Army
I graduated from Florida Atlantic University in 2013 and joined the United States Army in 2014. Since then I have been stationed in Oklahoma, Texas, and finally Washington.
Being stationed here in Washington has allowed me the opportunity to meet great people who work in the tech industry and now I have been inspired to pursue my dreams in computer science.
I was discharged from the Army in 2017 and decided to stay in the area to tackle this new challenge head on.
During my childhood I watched how important hair had been to the women in my household. I would constantly see my mother doing my sisters hair and my sisters doing my mothers hair.
As time went on I started to see the importance of hair wasn't just a characteristic that my family had but in fact it was cultural thing.


SIS 0.1-incubating Release notes
New Features
- [SIS-1] - Import LocalLucene code into SIS
- [SIS-5] - Website logo
- [SIS-6] - Create DOAP file for projects.a.o
- [SIS-7] - Top level pom for SIS
- [SIS-12] - Write basic SIS documentation for CLI and for WS layer
- [SIS-15] - Bake in Demo JSP page for initial release
Improvements
- [SIS-2] - Stand up SIS website
- [SIS-3] - Refactor SIS
- [SIS-14] - Use LatLon class instead of plain double points in QuadTreeData interface
- [SIS-23] - Brand demo.jsp with ApacheSIS TM logo
- [SIS-24] - Refactor sis.version into build process flowdown through Maven and rename to 0.1-incubating
Bug fixes
- [SIS-16] - SIS location service is configured incorrectly
- [SIS-17] - Context params need override set to false
- [SIS-18] - demo JSP has incorrect form get action set
- [SIS-19] - QTreeWriter and GeoRSSData.save don't check to make sure the directory exists before creating data there
- [SIS-20] - Make the qTreeIdxPath and geodata paths configurable properties
- [SIS-21] - Path to location service shouldn't include prefix slash
- [SIS-22] - Error obtaining geodata RSS causes Qtree index to not be written
Tasks


Huawei will reveal its iPad Pro competitor on November 25
The MatePad Pro takes some clear cues from Apple's tablet
by Napier LopezA few weeks ago, rumors and images of a supposed Huawei MatePad Pro started to hit the web. As you can probably guess by the name, it’s the company’s take on an iPad Pro competitor.
At the time we didn’t have a firm release date, but now it seems the tablet will arrive as soon as this month: Huawei CEO Richard Yu made a Weibo post teasing a MatePad even for November 25.
At a glance, it’d be easy to mistake the tablet for an iPad Pro. Granted, there are only so many ways to design a tablet, but it has relatively slim bezels, a very Apple-Pencil-looking stylus, and a grey keyboard. That said, it appears to come in several colorways, and the screen features a hole-punch camera, so there’s that.
Spec-wise, the tablet appears to sport the new Kirin 990 Chip in the Mate 30 Pro, and 8GB of RAM. As usual, it’s basically a flagship phone blown up into a larger form factor, but that’s not necessarily a bad thing, especially if Huawei has optimized thermals to eke a bit more performance out of the tablet than its smartphone.
The event is being held in Shanghai and we’ve seen little fanfare about the tablet from Huawei‘s European marketing, so it’s fair to assume the company is focusing on a China release for now. Still, we’re curious to see how the tablet holds up in the real world, given the scarcity of decent Android tablets.
Via Android Police
For more gear, gadget, and hardware news and reviews, follow Plugged on Twitter and Flipboard.


The World Wide Web ("WWW" or "The Web") is a global information medium which users can access via computers connected to
the InternetHTML (from English HyperText Markup Language - "hypertext markup language") is a standardized markup language for web
pages on the World Wide Web. HTML code is interpreted by browsers; the resulting page is displayed on a computer monitor
or mobile device.
The structure of the HTML
CSS
(Cascading Style Sheets)it is the language that is responsible for the visual presentation of documents to the user.


Lt Gen (R) Asim Bajwa to be appointed CPEC Authority's chairperson | Pakistan - Geo.tv
by Ayaz Akbar YousufzaiISLAMABAD: Lieutenant General (R) Asim Bajwa is to be appointed the chairperson of the China-Pakistan Economic Corridor (CPEC) Authority, sources informed Geo News.
According to sources, a summary pertaining to the appointment of Lt Gen (R) Bajwa as the CPEC Authority's chairperson has been sent to the Cabinet Division. The summary was prepared by the Prime Minister's Office.
It is expected that the approval of the summary to appoint Lt Gen (R) Bajwa as the CPEC Authority's head would be given in the cabinet's upcoming meeting, which is scheduled for Tuesday next week.
Prime Minister Imran Khan would preside over the cabinet meeting.


Git-Workshop Assignment
Who Am I?
Vithulan MV
Hey there,
I'm Vithulan from Hatton ;)
Now I'm learning about Git Hub. At the very beginning it was very complicated, but now I'm little fluent on this.I'm able to explain about its performance,benifits,tricks to my friends =D
Oh well it is supposed to be about myself!!
OK, Now I'm doing Computer Science and Engineering in University of Moratuwa
I'd like to learn about Information Security,Algorithms,Software development and Networks. As every Computer Science engineer's dream, I also like to join google, any anti virus software companies or NSA ;)
Copyright Vithulan MV
CSE
2014


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You may apply to the client area by completing this form.


'Love is His Meaning'
A talk given on Sunday 3 September 2017 at St Paul's Cathedral, London
{% include youtubeplayer.html id='kcTFlgOx9aI' %}Gresham College
Gresham College, where Professor Ward was Gresham Professor of Divinity from 2004 – 2008, holds a series of audio, video and transcripts of lectures. Link to full listing. You may download or stream the video from their web site.
Chapman University
Has Science Made God Obsolete? The Great Debate
{% include youtubeplayer.html id='aM2Q5REenmk' %}On March 7, 2016, noted British author, theologian and philosopher Keith Ward of Oxford University and scientist and skeptic Michael Shermer debated on the nature of reality, science and religion, and God’s existence.
Other
Philosophy, Science and The God Debate, a two-DVD set of filmed interviews with Keith Ward, Alister McGrath and John Lennox, and produced by the Nationwide Christian Trust.


The battery status should appear in the space below. After enabling battery state monitoring, try toggling the power saving mode to see the plugin in action.
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Battery level: [waiting for plugin...]


My student asked today how to interpret the AIC (Akaike’s Information Criteria) statistic for model selection. We ended up bashing out some R code to demonstrate how to calculate the AIC for a simple GLM (general linear model). I always think if you can understand the derivation of a statistic, it is much easier to remember how to use it.
Now if you google derivation of the AIC, you are likely to run into a lot of math. But the principles are really not that complex. So here we will fit some simple GLMs, then derive a means to choose the ‘best’ one.
Skip to the end if you just want to go over the basic principles.
Before we can understand the AIC though, we need to understand the statistical methodology of likelihoods.
Say you have some data that are normally distributed with a mean of 5
and an sd of 3
:
set.seed(126)
n <- 50 #sample size
a <- 5
sdy <- 3
y <- rnorm(n, mean = a, sd = sdy)
hist(y)
Now we want to estimate some parameters for the population that y
was
sampled from, like its mean and standard devaiation (which we know here
to be 5 and 3, but in the real world you won’t know that).
We are going to use frequentist statistics to estimate those parameters. Philosophically this means we believe that there is ‘one true value’ for each parameter, and the data we observed are generated by this true value.
m1 <- glm(y ~ 1, family = "gaussian")
sm1 <- summary(m1)
The estimate of the mean is stored here coef(m1)
=4.38, the estimated
variance here sm1$dispersion
= 5.91, or the SD sqrt(sm1$dispersion)
=2.43. Just to be totally clear, we also specified that we believe the
data follow a normal (AKA “Gaussian”) distribution.
We just fit a GLM asking R to estimate an intercept parameter (~1
),
which is simply the mean of y
. We also get out an estimate of the SD
(= $\sqrt variance$) You might think its overkill to use a GLM to
estimate the mean and SD, when we could just calculate them directly.
Well notice now that R also estimated some other quantities, like the residual deviance and the AIC statistic.
summary(m1)
##
## Call:
## glm(formula = y ~ 1, family = "gaussian")
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -5.7557 -0.9795 0.2853 1.7288 3.9583
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.3837 0.3438 12.75 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 5.910122)
##
## Null deviance: 289.6 on 49 degrees of freedom
## Residual deviance: 289.6 on 49 degrees of freedom
## AIC: 233.72
##
## Number of Fisher Scoring iterations: 2
You might also be aware that the deviance is a measure of model fit, much like the sums-of-squares. Note also that the value of the AIC is suspiciously close to the deviance. Despite its odd name, the concepts underlying the deviance are quite simple.
As I said above, we are observing data that are generated from a
population with one true mean and one true SD. Given we know have
estimates of these quantities that define a probability distribution, we
could also estimate the likelihood of measuring a new value of y
that
say = 7.
To do this, we simply plug the estimated values into the equation for
the normal distribution and ask for the relative likelihood of 7
. We
do this with the R function dnorm
sdest <- sqrt(sm1$dispersion)
dnorm(7, mean = coef(m1), sd = sdest)
## [1] 0.09196167
Formally, this is the relative likelihood of the value 7 given the values of the mean and the SD that we estimated (=4.8 and 2.39 respectively if you are using the same random seed as me).
You might ask why the likelihood is greater than 1, surely, as it comes
from a probability distribution, it should be <1. Well, the normal
distribution is continuous, which means it describes an infinte set of
possible y
values, so the probability of any given value will be zero.
The relative likelihood on the other hand can be used to calculate the
probability of a range of
values.
So you might realise that calculating the likelihood of all the data would be a sensible way to measure how well our ‘model’ (just a mean and SD here) fits the data.
Here’s what the likelihood looks like:
plot(y, dnorm(y, mean = coef(m1), sd = sdest), ylab = "Likelihood")
It’s just a normal distribution.
To do this, think about how you would calculate the probability of multiple (independent) events. Say the chance I ride my bike to work on any given day is 3/5 and the chance it rains is 161/365 (like Vancouver!), then the chance I will ride in the rain[1] is 3/5 * 161/365 = about 1/4, so I best wear a coat if riding in Vancouver.
We can do the same for likelihoods, simply multiply the likelihood of
each individual y
value and we have the total likelihood. This will be
a very small number, because we multiply a lot of small numbers by each
other. So one trick we use is to sum the log of the likelihoods instead
of multiplying them:
y_lik <- dnorm(y, mean = coef(m1), sd = sdest, log = TRUE)
sum(y_lik)
## [1] -114.8636
The larger (the less negative) the likelihood of our data given the model’s estimates, the ‘better’ the model fits the data. The deviance is calculated from the likelihood and for the deviance smaller values indicate a closer fit of the model to the data.
The parameter values that give us the smallest value of the -log-likelihood are termed the maximum likelihood estimates.
Now say we have measurements and two covariates, x1
and x2
, either
of which we think might affect y:
a <- 5
b <- 3
n <- 100
x1 <- rnorm(n)
x2 <- rnorm(n)
sdy <- 1
y <- a + b*x1 + rnorm(n, sd = sdy)
par(mfrow = c(1,2))
plot(x1, y)
plot(x2, y)
So x1 is a cause of y, but x2 does not affect y. How would we choose which hypothesis is most likely? Well one way would be to compare models with different combinations of covariates:
m1 <- glm(y ~ x1)
m2 <- glm(y ~ x2)
m3 <- glm(y ~ x1 + x2)
Now we are fitting a line to y, so our estimate of the mean is now the line of best fit, it varies with the value of x1. To visualise this:
plot(x1, y)
lines(x1, predict(m1))
The predict(m1)
gives the line of best fit, ie the mean value of y
given each x1 value. We then use predict to get the likelihoods for each
model:
sm1 <- summary(m1)
sum(dnorm(y, mean = predict(m1), sd = sqrt(sm1$dispersion), log = TRUE))
## [1] -125.6214
sm2 <- summary(m2)
sum(dnorm(y, mean = predict(m2), sd = sqrt(sm2$dispersion), log = TRUE))
## [1] -247.8059
sm3 <- summary(m3)
sum(dnorm(y, mean = predict(m3), sd = sqrt(sm3$dispersion), log = TRUE))
## [1] -125.4843
The likelihood of m1
is larger than m2
, which makes sense because
m2
has the ‘fake’ covariate in it. The likelihood for m3
(which has
both x1 and x2 in it) is fractionally larger than the likelihood m1
,
so should we judge that model as giving nearly as good a representation
of the data?
Because the likelihood is only a tiny bit larger, the addition of x2
has only explained a tiny amount of the variance in the data. But where
do you draw the line between including and excluding x2? You run into a
similar problem if you use R^2 for model selection.
So what if we penalize the likelihood by the number of paramaters we have to estimate to fit the model? Then if we include more covariates (and we estimate more slope parameters) only those that account for a lot of the variation will overcome the penalty.
What we want a statistic that helps us select the most parsimonious model.
One way we could penalize the likelihood by the number of parameters is to add an amount to it that is proportional to the number of parameters. First, let’s multiply the log-likelihood by -2, so that it is positive and smaller values indicate a closer fit.
LLm1 <- sum(dnorm(y, mean = predict(m1), sd = sqrt(sm1$dispersion), log = TRUE))
-2*LLm1
## [1] 251.2428
Why its -2 not -1, I can’t quite remember, but I think just historical reasons.
Then add 2*k, where k is the number of estimated parameters.
-2*LLm1 + 2*3
## [1] 257.2428
For m1
there are three parameters, one intercept, one slope and one
standard deviation. Now, let’s calculate the AIC for all three models:
-2*LLm1 + 2*3
## [1] 257.2428
LLm2 <- sum(dnorm(y, mean = predict(m2), sd = sqrt(sm2$dispersion), log = TRUE))
-2*LLm2 + 2*3
## [1] 501.6118
LLm3 <- sum(dnorm(y, mean = predict(m3), sd = sqrt(sm3$dispersion), log = TRUE))
-2*LLm3 + 2*4
## [1] 258.9686
We see that model 1 has the lowest AIC and therefore has the most parsimonious fit. Model 1 now outperforms model 3 which had a slightly higher likelihood, but because of the extra covariate has a higher penalty too.
 The function AIC(m3)
will just give it to you.
So to summarize, the basic principles that guide the use of the AIC are:
Lower indicates a more parsimonious model, relative to a model fit with a higher AIC.
It is a relative measure of model parsimony, so it only has meaning if we compare the AIC for alternate hypotheses (= different models of the data).
We can compare non-nested models. For instance, we could compare a linear to a non-linear model.
The comparisons are only valid for models that are fit to the same response data (ie values of y).
Model selection conducted with the AIC will choose the same model as leave-one-out cross validation (where we leave out one data point and fit the model, then evaluate its fit to that point) for large sample sizes.
You shouldn’t compare too many models with the AIC. You will run into the same problems with multiple model comparison as you would with p-values, in that you might by chance find a model with the lowest AIC, that isn’t truly the most appropriate model.
When using the AIC you might end up with multiple models that perform similarly to each other. So you have similar evidence weights for different alternate hypotheses. In the example above m3 is actually about as good as m1.
You should correct for small sample sizes if you use the AIC with small sample sizes, by using the AICc statistic.
You can access the AIC in R with the AIC()
function.
Message me on Twitter if you have comments on this post.
[1] Assuming it rains all day, which is reasonable for Vancouver.
Designed by Chris Brown. Source on Github


[Theta,LogPost,AR,Scale,FinalCov] = regen(Pos,NDraw,...)
Pos
[ poster ] - Initialised posterior simulator object.
NDraw
[ numeric ] - Length of the chain not including burn-in.
Theta
[ numeric ] - MCMC chain with individual parameters in rows.
LogPost
[ numeric ] - Vector of log posterior density (up to a constant) in each draw.
AR
[ numeric ] - Vector of cumulative acceptance ratios in each draw.
Scale
[ numeric ] - Vector of proposal scale factors in each draw.
FinalCov
[ numeric ] - Final proposal covariance matrix; the final covariance matrix of the random walk step is Scale(end)^2*FinalCov
.
Regeneration Times in MCMC Simulation, with Application to Adaptive Schemes," mimeo, Carnegie Mellon University.


Innovations is key in our present day world and Rick Sanchez stands for the craziest and most innovative ideas to solve our problems. Present your crazy idea that can shake the Tech industry and win a chance to make it your dream project.
| Venue: |
Seminar Hall |
| Timings: |
9:45 am - 12 pm |
| Registration Fee: |
Free |


| |
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What is IFTTT?
|
|
IFTTT (which stands for “If This, Then That”) is a free service that allows users to create simple conditional statements, called "recipes," that are triggered based on changes to other web services, such as Twitter, Facebook, Evernote, Code School, and hundreds more.
|
|
An example recipe might consist of automatically tweeting a celebratory message every time you complete a course.
|
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"... a form in which particular features recur recognisably and regularly, if not identically or symmetrically." — Philip Ball, Shapes, p. 18
Image attribution : muzina_shanghai
Image attribution : image_less_ordinary
Invisible Patterns
"... things which recur recognisably and regularly ..."
Image attribution : muzina_shanghai
Design Patterns
"... a design pattern is a general repeatable solution to a commonly occurring problem [in software design.]" — https://sourcemaking.com/design_patterns
VIDEO: Biomimicry
VIDEO: Some Maths
Visible and Invisible Patterns
Spirals
Image attribution : Brian McHugh
Image attribution : marc cornelis
phyllotaxis and the sunflower
Pulses
Flow
Image attribution : Antti Lipponen
Image attribution : NASA Goddard Space Flight Center
Image attribution : Anders Sandberg
Branches
Image attribution : www.paulslab.com
Image attribution : John Ash
Out and About with Patterns
explore Perth City Farm and collect one or several objects and / or observations which exemplify a pattern
"... there is going to be no Grand Theory of Pattern awaiting you at the rainbow's end. The universe is not made that way."
— Philip Ball, Branches, p. 180
Patterns In Design
Complexity
Image attribution : Mark Skipper
Patterns
Image attribution : Avital Pinnick
Pattern Languages
OR
"how do you make a Mediterranean hillside village?"
Image attribution : Miguel Virkkunen Carvalho
Image attribution : José Luis Mieza
Image attribution: Ethan Roland
Design Patterns
"... a design pattern is a general repeatable solution to a commonly occurring problem [in software design.]" — https://sourcemaking.com/design_patterns
"... the final act of the designer, once components have been assembled, is to make a sensible pattern assembly of the whole." — Mollison, Permaculture: A Designers' Manual, p. 70
"There are two aspects to patterning: the perception of the patterns that already exist and how these function, and the imposition of pattern on sites in order to achieve specific ends." — Mollison, Permaculture: A Designers' Manual, p. 95
exisiting patterns (eg. wind flow)
pattern imposition
Boundary / Edge Design Pattern
"The creation of complex boundary conditions is a basic design strategy for creating spacial and temporal niches." — Mollison, Permaculture: A Designers' Manual, p. 76
Keyholes
The Herb Spiral
Exodus and Illegibility
"The policy of encouraging or imposing legible, agrarian landscapes of appropriation seems hard-wired to state-making." — James Scott, The Art of Not Being Governed, p. 76
"... the farmers created methods for hiding their food that eventually became instrinsic to the foods themselves ... Miso was dried and hung in balls ... Dried vegetables were mixed with adobe and sometimes built into the walls of the house!" — Gaku Homa, The Folk Art of Japanese Country Cooking: A Traditional Diet for Today's World, p. 17
""Escape crops" may have one or more of several characteristics that facilitate evasion of raiding either by states or freebooters. In many cases they simple qualify as being well adapted to environmental niches that are difficult to map and control: high, rugged mountains, swamps, deltas, mangrove coasts, and so on. If, in addition, they are of staggered maturity, fast growing, and easily hidden, if they require little care, are of little value per unit weight and volume, and grow below ground, the aquire greater escape value." — James Scott, The Art of Not Being Governed, p. 199


Шапка
Основной текст
The Far File and Archive Manager
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Reporting Problems and Receiving Support
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
By following these simple rules, you have the best chances of
getting your problem successfully solved.
1. Make sure that the problem you've encountered is not an ordinary situation
or a known problem: see Far Help, FAQ, TechInfo and WhatsNew. If it's
possible, you should also search network resources for the answers (links to
the resources are located in help chapter "Overview of plugin capabilities").
Only if the problem is not known, move to step 2, otherwise there's no need
to produce extra traffic, it does not stimulate developers in any way,
but distracts them.
2. Because Far is commonly used with plug-ins, first it is necessary to
locate the source of the problem.
2.1. Start Far without plug-ins. You can do it with "far.exe /p" command.
Try to reproduce the problem situation. If the problem persists, it makes
sense to inform Far developers about the problem (see step 3).
2.2. If problem does not persist when running Far without plug-ins, you'll
have to find out which plug-in(s) are essential for the problem to emerge.
In order to do it, for example, you can remove plug-ins one by one, starting
with the most recently installed ones, and every time check whether the
problem still exists. If you have found the plugin causing the problem
and the plugin is not included in the Far distribution, you should report
the problem to the author of that plugin.
2.3. If the found "bad" plug-in is included in the Far distribution,
or you failed to find out which plug-in is essential to reproduce the
problem, inform Far developers about it (see step 3). If the problem is
in the MultiArc/ArcLite plugin, see Step 4. If the problem is in the FTP
plugin, see Step 5.
3. You should inform the Far Group about the problem (the contacts are in
the File_id.diz file).
3.1. Follow the usual rules when reporting a problem:
- Isolate the conditions under which the problem appears as narrow as
you can - it helps to find its cause faster. Try not to specify
irrelevant information (for example, do not list all the plug-ins you
have installed if it is not necessary to reproduce the problem).
If you are not sure whether some condition is relevant or not, you'd
better specify it just in case (and comment that you are not sure
if it's relevant).
- Make sure that the problem is not caused by macros you may have
recorded. Press Ctrl-. (Ctrl-point) to switch macros off, and try to
reproduce the problem again.
- If it's possible, try to reproduce the problem on different versions
of Far, Windows, other computers and with different Far settings.
Include a report on these experiments in your message about the
problem.
3.2. Specify the conditions to reproduce the problem (Windows version,
type of the filesystem, type of the local network, Far version, versions
of installed plug-ins, macros etc - the necessary and sufficient set of
conditions to repeat the problem).
3.3. If some specific files are necessary to reproduce the problem, you should
attach them to your message, only if the overall message size does not exceed
80k. If the size exceeds this value, you'd better put the necessary file to
an HTTP or FTP site and just add the URL to the file to the message.
3.4. If the problem concerns Plugins API, try to write a minimal test plug-in
which demonstrates the problem and attach the archive (compiled plug-in and
its sources) to your message.
3.5. Answer the following questions:
- what actions were performed?
- what result did you expect?
- what happened instead?
3.6. Supply additional comments (if applicable).


Posted on 04/06/2017 at 9:00 PM by The Vibe.
First week of Coding period of GSoC 2017 as per my timeline, started on the
29th of May 2017. This was something I was really looking up to, right from
the moment I was selected by SciRuby organization
( GitHub | Website )
for my project idea daru-io
. In my opinion, the first week was very
interesting and productive. Through this blog post, I'd like to document my
progress during this week.
During the earlier part of this week, I continued on my Pull Request
regarding the HTML Importer method for daru , Daru::DataFrame.from_html()
.
Through this method, I implemented web scraping to get tables from a HTML
page and convert them to an Array
of Daru::DataFrame
s. The Mechanize
gem has been used to implement web scraping.
Though the method is now able to properly parse the dataframes from a couple of websites, it does have a downside. The downside is that, there are websites that dynamically load the HTML tables by inline JS, and this method won't be able to parse such tables.
The logic behind detection of index and order of a table can be seen from this Pull Request. Have a look at a couple of screenshots documenting the method with an example -
The existing IO modules of daru - 5 importers and 2 exporters, are to be ported to the daru-io repository to ensure that subsequent IO modules that are to be newly formed, look consistent with the older ones. That way, daru-io can also be released as a plugin gem to daru, by August end. By Friday, I had ported the existing modules with updated YARD docs styling and modern syntax for RSpec tests. However, a review comment by mentor Victor (@zverok) seems to suggest a better inheritence layout and usage methodology to daru-io modules.
That is, something used like Daru::IO::Exporters::CSV.write('path/to/csv/file', options)
can now be used with lazy-calling feature such as,
csv_exporter = Daru::IO::Exporters::CSV.new('path/to/csv/file', options)
# Do some thing here
csv_exporter.write
This is still under progress with this Pull Request, and isn't merged yet. Once the lazy-calling feature has been approved for one module, it is easy to extend them to other modules. I've been putting effort from my side, and it hopefully doesn't delay my timeline. But if it does, I've a swiss-knife up my sleeve called 'Buffer period', which is meant for handling such delays.
Fixture files are the input files that are used in testing the modules (importers, in my case). As I'd like to tackle various format importers such as Redis, Mongodb, JSON, Avro, RData & RDS in my GSoC tenure, it's a good starting point to start adding the fixture files. Progress regarding this has been made in this Pull Request, and is yet to be merged.


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Mr. Pornchai Chaweewat
Committee: Dr. Jai Govind Singh (Chairperson)
Prof. Weerakorn Ongsakul
Dr. Shobhakar Dahkal
12nd April, 2018
In transmission system can be fomulted as minimize:
Subject to:
where:
C_{Gi} is generation cost of generator G_i ($/MWhr). P_{Gi} and Q_{Gi} are real and reactive power output of generator G_{i} (MW, MVar). P_{Gi}^{min} and P_{Gi}^{max} are minimum and maximum limit of P_{Gi} . Q_{Gi}^{min} and Q_{Gi}^{max} are minimum and maximum limit of Q_{Gi} .  F_{k} and F_{k}^{max} are line flow and its maximum limit at bus i. V_{i}^{min} and V_{i}^{max} are minimum and maximum voltage limit at bus i.
The LMP at bus i can be written as follows:
The LMP at any bus also be calculated in:
Part I: LMP sensitivity on load variation
PJM 5 bus system
Generation Parameters
| Gen. Name | Alta($/MWhr) | Park City | Solitude | Sundance | Brighton |
|---|---|---|---|---|---|
| Bus | A | A | C | D | E |
| Cost($/MWhr) | 14 | 15 | 30 | 40 | 10 |
| MW Limit | 40 | 170 | 520 | 200 | 600 |
| MVar Limit | +-30 | +-127.5 | +-390 | +-150 | +-450 |
Load data
| Bus | A | A | C | D | E |
|---|---|---|---|---|---|
| MW | 0 | 300 | 300 | 400 | 0 |
| MVar | 0 | 98.61 | 98.61 | 131.47 | 0 |
Line data
| Line | AB | AD | AE | BC | CD | DE |
|---|---|---|---|---|---|---|
| R(%) | 0.281 | 0.304 | 0.0064 | 0.108 | 0.297 | 0.297 |
| X(%) | 2.81 | 3.04 | 0.64 | 1.08 | 2.97 | 2.97 |
| B/2(%) | 3.56 | 3.29 | 15.63 | 9.26 | 3.37 | 3.37 |
| Limit (MVA) | 400 | - | - | - | - | 240 |
LMP at bus 2, 3 and 4 are differnced due to congestion between line A-B. The system marginal cost is 39.94 USD/MWhr
IEEE 118 bus test system
Load data
| Bus | MW | MVar | Bus | MW | MVar | Bus | MW | MVar | Bus | MW | MVar |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 51 | 27 | 33 | 23 | 9 | 60 | 78 | 3 | 95 | 42 | 31 |
| 2 | 20 | 9 | 34 | 59 | 26 | 62 | 77 | 14 | 96 | 38 | 15 |
| 3 | 39 | 10 | 35 | 33 | 9 | 66 | 39 | 18 | 97 | 15 | 9 |
| 4 | 39 | 12 | 36 | 31 | 17 | 67 | 28 | 7 | 98 | 34 | 8 |
| 6 | 52 | 22 | 39 | 27 | 11 | 70 | 66 | 20 | 99 | 42 | 0 |
| 7 | 19 | 2 | 40 | 66 | 23 | 72 | 12 | 0 | 100 | 37 | 18 |
| 8 | 28 | 0 | 41 | 37 | 10 | 73 | 6 | 0 | 101 | 22 | 15 |
| 11 | 70 | 23 | 42 | 96 | 23 | 74 | 68 | 27 | 102 | 5 | 3 |
| 12 | 47 | 10 | 43 | 18 | 7 | 75 | 47 | 11 | 103 | 23 | 16 |
| 13 | 34 | 16 | 44 | 16 | 8 | 76 | 68 | 36 | 104 | 38 | 25 |
| 14 | 14 | 1 | 45 | 53 | 22 | 77 | 61 | 28 | 105 | 31 | 26 |
| 15 | 90 | 30 | 46 | 28 | 10 | 78 | 71 | 26 | 106 | 43 | 16 |
| 16 | 25 | 10 | 47 | 34 | 0 | 79 | 39 | 32 | 107 | 50 | 12 |
| 17 | 11 | 3 | 48 | 20 | 11 | 80 | 130 | 26 | 108 | 2 | 1 |
| 18 | 60 | 34 | 49 | 87 | 30 | 82 | 54 | 27 | 109 | 8 | 3 |
| 19 | 45 | 25 | 50 | 17 | 4 | 83 | 20 | 10 | 110 | 39 | 30 |
| 20 | 18 | 3 | 51 | 17 | 8 | 84 | 11 | 7 | 112 | 68 | 13 |
| 21 | 14 | 8 | 52 | 18 | 5 | 85 | 24 | 15 | 113 | 6 | 0 |
| 22 | 10 | 5 | 53 | 23 | 11 | 86 | 21 | 10 | 114 | 8 | 3 |
| 23 | 7 | 3 | 54 | 113 | 32 | 88 | 48 | 10 | 115 | 22 | 7 |
| 24 | 13 | 0 | 55 | 63 | 22 | 90 | 163 | 42 | 116 | 184 | 0 |
| 27 | 71 | 13 | 56 | 84 | 18 | 91 | 10 | 0 | 117 | 20 | 8 |
| 28 | 17 | 7 | 57 | 12 | 3 | 92 | 65 | 10 | 118 | 33 | 15 |
| 31 | 43 | 27 | 58 | 12 | 3 | 93 | 12 | 7 | - | - | - |
| 32 | 59 | 23 | 59 | 277 | 113 | 94 | 30 | 16 | - | - | - |
Comparision between results from pure brute force and combine heuristic-brute force
MAE ($/MWhr)
where; p_{i} and \hat{p}_{i} are LMP values from prue brute force and combine heuristic-brute force. N is number of LMP vlues from any quantile in every bus.
| System | MAE($/MWhr) | MAPE(%) |
|---|---|---|
| PJM 5-bus | 1.6466576417902465e-05 | 5.337334019974992e-05 |
| IEEE 118-bus | 0.0027926042421568546 | 0.00705144530541964 |
The speed-up performance shows time reduction in percentage. The base calculation time are 65.67 and 1203.06 seconds for PJM 5 bus system and IEEE 118 bus system, respectively.
| System | F. Li and Bo (2009) | Purpose method |
|---|---|---|
| PJM 5-bus | 23.14% | 39.07% |
| IEEE 118-bus | 4.29% | 54.35% |
Part II: PJM dayly load (office day and holiday)
The key points of the overall performance analysis are disscussed as below:
Forecasting in Generation
Engage Demand response program
Integrate emission cost in LMP
Thank you for your attendtion
All questions are welcome


FilePond Plugins
| Name | Description | |
|---|---|---|
| Image Editor |
Allows client-side image editing libraries like Doka Image Editor to integrate with FilePond. |
Documentation |
| Image Preview |
Shows a preview of image files. |
Documentation |
| Image Transform |
Apply image transforms on the client before uploading. |
Documentation |
| Image Crop |
Set crop ratio for image files. |
Documentation |
| Image Resize |
Set output dimensions for image files. |
Documentation |
| Image EXIF Orientation |
Corrects orientation of pictures taken on mobile phones. |
Documentation |
| File Encode |
Encodes files as base64 data for submitting along a classic form post. |
Documentation |
| File Rename |
Rename files on the client. |
Documentation |
| File Size Validation |
Limit the size of files being added. |
Documentation |
| File Type Validation |
Limit the types of files being added. |
Documentation |
| Image Size Validation |
Limit dimensions of images being added. |
Documentation |
| File Metadata |
Automatically set file initial metadata object. |
Documentation |
| File poster |
Show a poster image inside the file item. |
Documentation |


Curriculum Vita
Education
Cornell University, Ithaca, New York
- Ph.D. Applied Mathematics, 2011-2016
- M.S. Applied Mathematics, 2011-2014
The University of Texas, Austin, Texas
- M.S. Mechanical Engineering (Acoustics), 2009-2011
- B.S. Mathematics, 2005-2009
- B.S. Mechanical Engineering, 2004-2008
Publications
-
A post-processing technique for stabilizing the discontinuous pressure projection operator in marginally- resolved incompressible inviscid flow, S. M. Joshi, P. J. Diamessis, D. T. Steinmoeller, M. Stastna, G. N. Thomsen, Computers and Fluids (submitted, arXiv)
-
Deflation-augmented preconditioning of the Poisson-Neumann Schur problem using a high-order discontinuous discretization on long domains, S. M. Joshi, G. N. Thomsen, P.J. Diamessis, Journal of Computational Physics, vol. 313 pp. 209-232 (arXiv)
-
Geoacoustic inversions of horizontal and vertical line array acoustic data from a surface ship source of opportunity, S. A. Stotts, R. A. Koch, S. M. Joshi, V. T. Nguyen and D. P. Knobles, IEEE J. Ocean Engineering, vol. 35 No. 1 p. 79-102 (2010)
-
Maximum entropy principle applied to propagation uncertainty and sonar performance for a submarine towed array, D. P. Knobles, S. M. Joshi and R. A. Koch, U.S. Navy Journal of Underwater Acoustics, (2009)
-
Analysis of wind-driven ambient noise in a shallow water environment with a sandy seabed, D. P. Knobles, S. M. Joshi, R. D. Gaul, H. C. Graber and N. J. Williams, J. Acoustical Society of America, vol. 124 No. 3 EL p.p. 157-162 (2008)
Conference Papers
-
A deflated Schur complement method for the iterative solution of a high-order discontinuous element discretization of the Poisson equation, S. M. Joshi and P. J. Diamessis, Submitted to the 14th Copper Mountain Conference on Iterative Methods, Copper Mountain CO,arxiv
-
Applicability of two-dimensional boundary scattering models as a proxy for three-dimensional models, B. Tran, S. M. Joshi, M. J. Isakson, Proceedings of Meetings on Acoustics 19 (1), 070079, 2013
-
Backscattering from a pressure-release rough surface, S. M. Joshi and M. J. Isakson, Underwater Acoustics Measurements Conference, Kos, Greece, June 2011
Talks
2015
-
A spectral multidomain penalty method model for the simulation of internal solitary wave shoaling and breaking over gentle slopes, P. J. Diamessis, S. M. Joshi, G. N. Thomsen, G. Rivera, 2015 American Geophysics Union Ocean Sciences Meeting, San Francisco, CA, December 2015
-
Enforcing the divergence-free condition in an under-resolved SMPM incompressible Euler simulation, S. M. Joshi, P. J. Diamessis, D. Steinmoeller, M. Stastna, G. N. Thomsen, Environmental Fluid Mechanics and Hydrology Seminar, Cornell University, October 2015
-
A quadrilateral spectral multidomain penalty method model for the simulation of environmental stratified flow processes, S. M. Joshi, G. N. Thomsen, and P. J. Diamessis. 2015 AMMCS-CAIMS Congress, Waterloo, Ontario, June 2015
-
A parallel deformed spectral multi-domain penalty method code for the incompressible Navier-Stokes equations in long domains†, S. M. Joshi, P. J. Diamessis, and G. N. Thomsen. Environmental Fluid Mechanics and Hydrology Seminar, Cornell University, February 26th, 2015
2014
-
Modeling of sound propagation through an incompressible flow†, S. M. Joshi. Acoustics Seminar, The University of Texas at Austin, October 1st 2014
-
Computational modeling of sound propagation through an incompressible flow†, S. M. Joshi. Cornell Fluid Dynamics Seminar given to the Sibley School of Mechanical and Aerospace Engineering, September 23rd 2014
-
A deformed quadrilateral high-order method for the incompressible Navier-Stokes equations for stratified environmental flows., S. M. Joshi, P. J. Diamessis, G. N. Thomsen. International Conference on Spectral and High-Order Methods, Salt Lake City, UT, June 2014
-
Coupling between sound and hydrodynamics.†, S. M. Joshi, Environmental Fluid Mechanics and Hydrology Seminar, Cornell University, March 2014
2013
-
Modeling sound propagation through internal waves using a spectral element method., S. M. Joshi, M. S. Ballard, P. J. Diamessis, Meeting of the Acoustical Society of America, San Francisco, December 2013
-
A deformed spectral quadrilateral multi-domain penalty model for the incompressible Navier-Stokes equations., S. M. Joshi, P.J. Diamessis, NYCAM 2013, Ithaca, New York, November 2013
-
A deformed spectral quadrilateral multi-domain penalty model for the incompressible Navier-Stokes equations., S. M. Joshi, P. J. Diamessis, SIAM Conference on Computational Science and Engineering, Boston MA, February 2013
2012
-
A deformed spectral quadrilateral multi-domain penalty model for the incompressible Navier-Stokes equations., S. M. Joshi, P. J. Diamessis, 65th Meeting of the Division of Fluid Dynamics of the American Physical Society, San Diego CA, November 2012
-
Functional analysis in computational acoustics†, S. M. Joshi, A workshop presented to the Graduate Program in Acoustics, Pennsylvania State University, State College PA, March 2012
-
Modeling in underwater acoustics†, S. M. Joshi, Brigham Young University, Provo UT, February 23rd 2012
2011
-
Energy conservation in the Kirchhoff approximation, S. M. Joshi and M. J. Isakson, 162nd Meeting of the Acoustical Society of America, San Diego CA, November 2011
-
Backscattering from a pressure-release rough surface, S. M. Joshi and M. J. Isakson, 161st Meeting of the Acoustical Society of America, May 26th, 2011 in Seattle WA, J. Acoust. Soc. Am. 129, 2630 (2011)
-
Modeling rough surface scattering using numerical methods†, S. M. Joshi, Seminar given to the Grad- uate Program in Acoustics, The University of Texas at Austin, Austin, Texas, April 2011
2010
-
Three-dimensional scattering from the ocean surface using finite elements, S. M. Joshi and M. J. Isak- son , 160th Meeting of the Acoustical Society of America, November 16th, 2010 in Cancun, Mexico, J. Acoust. Soc. Am. 128, 2326 (2010)
-
Three-dimensional scattering from pressure-release rough surfaces, S. M. Joshi and M. J. Isakson, 159th Meeting of the Acoustical Society of America, April 22nd, 2010 in Baltimore, MD, J. Acoust. Soc. Am. 127, 1963 (2010)
2009
- Three-dimensional rough surface scattering using finite elements, S. M. Joshi and M. J. Isakson, 158th Meeting of the Acoustical Society of America, October 29th, 2009 in San Antonio, TX, J. Acoust. Soc. Am. 126, 2166 (2009)
Awards
- National Defense Science and Engineering Graduate Fellowship, High Performance Computing Modernization Office, 2012-2015
- Charles C. & Lula May Wilson Endowed Fellowship, 2008
- Virginia & Ernest Cockrell, Jr. Scholarship in Engineering, 2004–2005
Graduate Coursework
Mathematics
- Partial Differential Equations II (Evans)
- Measure Theory and Lesbesgue Integration (Bartle)
- Functional Analysis (Len Gross Course Notes)
- Algebraic Topology (Massey)
- Methods of Applied Mathematics I (Todd Arbogast Course Notes)
Computational Science
- Matrix Computations (David Bindel Course Notes)
- Numerical Analysis II (No Text)
- Spectral Methods for Environmental Incompressible Flows (No Text)
Engineering
- Turbulence and Turbulent Mixing (No Text)
- Fluid Mechanics I (Panton)
- Physical Acoustics I/II (Blackstock)
- Nonlinear Acoustics (Hamilton)
- Architectural Acoustics (Long)
- Wave Phenomena (Williams)
- Wave Phyiscs (Graff)
In parenthesis is the text used.


CSS is used to style the content created in an HTML document, for laying out the items and specifying how they look...or another way to think of it is if the HTML page is a person's body then the CSS is the clothing.
This article will show you the different ways you can select your HTML elements to apply the styles to...in other words how you dress up your body to look good!
This article won’t really make much sense unless you already have some knowledge of HTML’s semantics…and of course the actual CSS styles to apply to the element you have selected
But if you know your
 from a
 and
 from  then read on...
Let’s start off simple with the easiest and most basic of them all....the simple selector.
The style is applied to one or more elements based on the type of element, it’s class, or it's id. Simple eh!
Element Selector
The style is applied to every element of that type and is useful for applying page wide or fallback styles

A paragraph element is normally one of the most commonly occurring elements in any html document

p {
color: blue
}
The end result is...
A paragraph element is normally one of the most commonly occurring elements in any html document
Class Selector
The class selector consists of a dot (.) followed by a class name (with no spaces in the class name).
Multiple elements in the document can have the same class name and a single element can have multiple class names (separated by spaces) which makes this selector type useful when applying a style to a variety of different types of elements.

Do I have an outline?

Or do I have an outline?

.box-outline {
border: 2px solid black;
}
The end result is...
Do I have an outline?
Or do I have an outline?
In this example each element with the class "box-outline" will have this style applied to it.
ID Selector
The ID selector consists of a hashtag (#) followed by ID name (must be unique) of a given element
This is different to class styling as multiple html elements can use the same class but only a single element can have an id (There can be only one!)

Description...

Content...

#top-box {
background-color: grey;
border: 2px dashed black;
}
The end result is...
Description...
Content...
In this example it is only the element with the id "top-box" that will have this style applied to it.
Universal Selector
The most simple selector of them all may be the universal selector
* is the universal selector and is rarely useful by itself but often used in combination with other selectors.

Listy McListface

Listy McListyFace

ul * {
margin: 0; padding: 0;
border: 1px dashed grey;
}
The end result is...
With style applied:
- Listy McListface
- Listy McListyFace
Without style applied:
- Listy McListface
- Listy McListyFace
I use this is to reset any default padding and margin on the html page itself so any browser presets do not mess up the look of my page
Now that we have warmed up our thinking caps with the easy stuff we can start getting to the more complicated (and therefore oh so much more fun) selectors we can play around with.
Attribute selectors match one or more elements based on their attributes/attribute values.
The generic syntax consists of square brackets [] containing an attribute name followed by optional condition to match against the value of the attribute (this makes much more sense when you see the examples below).
There are two categories of attribute selectors:
- Presence and Value
- Substring Value
Presence and Value
Try to match an exact attribute or attribute value of an element. This will make much more sense when you see the examples below (Well...a bit more sense)
- [attr] – this will select all elements with the attribute attr
- [attr=val] – this will select all elements with the attribute attr but only if attr value is val
- [attr~=val] – this will select all elements with the attribute attr but only if the value val is one of a space separated list of values for attr

No style

Style 1

Style 2

Style 3

li[class] {
list-style: none;
}
li[class="classy"] {
background-color: grey;
}
li[class~="class"] {
color: purple;
}
The end result is...
- No style
- Style 1
- Style 2
- Style 3
Substring Value
The next level to presence and value, this offers flexible matching in a similar fashion to regular expression (also known as RegExp-like selectors)
- [attr|=val] – this will select elements with the attribute attr for which the value is exactly val or starts with val
- [attr^=val] – this will select all elements with the attribute attr for which the value starts with val
- [attr$=val] – this will select all elements with the attribute attr for which the value ends with val
- [attr*=val] – this will select all elements with the attribute attr for which the value contains the string val (unlike [attr~=val] it does not treat spaces as value separators but as part of attribute name)

No style

Some style

More style

More style but not stylish

Style and more style but not stylish

li[class|="class"] {
font-size: 2rem;
}
li[class^="class"] {
background-color: grey;
}
li[class$="class"] {
color: purple;
}
li[class*="class"] {
text-decoration: underline;
}
The end result is...

Pseudo-classes allow you to select one or more elements that exist in a certain state such as mouse hover, or an element that is the first child of its parent in the DOM tree (like the first word in a paragraph, or the first div in an article, etc.)
A pseudo-class is a keyword preceded by a colon(:) added to the end of selectors to specify you want to style the selected elements but only when in a certain state such as mouse hover.

Learn

...

a:hover {
color: purple;
}
The end result is...
Pseudo-elements may be used more often than pseudo-classes and are used to target elements that do not technically exist in the HTML document in that they allow you to add content to an element in the HTML document.
Similar to pseudo-classes but are preceded with two colons (::) and are added to the end of selectors to select a certain part of the element.

Learn

...

[href^=http]::after {
content: '⤴';
}
The end result is...
Combinators & Multiple Selectors
Combinators
Not exactly selectors themselves but ways of combining two or more selectors in useful ways for very specific selections, such as only paragraphs that come directly after headings
CSS has several ways to select elements based on how they are related to one another:
- Group of selectors (A, B)
- Any element matching A and /or B
- Descendant selector (A B)
- Any element matching B which is a descendant of an element matching A
- Child selector (A > B)
- Any element matching B that is a direct child of an element matching A
- Adjacent sibling selector (A + B)
- Any element matching B that is the next sibling of an element matching A
- General sibling selector (A ~ B)
- Any element matching B that is one of the next siblings of an element matching A

My Sample page

This is what my page is about

Learn

News

Forum

...

...

This is the rest of my content...

li img, li a {
border: 2px solid red;
}
li a {
border: 1px dotted black;
}
li > ul {
background-color: grey;
}
h1 + p {
font-size: 1.5rem;
}
h1 ~ p {
color: darkgrey;
}
The end result is...
My Sample page
This is what my page is about
This is the rest of my content...
Multiple Selectors
Not separate selectors; the idea is that you can put multiple selectors on the same CSS rule, separated by commas, to apply a single set of declarations to all the elements selected by those selectors
h1, h2, h3, h4, h5, h6, p, li, a {
font: italic normal bold normal 1em/1.5 Helvetica, Arial, sans-serif;
}
Best Coding Practice - My rule of 3
- Keep it simple - start off with the basics and then evolve the style (Oh believe me, I have learned the hard way about biting off more than you can chew!)
- Don't repeat yourself - if all buttons have common styles use a class to apply this style and then target each button for the specific different styling
- Save yourself the headache of having to revisit (and revisit again) your HTML code by planning out your page first so you can add a class (or id) to your elements as you go along (and don’t forget about your container divs)...


What Is NUnit?
NUnit is a unit-testing framework for all .Net languages. Initially ported from JUnit, the current production release, version 3, has been completely rewritten with many new features and support for a wide range of .NET platforms.
License
NUnit is Open Source software and NUnit 3 is released under the MIT license. Earlier releases used the NUnit license.
Both of these licenses allow the use of NUnit in free and commercial applications and libraries without restrictions.
About Us
NUnit 3 was created by Charlie Poole, Rob Prouse, Simone Busoli, Neil Colvin and numerous community contributors.
Earlier versions of NUnit were developed by Charlie Poole, James Newkirk, Alexei Vorontsov, Michael Two and Philip Craig.
Donations
The NUnit team invests a great deal of time and effort to make NUnit a useful tool. In addition, we have expenses. We have to purchase domain names, arrange for web site hosting and acquire equipment.
Financial contributions are one way you can help to ensure that NUnit continues to develop and remains free and open software. For more information or to view a list of donors, see our Donations page.


--- title: Want to work with Data? Don’t wait. author: Jesse M date: '2017-08-11' slug: want-to-work-with-data-don-t-wait categories: [] tags: - data science - education - learning - R ---
Data is everywhere, and it seems you can’t go more than 48 hours without hearing how data scientists are going to rule the world–if only we can train enough of them.
The thing is, you don’t need to wait until after you get that Master’s (or PhD) in Data Science, or even until complete that online course that cost you several thousand dollars. You can start working with data now, and depending on your interests, skills, and commitment to learning, be job-ready in six months to a year.
With this in mind, I’d like to offer up my completely biased opinion on how to build the foundational skills you need to start moving towards a career in data.
That’s OK! There’s no time like the present to get started. As much as it pains me to say it, you’re going to want to get comfortable in Excel first. Why Excel? Because it’s quick, relatively easy, widely used, and once you go through the pain of learning data analysis in Excel you will have a whole new level of appreciation for R and/or Python.
So where do you start? Pick up the book Data Smart by John Foreman, who is currently the VP of Product Management at MailChimp. The book is engaging, insightful, and has you working directly with data in meaningful ways throughout the text.
This is a topic of much debate within the data science community–just do a Google search for “R vs. Python” and watch your computer melt.
My personal stance is that there’s no good answer, because you should eventually learn both. So try out both of them, see which one you gravitate towards, and dive in!
Obviously you’re welcome to hang out here and read the tutorials I (slowly) publish, and you can always reach out to me on Twitter. Speaking of Twitter, make sure you’re following the #rstats hashtag–there’s lots of good information being published daily!
One of the best beginner books I’ve found for R is R in Action, by Robert Kabacoff. Sure it’s a book, and so certain parts of it are going to be outdated or even obsolete by the time you get it, but that’s OK. You’re focused on building a foundation–you’re going to learn and grow the more you work with R, so don’t get distracted by the latest package releases or people who tell you that “=” is better than “<-”.
Update: since publishing this article, two additional texts have been mentioned to me that I cannot recommend enough:
Hands on Programming with R, by Garrett Grolemund, which will help build a strong foundation for the true programming beginner.
R for Data Science, by Garrett Grolemund and [Hadley Wickham](https://twitter.com/hadleywickham. This text sits firmly between beginner and intermediate levels of R and data science knowledge.
If books really aren’t your thing, check out STAT 545. Jenny Bryan has published her entire syllabus, notes, code, and supplementary articles. Following along with this course at your own pace will singlehandedly improve your data analysis game in a matter of weeks.
Yeah, you and me both. I don’t have a lot of good resources in learning Python for Data Science, because it’s still an area that I need to explore. That being said, I would highly recommend connecting with the Data Science Learning Club, an amazing, supportive, and friendly community created by BecomingDataSci.
You really don’t need to wait to start working on meaningful, real-world data. You should really go do data–now! If you don’t know where to start, see if there’s a small analytics project you can take on at work.
You can also check out Data for Democracy–they have a lot of work going on, and are a great place to start building up your experience.
Local non-profits also provide a great opportunity to hone your data science skills. Even if they aren’t hiring for an analytics staff member, try reaching out to their volunteer coordinator or analytics team to see if you can volunteer your data talents.


Genesis 27:32
And Isaac his father said unto him, Who art thou? And he said, I am thy son, thy firstborn Esau.


Before Change
def command(arg):
for root in ["include", "src", "python", "examples", "tutorial"]:
for dirname, dirnames, filenames in os.walk(os.path.join(arg.base, root)):
for filename in filenames:
basename, extname = os.path.splitext(filename)
After Change
os.chmod(fullname, 0o644)
// chmod document files
for dirname, _, filenames in os.walk(arg.base):
for filename in filenames:
_, extname = os.path.splitext(filename)
extname = extname.lower()
fullname = os.path.join(dirname, filename)
if extname in file_formatter.doc_extensions:
os.chmod(fullname, 0o644)


Hey there! Bat Company is a game developed by 3 Game Design & Development students. It is now available in the Google Play Store for free. Also for tablets. In the upcoming weeks the game will have multiple updates and new features.
Please download the game and give us usable feedback :)!


This article presents one of the neatest recursive pointer problems ever devised. This an advanced problem that uses pointers, binary trees, linked lists, and some significant recursion. This article includes the problem statement, a few explanatory diagrams, and sample solution code in Java and C. Thanks to Stuart Reges for originally showing me the problem.
Figure-1 -- ordered binary tree
All the nodes in the "small" sub-tree are less than or equal to the data in the parent node. All the nodes in the "large" sub-tree are greater than the parent node. So in the example above, all the nodes in the "small" sub-tree off the 4 node are less than or equal to 4, and all the nodes in "large" sub-tree are greater than 4. That pattern applies for each node in the tree. A null pointer effectively marks the end of a branch in the tree. Formally, a null pointer represents a tree with zero elements. The pointer to the topmost node in a tree is called the "root".
The circular doubly linked list is a standard linked list with two additional features...
The single node in a length-1 list is both the first and last node, so its pointers point to itself. Fortunately, the length-1 case obeys the rules above so no special case is required.
Figure-4 -- original tree with list "next" arrows added
This drawing shows the original tree drawn with plain black lines with
the "next" pointers for the desired list structure drawn as arrows. The
"previous" pointers are not shown.
Figure-5 -- original tree with "next" and "previous" list arrows added
Try the problem directly, or see the hints below.
// TreeList.java /* Demonstrates the greatest recursive pointer problem ever -- recursively changing an ordered binary tree into a circular doubly linked list. See http://cslibrary.stanford.edu/109/ This code is not especially OOP. This code is free for any purpose. Feb 22, 2000 Nick Parlante<EMAIL_ADDRESS>*/ /* This is the simple Node class from which the tree and list are built. This does not have any methods -- it's just used as dumb storage by TreeList. The code below tries to be clear where it treats a Node pointer as a tree vs. where it is treated as a list. */ class Node { int data; Node small; Node large; public Node(int data) { this.data = data; small = null; large = null; } } /* TreeList main methods: -join() -- utility to connect two list nodes -append() -- utility to append two lists -treeToList() -- the core recursive function -treeInsert() -- used to build the tree */ class TreeList { /* helper function -- given two list nodes, join them together so the second immediately follow the first. Sets the .next of the first and the .previous of the second. */ public static void join(Node a, Node b) { a.large = b; b.small = a; } /* helper function -- given two circular doubly linked lists, append them and return the new list. */ public static Node append(Node a, Node b) { // if either is null, return the other if (a==null) return(b); if (b==null) return(a); // find the last node in each using the .previous pointer Node aLast = a.small; Node bLast = b.small; // join the two together to make it connected and circular join(aLast, b); join(bLast, a); return(a); } /* --Recursion-- Given an ordered binary tree, recursively change it into a circular doubly linked list which is returned. */ public static Node treeToList(Node root) { // base case: empty tree -> empty list if (root==null) return(null); // Recursively do the subtrees (leap of faith!) Node aList = treeToList(root.small); Node bList = treeToList(root.large); // Make the single root node into a list length-1 // in preparation for the appending root.small = root; root.large = root; // At this point we have three lists, and it's // just a matter of appending them together // in the right order (aList, root, bList) aList = append(aList, root); aList = append(aList, bList); return(aList); } /* Given a non-empty tree, insert a new node in the proper place. The tree must be non-empty because Java's lack of reference variables makes that case and this method messier than they should be. */ public static void treeInsert(Node root, int newData) { if (newData<=root.data) { if (root.small!=null) treeInsert(root.small, newData); else root.small = new Node(newData); } else { if (root.large!=null) treeInsert(root.large, newData); else root.large = new Node(newData); } } // Do an inorder traversal to print a tree // Does not print the ending "\n" public static void printTree(Node root) { if (root==null) return; printTree(root.small); System.out.print(Integer.toString(root.data) + " "); printTree(root.large); } // Do a traversal of the list and print it out public static void printList(Node head) { Node current = head; while (current != null) { System.out.print(Integer.toString(current.data) + " "); current = current.large; if (current == head) break; } System.out.println(); } // Demonstrate tree->list with the list 1..5 public static void main(String[] args) { // first build the tree shown in the problem document // http://cslibrary.stanford.edu/109/ Node root = new Node(4); treeInsert(root, 2); treeInsert(root, 1); treeInsert(root, 3); treeInsert(root, 5); System.out.println("tree:"); printTree(root); // 1 2 3 4 5 System.out.println(); System.out.println("list:"); Node head = treeToList(root); printList(head); // 1 2 3 4 5 yay! } }
/* TreeList.c C code version of the great Tree-List recursion problem. See http://cslibrary.stanford.edu/109/ for the full discussion and the Java solution. This code is free for any purpose. Feb 22, 2000 Nick Parlante<EMAIL_ADDRESS>*/ #include <stdio.h> #include <stddef.h> #include <stdlib.h> /* The node type from which both the tree and list are built */ struct node { int data; struct node* small; struct node* large; }; typedef struct node* Node; /* helper function -- given two list nodes, join them together so the second immediately follow the first. Sets the .next of the first and the .previous of the second. */ static void join(Node a, Node b) { a->large = b; b->small = a; } /* helper function -- given two circular doubly linked lists, append them and return the new list. */ static Node append(Node a, Node b) { Node aLast, bLast; if (a==NULL) return(b); if (b==NULL) return(a); aLast = a->small; bLast = b->small; join(aLast, b); join(bLast, a); return(a); } /* --Recursion-- Given an ordered binary tree, recursively change it into a circular doubly linked list which is returned. */ static Node treeToList(Node root) { Node aList, bList; if (root==NULL) return(NULL); /* recursively solve subtrees -- leap of faith! */ aList = treeToList(root->small); bList = treeToList(root->large); /* Make a length-1 list ouf of the root */ root->small = root; root->large = root; /* Append everything together in sorted order */ aList = append(aList, root); aList = append(aList, bList); return(aList); /* Create a new node */ static Node newNode(int data) { Node node = (Node) malloc(sizeof(struct node)); node->data = data; node->small = NULL; node->large = NULL; return(node); } /* Add a new node into a tree */ static void treeInsert(Node* rootRef, int data) { Node root = *rootRef; if (root == NULL) *rootRef = newNode(data); else { if (data <= root->data) treeInsert(&(root->small), data); else treeInsert(&(root->large), data); } } static void printList(Node head) { Node current = head; while(current != NULL) { printf("%d ", current->data); current = current->large; if (current == head) break; } printf("\n"); } /* Demo that the code works */ int main() { Node root = NULL; Node head; treeInsert(&root, 4); treeInsert(&root, 2); treeInsert(&root, 1); treeInsert(&root, 3); treeInsert(&root, 5); head = treeToList(root); printList(head); /* prints: 1 2 3 4 5 */ return(0); }


This class implements numerous distributional semantic models based on information extracted from corpora, and evaluates them on standard datasets.
Usage:
java -cp DSM.jar dsm.DSM EvaluationDataset InputData VectorNormalization SimilarityMeasure WeightingScheme
minimumWordSimpleFrequency minimumFeatureSimpleFrequency minimumWordFeatureTupleSimpleFrequency
filterStopWords MinimumWeight Smooth FeatTransf DimRed
String EvaluationDataset: the test dataset, on which the DSM is evaluated (
EvalDatasetReader
). Some datasets are available for multiple languages.
The language of the test dataset used depends on the language of the InputData used.
- MenDev: the development part of the MEN dataset
- MenDevPart1: the first half of the development part of the MEN dataset
- MenDevPart2: the second half of the development part of the MEN dataset
- MenTest: the test part of the MEN dataset
- MenFull: the full MEN dataset
- RubensteinGoodenough65: the Rubenstein-Goodenough-65 dataset
- MillerCharles28: the Miller-Charles-28 dataset
- WordSim353: the WordSim-353 dataset
- SimLex999: the SimLex-999 dataset
- Toefl: the TOEFL dataset
- ToeflPart1: the first part of the TOEFL dataset
- ToeflPart2: the second part of the TOEFL dataset
- Moldovan: the Moldovan dataset
String InputData: the type of extracted information or word vectors used as input for the DSM. For more info see (
ExtractedInfoReader
).
String VectorNormalization: the type of vector normalization used before the comparison of word vectors (
VecNorm
).
- NN: no normalization
- L1: L1 norm
- L2: L2 norm
String SimilarityMeasure: the type of vector similarity measure used in the DSM. For more info see (
VecSim
).
String WeightingScheme: the type of weighting scheme used in the DSM. For more info see (
Weight
).
Integer minimumWordSimpleFrequency: minimum limit on word frequencies (
MiscParam
)
Integer minimumFeatureSimpleFrequency: minimum limit on feature frequencies (
MiscParam
)
Integer minimumWordFeatureTupleSimpleFrequency: minimum limit on word-feature tuple frequencies (
MiscParam
)
Boolean filterStopWords: the option to filter stop-words or not (
MiscParam
)
String MinimumWeight: minimum limit on word-feature tuple weights (
MiscParam
). For more info see the Section 4.2.9 of the dissertation.
- "null": no limit
- "Limit" minimumWordFeatureTupleWeightParameter: Limit type of limit, with a Double as the value of the limit
- "Zero" minimumWordFeatureTupleWeightParameter: Zero type of limit, with a Double as the value of the limit
String Smooth: the type of smoothing used on the word vectors (
Smooth
).
- "NoSmooth": no smoothing
- "WeightKNS" KNSParameter: Kneser-Ney smoothing on weights, with a Double parameter
- "FreqKNS" KNSParameter: Kneser-Ney smoothing on raw counts, with a Double parameter
- "FreqMKNS" KNSParameter1 KNSParameter2 KNSParameter3: Modified Kneser-Ney smoothing on raw counts, with 3 Double parameters
- "FreqMDKNSPOMD" KNSParameter1 KNSParameter2 KNSParameter3: Multi-D Kneser-Ney Smoothing Preserving the Original Marginal Distributions
on raw counts, with 3 Double parameters
String FeatTransf: the type of feature transformation used. For more info see (
FeatTransf
).
String DimRed: the type of dimensionality reduction used on the word vectors. For more info see (
DimRed
).


Generate randomized strings of a specified length, fast. Only the length is necessary, but you can optionally generate patterns using any combination of numeric, alpha-numeric, alphabetical, special or custom characters.
Install with npm
$ npm i randomatic --save
Install with bower
$ bower install randomatic --save
var randomize = require('randomatic');
randomize(pattern, length, options);
pattern
{String}: The pattern to use for randomizinglength
{Object}: The length of the string to generateThe pattern to use for randomizing
Patterns can contain any combination of the below characters, specified in any order.
Example:
To generate a 10-character randomized string using all available characters:
randomize('*', 10);
//=>
randomize('Aa0!', 10);
//=>
a
: Lowercase alpha characters (abcdefghijklmnopqrstuvwxyz'
)A
: Uppercase alpha characters (ABCDEFGHIJKLMNOPQRSTUVWXYZ'
)0
: Numeric characters<PHONE_NUMBER>'
)!
: Special characters (~!@#$%^&()_+-={}[];\',.
)*
: All characters (all of the above combined)?
: Custom characters (pass a string of custom characters to the options)the length of the string to generate
Examples:
randomize('A', 5)
will generate a 5-character, uppercase, alphabetical, randomized string, e.g. KDJWJ
.randomize('0', 2)
will generate a 2-digit random numberrandomize('0', 3)
will generate a 3-digit random numberrandomize('0', 12)
will generate a 12-digit random numberrandomize('A0', 16)
will generate a 16-character, alpha-numeric randomized stringIf length
is left undefined, the length of the pattern in the first parameter will be used. For example:
randomize('00')
will generate a 2-digit random numberrandomize('000')
will generate a 3-digit random numberrandomize('0000')
will generate a 4-digit random number...randomize('AAAAA')
will generate a 5-character, uppercase alphabetical random string...These are just examples, see the tests for more use cases and examples.
Type: String
Default: undefined
Define a custom string to be randomized.
Example:
randomize('?', 20, {chars: 'jonschlinkert'})
will generate a 20-character randomized string from the letters contained in jonschlinkert
.randomize('?', {chars: 'jonschlinkert'})
will generate a 13-character randomized string from the letters contained in jonschlinkert
.randomize('A', 4)
(whitespace insenstive) would result in randomized 4-digit uppercase letters, like, ZAKH
, UJSL
... etc.randomize('AAAA')
is equivelant to randomize('A', 4)
randomize('AAA0')
and randomize('AA00')
and randomize('A0A0')
are equivelant to randomize('A0', 4)
randomize('aa')
: results in double-digit, randomized, lower-case letters (abcdefghijklmnopqrstuvwxyz
)randomize('AAA')
: results in triple-digit, randomized, upper-case letters (ABCDEFGHIJKLMNOPQRSTUVWXYZ
)randomize('0', 6)
: results in six-digit, randomized nubmers<PHONE_NUMBER>
)randomize('!', 5)
: results in single-digit randomized, valid non-letter characters (~!@#$%^&()_+-={}[];\',.
)randomize('A!a0', 9)
: results in nine-digit, randomized characters (any of the above)The order in which the characters are defined is insignificant.
Pull requests and stars are always welcome. For bugs and feature requests, please create an issue.
Jon Schlinkert
Copyright © 2015 Jon Schlinkert Released under the MIT license.
This file was generated by verb on December 10, 2015.


| DOSE: T+ 0:00 |
|
oral |
LSD |
|
| T+ 3:00 |
|
smoked |
DMT |
(freebase) |
| T+ 4:00 |
|
smoked |
DMT |
(freebase) |
Tonight I was going to try LSD for the first time. Before then, I had experimented several times with DMT. My friend told me that having an acid trip would help make me more sensitive to DMT, and in turn would induce a very strong trip. It did exactly that.
While a few hours into the acid trip, I took a regular dose of DMT and I got the usual effects from it. I was one of those people that were insensitive to the substance. I waited for an hour or so before trying it again. During my wait I meditated on my thoughts. This time it was dramatically stronger than any DMT experience I ever had.
As soon as I took the second hit and exhaled, the walls of the room were suddenly covered with ancient runes, and my friend sitting next to me turned into something like a wooden and liquid-in-nature Native American. He turned into his “true” form. My eyes were wide open during almost the entire trip. During every second of the trip I began to see many dimensions that didn't exist in real life. I could see brand new spatial dimensions, including the original ones: length, height, and depth. Finally all space collapsed and time ceased to exist. I was stuck in the “real” world, the world that lasted for eternity. I closed my eyes in complete awe after realizing that I was dead. I found out that the living world was actually illusory, and that space and time did not even exist. At this point I was worried if I would ever come back to real life, after realizing that space and time were illusions.
The world was very elephant-like at this point. Then I saw the architect of the universe. It was a goddess, in the form of Indian deity Ganesha. The mere sight of this goddess gave me so much bliss that it was too difficult for my mind to comprehend. She was the soul of the universe, and she was constantly creating very intricate Indian-like structures out of thin air. She showed me the doorway to eternal bliss (heaven), and she invited me into her kingdom. I pleaded with her to take me back to the real world, because I needed to make sense of all this before I make such a decision. She was sad, but she let me go. Slowly time and space appeared again, and I came back to the realm of the living.
I ‘m not sure exactly what happened, but now I know that I have nothing to worry about when I die. To experience such dimensions and such bliss from a drug-induced trip was quite amazing. To this day I refuse to believe that what I experienced was merely a hallucination. I was stuck in that realm for eternity, because time actually didn’t exist there. But the trip, in reality, only lasted about five minutes.
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.


TopicBubbles is a general-purpose topic model visualization interface that is useful for getting an overview of a model, looking closely at topics, comparing topics, and looking at words associated with topics. Among the general-purpose interfaces, it stands out especially for facilitating the comparison of topics.
WE1S Topic Model Observatory Guide
Created by Sihwa Park, WE1S(WhatEvery1Says)


Hello
Currently trying to upload elapsed power consumption to emoncms3 (local installation)
Storing using the log to feed works ok. Although the timstamp is several hours ahead of my time. The time is correct on my NAS at which the emoncms is running. Is emoncms doing any interpretation of the time ?
when activating the "kwh to kwh/d " input procesor nothing is stored for that feed. Have tried using the the "kwhinc to kwh/d " input procesor and that stores data when i look in the tables using phpmyadmin. But the "kwh to kwh/d " input procesor is best for my task. Also have problems with getting the "update feed @ time " input procesor to work.
Anyone seing the same issues or is just my setup ?
Best regards Søren
Re: Emoncms 3 issues
Also get the following error text when i create a new data input.
Warning: Cannot modify header information - headers already sent by (output started at /share/MD0_DATA/Web/Controllers/process_controller.php:72) in /share/MD0_DATA/Web/Controllers/process_controller.php on line 65
But it works...
Note this is based on the latest emoncms3 release.
/Søren
Re: Emoncms 3 issues
Hello Søren
First as regard to time, emoncms gets the server time using php time() (line 25 api_controller.php) and converts it into date time format using: $time = date("Y-n-j H:i:s", $time); (line 119 feed_model.php). This is then stored in the mysql database. It is then retrieved using strtotime() which converts it back into a unix timestamp. The initial idea with storing it as a datetime string was that it was easy to analyze when browsing the database manually, however it may be that it isn't actually the best/most efficient format to store it in.
Sorry for the kwh to kwhd issue I had forgotten to include automatic setup for the database table needed for that processor to work. I've fixed it and committed the changes to the github repo.
I cant seem to replicate the "Cannot modify header information" warning,
Thanks
Trystan
Re: Emoncms 3 issues
Hello Trystan
Thanks for the feedback, highly appriciated.
Applied your newest release to my NAS server. Now the Kwh to Kwhd input processor works.
Still getting the "Cannot modify header information" warning.
Regarding the time issue. I am not an PHP expert,but I created a small test script containing the time function that you use. From that output I can see that the time reported from PHP to the database is pure UTC time without any timezone offset.
But my mySQL database is interpreting this somehow, because the time that gets in is not the one I can read out.
This is probaly for the new feature wish list. Set timezone info for each account. This would be a nice feature especially for the hosted version of EmonCMS, where people don't have the possibility to set the timezone for the mySQL database.
Best regards
Søren


| « CQ/XML Admin Docs | Generating Statistics | Updated: |
|---|
We regularly generate two types of statistics. The first, is a web page that records daily usage of the server. The script that generates the web page also clears out older live/output log files to free up disk space. The second classification of statistic is a detailed monthly report that contains information about users per day, unique users, etcetera.
The script cqxi_logmaint.pl
will count the number of requests from the previous day and record its findings on a web page. (The web page is located in the public log directory.) Additionally, cqxi_logmaint.pl
will also remove any live/output log files from 10 days ago. We have found that beyond that point, the live log files are not used but removing those files can free a substantial amount of disk space. To get optimal use of cqxi_logmaint.pl
it is best to schedule it run every day. The source repository file support/crontab.txt
shows how this script is setup in our production environment.
Creating the detailed monthly statistics requires very little effort. Navigate into the source repository and cd down into the support
directory. From there you should be able to see the file xmlstats.pl
. Running this file without arguments will force the script to run, logging activity from the last 30 days to standard-out (STDOUT
).
That's it! However it's more useful to redirect the output to a file. Specifying the -o
option with a filename will save the output to that file. In the past I have been saving all the files into ../docs/logs/[yy][mm]xmlusg.log
, where [yy]
is the 2-digit year and [mm]
is the 2-digit month.
The hardest part is remembering to run the script at the correct time. Since the CQ/XML Interface uses GMT, the best time to generate the monthly statistics is just after midnight GMT on the last day of the month. (In effect, the CQ/XML Interface already thinks it's tomorrow.) In the Pacific Timezone this is 16:00PST or 17:00PDT.
Once xmlstats.pl
completes execution you may need to modify the summary at the bottom of the output file. This is due to new users using the system every month. Those new users may not have LDAP mappings for their group and/or division. Those mappings are pulled from xmlstats.pm
so you can fix it and re-run the script or you can leave it for the next guy.
Remember to check-in the log file when you're done.
$ cd .../src//support $ xmlstats.pl -o ../docs/logs/0711xmlusg.log Searching logs from the last 30 days... From '07333' to '07303': 30 files found $


iot-2/evt/weight/fmt/json
2 msgs/sec
iot-2/evt/speed/fmt/json
iot-2/evt/power/fmt/json
iot-2/evt/temp/fmt/json
every 2 sec
iot-2/evt/sys/fmt/json
every 10 sec


LIFESTYLE
RUNNING
FOOTBALL
SKATEBOARDING
NEW
THE
RUNNING
HAS JUST BEGUN
Haurache
$ 200
Size
7
8
9
10
11
ADD TO CART
WISHLIST


#include <polar_match.h>
Public Attributes | |
| double | t |
| [s] Time when scan was taken. | |
| PM_TYPE | rx |
| [cm] Robot odometry X coordinate. | |
| PM_TYPE | ry |
| [cm] Robot odometry Y coordinate. | |
| PM_TYPE | th |
| [rad] Robot orientation. | |
| PM_TYPE | r [PM_L_POINTS] |
| [cm] Laser range readings. 0 or negative ranges denote invalid readings. | |
| PM_TYPE | x [PM_L_POINTS] |
| [cm] Laser reading X coordinates in Cartesian coordinates. | |
| PM_TYPE | y [PM_L_POINTS] |
| [cm] Laser reading Y coordinates in Cartesian coordinates. | |
| int | bad [PM_L_POINTS] |
| Tag describing the validity of a range measurement. 0 if OK; sources of invalidity - out of range reading; reading belongs to moving object (not implemented); occlusion; mixed pixel. | |
| int | seg [PM_L_POINTS] |
| Describes which segment the range reading belongs to. |
The robot pose (rx,ry,th) describes the odometry center if PM_LASER_Y does not equal 0, or the laser center if PM_LASER_Y=0.
In a laser scan, the middle laser bearing coincides with the laser coordinate frame's Y axis.
TODO: Consider using doubles for rx,ry in large environments.
| int PMScan::bad[PM_L_POINTS] |
Tag describing the validity of a range measurement. 0 if OK; sources of invalidity - out of range reading; reading belongs to moving object (not implemented); occlusion; mixed pixel.
| PM_TYPE PMScan::r[PM_L_POINTS] |
[cm] Laser range readings. 0 or negative ranges denote invalid readings.
| PM_TYPE PMScan::rx |
[cm] Robot odometry X coordinate.
| PM_TYPE PMScan::ry |
[cm] Robot odometry Y coordinate.
| int PMScan::seg[PM_L_POINTS] |
Describes which segment the range reading belongs to.
| double PMScan::t |
[s] Time when scan was taken.
| PM_TYPE PMScan::th |
[rad] Robot orientation.
| PM_TYPE PMScan::x[PM_L_POINTS] |
[cm] Laser reading X coordinates in Cartesian coordinates.
| PM_TYPE PMScan::y[PM_L_POINTS] |
[cm] Laser reading Y coordinates in Cartesian coordinates.


Here you can generate CSS and JavaScript 'hacks' that will fix the bugs in IE6-8 browsers. You can see the list of the bugs that will be fixed here. Just put your css and html files below and everything will be fine :)
Back Continue
(if you're not sure just click 'continue' )
border-radius
box-shadow


Suomeksi
På svenska
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General links
Helsinki University of Technology
Aalto University wiki
Studies & Academic Affairs
International Student Services
Faculties and departments
Teaching and Learning Development Unit
Library
Map of Otaniemi
Noppa-portal
>
Courses
> Faculty of Information and Natural Sciences
Faculty of Information and Natural Sciences
Search courses
Search tips
You can search by course name, part of the course name, course code or part of the course code.
Example searches
TU-53.1145
TU-53.1
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Leadership in product development organizations
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Code
Departments and other units of the faculties
IL-0
Common courses for the faculty
T3010
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T3020
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T3030
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T3040
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T3050
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T3060
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T3070
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T3080
BIT Research Centre
T3090
Language Centre
Printable version


Getting ready to upgrade my main machine to macOS 11. First things first, though: I have a ton of musical scores in Sibelius’ format, I’ve since moved to Dorico, and my old version of Sibelius doesn’t launch on Big Sur. So: spending my morning exporting everything to .mxl
.


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This plugins adds an MailArchive to your Shopware-Backend stored in Database.
This plugin is part of @FriendsOfShopware. Maintainer from the plugin is: Soner Sayakci
For questions or bugs please create a Github Issue


Ready to Cruise
Relax and enjoy the ride
Liberty 150
- Liberty 50
- Liberty 150
- Liberty 150 Sport
Engine
- Bore x Stroke
- 58mm x 58.6mm
- Clutch
- Automatic centrifugal dry clutch
- Consumption
- 36.8 Km/I (WMTC cycle)
- Cooling
- Air
- CO2 Emissions
- 65 g/Km
- Distribution
- Single overhead camshaft, 3 valves (2 input, 1 output)
- Engine
- Single cylinder 4-stroke -i-get
- Engine Capacity
- 155c
- Fuel system
- Electronic injection
- Lubrication
- Oil with wet sump
- Max Power
- 12.9hp (9.6kW) @ 7,750 rpm
- Max Torque
- 9.58 ft-lbs (13 Nm) @ 5250 rpm
- Transmission
- Automatic CVT
- Starter
- Electric
UNE MOTO TRES CLASSIC
AND EASY TO RIDE
Piaggio Liberty 150
$2999.00


I read in the paper today that the governors of a new primary school in Sheffield have decided the word “school” has “negative connotations” and it will thus be known as a “place for learning”. No doubt staff and pupils (or whatever euphemisms they use for them) will find their way around the place by following the “directional pointing devices” so favoured by John Birt when he was in charge of the BBC. I am disappointed, of course, that they neglected to use the correct phrase for “school”, which as we know is “community education hub”.
Horace Walpole wrote that “Sheffield is one of the foulest towns in England”, though he did buy a pair of “quite pretty” candlesticks while he was there.


Automated identification of printed array positions from high content microscopy images and the export of those positions as individual images written to output as multi-layered tiff files.
| Version: | 1.0 |
| Depends: | R (≥ 3.1.0), grDevices, jpeg, tiff, stringr, raster, utils, graphics, stats |
| Published: | 2016-09-26 |
| Author: | John Smestad [aut, cre] |
| Maintainer: | John Smestad <smestad.john at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | PersomicsArray results |
| Reference manual: | PersomicsArray.pdf |
| Package source: | PersomicsArray_1.0.tar.gz |
| Windows binaries: | r-devel: PersomicsArray_1.0.zip, r-release: PersomicsArray_1.0.zip, r-oldrel: PersomicsArray_1.0.zip |
| OS X binaries: | r-release: PersomicsArray_1.0.tgz, r-oldrel: PersomicsArray_1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=PersomicsArray to link to this page.


Dr Alison Roberts is testing a new questionnaire designed to measure how well people manage their emotions. This questionnaire has been designed on the basis of a well researched and accepted model of ‘emotion regulation’ but hasn’t yet been used with people who have a range of mental health difficulties. We want to collect answers to this questionnaire along with some other relevant information to find out how well it measures the ideas it is intended to measure. We need the help of NHS staff to collect some comparison data.
This project is part of a wider programme of research to explore whether Mindfulness Based Interventions can be adapted to provide a safe, effective and accessible treatment option for people who have problems managing emotional intensity (sometimes diagnosed as borderline or emotionally unstable personality disorder). This group of people often have difficulties accessing talking therapies and clinicians often find it challenging to know how best to support them.


{% extends "base.html" %} {% import 'bootstrap/wtf.html' as wtf %} {% block jumbotron_content %}
Retrieve Profiles from the online platform


--- layout: post permalink: artesanocoppersinks.html Tags: Array NameOfCoupon: Artesano Copper Sinks Cash Back Summary: See our Artesano Copper Sinks Coupon Code, Artesano Copper Sinks Rebate, Artesano Copper Sinks Promo Code, Artesano Copper Sinks Discount, Artesano Copper Sinks Coupon Cash Back Rebate here.   They are a boutique designer and manufacturer of hand hammered copper sinks They include outstanding copper bathroom sinks, copper kitchen sinks, copper bar sinks and copper bathtubs. Their accessories collection includes copper tiles and electric covers. You will get the best products when you buy from Artesano Copper Sinks. Their tradition of copper crafts and artwork has been handed down from generation to generation by the families of the artisans that Artesano Copper Sinks uses, from as far back as 1538. Use our discount coupons to get a great price.
Artesano Copper Sinks are 100% hand hammered copper sinks that are lead free. Artesano Copper Sinks are very easy to maintain and clean. Copper also has many antibacterial benefits. They can kill dangerous bacteria like E.Coli. Artesano Copper Sinks also offer perfect matching drains and accessories like such as tiles and electric covers. Artesano Copper Sinks offers a large variety of beautiful sinks that include styles like drop-in, under-mount, vessel and self-rimming. Check out Artesano Copper Sinks, you will love the beautiful copper color and the product. Use our coupons to save money.
Artesano Copper Sinks is a designer green, eco friendly company that makes beautiful copper sinks for your kitchen and bathrooms. They carry a large selection of different designed sinks and accessories that include tiles and electric covers. Buy Artesano Copper Sinks and reap the many benefits of having a copper sink, including anti-bacterial benefits. Buy it today!


Feedback is a tool to facilitate a clear and open conversation between one another. Feedback can strengthen relationships as well as provide personal development opportunities by gaining insights on behaviours you never were aware of.
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{% extends "_layouts/base.html" %} {% block content %}
This is all the users of your corporation in Auth. You might dump their ldap info, see their report, and most importantly, purge them.
| Character name | Auth status | Actions |
|---|---|---|
| {{user.characterName[0]}} | {{user.accountStatus[0]}} |


Hiatus
I fell asleep in the middle of the last day. And then life has hit and, for a while, I've genuinely been unable to find an hour in a day that I can devote to this project. Some days were busy and good. Some days were busy and bad. But then the busy days died away and I had trouble picking the project back up.
Just a short break though, since I've got some busy days ahead of me. Let's say we'll return on Sunday, July 24th. The key (that I tried to bring up last time) is to find something I want to make, that I can be passionate about and get lost in. So we're going to make a small game.


Magister Harne:
Oh no! Jasmine and Roselyn must both bear the responsibility for their actions this time.
Last time I only blamed Jasmine, but it's become clear that they both need to be taught a lesson!
You have a stake in this as well, my friend. You should go to Jasmine and give her this message in person!
Tell her that she's upset the Tetrarch terribly, and to keep her mouth shut! Can you do that?
And, as punishment, she should grant one of your wishes.
May the divine blessing of the Mother of the Abyss be with you always!


---
layout: post
title: 'Wonderful. Harper govt to cut GSC''s Ice Core Research Lab as well #EndOfTheWorldAsWeKnowIt'
date: '2012-02-11T18:22:00.001-05:00'
author: Bob Sica
tags:
modified_time: '2013-08-02T16:27:07.480-04:00'
blogger_id: tag:blogger.com,1999:blog-736339229338318027.post-8515348995098204158
blogger_orig_url: http://blog.purplecrowlidar.ca/2012/02/wonderful-harper-govt-to-cut-gsc-ice.html
---
Well got some feedback on the Harper Government cutting ozone research at Enviroment Canada. A colleague sent on this McLean's article about cutting the Geological Survey of Canada's Ice Core Research Laboratory as well.
That makes sense, right? If your trying to erase the present climate record to justify unchecked development you better cover your tracks and deny the last 150,000 years as well!
The horror...
You can download the story from Macleans Magazine
here (pdf).


It's bill time! Let's split the bill among everyone and update the values in the dictionary you have created in the previous stage.
Since we don't want to deal with too many decimals (who carries that much change anyway?), round the split amount to two decimal places and then update the dictionary with the split values.
In this stage your program should perform the following steps together with the steps of the previous stage:
"No one is joining for the party"
;Do not print the output of the previous stage (see examples).
The greater-than symbol followed by a space (>
) represents the user input. Note that it's not part of the input.
Example 1: Five people joining
Enter the number of friends joining (including you):
> 5
Enter the name of every friend (including you), each on a new line:
> Marc
> Jem
> Monica
> Anna
> Jason
Enter the total bill value:
> 100
{'Marc': 20, 'Jem': 20, 'Monica': 20, 'Anna': 20, 'Jason': 20}
Example 2: Seven people joining
Enter the number of friends joining (including you):
> 7
Enter the name of every friend (including you), each on a new line:
> Marc
> Jem
> Monica
> Anna
> Jason
> Ben
> Ned
Enter the total bill value:
> 41
{'Marc': 5.86, 'Jem': 5.86, 'Monica':5.86, 'Anna': 5.86, 'Jason': 5.86, 'Ben': 5.86, 'Ned': 5.86}
Example 3: Invalid input
Enter the number of friends joining (including you):
> 0
No one is joining for the party


EcoClub
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EcommCo’s Data Lake leverages Amazon S3 and Amazon Redshift for dataset management and transformation of data ingested from Data Providers. EcommCo’s internal Data Providers include its CRM system and e-commerce platform. Additionally, EcommCo uses publicly available demographics datasets to gain additional insights about their customers.
Submissions are provided to the Data Lake in their native format to keep the “price of entry” low for Data Providers. This enables business users to commission transforms on an as-needed basis for analytics that offer business value. They need not know all of their requirements up front.
Submissions can be added to S3 Buckets via a variety of submission mechanisms, which can differ from provider to provider (i.e.,SFTP, API, etc.).
Datasets under management are stored in S3 Buckets.
Curated Datasets are a key concept of Data Lakes. They can include:
The diagram below illustrates submissions from ECommCo’s Data Providers and how they are transformed into Curated Datasets.
When you click this button, the following steps will be performed within your AWS account:
Visit S3 in your AWS Management Console and review the following Buckets:


Your email is now registered. You will receive a welcome kit shortly on your registered email that will contain details to login to our system.


DeveloperTypescript - Anders Hejlsberg
is a strongly typed, object oriented, compiled language.
is just JavaScript
Summary:
TypeScript is JavaScript plus some additional features
Typescript support concepts like interfaces, generics, inheritance, and method access modifiers;
Example of interface:
interface IScore {
scorestudentId: string;
calculateScore(): number;
}
class OutputStudentScore implements IScore {
scorestudentId: string;
calculateScore(): number {
return 0;
}
}
Example of inheritance:
class Person{
Firstname:string;
}
class RSSchool extends Person {
Role:string
private AddPerson(){
this.Role="Student";
this.FirstName="Kitty";
}
}
Example of generic:
function identity (arg: T): T {
return arg;
}
let output = identity ("myString");
Example of access modifiers:
class RSSchool{
public taskName:string;
private taskSolution:string;
}
TypeScript will compile the code and generate compilation errors, if it finds some sort of syntax errors. This helps to highlight errors before the script is run.
Types are had to define when declaring variables and you cannot assign other values other than the type defined which is very possible in JavaScript.
Example:
let num: number = 209;
let name: string = 'Alena';
let isPresent: boolean = true;
There are two main ways to get the TypeScript tools:
*No animals were harmed in making this presentation)


--- layout: default description: Template for training material page --- {{ content }} {% for material in site.data.training %}


Inspired by @mattangriffel
This one is kind of technical, but every page on a product should append a unique parameter (something like ?id=532391) that way when I copy the URL and send it to a friend, they know who referred who. And a link could be permanently created between users
Add 1 line of JS to your website (or customize the param value and cookie, so 3 lines). Once added, you can then pass in a param and it will automatically keep that param as a visitor goes from page to page. It uses both cookies and localstorage, to keep track of the param, so you could remove one or the other and still be tracked. Take a look by clicking the example link below and then going to the About or Contact page.
Now visit the About and Contact Page, notice the jhid is automatically appended without any code changes to the link!
Jughandle uses cookies and localstorage to save the ID being tracked and automatically add it to a URL when loaded
A simple way to track visitors using a param referral without a huge or expensive system. Right now this is just a tracker, but planning to build a MVP product. For now, you have to keep track of the IDs you set e.g. track index.html?jhid=123 where 123 represents Acme Company.
This is still a work in progress, so feel free to reach out @johnmurch or on GitHub
I built Jughandle as a proof of concept with the intention of building a SAAS to track visitors and who they forward links to
Jughandle works on any website, but right now it's a bit manual. You have to manually keep track of an ids for each visitor or community you want to track.Be sure to add jh_track (or your customized name) as a param to ignore in Google Analytics
Feel free to reach out @johnmurch or on GitHub


Tidcombe Primary School Artwork Week 15 Nov 2019, 12:15 by Lewis Clarke Tidcombe Primary School pupils have been creating artwork with plastic Tidcombe Primary School pupils have been creating artwork with plastic Tidcombe Primary School pupils have been creating artwork with plastic Tidcombe Primary School pupils have been creating artwork with plastic Tidcombe Primary School pupils have been creating artwork with plastic Tidcombe Primary School pupils have been creating artwork with plastic Tidcombe Primary School pupils have been creating artwork with plastic Tidcombe Primary School pupils have been creating artwork with plastic Tidcombe Primary School pupils have been creating artwork with plastic Tidcombe Primary School pupils have been creating artwork with plastic


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