri said to have migrated from Israel and settled at Aguleri.
He said, “Today we commemorate and celebrate an African and global historical figure Nna Anyi (our father) Eri” the Eze begins rhetorically.
“I welcome my brother and friend from our brotherly land of Yoruba. I thank him for coming here to celebrate with us.
“I use this opportunity to call for peace and unity among the Yoruba and the Igbo and the rest of Nigerians.”


Including: Learning trace, Finance trace, Event trace, Library, and more.
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Released At: {{relDate}}


Barebones Git
11 Jul 2016Many people I know confuse Git with Github, and probably don’t have a good idea of what version control is. I guess, half the trouble with lies in the steep learning curve, not to mention how new and bizzare some concepts are. I’ve had my own million doubts before using Git and never knew where to start. Even if I did start, I’d have had given up on the second day not understanding why certain thing is the way it is.
This post is just for that, to de-mistify what a good tool Git is and how easy it is to integrate into the workflow and, really, master version controlling with only a handful of commands. I hope the days of copy-pasting a file in the name of ‘backup’ are a days of the past.
Firstly…
Git is a version control software, created by Linus Torvalds, the founder of Linux. Github, on the other hand is a neat platform to share and store code along with all the goodness of git. Github, is by default, a free platform at the cost of hosting the contents visible to the public. There’s a subscription service to have propitiatory projects stored online in all secrecy. Even though Git can seamlessly integrate with Github, it is however, also compatible with other code sharing platforms or just any location for that matter (ftps and shared networks, included)
Secondly…
It’s all about practice. Let’s dedicate this section to create a setup, to restore backups, without copy-pasting a single file.
Let’s start setting up in an empty folder (I’ll name it gittut)
The magic starts with initializing a Git repository
C:\...\>mkdir gittut
C:\...\>cd gittut
C:\...\gittut>git init
It means that the folder is ready for some version control action! The only visible difference would be that there is be .git folder created in the directory which is hidden by default. The takeaway point is that any location containing a .git folder means one or more (generally all) files have versions which Git is handling. Let’s create a text file named ‘my.txt’.
C:\...\gittut>more my.txt
First line, nothing more!!
Git still wouldn’t know that something new has been added. Technically, it hasn’t started tracking any files.
 to include in what will be committed)
my.txt
nothing added to commit but untracked files present (use "git add" to track)
Git status gives, well, the current status of the folder.
The way to start tracking a file is
C:\...\gittut>git add my.txt
C:\...\gittut>git status
On branch master
Initial commit
Changes to be committed:
(use "git rm --cached <file>..." to unstage)
new file: my.txt
To add all the files in a folder, one would git add -A
Let’s see what happens after editing the text file…
C:\...\gittut>more my.txt
First line, nothing more!! :3
Second line... ADDED!
There was a modification in the first line ‘:3’ and a second line was added.
 to unstage)
new file: my.txt
Changes not staged for commit:
(use "git add <file>..." to update what will be committed)
(use "git checkout -- <file>..." to discard changes in working directory)
modified: my.txt
Git notifies the modification that happend with respect to the state when a file was added. This is because, the moment we add
a file (to what is called as a stage), it freezes the state of the file for a possible commit. And any modifications after add
-ing a file will not be committed in the future. add
functions as a soft commit where if a code goes awry after modification, one could always go back to the state when it was add
-ed. git checkout my.txt
would essentially revert back the text file to the state when it was added for staging.
C:\...\gittut>git checkout my.txt
C:\...\gittut>more my.txt
First line, nothing more!!
And this way, it is always possible to revert a dysfunctional code back to a working state (given you add
the file to stage everytime a code works just fine). There’s your backup isn’t it!?
Of course! We’ve just scratched the surface of the shell. We barely tickled the tentacles and hardly glanced at the ginormous gizmo, which is Git. There’s more to git than simple add
command.


Introduction
Features
Running SoS
Documentation
Search
SoS
Workflow System for Data Analysis
Introduction
Features
Running SoS
Documentation


Class: ActionDispatch::Journey::Visitors::FunctionalVisitor
Do not use. This class is for internal use only.
| Relationships & Source Files | |
| Extension / Inclusion / Inheritance Descendants | |
|
Subclasses:
|
|
| Inherits: | Object |
| Defined in: | actionpack/lib/action_dispatch/journey/visitors.rb |
Constant Summary
-
DISPATCH_CACHE =
# File 'actionpack/lib/action_dispatch/journey/visitors.rb', line 96{}
Instance Method Summary
Instance Method Details
#accept(node, seed)
[ GitHub ]# File 'actionpack/lib/action_dispatch/journey/visitors.rb', line 98
def accept(node, seed) visit(node, seed) end
#binary(node, seed)
[ GitHub ]#nary(node, seed)
[ GitHub ]# File 'actionpack/lib/action_dispatch/journey/visitors.rb', line 111
def nary(node, seed) node.children.inject(seed) { |s, c| visit(c, s) } end
#terminal(node, seed)
[ GitHub ]# File 'actionpack/lib/action_dispatch/journey/visitors.rb', line 122
def terminal(node, seed); seed; end
#unary(node, seed)
[ GitHub ]# File 'actionpack/lib/action_dispatch/journey/visitors.rb', line 116
def unary(node, seed) visit(node.left, seed) end
#visit(node, seed)
[ GitHub ]# File 'actionpack/lib/action_dispatch/journey/visitors.rb', line 102
def visit(node, seed) send(DISPATCH_CACHE[node.type], node, seed) end
#visit_CAT(n, seed)
[ GitHub ]# File 'actionpack/lib/action_dispatch/journey/visitors.rb', line 109
def visit_CAT(n, seed); binary(n, seed); end
#visit_DOT(n, seed)
[ GitHub ]# File 'actionpack/lib/action_dispatch/journey/visitors.rb', line 126
def visit_DOT(n, seed); terminal(n, seed); end
#visit_GROUP(n, seed)
[ GitHub ]# File 'actionpack/lib/action_dispatch/journey/visitors.rb', line 119
def visit_GROUP(n, seed); unary(n, seed); end
#visit_LITERAL(n, seed)
[ GitHub ]# File 'actionpack/lib/action_dispatch/journey/visitors.rb', line 123
def visit_LITERAL(n, seed); terminal(n, seed); end
#visit_OR(n, seed)
[ GitHub ]# File 'actionpack/lib/action_dispatch/journey/visitors.rb', line 114
def visit_OR(n, seed); nary(n, seed); end
#visit_SLASH(n, seed)
[ GitHub ]# File 'actionpack/lib/action_dispatch/journey/visitors.rb', line 125
def visit_SLASH(n, seed); terminal(n, seed); end
#visit_STAR(n, seed)
[ GitHub ]# File 'actionpack/lib/action_dispatch/journey/visitors.rb', line 120
def visit_STAR(n, seed); unary(n, seed); end
#visit_SYMBOL(n, seed)
[ GitHub ]# File 'actionpack/lib/action_dispatch/journey/visitors.rb', line 124
def visit_SYMBOL(n, seed); terminal(n, seed); end


| File Name |
Brief Description |
Page Number(s) |
Tags |
| alarmBelPropDemo |
Compare speed and accuracy of belief propagation on the alarm network |
|
* |
| alarmDgmTimingComparison |
Test inference on the Alarm Network |
|
|
| alarmNetworkFitTest |
Test dgmTrain on the alarm network |
|
|
| alarmTest |
Simple inference test in the alarm network |
|
|
| anscombe |
Display four data sets each with the same correlation of 0.81. |
|
|
| bayesfactorGeneDemo |
Bayes Factor Gene Demo |
|
S |
| bayesFactorHandedness |
Bayes factor for handedness/ gender data |
|
|
| bayesRiskDemo |
draw_bayesrisk.m |
|
|
| bayesTtestDemo |
Bayesian T test demo |
|
|
| bayesVsErmDemo |
Compare Bayes and ERM |
|
|
| belPropTest |
Test that belief propagation gives exact results on a tree |
|
|
| bernoulliBetaSequentialUpdate |
Sequential Bayesian Updating of a Beta-Bernoulli model. |
|
|
| betaMCdemo |
Compare Monte-Carlo and Numerical Integration |
|
|
| betaSampleDemo |
Monte Carlo approximation to a probability of being in an interval |
|
|
| biasVarianceFigure |
Visualization of error versus amount of regularization |
|
|
| biasVarModelComplexity |
Illustration of the bias variance trade off as we vary the L2 regularizer. |
|
|
| biasVarModelComplexity2 |
Illustration of the bias variance trade off as we vary the L2 regularizer. |
|
|
| biclusterDemo |
Bicluster Demo |
|
** |
| bigO |
Illustration of bigO and bigTheta. |
|
|
| binaryFaDemoNewsgroups |
Demo of factor analysis applied to binary newsgroups bag of words |
|
** |
| bolassoSimpleDemo |
Simple bolasso demo |
|
|
| boundOptPicture |
Bound optimization illustration |
|
|
| boxplotMorley |
boxplot of the Michelson-Morley data |
|
S |
| cancerRatesMh |
Hierarchical Bayesian Estimation of Some Binomial Proportions |
|
|
| catFAdemo |
Factor analysis with categorical and continuous data |
|
|
| catFAdemoAuto |
Factor analysis with categorical and continuous data |
|
|
| catFAtest |
Categorical factor analysis - check the code is syntactically correct |
|
|
| changeOfVarsDemoPolar |
change of variables demo |
|
|
| chisquaredTestDemo |
Demo of pearson's chi squared test |
|
|
| classificationShootout |
Compare different classification algorithms on a number of data sets |
|
** |
| classificationShootoutCvLambdaOnly |
Compare different classification algorithms on a number of data sets |
|
* |
| classificationShootoutCvLambdaOnlyLinear |
Compare different classification algorithms on a number of data sets |
|
* |
| clusterYeast |
Yeast Clustergram Demo |
|
S B |
| coinBayesFactorDemo |
Simple demo of Bayes factor computation |
|
|
| confintDemo |
Larsen and Marx p332 |
|
S |
| conjugateFnExp |
Conjugate Function |
|
|
| conjugateFnExpLowerBound |
Conjugate Function Lower Bound |
|
|
| conjugateFunction |
Illustration of a conjugate function |
|
|
| convRateDemo |
Convergence Rate Demo |
|
|
| crf2ChainTrainDemo |
Demonstrate training a chain-structured CRF |
|
|
| crf2ImgTrainDemo |
Demonstrate training a lattice-structured CRF |
|
|
| crfFitStructDemo |
Learn the structure of a small CRF using group L1 regularization |
|
|
| cubicPlot |
Plot a cubic curve to illustrate a saddle point in 1d |
|
|
| dagStructLearnDemoCollegeCompleteData |
Posterior over 5-node DAGs on Sewell-Shah college data |
|
|
| dboundaries3bumps |
Illustration of decision regions for the 3 class case |
|
|
| decisionBoundaryLinearVSwiggly |
Illustration of a linear vs 'wiggly' decision boundary. |
|
|
| deepBelNetClassifyDemo |
Classify some MNIST digits using a deepBelNet |
|
** |
| deepBelNetDemo |
Classify some MNIST digits using a binary RBM |
|
** |
| deflationDemo |
Compute largest K eigenvectors and eigenvalues of a symmetric matrix |
|
|
| demoFAemt |
|
|
|
| demoLagrange |
Lagrange Demo |
|
|
| demoMinfunc |
Minfunc Rosen Demo |
|
|
| demoMinfuncHighdim |
Optimization in high dimensions using minFunc |
|
* |
| demoPeaksTraj |
Peaks Demo |
|
O |
| demoRosen2d |
Rosenbrock 2D demo |
|
O |
| demoRosenConstrained |
Rosenbrock constrained optimization demo |
|
|
| demoRosenHighDim |
RosenBrock Demo |
|
O I |
| demoRosenPlot2d |
Plot the Rosenbrock function |
|
|
| derivComplexTrick |
This function returns the numerical derivative of an analytic function. |
|
|
| dgmDiscreteHmmFitTest |
Fit a discrete HMM via hmmFit and dgmTrain, comparing the results |
|
|
| dgmGaussHmmFitTest |
Fit an HMM and an equivalent DGM, making sure the results agree |
|
|
| dgmGaussHmmFullyObsFitTest |
Fit an HMM and an equivalent DGM and make sure the results agree |
|
|
| dgmLogprobTest |
Simple test to make sure the logZ calculation is correct |
|
|
| dgmMixGaussFitTest |
Fit a mixture of Gaussians comparing mixModelFit and dgmTrain |
|
|
| dgmMrf2Demo |
Convert a dgm to a pairwise mrf |
|
|
| dgmNaiveBayesFitTest |
Compare dgmTrain and naiveBayesFit |
|
|
| dgmNodeOrderDemo |
When queries are not in topogical order |
|
|
| dirichlet3dPlot |
Plot a Dirichlet distribution |
|
|
| dirichlet3dPlotWedge |
Plot a Dirichlet distribution in 3d |
|
|
| discreteDagSmallStructureLearnDemo |
Example from Neapolitan's book "Learning Bayesian networks" p438 |
|
|
| discreteDensityModelsShootout |
PMTKslow |
|
* |
| discrimAnalysisFisherIrisDemo |
Discriminant Analysis On the Fisher Iris Data |
|
|
| elasticNetDemo |
Reproduce figure 3 of Zou and Hastie JRSSB 2005 |
|
|
| emtShootout |
This is a simplified version of 'discreteDensityModelsShootout' |
|
|
| epigraph |
Illustration that the epigraph of a convex function is a convex set. |
|
|
| errorCorrectingCodeDemo |
Demo of error correcting code |
|
|
| factorGraph2PairwiseTest |
Test that factorGraphMakePairwise creates an equivalent model |
|
|
| fwdBackDemo |
Forwards Backwards Algorithm Demo |
|
|
| gauss2dMargCondPlot |
2D Gaussian contour plot along with its 1D marginals and conditionals. |
|
|
| gaussBayesMultiPoints |
Bayesian Sequential Updating of a Gaussian |
|
|
| gaussBayesUnequalCov |
Bayesian updating of a 2D Gaussian |
|
|
| gaussCondition2Ddemo |
Conditioning a 2D Gaussian |
|
|
| gaussEvec |
|
|
|
| gaussianEllipsoids |
Visualize the Projection of 3D Gaussian Ellipsoids onto the 2D Plain |
|
|
| gaussImputationCompare |
Compare the results of imputation on a MVN using three imputation methods: EM, ICM, and Gibbs |
|
|
| gaussImputationDemoColorHintonDiag |
Gauss Imputation Demo |
|
|
| gaussImputationDemoEM |
Gauss Imputation Demo |
|
S |
| gaussImputationDemoOld |
Gauss Imputation Demo |
|
|
| gaussImputationGibbsDemo |
Gauss Imputation Demo using Gibbs |
|
|
| gaussImputationICMDemo |
Gauss Imputation Demo using ICM |
|
|
| gaussImputationPima |
Impute missing entries of the 4d Pima Indians data using an MVN |
|
|
| gaussInferMuSigmaDemo |
Plot p(mu, sigma^2 | D) in 2d using an uninformative prior for different n |
|
|
| gaussInterpDemo2 |
Interpolate some data using a joint Gaussian |
|
|
| gaussInterpNoisyDemoOld |
Interpolate some data using a joint Gaussian |
|
|
| gaussPlot2dDemo2 |
Plot a 2D Gaussian and its decorrelated and whitened transformations |
|
|
| gaussSeqlUpdateMuSigma1D |
Sequential Bayesian updating of (mu,sigma) for a 1D gaussian |
|
|
| gaussVsStudentOutlierDemo |
Illustrate the robustness of the t-distribution compared to the Gaussian |
|
|
| getCountsMarkovLanguageEx |
counts1(i,l) = num times word i occurs in language l |
|
|
| gibbsDemoNoBlindspot |
Denoising an Image Using Gibbs Sampling |
|
|
| gibbsGauss2dDemo |
Use Gibbs sampling to sample from a 2D Gaussian |
|
|
| gibbsGaussParamsDemo |
Gibbs sample mu and Sigma given complete observations of an MVN |
|
S |
| glmnetDemo |
|
|
|
| gmmSingularity |
Illustration of a GMM singularity |
|
|
| gpDemoChangeHparams2d |
Visualize the effect of change the hyper-params for a 2d GP regression |
|
* |
| gpnnCov |
Compute the covariance matrix for a 1 hidden layer neural net |
|
|
| graphicalIndepTest |
We compare the posterior on 2 graphs, X1-X2 and X1 indep of X2 |
|
|
| hastieCvsGammaDemo |
Plot test error vs C for differnet gammas in an SVM |
|
|
| hastieSvmLrDemo |
Reproduce figures 12.2, 12.3 and 12.5 in Hastie ESL 2nd ed. |
|
O * |
| healthyLevels |
Josh Tenenbaum's Healthy Levels Game |
|
|
| heteroscedasticDataPlot |
Plot of heteroscedastic linear regression |
|
|
| hingePlot |
Hinge loss illustration |
|
|
| hmm2DgmTest |
Compare different inference methods for an HMM |
|
|
| hmmDiscreteTest |
Simple test of hmmDiscreteFitEm |
|
|
| hmmGaussTest |
Simple test of hmmFitEm with Gaussian observations |
|
|
| hmmMixGaussTiedTest |
Simple test of hmmFit with tied mix Gauss observations |
|
|
| hmmNbestDemo |
Sequentially compute N best list for an HMM |
|
|
| hmmStudentTest |
Simple test of hmmFitEm with Student observations |
|
|
| hoffNormalmsePlots |
Relative MSE plot |
|
S |
| hopfieldDemo |
Demonstrate the use of a Hopfield network for associative memory. |
|
* |
| imputationGeneMicroarrayDemo |
Comparison of methods for imputing missing values in a gene microarray |
|
S * |
| imputationMixedAdultDemo |
Comparison of methods for imputing missing values in UCI adult census data |
|
S * |
| invGammaPlotDemo |
Plot Inverse Gamma Distributions |
|
|
| IPFdemo4Cycle |
Fit loopy MRF 1-2-3-1 using iterative proportional fitting |
|
|
| IPFdemoIndep2d |
Approximate joint density as a product of two marginals |
|
|
| irmGraph |
make graph for figure 1 of IRM paper by Kemp et al |
|
|
| isolatedWordClassificationWithHmmsDemo |
Classifying a speech signal with an HMM as "four" or "five" |
|
|
| jensensInequalityFigure |
Illustration of Jensen's inequality |
|
|
| kmeansDemoFaithful |
Run Kmeans on the old faithful data |
|
|
| kmeansModelSel2d |
Kmeans Model Selection in 2D |
|
|
| kmedoidsDemoFaithful |
Run Kmedoids on the old faithful data |
|
|
| knnDensityEstimationDemo |
An illustration of density estimation with k-nearest neighbours. |
|
|
| kpcaDemo |
Kpca demo |
|
|
| laplacePdfPlot |
Compare Gauss, and Laplacian distributions |
|
|
| laplacePlotDemo |
Compare a Gauss and Laplace distribution |
|
|
| lassoPathProstateClassif |
Compute the full L1 regularization path for a classification |
|
|
| lassoPostPlot |
Reproduce fig 1 of "Bayesian lasso regression", Chris Hans, 2009 |
|
|
| ldaQdaDemoIris |
Demonstrate LDA/QDA on 2d 3class iris data |
|
|
| linearKernelDemo |
Compare different linear kernel classifiers on several datasets |
|
* |
| linesearchPerp |
|
|
|
| linregAllSubsetsGraycodeDemo |
All subsets regression |
|
|
| linregBFCaterpillar |
Bayes factors for simple linear regression |
|
|
| linregFreqCaterpillar |
Linreg frequentist caterpillar demo |
|
S |
| linregGroupLassoDemo |
Demo of group lasso compared to vanilla lasso |
|
|
| linregGroupLassoEmDemo |
Compare group lasso solved using EM and projected gradients |
|
|
| linregLogTransformDemo |
Illustrate log transform for linear regression |
|
|
| linregMlcompDemo |
Run linear regression on the mlcomp system |
|
|
| linregPostPredLinearDemo |
Posterior predictive density for Bayesian linear Regression in 1d |
|
|
| linregRobustDemo |
Robust linear regression demo |
|
O |
| linregSparseEmBiscuitsDemo |
Sparse linear regression with EM on biscuits data |
|
** |
| linregSparseEmSynthDemo |
Sparse linear regression with EM on synthetic data |
|
S ** |
| linregSparseEmSynthDemoOld |
Sparse linear regression with EM on synthetic data |
|
S ** |
| linregStudentStacklossDemo |
Reproduce table 1 from "Robust statistical modeling using the T distribution |
|
|
| linregStudentTest |
Compare speed and accuracy of different linregStudent methods |
|
|
| linregWeightedDemo |
Several ways of implementing weighted least squares |
|
|
| LMSdemoSimple |
LMS Demo |
|
|
| localMinima1d |
A non convex function in 1d: exponentially damped sinuosoid |
|
|
| logcon |
Illustration of the concavity of the log function |
|
|
| logregBasisFnDemo |
Logistic Regression with RBF Basis |
|
|
| logregDemoGirolami |
2D Logistic Regression Demo |
|
|
| logregFitL2DualTest |
Check that fitting L2-regularized logisitc regression using SVD trick |
|
|
| logregIrisDemo |
Multi-class logistic regression on iris data |
|
|
| logregKernelDemo |
Fit a rbf kernelized logistic regression model |
|
|
| logregL2FitTest |
Test that the minfunc method and IRLS give same result |
|
|
| logregMultinomKernelMinfuncDemo |
Minfunc Kernelized Logreg Demo |
|
|
| logregMultinomMinfuncDemo |
Minfunc Logreg Demo |
|
|
| logregOptDemo |
Compare various optimizers on a binary logistic regression problem |
|
* |
| logregSgdDemo |
Stochastic gradient descent for logistic regression problem |
|
* |
| logregWeightedDemo |
weighted logistic regression |
|
|
| logregXorLinearDemo |
Apply L2 Logistic Regression to the XOR problem in original feature |
|
|
| lungcancerGMdemo |
Lung cancer network Example |
|
|
| makeData3cousins |
Simulation example from "a tale of three cousins: lasso, l2boosting, and dantzig" |
|
|
| mcmcMvn2d |
MCMC Sampling from a 2d Gaussians |
|
|
| mdgGridDemoSutton |
do the example in Sutton and Barto (1998) p79 |
|
|
| mdpGridDemoRussell |
Grid world MDP |
|
|
| memoryMapDemo |
This is a simple demo of Matlab's memmapfile class |
|
|
| miDemo |
Computing pairwise mutual information, timing comparison |
|
S * |
| minFunc_applications |
Minfunc demo |
|
I * |
| minfuncRosen2dDemo |
Minfunc Rosen Demo |
|
|
| misconceptionRank |
Figure out rank of linear dependencies in misconception Markov network |
|
|
| mixdirDemo3 |
Biosequence analysis Demo |
|
|
| mixDirDnaDemo |
Mixture of Discrete Distributions DNA Demo |
|
|
| mixDiscreteDemo |
Simple test of a discrete mixture model |
|
|
| mixGaussImputationDemo |
Imputation for a mixture of Gaussians |
|
S |
| mixGaussImputationDemoOld |
Imputation for a mixture of Gaussians |
|
S |
| mixGaussImputationTest |
Simple test of mixGaussMissingFitEM |
|
|
| mixGaussLikelihoodConvexHull |
Illustration of the likelihood surface created by a GMM |
|
|
| mixGaussMultiModesPlot |
Plot Mixtures of 2, 2D Gaussians with 1,2, and 3 modes Respectively. |
|
|
| mlpClassifDemo |
MLP Classification Demo |
|
|
| mlpClassifDemoSchmidt |
MLP Classification Demo |
|
|
| mlpRegressDemo |
MLP Regression Demo |
|
|
| mlpRegressDemoBishop |
Reproduced fig 5.3 from Bishop's book |
|
|
| mlpRegressDemoSchmidt |
MLP Regression Demo |
|
|
| mnistKNNdemo |
Classify the MNIST digits using a one nearest neighbour classifier and Euclidean distance |
|
|
| mrf2ChainInferDemo |
Demonstrate inference in undirected markov chain |
|
|
| mrf2ChainTrainDemo |
Demonstrate training a chain-structured MRF |
|
|
| mrf2CutsetInferDemo |
Demonstrate cutset conditioning in cyclic undirected markov chain |
|
|
| mrf2ImgBlockInferDemo |
Demonstrate block-based inference in a 2d grid of a noisy image of an X |
|
* |
| mrf2ImgInferDemo |
Demonstrate inference in a 2d grid of a noisy image of an X |
|
|
| mrf2ImgRgbInferDemo |
Demonstrate inference in a 2d grid of a multi-state noisy image of an X |
|
|
| mrf2ImgSmallInferDemo |
Demonstrate inference in a small 2d grid of a noisy image of an X |
|
|
| mrf2NaiveInferDemo |
Demonstrate inference in pairwise mrf using 4 node network |
|
|
| mrfFitImgSemiObsDemo |
Fit the local CPDs of a lattice mrf given a noisy image and its underlying |
|
* |
| mrfImageDenoisingApprox |
Image denoising using an mrf and approximate inference |
|
|
| mrfImageDenoisingExact |
Image denoising using an mrf and exact inference |
|
|
| mrfTest |
Test PMTK mrf code against mrf2 |
|
|
| multinomIndepLogBF |
Multinomial Bayes Factor demo |
|
|
| multinomSamplePlotDemo |
Plot samples drawn from a multinomial |
|
|
| multitaskRegDemo |
Multitask regression demo |
|
* |
| naiveBayesBayesianDemo |
Illustrate benefit of using T distribution instead of plugin estimate |
|
|
| naiveBayesMnistDemo |
Naive Bayes classifier for binary MNIST digits |
|
* |
| naiveBayesMnistSample |
Run NaiveBayes on the mnist data |
|
|
| negBinomDemo |
Mixtures of negative binomials |
|
|
| newcombDemo |
Determine if Newcomb's speed of light data is Gaussian |
|
|
| newcombPlugin |
Test to see if Newcomb's speed of light data is Gaussian |
|
|
| newcombPostPred |
|
|
|
| newtonIteration1d |
An illustration of Newton's method in 1D |
|
|
| newtonsMethodMin1d |
Illustration of Newton's method for minimizing a 1d function |
|
|
| neymanPearson |
Illustration of the Neyman-Pearson testing paradigm |
|
|
| neymanPearson2 |
Illustration of the Neyman-Pearson testing paradigm |
|
|
| noisyMixDemo |
Z -> Xj -> Yj |
|
|
| nonConvexFn |
A non convex function in 1d |
|
|
| normalGammaPlotDemo |
Normal Gamma Distribution Plot |
|
|
| optLowerbound |
Lowerbound illustration |
|
|
| optMaxLambdaVal |
Optimization illustration |
|
|
| pagerankDemoMoler |
Google's PageRank |
|
I |
| pagerankpow |
PageRank by power method with no matrix operations. |
|
|
| pairedTestDemo |
Plot posterior on difference in rates given paired contingency table |
|
S |
| pairedVsUnpairedTestDemo |
Compare paired and unpaired Bayesian posteriors on contingency table differences |
|
S |
| paretoDemoTaxicab |
Pareto Taxicab Example |
|
|
| parzenWindowDemo |
Demonstrate a non-parametric (parzen) density estimator in 1D |
|
|
| pcaDemo2dto10d |
PCA Scree Plot |
|
|
| pcaDigitsVis10classes |
PCA digit visualization demo |
|
|
| pcaDigitsVisGrid |
Embed the digit 3 into 2D and sample from the space |
|
|
| pcaEmTest |
Compare pcaPmtk and pcaFitEm |
|
|
| pcaFaceDemo |
PCA Face Demo |
|
|
| pcaTimingTest |
Time various pca methods w.r.t. n and d |
|
|
| pcaYeast |
PCA Yeast Demo |
|
I |
| peaksPlotDemo |
Display various plots of a multimodal 2D function, peaks() |
|
|
| perfectElimDemo |
Demonstrate perfect elimination ordering |
|
|
| piecewiseLinearSurface |
Illustration of a piecewise linear surface |
|
|
| plotMFCC |
Plot a spectogram and its MFCC representation |
|
|
| plotNoLocalMin |
Illustration of a function without a distinct local minimum |
|
|
| plotPdfCdfDemo |
Illustration of an arbitrary PDF and its corresponding CDF |
|
|
| powerCurvesDemo |
Power vs sample size for excluding a null value from binary trials |
|
|
| powerMethodDemo |
Power Method Demo |
|
|
| ppcaDemoNetlab |
Compare ppcaFit to Netlab's verson |
|
|
| ppcaDemoOilflowMissing |
PPCA Imputation Demo |
|
|
| ppcaEmTest |
Test ppcaFit by comparing to Verbeek's code with no missing data |
|
|
| ppcaEvidenceDemo |
Plot the posterior over model dimensionality |
|
|
| ppcaVBdemo |
Bayesian Probabilistic PCA Imputation Demo |
|
|
| probitVsLogregCrabsDemo |
Compare logistic regresion and probit regression on the crabs dataset |
|
|
| prostateOLS |
OLS on the prostate data set |
|
|
| prostateScatterDemo |
Prostate pscatter demo |
|
|
| qmrDemo |
Compare inference in a QMR like network (dgm) |
|
|
| qpDemo |
Solve a simple quadratic program |
|
O |
| randDgmTimingTest |
Compare inference method speed w.r.t model size |
|
* |
| rankDefDemo |
Rank Deficiency Demo |
|
|
| rbfGramDemo |
Demo of an RBF gram matrix |
|
|
| rbfNetworkSarcosDemo |
RBF Network Demo |
|
* |
| rbmClassifyDemo |
Classify some MNIST digits using a binary RBM |
|
|
| rbmDenoiseDemo |
Denoise some MNIST digits using a binary RBM |
|
|
| readlines |
Returns a cell array with the lines of the file converted to numbers (streams) |
|
|
| ridgeLassoOLS |
|
|
|
| ridgeSplineDemoCV |
Demo of L2 regualrization of a piecewise constant function (splines) with fixed knots |
|
|
| ridgeTest |
Compare PMTK ridge with stats toolbox |
|
S |
| riskFnBernoulli |
Bernoulli Risk Function Demo |
|
|
| robustDiscrimAnalysisBankruptcyDemo |
Fit a student distribution as class conditional density to the bankruptcy data |
|
|
| robustPdfPlot |
Compare Gauss, Student T, and Laplacian distributions |
|
|
| ROChand |
ROC curves for two hypothetical classification systems |
|
|
| rosen2d |
2D Rosenbrock Function |
|
|
| rotationDemo |
Rotation Matrix Demo |
|
|
| saCoolingSchedule |
Illustration of cooling schedule for simulated annealing |
|
|
| saddle |
Illustration of a saddle point |
|
|
| saDemoCamel |
Demo of simulated annealing on a 2D function |
|
|
| saDemoHisto |
Anneal a histogram to illustrate the effect of temperature |
|
|
| saDemoMOG |
Find the mode of a mixture of 2, 1D Gaussians using Simulated Annealing |
|
|
| scadPlot |
LLA and LQA approximation - fig 1 of Zou & Li 2008 |
|
|
| shatteredSin |
Shattered sin |
|
|
| sigmoidBounds |
|
|
|
| sigmoidplot2D_new |
Plot 2d sigmoid functions for various values of w1, w2. |
|
|
| smlrPathDemo |
Compare SMLR selecting lambda on grid or on path. |
|
* |
| sparseLinRegBabackDemo |
Demo of what constitutes plausible sparsity prior for linear regression |
|
|
| sparseLinRegMargDistDemo |
Compute empirical distribution of coefficients drawn from a Gaussian ensemble |
|
|
| splineDemoWiki |
http://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines |
|
|
| sprinklerGMdemo |
Water sprinkler network Example |
|
|
| stream2text |
converts an array of integers representing letters and spaces, to a |
|
|
| studentFitDemo |
Compare EM to matlab stats toolbox |
|
S |
| studentFitMultiDemo |
Fit multivariate student T distribution usign ECME |
|
|
| studentPdfPlot |
Compare Gauss, Student T, and Laplacian distributions |
|
|
| studentPlotDemo |
Student T Plot Demo |
|
|
| studentVsGauss |
Compare Student and Gaussian pdfs |
|
|
| surfer |
Create the adjacency graph of a portion of the Web. |
|
I |
| svmBinaryClassifDemo |
Compare SVM and kernelized logreg on synthetic 2 class data in 2d |
|
X |
| svmFitTest |
Simple Tests of the svmFit interface |
|
O * |
| svmLogregComparison |
Compare kernalized L1 logistic regession to an svm. |
|
|
| svmOneVsRestDemo |
Test the oneVsRestClassifFit function |
|
O * |
| svmSmoothRegrDemo |
Smooth support vector machine regression |
|
|
| tabularFactorTest |
|
|
|
| testLanguageClassifier |
Test classifier performance as a function of length (averaged over streams) |
|
|
| testSprinklerDemo |
Low level test of the TabularFactor code using the water sprinkeler BN |
|
|
| text2stream |
Converts a charachter array to an array of integers |
|
|
| tikhonovDemo |
Tikhonov regularization for a piecewise smooth function |
|
|
| toposortDemo |
Demo of topological sorting |
|
|
| treeInferDemo |
{ |
|
|
| treeInferHmmDemo |
Compare inference in an HMM and a tree - should be same! |
|
|
| twoPowerCurves |
Two hypothetical two-sided power curves: B dominates A |
|
|
| unpairedTestHandednessDemo |
Analysis of binomial differences on lefthand/righthand data |
|
S |
| varSelDemo |
Variable Selection Demo |
|
* |
| xwindowsDocVisualize |
Visualize word-document binary matrix |
|
|


{# YOUR HOMEPAGE #} {# First time seeing extend or blocks? Want to know how it works? Tutorial here: http://www.webhook.com #} {% extends "templates/partials/base.html" %} {% block content %} {# Grabs the data to list all the content types (set as multiple) from the CMS. #} {% set types = getTypes() %} {# If you have at least content page type, we show a list. #} {% if types|size !== 0 %} {# AD PLACEHOLDER #} {# INTRO SECTION#}
WorkWisdom is a place to find wise saying for business from around the world. You can submit your own sayings or browse our collection. As sayings are added to the site, you will see them appear below in the 'Latest Quotes' section.
Submit Browse{{ quotes.quotation }}- {{quotes.author.first}}{% if quotes.author.last %} {{quotes.author.last}}{% endif %}


| Figure 1 |
Change in attainable yield from the reference period to 2041 ... |
| Figure 2 |
Yield vulnerability of dominant crop. Maps show grid-cells w ... |
| Figure 3 |
The effect of climate change on production increases through ... |
| Figure 4 |
Areas with climate suitable to provide attainable yields gre ... |


| abs readonly |
Returns the absolute value of a number. |
| acos readonly |
Returns the arc cosine(in radians) of a number. |
| asin readonly |
Returns the arc sin(in radians) of a number. |
| atan readonly |
Returns the arc tangent(in radians) of a number. |
| atan2 readonly |
Returns the arc tangent of the quotient of its arguments (y/x). |
| ceil readonly |
Rounds the number up to the nearest integer. |
| cos readonly |
Returns the cosine of an angle provided in radians. |
| exp readonly |
Returns Math.E raised to the power of a number. |
| floor readonly |
Rounds a number down to the nearest integer. |
| log readonly |
Returns the natural logarithm of a number. |
| max readonly |
Returns the largest of zero or more numbers. |
| min readonly |
Returns the smallest of zero or more numbers. |
| pow readonly |
Returns x raised to the power of y. |
| random readonly |
Returns a pseudo-random number from 0.0 up to but not including 1.0. |
| round readonly |
Rounds a number to the nearest integer. |
| sin readonly |
Returns the sine of an angle provided in radians. |
| sqrt readonly |
Returns the square root of a number. |
| tan readonly |
Returns the tangent of an angle provided in radians. |


Talking : Etienne Roudeix / @__etienne
mouse actions - key presses
different logic
aim : modularity
all emails in the same MailSubscriber
all admin management in AdminSubscriber
easy pre/post crud modularity
testing => no MailSubscriber, no LogSubscriber
prod => no DebugSubscriber
by Etienne Roudeix / @__etienne


In The Future, Football Will Be Mechanised
by Luke PlunkettJake Woodruff is an artist at Playground Games, the developers of the Forza Horizon series.
Below is a series of pieces for the National Mech League, an idea that in the future, when a bunch of military and service mechs are no longer needed, they’re put to work playing football.
It’s a collaboration between Woodruff and his friend Kyle Moody. You can see more of Jake’s stuff at his ArtStation page.


Former Apple chip execs launch startup to take on AMD, Intel in the data center
Nuvia sees a big opportunity in the data center space
by Shawn KnightIn brief: Nuvia’s goal is to take the lessons the team learned from developing mobile processors for battery-powered devices and apply them to electricity-hungry data centers. Efficiency gains could be highly attractive to data center operators.
Look out AMD and Intel, there’s a new competitor in town.
Three former semiconductor executives that worked on Apple’s iPhone chips launched a startup earlier this year called Nuvia Inc. that aims to design processors for use in data centers. On Friday, the trio – John Bruno, Manu Gulati and Gerard Williams III – announced they have raised $53 million in funding to help expand their outfit to around 100 employees by the end of the year.
In an interview with Reuters, Williams said this area is ripe for innovation and advancement. Patrick Moorhead, an analyst at Moor Insights & Strategies, believes the company could be on to something.
“I’ve been in and around semiconductor companies for 30 years. There are never any guarantees, but one thread that I’ve seen is that the most successful chip companies have rock star architects and developers,” Moorhead said.
Interestingly enough, one of Nuvia’s key investors is Dell Technologies Capital which is one of Intel’s biggest customers. That doesn’t necessarily mean Dell will use Nuvia’s chips, however.
Scott Darling, president of the venture group, said you have to have a world-class team to do something disruptive enough that it will prevail despite the response of competitors. “At Nuvia, we think we have found one,” he added.
Masthead credit: CPUs by Yurchanka Sisrhei


For detailed test output see Firebug console (if Firefox). This test should be run on Firefox, Internet Explorer, and Safari to confirm that Dojox Flash is working correctly.


Your browser doesn't support the features required by impress.js, so you are presented with a simplified version of this presentation.
For the best experience please use the latest Chrome, Safari or Firefox browser.
Rich Hildred -<EMAIL_ADDRESS>-<PHONE_NUMBER>
Use a spacebar or arrow keys to navigate


HTML page without CCS styling looks a bit boring!!
Headings and paragraphs are BLOCK elements
There are 6 levels of heading. Do not skip levels. If you want to change the size of a heading, don't change its level - change the CSS.
Paragraphs contain the body text of a web page. You cannot put another block element inside a paragraph.
HTML supports special characters. For example ® and ©.
There are two types of lists: unordered and ordered
The subjects I'm doing are:
To get your drink, follow the instructions as detailed:
| ID | Name | State |
|---|---|---|
| 10 | Amy | VIC |
| 11 | George | NSW |


The narrative visualization presented here uses the Martini glass structure. It starts with a basic introduction of the area, some insights about the data and then allows the user to further explore the dataset on one's own on the last dashboard.
As mentioned above the visualization contains five scenes. The first scens provides a high level visualization of the data, second scene presents a parallel coordinate chart to allow multi-dimensional visualization easy. Third and fourth scenes use a force directed layout to provide more insights in the variance for profits overall and across each sector. The final scene provides a dashboard for users with three linked charts to explore the data on one's own. I have also added some slides in the beginning to set the context and provide information about the data and problem domain as well as there is also a summary slide added in the end.
Specific annotations are used in some charts to provide contextual details. Tooltips are also added in all the charts. The colors for the tooltips are annotations are chosen so as they appear soft on eyes. Consistency is maintained across all the charts by abstracting both the tooltip and annotation classes.
The last scens is an interactive dashboard with three charts. The top chart is a bar chart which shows average profits across each sector. The other two charts show the profit for companies in the selected sector, which is used as a parameter here. The selection of the paramter is implicit, one needs to click on any sector's bar to activate it.
The interactive dashboard uses sector as a parameter. The selection of sector is used as a trigger to update the other two linked charts which shows the top and bottom 20 companies in the selected sector with respect to the profit rates.
Colors for the charts are selected using ColorBrewer. Specific care has been taken to select correct colors (ordinal/quantitative). The canvas used for all the charts use soft colors for background information. It also provides dotted lines for minor values to make it easier to read the data. For comparing the profits across sectors note that I have used a force directed layout and not a sorted bar chart. This was done mostly because the no of data points were large and FDL provided a simpler more visually appealing layout which serves the purpose.


Known Bugs
There are likely a
whole host of bugs and issues with this software -- it should be considred "Beta" for the moment. Here are
the ones we know about:
-
Putting a semicolon in any of the input fields causes the application to die
- Fixed on 5/3/2011. Semicolons can no longer be entered. Thanks to Stefan German for fiding this one.
-
BST and AVL trees have broken undos in HTML5 version
-
Find and Delete broken in open hashing
- Fixed on 5/10/2011. (thanks to Eduardo Bonet, of Santa Catarina Federal University, Brazil, for finding this one!)
- All algorithms that compute an ordering (BST, AVL Trees, etc) don't sort floating point numbers correctly -- 9.2 > 10.3
- This one is a design decision -- since those algoritms can take any string, the decision was made to sort based on lexicographic order.
We tried to mitigate confusion somehwat by automatically converting positive integers into equivalent numbers with leading zeroes, so that positive
integers would always be ordered as expected (though negative numbers are alas off). It turns out that if you are trying to sort a group of any strings,
using a mixed comparison strategy gets confusing
fast -- how should 10.4 compare to 3.1.2? What about 3A -- is that a string or a hexidecimal number?
What about AF? Should BC come before or after AFF? We made the executive decision to just do string comparisons.
-
When running connected components, if you run the algorithm, then undo, then run it again, the application breaks. We're working on a fix --
the current workaroud is to create a new graph after undo before running the algorithm again.
- Various typos
- Please continue to send in any typos you see, and I will correct them as I get to them (at a somewhat lower priority than functionaility
bugs, of course). Typo corrections will not appear on this page.
Featue Requests
Find a bug? Have a feature or new algorithm to request? Send an email to galles <at> usfca <dot> edu


MDNS¶
MDNS Base¶
MDNS Handler¶
-
class
designate.mdns.handler.
RequestHandler
(storage, tg)[source]¶ Bases:
designate.mdns.xfr.XFRMixin
-
central_api
¶
-
MDNS Notify¶
-
class
designate.mdns.notify.
NotifyEndpoint
(tg)[source]¶ Bases:
designate.mdns.base.BaseEndpoint
-
RPC_API_NAMESPACE
= 'notify'¶
-
RPC_API_VERSION
= '2.0'¶
-
get_serial_number
(context, zone, host, port, timeout, retry_interval, max_retries, delay)[source]¶ Get zone serial number from a resolver using retries.
Parameters: - context – The user context.
- zone – The designate zone object. This contains the zone name. zone.serial = expected_serial
- host – A notify is sent to this host.
- port – A notify is sent to this port.
- timeout – The time (in seconds) to wait for a SOA response from nameserver.
- retry_interval – The time (in seconds) between retries.
- max_retries – The maximum number of retries mindns would do for an expected serial number. After this many retries, mindns returns an ERROR.
- delay – The time to wait before sending the first request.
Returns: a tuple of (status, actual_serial, retries) status is either “SUCCESS” or “ERROR”. actual_serial is either the serial number returned in the SOA message from the nameserver or None. retries is the number of retries left. The return value is just used for testing and not by pool manager. The pool manager is informed of the status with update_status.
-
notify_zone_changed
(*args, **kwargs)[source]¶ Parameters: - context – The user context.
- zone – The designate zone object. This contains the zone name.
- host – A notify is sent to this host.
- port – A notify is sent to this port.
- timeout – The time (in seconds) to wait for a NOTIFY response from server.
- retry_interval – The time (in seconds) between retries.
- max_retries – The maximum number of retries mindns would do for sending a NOTIFY message. After this many retries, mindns gives up.
- delay – The time to wait before sending the first NOTIFY request.
Returns: a tuple of (response, current_retry) where response is the response on success or None on failure. current_retry is the current retry number. The return value is just used for testing and not by pool manager.
-
poll_for_serial_number
(context, zone, nameserver, timeout, retry_interval, max_retries, delay)[source]¶ Get the serial number of a zone on a resolver, then call update_status on Pool Manager to update the zone status.
Parameters: - context – The user context.
- zone – The designate zone object. This contains the zone name. zone.serial = expected_serial
- nameserver – Destination for the poll
- timeout – The time (in seconds) to wait for a SOA response from nameserver.
- retry_interval – The time (in seconds) between retries.
- max_retries – The maximum number of retries mindns would do for an expected serial number. After this many retries, mindns returns an ERROR.
- delay – The time to wait before sending the first request.
Returns: None
-
MDNS RPC API¶
-
class
designate.mdns.rpcapi.
MdnsAPI
(topic=None)[source]¶ Bases:
object
Client side of the mdns RPC API.
Notify API version history:
1.0 - Added notify_zone_changed and poll_for_serial_number. 1.1 - Added get_serial_number. 2.0 - Changed method signatures- XFR API version history:
- 1.0 - Added perform_zone_xfr.
-
RPC_NOTIFY_API_VERSION
= '2.0'¶
-
RPC_XFR_API_VERSION
= '1.0'¶
-
classmethod
get_instance
()[source]¶ The rpc.get_client() which is called upon the API object initialization will cause a assertion error if the designate.rpc.TRANSPORT isn’t setup by rpc.init() before.
This fixes that by creating the rpcapi when demanded.
-
get_serial_number
(*args, **kwargs)¶
-
notify_zone_changed
(*args, **kwargs)¶
-
perform_zone_xfr
(*args, **kwargs)¶
-
poll_for_serial_number
(*args, **kwargs)¶
MDNS Service¶
MDNS XFR¶
-
class
designate.mdns.xfr.
XFRMixin
[source]¶ Bases:
object
Utility mixin that holds common methods for XFR functionality.
-
class
designate.mdns.xfr.
XfrEndpoint
(tg)[source]¶ Bases:
designate.mdns.base.BaseEndpoint
,designate.mdns.xfr.XFRMixin
-
RPC_API_NAMESPACE
= 'xfr'¶
-
RPC_API_VERSION
= '1.0'¶
-


The constructor creates a ScaleAll action for use by an emitter. To add a ScaleAll to all particles created by an emitter, use the emitter's addAction method.
The scale factor for the particle when its energy is 1 - usually at the start of its lifetime. A scale of 1 is normal size.
The scale factor for the particle when its energy is 0 - usually at the end of its lifetime. A scale of 1 is normal size.
The scale factor for the particle when its energy is 0 - usually at the end of its lifetime. A scale of 1 is normal size.
The scale factor for the particle when its energy is 0 - usually at the end of its lifetime. A scale of 1 is normal size.
The scale factor for the particle when its energy is 1 - usually at the start of its lifetime. A scale of 1 is normal size.
The scale factor for the particle when its energy is 1 - usually at the start of its lifetime. A scale of 1 is normal size.
Generated using TypeDoc
The ScaleAll action adjusts the size of the particle, its collision radius and its mass as it ages. It uses the particle's energy level to decide what size the particle should be.
Usually a particle's energy changes from 1 to 0 over its lifetime, but this can be altered via the easing set within the age action.
This action should be used in conjunction with the Age action.


A special kind of move uniquely offered by this puzzle takes the form (2/3)(RU),(2/3)(LF),FU,(2/3)(RU)i,(2/3)(LF)i
along with its symmetric varient acting on the same edge FU
. Note that 2/3
is an estimate here. The actual fraction is hard to calculate.
Note that this special move will swap orbits for a pair of peddal peices on opposite sides of the cube.
Locate the face of your choice by finding the desired color in common amongst the four edge peices touching the face. Now simply orient those edge peices with the face using zero or one trivial turns per edge peice.
We now position the peddal peices into the face chosen in step 1. This is trivial for the most part. What you may find, however, is that two peddal peices of the same color occupy the same orbit. To fix this, use the special kind of move uniquely offered by this puzzle.
In this step we complete the face chosen in step 1 by solving the corner peice positions and orientations for that face. It is trivial. As you do it, make sure that the edge peices in the middle layer and adjacent to those corner peices are oriented properly with respect to the corner peices.
Now position the 8 peddal peices adjacent to the face chosen in step 1. Note that you may have to trade orbits of peddals in the top layer before positioning them in the bottom layer. The basic move for moving them from the top to bottom layer is FR,FU,RU,FUi,FRi
, along with obvious varients, which is pretty trivial, and really goes without saying.
Now position all the peddals adjacent to the face opposite that chosen in step 1. If the chosen face was, say, the black face, note that this step will also, as a consequence, position all peddals in the yellow face.
Trade orbits as necessary and ignore the corner and edge peices in the top layer. There is just one case that comes up during this step that is a bit tricky, but it has a trivial solution. If you find that all peddals are in the correct orbits, but not all peddals can be positioned correctly using only 180-degree rotations of the edges in the top layer, then you need only trade the orbit of any two yellow peddals. Once done, the peddals will solve using only 180-degree rotations of the edges in the top layer.
Now solve the orientation of the edges in the final layer. For each mis-oriented edge, simply use
(2/3)(RU),(2/3)(LF),FU,(2/3)(RU)i,(2/3)(LF)i
immediately followed by (2/3)(RF)i,(2/3)(LU)i,FU,(2/3)(RF),(2/3)(LU)
and then by FU
.
Position the corners in the final layer. A tri-cycle for this is 2[BU,RU,FU,RU]
along with its obvious symmetric varient 2[LU,FU,RU,FU]
. In both cases, the corner peice in the FRU
position is preserved, while the other three are tri-cycled.
Finally, orient the corners. The needed sequences are 3[LU,FU,RU,BU]
and 3[BU,RU,FU,LU]
. Each of these will preserve the orientation of the corner in the LUB
position, while rotating all remaining corners in the top layer clock-wise or counter-clock-wise, respectively. Note that an even number of these sequences must be applied as they also flip the orientation of all 4 edge peices in the top layer. With practice, it is easy to get this step done with just 2 applications of these sequences.


4. The AMP audio skin¶
Soldering and using the AMP audio skin.
The following video shows how to solder the headers, microphone and speaker onto the AMP skin.
For circuit schematics and datasheets for the components on the skin see The pyboard hardware.
4.1. Example code¶
The AMP skin has a speaker which is connected to DAC(1)
via a small
power amplifier. The volume of the amplifier is controlled by a digital
potentiometer, which is an I2C device with address 46 on the IC2(1)
bus.
To set the volume, define the following function:
import pyb
def volume(val):
pyb.I2C(1, pyb.I2C.MASTER).mem_write(val, 46, 0)
Then you can do:
>>> volume(0) # minimum volume
>>> volume(127) # maximum volume
To play a sound, use the write_timed
method of the DAC
object.
For example:
import math
from pyb import DAC
# create a buffer containing a sine-wave
buf = bytearray(100)
for i in range(len(buf)):
buf[i] = 128 + int(127 * math.sin(2 * math.pi * i / len(buf)))
# output the sine-wave at 400Hz
dac = DAC(1)
dac.write_timed(buf, 400 * len(buf), mode=DAC.CIRCULAR)
You can also play WAV files using the Python wave
module. You can get
the wave module here and you will also need
the chunk module available here. Put these
on your pyboard (either on the flash or the SD card in the top-level directory). You will need an
8-bit WAV file to play, such as this one,
or to convert any file you have with the command:
avconv -i original.wav -ar 22050 -codec pcm_u8 test.wav
Then you can do:
>>> import wave
>>> from pyb import DAC
>>> dac = DAC(1)
>>> f = wave.open('test.wav')
>>> dac.write_timed(f.readframes(f.getnframes()), f.getframerate())
This should play the WAV file.


| DOSE: |
300 mg |
oral |
DXM |
(pill / tablet) |
| |
40 mg |
oral |
Chlorpheniramine Maleate |
(pill / tablet) |
[Erowid Note: This report includes a second-hand mention of heart injury attributed to Coricidin, which was submitted with no verifiable details, no contact information, and no way to confirm it. Erowid is choosing to publish this report to invite public comment and to allow anyone who knows about this event to submit details which can be verified. Email sage at erowid.org.]
I experimented with Coricidin as a teenager under the recommendation of a friend of mine. All I had ever done was habitually smoke pot and upon trying Coricidin approx. 4-6 pills I felt good eventually building an immunity. Having increase the dosage with each use I eventually got up 15 pills for the effects and by then I had grown sick of this disgusting high so I quit taking them. My friend on the other hand wasnt as apt to being sober as I was and continued taking them, quickly building an immunity till he had to take at least 30+ to feel the effects.
He suffered severe heart damage and now requires medication to correct the effects of the damage.  Finally it wore off and I went to sleep then for the following month and a half I felt retarted with slower motor skills inability to think straight or for a long period of time and when I smoked weed these effect seemed to amplify finally after about 35 - 40 days later I was back to normal. This is a caution to any one thinking that coricidin is worth taking for a trip....it most sertianly is not.... No trip is worth your life or your health
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.


Just when we all thought we were comfortable with Ruby and working through some pretty complex problems, DBC pulled fast one and dropped JavaScript on us!
By now we are very familiar with classes and instance methods in Ruby. How might we set up the same code in JS to perform the same output?
Lets look at a few lines of code in Ruby and JavaScript that are doing the same thing. For the examples I considered how we would set up a class/function ask if a dish is clean and say if it is clean or dirty. If the dish is dirty, the code washes the dish and says that it is clean.
class Dishes
def initialize(dish)
@dish = dish;
end
def dirty
if true
puts "Lets wash this dish!"
wash
else
return "This dish is already clean."
end
end
def wash
puts "This dish is clean now!"
end
end
plate = Dishes.new("plate")
p plate.dirty
That was pretty straight forward. Now pay attention to how some of the syntax changes when I move this code into Javascript. How are different strings returned? How are functions called?
function dishes(dish) {
this.dish = dish;
this.dirty = function(){
if (true) {
console.log("Lets wash this dish");
this.wash();
}
else {
console.log("This dish is already clean.");
}
}
this.wash = function() {
console.log("This dish is clean now!");
}
}
plate = new dishes("plate");
plate.dirty();
As you can see, both pieces of code output the same results while using different syntax. Can you pick out the differences and similarities? What are some things to remember as you transition from Ruby to JavaScript? While not required all the time, it is a best practice to include semi-colons after each line in JS. Similar to having to close out def and class, the curly braces act as a way of closing out a certain statement.
When I worked on converting the Ruby code to JavaScript, I ran into a few issues that are important to note when working in JS. First, using a method with a question mark at the end is a big no-no in JavaScript. Also, while I use console.log to call the various string prompts, to call functions, I simply write that function and add a parenthesis at the end.


Travel
I love to travel Ive been to man places all over the world including australia, Ios, Paris, New York, London, Spain and Austria.I love to see different places.I went skiing in Austria for 2 weeks last year and absouluty loved it!
We flew into Munich and styaed there for a night and just went walking through the city,we then got the bus to our accommadation in Austria.We went skiing for two weeks straight then.I've been to Australia to visit my brother
when he was living in Sydney and we travelled to Brisbane and melbourne in the two weeks.
I regularly travel to London because my grandmother is from there orginally and my mum has an apartment over there now. My favourite city that i have visited is Paris because of the architecture and the shopping.
Also i go to Marbella, Spain every year with my family during the month of July for two weeks. We go surfing, jet skiing and on boat trips durning these weeks.


| 95 | (A) | whate Medecyns moste that that Maye vses |
| 90 | (C) | what medycyn medycyn moste is . þat þe mayden vsetħ |
| 91 | (D) | what medicyne most is þat þat may vsitħ |
| 89 | (E) | what is þe mooste medicyne . þat þe may vseth . |
| (Ex) | ---- no text ---- | |
| 94 | (L) | what medecyn is most þat þat may vseþ |
| 89 | (P) | what medycyn es maste þat þat may vses |
| 90 | (U) | What medicine most is / that that may vsetħ |
| (V) | ---- no text ---- |


“Empathy is about standing in someone else’s shoes, feeling with his or her heart, seeing with his or her eyes. Not only is empathy hard to outsource and automate, but it makes the world a better place.” — Daniel Pink
Assholes, Jerks and Dickheads Are People Too - by Neil McGregor. Takeaway: “There is worrying trend on LinkedIn and in the business media in general. The labelling of people into categories labelled Assholes, Jerks and Dickheads. I have to say this worries me for a number of reasons, not the least of which, is the fact that some pretty heavy hitters are using the terms and in turn influencing many others to think along similar narrow minded lines. No doubt many if not all of us have had the unfortunate opportunity to work with some difficult folk, but labelling and discarding them helps no-one.” The article encourages empathy and staying focused on someone’s humanity.
Building Software with the Empathy Spectrum - by Andrea Goulet. Empathy is a skill, and requires context and cultivation. Leaders are wise to master it. “Empathy is a competitive advantage. It helps us build better products and solve better problems by getting to the heart of what’s important to those of us who are human — connection to each other. As leaders, it’s our job to build software organizations where empathy is encouraged and people can grow both intellectually and emotionally.”
Dealing with Surprising Human Emotions: Desk Moves - by Lara Hogan. Takeaway: an examination of people’s emotional reactions when asked to move seats, using Paloma Medina’s BICEPS model and a pitch to understand people’s core needs.
Empathy: The Missing Ingredient in Effective Software Development (video) - by Daniel Bryant. Takeaway: “When gathering requirements and performing business analysis, it is obvious that the ability to experience from within another user/customer/being’s frame of reference is a valuable skill, but the same can be said when writing code. If we follow Martin Fowler’s train of thought where ‘any fool can write code that a computer can understand. Good programmers write code that humans can understand’ we can see that empathy is at the heart of this skill.”
Forming Stronger Bonds with People at Work - by Monica C. Worline, Jane E. Dutton and Ashley E. Hardin. Takeaway: “Too many people think of compassion and connection with others as a nice-to-have in organizations. But if people feel like they belong and genuinely care about one another, they will be more creative, resilient, and eager to contribute at work.  But the human experience of pain is going to show up, whether we invite it or not, and the only way to respond is with compassion.”
Hanlon’s Razor: Relax, Not Everything Is Out to Get You — by Farnam Street. Takeaway: Don’t assign malicious intent to people and situations when unintentional mistakes might be the cause of things not working as you’d expected or wanted.
How the Former CTO of Kickstarter Finds Meaning in Leading - by Michelle. Takeaway: a profile of James Turnbull that covers leadership basics but also delves into his thoughts and implementations of empathy, including his work at Empatico (a startup for facilitating empathy in schoolchildren).
The Secret to Leading Organizational Change Is Empathy - by Patti Sanchez. Takeaway: “I’ve observed the same thing time and time again: How information is communicated to employees during a change matters more than what information is communicated … A lack of audience empathy when conveying news about an organizational transformation can cause it to fail.”
Weathering the Emotional Storms of a Crisis — A Tactical Guide for Individual Contributors and Managers - by First Round. Takeaway: lessons in crisis management by Liz Fosslien and Mollie West Duffy, authors of No Hard Feelings: The Secret Power of Embracing Emotions at Work.
Which Factors Shape Our Empathy? - by Emiliana R. Simon-Thomas. Takeaway: A summary of what empathy is from a neuroscientific and psychological approach, with a link to an empathy quiz included. Offered by UC Berkeley’s “Greater Good” magazine.
Your Most Important Skill: Empathy - by Chad Fowler. Takeaway: Includes a long-ish list of compelling reasons why empathy truly is your most important skill as a leader, and several suggestions on how to practice empathy: listen; watch and wonder; know your enemies, and choose the other side. “We all have friends and loved ones that complain to us about how they have been treated by other people. It’s human nature to complain and it’s the duty of a loved one to listen sympathetically. The assumption is that the listener is on the side of the complainer. A supportive friend or loved one almost always is, instinctually. Try practicing (internally) taking the opposing view point. Don’t go with your default reaction immediately. Start on the other side and work your way back.”
If I Understood You, Would I Have This Look on My Face?: My Adventures in the Art and Science of Relating and Communicating - by Alan Alda. Takeaway: “We were developing empathy and the ability to be aware of what was happening in the mind of another person. This, we realized, is the key, the fundamental ingredient without which real communication can’t happen. Developing empathy and learning to recognize what the other person is thinking are both essential to good communication, and are what this book is about.”
Listening Is the Job - by Andrew Bosworth. Takeaway: Have a system for taking in/consuming information; maximize signal-to-noise ratio; give feedback; proactively identify gaps in your information flows; write notes to remember reactions you have through the day; tell your story; be clear about the information you seek; and listen.
The Power of Listening in Helping People Change - by Guy Itzchakov and Avraham N. (Avi) Kluger. Takeaway: “More than 20 years ago, one of us (Kluger) analyzed 607 experiments on feedback effectiveness and found that feedback caused performance to decline in 38% of cases. This happened with both positive and negative feedback, mostly when the feedback threatened how people saw themselves.”
Why Is It So Hard to Listen - by Happy Melly. Takeaway: “Not only is listening a key factor in being an effective communicator and a strong leader, it’s also one of the basic necessities when it comes to enhancing our emotional intelligence and living in the moment — which ultimately contributes to our overall happiness at work and in life.” Includes listening challenges to help you improve.


Using block programming and a pre-designed course, students moved from zero knowledge to working with the basics. At the end of 4 weeks the entire class was using control flow, variables, loops, and simple functions.
Intro to Python
After completing the introductory course, we moved on to translating those concepts into actual code in the Python language. We did this using Raspberry Pi computers, a robot car kit, and Minecraft Pi Edition.
Python in Robotics
Some students focused on learning to write code to create things in Minecraft, while others tried to program a robotic car to drive itself safely through an obstacle course. In the end, all covered similar concepts.


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Components—reusable patterns
Besides "atomic classes", Co-Design comes with a rich and powerful set of standard components that create a consistent, beautiful look-and-feel.
You can, however, override these components with the utilities provided to you by Co-Design.
Click on any of the tabs above, in order to see the component and an explanation of it.


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You're invited to play a game. The game begins with one dollar in the pot. You flip a coin.
How much would you pay to play this game? Let's simulate a few times to get a sense of how it works


Samsung SmartView SDK allows you to connect mobile devices and SmartTVs. By using the SmartView SDK API you can create compelling experiences that extend mobile applications to a Samsung SmartTV. The JavaScript API can be used in your SmartTV application to enable mobile applications to discover, connect, and communicate with the SmartTV.
<script src="samsung-msf-2.x.js"></script>
NOTE: bower and npm support coming soon.
Before we get started it is important to understand that a multiscreen application consist of two parts. The SmartTV application, which can be an installed application or a hosted web application, and a client application (often a mobile app). Most SmartTV applications are created using HTML/CSS/JavaScript. Mobile applications can be built in many languages including JAVA/Objective C/JS. The MultiScreen SDK family provides client APIs for Android, iOS, and JavaScript.
In the example to follow we will start with our SmartTV application since it will be commonly shared with all other clients.
<!DOCTYPE html>
<html>
<head lang="en">
<!-- Add the script to your html (exposes a `msf` object to the window) -->
<script src="scripts/vendor/msf-2.0.x.min.js"></script>
</head>
...
</html>
msf.local(function(err, service){
var channel = service.channel('com.mydomain.myapp');
channel.connect({name: 'TV'}, function (err) {
if(err) return console.error(err);
console.log('You are connected');
});
channel.on('say', function(msg, from){
console.log(from.attributes.name + ' says, ' + msg);
});
channel.on('clientConnect', function(client){
channel.publish('say', 'Hello '+client.attributes.name, client.id);
});
});
<!DOCTYPE html>
<html>
<head lang="en">
<!-- Add the script to your html (exposes a `msf` object to the window) -->
<script src="scripts/vendor/msf-2.0.x.min.js"></script>
</head>
...
</html>
msf.search(function(err,services){
// Lets assume we found 1 service and want to use it
var app = service[0].application('http://mydomain.com/myapp','com.mydomain.myapp');
app.connect({name: 'Mobile Browser'}, function (err) {
if(err) return console.error(err);
app.publish('say', 'Hello Everyone');
});
app.on('say', function(msg, from){
console.log(from.attributes.name + ' says, ' + msg);
});
});
The MultiScreen API provides three primary features.
In order for a client to start or join a MultiScreen experience, the client application must be able to discover a compatible SmartTV service. The API manages searching for compatible services and only requires a few lines of code. Below is a chunk of code from the Hello World mobile web application. For more details look review the Search & Service classes.
var search = msf.search();
search.on('found', function(services){
for(var i=0; i<services.length; i++){
console.log('Found service '+services[i].name);
}
});
search.start();
Once a compatible SmartTV is discovered, you can launch and connect to your SmartTV application from you mobile client. Application management is centeralized to a single class (Application) that handles starting, stopping, installing, and connecting to your application. Here is a quick example.
// The first argument is the application id or hosted url
// The second is the channel (communication) id you will use for your application
var app = service.application('http://mydomain.com/myapp','com.mydomain.myapp');
// App connect supports callback and event listener coding styles
app.connect({name: 'John Doe'}, function (err) {
if(err) return console.error(err);
console.log('callback : you are connected');
});
Once your client has started and established a connection with the SmartTV application, You can use the applications channel to send and receive messages between the devices. The Application class extends Channel, so in the example below we will continue with application instance we created earlier. Applications also provide notifications when someone connects, disconnects, and publishes a message.
app.on('clientConnect', function(client){
// This will publish the say event to the client that connected
app.publish('say', 'Hello '+client.attributes.name, client.id);
});
// This event will fire when another client publishes the `say` event to a target including your client
app.on('say', function(msg, fromClient){
console.log(fromClient.attributes.name + ' says, ' + msg);
});


Welcome to the place where you can find anything and everything you'll ever need about the RHS Code Club.
 Each week, you will learn new code and apply it in creative ways to your own coding creations, such as personal websites, online games, and other fun, cool, and hilarious projects.
Q: I don't know any coding at all, can I still join?
A: There is so much code out there, and as such, the RHS Code Club not only accepts members with litte to no coding knowledge, but teaches a lot of code that even advanced coders may have looked over, so you will be just as knowledgable as anyone else.
Q: Is the RHS Code Club the dankest club of them all?
A: Yes.
Start designing the website that you want
A navigation bar across the top of the screen
New tags and other fun additions to your personal webiste
Add a sliding animation to a navigation bar or checkbox to create music
See how fast you can click the buttons with this introductory workshop to javascript integration
Apply sleek, cool, and fancy animation to anything you want
A classic runner game RHS Code Club style
To find out what else we will do, join RHS Code Club now!
Need help? Shoot us an email or direct message any member of the board and we will get back to you as soon as possible!
Jeffrey Yu
Sam Kaplan
Sarah Pethani
Sam Miller:<PHONE_NUMBER> | Facebook


The header will usuall be th title of the post, as well as usually being the largest. Heading sizes start at 1 (biggest) and get smaller as you go (h6).It is supposed to indicate the most important information on the page.

h2 is apart of the heading elements that go from h1 (biggest) to h6(smallest). Heading elements imply. h2 is the 2nd to the largest.

h3 is another heading element apart of that 1-6 deal we are talking about above. h3 is third to the biggest. Having these different sizes helps us create hierarchy.

h4, obviously, is fourth to biggest, second to the smallest.

Another heading element to play with. Second to the smallest header.

This is the smallest size that you can do with the heading elements. Playing around with all of these can lead to interesting hierarchy.
 </6>
The element p represents a paragraph. Paragrahs are usually represented in visual media as blocks of text.

This element is an unordered list. You use ul and li tags to create unordered lists.

Element used to define an ordered list. A list where each item is preceded by a numerical/alphabetical identifier.

The li tag defines a list item. It is used in ordered, unordered, and menu lists.

This tag defines a hyperlink, which is used to link from one page to another. Most important attribute of the s element is the href tag, which indicates the link's destination.
This is an image that was put into an HTML page. It has two required attributes - src and alt.
HTML is a texted based approach to describe how content is contained within HTML file structure. This tells the good brower how to display the text, images and other forms of multimedia on we webpage.
Much like our header elments above - head is a place for all of the head elemtents. That being titles for documentsm scripts, styles, meta information and more.
This is how to navigate between oages and Web sites. There are 4 types: text hyperlink, image hyperlink, bookmark hyperklink, and email hyperlink. <link> </link>
This is where the name of what you are doing is. It usually is at the top. <title> <title>
This tag is required in all HTML documents and it defines the title of the document. The title element: defines a title in the browser toolbar.
This is a lot like the div tag. It has no meaning and is mostly used for styling by using an id or clas. The difference between the two is that div is a block element. While span is an inline element. Which means it can be on a line with other elements.
This tag defines a division or section in an HTML document. The element is used as a container for other HTML elements to style them with CSS or to perform tasks with Javascript.

Header element represents a container for introductory content or a set of navigational links. Typically contains: one or more heading elements.
Element is intended only for major blocks of navigational links.
A footer is a small section at the bottom of each page within the document. It often displays company data or copyright information.
This is a way to break up your page. It defines sections ina doctument such as, chapters, headers, footers, or any other sections of the document.

This repesents a section of the page that consists of content that is tangentially related to the content around the aside element, which could be considered seperate from that content. Examples of this, sidebars that are on posters and pages.


I'm a final year CS Undergrad pursuing Bachelor of Technology at Rajasthan Institute of Engineering and Technology, Jaipur.
Doing an internship at Celebal Technologies as an Associate Cloud Developer.
I love using modern technologies to build projects that look great, are fluid and functions correctly.
I can make your data do the storytelling.
Profiency in the following skills


| 26 Jul 1952 |
Evita Peron dies of uterine cancer, Buenos Aires. After her death her body is mummified. |
| 20 May 1989 |
Saturday Night Live actress Gilda Radner dies of ovarian cancer, Los Angeles CA. |
| 24 Feb 1994 |
Singer Dinah Shore (known for See the U.S.A. in a Chevrolet) dies of ovarian cancer, Beverly Hills CA. |
| 11 Sep 1994 |
Actress Jessica Tandy dies of ovarian cancer, Easton CT. |
| 3 Dec 1999 |
Actress Madeline Kahn dies after a year long battle with ovarian cancer, at a Manhattan hospital. |
| 29 Jun 2001 |
Actress Lana Turner dies of cervical cancer, Century City CA. She also developed throat cancer in 1981 caused by her smoking. |


Privacy policy
What kind of personal information the MOT history service and related services holds about you, how it's protected and how you can find out about it.
What information DVSA holds about you
Using MOT reminders
This service links to the MOT reminder service. If you choose to sign up for MOT reminders, DVSA will, typically, hold some or all of the following information about you:
- your email address or mobile phone number to allow us to send you an MOT reminder
- information on how you use the service, using cookies and page tagging techniques
- questions, queries or feedback you leave
- details of which version of web browser you used
Information is stored on our servers in the EU.
Using MOT history API
If you apply to use the MOT history API, DVSA will, typically, hold some or all of the following information about your business:
- business name and address
- contact details for a technical contact, eg name, email address, telephone number
- IP addresses you will use to access the API
This data is collected on a third-party site, which stores the information on ISO certified servers in the UK.
We use this information to assess your application to use the API, and to contact you as necessary in relation to the smooth operation of your use of the API.
Any email sent to us, including any attachments, may be monitored and used by us for reasons of security and for monitoring compliance with office policy. Email monitoring or blocking software may also be used. Please be aware that you have a responsibility to ensure that any email you send to us is within the bounds of the law.
How DVSA protects your personal information
DVSA complies with the Data Protection Act 1998.
DVSA is an executive agency of the Department for Transport who are the data controller for the information. They are registered as a data controller with the Information Commissioner's Office. You can view the details of the registration on their website.
DVSA will:
- use the information only for the effective administration of DVSA services
- ask only for what's needed
- make sure nobody has access to it who shouldn't
- not share the information with other organisations other than where it has a legal responsibility to do so (you cannot say no to this)
- only keep the information for as long as it's needed
- not make it available for commercial use, eg marketing
You must:
- give accurate information
- tell DVSA about any changes, eg to your address
Sharing your information
Your information can be shared with other teams in DVSA where this is needed to process your application.
There are some cases when your information can be shared for other reasons, eg to prevent crime.
DVSA staff handling your information
DVSA's staff are trained in handling information and understand how important it is to protect personal and other sensitive information.
Asking to see your personal information
You can ask to see the personal information DVSA holds about you. You can:
- send an email to<EMAIL_ADDRESS>- or write to: Information Access, The Axis Building, 112 Upper Parliament Street, Nottingham, NG1 6LP
There's no charge.
Sometimes DVSA can withhold information, for example where it also refers to another individual.
How to make a complaint
If you're unhappy with the way DVSA handled your personal information, you can:
- send an email to<EMAIL_ADDRESS>- or write to: Information Access, The Axis Building, 112 Upper Parliament Street, Nottingham, NG1 6LP
You'll get a confirmation that DVSA has received your complaint within 5 days and a full answer within 20 days. DVSA Information Assurance will tell you if there's going to be a delay.
If you're unhappy with the answer or need any advice, contact the Information Commissioner's Office (ICO).
The ICO can investigate your complaint and take action against anyone who has misused personal data.
This privacy notice does not cover the links within this site linking to other websites. We encourage you to read the privacy statements on the other websites you visit.
Changes to this privacy notice
We keep our privacy notice under regular review. This privacy notice was last updated on 24 July 2017.


The A-Frame javascript library lets you create WebVR applications using custom DOM elements, allowing 3D content to be manipulated similarly to 2D HTML elements.
We have adapted A-Frame to work with Argon, creating a new ar-scene element and a number of entities and components to support AR. The documentation and source are on the github page.


Text with rem units
Text with px units
A paragraph floated to the left
Another paragraph floated to the left


This a Landing page of Anitas kitchen project,this website does not have a navigation bar, but it has some images that you can click to make an order .you can go through this website and you can find that there is a part showing some of our clients that have visited website.
Language used
Mkulima Market project, this project will help farmers get a platform they use to get a direct market to sellers Farmers will create account on this platform and they can upload images of their products.
Languages used
I'm Benjamin Kipkorir Tonui, currently student at Moringa School specializing in Full Stack Software Development and as well a UI/UX Designer.
Have been having a passion of becoming a Full stack developer and this passion has really given me desire to learn coding, have a look at some of my projects on my Github account.
Graduated from Karatina University with degree in BSc Computer Science with Second class honors Upper Division
From 6th June to 31st August 2017, Kenya Electricity Generating Company


CLOCKWATCHERS
Reviewed by Harvey Karten, Ph.D. BMG Independents/Artistic License Films Director: Jill Sprecher Writer: Jill Sprecher, Karen Sprecher Cast: Toni Collette, Parker Posey, Lisa Kudrow, Alanna Ubach, Helen Fitzgerald, Stanley DeSantis, Jamie Kennedy, David James Elliott, Debra Jo Rupp, Kevin Cooney, Bob Balaban, Paul Dooley
As Michael Moore points out so incisively in his documentary "The Big One," greedy corporations will do anything to amass profits. Even companies on a roll thanks to the faithful toil of their workers will throw their hirelings to the wolves if doing so will garner more specie for the stockholders. "Clockwatchers," neither as flat-out hilarious as "The Big One" nor as lacerating, treats the dilemma of downsizing in a more heartfelt way. In Jill Sprecher's sincere motion picture, educated women are unable to find permanent jobs, implicitly because American companies prefer to hire temporary workers in order to avoid paying benefits. These temps are kept on simply because the enterprises who hire them can get away with doing so. What had been during the 1950s simply a small sector of the economy has now become almost a norm. Lower-level workers are anonymous and dispensable, shunned as pariahs and as people of little consequence by their colleagues who are "permanent" as well as by the executives who exploit them.
Obviously, their low status on the corporate totem pole is going to have effects on the temps' character. Jill and Karen Sprecher have captured the consequence of company policy so well that they've either done their homework or have been temps themselves. The Sprechers are not so doctrinaire that they blame the economy for every blemish in the employees' make-up: they are more interested in providing us with three- dimensional portraits of four particular women working for a large credit firm, women who even without the abuse they abide are quirky gals and therefore thoroughly entertaining.
Take Iris (Toni Collette), for example. Here's a young woman whose flaws were likely present long before she took on a job as a temporary worker. Not so much announcing her presence at her new assignment as skulking into the post, she acts like a scared deer gazing in terror at the headlights of an oncoming Mack truck.  Luckily, she is befriended by the office extrovert, Margaret (Parker Posey), who steals the movie from that point. Margaret shows her the ropes. If a client phones, she may simply hang up ("If its' important he'll call back") or gives him advice on buying and selling as though she were really instructed by an executive to do so: ("He says go ahead an sell"). Jane (Alanna Ubach), yet another temp, whines that nobody mixes with her kind or tells her anything of importance, illustrating her gripe by an experience she had while working at a bank: "There was this button on the desk and I kept looking at it every day for a month and finally I just pushed it. It was the alarm." Paula (Lisa Kudrow) lives a fantasy, allegedly going to auditions and telling her co-workers that she's a temp only until she gets the starring role she deserves in a movie.
Aside from this clearly differentiated quartet, director Sprecher hones in on familiar office types such as the anal- retentive supplies person who seems to develop stomach pains when he has to issue a pencil; the suave and handsome junior exec who looks right through the women who help him out and seems never to learn their names; the efficient office manager who chirps instructions on how to arrange the papers on the desk for maximum neatness; the CEO who baldly insists that everyone in the office is like family.
The movie gets its name from the principal activity of the officer workers, which is to stare at the big wall timepiece with the passion of fourth-graders eager to get out of school and watch the Jerry Springer show. While we in the audience are unlikely to gaze at our own watches, we do become aware of the passage of time during the second half of the movie, when a contrived element is introduced into the plot. A series of thefts puts the office on security alert, making the daily grind at once more exciting and more strained. Naturally everyone concludes that the temps--who have no roots in the job--are the culprits. What should have been a passing plot point changes the focus of the movie, which begins to lose its comic good will.
Sprecher never really goes for the throat the way Michael Moore does in "Roger and Me" and in "The Big One," but her laid-back and frequently mirthful film serves as a gentle warning on the way our society is headed. The dissolution of the bonds connecting labor and management may lead to higher profits, at least in the short run. But what sort of country will we have as wealth continues to become concentrated in fewer and fewer hands and the majority of the population are as alienated as Karl Marx's proverbial proletariat? Rated PG-13. Running time: 96 minutes. (C) Harvey Karten 1998
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Android App - Kotlin & React Native (from scratch)
All-in-one transport application in the context of Collaborative Economy, including Carpooling, Carsharing, and private car or parking rental.
Founder. Visual search comparison tool to match against e-commerce catalogues.
Visual search engine able to match a picture with an item within a catalog providing a powerful, scalable purchase comparator platform.
Air department. Java Software developer engineer in Tribe agile team. E-commerce product and bookings.
Android developer. NFC Android application for SEAT:
SeatTap as an interactive controller through NFC to several vehicle
interfaces such as the dashbord or engine level.
Final Degree project at UPC promoted to EWSN challenge 2016.
Dependability Competition within the International Conference on Embedded
Wireless Systems and Networks [EWSN] 2016 at TU Graz, Austria.
Hackathon organized by Facebook at EPFL.
First prize was an Internship at
Facebook California 🇺🇸
Swiss-European Mobility Programme (formerly Erasmus) at . DISAL department.
ThirdEye : SmartBike idea promoted to National Enterpreneurship Challenge.
In charge of the design of the communication layers, as well as tinkering with the
WiFi driver and the source code of Android Hammerhead.
Communication department at ABS
[Android Beyond Stratosphere]
Project granted by Google,
aiming to send a Satellite controlled by a Nexus5 device.
In the context of
at UPC,
belonging to CubeSat worldwide project, targetingt to send 10x10x10cm Satellites to
the Space.
Telecommunications Engineering degree, Telematics branch.
Exchange project at
Final degree project promoted to EWSN 2016 in Graz, Austria.
Final HighSchool project presented to RoboSumo UPC challenge 2010.
Startup and company IT consulting and development.
Experience in e-Commerce platform deployments.
End-To-End security, technologies and tool analysis for your business.


Hungry but sick of eating the same food? Enter your postal code and we will recommend awesome food places near you!
With our amazing online payment portal, you can collect your food without taking out your wallet at the counter!
Our carefully curated restaurant partners ensure that you'll only ever order quality food we know you'll love!
"This is fantastic! Thanks so much guys!"
"Bragdoof is amazing. I've been using it to order all kinds of great food around me!"
"Thanks so much for helping us beat the lunchtime queue!"


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Hey, there's nothing to see here.
Try viewing the list of podcast episodes or interviews.


Class: 'exponentially damped J-modulation window function'
http://nmrML.org/nmrCV#NMR:1000307
Annotations (3)
-
has_exact_synonym "exponentially damped J-modulation apodization" ()
-
comment "Functional form:
jmod(x_i) = exp(-e)*sin( pi*off + pi*(end-off)*i/(size-1))
Parameters:
* data Array of spectral data.
* e Exponential term.
* off Start of modulation in fraction of pi radians.
* end End of modulation in fraction of pi radians.
* inv Set True for inverse apodization" ()
-
label "exponentially damped J-modulation window function" (en)
Superclasses (1)
Usage (1)
-
Class: 'exponentially damped J-modulation window function'


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03/03/2017 10:01:58 ¶ ● 2jam4u: micdotcom: Sportscaster Dale Hansen defends student wrestler Mack Beggs and takes a stand against transphobia WHOS GRANDPA IS THIS!?!