|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object com.hp.hpl.jena.vocabulary.ReasonerVocabulary
public class ReasonerVocabulary
A collection of RDF terms used in driving or configuring some of the builtin reasoners.
| Field Summary | |
|---|---|
static Property |
configurationP
Reasoner description property: a configuration property supported by the reasoner |
static Property |
descriptionP
Reasoner description property: text description of the reasoner |
static Property |
directRDFType
The property that represents the direct/minimal version of the rdf:type relationship |
static Property |
directSubClassOf
The property that represents the direct/minimal version of the subClassOf relationship |
static Property |
directSubPropertyOf
The property that represents the direct/minimal version of the subPropertyOf relationship |
static Property |
EXT_REASONER_AXIOMS
Property to denote the axioms file that the reasoner will use for background knowledge. |
static Property |
EXT_REASONER_ONT_LANG
Property to denote the ontology language (OWL, DAML, RDFS) an external reasoner will process. |
static Property |
EXT_REASONER_URL
Property to denote the URL of an external reasoner. |
static Property |
hasRule
Property of a GRR rule-set config that specifies a rule as a string in the Jena rules language. |
static Property |
individualAsThingP
A faux property used in reasoner capabilty models to denote reasoners that infer that individuals have rdf:type owl:Thing (or daml:Thing) |
static java.lang.String |
JenaReasonerNS
The namespace used for system level descriptive properties of any reasoner |
static Property |
nameP
Reasoner description property: name of the reasoner |
static Property |
PROPderivationLogging
Property used to configure the derivation logging behaviour of a reasoner. |
static Property |
PROPenableCMPScan
Property used to switch on/off scanning of data for container membership properties in RDFS preprocessing. |
static Property |
PROPenableFunctorFiltering
Property used to switch on/off filtering of functors from returned results in the generic rule engine. |
static Property |
PROPenableOWLTranslation
Property used to switch on/off OWL schema translation on a generic rule reasoner. |
static Property |
PROPenableTGCCaching
Property used to switch on/off use of the dedicated subclass/subproperty caching in a generic rule reasoner. |
static Property |
PROPruleMode
Property used to set the mode of a generic rule reasoner. |
static Property |
PROPruleSet
Property used to attach a file a rules to a generic rule reasoner. |
static Property |
PROPsetRDFSLevel
Property used to switch to different RDFS processing levles. |
static Property |
PROPtraceOn
Property used to configure the tracing behaviour of a reasoner. |
static java.lang.String |
PropURI
Base URI used for configuration properties for rule reasoners |
static Property |
RB_VALIDATION
Property used to switch on validation in owl ruleset |
static Property |
RB_VALIDATION_REPORT
Property used for validation reports in owl ruleset |
static java.lang.String |
RBNamespace
A namespace used for Rubrik specific properties |
static java.lang.String |
RDFS_DEFAULT
Constant for PROPsetRDFSLevel - default behaviour |
static java.lang.String |
RDFS_FULL
Constant for PROPsetRDFSLevel - fullest implementation supported. |
static java.lang.String |
RDFS_SIMPLE
Constant for PROPsetRDFSLevel - simplified, higher performance rules. |
static Resource |
ReasonerClass
The RDF class to which all Reasoners belong |
static Property |
ruleSet
Property of a GRR config with object a node with rule set properties. |
static Property |
ruleSetURL
Property of a GRR config with object a resource who's URI is the URL of a Jena rules text. |
static Property |
schemaURL
Property of a reasoner that specifies the URL of a schema to load. |
static Property |
supportsP
Reasoner description property: a schema property supported by the reasoner |
static Property |
versionP
Reasoner description property: version of the reasoner |
| Constructor Summary | |
|---|---|
ReasonerVocabulary()
|
| Method Summary | |
|---|---|
static java.lang.String |
getJenaReasonerNS()
Return namespace used for system level descriptive properties of any reasoner |
static java.lang.String |
getRBNamespace()
Return namespace used for Rubric specific properties |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public static final java.lang.String JenaReasonerNS
public static final Resource ReasonerClass
public static final Property nameP
public static final Property descriptionP
public static final Property versionP
public static final Property supportsP
public static final Property configurationP
public static final Property individualAsThingP
public static final Property directSubClassOf
public static final Property directSubPropertyOf
public static final Property directRDFType
public static final java.lang.String PropURI
public static final Property PROPderivationLogging
public static final Property PROPtraceOn
public static final Property PROPruleMode
public static final Property PROPruleSet
public static final Property PROPenableOWLTranslation
public static final Property PROPenableTGCCaching
public static final Property PROPenableCMPScan
public static final Property PROPsetRDFSLevel
public static final Property PROPenableFunctorFiltering
public static final java.lang.String RDFS_DEFAULT
public static final java.lang.String RDFS_FULL
public static final java.lang.String RDFS_SIMPLE
public static final java.lang.String RBNamespace
public static final Property RB_VALIDATION
public static final Property RB_VALIDATION_REPORT
public static final Property EXT_REASONER_URL
public static final Property EXT_REASONER_ONT_LANG
ProfileRegistry
. Default is OWL.
public static final Property EXT_REASONER_AXIOMS
public static final Property ruleSet
public static final Property ruleSetURL
public static final Property hasRule
public static final Property schemaURL
| Constructor Detail |
|---|
public ReasonerVocabulary()
| Method Detail |
|---|
public static final java.lang.String getRBNamespace()
public static final java.lang.String getJenaReasonerNS()
|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Chocolate Pizza
POSTED ON 15 DEC 2013 / DESSERTS
Vanessa Stevenson
Food enthusiast, photography fan. Add a pinch of raw foodism and that's pretty much who I am.
POSTED ON 15 DEC 2013 / DESSERTS


Fake news has emerged to become a significant issue since the Brexit referendum and subsequently the US presidential elections in 2016. There were claims that such misinformation had influenced the outcome of the two vital polls to a certain degree.
Now “fake news” is not only a word that referring to news which is factually wrong that deliberately misinforms the public, but it’s also a word frequently used by US president Donald Trump to criticise certain media organisations and news reports.
Thanks to Trump, the usage of the term “fake news” had increased by 365% since 2016, even before he was inaugurated in January this year, according to Collins Dictionary’s lexicographers. As such, “Fake news” was named as the word of the year in 2017.
The recognition was mostly due to Trump’s hostile relationship with some prominent media organisations, such as the New York Times and CNN. He often fires against those media outlets for allegedly spreading the fake news over reports that he disagrees with.
As a Twitter’s heavy user, Trump frequently trains his gun on those reports via his personal Twitter account, besides publicly criticising the reporters from those news organisations for allegedly producing fake news in press conferences.
The term has become a trend and a popular hashtag among Twitter users. This report is to analyse how Twitter users engage with #fakenews in a specific timeframe.
Methodology
The sample of Twitter data containing the word “fake news” and #fakenews was collected by using Twitter streaming application programming interface (API) script written in Python between 5pm UTC December 9 and 12am UTC December 14, 2017.
The script was written so that it could distinguish and scrape truncated original tweets, the full text of retweets and quoted tweets (retweets with users’ comment), besides other information, for more precise analysis.
The analysis focuses on understanding the peaks of the tweets timeline, popular retweets and quoted tweets, percentage of verified users, identifying bots among top 10 tweeters, sentiments etc.
Findings
- Trump’s tweets against certain media organisations had driven the usage of the fakenews hashtag and “fake news” term.
- More than 556,000 Twitter users had tweeted over 150 million tweets that carried #fakenews or “fake news” in 103 hours of scrapping.
- Of all the tweets, close to three-quarters of the tweets were retweets. 8.6% were quoted tweets, which were retweets with original text or opinion of the users, while the remaining 16.52% were original tweets.
- Less than 1% of the users who tweeted in the range of the scrapping period were verified users.
- Less than half of the top ten most active tweeters were genuine users, whom among others were using web services for retweet purpose.
- More than 550,000 users tweeted less than 50 tweets, while two users tweeted more than 400 times during the time span.


Note
Click here to download the full example code
Rhythmicity of Time Series¶
Exploring the rhythmicity of time series and their frequency representations.
Rhythmicity of Time Series¶
Central to the motivation for parameterizing neural power is the claim that power at a given frequency is not sufficient to claim that there is evidence for rhythmic, or periodic, activity at that frequency.
In this example, we will explore this idea by examining some example signals in the time domain, and their frequency domain representations. We will use these signals to motivate if and when signals should be interpreted as containing periodic activity.
The Fourier Theorem¶
Stated informally, the Fourier theorem tells us that any time series can be represented as a sum of sinusoids.
This is a powerful and useful theorem, as it means that we can use tools such as the Fourier transform and other similar measures, to compute frequency representations of any time series data.
However, just because a signal can be represented by sinusoids does not mean that any given signal, or any given aspect of a signal, for which a power spectrum can be computed should be conceptualized as being comprised of rhythmic components.
The power spectrum is just a possible representation of the original data, not a descriptive claim of the actual components of the data.
# Import numpy
import numpy as np
# Use NeuroDSP for time series simulations & analyses
from neurodsp import sim
from neurodsp.utils import create_times, set_random_seed
from neurodsp.spectral import compute_spectrum_welch
from neurodsp.plts import plot_time_series, plot_power_spectra
Out:
/home/john/Software/miniconda3/envs/jupyter_py3/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject
return f(*args, **kwds)
# Set random seed, for consistency generating simulated data
set_random_seed(21)
# Simulation Settings
n_seconds = 2
s_rate = 1000
# Compute an array of time values, for plotting, and check length of data
times = create_times(n_seconds, s_rate)
n_points = len(times)
Frequency Representations of Aperiodic Signals¶
Let’s start with aperiodic signals, and examine how different types of aperiodic signals are represented in the frequency domain.
The Dirac Delta¶
The Dirac delta is arguably the simplest signal, as it’s a signal of all zeros, except for a single value of 1.
# Simulate a delta function
dirac_sig = np.zeros([n_points])
dirac_sig[500] = 1
# Plot the time series of the delta signal
plot_time_series(times, dirac_sig)
Next, lets compute the frequency representation of the delta function.
# Compute a power spectrum of the Dirac delta
freqs, powers = compute_spectrum_welch(dirac_sig, 100)
# Plot the power spectrum of the Dirac delta
plot_power_spectra(freqs, powers)
Section Conclusions¶
As we can see above, the power spectrum of the Dirac delta function has power across all frequencies.
This is despite it containing containing a single non-zero value, and thus having no rhythmic properties to it in the time domain.
The Dirac delta example can be taken as a proof of principle that observing power at a particular frequency does not necessarily imply that one should consider that there are any rhythmic properties at that frequency in the original time series.
In this case, and many like it, power across all frequencies is a representation of transient (or aperiodic) activity in the time series. Broadly, when there are transients, or aperiodic components, lots of sinusoids have to be added together in order to represent aperiodic activity out of a basis set of periodic sine waves, and this is why such signals typically look very broadband in the frequency domain.
Colored Noise Signals¶
Let’s now look at ‘noise’ signals.
In the signals below, we will simulate colored noise signals, in which samples are drawn randomly from noise distributions, with no rhythmic properties.
As we will see, in the power spectrum, these signals exhibit power at all frequencies, with specific patterns of powers across frequencies, which is dependent on the ‘color’ of the noise.
White Noise¶
A ‘white noise’ signal is one that is generated with uncorrelated samples drawn from a random distribution. Since each element of the signal is sampled randomly, there is no consistent rhythmic structure in the signal.
# Generate a white noise time series signal
white_sig = np.random.normal(0, 1, n_points)
# Plot the white noise time series
plot_time_series(times, white_sig)
As before, we can compute and visualize the power spectrum of this signal.
# Compute the power spectrum of the white noise signal
freqs, powers = compute_spectrum_welch(white_sig, s_rate)
# Visualize the power spectrum of the white noise signal
plot_power_spectra(freqs, powers)
In the frequency representation, we can see that white noise has a flat power spectrum, with equal power across all frequencies. This is the definition of white noise.
This is similar to the delta function, though note that in this case the power across frequencies is representing continuous aperiodic activity, rather than a single transient.
Pink Noise¶
Other ‘colors’ of noise refer to different patterns of power distributions in the power spectrum.
For example, pink noise is a signal where power systematically decreases across frequencies in the power spectrum.
# Generate a pink noise signal
pink_sig = sim.sim_powerlaw(n_seconds, s_rate, exponent=-1)
# Plot the pink noise time series
plot_time_series(times, pink_sig)
# Compute the power spectrum of the pink noise signal
freqs, powers = compute_spectrum_welch(pink_sig, s_rate)
# Visualize the power spectrum of the pink noise signal
plot_power_spectra(freqs, powers)
Section Conclusion¶
The ‘colored noise’ signals above are simulated signals with no rhythmic properties, in the sense that there are no characteristic frequencies or visible rhythms in the data.
Nevertheless, and by definition, in the power spectra of such signals, there is power across all frequencies, with some pattern of power across frequencies.
However, there are no frequencies at which power is different from expected from an aperiodic noise signal. These signals are statistically, by definition, aperiodic.
Frequency Representations of Rhythmic Signals¶
Next, lets check what frequency representations look like for time series that do have rhythmic activity.
Sinusoidal Signals¶
There are many different rhythmic signals we could simulate, in terms of different rhythmic shapes, and or temporal properties (such as rhythmic bursts). For this example, we will stick to simulating continuous sinusoidal signals.
# Generate an oscillating signal
osc_sig = sim.sim_oscillation(n_seconds, s_rate, freq=10)
# Plot the oscillating time series
plot_time_series(times, osc_sig)
# Compute the power spectrum of the oscillating signal
freqs, powers = compute_spectrum_welch(osc_sig, s_rate)
# Visualize the power spectrum of the oscillating signal
plot_power_spectra(freqs, powers)
Section Conclusion¶
When there is rhythmic activity at a particular frequency, this exhibits as a ‘peak’ of power in the frequency domain. This peak indicates high power at a specific frequency, where as the power values at all other frequencies are effectively zero.
Frequency Representations of Complex Signals¶
Now let’s consider the case whereby one could have a signal comprised of multiple components, for example one or more oscillations combined with an aperiodic component.
Combined Aperiodic & Periodic Signals¶
To examine this, we will simulate combined signals, comprising both periodic and aperiodic components, and see what the frequency representations look like.
# Define component of a combined signal: an oscillation and an aperiodic component
components = {
'sim_oscillation' : {'freq' : 10},
'sim_powerlaw' : {'exponent' : -1}
}
# Generate a combined signal
combined_sig = sim.sim_combined(n_seconds, s_rate, components)
# Plot the combined time series
plot_time_series(times, combined_sig)
# Compute the power spectrum of the combined signal
freqs, powers = compute_spectrum_welch(combined_sig, s_rate)
# Visualize the power spectrum of the combined signal
plot_power_spectra(freqs, powers)
Section Conclusion¶
In the power spectrum above, we can see that combined signals, with aperiodic & periodic activity reflect elements of both components. The periodic power can be seen as a peak of power over and above the rest of the spectrum, at the frequency of the simulated rhythm. Across all frequencies, we also see the power contributed by the aperiodic component.
Conclusion¶
In this example, we have seen that, in the frequency domain:
transients and aperiodic activity exhibit power across all frequencies
oscillations exhibit specific power, or a ‘peak’, at the frequency of the rhythm
combined signals display a combination of these properties, with power across all frequencies, and overlying ‘peaks’ at frequencies with periodic activity
Collectively, we have seen cases that motivate that simply having power at a particularly frequency does not imply any rhythmic component at that frequency. Peaks of frequency specific power are associated with rhythmic activity in the time series.
What we have covered here are just a starting point for some properties of time series analysis and digital signal processing. For neural data, these properties alone do not tell us how to interpret neural power spectra. However, here we take them as a starting point that motivate why prominent rhythms in the time series can be measured as peaks in the power spectrum, but that absent a peak, we should not automatically interpret power at any given frequency as necessarily reflecting rhythmic activity.
Total running time of the script: ( 0 minutes 7.214 seconds)


FIRST LOOK: Nando’s opens new flagship store at The Dubai Mall
Check out the cheeky new store in one of the world’s biggest mall
by Tim SkinnerSometimes absolutely nothing beats a cheeky Nando’s (just ask literally anyone from London), and the world famous chicken-grilling restaurant is opening its brand-new flagship store in Dubai.
The South African chain is swinging open the doors of its newest store, its 22nd in the UAE, in The Dubai Mall, and Time Out Dubai got a cheeky first look inside before anyone else.
Sat on the Waterfront Promenade in the world’s most famous mall, you can now enjoy your hot, medium or mild chicken with views of the Burj Khalifa, the world’s tallest tower (among other records), and The Dubai Fountain, the world’s largest synchronised fountain.
“We are very proud of this milestone, opening our 22nd Casa in the UAE, and in this grand location at The Dubai Mall, home to some of the most prestigious brands and iconic attractions in the country,” said Suhail Gidwani, Chairman and CEO of Nando’s UAE. “We couldn’t be more grateful to our UAE customers for their continued loyalty, which has led to our expansion here.”
The new restaurant features a series of specially-commissioned pieces of art from renowned South African artist Dion Cupido. It’s also home to the classic dishes we’ve all come to love from Nando’s, plus this branch will be the first in the UAE to offer Nando’s breakfast – including classic South African breakfasts like doughnuts, and roast almond and peanut butter oats.
You’ll find it open daily from 11am until 1am. Check out the pictures above to get a cheeky glimpse inside.
Open daily 11am-1am. The Dubai Mall, Downtown Dubai (04 362 7500).
Be the first to get all the latest Dubai news, reviews and deals into your inbox by signing up to our free newsletter, click here to sign up.


At Pats Vegan Salmon Cookies, we strive to bring you the best cruelty free and ethically sources salmon cookies. All our products and ingredients are locally sourced and only use natural ingredients. Interested in becoming Vegan or plant-based? Check out a flyer from our friends at Happy Cow.
Locations
Below is the founder Pat with his beautiful family.


#parse ( "header.html" )
#if ( $errorMsg )
$errorMsg
#end
Event $event.identifier
$event.eventId
$event.topoKey
#if ( $activeCg ) #if ( $activeCg.primary == $event )
Primary in
$activeCg.cgSpec.name at $activeCg.anchorTopology
Secondaries
#foreach ( $secondary in $activeCg.secondaries )
$secondary.eventId at $secondary.topoKey
#end
#else
Secondary to
$activeCg.primary.eventId on $activeCg.primary.topoKey
In correlation
$activeCg.cgSpec.name at $activeCg.anchorTopology
#end #end
#parse ( "footer.html" )


Implementations of a SiO2:Al2O3 Stull Chart using various Javascript libraries.
The Glazy Stull Chart overlay was inspired by the original work of R.T. Stull and the continued research of Matt Katz.
For more information, see R.T. Stull's original article in Transactions of the American Ceramic Society, Volume 14, pages 62-70.
Also see Matt Katz's online courses on Ceramic Materials Workshop
The primary difficulty with making Stull Charts in Javascript is combining the bottom "region" layers with an overlapping scatter plot of actual UMF analyses for recipes. Furthermore, each point in the scatter plot contains additional color-coded information (R2O:RO).
After experimenting with a number of Javascript charting libraries, it seems that d3 is ultimately the best choice due to its ability to draw complex charts with multiple layers. This d3 example uses actual recipe data from an externally loaded JSON file.
While having a much simpler interface than d3, Chart.js does allow one to combine a line chart (for drawing the underlying Stull regions) with scatter plot data.
Hover over link with mouse to highlight the recipe on the Stull Chart.
This is a static version of the Highcharts implementation on https://glazy.org
Full implementation on Glazy


People who are friends with me on Facebook are perfectly aware of the fact that I go on a status marathon whenever I've vacations. But this time, I'm taking that up a notch and so, tada!!, I present to you my new blog where I'll be pouring my heart out during these two months.
But why a blog?
In the past few months, some really screwed up decisions led me to some place (known as Gurgaon) other than where most of my friends have landed. My friends would die without me and this blog will also serve to rejoice them these two months.
Since memories are the only constant in this ever changing worlds (I know it's not scientifically true CR/Dwivedi/Mathur so don't even say that), I hope this blog will create some memeories for all of us and will be the right way to begin a story that will never end.
So what about the old one?
Ohh I completely forgot about that!!!!
Just Kidding, actually my old blog got cluttered with a lot of nonsensical posts and so I thought of starting from scratch. Also, having your own site is way much cooler.
Anything else?
Not really but since I don't have much to do right now here's a poem for you
Roses are red,
Sky is blue,
Click Here for more,
Ya I got you
HAHAHA...
So I bid you farewell for now.
Till my next post,
Sayo Nara


| FILE |
DESCRIPTION |
TAGS |
| mixModelCreate |
Create a mixture models |
|
| mixModelFit |
Fit a mixture model via MLE/MAP (using EM) |
|
| mixModelInferLatent |
Compute pZ(i, k) = p( Z = k | X(i, :), model) |
|
| mixModelLogprob |
Calculate logp(i) = log p(X(i,:) | model) |
|
| mixModelMapLatent |
Compute argmax_k p( Z = k | X(i, :), model) i.e. the hard clustering |
|
| mixModelReconstruct |
Compress X then reconstruct it in Xhat |
|
| mixModelSample |
Sample from a mixture model |
|
| sub\kmeansInitMixGauss |
Initialize params of GMM with Kmeans |
|
| sub\mixDiscreteSample |
Sample nsamples from a mixture of discrete distributions. |
|
| sub\mixGaussImpute |
Fill in NaN entries of X using posterior mode on each row |
|
| sub\mixGaussInfer |
z(i) = argmax_k p(z=k|X(i,:), model) hard clustering |
|
| sub\mixGaussLogprob |
logp(i) = log p(X(i,:) | mu(:, i), Sigma(:, :, i), mixWeight(i)s) |
|
| sub\mixGaussMissingFitEm |
Fit a mixture of Gaussians where the data may have NaN entries |
|
| sub\mixGaussSample |
Sample n samples from a mixture of multivariate gaussians |
|
| sub\mixModelFitEm |
Fit a mixture model via EM |
|


[name]
Class representing a [link:https://en.wikipedia.org/wiki/Spline_(mathematics) spline].
Examples
[example:webgl_lines_splines WebGL / lines / splines]
[example:webgl_lines_dashed WebGL / lines / dashed]
[example:software_sandbox Software / sandbox]
[example:canvas_lines_dashed Canvas / lines / dashed]
Constructor
[name]( [page:Array points] )
Initialises the spline with [page:Array points], which are the [page:Vector3 Vector3s]
through which the spline will pass.
Properties
[property:Array points]
Methods
[method:Array getControlPointsArray]( )
Returns an array with triplets of [ x, y, z ] coordinates that correspond to the
current control points.
[method:Vector3 getPoint]( [page:Integer k] )
[page:Integer k] — point index
Return the interpolated point at [page:Integer k].
[method:Object getLength]( [page:Integer nSubDivisions] )
[page:Integer nSubDivisions] — number of subdivisions between control points. Default is *100*.
Returns an object with the two properties. The property [page:Number total] contains
the length of the spline when using [page:Integer nSubDivisions]. The property [page:Array chunkLength]
contains an array with the total length from the beginning of the spline to the end of that chunk.
[method:null initFromArray]( [page:Array a] )
[page:Array a] — array of the form
var a = [ [x1, y1, z1], [x2, y2, z2], ... ];
from which to initialise the points array.
Initialises using the data in the array as a series of points. Each value in *a* must
be another array with three values, where a[n] is v, the value for the *nth* point,
and v[0], v[1] and v[2] are the x, y and z coordinates of that point n, respectively.
[method:Vector3 interpolate]( p0, p1, p2, p3, t, t2, t3 )
[method:null reparametrizeByArcLength]( [page:Float samplingCoef] )
[page:Float samplingCoef] — how many intermediate values to use between spline points
Modifies the spline so that it looks similar to the original but has its points distributed
in such way that moving along the spline it's done at a more or less constant speed.
The points should also appear more uniformly spread along the curve.
This is done by resampling the original spline, with the density of sampling controlled by [page:Float samplingCoef].
Here it's interesting to note that denser sampling is not necessarily better:
if sampling is too high, you may get weird kinks in curvature.
Source
[link:https://github.com/mrdoob/three.js/blob/master/src/[path].js src/[path].js]


Construct using coordinates x,y,z.
x value, default = 0.
y value, default = 0.
z value, default = 0.
x value.
y value.
z value.
Adds the vector 3 to this vector 3. The result is stored in this vector 3.
Vector 3 to add.
Creates a copy of the vector 3.
Copy of this vector 3.
Gets the negative of this vector 3.
Negative copy of this vector 3.
Vector multiplication. Stores the result in this vector 3.
Value to multiple the vector's values by.
Sets this vector 3 to its negative.
Sets the vector 3.
x value, default is 0.
y value, default is 0.
z value, default is 0.
Sets the vector 3 from a vector 3.
Vector 3 to copy values from.
Sets the vector 3 to zero values.
Subtracts the vector 3 from this vector 3. The result is stored in this vector 3.
Vector 3 to subtract.
Generated using TypeDoc
A 2D column vector with 3 elements.


Test the different app-route types by navigating through these links.
app-route
Should ignore trailing slashes and match "/custom-element" and "/custom-element/" with the same route.


These are some of the interesting ML projects that I've worked on over the last couple months. All of my projects can be found on GitHub
(IN PROGRESS)
(IN PROGRESS)
I implemeneted the style transfer network described in the paper A Neural Algorithm of Artistic Style by Gatys, Ecker and Bethge.
The basic premise is to take two existing images - typically one image is thought of as the "content" image while the other is thought of as the "style" image - along with a third image of random noise and incrementally change the pixel values of the noisey image.
The goal of continually tweaking the pixel values of the noisey image is to have a resultant new image which is both "close" to the content image and "close" to the style image. Of course, here images being "close" to each other simply means that they minimize some predefined cost/objective function.
Specifically, in this setting of style transfer, we feed each of the three images (content, style and noise) though a pretrained Convolutional Neural Network (CNN) - in this case we used the VGG16 network trained on ImageNet data, and use the output values of each layer to measure how close the images are to each other.
Loosely speaking, we say that the the noise image is close to the content image if the outputs of the convolutional layers earlier on in the network agree, whereas we say that the noise image and the style image are close if the outputs of all the convolutional layers agree. Here when we talk about outputs of the convolutional layers, we really mean the outputs of the convolutional layers found immediately before the pooling layers.
The images below (mobile)/to the right(desktop) show an example of style tansfer in action. My girlfriend is a very talented illustrator/graphic designer. Our anniversary was around the corner and in addition to a nice dinner and some gifts I wanted to give her something more personal, and handful of style transfered images of the two of us blended with some of her artwork seemed like just the thing! In the process I was able to not only give her a more personal gift, but learned some new machine learning concepts along the way! Win-win :)
DESCRIPTION
DESCRIPTION


elFinder is an open-source file manager for web, written in JavaScript using jQuery UI. As you can see its creation is inspired by simplicity and convenience of Finder program used in Mac OS X operating system.
* Homepage - http://elfinder.org
* Wiki - https://github.com/Studio-42/elFinder/wiki


Pig_Dice Game
Rules of the game:
Each turn, a player repeatedly rolls a die until either a 1 is rolled or the player decides to "hold":
- If the player rolls a 1, they score nothing and it becomes the next player's turn.
- If the player rolls any other number, it is added to their turn total and the player's turn continues.
- If a player chooses to "hold", their turn total is added to their score, and it becomes the next player's turn.
Choose difficulty level:


Awwdorable! Freida Pinto and Cory Tran look absolutely blissful in love in THESE pictures
by TIMESOFINDIA.COMFreida Pinto got engaged to her beau Cory Tran recently. The actress took to her social media handles to announce the exciting news, gushing about her fiancé Cory Tran. On her fiancé's B'day she decided to share the engagement news with her fans and followers. Cory Tran is a adventure photographer and had met Freida through her close friend, Aaron Paul. After attending the Oscars together this year, the couple made their relationship official and are reportedly tying the knot next year.
Early in the day, Freida shared a series of photos of herself alongside her fiancé and wrote, "It all makes sense now. Life makes sense, the world makes sense, the past tears and trials make sense, what wise old lovers said about love makes sense, where I am making sense and where I want to go completely makes sense.  And you are here to stay. Well, I am making you stay. Ha! 😂 All my love with all my heart♥. Oh and Happiest Birthday sweet Fiancé!"
In the past also the actress has shared many endearing pics of them indulging in social media PDA. Their lovey-dovey pictures give out major relationship goals to many out there.
THESE beautiful pictures of the couple speak volume about their awwdorable love story, take a look
Pictures Courtesy: Freida Pinto Instagram


--- layout: post status: publish published: true title: 'Podcast #18 - 8/19/09' author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 1251 wordpress_url: http://ivyenvy.com/?p=1251 date: '2009-08-19 20:16:34 +0000' date_gmt: '2009-08-20 02:16:34 +0000' categories: - Ivy Envy - Minor Leagues - Podcast - Cubs fans tags: - Carlos Zambrano - Peoria Chiefs - Twenty Sports Grille - Peoria - Wrigley Bound comments: - id: 376 author: Kurt author_email<EMAIL_ADDRESS>author_url: http:// date: '2009-08-21 10:23:06 +0000' date_gmt: '2009-08-21 16:23:06 +0000' content: I love Journey. ---
Just a quick little update with what's going on. On the way out, I play a clip about Cubs fans by Rich Halten, an independent producer.
* Live-blogging from Zambrano's rehab start in Peoria.
* Event at Twenty Sports Grille on Tuesday August 25th.
* Future segments on the podcast from Matt at Wrigley Bound.


{% extends "base.html" %} {% block htmltitle %} {{ section.title }} {% endblock %} {% block description -%} {{ section.description }} {%- endblock %} {% block head_extra %} {% endblock %} {% block content %} {% set links = load_data(path="content/links.json") %} The name is Darshak. I write apps for elementary OS and the web. And I read books and stuff. This is my site. Make yourself at home. {% for link in links.pages %} {{ link.long }} {% endfor %} {% endblock %} {% block footer %} {% endblock %}
I write apps for elementary OS and the web. And I read books and stuff.
This is my site. Make yourself at home.


About APIs
An Application Programming Interface (API) is an interface that provides developers access to a application's functionality so that a developer can write customized applications to it. An example of an API is a web browser's Document Object Model (DOM) which is the web browser's API that allows scripts to access and change its contents. There are many other types of APIs published for just about any software "product" or "platform", for example Apple OS X, Microsoft Office, Google Maps, or even Facebook, all of which allows a developer to create interesting projects that interface with what these APIs have to offer.
YouTube API
YouTube is a popular website that allows people to view, create, and share videos. Our team would like this to be an educational website for Code Fellows students to learn about APIs by investigating YouTube's API.
Choose a Programming Language!
Code Fellows offers many courses covering the three basic languages of web design (HTML, CSS, and Javascript), to specific languages including Ruby, Python, and Objective-C and Swift (for iOS Development). By using the following form you can discover interesting educational videos from YouTube.
On the next page, we will explain how this works!


A web based interface for Docker.
Fast, consistent delivery
Responsive deployment and scaling
Running workloads on same hardware
A web based interface for Kubernetes.
Automated rollouts and rollbacks
Service discovery and load balancing
Scale your application up and down


Audit access to "credit_card" table by employee Bob
You must log into the database as Bob before proceeding with this page.
Operation
Execute the SQL statements in the right pane.
Solution
Bob is granted with SELECT, INSERT, and UPDATE privileges on 'credit_card' table through role 'custService'.
When he executes SQL statements, he will be able to retrieve data from 'credit_card' table.
SELECT, INSERT, UPDATE statement succeeds and DELETE statement fails. An entry will be made in audit log files.


A Grid with subrows (no ColumnSets)
Buttons to test resetting subRows:
A Grid with locking columns but only one subrow
A Grid with complex columns including locking columns and subrows
Buttons to test resetting columnSets:
(set 3 tests 3 columnsets, to test proper cleanup of scrollers when
going back to 2)
Same grid, instantiated programmatically from HTML table
| Column 1 |
Column 2 |
Column 1 |
Column 4 |
| Column 3 |
Column 5 |
Another complex grid instantiated from an HTML table
| rowspan=3 |
colspan=2 |
rowspan=2 |
colspan=2 |
rowspan=2 |
| rowspan=2 |
Column 3 |
rowspan=2 |
Column 5 |
| Column 2 |
Column 3 |
colspan=2 |
Same as previous two tables, instantiated declaratively
| Column 1 |
Column 2 |
Column 1 |
Column 4 |
| Column 3 |
Column 5 |
| rowspan=3 |
colspan=2 |
rowspan=2 |
colSpan:2 |
rowSpan:2 |
| rowspan=2 |
Column 3 |
rowspan=2 |
Column 5 |
| Column 2 |
Column 3 |
colspan=2 |


|
MindScape
1.0
MindScape é um jogo plataforma com elementos de terror e suspense. Como o jogo se situa na mente da protagonista, há monstros surreais que são criados com base em seus medos.
|
#include <image.hpp>
Public Member Functions | |
| Image (SDL_Renderer *p_renderer, std::string path, bool is_active, std::pair< int, int > displacement, int priority) | |
| void | set_values (std::pair< int, int > dimension_on_screen, std::pair< int, int > dimensionOnTexture, std::pair< int, int > coordinatesOnTexture) |
| Methods Set values. More... | |
| bool | load () |
| Method Load. More... | |
| int | get_width () |
| Get Width. More... | |
| int | get_height () |
| Get Height. More... | |
| void | free () |
| Free Method. More... | |
| void | draw (int x, int y) |
| Draw. More... | |
| Public Member Functions inherited from engine::Component | |
| Component (std::string p_name, std::pair< int, int > p_displacement, bool p_active, int p_priority) | |
| bool | is_active () |
| This Routine verifys if the component is active or deactive. More... | |
| void | activate () |
| This Routine activate the current component. More... | |
| void | deactivate () |
| This Routine deactivate the current component. More... | |
| std::pair< int, int > | get_displacement () |
| This Routine get the current component displacement. More... | |
| void | set_displacement (std::pair< int, int >) |
| This Routine set the current component displacement. More... | |
| int | get_priority () |
| This Routine get the current component priority. More... | |
| void | set_priority (int) |
| This Routine set the current component priority. More... | |
| std::string | get_name () |
| This Routine get the current component name. More... | |
| void | set_name (std::string) |
| This Routine set the current component name. More... | |
Public Attributes | |
| SDL_Texture * | texture |
| SDL_Renderer * | renderer |
| std::string | image_path |
| std::pair< int, int > | dimension_on_screen |
| std::pair< int, int > | dimensionOnTexture |
| std::pair< int, int > | coordinatesOnTexture |
|
virtual |
Draw.
Draw the image on the screen
| x | position in axis x on the screen |
| y | position in axis y on the screen |
< rectangle holding the images positions and dimensions
< on screen rendering rectangle
Reimplemented from engine::Component.
|
virtual |
Free Method.
Method responsible for close the game
Reimplemented from engine::Component.
| int Image::get_height | ( | ) |
Get Height.
| int Image::get_width | ( | ) |
Get Width.
|
virtual |
Method Load.
Responsible for load the game images and create the texture
< sdl texture new texture converted
< surface loaded to be converted
Reimplemented from engine::Component.
| void Image::set_values | ( | std::pair< int, int > | _dimension_on_screen, |
| std::pair< int, int > | _dimension_on_texture, | ||
| std::pair< int, int > | _coordinates_on_texture | ||
| ) |
Methods Set values.
Set the position and dimension of the texture on the screen
| _dimension_on_screen | size in the screen of the texture |
| _dimension_on_texture | size of the texture |
| _coordinates_on_texture | coordinates in axis x,y |
| std::pair<int, int> engine::Image::coordinatesOnTexture |
coordinates on the texture
| std::pair<int, int> engine::Image::dimension_on_screen |
pair of x and y on screen dimensions
| std::pair<int, int> engine::Image::dimensionOnTexture |
pair of dimensions on the texture
| std::string engine::Image::image_path |
path of the image
| SDL_Renderer* engine::Image::renderer |
renderer from type SDL_Renderer
| SDL_Texture* engine::Image::texture |
texture from type SDL_Texture


Key-Combo Simulation:
Key Combo "Shift+a"
Key Combo "A+d"
Custom Combo:
Execute custom combo
Key-Sequence Simulation:
Key Sequence "asdf"
Key Sequence "fooob{leftarrow}{backspace}{rightarrow}ar"
Delay:
ms
Custom Sequence:
Execute custom sequence
Drag'n'Drop Simulation:
Note:
Avoid moving the mouse during interpolated drag'n'drop simulations.
Interpolation:
Step Count
Step Width
:
Step Delay
Duration
:
ms
Shaky:
pixels
Slider:
Current slider value:
Press 'd' to simulate a drop while dragging.
Drag 50px right
Drag'n'Drop 50px left
Drag'n'Drop 50px right
Drag 350px right
Drag'n'Drop 350px left
Drag'n'Drop 350px right
Drawing:
Clear Area
Draw 100px right
Draw 100px up
Draw 100px bottom-left
dx:
, dy:
Execute custom draw
Event Log:
Clear Log
Note:
mousemove events are logged only during drag simulation to avoid spamming
MouseMove Position:


Enter the name of the topic that you want to send messages to. This setting overrides topic configured in the global configuration.
You can use Jenkins build environment variables like build or project name in the topic name.
If left blank both here and in global configuration, project name will be used as topic.


|
ReactESP 2.1.0
Asynchronous programming for the ESP microcontrollers
|
Reaction that is triggered repeatedly. More...
#include <ReactESP.h>
Public Member Functions | |
| RepeatReaction (const uint32_t interval, const react_callback callback) | |
| Construct a new Repeat Reaction object. More... | |
| RepeatReaction (const uint64_t interval, const react_callback callback) | |
| Construct a new Repeat Reaction object. More... | |
| void | tick () |
| Public Member Functions inherited from reactesp::TimedReaction | |
| TimedReaction (const uint32_t interval, const react_callback callback) | |
| Construct a new Timed Reaction object. More... | |
| TimedReaction (const uint64_t interval, const react_callback callback) | |
| Construct a new Timed Reaction object. More... | |
| virtual | ~TimedReaction () |
| bool | operator< (const TimedReaction &other) |
| Return the current time since the device restart in microseconds. More... | |
| void | add (ReactESP *app=nullptr) override |
| void | remove (ReactESP *app=nullptr) override |
| uint32_t | getTriggerTime () |
| uint64_t | getTriggerTimeMicros () |
| bool | isEnabled () |
| Public Member Functions inherited from reactesp::Reaction | |
| Reaction (react_callback callback) | |
| Construct a new Reaction object. More... | |
Additional Inherited Members | |
| Protected Attributes inherited from reactesp::TimedReaction | |
| const uint64_t | interval |
| uint64_t | last_trigger_time |
| bool | enabled |
| ReactESP * | app_context = nullptr |
| Protected Attributes inherited from reactesp::Reaction | |
| const react_callback | callback |
Reaction that is triggered repeatedly.
Definition at line 119 of file ReactESP.h.
|
inline |
Construct a new Repeat Reaction object.
| interval | Repetition interval, in milliseconds |
| callback | Function to be called at every repetition |
Definition at line 127 of file ReactESP.h.
|
inline |
Construct a new Repeat Reaction object.
| interval | Repetition interval, in microseconds |
| callback | Function to be called at every repetition |
Definition at line 135 of file ReactESP.h.
|
virtual |
Implements reactesp::TimedReaction.
Definition at line 59 of file ReactESP.cpp.


Reports methods declared final and static.
When a static method is overridden in a subclass it can still be
accessed via the super class, making a final declaration not very necessary.
Declaring a static method final
does prevent subclasses from defining a static method with the same
signature.
Powered by InspectionGadgets


Represents real valued or numerical features.
tf.compat.v1.feature_column.numeric_column
tf.compat.v2.feature_column.numeric_column
tf.feature_column.numeric_column( key, shape=(1,), default_value=None, dtype=tf.dtypes.float32, normalizer_fn=None )
price = numeric_column('price') columns = [price, ...] features = tf.io.parse_example(..., features=make_parse_example_spec(columns)) dense_tensor = input_layer(features, columns) # or bucketized_price = bucketized_column(price, boundaries=[...]) columns = [bucketized_price, ...] features = tf.io.parse_example(..., features=make_parse_example_spec(columns)) linear_prediction = linear_model(features, columns)
key
: A unique string identifying the input feature. It is used as the
column name and the dictionary key for feature parsing configs, feature
Tensor
objects, and feature columns.shape
: An iterable of integers specifies the shape of the Tensor
. An
integer can be given which means a single dimension Tensor
with given
width. The Tensor
representing the column will have the shape of
[batch_size] + shape
.default_value
: A single value compatible with dtype
or an iterable of
values compatible with dtype
which the column takes on during
tf.Example
parsing if data is missing. A default value of None
will
cause tf.io.parse_example
to fail if an example does not contain this
column. If a single value is provided, the same value will be applied as
the default value for every item. If an iterable of values is provided,
the shape of the default_value
should be equal to the given shape
.dtype
: defines the type of values. Default value is tf.float32
. Must be a
non-quantized, real integer or floating point type.normalizer_fn
: If not None
, a function that can be used to normalize the
value of the tensor after default_value
is applied for parsing.
Normalizer function takes the input Tensor
as its argument, and returns
the output Tensor
. (e.g. lambda x: (x - 3.0) / 4.2). Please note that
even though the most common use case of this function is normalization, it
can be used for any kind of Tensorflow transformations.A NumericColumn
.
TypeError
: if any dimension in shape is not an intValueError
: if any dimension in shape is not a positive integerTypeError
: if default_value
is an iterable but not compatible with shape
TypeError
: if default_value
is not compatible with dtype
.ValueError
: if dtype
is not convertible to tf.float32
.


| 9 | | When you first go to the query page the default filters will display all open tickets, or if you're logged in it will display open tickets assigned to you. Current filters can be removed by clicking the button to the right with the minus sign on the label. New filters are added from the pulldown list in the bottom-right corner of the filters box. Filters with either a text box or a pulldown menu of options can be added multiple times to perform an ''or'' of the criteria. |
| | 9 | |
| | 10 | When you first go to the query page the default filter will display tickets relevant to you: |
| | 11 | * If logged in then all open tickets it will display open tickets assigned to you. |
| | 12 | * If not logged in but you have specified a name or email address in the preferences then it will display all open tickets where your email (or name if email not defined) is in the CC list. |
| | 13 | * If not logged and no name/email defined in the preferences then all open issues are displayed. |
| | 14 | |
| | 15 | Current filters can be removed by clicking the button to the right with the minus sign on the label. New filters are added from the pulldown lists at the bottom corners of the filters box ('And' conditions on the left, 'Or' conditions on the right). Filters with either a text box or a pulldown menu of options can be added multiple times to perform an ''or'' of the criteria. |
| 66 | | [[TicketQuery(version=1.0|2.0&resolution=duplicate, compact)]] |
| | 75 | [[TicketQuery(version=0.6|0.7&resolution=duplicate, compact)]] |
| | 76 | |
| | 77 | Finally if you wish to receive only the number of defects that match the query using the ``count`` parameter. |
| | 78 | |
| | 79 | {{{ |
| | 80 | [[TicketQuery(version=0.6|0.7&resolution=duplicate, count)]] |
| | 81 | }}} |
| | 82 | |
| | 83 | This is displayed as: |
| | 84 | [[TicketQuery(version=0.6|0.7&resolution=duplicate, count)]] |
| | 85 | |
| | 86 | === Customizing the ''table'' format === |
| | 87 | You can also customize the columns displayed in the table format (''format=table'') by using ''col=<field>'' - you can specify multiple fields and what order they are displayed by placing pipes (`|`) between the columns like below: |
| | 88 | |
| | 89 | {{{ |
| | 90 | [[TicketQuery(max=3,status=closed,order=id,desc=1,format=table,col=resolution|summary|owner|reporter)]] |
| | 91 | }}} |
| | 92 | |
| | 93 | This is displayed as: |
| | 94 | [[TicketQuery(max=3,status=closed,order=id,desc=1,format=table,col=resolution|summary|owner|reporter)]] |
| | 95 | |
| | 96 | ==== Full rows ==== |
| | 97 | In ''table'' format you can also have full rows by using ''rows=<field>'' like below: |
| | 98 | |
| | 99 | {{{ |
| | 100 | [[TicketQuery(max=3,status=closed,order=id,desc=1,format=table,col=resolution|summary|owner|reporter,rows=description)]] |
| | 101 | }}} |
| | 102 | |
| | 103 | This is displayed as: |
| | 104 | [[TicketQuery(max=3,status=closed,order=id,desc=1,format=table,col=resolution|summary|owner|reporter,rows=description)]] |
| | 105 | |
| 73 | | || '''=''' || the field content exactly matches the one of the values || |
| 74 | | || '''~=''' || the field content contains one or more of the values || |
| 75 | | || '''!^=''' || the field content starts with one of the values || |
| 76 | | || '''$=''' || the field content ends with one of the values || |
| | 112 | || '''`=`''' || the field content exactly matches the one of the values || |
| | 113 | || '''`~=`''' || the field content contains one or more of the values || |
| | 114 | || '''`^=`''' || the field content starts with one of the values || |
| | 115 | || '''`$=`''' || the field content ends with one of the values || |
| 79 | | || '''!=''' || the field content matches none of the values || |
| 80 | | || '''!~=''' || the field content does not contain any of the values || |
| 81 | | || '''!!^=''' || the field content does not start with any of the values || |
| 82 | | || '''!$=''' || the field content does not end with any of the values || |
| | 118 | || '''`!=`''' || the field content matches none of the values || |
| | 119 | || '''`!~=`''' || the field content does not contain any of the values || |
| | 120 | || '''`!^=`''' || the field content does not start with any of the values || |
| | 121 | || '''`!$=`''' || the field content does not end with any of the values || |
| | 122 | |
| | 123 | The date fields `created` and `modified` can be constrained by using the `=` operator and specifying a value containing two dates separated by two dots (`..`). Either end of the date range can be left empty, meaning that the corresponding end of the range is open. The date parser understands a few natural date specifications like "3 weeks ago", "last month" and "now", as well as Bugzilla-style date specifications like "1d", "2w", "3m" or "4y" for 1 day, 2 weeks, 3 months and 4 years, respectively. Spaces in date specifications can be left out to avoid having to quote the query string. |
| | 124 | || '''`created=2007-01-01..2008-01-01`''' || query tickets created in 2007 || |
| | 125 | || '''`created=lastmonth..thismonth`''' || query tickets created during the previous month || |
| | 126 | || '''`modified=1weekago..`''' || query tickets that have been modified in the last week || |
| | 127 | || '''`modified=..30daysago`''' || query tickets that have been inactive for the last 30 days || |


From 1949 to 2014: Columbia Then and Now
Columbia is rich in history, and a vital aspect in documenting Columbia's historical journey has been through the development of photojournalism.
Photography has an evocative power to explain stories to others in a way that can be understood by all ages and literary levels. Even everyday scenes can take a deeper meaning when photographed. “It distills the reality into something that’s more palpable and which sometimes feels very confronting. And, we can’t dismiss it. We can’t just turn our eyes. We look at it and sometimes we see for the first time what’s really going on […] that’s the real strength of still photography,” said David Rees, a Missouri Photo Workshop Co-Director and head of the MU Photojournalism Sequence.


| Modifier and Type | Method and Description |
|---|---|
static GenericTree |
GlowTree.newInstance(org.bukkit.TreeType type,
Random random,
BlockStateDelegate delegate)
Creates a tree of any vanilla type.
|
| Modifier and Type | Method and Description |
|---|---|
BiFunction<Random,BlockStateDelegate,? extends GenericTree> |
TreeDecorator.TreeDecoration.getConstructor() |
| Constructor and Description |
|---|
TreeDecoration(BiFunction<Random,BlockStateDelegate,? extends GenericTree> constructor,
int weight) |
| Modifier and Type | Field and Description |
|---|---|
protected BlockStateDelegate |
GenericTree.delegate |
| Constructor and Description |
|---|
AcaciaTree(Random random,
BlockStateDelegate delegate)
Initializes this tree with a random height, preparing it to attempt to generate.
|
BigOakTree(Random random,
BlockStateDelegate delegate) |
BigTree(Random random,
BlockStateDelegate delegate)
Initializes this tree with a random height, preparing it to attempt to generate.
|
BirchTree(Random random,
BlockStateDelegate delegate)
Initializes this tree with a random height, preparing it to attempt to generate.
|
BrownMushroomTree(Random random,
BlockStateDelegate delegate)
Initializes this mushroom with a random height, preparing it to attempt to generate.
|
CocoaTree(Random random,
BlockStateDelegate delegate)
Initializes this tree, preparing it to attempt to generate.
|
DarkOakTree(Random random,
BlockStateDelegate delegate)
Initializes this tree with a random height, preparing it to attempt to generate.
|
GenericTree(Random random,
BlockStateDelegate delegate)
Initializes this tree with a random height, preparing it to attempt to generate.
|
JungleBush(Random random,
BlockStateDelegate delegate)
Initializes this bush, preparing it to attempt to generate.
|
JungleTree(Random random,
BlockStateDelegate delegate)
Initializes this tree with a random height, preparing it to attempt to generate.
|
MegaJungleTree(Random random,
BlockStateDelegate delegate)
Initializes this tree with a random height, preparing it to attempt to generate.
|
MegaPineTree(Random random,
BlockStateDelegate delegate)
Initializes this tree with a random height, preparing it to attempt to generate.
|
MegaRedwoodTree(Random random,
BlockStateDelegate delegate)
Initializes this tree with a random height, preparing it to attempt to generate.
|
MegaSpruceTree(Random random,
BlockStateDelegate delegate)
Initializes this tree, preparing it to attempt to generate.
|
RedMushroomTree(Random random,
BlockStateDelegate delegate)
Initializes this mushroom, preparing it to attempt to generate.
|
RedwoodTree(Random random,
BlockStateDelegate delegate)
Initializes this tree with a random height, preparing it to attempt to generate.
|
SwampTree(Random random,
BlockStateDelegate delegate)
Initializes this tree with a random height, preparing it to attempt to generate.
|
TallBirchTree(Random random,
BlockStateDelegate delegate) |
TallRedwoodTree(Random random,
BlockStateDelegate delegate)
Initializes this tree with a random height and radius, preparing it to attempt to generate.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
GlowStructure.generate(Random random,
int x,
int z,
BlockStateDelegate delegate)
Attempts to generate this structure.
|
boolean |
GlowDesertWell.generate(org.bukkit.World world,
Random random,
StructureBoundingBox genBoundingBox,
BlockStateDelegate delegate) |
boolean |
GlowWitchHut.generate(org.bukkit.World world,
Random random,
StructureBoundingBox genBoundingBox,
BlockStateDelegate delegate) |
boolean |
GlowJungleTemple.generate(org.bukkit.World world,
Random random,
StructureBoundingBox genBoundingBox,
BlockStateDelegate delegate) |
boolean |
GlowDungeon.generate(org.bukkit.World world,
Random random,
StructureBoundingBox genBoundingBox,
BlockStateDelegate delegate) |
boolean |
GlowDesertTemple.generate(org.bukkit.World world,
Random random,
StructureBoundingBox genBoundingBox,
BlockStateDelegate delegate) |
boolean |
GlowStructurePiece.generate(org.bukkit.World world,
Random random,
StructureBoundingBox boundingBox,
BlockStateDelegate delegate) |
| Constructor and Description |
|---|
StructureBuilder(org.bukkit.World world,
GlowStructurePiece structure,
StructureBoundingBox boundingBox,
BlockStateDelegate delegate) |
Copyright © 2021. All rights reserved.


Demo
Copy <input>
Copy Me
Copy <textarea>
Copy content
Copy

The roses are red and violet are blue. Sugar is sweet and so are you. 🌹 ❤ --- nobody
Copy quote
Copy
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About Us
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{% extends 'weblab-master.html' %} {% block body %}
{{ gettext("You can add multiple users in a single operation. So as to do this, you must select first an authentication type from the following. Then, you will need to fill a text in a particular format, which depends on each authentication type.") }}
| {{ gettext("Type") }} | {{ gettext("Description") }} | {{ gettext("Action") }} |
| {{ gettext("Database") }} | {{ gettext("Simplest method: a passwords is stored in WebLab-Deusto") }} | {{ gettext("Add users") }} |
| {{ gettext("OpenID") }} | {{ gettext("Rely on an external system for authentication") }} | {{ gettext("Add users") }} |
| {{ gettext("LDAP") }} | {{ gettext("Rely on a local authentication system, if available") }} | {{ gettext("Add users") }} |


The Next Big Avengers Threat Just Crashed a Massive Space Prison
by Tim AdamsWARNING: The following article contains spoilers for Avengers #27 by Jason Aaron, Ed McGuinness, Mark Morales, Jason Keith and VC's Cory Petit, on sale Wednesday, Nov. 27.
The Marvel Universe is full of various cosmic entities, each possessing the power to wipe out entire galaxies. Fans are familiar with the likes of the Phoenix Force and Galactus' numerous heralds, but one force that often goes overlooked is the cosmic Starbrand.
The planetary defense mechanism returned with a splash during Jonathan Hickman's run on Avengers and New Avengers, when a college student named Kevin Conner became the Starbrand's newest host. After co-starring in a shortlived comic with the Nightmask, Starbrand made a surprising appearance in Marvel Legacy #1. A seemingly-mad Starbrand confronted Ghost Rider (Robbie Reyes), resulting in a fight that left Kevin Conner dead and the Starbrand looking for a new body to possess.
Click the button below to start this article in quick view.
Now, Marvel is ready to reveal the next tale in the Starbrand saga in Avengers. "Starbrand Reborn" takes Earth's Mightiest Heroes to an alien prison galaxy, where they will come across the new wielder of the powerful Starbrand. Fans got a short teaser for the story arc in last year’s Free Comic Book Day release, when a very different looking team of Avengers were shown heading towards a planet with the Starbrand symbol burned into it.
"Starbrand Reborn" kicks off in Avengers #27, and a preview of the issue features the Shi'ar Majestor Gladiator aboard an outpost on the edge of Shi'ar-controlled space. Someone working on the outpost informs Gladiator that a large explosion occurred inside the prison, with a dreadnought ship dispatched to investigate. Only two of the crewmen returned, and the preview of Avengers #27 reveals they have the Starbrand burned into their foreheads.
 Also, the cause of the explosion is tied to an automated transport ship carrying new prisoners of the planet. Gladiator decides no more of his people will be sacrificed to learn answers to this growing mystery, and volunteers to follow the dreadnought's last coordinates.
"If you haven't from me after seventeen starsecs, here's who I want you to call," Gladiator relays to a soldier. The identity of this person remains a mystery until Avengers #27 goes on sale Nov. 27, but it more than likely isn't a hero from the team. It would be odd for the Shi'ar to rely on the Avengers for help, even though they did work together against the Builders in Hickman's run. The Shi'ar already find themselves at odds with the New Mutants, so perhaps a team-up with the Avengers may be in order.
The extra-sized Avengers #10/#700 teased the return of the Starbrand in a splash page featuring future storylines, and artwork revealed hands extended in the air with the Starbrand symbol on them. The cover to Avengers #27 shows alien hands with the same symbol marked on them, so we may be looking at more than one character becoming a Starbrand. An army of Starbrands may be too much for even the Avengers to contend with.
https://schema.orgBreadcrumbListListItem1Homehttps://www.cbr.com/ListItem2Comicshttps://www.cbr.com/category/comics/ListItem3Comic Newshttps://www.cbr.com/category/comics/news/ListItem4The Next Big Avengers Threat Just Crashed a Massive Space Prisonhttps://www.cbr.com/avengers-starbrand-space-prison/


{% include google-analytics.html %} {% seo %}
Visual Music Sheets (tablatures) for the game Sky: Children of the Light


Poisson Kriging - Area to Point Kriging¶
Table of Contents:¶
Load areal and point data,
Load semivariogram (regularized),
Build point-based map of better spatial resolution.
Level: Advanced¶
Changelog¶
Date |
Change description |
Author |
|---|---|---|
2021-05-28 |
Updated paths for input/output data |
@szymon-datalions |
2021-05-11 |
Refactored TheoreticalSemivariogram class |
@szymon-datalions |
2021-03-31 |
Update related to the change of semivariogram weighting. Updated cancer rates data. |
@szymon-datalions |
Introduction¶
To start this tutorial it is required to understand concepts in the Ordinary and Simple Kriging and Semivariogram Regularization tutorials. The good idea is to end Poisson Kriging - Centroid based and Poisson Kriging - Area to Area tutorials before this one.
Poisson Kriging technique is used to model spatial count data. We are analyzing special case where data is counted over areas. Those areas may have irregular shapes and sizes beacuse they represent administrative regions.
In this tutorial we try to predict rates of Breast Cancer in Pennsylvania counties. Along with areal data we use U.S. Census 2010 data for population blocks.
Breast cancer rates data is stored in the shapefile in folder
sample_data/areal_data/cancer_data.shp
.
Population blocks data is stored in the shapefile in folder
sample_data/population_data/cancer_population_base.shp
This tutorial covers following steps:
Read and explore data,
Load semivariogram model,
Perform Area to Point smoothing of areal data.
Visualize points.
1) Read and explore data¶
[1]:
import numpy as np
import pandas as pd
import geopandas as gpd
from pyinterpolate.io_ops import prepare_areal_shapefile, get_points_within_area
from pyinterpolate.semivariance import TheoreticalSemivariogram
from pyinterpolate.kriging import ArealKriging
[2]:
areal_data_file = '../sample_data/areal_data/cancer_data.shp'
point_data_file = '../sample_data/population_data/cancer_population_base.shp'
areal_id_column_name = 'FIPS'
areal_val_column_name = 'rate'
points_val_column_name = 'POP10'
areal_data = prepare_areal_shapefile(areal_data_file,
id_column_name=areal_id_column_name,
value_column_name=areal_val_column_name)
point_data = get_points_within_area(areal_data_file, point_data_file, areal_id_col_name=areal_id_column_name,
points_val_col_name=points_val_column_name)
[3]:
# Lets take a look into a map of areal counts
gdf = gpd.read_file(areal_data_file)
gdf.plot(column='rate', cmap='Spectral_r', legend=True)
[3]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fb1324f7a90>
Clarification:¶
It is good idea to look into the spatial patterns in dataset and to visually check if our data do not have any NaN values. We use geopandas GeoDataFrame plot function for it with color map which is diverging color classes well.
2) Load semivariogram model¶
In this step we load regularized semivariogram from the tutorial Semivariogram Regularization (Intermediate). You can always perform semivariogram regulariztion along with the Poisson Kriging but it is a very long process and it is more convenient to separate those two steps.
[4]:
semivariogram = TheoreticalSemivariogram() # Create TheoreticalSemivariogram object
semivariogram.import_model('output/regularized_model.csv') # Load regularized semivariogram
3) Perform Area to Point smoothing of areal data.¶
The process of map smoothing is straightforward. We need to initialize Kriging model then invoke method regularize_data. This method takes three parameters:
=> number of observations (the most important parameter - how many neighbors are affecting your area of analysis),
=> minimum search radius (minimum search radius for analysis from your area of interest, if there are less areas than number of observations then the new, next closest neighbors are included in the analysis),
=> data_crs with default EPSG 4326.
Method returns GeoDataFrame with points and predicted values. It iteratively re-calculates each area risk and returns predictions per point. In Area to Area Kriging those predictions where aggregated, now we leave them and use them as a smooth map of areal risk.
[5]:
number_of_obs = 8
radius = 60000 # 60 km
kriging_model = ArealKriging(semivariogram_model=semivariogram,
known_areas=areal_data,
known_areas_points=point_data,
kriging_type='atp')
[6]:
smoothed_area = kriging_model.regularize_data(number_of_neighbours=number_of_obs,
max_search_radius=radius,
data_crs=gdf.crs)
4) Visualize data¶
The last step is data visualization. We use choropleth map from the GeoPandas package, but you can store smoothed map to shapefile of points and process it in a different place or with specific software (in our idea the best for it is QGIS).
[7]:
smoothed_area.head()
[7]:
| id | geometry | reg.est | reg.err | rmse | |
|---|---|---|---|---|---|
| 0 | 25019 | POINT (2117322.312 556124.507) | 0.094379 | 6.705419 | 30.791424 |
| 1 | 25019 | POINT (2134642.820 556124.507) | 161.314196 | 6.682668 | 30.791424 |
| 2 | 36121 | POINT (1424501.989 556124.507) | 5.668237 | 7.184250 | 26.042054 |
| 3 | 36121 | POINT (1424501.989 546124.507) | 6.426163 | 6.751145 | 26.042054 |
| 4 | 36121 | POINT (1433162.243 561124.507) | 20.399275 | 7.594203 | 26.042054 |
[9]:
base = gdf.plot(figsize=(14, 14), color='white')
smoothed_area.plot(ax=base, column='reg.est', cmap='Spectral_r', legend=True, markersize=20, vmax=500)
[9]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fb13289fc10>


Dropbox tops revenue, profit estimates on higher subscriber additions
Dropbox Inc beat analysts' estimates for third-quarter revenue on Thursday, as the online file hosting company added more individual and business customers.
REUTERS: Dropbox Inc beat analysts' estimates for third-quarter revenue and profit on Thursday, as the online file hosting company signed up more individual and business customers to its platform.
Dropbox, which competes with Alphabet Inc's Google, Microsoft Corp as well as Box Inc , has relied on its "freemium" model to attract more customers. The model allows customers to use the platform's basic features for free, but charges for additional services.
It acquired e-signature company HelloSign earlier this year and has partnered with rivals Microsoft and Google's file storage platforms, aiming to expand its reach and convert free users to paid customers.
Dropbox had 14 million subscribers at the end of the quarter, up from 12.3 million a year earlier and ahead of estimates of 13.89 million, according to FactSet.
Average revenue per user rose to US$123.15, beating Refinitiv estimates of US$122.82.
The company's deferred revenue, which measures future business for subscription-based software vendors, was US$541.1 million, above estimates of US$534 million, according to IBES data from Refinitiv.
Revenue rose 19per cent to US$428.20 million in the third quarter, beating analysts' estimates of US$423.48 million, according to IBES data from Refinitiv.
However, net loss widened to US$17 million, or 4 cents per share, in the three months ended Sept. 30, from US$5.8 million, or 1 cent per share, a year earlier.
On an adjusted basis, Dropbox earned 13 cents per share, beating analysts' estimates of 11 cents per share.
Shares of the company, which initially went up 5per cent in extended trading, were last down 3per cent.
(Reporting by Munsif Vengattil in Bengaluru; Editing by Shounak Dasgupta and Sriraj Kalluvila)


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| Test method | Required value | Average values | ||
| Water absorption | ASTM C373 | ≤ 0.5% | 0.05% | |
| Breaking strengh | ASTM C648 | ≥ 250 lbf | CONFORMS | |
| Deep abrasion resistance | ASTM C1243 | ≤ 175 mm3 | 145 mm3 | |
| Linear thermal expansion | ASTM C372 | NO PROVISION |
α ≤ 8 x 10-6 °C-1 α ≤ 4.4 x 10-6 °F-1 |
|
| Stain resistance | ASTM C1378 | As indicated by manufacturer | A RESISTANT | |
| Chemical resistance Lead and cadmium given off |
ASTM C650 ASTM C895 |
As indicated by manufacturer NO PROVISION |
A RESISTANT (Excluding hydrofluoridric acid and its derivates) NOT APPLICABLE |
|
| Frost resistance | ASTM C1026 | As indicated by manufacturer | RESISTANT | |
| Slip resistance | DIN 51130 BCR-TORTUS ANSI A326.3 DIN 51097 PENDULUM AS/NZS 4586 PENDULUM UNE-ENV 12633 PENDULUM BS 7976-2 |
- DCOF ≥ 0.42 |
R10 > 0,40 ≥ 0.42 A+B - 2 |
|
| Flame spread Fire reaction |
ASTM E84 EN 9239-1 - EN 13823 |
- | CLASS A CLASSE A1 FL (floor installation) CLASSE A1 (wall installation) |
|
| Thermal conductivity | EN 12524 | - | λ = 1,3 W/m °K / 0.7 BTU/ft h °F | |
| Thermal shock resistance | ISO 10545-9 | No alterations |
| Test method | Required value | Average values | ||
| Water absorption | ASTM C373 | ≤ 0.5% | 0.05% | |
| Breaking strengh | ASTM C648 | ≥ 250 lbf | 2800 lbf | |
| Deep abrasion resistance | ASTM C1243 | ≤ 175 mm3 | 145 mm3 | |
| Linear thermal expansion | ASTM C372 | NO PROVISION |
α ≤ 8 x 10-6 °C-1 α ≤ 4.4 x 10-6 °F-1 |
|
| Stain resistance | ASTM C1378 | As indicated by manufacturer | A RESISTANT | |
| Chemical resistance | ASTM C650 | As indicated by manufacturer | A RESISTANT (Excluding hydrofluoridric acid and its derivates) | |
| Frost resistance | ASTM C1026 | As indicated by manufacturer | RESISTANT | |
| Slip resistance | DIN 51130 BCR-TORTUS ANSI A326.3 DIN 51097 PENDULUM AS/NZS 4586 PENDULUM UNE-ENV 12633 PENDULUM BS 7976-2 |
- DCOF ≥ 0.42 |
R11 (STRUCT/20mm) > 0,50 ≥ 0.62 A+B+C P4 3 36+ |
|
| Flame spread Fire reaction |
ASTM E84 EN 9239-1 |
- | CLASS A CLASSE A1 FL (Floor installation) |
|
| Thermal conductivity | EN 12524 | - | λ = 1,3 W/m °K / 0.7 BTU/ft h °F | |
| Thermal shock resistance | ISO 10545-9 | No alterations | Resistant |